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50349, + "<|mi|>": 50295, + "<|mk|>": 50308, + "<|ml|>": 50296, + "<|mn|>": 50314, + "<|mr|>": 50320, + "<|ms|>": 50282, + "<|mt|>": 50343, + "<|my|>": 50346, + "<|ne|>": 50313, + "<|nl|>": 50271, + "<|nn|>": 50342, + "<|nospeech|>": 50363, + "<|notimestamps|>": 50364, + "<|no|>": 50288, + "<|oc|>": 50328, + "<|pa|>": 50321, + "<|pl|>": 50269, + "<|ps|>": 50340, + "<|pt|>": 50267, + "<|ro|>": 50284, + "<|ru|>": 50263, + "<|sa|>": 50344, + "<|sd|>": 50332, + "<|si|>": 50322, + "<|sk|>": 50298, + "<|sl|>": 50305, + "<|sn|>": 50324, + "<|so|>": 50326, + "<|sq|>": 50317, + "<|sr|>": 50303, + "<|startoflm|>": 50361, + "<|startofprev|>": 50362, + "<|startoftranscript|>": 50258, + "<|su|>": 50357, + "<|sv|>": 50273, + "<|sw|>": 50318, + "<|ta|>": 50287, + "<|te|>": 50299, + "<|tg|>": 50331, + "<|th|>": 50289, + "<|tk|>": 50341, + "<|tl|>": 50348, + "<|transcribe|>": 50360, + "<|translate|>": 50359, + "<|tr|>": 50268, + "<|tt|>": 50351, + "<|uk|>": 50280, + "<|ur|>": 50290, + "<|uz|>": 50337, + "<|vi|>": 50278, + "<|yi|>": 50335, + "<|yo|>": 50325, + "<|yue|>": 50358, + "<|zh|>": 50260 +} diff --git a/debug/debug-final.py b/debug/debug-final.py new file mode 100644 index 0000000000000000000000000000000000000000..db97fc9f5629bb0a956267a03e7a85fb0a1a651b --- /dev/null +++ b/debug/debug-final.py @@ -0,0 +1,24446 @@ +# from tvm.script import ir as I +# from tvm.script import tir as T +# from tvm.script import relax as R + +@I.ir_module +class Module: + I.module_attrs({"external_mods": [metadata["runtime.Module"][0], metadata["runtime.Module"][1], metadata["runtime.Module"][2], metadata["runtime.Module"][3], metadata["runtime.Module"][4], metadata["runtime.Module"][5], metadata["runtime.Module"][6], metadata["runtime.Module"][7], metadata["runtime.Module"][8], metadata["runtime.Module"][9], metadata["runtime.Module"][10], metadata["runtime.Module"][11], metadata["runtime.Module"][12], metadata["runtime.Module"][13], metadata["runtime.Module"][14]]}) + @T.prim_func + def NT_matmul(layer_norm356: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16"), model_decoder_layers_0_self_attn_q_proj_weight5: T.Buffer((T.int64(1280), T.int64(1280)), "float16"), NT_matmul: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16")): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + # with T.block("root"): + NT_matmul_rf_local = T.alloc_buffer((T.int64(128), T.int64(1), T.int64(1), T.int64(1280)), "float16", scope="local") + NT_matmul_rf_local_1 = T.alloc_buffer((T.int64(32), T.int64(1), T.int64(1), T.int64(1280)), "float16", scope="local") + model_decoder_layers_0_self_attn_q_proj_weight5_local = T.alloc_buffer((T.int64(1280), T.int64(1280)), "float16", scope="local") + layer_norm356_shared = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16", scope="shared") + for u_fused_ax0_fused_fused_0 in T.thread_binding(T.int64(80), thread="blockIdx.x"): + for u_fused_ax0_fused_fused_1 in T.thread_binding(T.int64(16), thread="threadIdx.y"): + for ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 in T.thread_binding(T.int64(32), thread="threadIdx.x"): + for ax0, ax1 in T.grid(T.int64(1), T.int64(1)): + for ax2_0 in T.serial(T.int64(3), annotations={"pragma_unroll_explicit": 256, "pragma_vectorize": 1}): + for ax2_1 in T.thread_binding(T.int64(16), thread="threadIdx.y"): + for ax2_2 in T.thread_binding(T.int64(32), thread="threadIdx.x"): + for ax2_3 in T.vectorized(T.int64(1)): + with T.block("layer_norm356_shared"): + v0, v1 = T.axis.remap("SS", [ax0, ax1]) + v2 = T.axis.spatial(T.int64(1280), ax2_0 * T.int64(512) + ax2_1 * T.int64(32) + ax2_2 + ax2_3) + T.where((ax2_0 * T.int64(16) + ax2_1) * T.int64(32) + ax2_2 + ax2_3 < T.int64(1280)) + T.reads(layer_norm356[v0, v1, v2]) + T.writes(layer_norm356_shared[v0, v1, v2]) + layer_norm356_shared[v0, v1, v2] = layer_norm356[v0, v1, v2] + for u_fused_ax0_fused_fused_2_init in range(T.int64(1)): + for ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1_init in T.vectorized(T.int64(4)): + with T.block("NT_matmul_rf_init"): + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused = T.axis.spatial(T.int64(128), ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 * T.int64(4) + ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1_init) + v0 = T.axis.spatial(T.int64(1280), u_fused_ax0_fused_fused_0 * T.int64(16) + u_fused_ax0_fused_fused_1 + u_fused_ax0_fused_fused_2_init) + T.reads() + T.writes(NT_matmul_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0]) + NT_matmul_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0] = T.float16(0) + for ax1_fused_u_fused_0 in T.serial(T.int64(5), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): + for ax0_ax1_fused_0 in range(T.int64(4)): + for ax0_ax1_fused_1 in T.vectorized(T.int64(2)): + with T.block("model_decoder_layers_0_self_attn_q_proj_weight5_local"): + v0 = T.axis.spatial(T.int64(1280), u_fused_ax0_fused_fused_0 * T.int64(16) + u_fused_ax0_fused_fused_1) + v1 = T.axis.spatial(T.int64(1280), ax1_fused_u_fused_0 * T.int64(256) + ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 * T.int64(8) + ax0_ax1_fused_0 * T.int64(2) + ax0_ax1_fused_1) + T.reads(model_decoder_layers_0_self_attn_q_proj_weight5[v0, v1]) + T.writes(model_decoder_layers_0_self_attn_q_proj_weight5_local[v0, v1]) + model_decoder_layers_0_self_attn_q_proj_weight5_local[v0, v1] = model_decoder_layers_0_self_attn_q_proj_weight5[v0, v1] + for u_fused_ax0_fused_fused_2, ax1_fused_u_fused_2 in T.grid(T.int64(1), T.int64(2)): + for ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1 in T.vectorized(T.int64(4)): + with T.block("NT_matmul_rf_update"): + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused = T.axis.spatial(T.int64(128), ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 * T.int64(4) + ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1) + v0 = T.axis.spatial(T.int64(1280), u_fused_ax0_fused_fused_0 * T.int64(16) + u_fused_ax0_fused_fused_1 + u_fused_ax0_fused_fused_2) + vax1_fused_u_fused_0, vax1_fused_u_fused_2 = T.axis.remap("RR", [ax1_fused_u_fused_0, ax1_fused_u_fused_2]) + T.reads(NT_matmul_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0], layer_norm356_shared[T.int64(0), T.int64(0), vax1_fused_u_fused_0 * T.int64(256) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused // T.int64(4) * T.int64(8) + vax1_fused_u_fused_2 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused % T.int64(4)], model_decoder_layers_0_self_attn_q_proj_weight5_local[v0, vax1_fused_u_fused_0 * T.int64(256) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused // T.int64(4) * T.int64(8) + vax1_fused_u_fused_2 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused % T.int64(4)]) + T.writes(NT_matmul_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0]) + NT_matmul_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0] = NT_matmul_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0] + layer_norm356_shared[T.int64(0), T.int64(0), vax1_fused_u_fused_0 * T.int64(256) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused // T.int64(4) * T.int64(8) + vax1_fused_u_fused_2 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused % T.int64(4)] * model_decoder_layers_0_self_attn_q_proj_weight5_local[v0, vax1_fused_u_fused_0 * T.int64(256) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused // T.int64(4) * T.int64(8) + vax1_fused_u_fused_2 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused % T.int64(4)] + for ax2_fused_0_ax2_fused_1_fused in T.thread_binding(T.int64(16), thread="threadIdx.y"): + for ax0 in T.thread_binding(T.int64(32), thread="threadIdx.x"): + for ax2_fused_2_0 in T.serial(T.int64(1), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): + for ax2_fused_2_1 in T.vectorized(T.int64(1)): + with T.block("NT_matmul_rf_init"): + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 = T.axis.spatial(T.int64(32), ax0) + v0 = T.axis.spatial(T.int64(1280), u_fused_ax0_fused_fused_0 * T.int64(16) + ax2_fused_0_ax2_fused_1_fused + ax2_fused_2_0 + ax2_fused_2_1) + T.reads() + T.writes(NT_matmul_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0]) + NT_matmul_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0] = T.float16(0) + for ax1 in range(T.int64(4)): + with T.block("NT_matmul_rf_update"): + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1 = T.axis.remap("SR", [ax0, ax1]) + v0 = T.axis.spatial(T.int64(1280), u_fused_ax0_fused_fused_0 * T.int64(16) + ax2_fused_0_ax2_fused_1_fused + ax2_fused_2_0 + ax2_fused_2_1) + T.reads(NT_matmul_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0], NT_matmul_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1, T.int64(0), T.int64(0), v0]) + T.writes(NT_matmul_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0]) + NT_matmul_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0] = NT_matmul_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0] + NT_matmul_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1, T.int64(0), T.int64(0), v0] + for ax1_fused_2 in range(T.int64(1)): + for ax1_fused_0_ax1_fused_1_fused in T.thread_binding(T.int64(16), thread="threadIdx.y"): + for ax0 in T.thread_binding(T.int64(32), thread="threadIdx.x"): + with T.block("NT_matmul"): + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 = T.axis.reduce(T.int64(32), ax0) + v0 = T.axis.spatial(T.int64(1280), u_fused_ax0_fused_fused_0 * T.int64(16) + ax1_fused_0_ax1_fused_1_fused + ax1_fused_2) + T.reads(NT_matmul_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0]) + T.writes(NT_matmul[T.int64(0), T.int64(0), v0]) + with T.init(): + NT_matmul[T.int64(0), T.int64(0), v0] = T.float16(0) + NT_matmul[T.int64(0), T.int64(0), v0] = NT_matmul[T.int64(0), T.int64(0), v0] + NT_matmul_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0] + + @T.prim_func + def NT_matmul3(layer_norm452: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16"), model_decoder_embed_tokens_weight5: T.Buffer((T.int64(51866), T.int64(1280)), "float16"), NT_matmul: T.Buffer((T.int64(1), T.int64(1), T.int64(51866)), "float32")): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + # with T.block("root"): + NT_matmul_rf_local = T.alloc_buffer((T.int64(256), T.int64(1), T.int64(1), T.int64(51866)), scope="local") + NT_matmul_rf_local_1 = T.alloc_buffer((T.int64(64), T.int64(1), T.int64(1), T.int64(51866)), scope="local") + model_decoder_embed_tokens_weight5_local = T.alloc_buffer((T.int64(51866), T.int64(1280)), "float16", scope="local") + layer_norm452_shared = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16", scope="shared") + for u_fused_ax0_fused_fused_0 in T.thread_binding(T.int64(12967), thread="blockIdx.x"): + for u_fused_ax0_fused_fused_1 in T.thread_binding(T.int64(4), thread="threadIdx.y"): + for ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 in T.thread_binding(T.int64(64), thread="threadIdx.x"): + for ax0, ax1 in T.grid(T.int64(1), T.int64(1)): + for ax2_0 in T.serial(T.int64(5), annotations={"pragma_unroll_explicit": 256, "pragma_vectorize": 1}): + for ax2_1 in T.thread_binding(T.int64(4), thread="threadIdx.y"): + for ax2_2 in T.thread_binding(T.int64(64), thread="threadIdx.x"): + for ax2_3 in T.vectorized(T.int64(1)): + with T.block("layer_norm452_shared"): + v0, v1 = T.axis.remap("SS", [ax0, ax1]) + v2 = T.axis.spatial(T.int64(1280), ax2_0 * T.int64(256) + ax2_1 * T.int64(64) + ax2_2 + ax2_3) + T.reads(layer_norm452[v0, v1, v2]) + T.writes(layer_norm452_shared[v0, v1, v2]) + layer_norm452_shared[v0, v1, v2] = layer_norm452[v0, v1, v2] + for u_fused_ax0_fused_fused_2_init in range(T.int64(1)): + for ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1_init in T.vectorized(T.int64(4)): + with T.block("NT_matmul_rf_init"): + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused = T.axis.spatial(T.int64(256), ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 * T.int64(4) + ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1_init) + v0 = T.axis.spatial(T.int64(51866), u_fused_ax0_fused_fused_0 * T.int64(4) + u_fused_ax0_fused_fused_1 + u_fused_ax0_fused_fused_2_init) + T.where(u_fused_ax0_fused_fused_0 * T.int64(4) + u_fused_ax0_fused_fused_1 + u_fused_ax0_fused_fused_2_init < T.int64(51866)) + T.reads() + T.writes(NT_matmul_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0]) + NT_matmul_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0] = T.float32(0) + for ax1_fused_u_fused_0 in T.serial(T.int64(5), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): + for ax0_ax1_fused_0 in range(T.int64(2)): + for ax0_ax1_fused_1 in T.vectorized(T.int64(2)): + with T.block("model_decoder_embed_tokens_weight5_local"): + v0 = T.axis.spatial(T.int64(51866), u_fused_ax0_fused_fused_0 * T.int64(4) + u_fused_ax0_fused_fused_1) + v1 = T.axis.spatial(T.int64(1280), ax1_fused_u_fused_0 * T.int64(256) + ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 * T.int64(4) + ax0_ax1_fused_0 * T.int64(2) + ax0_ax1_fused_1) + T.where(u_fused_ax0_fused_fused_0 * T.int64(4) + u_fused_ax0_fused_fused_1 < T.int64(51866)) + T.reads(model_decoder_embed_tokens_weight5[v0, v1]) + T.writes(model_decoder_embed_tokens_weight5_local[v0, v1]) + model_decoder_embed_tokens_weight5_local[v0, v1] = model_decoder_embed_tokens_weight5[v0, v1] + for u_fused_ax0_fused_fused_2, ax1_fused_u_fused_2 in T.grid(T.int64(1), T.int64(1)): + for ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1 in T.vectorized(T.int64(4)): + with T.block("NT_matmul_rf_update"): + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused = T.axis.spatial(T.int64(256), ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 * T.int64(4) + ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1) + v0 = T.axis.spatial(T.int64(51866), u_fused_ax0_fused_fused_0 * T.int64(4) + u_fused_ax0_fused_fused_1 + u_fused_ax0_fused_fused_2) + vax1_fused_u_fused_2, vax1_fused_u_fused_0 = T.axis.remap("RR", [ax1_fused_u_fused_2, ax1_fused_u_fused_0]) + T.where(u_fused_ax0_fused_fused_0 * T.int64(4) + u_fused_ax0_fused_fused_1 + u_fused_ax0_fused_fused_2 < T.int64(51866)) + T.reads(NT_matmul_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0], layer_norm452_shared[T.int64(0), T.int64(0), vax1_fused_u_fused_0 * T.int64(256) + vax1_fused_u_fused_2 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused], model_decoder_embed_tokens_weight5_local[v0, vax1_fused_u_fused_0 * T.int64(256) + vax1_fused_u_fused_2 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused]) + T.writes(NT_matmul_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0]) + NT_matmul_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0] = NT_matmul_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0] + T.Cast("float32", layer_norm452_shared[T.int64(0), T.int64(0), vax1_fused_u_fused_0 * T.int64(256) + vax1_fused_u_fused_2 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused]) * T.Cast("float32", model_decoder_embed_tokens_weight5_local[v0, vax1_fused_u_fused_0 * T.int64(256) + vax1_fused_u_fused_2 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused]) + for ax2_fused_0_ax2_fused_1_fused in T.thread_binding(T.int64(4), thread="threadIdx.y"): + for ax0 in T.thread_binding(T.int64(64), thread="threadIdx.x"): + for ax2_fused_2_0 in T.serial(T.int64(1), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): + for ax2_fused_2_1 in T.vectorized(T.int64(1)): + with T.block("NT_matmul_rf_init"): + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 = T.axis.spatial(T.int64(64), ax0) + v0 = T.axis.spatial(T.int64(51866), u_fused_ax0_fused_fused_0 * T.int64(4) + ax2_fused_0_ax2_fused_1_fused + ax2_fused_2_0 + ax2_fused_2_1) + T.where(u_fused_ax0_fused_fused_0 * T.int64(4) + (T.Mul(T.int64(0), T.int64(4)) + ax2_fused_0_ax2_fused_1_fused % T.int64(4) + (ax2_fused_2_0 + ax2_fused_2_1)) < T.int64(51866)) + T.reads() + T.writes(NT_matmul_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0]) + NT_matmul_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0] = T.float32(0) + for ax1 in range(T.int64(4)): + with T.block("NT_matmul_rf_update"): + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1 = T.axis.remap("SR", [ax0, ax1]) + v0 = T.axis.spatial(T.int64(51866), u_fused_ax0_fused_fused_0 * T.int64(4) + ax2_fused_0_ax2_fused_1_fused + ax2_fused_2_0 + ax2_fused_2_1) + T.where(u_fused_ax0_fused_fused_0 * T.int64(4) + (T.Mul(T.int64(0), T.int64(4)) + ax2_fused_0_ax2_fused_1_fused % T.int64(4) + (ax2_fused_2_0 + ax2_fused_2_1)) < T.int64(51866)) + T.reads(NT_matmul_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0], NT_matmul_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1, T.int64(0), T.int64(0), v0]) + T.writes(NT_matmul_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0]) + NT_matmul_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0] = NT_matmul_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0] + NT_matmul_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1, T.int64(0), T.int64(0), v0] + for ax1_fused_2 in range(T.int64(1)): + for ax1_fused_0_ax1_fused_1_fused in T.thread_binding(T.int64(4), thread="threadIdx.y"): + for ax0 in T.thread_binding(T.int64(64), thread="threadIdx.x"): + with T.block("NT_matmul"): + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 = T.axis.reduce(T.int64(64), ax0) + v0 = T.axis.spatial(T.int64(51866), u_fused_ax0_fused_fused_0 * T.int64(4) + ax1_fused_0_ax1_fused_1_fused + ax1_fused_2) + T.where(u_fused_ax0_fused_fused_0 * T.int64(4) + (T.Mul(T.int64(0), T.int64(4)) + ax1_fused_0_ax1_fused_1_fused % T.int64(4) + ax1_fused_2) < T.int64(51866)) + T.reads(NT_matmul_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0]) + T.writes(NT_matmul[T.int64(0), T.int64(0), v0]) + with T.init(): + NT_matmul[T.int64(0), T.int64(0), v0] = T.float32(0) + NT_matmul[T.int64(0), T.int64(0), v0] = NT_matmul[T.int64(0), T.int64(0), v0] + NT_matmul_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0] + + @T.prim_func + def add(var_reshape708: T.handle, var_reshape709: T.handle, var_T_add: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + batch_size = T.int64() + reshape708 = T.match_buffer(var_reshape708, (batch_size, T.int64(1), T.int64(1280)), "float16") + reshape709 = T.match_buffer(var_reshape709, (batch_size, T.int64(1), T.int64(1280)), "float16") + T_add = T.match_buffer(var_T_add, (batch_size, T.int64(1), T.int64(1280)), "float16") + # with T.block("root"): + for ax0_ax1_fused_0 in T.thread_binding((batch_size * T.int64(1280) + T.int64(1023)) // T.int64(1024), thread="blockIdx.x"): + for ax0_ax1_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_add"): + v0 = T.axis.spatial(batch_size, (ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1) // T.int64(1280)) + v1 = T.axis.spatial(T.int64(1280), (ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1) % T.int64(1280)) + T.where(ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1 < batch_size * T.int64(1280)) + T.reads(reshape708[v0, T.int64(0), v1], reshape709[v0, T.int64(0), v1]) + T.writes(T_add[v0, T.int64(0), v1]) + T_add[v0, T.int64(0), v1] = reshape708[v0, T.int64(0), v1] + reshape709[v0, T.int64(0), v1] + + @T.prim_func + def add4(var_add: T.handle, var_lv610: T.handle, var_T_add: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + batch_size = T.int64() + add = T.match_buffer(var_add, (batch_size, T.int64(1500), T.int64(1280)), "float16") + lv610 = T.match_buffer(var_lv610, (batch_size, T.int64(1500), T.int64(1280)), "float16") + T_add = T.match_buffer(var_T_add, (batch_size, T.int64(1500), T.int64(1280)), "float16") + # with T.block("root"): + for ax0_ax1_ax2_fused_0 in T.thread_binding(batch_size * T.int64(1875), thread="blockIdx.x"): + for ax0_ax1_ax2_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_add"): + v0 = T.axis.spatial(batch_size, (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) // T.int64(1920000)) + v1 = T.axis.spatial(T.int64(1500), (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) % T.int64(1920000) // T.int64(1280)) + v2 = T.axis.spatial(T.int64(1280), (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) % T.int64(1280)) + T.reads(add[v0, v1, v2], lv610[v0, v1, v2]) + T.writes(T_add[v0, v1, v2]) + T_add[v0, v1, v2] = add[v0, v1, v2] + lv610[v0, v1, v2] + + @T.prim_func + def add5(var_reshape385: T.handle, var_reshape386: T.handle, var_T_add: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + seq_len = T.int64() + reshape385 = T.match_buffer(var_reshape385, (T.int64(1), seq_len, T.int64(1280)), "float16") + reshape386 = T.match_buffer(var_reshape386, (T.int64(1), seq_len, T.int64(1280)), "float16") + T_add = T.match_buffer(var_T_add, (T.int64(1), seq_len, T.int64(1280)), "float16") + # with T.block("root"): + for ax0_ax1_fused_0 in T.thread_binding((seq_len * T.int64(1280) + T.int64(1023)) // T.int64(1024), thread="blockIdx.x"): + for ax0_ax1_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_add"): + v0 = T.axis.spatial(seq_len, (ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1) // T.int64(1280)) + v1 = T.axis.spatial(T.int64(1280), (ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1) % T.int64(1280)) + T.where(ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1 < seq_len * T.int64(1280)) + T.reads(reshape385[T.int64(0), v0, v1], reshape386[T.int64(0), v0, v1]) + T.writes(T_add[T.int64(0), v0, v1]) + T_add[T.int64(0), v0, v1] = reshape385[T.int64(0), v0, v1] + reshape386[T.int64(0), v0, v1] + + @T.prim_func + def apply_bitmask_inplace(var_logits: T.handle, var_seq_ids: T.handle, var_bitmask: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": T.bool(True), "tir.noalias": T.bool(True)}) + batch_size, vocab_size = T.int32(is_size_var=True), T.int32(is_size_var=True) + logits = T.match_buffer(var_logits, (batch_size, vocab_size)) + num_seq = T.int32(is_size_var=True) + seq_ids = T.match_buffer(var_seq_ids, (num_seq,), "int32") + bitmask = T.match_buffer(var_bitmask, (batch_size, (vocab_size + 31) // 32), "int32") + # with T.block("root"): + for fused_s_v_0 in T.thread_binding((num_seq * vocab_size + 1023) // 1024, thread="blockIdx.x"): + for fused_s_v_1 in T.thread_binding(1024, thread="threadIdx.x"): + with T.block("block"): + vs = T.axis.spatial(num_seq, (fused_s_v_0 * 1024 + fused_s_v_1) // vocab_size) + vv = T.axis.spatial(vocab_size, (fused_s_v_0 * 1024 + fused_s_v_1) % vocab_size) + T.where(fused_s_v_0 * 1024 + fused_s_v_1 < num_seq * vocab_size) + T.reads(bitmask[seq_ids[vs], vv // 32], seq_ids[vs], logits[seq_ids[vs], vv]) + T.writes(logits[seq_ids[vs], vv]) + logits[seq_ids[vs], vv] = T.if_then_else(T.bitwise_and(T.shift_right(bitmask[seq_ids[vs], vv // 32], vv % 32), 1) == 1, logits[seq_ids[vs], vv], T.float32(-3.4028234663852886e+38)) + + @T.prim_func + def apply_logit_bias_inplace(var_logits: T.handle, var_pos2seq_id: T.handle, var_token_ids: T.handle, var_logit_bias: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": T.bool(True), "tir.noalias": T.bool(True)}) + batch_size, vocab_size = T.int32(is_size_var=True), T.int32(is_size_var=True) + logits = T.match_buffer(var_logits, (batch_size, vocab_size)) + num_token = T.int32(is_size_var=True) + pos2seq_id = T.match_buffer(var_pos2seq_id, (num_token,), "int32") + token_ids = T.match_buffer(var_token_ids, (num_token,), "int32") + logit_bias = T.match_buffer(var_logit_bias, (num_token,)) + # with T.block("root"): + for p0 in T.thread_binding((num_token + 1023) // 1024, thread="blockIdx.x"): + for p1 in T.thread_binding(1024, thread="threadIdx.x"): + with T.block("block"): + vp = T.axis.spatial(num_token, p0 * 1024 + p1) + T.where(p0 * 1024 + p1 < num_token) + T.reads(logits[pos2seq_id[vp], token_ids[vp]], pos2seq_id[vp], token_ids[vp], logit_bias[vp]) + T.writes(logits[pos2seq_id[vp], token_ids[vp]]) + logits[pos2seq_id[vp], token_ids[vp]] = logits[pos2seq_id[vp], token_ids[vp]] + logit_bias[vp] + + @T.prim_func + def apply_penalty_inplace(var_logits: T.handle, var_seq_ids: T.handle, var_pos2seq_id: T.handle, var_token_ids: T.handle, var_token_cnt: T.handle, var_penalties: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": T.bool(True), "tir.noalias": T.bool(True)}) + batch_size, vocab_size = T.int32(is_size_var=True), T.int32(is_size_var=True) + logits = T.match_buffer(var_logits, (batch_size, vocab_size)) + num_seq = T.int32(is_size_var=True) + seq_ids = T.match_buffer(var_seq_ids, (num_seq,), "int32") + num_token = T.int32(is_size_var=True) + pos2seq_id = T.match_buffer(var_pos2seq_id, (num_token,), "int32") + token_ids = T.match_buffer(var_token_ids, (num_token,), "int32") + token_cnt = T.match_buffer(var_token_cnt, (num_token,), "int32") + penalties = T.match_buffer(var_penalties, (num_seq, 3)) + # with T.block("root"): + for p0 in T.thread_binding((num_token + 1023) // 1024, thread="blockIdx.x"): + for p1 in T.thread_binding(1024, thread="threadIdx.x"): + with T.block("block"): + vp = T.axis.spatial(num_token, p0 * 1024 + p1) + T.where(p0 * 1024 + p1 < num_token) + T.reads(logits[seq_ids[pos2seq_id[vp]], token_ids[vp]], seq_ids[pos2seq_id[vp]], pos2seq_id[vp], token_ids[vp], penalties[pos2seq_id[vp], 0:3], token_cnt[vp]) + T.writes(logits[seq_ids[pos2seq_id[vp]], token_ids[vp]]) + logits[seq_ids[pos2seq_id[vp]], token_ids[vp]] = logits[seq_ids[pos2seq_id[vp]], token_ids[vp]] - (penalties[pos2seq_id[vp], 0] + T.Cast("float32", token_cnt[vp]) * penalties[pos2seq_id[vp], 1]) + logits[seq_ids[pos2seq_id[vp]], token_ids[vp]] = T.if_then_else(logits[seq_ids[pos2seq_id[vp]], token_ids[vp]] > T.float32(0), logits[seq_ids[pos2seq_id[vp]], token_ids[vp]] * penalties[pos2seq_id[vp], 2], logits[seq_ids[pos2seq_id[vp]], token_ids[vp]] / penalties[pos2seq_id[vp], 2]) + + @T.prim_func + def argsort_thrust(var_probs: T.handle, var_lv: T.handle, var_topk_gpu_v1: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + batch_size, vocab_size = T.int64(), T.int64() + data_buf = T.match_buffer(var_probs, (batch_size, vocab_size), align=8) + workspace_buf = T.match_buffer(var_lv, (T.int64(8) * (batch_size * vocab_size * T.int64(4)) + T.int64(8388608) + batch_size * vocab_size * T.int64(12),), "uint8", align=8) + indices_buf = T.match_buffer(var_topk_gpu_v1, (batch_size, vocab_size), "int32", align=8) + # with T.block("root"): + value_buf = T.alloc_buffer((batch_size, vocab_size), align=8) + with T.block("topk_gpu"): + T.reads() + T.writes() + T.call_packed("tvm.contrib.thrust.sort", T.tvm_stack_make_array(data_buf.data, T.tvm_stack_make_shape(batch_size, vocab_size), 0, 2, T.float32(0), T.int64(0)), T.tvm_stack_make_array(value_buf.data, T.tvm_stack_make_shape(batch_size, vocab_size), 0, 2, T.float32(0), T.int64(0)), T.tvm_stack_make_array(indices_buf.data, T.tvm_stack_make_shape(batch_size, vocab_size), 0, 2, 0, T.int64(0)), 0, T.tvm_stack_make_array(workspace_buf.data, T.tvm_stack_make_shape(T.int64(8) * (batch_size * vocab_size * T.int64(4)) + T.int64(8388608) + batch_size * vocab_size * T.int64(12)), 0, 1, T.uint8(0), T.int64(0))) + + @T.prim_func + def batch_decode_paged_kv(_0: T.int32, Q_handle: T.handle, pages_handle: T.handle, page_table_indptr_handle: T.handle, page_table_values_handle: T.handle, var_length_info: T.handle, k_rope_pos_offset_handle: T.handle, q_rope_position_handle: T.handle, output_handle: T.handle, lse_handle: T.handle, rotary_mode: T.int32, rope_scale: T.float32, rope_theta: T.float32, attn_score_scaling_factor: T.float32): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) + B = T.int32(is_size_var=True) + Q = T.match_buffer(Q_handle, (B, 20, 64), "float16") + max_num_pages = T.int32(is_size_var=True) + pages = T.match_buffer(pages_handle, (max_num_pages, 2, 20, 16, 64), "float16") + page_table_indptr = T.match_buffer(page_table_indptr_handle, (B + 1,), "int32", offset_factor=1) + nnz_pages = T.int32(is_size_var=True) + page_table_values = T.match_buffer(page_table_values_handle, (nnz_pages,), "int32", offset_factor=1) + length_info = T.match_buffer(var_length_info, (B,), "int32", offset_factor=1) + k_rope_pos_offset = T.match_buffer(k_rope_pos_offset_handle, (B,), "int32", offset_factor=1) + q_rope_position = T.match_buffer(q_rope_position_handle, (B,), "int32", offset_factor=1) + output = T.match_buffer(output_handle, (B, 20, 64), "float16") + lse = T.match_buffer(lse_handle, (B, 20)) + # with T.block("root"): + sm_scale: T.float32 = T.float32(0.18033688011112042) + for bx in T.thread_binding(B, thread="blockIdx.x"): + for fused_by_bz in T.thread_binding(20, thread="blockIdx.y"): + for ty in T.thread_binding(1, thread="threadIdx.y"): + for tx in T.thread_binding(16, thread="threadIdx.x"): + for tz in T.thread_binding(32, thread="threadIdx.z"): + with T.block("attn"): + T.reads(page_table_indptr[bx:bx + 2], length_info[bx], q_rope_position[bx], Q[bx, fused_by_bz // 20 + ty + fused_by_bz % 20, tx * 4 - 32:tx * 4 - 32 + 68]) + T.writes(output[bx, fused_by_bz % 20 + fused_by_bz // 20 + ty, tx * 4:tx * 4 + 4], lse[bx, fused_by_bz % 20 + fused_by_bz // 20 + ty]) + Q_local = T.alloc_buffer((4,), "float16", scope="local") + kv_chunk_len = T.alloc_buffer((1,), "int32", scope="local") + K_smem = T.alloc_buffer((64, 64), "float16", scope="shared") + V_smem = T.alloc_buffer((64, 64), "float16", scope="shared") + O_allreduce = T.alloc_buffer((32, 1, 64), scope="shared") + md_allreduce = T.alloc_buffer((32, 1, 2), scope="shared") + S_reduce_local = T.alloc_buffer((1,), scope="local") + t0 = T.alloc_buffer((1,), scope="local") + S_local = T.alloc_buffer((2,), scope="local") + QK_local = T.alloc_buffer((4,), scope="local") + V_local = T.alloc_buffer((4,), "float16", scope="local") + m_prev = T.alloc_buffer((1,), scope="local") + d_prev = T.alloc_buffer((1,), scope="local") + other_m = T.alloc_buffer((1,), scope="local") + other_d = T.alloc_buffer((1,), scope="local") + exp_mprev = T.alloc_buffer((1,), scope="local") + exp_otherm = T.alloc_buffer((1,), scope="local") + other_o = T.alloc_buffer((4,), scope="local") + st_m = T.alloc_buffer((1,), scope="local") + st_d = T.alloc_buffer((1,), scope="local") + O_local = T.alloc_buffer((4,), scope="local") + by: T.int32 = fused_by_bz % 20 + bz: T.int32 = fused_by_bz // 20 + batch_idx: T.int32 = bx + cur_page_indptr_begin: T.int32 = page_table_indptr[batch_idx] + cur_page_indptr_end: T.int32 = page_table_indptr[batch_idx + 1] + kv_chunk_len[0] = T.if_then_else(cur_page_indptr_begin != cur_page_indptr_end, (cur_page_indptr_end - cur_page_indptr_begin - 1) * 16 + length_info[batch_idx], 0) + st_m[0] = T.float32(-50000) + st_d[0] = T.float32(1) + for vec in T.vectorized(4): + O_local[vec] = T.float32(0) + for vec in T.vectorized(4): + Q_local[vec] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", q_rope_position[batch_idx]) * rope_scale / T.pow(rope_theta, T.Cast("float32", (tx * 4 + vec) * 2 % 64) / T.float32(64))) * T.Cast("float32", Q[bx, by + bz + ty, tx * 4 + vec]) + T.sin(T.Cast("float32", q_rope_position[batch_idx]) * rope_scale / T.pow(rope_theta, T.Cast("float32", (tx * 4 + vec) * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(tx * 4 + vec < 32, Q[bx, by + bz + ty, tx * 4 + vec + 32] * T.float16(-1), Q[bx, by + bz + ty, tx * 4 + vec - 32]))), Q[bx, by + bz + ty, tx * 4 + vec]) + for iterator in range((kv_chunk_len[0] + 63) // 64): + tile_start_s: T.int32 = (tz + ty) * 2 + tile_start_g: T.int32 = (iterator * 32 + tz + ty) * 2 + for j in range(2): + with T.block("KV_load"): + T.reads() + T.writes() + row_g: T.int32 = tile_start_g + j + if row_g < kv_chunk_len[0]: + seq_offset: T.int32 = row_g + page_no: T.int32 = page_table_values[cur_page_indptr_begin + seq_offset // 16] + page_offset: T.int32 = seq_offset % 16 + for vec in T.vectorized(4): + K_smem[tile_start_s + j, tx * 4 + vec] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", k_rope_pos_offset[batch_idx] + row_g) * rope_scale / T.pow(rope_theta, T.Cast("float32", (tx * 4 + vec) * 2 % 64) / T.float32(64))) * T.Cast("float32", pages[page_no, 0, by, page_offset, tx * 4 + vec]) + T.sin(T.Cast("float32", k_rope_pos_offset[batch_idx] + row_g) * rope_scale / T.pow(rope_theta, T.Cast("float32", (tx * 4 + vec) * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(tx * 4 + vec < 32, pages[page_no, 0, by, page_offset, tx * 4 + vec + 32] * T.float16(-1), pages[page_no, 0, by, page_offset, tx * 4 + vec - 32]))), pages[page_no, 0, by, page_offset, tx * 4 + vec]) + V_smem[tile_start_s + j, tx * 4 + vec] = pages[page_no, 1, by, page_offset, tx * 4 + vec] + else: + for vec in T.vectorized(4): + K_smem[tile_start_s + j, tx * 4 + vec] = T.float16(0) + V_smem[tile_start_s + j, tx * 4 + vec] = T.float16(0) + T.tvm_storage_sync("shared") + m_prev[0] = st_m[0] + for j in range(2): + for vec in T.vectorized(4): + QK_local[vec] = T.Cast("float32", Q_local[vec]) * T.Cast("float32", K_smem[tz * 2 + j, tx * 4 + vec]) * attn_score_scaling_factor * sm_scale + S_reduce_local[0] = T.float32(0) + for vec in T.unroll(4): + S_reduce_local[0] = S_reduce_local[0] + QK_local[vec] + with T.block("block_cross_thread"): + T.reads(S_reduce_local[0]) + T.writes(t0[0]) + T.attr(T.comm_reducer(lambda x0, y0: x0 + y0, [T.float32(0)]), "reduce_scope", T.reinterpret("handle", T.uint64(0))) + T.tvm_thread_allreduce(T.uint32(1), S_reduce_local[0], T.bool(True), t0[0], tx) + S_local[j] = T.float32(-50000) + if (iterator * 32 + tz) * 2 + j < kv_chunk_len[0]: + S_local[j] = t0[0] + st_m[0] = T.max(st_m[0], S_local[j]) + o_scale: T.float32 = T.exp2(m_prev[0] - st_m[0]) + st_d[0] = st_d[0] * o_scale + for j in range(2): + S_local[j] = T.exp2(S_local[j] - st_m[0]) + st_d[0] = st_d[0] + S_local[j] + for j in T.vectorized(4): + O_local[j] = O_local[j] * o_scale + for j in range(2): + for vec in T.vectorized(4): + V_local[vec] = V_smem[tz * 2 + j, tx * 4 + vec] + for vec in T.vectorized(4): + O_local[vec] = O_local[vec] + T.Cast("float32", V_local[vec]) * S_local[j] + for vec in T.vectorized(4): + O_allreduce[tz, ty, tx * 4 + vec] = O_local[vec] + md_allreduce[tz, ty, 0] = st_m[0] + md_allreduce[tz, ty, 1] = st_d[0] + T.tvm_storage_sync("shared") + st_m[0] = T.float32(-50000) + st_d[0] = T.float32(1) + for vec in T.vectorized(4): + O_local[vec] = T.float32(0) + for j in range(32): + m_prev[0] = st_m[0] + d_prev[0] = st_d[0] + other_m[0] = md_allreduce[j, ty, 0] + other_d[0] = md_allreduce[j, ty, 1] + for vec in T.vectorized(4): + other_o[vec] = O_allreduce[j, ty, tx * 4 + vec] + st_m[0] = T.max(st_m[0], other_m[0]) + st_d[0] = d_prev[0] * T.exp2(m_prev[0] - st_m[0]) + other_d[0] * T.exp2(other_m[0] - st_m[0]) + exp_mprev[0] = T.exp2(m_prev[0] - st_m[0]) + exp_otherm[0] = T.exp2(other_m[0] - st_m[0]) + for vec in T.vectorized(4): + O_local[vec] = O_local[vec] * exp_mprev[0] + other_o[vec] * exp_otherm[0] + for vec in T.vectorized(4): + O_local[vec] = O_local[vec] / st_d[0] + for vec in T.vectorized(4): + output[batch_idx, by + bz + ty, tx * 4 + vec] = T.Cast("float16", O_local[vec]) + lse[batch_idx, by + bz + ty] = st_m[0] + T.log2(st_d[0]) + + @T.prim_func + def batch_decode_paged_kv_sliding_window(_0: T.int32, Q_handle: T.handle, pages_handle: T.handle, page_table_indptr_handle: T.handle, page_table_values_handle: T.handle, var_length_info: T.handle, k_rope_pos_offset_handle: T.handle, q_rope_position_handle: T.handle, output_handle: T.handle, lse_handle: T.handle, rotary_mode: T.int32, rope_scale: T.float32, rope_theta: T.float32, attn_score_scaling_factor: T.float32): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) + B = T.int32(is_size_var=True) + Q = T.match_buffer(Q_handle, (B, 20, 64), "float16") + max_num_pages = T.int32(is_size_var=True) + pages = T.match_buffer(pages_handle, (max_num_pages, 2, 20, 16, 64), "float16") + page_table_indptr = T.match_buffer(page_table_indptr_handle, (B + 1,), "int32", offset_factor=1) + nnz_pages = T.int32(is_size_var=True) + page_table_values = T.match_buffer(page_table_values_handle, (nnz_pages,), "int32", offset_factor=1) + length_info = T.match_buffer(var_length_info, (3, B), "int32", offset_factor=1) + k_rope_pos_offset = T.match_buffer(k_rope_pos_offset_handle, (B,), "int32", offset_factor=1) + q_rope_position = T.match_buffer(q_rope_position_handle, (B,), "int32", offset_factor=1) + output = T.match_buffer(output_handle, (B, 20, 64), "float16") + lse = T.match_buffer(lse_handle, (B, 20)) + # with T.block("root"): + sm_scale: T.float32 = T.float32(0.18033688011112042) + for bx in T.thread_binding(B, thread="blockIdx.x"): + for fused_by_bz in T.thread_binding(20, thread="blockIdx.y"): + for ty in T.thread_binding(1, thread="threadIdx.y"): + for tx in T.thread_binding(16, thread="threadIdx.x"): + for tz in T.thread_binding(32, thread="threadIdx.z"): + with T.block("attn"): + T.reads(page_table_indptr[bx:bx + 2], length_info[0:3, bx], q_rope_position[bx], Q[bx, fused_by_bz // 20 + ty + fused_by_bz % 20, tx * 4 - 32:tx * 4 - 32 + 68]) + T.writes(output[bx, fused_by_bz % 20 + fused_by_bz // 20 + ty, tx * 4:tx * 4 + 4], lse[bx, fused_by_bz % 20 + fused_by_bz // 20 + ty]) + Q_local = T.alloc_buffer((4,), "float16", scope="local") + kv_chunk_len = T.alloc_buffer((1,), "int32", scope="local") + K_smem = T.alloc_buffer((64, 64), "float16", scope="shared") + V_smem = T.alloc_buffer((64, 64), "float16", scope="shared") + O_allreduce = T.alloc_buffer((32, 1, 64), scope="shared") + md_allreduce = T.alloc_buffer((32, 1, 2), scope="shared") + S_reduce_local = T.alloc_buffer((1,), scope="local") + t0 = T.alloc_buffer((1,), scope="local") + S_local = T.alloc_buffer((2,), scope="local") + QK_local = T.alloc_buffer((4,), scope="local") + V_local = T.alloc_buffer((4,), "float16", scope="local") + m_prev = T.alloc_buffer((1,), scope="local") + d_prev = T.alloc_buffer((1,), scope="local") + other_m = T.alloc_buffer((1,), scope="local") + other_d = T.alloc_buffer((1,), scope="local") + exp_mprev = T.alloc_buffer((1,), scope="local") + exp_otherm = T.alloc_buffer((1,), scope="local") + other_o = T.alloc_buffer((4,), scope="local") + st_m = T.alloc_buffer((1,), scope="local") + st_d = T.alloc_buffer((1,), scope="local") + O_local = T.alloc_buffer((4,), scope="local") + by: T.int32 = fused_by_bz % 20 + bz: T.int32 = fused_by_bz // 20 + batch_idx: T.int32 = bx + cur_page_indptr_begin: T.int32 = page_table_indptr[batch_idx] + cur_page_indptr_end: T.int32 = page_table_indptr[batch_idx + 1] + kv_chunk_len[0] = T.if_then_else(cur_page_indptr_begin != cur_page_indptr_end, (cur_page_indptr_end - cur_page_indptr_begin - 1) * 16 + length_info[0, batch_idx] - length_info[1, batch_idx] + length_info[2, batch_idx], 0) + st_m[0] = T.float32(-50000) + st_d[0] = T.float32(1) + for vec in T.vectorized(4): + O_local[vec] = T.float32(0) + for vec in T.vectorized(4): + Q_local[vec] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", q_rope_position[batch_idx]) * rope_scale / T.pow(rope_theta, T.Cast("float32", (tx * 4 + vec) * 2 % 64) / T.float32(64))) * T.Cast("float32", Q[bx, by + bz + ty, tx * 4 + vec]) + T.sin(T.Cast("float32", q_rope_position[batch_idx]) * rope_scale / T.pow(rope_theta, T.Cast("float32", (tx * 4 + vec) * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(tx * 4 + vec < 32, Q[bx, by + bz + ty, tx * 4 + vec + 32] * T.float16(-1), Q[bx, by + bz + ty, tx * 4 + vec - 32]))), Q[bx, by + bz + ty, tx * 4 + vec]) + for iterator in range((kv_chunk_len[0] + 63) // 64): + tile_start_s: T.int32 = (tz + ty) * 2 + tile_start_g: T.int32 = (iterator * 32 + tz + ty) * 2 + for j in range(2): + with T.block("KV_load"): + T.reads() + T.writes() + row_g: T.int32 = tile_start_g + j + if row_g < kv_chunk_len[0]: + seq_offset: T.int32 = T.if_then_else(row_g < length_info[2, batch_idx], row_g, row_g - length_info[2, batch_idx] + length_info[1, batch_idx]) + page_no: T.int32 = page_table_values[cur_page_indptr_begin + seq_offset // 16] + page_offset: T.int32 = seq_offset % 16 + for vec in T.vectorized(4): + K_smem[tile_start_s + j, tx * 4 + vec] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", k_rope_pos_offset[batch_idx] + row_g) * rope_scale / T.pow(rope_theta, T.Cast("float32", (tx * 4 + vec) * 2 % 64) / T.float32(64))) * T.Cast("float32", pages[page_no, 0, by, page_offset, tx * 4 + vec]) + T.sin(T.Cast("float32", k_rope_pos_offset[batch_idx] + row_g) * rope_scale / T.pow(rope_theta, T.Cast("float32", (tx * 4 + vec) * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(tx * 4 + vec < 32, pages[page_no, 0, by, page_offset, tx * 4 + vec + 32] * T.float16(-1), pages[page_no, 0, by, page_offset, tx * 4 + vec - 32]))), pages[page_no, 0, by, page_offset, tx * 4 + vec]) + V_smem[tile_start_s + j, tx * 4 + vec] = pages[page_no, 1, by, page_offset, tx * 4 + vec] + else: + for vec in T.vectorized(4): + K_smem[tile_start_s + j, tx * 4 + vec] = T.float16(0) + V_smem[tile_start_s + j, tx * 4 + vec] = T.float16(0) + T.tvm_storage_sync("shared") + m_prev[0] = st_m[0] + for j in range(2): + for vec in T.vectorized(4): + QK_local[vec] = T.Cast("float32", Q_local[vec]) * T.Cast("float32", K_smem[tz * 2 + j, tx * 4 + vec]) * attn_score_scaling_factor * sm_scale + S_reduce_local[0] = T.float32(0) + for vec in T.unroll(4): + S_reduce_local[0] = S_reduce_local[0] + QK_local[vec] + with T.block("block_cross_thread"): + T.reads(S_reduce_local[0]) + T.writes(t0[0]) + T.attr(T.comm_reducer(lambda x0, y0: x0 + y0, [T.float32(0)]), "reduce_scope", T.reinterpret("handle", T.uint64(0))) + T.tvm_thread_allreduce(T.uint32(1), S_reduce_local[0], T.bool(True), t0[0], tx) + S_local[j] = T.float32(-50000) + if (iterator * 32 + tz) * 2 + j < kv_chunk_len[0]: + S_local[j] = t0[0] + st_m[0] = T.max(st_m[0], S_local[j]) + o_scale: T.float32 = T.exp2(m_prev[0] - st_m[0]) + st_d[0] = st_d[0] * o_scale + for j in range(2): + S_local[j] = T.exp2(S_local[j] - st_m[0]) + st_d[0] = st_d[0] + S_local[j] + for j in T.vectorized(4): + O_local[j] = O_local[j] * o_scale + for j in range(2): + for vec in T.vectorized(4): + V_local[vec] = V_smem[tz * 2 + j, tx * 4 + vec] + for vec in T.vectorized(4): + O_local[vec] = O_local[vec] + T.Cast("float32", V_local[vec]) * S_local[j] + for vec in T.vectorized(4): + O_allreduce[tz, ty, tx * 4 + vec] = O_local[vec] + md_allreduce[tz, ty, 0] = st_m[0] + md_allreduce[tz, ty, 1] = st_d[0] + T.tvm_storage_sync("shared") + st_m[0] = T.float32(-50000) + st_d[0] = T.float32(1) + for vec in T.vectorized(4): + O_local[vec] = T.float32(0) + for j in range(32): + m_prev[0] = st_m[0] + d_prev[0] = st_d[0] + other_m[0] = md_allreduce[j, ty, 0] + other_d[0] = md_allreduce[j, ty, 1] + for vec in T.vectorized(4): + other_o[vec] = O_allreduce[j, ty, tx * 4 + vec] + st_m[0] = T.max(st_m[0], other_m[0]) + st_d[0] = d_prev[0] * T.exp2(m_prev[0] - st_m[0]) + other_d[0] * T.exp2(other_m[0] - st_m[0]) + exp_mprev[0] = T.exp2(m_prev[0] - st_m[0]) + exp_otherm[0] = T.exp2(other_m[0] - st_m[0]) + for vec in T.vectorized(4): + O_local[vec] = O_local[vec] * exp_mprev[0] + other_o[vec] * exp_otherm[0] + for vec in T.vectorized(4): + O_local[vec] = O_local[vec] / st_d[0] + for vec in T.vectorized(4): + output[batch_idx, by + bz + ty, tx * 4 + vec] = T.Cast("float16", O_local[vec]) + lse[batch_idx, by + bz + ty] = st_m[0] + T.log2(st_d[0]) + + @T.prim_func + def batch_prefill_paged_kv(_0: T.int32, var_q: T.handle, var_q_indptr: T.handle, var_pages: T.handle, var_page_indptr: T.handle, var_page_values: T.handle, var_length_info: T.handle, var_k_rope_pos_offset: T.handle, var_q_rope_position: T.handle, var_output: T.handle, var_lse: T.handle, causal: T.int32, rotary_mode: T.int32, rope_scale: T.float32, rope_theta: T.float32, attn_score_scaling_factor: T.float32): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) + total_len = T.int32(is_size_var=True) + q = T.match_buffer(var_q, (total_len, 20, 64), "float16") + batch_size = T.int32(is_size_var=True) + q_indptr = T.match_buffer(var_q_indptr, (batch_size + 1,), "int32", offset_factor=1) + max_num_pages = T.int32(is_size_var=True) + pages = T.match_buffer(var_pages, (max_num_pages, 2, 20, 16, 64), "float16") + page_indptr = T.match_buffer(var_page_indptr, (batch_size + 1,), "int32", offset_factor=1) + nnz_pages = T.int32(is_size_var=True) + page_values = T.match_buffer(var_page_values, (nnz_pages,), "int32", offset_factor=1) + length_info = T.match_buffer(var_length_info, (batch_size,), "int32", offset_factor=1) + k_rope_pos_offset = T.match_buffer(var_k_rope_pos_offset, (batch_size,), "int32", offset_factor=1) + q_rope_position = T.match_buffer(var_q_rope_position, (total_len,), "int32", offset_factor=1) + output = T.match_buffer(var_output, (total_len, 20, 64), "float16") + lse = T.match_buffer(var_lse, (total_len, 20)) + # with T.block("root"): + for lbx in T.thread_binding(16, thread="blockIdx.x"): + for lby in T.thread_binding(20, thread="blockIdx.y"): + for lty in T.thread_binding(4, thread="threadIdx.y"): + for ltx in T.thread_binding(32, thread="threadIdx.x"): + with T.block("attn"): + bx, by, ty, tx = T.axis.remap("SSSS", [lbx, lby, lty, ltx]) + T.reads() + T.writes() + tile_id = T.alloc_buffer((1,), "int32", scope="local") + batch_idx = T.alloc_buffer((1,), "int32", scope="local") + batch_tiles = T.alloc_buffer((1,), "int32", scope="local") + batch_rows = T.alloc_buffer((1,), "int32", scope="local") + iterator = T.alloc_buffer((1,), "int32", scope="local") + kv_chunk_len = T.alloc_buffer((1,), "int32", scope="local") + Q_smem = T.alloc_buffer((32, 64), "float16", scope="shared") + K_smem = T.alloc_buffer((16, 64), "float16", scope="shared") + V_smem = T.alloc_buffer((16, 64), "float16", scope="shared") + S_smem = T.alloc_buffer((32, 16), scope="shared") + S_local = T.alloc_buffer((32, 16), scope="local") + O_local = T.alloc_buffer((32, 64), scope="local") + m_smem = T.alloc_buffer((32,), scope="shared") + m_prev_smem = T.alloc_buffer((32,), scope="shared") + d_smem = T.alloc_buffer((32,), scope="shared") + m_new = T.alloc_buffer((1,), scope="local") + m_prev = T.alloc_buffer((1,), scope="local") + d_new = T.alloc_buffer((1,), scope="local") + tile_id[0] = bx + batch_idx[0] = 0 + batch_rows[0] = q_indptr[1] - q_indptr[0] + batch_tiles[0] = (batch_rows[0] + 32 - 1) // 32 + while T.tvm_thread_invariant(batch_idx[0] < batch_size): + while tile_id[0] >= batch_tiles[0] and batch_idx[0] < batch_size: + tile_id[0] = tile_id[0] - batch_tiles[0] + batch_idx[0] = batch_idx[0] + 1 + if batch_idx[0] < batch_size: + b_idx: T.int32 = batch_idx[0] + batch_rows[0] = q_indptr[b_idx + 1] - q_indptr[b_idx] + batch_tiles[0] = (batch_rows[0] + 32 - 1) // 32 + if T.tvm_thread_invariant(batch_idx[0] < batch_size): + b_idx: T.int32 = batch_idx[0] + LH_start: T.int32 = tile_id[0] * 32 + q_indptr_val: T.int32 = q_indptr[b_idx] + cur_page_indptr_begin: T.int32 = page_indptr[b_idx] + cur_page_indptr_end: T.int32 = page_indptr[b_idx + 1] + kv_chunk_len[0] = T.if_then_else(cur_page_indptr_begin != cur_page_indptr_end, (cur_page_indptr_end - cur_page_indptr_begin - 1) * 16 + length_info[b_idx], 0) + T.tvm_storage_sync("shared") + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + m_smem[row] = T.float32(-50000) + d_smem[row] = T.float32(1) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(4, 4): + with T.block("O_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + T.reads() + T.writes(O_local[i, j]) + O_local[i, j] = T.float32(0) + T.tvm_storage_sync("shared") + for li_lj_fused_0 in range(4): + for li_lj_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for li_lj_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for li_lj_fused_3 in T.vectorized(4): + with T.block("Q_load"): + i = T.axis.spatial(32, (li_lj_fused_0 * 512 + li_lj_fused_1 * 128 + li_lj_fused_2 * 4 + li_lj_fused_3) // 64) + j = T.axis.spatial(64, (li_lj_fused_0 * 512 + li_lj_fused_1 * 128 + li_lj_fused_2 * 4 + li_lj_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = q_indptr_val + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + Q_smem[i, j] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", q[cur_L, cur_H_qo, j]) + T.sin(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(j < 32, q[cur_L, cur_H_qo, j + 32] * T.float16(-1), q[cur_L, cur_H_qo, j - 32]))), q[cur_L, cur_H_qo, j]) + else: + Q_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + for iterator_1 in range((kv_chunk_len[0] + 15) // 16): + L_kv_start: T.int32 = iterator_1 * 16 + for lz_ly_fused_0 in range(2): + for lz_ly_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for lz_ly_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for lz_ly_fused_3 in T.vectorized(4): + with T.block("K_load"): + i = T.axis.spatial(16, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) // 64) + j = T.axis.spatial(64, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = L_kv_start + i + if cur_L < kv_chunk_len[0]: + seq_offset: T.int32 = cur_L + page_no: T.int32 = page_values[cur_page_indptr_begin + seq_offset // 16] + page_offset: T.int32 = seq_offset % 16 + K_smem[i, j] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", k_rope_pos_offset[b_idx] + cur_L) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", pages[page_no, 0, by, page_offset, j]) + T.sin(T.Cast("float32", k_rope_pos_offset[b_idx] + cur_L) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(j < 32, pages[page_no, 0, by, page_offset, j + 32] * T.float16(-1), pages[page_no, 0, by, page_offset, j - 32]))), pages[page_no, 0, by, page_offset, j]) + else: + K_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + for lz_ly_fused_0 in range(2): + for lz_ly_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for lz_ly_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for lz_ly_fused_3 in T.vectorized(4): + with T.block("V_load"): + i = T.axis.spatial(16, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) // 64) + j = T.axis.spatial(64, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = L_kv_start + i + if cur_L < kv_chunk_len[0]: + seq_offset: T.int32 = cur_L + page_no: T.int32 = page_values[cur_page_indptr_begin + seq_offset // 16] + page_offset: T.int32 = seq_offset % 16 + V_smem[i, j] = pages[page_no, 1, by, page_offset, j] + else: + V_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + with T.block(""): + T.reads(Q_smem[0:32, 0:64], K_smem[0:16, 0:64]) + T.writes(S_local[0:32, 0:16]) + for li_0_lj_0_fused_0_init in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1_init in T.thread_binding(32, thread="threadIdx.x"): + for li_1_init, lj_1_init in T.grid(2, 2): + with T.block("S_gemm_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) // 8 * 2 + li_1_init) + j = T.axis.spatial(16, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) % 8 * 2 + lj_1_init) + T.reads() + T.writes(S_local[i, j]) + S_local[i, j] = T.float32(0) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for lk_0, li_1, lj_1, lk_1 in T.grid(8, 2, 2, 8): + with T.block("S_gemm_update"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 8 * 2 + li_1) + j = T.axis.spatial(16, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 8 * 2 + lj_1) + k = T.axis.reduce(64, lk_0 * 8 + lk_1) + T.reads(S_local[i, j], Q_smem[i, k], K_smem[j, k]) + T.writes(S_local[i, j]) + S_local[i, j] = S_local[i, j] + T.Cast("float32", Q_smem[i, k]) * T.Cast("float32", K_smem[j, k]) * attn_score_scaling_factor * T.float32(0.18033688011112042) + T.tvm_storage_sync("shared") + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(2, 2): + with T.block("S_store"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 8 * 2 + li_1) + j = T.axis.spatial(16, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 8 * 2 + lj_1) + T.reads(S_local[i, j]) + T.writes(S_smem[i, j]) + S_smem[i, j] = S_local[i, j] + T.tvm_storage_sync("shared") + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + with T.block("update1"): + T.reads(m_smem[row], kv_chunk_len[0], q_indptr[b_idx:b_idx + 2], m_new[i], S_smem[row, 0:16], d_smem[row], m_prev[i]) + T.writes(m_prev[i], m_new[i], d_new[i]) + m_prev[i] = m_smem[row] + m_new[i] = m_smem[row] + row_: T.int32 = LH_start + row + for j in range(16): + if T.if_then_else(causal > 0, L_kv_start + j < kv_chunk_len[0] - (q_indptr[b_idx + 1] - q_indptr[b_idx]) + row_ + 1, L_kv_start + j < kv_chunk_len[0]): + m_new[i] = T.max(m_new[i], S_smem[row, j]) + d_new[i] = d_smem[row] * T.exp2(m_prev[i] - m_new[i]) + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + with T.block("update"): + T.reads(kv_chunk_len[0], q_indptr[b_idx:b_idx + 2], S_smem[row, 0:16], m_new[i]) + T.writes(S_smem[row, 0:16]) + for j in range(16): + if row < 32: + row_: T.int32 = LH_start + row + if T.if_then_else(causal > 0, L_kv_start + j < kv_chunk_len[0] - (q_indptr[b_idx + 1] - q_indptr[b_idx]) + row_ + 1, L_kv_start + j < kv_chunk_len[0]): + S_smem[row, j] = T.exp2(S_smem[row, j] - m_new[i]) + else: + S_smem[row, j] = T.exp2(T.float32(-50000) - m_new[i]) + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + with T.block("update"): + T.reads(d_new[i], S_smem[row, 0:16], m_new[i], m_prev[i]) + T.writes(d_new[i], m_smem[row], d_smem[row], m_prev_smem[row]) + for j in range(16): + d_new[i] = d_new[i] + S_smem[row, j] + m_smem[row] = m_new[i] + d_smem[row] = d_new[i] + m_prev_smem[row] = m_prev[i] + T.tvm_storage_sync("shared") + with T.block(""): + T.reads(m_prev_smem[0:32], m_smem[0:32], S_smem[0:32, 0:16], V_smem[0:16, 0:64]) + T.writes(O_local[0:32, 0:64]) + for li_0_lj_0_fused_0_init in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1_init in T.thread_binding(32, thread="threadIdx.x"): + for li_1_init, lj_1_init in T.grid(4, 4): + with T.block("O_gemm_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) // 16 * 4 + li_1_init) + j = T.axis.spatial(64, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) % 16 * 4 + lj_1_init) + T.reads() + T.writes(O_local[i, j]) + O_local[i, j] = O_local[i, j] * T.exp2(m_prev_smem[i] - m_smem[i]) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for lk_0, lk_1, li_1, lj_1 in T.grid(2, 8, 4, 4): + with T.block("O_gemm_update"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + k = T.axis.reduce(16, lk_0 * 8 + lk_1) + T.reads(O_local[i, j], m_prev_smem[i], m_smem[i], S_smem[i, k], V_smem[k, j]) + T.writes(O_local[i, j]) + O_local[i, j] = O_local[i, j] + S_smem[i, k] * T.Cast("float32", V_smem[k, j]) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(4, 4): + with T.block("O_store"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + T.reads(q_indptr[b_idx:b_idx + 2], O_local[i, j], d_smem[i]) + T.writes(output[q_indptr[b_idx] + (LH_start + i), by, j]) + cur_L: T.int32 = q_indptr[b_idx] + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + output[cur_L, cur_H_qo, j] = T.Cast("float16", O_local[i, j] / d_smem[i]) + for li_0 in range(1): + for li_1 in T.thread_binding(4, thread="threadIdx.y"): + for li_2 in T.thread_binding(32, thread="threadIdx.x"): + with T.block("lse_store"): + i = T.axis.spatial(32, li_0 * 128 + li_1 * 32 + li_2) + T.where((li_0 * 4 + li_1) * 32 + li_2 < 32) + T.reads(q_indptr[b_idx:b_idx + 2], m_smem[i], d_smem[i]) + T.writes(lse[q_indptr[b_idx] + (LH_start + i), by]) + cur_L: T.int32 = q_indptr[b_idx] + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + lse[cur_L, cur_H_qo] = m_smem[i] + T.log2(d_smem[i]) + tile_id[0] = tile_id[0] + 16 + + @T.prim_func + def batch_prefill_paged_kv_sliding_window(_0: T.int32, var_q: T.handle, var_q_indptr: T.handle, var_pages: T.handle, var_page_indptr: T.handle, var_page_values: T.handle, var_length_info: T.handle, var_k_rope_pos_offset: T.handle, var_q_rope_position: T.handle, var_output: T.handle, var_lse: T.handle, causal: T.int32, rotary_mode: T.int32, rope_scale: T.float32, rope_theta: T.float32, attn_score_scaling_factor: T.float32): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) + total_len = T.int32(is_size_var=True) + q = T.match_buffer(var_q, (total_len, 20, 64), "float16") + batch_size = T.int32(is_size_var=True) + q_indptr = T.match_buffer(var_q_indptr, (batch_size + 1,), "int32", offset_factor=1) + max_num_pages = T.int32(is_size_var=True) + pages = T.match_buffer(var_pages, (max_num_pages, 2, 20, 16, 64), "float16") + page_indptr = T.match_buffer(var_page_indptr, (batch_size + 1,), "int32", offset_factor=1) + nnz_pages = T.int32(is_size_var=True) + page_values = T.match_buffer(var_page_values, (nnz_pages,), "int32", offset_factor=1) + length_info = T.match_buffer(var_length_info, (3, batch_size), "int32", offset_factor=1) + k_rope_pos_offset = T.match_buffer(var_k_rope_pos_offset, (batch_size,), "int32", offset_factor=1) + q_rope_position = T.match_buffer(var_q_rope_position, (total_len,), "int32", offset_factor=1) + output = T.match_buffer(var_output, (total_len, 20, 64), "float16") + lse = T.match_buffer(var_lse, (total_len, 20)) + # with T.block("root"): + for lbx in T.thread_binding(16, thread="blockIdx.x"): + for lby in T.thread_binding(20, thread="blockIdx.y"): + for lty in T.thread_binding(4, thread="threadIdx.y"): + for ltx in T.thread_binding(32, thread="threadIdx.x"): + with T.block("attn"): + bx, by, ty, tx = T.axis.remap("SSSS", [lbx, lby, lty, ltx]) + T.reads() + T.writes() + tile_id = T.alloc_buffer((1,), "int32", scope="local") + batch_idx = T.alloc_buffer((1,), "int32", scope="local") + batch_tiles = T.alloc_buffer((1,), "int32", scope="local") + batch_rows = T.alloc_buffer((1,), "int32", scope="local") + iterator = T.alloc_buffer((1,), "int32", scope="local") + kv_chunk_len = T.alloc_buffer((1,), "int32", scope="local") + Q_smem = T.alloc_buffer((32, 64), "float16", scope="shared") + K_smem = T.alloc_buffer((16, 64), "float16", scope="shared") + V_smem = T.alloc_buffer((16, 64), "float16", scope="shared") + S_smem = T.alloc_buffer((32, 16), scope="shared") + S_local = T.alloc_buffer((32, 16), scope="local") + O_local = T.alloc_buffer((32, 64), scope="local") + m_smem = T.alloc_buffer((32,), scope="shared") + m_prev_smem = T.alloc_buffer((32,), scope="shared") + d_smem = T.alloc_buffer((32,), scope="shared") + m_new = T.alloc_buffer((1,), scope="local") + m_prev = T.alloc_buffer((1,), scope="local") + d_new = T.alloc_buffer((1,), scope="local") + tile_id[0] = bx + batch_idx[0] = 0 + batch_rows[0] = q_indptr[1] - q_indptr[0] + batch_tiles[0] = (batch_rows[0] + 32 - 1) // 32 + while T.tvm_thread_invariant(batch_idx[0] < batch_size): + while tile_id[0] >= batch_tiles[0] and batch_idx[0] < batch_size: + tile_id[0] = tile_id[0] - batch_tiles[0] + batch_idx[0] = batch_idx[0] + 1 + if batch_idx[0] < batch_size: + b_idx: T.int32 = batch_idx[0] + batch_rows[0] = q_indptr[b_idx + 1] - q_indptr[b_idx] + batch_tiles[0] = (batch_rows[0] + 32 - 1) // 32 + if T.tvm_thread_invariant(batch_idx[0] < batch_size): + b_idx: T.int32 = batch_idx[0] + LH_start: T.int32 = tile_id[0] * 32 + q_indptr_val: T.int32 = q_indptr[b_idx] + cur_page_indptr_begin: T.int32 = page_indptr[b_idx] + cur_page_indptr_end: T.int32 = page_indptr[b_idx + 1] + kv_chunk_len[0] = T.if_then_else(cur_page_indptr_begin != cur_page_indptr_end, (cur_page_indptr_end - cur_page_indptr_begin - 1) * 16 + length_info[0, b_idx] - length_info[1, b_idx] + length_info[2, b_idx], 0) + T.tvm_storage_sync("shared") + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + m_smem[row] = T.float32(-50000) + d_smem[row] = T.float32(1) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(4, 4): + with T.block("O_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + T.reads() + T.writes(O_local[i, j]) + O_local[i, j] = T.float32(0) + T.tvm_storage_sync("shared") + for li_lj_fused_0 in range(4): + for li_lj_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for li_lj_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for li_lj_fused_3 in T.vectorized(4): + with T.block("Q_load"): + i = T.axis.spatial(32, (li_lj_fused_0 * 512 + li_lj_fused_1 * 128 + li_lj_fused_2 * 4 + li_lj_fused_3) // 64) + j = T.axis.spatial(64, (li_lj_fused_0 * 512 + li_lj_fused_1 * 128 + li_lj_fused_2 * 4 + li_lj_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = q_indptr_val + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + Q_smem[i, j] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", q[cur_L, cur_H_qo, j]) + T.sin(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(j < 32, q[cur_L, cur_H_qo, j + 32] * T.float16(-1), q[cur_L, cur_H_qo, j - 32]))), q[cur_L, cur_H_qo, j]) + else: + Q_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + for iterator_1 in range((kv_chunk_len[0] + 15) // 16): + L_kv_start: T.int32 = iterator_1 * 16 + for lz_ly_fused_0 in range(2): + for lz_ly_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for lz_ly_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for lz_ly_fused_3 in T.vectorized(4): + with T.block("K_load"): + i = T.axis.spatial(16, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) // 64) + j = T.axis.spatial(64, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = L_kv_start + i + if cur_L < kv_chunk_len[0]: + seq_offset: T.int32 = T.if_then_else(cur_L < length_info[2, b_idx], cur_L, cur_L - length_info[2, b_idx] + length_info[1, b_idx]) + page_no: T.int32 = page_values[cur_page_indptr_begin + seq_offset // 16] + page_offset: T.int32 = seq_offset % 16 + K_smem[i, j] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", k_rope_pos_offset[b_idx] + cur_L) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", pages[page_no, 0, by, page_offset, j]) + T.sin(T.Cast("float32", k_rope_pos_offset[b_idx] + cur_L) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(j < 32, pages[page_no, 0, by, page_offset, j + 32] * T.float16(-1), pages[page_no, 0, by, page_offset, j - 32]))), pages[page_no, 0, by, page_offset, j]) + else: + K_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + for lz_ly_fused_0 in range(2): + for lz_ly_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for lz_ly_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for lz_ly_fused_3 in T.vectorized(4): + with T.block("V_load"): + i = T.axis.spatial(16, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) // 64) + j = T.axis.spatial(64, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = L_kv_start + i + if cur_L < kv_chunk_len[0]: + seq_offset: T.int32 = T.if_then_else(cur_L < length_info[2, b_idx], cur_L, cur_L - length_info[2, b_idx] + length_info[1, b_idx]) + page_no: T.int32 = page_values[cur_page_indptr_begin + seq_offset // 16] + page_offset: T.int32 = seq_offset % 16 + V_smem[i, j] = pages[page_no, 1, by, page_offset, j] + else: + V_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + with T.block(""): + T.reads(Q_smem[0:32, 0:64], K_smem[0:16, 0:64]) + T.writes(S_local[0:32, 0:16]) + for li_0_lj_0_fused_0_init in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1_init in T.thread_binding(32, thread="threadIdx.x"): + for li_1_init, lj_1_init in T.grid(2, 2): + with T.block("S_gemm_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) // 8 * 2 + li_1_init) + j = T.axis.spatial(16, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) % 8 * 2 + lj_1_init) + T.reads() + T.writes(S_local[i, j]) + S_local[i, j] = T.float32(0) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for lk_0, li_1, lj_1, lk_1 in T.grid(8, 2, 2, 8): + with T.block("S_gemm_update"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 8 * 2 + li_1) + j = T.axis.spatial(16, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 8 * 2 + lj_1) + k = T.axis.reduce(64, lk_0 * 8 + lk_1) + T.reads(S_local[i, j], Q_smem[i, k], K_smem[j, k]) + T.writes(S_local[i, j]) + S_local[i, j] = S_local[i, j] + T.Cast("float32", Q_smem[i, k]) * T.Cast("float32", K_smem[j, k]) * attn_score_scaling_factor * T.float32(0.18033688011112042) + T.tvm_storage_sync("shared") + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(2, 2): + with T.block("S_store"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 8 * 2 + li_1) + j = T.axis.spatial(16, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 8 * 2 + lj_1) + T.reads(S_local[i, j]) + T.writes(S_smem[i, j]) + S_smem[i, j] = S_local[i, j] + T.tvm_storage_sync("shared") + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + with T.block("update1"): + T.reads(m_smem[row], kv_chunk_len[0], q_indptr[b_idx:b_idx + 2], m_new[i], S_smem[row, 0:16], d_smem[row], m_prev[i]) + T.writes(m_prev[i], m_new[i], d_new[i]) + m_prev[i] = m_smem[row] + m_new[i] = m_smem[row] + row_: T.int32 = LH_start + row + for j in range(16): + if T.if_then_else(causal > 0, L_kv_start + j < kv_chunk_len[0] - (q_indptr[b_idx + 1] - q_indptr[b_idx]) + row_ + 1, L_kv_start + j < kv_chunk_len[0]): + m_new[i] = T.max(m_new[i], S_smem[row, j]) + d_new[i] = d_smem[row] * T.exp2(m_prev[i] - m_new[i]) + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + with T.block("update"): + T.reads(kv_chunk_len[0], q_indptr[b_idx:b_idx + 2], S_smem[row, 0:16], m_new[i]) + T.writes(S_smem[row, 0:16]) + for j in range(16): + if row < 32: + row_: T.int32 = LH_start + row + if T.if_then_else(causal > 0, L_kv_start + j < kv_chunk_len[0] - (q_indptr[b_idx + 1] - q_indptr[b_idx]) + row_ + 1, L_kv_start + j < kv_chunk_len[0]): + S_smem[row, j] = T.exp2(S_smem[row, j] - m_new[i]) + else: + S_smem[row, j] = T.exp2(T.float32(-50000) - m_new[i]) + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + with T.block("update"): + T.reads(d_new[i], S_smem[row, 0:16], m_new[i], m_prev[i]) + T.writes(d_new[i], m_smem[row], d_smem[row], m_prev_smem[row]) + for j in range(16): + d_new[i] = d_new[i] + S_smem[row, j] + m_smem[row] = m_new[i] + d_smem[row] = d_new[i] + m_prev_smem[row] = m_prev[i] + T.tvm_storage_sync("shared") + with T.block(""): + T.reads(m_prev_smem[0:32], m_smem[0:32], S_smem[0:32, 0:16], V_smem[0:16, 0:64]) + T.writes(O_local[0:32, 0:64]) + for li_0_lj_0_fused_0_init in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1_init in T.thread_binding(32, thread="threadIdx.x"): + for li_1_init, lj_1_init in T.grid(4, 4): + with T.block("O_gemm_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) // 16 * 4 + li_1_init) + j = T.axis.spatial(64, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) % 16 * 4 + lj_1_init) + T.reads() + T.writes(O_local[i, j]) + O_local[i, j] = O_local[i, j] * T.exp2(m_prev_smem[i] - m_smem[i]) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for lk_0, lk_1, li_1, lj_1 in T.grid(2, 8, 4, 4): + with T.block("O_gemm_update"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + k = T.axis.reduce(16, lk_0 * 8 + lk_1) + T.reads(O_local[i, j], m_prev_smem[i], m_smem[i], S_smem[i, k], V_smem[k, j]) + T.writes(O_local[i, j]) + O_local[i, j] = O_local[i, j] + S_smem[i, k] * T.Cast("float32", V_smem[k, j]) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(4, 4): + with T.block("O_store"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + T.reads(q_indptr[b_idx:b_idx + 2], O_local[i, j], d_smem[i]) + T.writes(output[q_indptr[b_idx] + (LH_start + i), by, j]) + cur_L: T.int32 = q_indptr[b_idx] + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + output[cur_L, cur_H_qo, j] = T.Cast("float16", O_local[i, j] / d_smem[i]) + for li_0 in range(1): + for li_1 in T.thread_binding(4, thread="threadIdx.y"): + for li_2 in T.thread_binding(32, thread="threadIdx.x"): + with T.block("lse_store"): + i = T.axis.spatial(32, li_0 * 128 + li_1 * 32 + li_2) + T.where((li_0 * 4 + li_1) * 32 + li_2 < 32) + T.reads(q_indptr[b_idx:b_idx + 2], m_smem[i], d_smem[i]) + T.writes(lse[q_indptr[b_idx] + (LH_start + i), by]) + cur_L: T.int32 = q_indptr[b_idx] + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + lse[cur_L, cur_H_qo] = m_smem[i] + T.log2(d_smem[i]) + tile_id[0] = tile_id[0] + 16 + + @T.prim_func + def batch_prefill_ragged_kv(var_q: T.handle, var_q_indptr: T.handle, var_k: T.handle, var_v: T.handle, var_kv_indptr: T.handle, var_q_rope_position: T.handle, var_k_rope_pos_offset: T.handle, var_output: T.handle, var_lse: T.handle, causal: T.int32, rotary_mode: T.int32, rope_scale: T.float32, rope_theta: T.float32, attn_score_scaling_factor: T.float32): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) + qo_len = T.int32(is_size_var=True) + q = T.match_buffer(var_q, (qo_len, 20, 64), "float16") + batch_size = T.int32(is_size_var=True) + q_indptr = T.match_buffer(var_q_indptr, (batch_size + 1,), "int32", offset_factor=1) + kv_len = T.int32(is_size_var=True) + k = T.match_buffer(var_k, (kv_len, 20, 64), "float16") + v = T.match_buffer(var_v, (kv_len, 20, 64), "float16") + kv_indptr = T.match_buffer(var_kv_indptr, (batch_size + 1,), "int32", offset_factor=1) + q_rope_position = T.match_buffer(var_q_rope_position, (qo_len,), "int32", offset_factor=1) + k_rope_pos_offset = T.match_buffer(var_k_rope_pos_offset, (batch_size,), "int32", offset_factor=1) + output = T.match_buffer(var_output, (qo_len, 20, 64), "float16") + lse = T.match_buffer(var_lse, (qo_len, 20)) + # with T.block("root"): + for lbx in T.thread_binding(16, thread="blockIdx.x"): + for lby in T.thread_binding(20, thread="blockIdx.y"): + for lty in T.thread_binding(4, thread="threadIdx.y"): + for ltx in T.thread_binding(32, thread="threadIdx.x"): + with T.block("attn"): + bx, by, ty, tx = T.axis.remap("SSSS", [lbx, lby, lty, ltx]) + T.reads() + T.writes() + tile_id = T.alloc_buffer((1,), "int32", scope="local") + batch_idx = T.alloc_buffer((1,), "int32", scope="local") + batch_tiles = T.alloc_buffer((1,), "int32", scope="local") + batch_rows = T.alloc_buffer((1,), "int32", scope="local") + iterator = T.alloc_buffer((1,), "int32", scope="local") + kv_chunk_len = T.alloc_buffer((1,), "int32", scope="local") + Q_smem = T.alloc_buffer((32, 64), "float16", scope="shared") + K_smem = T.alloc_buffer((16, 64), "float16", scope="shared") + V_smem = T.alloc_buffer((16, 64), "float16", scope="shared") + S_smem = T.alloc_buffer((32, 16), scope="shared") + S_local = T.alloc_buffer((32, 16), scope="local") + O_local = T.alloc_buffer((32, 64), scope="local") + m_smem = T.alloc_buffer((32,), scope="shared") + m_prev_smem = T.alloc_buffer((32,), scope="shared") + d_smem = T.alloc_buffer((32,), scope="shared") + m_new = T.alloc_buffer((1,), scope="local") + m_prev = T.alloc_buffer((1,), scope="local") + d_new = T.alloc_buffer((1,), scope="local") + tile_id[0] = bx + batch_idx[0] = 0 + batch_rows[0] = q_indptr[1] - q_indptr[0] + batch_tiles[0] = (batch_rows[0] + 32 - 1) // 32 + while T.tvm_thread_invariant(batch_idx[0] < batch_size): + while tile_id[0] >= batch_tiles[0] and batch_idx[0] < batch_size: + tile_id[0] = tile_id[0] - batch_tiles[0] + batch_idx[0] = batch_idx[0] + 1 + if batch_idx[0] < batch_size: + b_idx: T.int32 = batch_idx[0] + batch_rows[0] = q_indptr[b_idx + 1] - q_indptr[b_idx] + batch_tiles[0] = (batch_rows[0] + 32 - 1) // 32 + if T.tvm_thread_invariant(batch_idx[0] < batch_size): + b_idx: T.int32 = batch_idx[0] + q_indptr_val: T.int32 = q_indptr[b_idx] + LH_start: T.int32 = tile_id[0] * 32 + kv_chunk_len[0] = kv_indptr[b_idx + 1] - kv_indptr[b_idx] + T.tvm_storage_sync("shared") + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + m_smem[row] = T.float32(-50000) + d_smem[row] = T.float32(1) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(4, 4): + with T.block("O_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + T.reads() + T.writes(O_local[i, j]) + O_local[i, j] = T.float32(0) + T.tvm_storage_sync("shared") + for li_lj_fused_0 in range(4): + for li_lj_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for li_lj_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for li_lj_fused_3 in T.vectorized(4): + with T.block("Q_load"): + i = T.axis.spatial(32, (li_lj_fused_0 * 512 + li_lj_fused_1 * 128 + li_lj_fused_2 * 4 + li_lj_fused_3) // 64) + j = T.axis.spatial(64, (li_lj_fused_0 * 512 + li_lj_fused_1 * 128 + li_lj_fused_2 * 4 + li_lj_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = q_indptr_val + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + Q_smem[i, j] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", q[cur_L, cur_H_qo, j]) + T.sin(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(j < 32, q[cur_L, cur_H_qo, j + 32] * T.float16(-1), q[cur_L, cur_H_qo, j - 32]))), q[cur_L, cur_H_qo, j]) + else: + Q_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + for iterator_1 in range((kv_chunk_len[0] + 15) // 16): + L_kv_start: T.int32 = iterator_1 * 16 + L_kv_base: T.int32 = kv_indptr[b_idx] + for lz_ly_fused_0 in range(2): + for lz_ly_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for lz_ly_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for lz_ly_fused_3 in T.vectorized(4): + with T.block("K_load"): + i = T.axis.spatial(16, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) // 64) + j = T.axis.spatial(64, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = L_kv_start + i + if cur_L < kv_chunk_len[0]: + K_smem[i, j] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", k_rope_pos_offset[b_idx] + cur_L) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", k[L_kv_base + cur_L, by, j]) + T.sin(T.Cast("float32", k_rope_pos_offset[b_idx] + cur_L) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(j < 32, k[L_kv_base + cur_L, by, j + 32] * T.float16(-1), k[L_kv_base + cur_L, by, j - 32]))), k[L_kv_base + cur_L, by, j]) + else: + K_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + for lz_ly_fused_0 in range(2): + for lz_ly_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for lz_ly_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for lz_ly_fused_3 in T.vectorized(4): + with T.block("V_load"): + i = T.axis.spatial(16, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) // 64) + j = T.axis.spatial(64, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = L_kv_start + i + if cur_L < kv_chunk_len[0]: + V_smem[i, j] = v[L_kv_base + cur_L, by, j] + else: + V_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + with T.block(""): + T.reads(Q_smem[0:32, 0:64], K_smem[0:16, 0:64]) + T.writes(S_local[0:32, 0:16]) + for li_0_lj_0_fused_0_init in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1_init in T.thread_binding(32, thread="threadIdx.x"): + for li_1_init, lj_1_init in T.grid(2, 2): + with T.block("S_gemm_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) // 8 * 2 + li_1_init) + j = T.axis.spatial(16, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) % 8 * 2 + lj_1_init) + T.reads() + T.writes(S_local[i, j]) + S_local[i, j] = T.float32(0) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for lk_0, li_1, lj_1, lk_1 in T.grid(8, 2, 2, 8): + with T.block("S_gemm_update"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 8 * 2 + li_1) + j = T.axis.spatial(16, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 8 * 2 + lj_1) + k_1 = T.axis.reduce(64, lk_0 * 8 + lk_1) + T.reads(S_local[i, j], Q_smem[i, k_1], K_smem[j, k_1]) + T.writes(S_local[i, j]) + S_local[i, j] = S_local[i, j] + T.Cast("float32", Q_smem[i, k_1]) * T.Cast("float32", K_smem[j, k_1]) * attn_score_scaling_factor * T.float32(0.18033688011112042) + T.tvm_storage_sync("shared") + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(2, 2): + with T.block("S_store"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 8 * 2 + li_1) + j = T.axis.spatial(16, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 8 * 2 + lj_1) + T.reads(S_local[i, j]) + T.writes(S_smem[i, j]) + S_smem[i, j] = S_local[i, j] + T.tvm_storage_sync("shared") + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + with T.block("update1"): + T.reads(m_smem[row], kv_chunk_len[0], q_indptr[b_idx:b_idx + 2], m_new[i], S_smem[row, 0:16], d_smem[row], m_prev[i]) + T.writes(m_prev[i], m_new[i], d_new[i]) + m_prev[i] = m_smem[row] + m_new[i] = m_smem[row] + row_: T.int32 = LH_start + row + for j in range(16): + if T.if_then_else(causal > 0, L_kv_start + j < kv_chunk_len[0] - (q_indptr[b_idx + 1] - q_indptr[b_idx]) + row_ + 1, L_kv_start + j < kv_chunk_len[0]): + m_new[i] = T.max(m_new[i], S_smem[row, j]) + d_new[i] = d_smem[row] * T.exp2(m_prev[i] - m_new[i]) + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + with T.block("update"): + T.reads(kv_chunk_len[0], q_indptr[b_idx:b_idx + 2], S_smem[row, 0:16], m_new[i]) + T.writes(S_smem[row, 0:16]) + for j in range(16): + if row < 32: + row_: T.int32 = LH_start + row + if T.if_then_else(causal > 0, L_kv_start + j < kv_chunk_len[0] - (q_indptr[b_idx + 1] - q_indptr[b_idx]) + row_ + 1, L_kv_start + j < kv_chunk_len[0]): + S_smem[row, j] = T.exp2(S_smem[row, j] - m_new[i]) + else: + S_smem[row, j] = T.exp2(T.float32(-50000) - m_new[i]) + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + with T.block("update"): + T.reads(d_new[i], S_smem[row, 0:16], m_new[i], m_prev[i]) + T.writes(d_new[i], m_smem[row], d_smem[row], m_prev_smem[row]) + for j in range(16): + d_new[i] = d_new[i] + S_smem[row, j] + m_smem[row] = m_new[i] + d_smem[row] = d_new[i] + m_prev_smem[row] = m_prev[i] + T.tvm_storage_sync("shared") + with T.block(""): + T.reads(m_prev_smem[0:32], m_smem[0:32], S_smem[0:32, 0:16], V_smem[0:16, 0:64]) + T.writes(O_local[0:32, 0:64]) + for li_0_lj_0_fused_0_init in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1_init in T.thread_binding(32, thread="threadIdx.x"): + for li_1_init, lj_1_init in T.grid(4, 4): + with T.block("O_gemm_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) // 16 * 4 + li_1_init) + j = T.axis.spatial(64, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) % 16 * 4 + lj_1_init) + T.reads() + T.writes(O_local[i, j]) + O_local[i, j] = O_local[i, j] * T.exp2(m_prev_smem[i] - m_smem[i]) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for lk_0, lk_1, li_1, lj_1 in T.grid(2, 8, 4, 4): + with T.block("O_gemm_update"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + k_1 = T.axis.reduce(16, lk_0 * 8 + lk_1) + T.reads(O_local[i, j], m_prev_smem[i], m_smem[i], S_smem[i, k_1], V_smem[k_1, j]) + T.writes(O_local[i, j]) + O_local[i, j] = O_local[i, j] + S_smem[i, k_1] * T.Cast("float32", V_smem[k_1, j]) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(4, 4): + with T.block("O_store"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + T.reads(q_indptr[b_idx:b_idx + 2], O_local[i, j], d_smem[i]) + T.writes(output[q_indptr[b_idx] + (LH_start + i), by, j]) + cur_L: T.int32 = q_indptr[b_idx] + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + output[cur_L, cur_H_qo, j] = T.Cast("float16", O_local[i, j] / d_smem[i]) + for li_0 in range(1): + for li_1 in T.thread_binding(4, thread="threadIdx.y"): + for li_2 in T.thread_binding(32, thread="threadIdx.x"): + with T.block("lse_store"): + i = T.axis.spatial(32, li_0 * 128 + li_1 * 32 + li_2) + T.where((li_0 * 4 + li_1) * 32 + li_2 < 32) + T.reads(q_indptr[b_idx:b_idx + 2], m_smem[i], d_smem[i]) + T.writes(lse[q_indptr[b_idx] + (LH_start + i), by]) + cur_L: T.int32 = q_indptr[b_idx] + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + lse[cur_L, cur_H_qo] = m_smem[i] + T.log2(d_smem[i]) + tile_id[0] = tile_id[0] + 16 + + @T.prim_func + def batch_tree_attn(var_q: T.handle, var_q_indptr: T.handle, var_k: T.handle, var_v: T.handle, var_kv_indptr: T.handle, var_q_rope_position: T.handle, var_mn_indptr: T.handle, var_mask: T.handle, var_output: T.handle, var_lse: T.handle, rotary_mode: T.int32, rope_scale: T.float32, rope_theta: T.float32, attn_score_scaling_factor: T.float32, batch_size: T.int32): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) + qo_len = T.int32(is_size_var=True) + q = T.match_buffer(var_q, (qo_len, 20, 64), "float16") + q_indptr = T.match_buffer(var_q_indptr, (batch_size + 1,), "int32", offset_factor=1) + kv_len = T.int32(is_size_var=True) + k = T.match_buffer(var_k, (kv_len, 20, 64), "float16") + v = T.match_buffer(var_v, (kv_len, 20, 64), "float16") + kv_indptr = T.match_buffer(var_kv_indptr, (batch_size + 1,), "int32", offset_factor=1) + q_rope_position = T.match_buffer(var_q_rope_position, (qo_len,), "int32", offset_factor=1) + mn_indptr = T.match_buffer(var_mn_indptr, (batch_size + 1,), "int32", offset_factor=1) + tree_size = T.int32(is_size_var=True) + mask = T.match_buffer(var_mask, (tree_size,), "int32", offset_factor=1) + output = T.match_buffer(var_output, (qo_len, 20, 64), "float16") + lse = T.match_buffer(var_lse, (qo_len, 20)) + # with T.block("root"): + for lbx in T.thread_binding(16, thread="blockIdx.x"): + for lby in T.thread_binding(20, thread="blockIdx.y"): + for lty in T.thread_binding(4, thread="threadIdx.y"): + for ltx in T.thread_binding(32, thread="threadIdx.x"): + with T.block("attn"): + bx, by, ty, tx = T.axis.remap("SSSS", [lbx, lby, lty, ltx]) + T.reads() + T.writes() + tile_id = T.alloc_buffer((1,), "int32", scope="local") + batch_idx = T.alloc_buffer((1,), "int32", scope="local") + batch_tiles = T.alloc_buffer((1,), "int32", scope="local") + batch_rows = T.alloc_buffer((1,), "int32", scope="local") + iterator = T.alloc_buffer((1,), "int32", scope="local") + kv_chunk_len = T.alloc_buffer((1,), "int32", scope="local") + Q_smem = T.alloc_buffer((32, 64), "float16", scope="shared") + K_smem = T.alloc_buffer((16, 64), "float16", scope="shared") + V_smem = T.alloc_buffer((16, 64), "float16", scope="shared") + S_smem = T.alloc_buffer((32, 16), scope="shared") + S_local = T.alloc_buffer((32, 16), scope="local") + O_local = T.alloc_buffer((32, 64), scope="local") + m_smem = T.alloc_buffer((32,), scope="shared") + m_prev_smem = T.alloc_buffer((32,), scope="shared") + d_smem = T.alloc_buffer((32,), scope="shared") + m_new = T.alloc_buffer((1,), scope="local") + m_prev = T.alloc_buffer((1,), scope="local") + d_new = T.alloc_buffer((1,), scope="local") + tile_id[0] = bx + batch_idx[0] = 0 + batch_rows[0] = q_indptr[1] - q_indptr[0] + batch_tiles[0] = (batch_rows[0] + 32 - 1) // 32 + while T.tvm_thread_invariant(batch_idx[0] < batch_size): + while tile_id[0] >= batch_tiles[0] and batch_idx[0] < batch_size: + tile_id[0] = tile_id[0] - batch_tiles[0] + batch_idx[0] = batch_idx[0] + 1 + if batch_idx[0] < batch_size: + b_idx: T.int32 = batch_idx[0] + batch_rows[0] = q_indptr[b_idx + 1] - q_indptr[b_idx] + batch_tiles[0] = (batch_rows[0] + 32 - 1) // 32 + if T.tvm_thread_invariant(batch_idx[0] < batch_size): + b_idx: T.int32 = batch_idx[0] + LH_start: T.int32 = tile_id[0] * 32 + q_indptr_val: T.int32 = q_indptr[b_idx] + kv_chunk_len[0] = kv_indptr[b_idx + 1] - kv_indptr[b_idx] + T.tvm_storage_sync("shared") + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + m_smem[row] = T.float32(-50000) + d_smem[row] = T.float32(1) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(4, 4): + with T.block("O_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + T.reads() + T.writes(O_local[i, j]) + O_local[i, j] = T.float32(0) + T.tvm_storage_sync("shared") + for li_lj_fused_0 in range(4): + for li_lj_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for li_lj_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for li_lj_fused_3 in T.vectorized(4): + with T.block("Q_load"): + i = T.axis.spatial(32, (li_lj_fused_0 * 512 + li_lj_fused_1 * 128 + li_lj_fused_2 * 4 + li_lj_fused_3) // 64) + j = T.axis.spatial(64, (li_lj_fused_0 * 512 + li_lj_fused_1 * 128 + li_lj_fused_2 * 4 + li_lj_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = q_indptr_val + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + Q_smem[i, j] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64)))) * q[cur_L, cur_H_qo, j] + T.Cast("float16", T.sin(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64)))) * T.if_then_else(j < 32, q[cur_L, cur_H_qo, j + 32] * T.float16(-1), q[cur_L, cur_H_qo, j - 32]), q[cur_L, cur_H_qo, j]) + else: + Q_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + for iterator_1 in range((kv_chunk_len[0] + 15) // 16): + L_kv_start: T.int32 = iterator_1 * 16 + L_kv_base: T.int32 = kv_indptr[b_idx] + for lz_ly_fused_0 in range(2): + for lz_ly_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for lz_ly_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for lz_ly_fused_3 in T.vectorized(4): + with T.block("KV_load"): + i = T.axis.spatial(16, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) // 64) + j = T.axis.spatial(64, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = L_kv_base + L_kv_start + i + if L_kv_start + i < kv_chunk_len[0]: + K_smem[i, j] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64)))) * k[cur_L, by, j] + T.Cast("float16", T.sin(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64)))) * T.if_then_else(j < 32, k[cur_L, by, j + 32] * T.float16(-1), k[cur_L, by, j - 32]), k[cur_L, by, j]) + V_smem[i, j] = v[cur_L, by, j] + else: + K_smem[i, j] = T.float16(0) + V_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + with T.block(""): + T.reads(Q_smem[0:32, 0:64], K_smem[0:16, 0:64]) + T.writes(S_local[0:32, 0:16]) + for li_0_lj_0_fused_0_init in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1_init in T.thread_binding(32, thread="threadIdx.x"): + for li_1_init, lj_1_init in T.grid(2, 2): + with T.block("S_gemm_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) // 8 * 2 + li_1_init) + j = T.axis.spatial(16, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) % 8 * 2 + lj_1_init) + T.reads() + T.writes(S_local[i, j]) + S_local[i, j] = T.float32(0) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for lk_0, li_1, lj_1, lk_1 in T.grid(8, 2, 2, 8): + with T.block("S_gemm_update"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 8 * 2 + li_1) + j = T.axis.spatial(16, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 8 * 2 + lj_1) + k_1 = T.axis.reduce(64, lk_0 * 8 + lk_1) + T.reads(S_local[i, j], Q_smem[i, k_1], K_smem[j, k_1]) + T.writes(S_local[i, j]) + S_local[i, j] = S_local[i, j] + T.Cast("float32", Q_smem[i, k_1]) * T.Cast("float32", K_smem[j, k_1]) * attn_score_scaling_factor * T.float32(0.18033688011112042) + T.tvm_storage_sync("shared") + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(2, 2): + with T.block("S_store"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 8 * 2 + li_1) + j = T.axis.spatial(16, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 8 * 2 + lj_1) + T.reads(S_local[i, j]) + T.writes(S_smem[i, j]) + S_smem[i, j] = S_local[i, j] + T.tvm_storage_sync("shared") + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + with T.block("update1"): + T.reads(m_smem[row], kv_chunk_len[0], mask[mn_indptr[b_idx] + (LH_start + row) * (q_indptr[b_idx + 1] - q_indptr[b_idx]) + L_kv_start:mn_indptr[b_idx] + (LH_start + row) * (q_indptr[b_idx + 1] - q_indptr[b_idx]) + L_kv_start + 16], mn_indptr[b_idx], q_indptr[b_idx:b_idx + 2], m_new[i], S_smem[row, 0:16], d_smem[row], m_prev[i]) + T.writes(m_prev[i], m_new[i], d_new[i]) + m_prev[i] = m_smem[row] + m_new[i] = m_smem[row] + row_: T.int32 = LH_start + row + for j in range(16): + if L_kv_start + j < kv_chunk_len[0] and mask[mn_indptr[b_idx] + row_ * (q_indptr[b_idx + 1] - q_indptr[b_idx]) + (L_kv_start + j)] == 1: + m_new[i] = T.max(m_new[i], S_smem[row, j]) + d_new[i] = d_smem[row] * T.exp2(m_prev[i] - m_new[i]) + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + with T.block("update"): + T.reads(kv_chunk_len[0], mask[mn_indptr[b_idx] + (LH_start + row) * (q_indptr[b_idx + 1] - q_indptr[b_idx]) + L_kv_start:mn_indptr[b_idx] + (LH_start + row) * (q_indptr[b_idx + 1] - q_indptr[b_idx]) + L_kv_start + 16], mn_indptr[b_idx], q_indptr[b_idx:b_idx + 2], S_smem[row, 0:16], m_new[i]) + T.writes(S_smem[row, 0:16]) + for j in range(16): + if row < 32: + row_: T.int32 = LH_start + row + if L_kv_start + j < kv_chunk_len[0] and mask[mn_indptr[b_idx] + row_ * (q_indptr[b_idx + 1] - q_indptr[b_idx]) + (L_kv_start + j)] == 1: + S_smem[row, j] = T.exp2(S_smem[row, j] - m_new[i]) + else: + S_smem[row, j] = T.exp2(T.float32(-50000) - m_new[i]) + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + with T.block("update"): + T.reads(d_new[i], S_smem[row, 0:16], m_new[i], m_prev[i]) + T.writes(d_new[i], m_smem[row], d_smem[row], m_prev_smem[row]) + for j in range(16): + d_new[i] = d_new[i] + S_smem[row, j] + m_smem[row] = m_new[i] + d_smem[row] = d_new[i] + m_prev_smem[row] = m_prev[i] + T.tvm_storage_sync("shared") + with T.block(""): + T.reads(m_prev_smem[0:32], m_smem[0:32], S_smem[0:32, 0:16], V_smem[0:16, 0:64]) + T.writes(O_local[0:32, 0:64]) + for li_0_lj_0_fused_0_init in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1_init in T.thread_binding(32, thread="threadIdx.x"): + for li_1_init, lj_1_init in T.grid(4, 4): + with T.block("O_gemm_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) // 16 * 4 + li_1_init) + j = T.axis.spatial(64, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) % 16 * 4 + lj_1_init) + T.reads() + T.writes(O_local[i, j]) + O_local[i, j] = O_local[i, j] * T.exp2(m_prev_smem[i] - m_smem[i]) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for lk_0, lk_1, li_1, lj_1 in T.grid(2, 8, 4, 4): + with T.block("O_gemm_update"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + k_1 = T.axis.reduce(16, lk_0 * 8 + lk_1) + T.reads(O_local[i, j], m_prev_smem[i], m_smem[i], S_smem[i, k_1], V_smem[k_1, j]) + T.writes(O_local[i, j]) + O_local[i, j] = O_local[i, j] + S_smem[i, k_1] * T.Cast("float32", V_smem[k_1, j]) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(4, 4): + with T.block("O_store"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + T.reads(q_indptr[b_idx:b_idx + 2], O_local[i, j], d_smem[i]) + T.writes(output[q_indptr[b_idx] + (LH_start + i), by, j]) + cur_L: T.int32 = q_indptr[b_idx] + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + output[cur_L, cur_H_qo, j] = T.Cast("float16", O_local[i, j] / d_smem[i]) + for li_0 in range(1): + for li_1 in T.thread_binding(4, thread="threadIdx.y"): + for li_2 in T.thread_binding(32, thread="threadIdx.x"): + with T.block("lse_store"): + i = T.axis.spatial(32, li_0 * 128 + li_1 * 32 + li_2) + T.where((li_0 * 4 + li_1) * 32 + li_2 < 32) + T.reads(q_indptr[b_idx:b_idx + 2], m_smem[i], d_smem[i]) + T.writes(lse[q_indptr[b_idx] + (LH_start + i), by]) + cur_L: T.int32 = q_indptr[b_idx] + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + lse[cur_L, cur_H_qo] = m_smem[i] + T.log2(d_smem[i]) + tile_id[0] = tile_id[0] + 16 + + @T.prim_func + def batch_verify_on_gpu_single_kernel(var_draft_probs: T.handle, var_draft_tokens: T.handle, var_model_probs: T.handle, var_token_tree_first_child: T.handle, var_token_tree_next_sibling: T.handle, var_uniform_samples: T.handle, var_token_tree_parent_ptr: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + num_nodes, vocab_size = T.int32(is_size_var=True), T.int64() + draft_probs = T.match_buffer(var_draft_probs, (num_nodes, vocab_size)) + draft_tokens = T.match_buffer(var_draft_tokens, (num_nodes,), "int32") + model_probs = T.match_buffer(var_model_probs, (num_nodes, vocab_size)) + token_tree_first_child = T.match_buffer(var_token_tree_first_child, (num_nodes,), "int32") + token_tree_next_sibling = T.match_buffer(var_token_tree_next_sibling, (num_nodes,), "int32") + uniform_samples = T.match_buffer(var_uniform_samples, (num_nodes,)) + nbatch = T.int32(is_size_var=True) + token_tree_parent_ptr = T.match_buffer(var_token_tree_parent_ptr, (nbatch,), "int32") + # with T.block("root"): + child_ptr = T.alloc_buffer((1,), "int32", scope="local") + parent_ptr = T.alloc_buffer((1,), "int32", scope="local") + child_token = T.alloc_buffer((1,), "int32", scope="local") + done = T.alloc_buffer((1,), "bool", scope="local") + psum = T.alloc_buffer((1,), scope="local") + t0 = T.alloc_buffer((1,), scope="local") + model_prob_local = T.alloc_buffer((1,), scope="local") + draft_prob_local = T.alloc_buffer((1,), scope="local") + p_child = T.alloc_buffer((1,), scope="local") + q_child = T.alloc_buffer((1,), scope="local") + uniform_sample = T.alloc_buffer((1,), scope="local") + pred_shared = T.alloc_buffer((1,), "bool", scope="shared") + pred_local = T.alloc_buffer((1,), "bool", scope="local") + for _bx in T.thread_binding(nbatch, thread="blockIdx.x"): + for _tx in T.thread_binding(1024, thread="threadIdx.x"): + with T.block("CTA"): + b, tx = T.axis.remap("SS", [_bx, _tx]) + T.reads(token_tree_parent_ptr[b], token_tree_first_child[T.min(parent_ptr[0], child_ptr[0]):T.min(parent_ptr[0], child_ptr[0]) + (T.max(parent_ptr[0], child_ptr[0]) + 1 - T.min(parent_ptr[0], child_ptr[0]))], parent_ptr[0], done[0], child_ptr[0], draft_tokens[child_ptr[0]], model_probs[parent_ptr[0], T.min(T.Cast("int64", child_token[0]), T.Cast("int64", tx)):T.min(T.Cast("int64", child_token[0]), T.Cast("int64", tx)) + (T.max(T.Cast("int64", child_token[0]), (vocab_size + T.int64(1023)) // T.int64(1024) * T.int64(1024) + T.Cast("int64", tx) - T.int64(1024)) + T.int64(1) - T.min(T.Cast("int64", child_token[0]), T.Cast("int64", tx)))], child_token[0], draft_probs[child_ptr[0], T.min(T.Cast("int64", child_token[0]), T.Cast("int64", tx)):T.min(T.Cast("int64", child_token[0]), T.Cast("int64", tx)) + (T.max(T.Cast("int64", child_token[0]), (vocab_size + T.int64(1023)) // T.int64(1024) * T.int64(1024) + T.Cast("int64", tx) - T.int64(1024)) + T.int64(1) - T.min(T.Cast("int64", child_token[0]), T.Cast("int64", tx)))], uniform_samples[child_ptr[0]], p_child[0], uniform_sample[0], q_child[0], pred_shared[0], pred_local[0], model_prob_local[0], draft_prob_local[0], psum[0], t0[0], token_tree_next_sibling[child_ptr[0]]) + T.writes(parent_ptr[0], child_ptr[0], done[0], child_token[0], p_child[0], q_child[0], uniform_sample[0], pred_shared[0], pred_local[0], psum[0], model_prob_local[0], draft_prob_local[0], t0[0], model_probs[parent_ptr[0], T.Cast("int64", tx):T.Cast("int64", tx) + ((vocab_size + T.int64(1023)) // T.int64(1024) * T.int64(1024) - T.int64(1023))], token_tree_parent_ptr[b]) + parent_ptr[0] = token_tree_parent_ptr[b] + child_ptr[0] = token_tree_first_child[parent_ptr[0]] + done[0] = T.bool(False) + while not done[0]: + T.tvm_storage_sync("shared") + if child_ptr[0] == -1: + done[0] = T.bool(True) + T.tvm_storage_sync("shared") + else: + if tx == 0: + child_token[0] = draft_tokens[child_ptr[0]] + p_child[0] = model_probs[parent_ptr[0], child_token[0]] + q_child[0] = draft_probs[child_ptr[0], child_token[0]] + uniform_sample[0] = uniform_samples[child_ptr[0]] + pred_shared[0] = p_child[0] >= uniform_sample[0] * q_child[0] + T.tvm_storage_sync("shared") + pred_local[0] = pred_shared[0] + if pred_local[0]: + parent_ptr[0] = child_ptr[0] + child_ptr[0] = token_tree_first_child[child_ptr[0]] + else: + psum[0] = T.float32(0) + for i in range((vocab_size + T.int64(1023)) // T.int64(1024)): + if i * T.int64(1024) + T.Cast("int64", tx) < vocab_size: + model_prob_local[0] = model_probs[parent_ptr[0], i * T.int64(1024) + T.Cast("int64", tx)] + draft_prob_local[0] = draft_probs[child_ptr[0], i * T.int64(1024) + T.Cast("int64", tx)] + model_prob_local[0] = T.max(model_prob_local[0] - draft_prob_local[0], T.float32(0)) + psum[0] = psum[0] + model_prob_local[0] + with T.block("block_cross_thread"): + T.reads(psum[0]) + T.writes(t0[0]) + T.attr(T.comm_reducer(lambda x0, y0: x0 + y0, [T.float32(0)]), "reduce_scope", T.reinterpret("handle", T.uint64(0))) + T.tvm_thread_allreduce(T.uint32(1), psum[0], T.bool(True), t0[0], tx) + if t0[0] < T.float32(9.9999999999999995e-08): + parent_ptr[0] = child_ptr[0] + child_ptr[0] = token_tree_first_child[child_ptr[0]] + else: + for i in range((vocab_size + T.int64(1023)) // T.int64(1024)): + if i * T.int64(1024) + T.Cast("int64", tx) < vocab_size: + model_prob_local[0] = model_probs[parent_ptr[0], i * T.int64(1024) + T.Cast("int64", tx)] + draft_prob_local[0] = draft_probs[child_ptr[0], i * T.int64(1024) + T.Cast("int64", tx)] + model_prob_local[0] = T.max(model_prob_local[0] - draft_prob_local[0], T.float32(0)) + model_probs[parent_ptr[0], i * T.int64(1024) + T.Cast("int64", tx)] = model_prob_local[0] / t0[0] + child_ptr[0] = token_tree_next_sibling[child_ptr[0]] + if tx == 0: + token_tree_parent_ptr[b] = parent_ptr[0] + + @T.prim_func + def chunk_lse(var_A: T.handle, var_temperature: T.handle, var_chunked_sum: T.handle, var_chunked_max: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + batch_size, vocab_size = T.int64(is_size_var=True), T.int64(is_size_var=True) + A = T.match_buffer(var_A, (batch_size, vocab_size)) + temperature = T.match_buffer(var_temperature, (batch_size,)) + num_chunks = T.int64(is_size_var=True) + chunked_sum = T.match_buffer(var_chunked_sum, (batch_size, num_chunks)) + chunked_max = T.match_buffer(var_chunked_max, (batch_size, num_chunks)) + # with T.block("root"): + temp_max_shared = T.alloc_buffer((batch_size, num_chunks), scope="shared") + temp_sum_shared = T.alloc_buffer((batch_size, num_chunks), scope="shared") + for ax0_ax1_fused in T.thread_binding(batch_size * num_chunks, thread="blockIdx.x"): + for ax0, ax1 in T.grid(T.int64(1), T.int64(1)): + for ax2_fused_1 in T.thread_binding(T.int64(256), thread="threadIdx.x"): + for ax2_fused_0 in T.serial(T.int64(16), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): + with T.block("max"): + v0 = T.axis.spatial(batch_size, ax0_ax1_fused % (num_chunks * batch_size) // num_chunks + ax0) + v1 = T.axis.spatial(num_chunks, ax0_ax1_fused % num_chunks + ax1) + v2 = T.axis.reduce(T.int64(4096), ax2_fused_0 * T.int64(256) + ax2_fused_1) + T.reads(temperature[v0], A[v0, v1 * T.int64(4096) + v2]) + T.writes(temp_max_shared[v0, v1]) + with T.init(): + temp_max_shared[v0, v1] = T.float32(-3.4028234663852886e+38) + temp_max_shared[v0, v1] = T.max(temp_max_shared[v0, v1], T.if_then_else(v1 * T.int64(4096) + v2 < vocab_size, T.if_then_else(temperature[v0] > T.float32(1.0000000000000001e-05), A[v0, v1 * T.int64(4096) + v2] / temperature[v0], A[v0, v1 * T.int64(4096) + v2]), T.float32(-3.4028234663852886e+38))) + for ax0, ax1 in T.grid(T.int64(1), T.int64(1)): + for ax2_fused_1 in T.thread_binding(T.int64(256), thread="threadIdx.x"): + for ax2_fused_0 in T.serial(T.int64(16), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): + with T.block("sum_exp"): + v0 = T.axis.spatial(batch_size, ax0_ax1_fused % (num_chunks * batch_size) // num_chunks + ax0) + v1 = T.axis.spatial(num_chunks, ax0_ax1_fused % num_chunks + ax1) + v2 = T.axis.reduce(T.int64(4096), ax2_fused_0 * T.int64(256) + ax2_fused_1) + T.reads(temperature[v0], A[v0, v1 * T.int64(4096) + v2], temp_max_shared[v0, v1]) + T.writes(temp_sum_shared[v0, v1]) + with T.init(): + temp_sum_shared[v0, v1] = T.float32(0) + temp_sum_shared[v0, v1] = temp_sum_shared[v0, v1] + T.if_then_else(v1 * T.int64(4096) + v2 < vocab_size, T.Select(temperature[v0] > T.float32(1.0000000000000001e-05), T.exp(T.if_then_else(v1 * T.int64(4096) + v2 < vocab_size, T.if_then_else(temperature[v0] > T.float32(1.0000000000000001e-05), A[v0, v1 * T.int64(4096) + v2] / temperature[v0], A[v0, v1 * T.int64(4096) + v2]), T.float32(-3.4028234663852886e+38)) - temp_max_shared[v0, v1]), T.Cast("float32", T.if_then_else(v1 * T.int64(4096) + v2 < vocab_size, T.if_then_else(temperature[v0] > T.float32(1.0000000000000001e-05), A[v0, v1 * T.int64(4096) + v2] / temperature[v0], A[v0, v1 * T.int64(4096) + v2]), T.float32(-3.4028234663852886e+38)) == temp_max_shared[v0, v1])), T.float32(0)) + for ax2_1 in T.thread_binding(T.int64(256), thread="threadIdx.x"): + for ax2_0 in T.serial(T.int64(1), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): + with T.block("log"): + v0 = T.axis.spatial(batch_size, ax0_ax1_fused % (num_chunks * batch_size) // num_chunks) + v1 = T.axis.spatial(num_chunks, ax0_ax1_fused % num_chunks) + v2 = T.axis.spatial(T.int64(1), ax2_0 * T.int64(256) + ax2_1) + T.where(ax2_0 * T.int64(256) + ax2_1 < T.int64(1)) + T.reads(temperature[v0], temp_sum_shared[v0, v1], temp_max_shared[v0, v1]) + T.writes(chunked_sum[v0, v1], chunked_max[v0, v1]) + chunked_sum[v0, v1] = T.Select(temperature[v0] > T.float32(1.0000000000000001e-05), T.log(temp_sum_shared[v0, v1]), temp_sum_shared[v0, v1]) + chunked_max[v0, v1] = temp_max_shared[v0, v1] + + @T.prim_func + def compact_kv_copy(var_pages: T.handle, var_copy_length_indptr: T.handle, var_copy_src_dst_pos: T.handle, batch_size: T.int32): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) + num_pages = T.int32() + pages = T.match_buffer(var_pages, (num_pages, 2, 20, 16, 64), "float16") + copy_length_indptr = T.match_buffer(var_copy_length_indptr, (batch_size + 1,), "int32", offset_factor=1) + total_copy_length = T.int32() + copy_src_dst_pos = T.match_buffer(var_copy_src_dst_pos, (2, total_copy_length), "int32", offset_factor=1) + with T.block("root"): + T.reads() + T.writes() + for bhd_o in T.thread_binding((batch_size * 1280 + 1023) // 1024, thread="blockIdx.x"): + for bhd_i in T.thread_binding(1024, thread="threadIdx.x"): + b: T.int32 = (bhd_o * 1024 + bhd_i) // 1280 + h: T.int32 = (bhd_o * 1024 + bhd_i) // 64 % 20 + d: T.int32 = (bhd_o * 1024 + bhd_i) % 64 + if bhd_o * 1024 + bhd_i < batch_size * 20 * 64: + for i in range(copy_length_indptr[b + 1] - copy_length_indptr[b]): + src_pos: T.int32 = copy_src_dst_pos[0, copy_length_indptr[b] + i] + dst_pos: T.int32 = copy_src_dst_pos[1, copy_length_indptr[b] + i] + pages[dst_pos // 16, 0, h, dst_pos % 16, d] = pages[src_pos // 16, 0, h, src_pos % 16, d] + pages[dst_pos // 16, 1, h, dst_pos % 16, d] = pages[src_pos // 16, 1, h, src_pos % 16, d] + + @T.prim_func + def concatenate(var_reshape710: T.handle, var_reshape711: T.handle, var_reshape712: T.handle, var_T_concat: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + batch_size = T.int64() + reshape710 = T.match_buffer(var_reshape710, (batch_size, T.int64(1), T.int64(20), T.int64(64)), "float16") + reshape711 = T.match_buffer(var_reshape711, (batch_size, T.int64(1), T.int64(20), T.int64(64)), "float16") + reshape712 = T.match_buffer(var_reshape712, (batch_size, T.int64(1), T.int64(20), T.int64(64)), "float16") + T_concat = T.match_buffer(var_T_concat, (batch_size, T.int64(1), T.int64(60), T.int64(64)), "float16") + # with T.block("root"): + for ax0_ax1_ax2_fused_0 in T.thread_binding((batch_size * T.int64(3840) + T.int64(1023)) // T.int64(1024), thread="blockIdx.x"): + for ax0_ax1_ax2_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_concat"): + v0 = T.axis.spatial(batch_size, (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) // T.int64(3840)) + v1 = T.axis.spatial(T.int64(60), (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) % T.int64(3840) // T.int64(64)) + v2 = T.axis.spatial(T.int64(64), (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) % T.int64(64)) + T.where(ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1 < batch_size * T.int64(3840)) + T.reads(reshape712[v0, T.int64(0), v1 + T.int64(-40), v2], reshape711[v0, T.int64(0), v1 + T.int64(-20), v2], reshape710[v0, T.int64(0), v1, v2]) + T.writes(T_concat[v0, T.int64(0), v1, v2]) + T_concat[v0, T.int64(0), v1, v2] = T.if_then_else(T.int64(40) <= v1, reshape712[v0, T.int64(0), v1 - T.int64(40), v2], T.if_then_else(T.int64(20) <= v1, reshape711[v0, T.int64(0), v1 + T.int64(-20), v2], reshape710[v0, T.int64(0), v1, v2])) + + @T.prim_func + def concatenate1(var_reshape387: T.handle, var_reshape388: T.handle, var_reshape389: T.handle, var_T_concat: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + seq_len = T.int64() + reshape387 = T.match_buffer(var_reshape387, (T.int64(1), seq_len, T.int64(20), T.int64(64)), "float16") + reshape388 = T.match_buffer(var_reshape388, (T.int64(1), seq_len, T.int64(20), T.int64(64)), "float16") + reshape389 = T.match_buffer(var_reshape389, (T.int64(1), seq_len, T.int64(20), T.int64(64)), "float16") + T_concat = T.match_buffer(var_T_concat, (T.int64(1), seq_len, T.int64(60), T.int64(64)), "float16") + # with T.block("root"): + for ax0_ax1_ax2_fused_0 in T.thread_binding((seq_len * T.int64(3840) + T.int64(1023)) // T.int64(1024), thread="blockIdx.x"): + for ax0_ax1_ax2_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_concat"): + v0 = T.axis.spatial(seq_len, (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) // T.int64(3840)) + v1 = T.axis.spatial(T.int64(60), (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) % T.int64(3840) // T.int64(64)) + v2 = T.axis.spatial(T.int64(64), (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) % T.int64(64)) + T.where(ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1 < seq_len * T.int64(3840)) + T.reads(reshape389[T.int64(0), v0, v1 + T.int64(-40), v2], reshape388[T.int64(0), v0, v1 + T.int64(-20), v2], reshape387[T.int64(0), v0, v1, v2]) + T.writes(T_concat[T.int64(0), v0, v1, v2]) + T_concat[T.int64(0), v0, v1, v2] = T.if_then_else(T.int64(40) <= v1, reshape389[T.int64(0), v0, v1 - T.int64(40), v2], T.if_then_else(T.int64(20) <= v1, reshape388[T.int64(0), v0, v1 + T.int64(-20), v2], reshape387[T.int64(0), v0, v1, v2])) + + @T.prim_func + def copy_single_page(var_pages: T.handle, src_page_id: T.int64, tgt_page_id: T.int64, copy_length: T.int64): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) + num_pages, page_size = T.int32(), T.int64() + pages = T.match_buffer(var_pages, (num_pages, 2, 20, page_size, 64), "float16") + # with T.block("root"): + for b in T.thread_binding((copy_length * T.int64(1280) + T.int64(1023)) // T.int64(1024), thread="blockIdx.x"): + for t in T.thread_binding(1024, thread="threadIdx.x"): + with T.block("copy"): + vh = T.axis.spatial(20, T.Cast("int32", (b * T.int64(1024) + T.Cast("int64", t)) // (copy_length * T.int64(64)))) + vp = T.axis.spatial(copy_length, (b * T.int64(1024) + T.Cast("int64", t)) % (copy_length * T.int64(64)) // T.int64(64)) + vd = T.axis.spatial(64, T.Cast("int32", (b * T.int64(1024) + T.Cast("int64", t)) % T.int64(64))) + T.reads(pages[src_page_id, 0:2, vh, vp, vd]) + T.writes(pages[tgt_page_id, 0:2, vh, vp, vd]) + pages[tgt_page_id, 0, vh, vp, vd] = pages[src_page_id, 0, vh, vp, vd] + pages[tgt_page_id, 1, vh, vp, vd] = pages[src_page_id, 1, vh, vp, vd] + + @T.prim_func + def cumsum(var_sorted_probs: T.handle, var_lv1: T.handle, var_exclusive_scan_thrust: T.handle): + T.func_attr({"tir.noalias": T.bool(True)}) + batch_size, vocab_size = T.int64(), T.int64() + data_buf = T.match_buffer(var_sorted_probs, (batch_size, vocab_size), align=8) + workspace_buf = T.match_buffer(var_lv1, (T.int64(8) * (batch_size * vocab_size * T.int64(4)) + T.int64(8388608) + batch_size * vocab_size * T.int64(12),), "uint8", align=8) + output_buf = T.match_buffer(var_exclusive_scan_thrust, (batch_size, vocab_size), align=8) + with T.block("exclusive_scan_thrust"): + T.reads() + T.writes() + T.call_packed("tvm.contrib.thrust.sum_scan", T.tvm_stack_make_array(data_buf.data, T.tvm_stack_make_shape(batch_size, vocab_size), 0, 2, T.float32(0), T.int64(0)), T.tvm_stack_make_array(output_buf.data, T.tvm_stack_make_shape(batch_size, vocab_size), 0, 2, T.float32(0), T.int64(0)), T.bool(False), T.tvm_stack_make_array(workspace_buf.data, T.tvm_stack_make_shape(T.int64(8) * (batch_size * vocab_size * T.int64(4)) + T.int64(8388608) + batch_size * vocab_size * T.int64(12)), 0, 1, T.uint8(0), T.int64(0))) + + @T.prim_func + def full(var_result: T.handle, value: T.int32): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) + batch_size = T.int32(is_size_var=True) + result = T.match_buffer(var_result, (batch_size, 1), "int32") + # with T.block("root"): + for ax0_fused_0 in T.thread_binding((batch_size + 1023) // 1024, thread="blockIdx.x"): + for ax0_fused_1 in T.thread_binding(1024, thread="threadIdx.x"): + with T.block("block"): + v0 = T.axis.spatial(batch_size, ax0_fused_0 * 1024 + ax0_fused_1) + T.where(ax0_fused_0 * 1024 + ax0_fused_1 < batch_size) + T.reads() + T.writes(result[v0, 0]) + result[v0, 0] = value + + @T.prim_func + def fused_NT_matmul1_add8_gelu2(layer_norm358: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16"), model_decoder_layers_0_fc1_weight5: T.Buffer((T.int64(5120), T.int64(1280)), "float16"), model_decoder_layers_0_fc1_bias5: T.Buffer((T.int64(5120),), "float16"), T_multiply_intermediate: T.Buffer((T.int64(1), T.int64(1), T.int64(5120)), "float16")): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + # with T.block("root"): + NT_matmul_intermediate_local = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(5120)), "float16", scope="local") + NT_matmul_intermediate_rf_local = T.alloc_buffer((T.int64(256), T.int64(1), T.int64(1), T.int64(5120)), "float16", scope="local") + NT_matmul_intermediate_rf_local_1 = T.alloc_buffer((T.int64(64), T.int64(1), T.int64(1), T.int64(5120)), "float16", scope="local") + model_decoder_layers_0_fc1_weight5_local = T.alloc_buffer((T.int64(5120), T.int64(1280)), "float16", scope="local") + layer_norm358_shared = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16", scope="shared") + for u_fused_ax0_fused_fused_0 in T.thread_binding(T.int64(1280), thread="blockIdx.x"): + for u_fused_ax0_fused_fused_1 in T.thread_binding(T.int64(4), thread="threadIdx.y"): + for ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 in T.thread_binding(T.int64(64), thread="threadIdx.x"): + for ax0, ax1 in T.grid(T.int64(1), T.int64(1)): + for ax2_0 in T.serial(T.int64(5), annotations={"pragma_unroll_explicit": 256, "pragma_vectorize": 1}): + for ax2_1 in T.thread_binding(T.int64(4), thread="threadIdx.y"): + for ax2_2 in T.thread_binding(T.int64(64), thread="threadIdx.x"): + for ax2_3 in T.vectorized(T.int64(1)): + with T.block("layer_norm358_shared"): + v0, v1 = T.axis.remap("SS", [ax0, ax1]) + v2 = T.axis.spatial(T.int64(1280), ax2_0 * T.int64(256) + ax2_1 * T.int64(64) + ax2_2 + ax2_3) + T.reads(layer_norm358[v0, v1, v2]) + T.writes(layer_norm358_shared[v0, v1, v2]) + layer_norm358_shared[v0, v1, v2] = layer_norm358[v0, v1, v2] + for u_fused_ax0_fused_fused_2_init in range(T.int64(1)): + for ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1_init in T.vectorized(T.int64(4)): + with T.block("NT_matmul_rf_init"): + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused = T.axis.spatial(T.int64(256), ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 * T.int64(4) + ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1_init) + v0 = T.axis.spatial(T.int64(5120), u_fused_ax0_fused_fused_0 * T.int64(4) + u_fused_ax0_fused_fused_1 + u_fused_ax0_fused_fused_2_init) + T.reads() + T.writes(NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0]) + NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0] = T.float16(0) + for ax1_fused_u_fused_0 in T.serial(T.int64(5), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): + for ax0_ax1_fused_0 in range(T.int64(2)): + for ax0_ax1_fused_1 in T.vectorized(T.int64(2)): + with T.block("model_decoder_layers_0_fc1_weight5_local"): + v0 = T.axis.spatial(T.int64(5120), u_fused_ax0_fused_fused_0 * T.int64(4) + u_fused_ax0_fused_fused_1) + v1 = T.axis.spatial(T.int64(1280), ax1_fused_u_fused_0 * T.int64(256) + ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 * T.int64(4) + ax0_ax1_fused_0 * T.int64(2) + ax0_ax1_fused_1) + T.reads(model_decoder_layers_0_fc1_weight5[v0, v1]) + T.writes(model_decoder_layers_0_fc1_weight5_local[v0, v1]) + model_decoder_layers_0_fc1_weight5_local[v0, v1] = model_decoder_layers_0_fc1_weight5[v0, v1] + for u_fused_ax0_fused_fused_2, ax1_fused_u_fused_2 in T.grid(T.int64(1), T.int64(1)): + for ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1 in T.vectorized(T.int64(4)): + with T.block("NT_matmul_rf_update"): + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused = T.axis.spatial(T.int64(256), ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 * T.int64(4) + ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1) + v0 = T.axis.spatial(T.int64(5120), u_fused_ax0_fused_fused_0 * T.int64(4) + u_fused_ax0_fused_fused_1 + u_fused_ax0_fused_fused_2) + vax1_fused_u_fused_2, vax1_fused_u_fused_0 = T.axis.remap("RR", [ax1_fused_u_fused_2, ax1_fused_u_fused_0]) + T.reads(NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0], layer_norm358_shared[T.int64(0), T.int64(0), vax1_fused_u_fused_0 * T.int64(256) + vax1_fused_u_fused_2 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused], model_decoder_layers_0_fc1_weight5_local[v0, vax1_fused_u_fused_0 * T.int64(256) + vax1_fused_u_fused_2 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused]) + T.writes(NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0]) + NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0] = NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0] + layer_norm358_shared[T.int64(0), T.int64(0), vax1_fused_u_fused_0 * T.int64(256) + vax1_fused_u_fused_2 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused] * model_decoder_layers_0_fc1_weight5_local[v0, vax1_fused_u_fused_0 * T.int64(256) + vax1_fused_u_fused_2 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused] + for ax2_fused_0_ax2_fused_1_fused in T.thread_binding(T.int64(4), thread="threadIdx.y"): + for ax0 in T.thread_binding(T.int64(64), thread="threadIdx.x"): + for ax2_fused_2_0 in T.serial(T.int64(1), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): + for ax2_fused_2_1 in T.vectorized(T.int64(1)): + with T.block("NT_matmul_rf_init"): + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 = T.axis.spatial(T.int64(64), ax0) + v0 = T.axis.spatial(T.int64(5120), u_fused_ax0_fused_fused_0 * T.int64(4) + ax2_fused_0_ax2_fused_1_fused + ax2_fused_2_0 + ax2_fused_2_1) + T.reads() + T.writes(NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0]) + NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0] = T.float16(0) + for ax1 in range(T.int64(4)): + with T.block("NT_matmul_rf_update"): + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1 = T.axis.remap("SR", [ax0, ax1]) + v0 = T.axis.spatial(T.int64(5120), u_fused_ax0_fused_fused_0 * T.int64(4) + ax2_fused_0_ax2_fused_1_fused + ax2_fused_2_0 + ax2_fused_2_1) + T.reads(NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0], NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1, T.int64(0), T.int64(0), v0]) + T.writes(NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0]) + NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0] = NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0] + NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1, T.int64(0), T.int64(0), v0] + for ax1_fused_2 in range(T.int64(1)): + for ax1_fused_0_ax1_fused_1_fused in T.thread_binding(T.int64(4), thread="threadIdx.y"): + for ax0 in T.thread_binding(T.int64(64), thread="threadIdx.x"): + with T.block("NT_matmul"): + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 = T.axis.reduce(T.int64(64), ax0) + v0 = T.axis.spatial(T.int64(5120), u_fused_ax0_fused_fused_0 * T.int64(4) + ax1_fused_0_ax1_fused_1_fused + ax1_fused_2) + T.reads(NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0]) + T.writes(NT_matmul_intermediate_local[T.int64(0), T.int64(0), v0]) + with T.init(): + NT_matmul_intermediate_local[T.int64(0), T.int64(0), v0] = T.float16(0) + NT_matmul_intermediate_local[T.int64(0), T.int64(0), v0] = NT_matmul_intermediate_local[T.int64(0), T.int64(0), v0] + NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0] + for ax0_fused_0_ax0_fused_1_fused in T.thread_binding(T.int64(4), thread="threadIdx.y"): + for ax0_fused_2 in range(T.int64(1)): + with T.block("T_multiply_2"): + v0 = T.axis.spatial(T.int64(5120), u_fused_ax0_fused_fused_0 * T.int64(4) + ax0_fused_0_ax0_fused_1_fused + ax0_fused_2) + T.reads(NT_matmul_intermediate_local[T.int64(0), T.int64(0), v0], model_decoder_layers_0_fc1_bias5[v0]) + T.writes(T_multiply_intermediate[T.int64(0), T.int64(0), v0]) + T_multiply_intermediate[T.int64(0), T.int64(0), v0] = (NT_matmul_intermediate_local[T.int64(0), T.int64(0), v0] + model_decoder_layers_0_fc1_bias5[v0]) * (T.float16(0.5) + T.Cast("float16", T.erf(T.Cast("float32", (NT_matmul_intermediate_local[T.int64(0), T.int64(0), v0] + model_decoder_layers_0_fc1_bias5[v0]) * T.float16(0.70710678118654757)))) * T.float16(0.5)) + + @T.prim_func + def fused_NT_matmul2_add7_add6(gelu130: T.Buffer((T.int64(1), T.int64(1), T.int64(5120)), "float16"), model_decoder_layers_0_fc2_weight5: T.Buffer((T.int64(1280), T.int64(5120)), "float16"), model_decoder_layers_0_fc2_bias5: T.Buffer((T.int64(1280),), "float16"), add1227: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16"), T_add_intermediate_1: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16")): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + # with T.block("root"): + NT_matmul_intermediate_local = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16", scope="local") + NT_matmul_intermediate_rf_local = T.alloc_buffer((T.int64(128), T.int64(1), T.int64(1), T.int64(1280)), "float16", scope="local") + NT_matmul_intermediate_rf_local_1 = T.alloc_buffer((T.int64(32), T.int64(1), T.int64(1), T.int64(1280)), "float16", scope="local") + model_decoder_layers_0_fc2_weight5_local = T.alloc_buffer((T.int64(1280), T.int64(5120)), "float16", scope="local") + gelu130_shared = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(5120)), "float16", scope="shared") + for u_fused_ax0_fused_fused_0 in T.thread_binding(T.int64(80), thread="blockIdx.x"): + for u_fused_ax0_fused_fused_1 in T.thread_binding(T.int64(16), thread="threadIdx.y"): + for ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 in T.thread_binding(T.int64(32), thread="threadIdx.x"): + for ax0, ax1 in T.grid(T.int64(1), T.int64(1)): + for ax2_0 in T.serial(T.int64(5), annotations={"pragma_unroll_explicit": 256, "pragma_vectorize": 1}): + for ax2_1 in T.thread_binding(T.int64(16), thread="threadIdx.y"): + for ax2_2 in T.thread_binding(T.int64(32), thread="threadIdx.x"): + for ax2_3 in T.vectorized(T.int64(2)): + with T.block("gelu130_shared"): + v0, v1 = T.axis.remap("SS", [ax0, ax1]) + v2 = T.axis.spatial(T.int64(5120), ax2_0 * T.int64(1024) + ax2_1 * T.int64(64) + ax2_2 * T.int64(2) + ax2_3) + T.reads(gelu130[v0, v1, v2]) + T.writes(gelu130_shared[v0, v1, v2]) + gelu130_shared[v0, v1, v2] = gelu130[v0, v1, v2] + for u_fused_ax0_fused_fused_2_init in range(T.int64(1)): + for ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1_init in T.vectorized(T.int64(4)): + with T.block("NT_matmul_rf_init"): + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused = T.axis.spatial(T.int64(128), ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 * T.int64(4) + ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1_init) + v0 = T.axis.spatial(T.int64(1280), u_fused_ax0_fused_fused_0 * T.int64(16) + u_fused_ax0_fused_fused_1 + u_fused_ax0_fused_fused_2_init) + T.reads() + T.writes(NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0]) + NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0] = T.float16(0) + for ax1_fused_u_fused_0 in T.serial(T.int64(20), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): + for ax0_ax1_fused_0 in range(T.int64(4)): + for ax0_ax1_fused_1 in T.vectorized(T.int64(2)): + with T.block("model_decoder_layers_0_fc2_weight5_local"): + v0 = T.axis.spatial(T.int64(1280), u_fused_ax0_fused_fused_0 * T.int64(16) + u_fused_ax0_fused_fused_1) + v1 = T.axis.spatial(T.int64(5120), ax1_fused_u_fused_0 * T.int64(256) + ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 * T.int64(8) + ax0_ax1_fused_0 * T.int64(2) + ax0_ax1_fused_1) + T.reads(model_decoder_layers_0_fc2_weight5[v0, v1]) + T.writes(model_decoder_layers_0_fc2_weight5_local[v0, v1]) + model_decoder_layers_0_fc2_weight5_local[v0, v1] = model_decoder_layers_0_fc2_weight5[v0, v1] + for u_fused_ax0_fused_fused_2, ax1_fused_u_fused_2 in T.grid(T.int64(1), T.int64(2)): + for ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1 in T.vectorized(T.int64(4)): + with T.block("NT_matmul_rf_update"): + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused = T.axis.spatial(T.int64(128), ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 * T.int64(4) + ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1) + v0 = T.axis.spatial(T.int64(1280), u_fused_ax0_fused_fused_0 * T.int64(16) + u_fused_ax0_fused_fused_1 + u_fused_ax0_fused_fused_2) + vax1_fused_u_fused_0, vax1_fused_u_fused_2 = T.axis.remap("RR", [ax1_fused_u_fused_0, ax1_fused_u_fused_2]) + T.reads(NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0], gelu130_shared[T.int64(0), T.int64(0), vax1_fused_u_fused_0 * T.int64(256) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused // T.int64(4) * T.int64(8) + vax1_fused_u_fused_2 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused % T.int64(4)], model_decoder_layers_0_fc2_weight5_local[v0, vax1_fused_u_fused_0 * T.int64(256) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused // T.int64(4) * T.int64(8) + vax1_fused_u_fused_2 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused % T.int64(4)]) + T.writes(NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0]) + NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0] = NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0] + gelu130_shared[T.int64(0), T.int64(0), vax1_fused_u_fused_0 * T.int64(256) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused // T.int64(4) * T.int64(8) + vax1_fused_u_fused_2 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused % T.int64(4)] * model_decoder_layers_0_fc2_weight5_local[v0, vax1_fused_u_fused_0 * T.int64(256) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused // T.int64(4) * T.int64(8) + vax1_fused_u_fused_2 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused % T.int64(4)] + for ax2_fused_0_ax2_fused_1_fused in T.thread_binding(T.int64(16), thread="threadIdx.y"): + for ax0 in T.thread_binding(T.int64(32), thread="threadIdx.x"): + for ax2_fused_2_0 in T.serial(T.int64(1), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): + for ax2_fused_2_1 in T.vectorized(T.int64(1)): + with T.block("NT_matmul_rf_init"): + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 = T.axis.spatial(T.int64(32), ax0) + v0 = T.axis.spatial(T.int64(1280), u_fused_ax0_fused_fused_0 * T.int64(16) + ax2_fused_0_ax2_fused_1_fused + ax2_fused_2_0 + ax2_fused_2_1) + T.reads() + T.writes(NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0]) + NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0] = T.float16(0) + for ax1 in range(T.int64(4)): + with T.block("NT_matmul_rf_update"): + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1 = T.axis.remap("SR", [ax0, ax1]) + v0 = T.axis.spatial(T.int64(1280), u_fused_ax0_fused_fused_0 * T.int64(16) + ax2_fused_0_ax2_fused_1_fused + ax2_fused_2_0 + ax2_fused_2_1) + T.reads(NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0], NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1, T.int64(0), T.int64(0), v0]) + T.writes(NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0]) + NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0] = NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0] + NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1, T.int64(0), T.int64(0), v0] + for ax1_fused_2 in range(T.int64(1)): + for ax1_fused_0_ax1_fused_1_fused in T.thread_binding(T.int64(16), thread="threadIdx.y"): + for ax0 in T.thread_binding(T.int64(32), thread="threadIdx.x"): + with T.block("NT_matmul"): + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 = T.axis.reduce(T.int64(32), ax0) + v0 = T.axis.spatial(T.int64(1280), u_fused_ax0_fused_fused_0 * T.int64(16) + ax1_fused_0_ax1_fused_1_fused + ax1_fused_2) + T.reads(NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0]) + T.writes(NT_matmul_intermediate_local[T.int64(0), T.int64(0), v0]) + with T.init(): + NT_matmul_intermediate_local[T.int64(0), T.int64(0), v0] = T.float16(0) + NT_matmul_intermediate_local[T.int64(0), T.int64(0), v0] = NT_matmul_intermediate_local[T.int64(0), T.int64(0), v0] + NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0] + for ax0_fused_0_ax0_fused_1_fused in T.thread_binding(T.int64(16), thread="threadIdx.y"): + for ax0_fused_2 in range(T.int64(1)): + with T.block("T_add_1"): + v0 = T.axis.spatial(T.int64(1280), u_fused_ax0_fused_fused_0 * T.int64(16) + ax0_fused_0_ax0_fused_1_fused + ax0_fused_2) + T.reads(add1227[T.int64(0), T.int64(0), v0], NT_matmul_intermediate_local[T.int64(0), T.int64(0), v0], model_decoder_layers_0_fc2_bias5[v0]) + T.writes(T_add_intermediate_1[T.int64(0), T.int64(0), v0]) + T_add_intermediate_1[T.int64(0), T.int64(0), v0] = add1227[T.int64(0), T.int64(0), v0] + (NT_matmul_intermediate_local[T.int64(0), T.int64(0), v0] + model_decoder_layers_0_fc2_bias5[v0]) + + @T.prim_func + def fused_NT_matmul_add7(layer_norm356: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16"), model_decoder_layers_0_self_attn_q_proj_weight5: T.Buffer((T.int64(1280), T.int64(1280)), "float16"), model_decoder_layers_0_self_attn_q_proj_bias5: T.Buffer((T.int64(1280),), "float16"), T_add_intermediate: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16")): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + # with T.block("root"): + NT_matmul_intermediate_local = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16", scope="local") + NT_matmul_intermediate_rf_local = T.alloc_buffer((T.int64(128), T.int64(1), T.int64(1), T.int64(1280)), "float16", scope="local") + NT_matmul_intermediate_rf_local_1 = T.alloc_buffer((T.int64(32), T.int64(1), T.int64(1), T.int64(1280)), "float16", scope="local") + model_decoder_layers_0_self_attn_q_proj_weight5_local = T.alloc_buffer((T.int64(1280), T.int64(1280)), "float16", scope="local") + layer_norm356_shared = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16", scope="shared") + for u_fused_ax0_fused_fused_0 in T.thread_binding(T.int64(80), thread="blockIdx.x"): + for u_fused_ax0_fused_fused_1 in T.thread_binding(T.int64(16), thread="threadIdx.y"): + for ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 in T.thread_binding(T.int64(32), thread="threadIdx.x"): + for ax0, ax1 in T.grid(T.int64(1), T.int64(1)): + for ax2_0 in T.serial(T.int64(3), annotations={"pragma_unroll_explicit": 256, "pragma_vectorize": 1}): + for ax2_1 in T.thread_binding(T.int64(16), thread="threadIdx.y"): + for ax2_2 in T.thread_binding(T.int64(32), thread="threadIdx.x"): + for ax2_3 in T.vectorized(T.int64(1)): + with T.block("layer_norm356_shared"): + v0, v1 = T.axis.remap("SS", [ax0, ax1]) + v2 = T.axis.spatial(T.int64(1280), ax2_0 * T.int64(512) + ax2_1 * T.int64(32) + ax2_2 + ax2_3) + T.where((ax2_0 * T.int64(16) + ax2_1) * T.int64(32) + ax2_2 + ax2_3 < T.int64(1280)) + T.reads(layer_norm356[v0, v1, v2]) + T.writes(layer_norm356_shared[v0, v1, v2]) + layer_norm356_shared[v0, v1, v2] = layer_norm356[v0, v1, v2] + for u_fused_ax0_fused_fused_2_init in range(T.int64(1)): + for ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1_init in T.vectorized(T.int64(4)): + with T.block("NT_matmul_rf_init"): + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused = T.axis.spatial(T.int64(128), ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 * T.int64(4) + ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1_init) + v0 = T.axis.spatial(T.int64(1280), u_fused_ax0_fused_fused_0 * T.int64(16) + u_fused_ax0_fused_fused_1 + u_fused_ax0_fused_fused_2_init) + T.reads() + T.writes(NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0]) + NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0] = T.float16(0) + for ax1_fused_u_fused_0 in T.serial(T.int64(5), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): + for ax0_ax1_fused_0 in range(T.int64(4)): + for ax0_ax1_fused_1 in T.vectorized(T.int64(2)): + with T.block("model_decoder_layers_0_self_attn_q_proj_weight5_local"): + v0 = T.axis.spatial(T.int64(1280), u_fused_ax0_fused_fused_0 * T.int64(16) + u_fused_ax0_fused_fused_1) + v1 = T.axis.spatial(T.int64(1280), ax1_fused_u_fused_0 * T.int64(256) + ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 * T.int64(8) + ax0_ax1_fused_0 * T.int64(2) + ax0_ax1_fused_1) + T.reads(model_decoder_layers_0_self_attn_q_proj_weight5[v0, v1]) + T.writes(model_decoder_layers_0_self_attn_q_proj_weight5_local[v0, v1]) + model_decoder_layers_0_self_attn_q_proj_weight5_local[v0, v1] = model_decoder_layers_0_self_attn_q_proj_weight5[v0, v1] + for u_fused_ax0_fused_fused_2, ax1_fused_u_fused_2 in T.grid(T.int64(1), T.int64(2)): + for ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1 in T.vectorized(T.int64(4)): + with T.block("NT_matmul_rf_update"): + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused = T.axis.spatial(T.int64(128), ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 * T.int64(4) + ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1) + v0 = T.axis.spatial(T.int64(1280), u_fused_ax0_fused_fused_0 * T.int64(16) + u_fused_ax0_fused_fused_1 + u_fused_ax0_fused_fused_2) + vax1_fused_u_fused_0, vax1_fused_u_fused_2 = T.axis.remap("RR", [ax1_fused_u_fused_0, ax1_fused_u_fused_2]) + T.reads(NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0], layer_norm356_shared[T.int64(0), T.int64(0), vax1_fused_u_fused_0 * T.int64(256) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused // T.int64(4) * T.int64(8) + vax1_fused_u_fused_2 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused % T.int64(4)], model_decoder_layers_0_self_attn_q_proj_weight5_local[v0, vax1_fused_u_fused_0 * T.int64(256) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused // T.int64(4) * T.int64(8) + vax1_fused_u_fused_2 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused % T.int64(4)]) + T.writes(NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0]) + NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0] = NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0] + layer_norm356_shared[T.int64(0), T.int64(0), vax1_fused_u_fused_0 * T.int64(256) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused // T.int64(4) * T.int64(8) + vax1_fused_u_fused_2 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused % T.int64(4)] * model_decoder_layers_0_self_attn_q_proj_weight5_local[v0, vax1_fused_u_fused_0 * T.int64(256) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused // T.int64(4) * T.int64(8) + vax1_fused_u_fused_2 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused % T.int64(4)] + for ax2_fused_0_ax2_fused_1_fused in T.thread_binding(T.int64(16), thread="threadIdx.y"): + for ax0 in T.thread_binding(T.int64(32), thread="threadIdx.x"): + for ax2_fused_2_0 in T.serial(T.int64(1), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): + for ax2_fused_2_1 in T.vectorized(T.int64(1)): + with T.block("NT_matmul_rf_init"): + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 = T.axis.spatial(T.int64(32), ax0) + v0 = T.axis.spatial(T.int64(1280), u_fused_ax0_fused_fused_0 * T.int64(16) + ax2_fused_0_ax2_fused_1_fused + ax2_fused_2_0 + ax2_fused_2_1) + T.reads() + T.writes(NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0]) + NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0] = T.float16(0) + for ax1 in range(T.int64(4)): + with T.block("NT_matmul_rf_update"): + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1 = T.axis.remap("SR", [ax0, ax1]) + v0 = T.axis.spatial(T.int64(1280), u_fused_ax0_fused_fused_0 * T.int64(16) + ax2_fused_0_ax2_fused_1_fused + ax2_fused_2_0 + ax2_fused_2_1) + T.reads(NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0], NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1, T.int64(0), T.int64(0), v0]) + T.writes(NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0]) + NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0] = NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0] + NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1, T.int64(0), T.int64(0), v0] + for ax1_fused_2 in range(T.int64(1)): + for ax1_fused_0_ax1_fused_1_fused in T.thread_binding(T.int64(16), thread="threadIdx.y"): + for ax0 in T.thread_binding(T.int64(32), thread="threadIdx.x"): + with T.block("NT_matmul"): + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 = T.axis.reduce(T.int64(32), ax0) + v0 = T.axis.spatial(T.int64(1280), u_fused_ax0_fused_fused_0 * T.int64(16) + ax1_fused_0_ax1_fused_1_fused + ax1_fused_2) + T.reads(NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0]) + T.writes(NT_matmul_intermediate_local[T.int64(0), T.int64(0), v0]) + with T.init(): + NT_matmul_intermediate_local[T.int64(0), T.int64(0), v0] = T.float16(0) + NT_matmul_intermediate_local[T.int64(0), T.int64(0), v0] = NT_matmul_intermediate_local[T.int64(0), T.int64(0), v0] + NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0] + for ax0_fused_0_ax0_fused_1_fused in T.thread_binding(T.int64(16), thread="threadIdx.y"): + for ax0_fused_2 in range(T.int64(1)): + with T.block("T_add"): + v0 = T.axis.spatial(T.int64(1280), u_fused_ax0_fused_fused_0 * T.int64(16) + ax0_fused_0_ax0_fused_1_fused + ax0_fused_2) + T.reads(NT_matmul_intermediate_local[T.int64(0), T.int64(0), v0], model_decoder_layers_0_self_attn_q_proj_bias5[v0]) + T.writes(T_add_intermediate[T.int64(0), T.int64(0), v0]) + T_add_intermediate[T.int64(0), T.int64(0), v0] = NT_matmul_intermediate_local[T.int64(0), T.int64(0), v0] + model_decoder_layers_0_self_attn_q_proj_bias5[v0] + + @T.prim_func + def fused_NT_matmul_add7_add6(reshape1361: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16"), model_decoder_layers_0_self_attn_out_proj_weight5: T.Buffer((T.int64(1280), T.int64(1280)), "float16"), model_decoder_layers_0_self_attn_out_proj_bias5: T.Buffer((T.int64(1280),), "float16"), add1220: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16"), T_add_intermediate_1: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16")): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + # with T.block("root"): + NT_matmul_intermediate_local = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16", scope="local") + NT_matmul_intermediate_rf_local = T.alloc_buffer((T.int64(128), T.int64(1), T.int64(1), T.int64(1280)), "float16", scope="local") + NT_matmul_intermediate_rf_local_1 = T.alloc_buffer((T.int64(32), T.int64(1), T.int64(1), T.int64(1280)), "float16", scope="local") + model_decoder_layers_0_self_attn_out_proj_weight5_local = T.alloc_buffer((T.int64(1280), T.int64(1280)), "float16", scope="local") + reshape1361_shared = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16", scope="shared") + for u_fused_ax0_fused_fused_0 in T.thread_binding(T.int64(80), thread="blockIdx.x"): + for u_fused_ax0_fused_fused_1 in T.thread_binding(T.int64(16), thread="threadIdx.y"): + for ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 in T.thread_binding(T.int64(32), thread="threadIdx.x"): + for ax0, ax1 in T.grid(T.int64(1), T.int64(1)): + for ax2_0 in T.serial(T.int64(3), annotations={"pragma_unroll_explicit": 256, "pragma_vectorize": 1}): + for ax2_1 in T.thread_binding(T.int64(16), thread="threadIdx.y"): + for ax2_2 in T.thread_binding(T.int64(32), thread="threadIdx.x"): + for ax2_3 in T.vectorized(T.int64(1)): + with T.block("reshape1361_shared"): + v0, v1 = T.axis.remap("SS", [ax0, ax1]) + v2 = T.axis.spatial(T.int64(1280), ax2_0 * T.int64(512) + ax2_1 * T.int64(32) + ax2_2 + ax2_3) + T.where((ax2_0 * T.int64(16) + ax2_1) * T.int64(32) + ax2_2 + ax2_3 < T.int64(1280)) + T.reads(reshape1361[v0, v1, v2]) + T.writes(reshape1361_shared[v0, v1, v2]) + reshape1361_shared[v0, v1, v2] = reshape1361[v0, v1, v2] + for u_fused_ax0_fused_fused_2_init in range(T.int64(1)): + for ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1_init in T.vectorized(T.int64(4)): + with T.block("NT_matmul_rf_init"): + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused = T.axis.spatial(T.int64(128), ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 * T.int64(4) + ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1_init) + v0 = T.axis.spatial(T.int64(1280), u_fused_ax0_fused_fused_0 * T.int64(16) + u_fused_ax0_fused_fused_1 + u_fused_ax0_fused_fused_2_init) + T.reads() + T.writes(NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0]) + NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0] = T.float16(0) + for ax1_fused_u_fused_0 in T.serial(T.int64(5), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): + for ax0_ax1_fused_0 in range(T.int64(4)): + for ax0_ax1_fused_1 in T.vectorized(T.int64(2)): + with T.block("model_decoder_layers_0_self_attn_out_proj_weight5_local"): + v0 = T.axis.spatial(T.int64(1280), u_fused_ax0_fused_fused_0 * T.int64(16) + u_fused_ax0_fused_fused_1) + v1 = T.axis.spatial(T.int64(1280), ax1_fused_u_fused_0 * T.int64(256) + ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 * T.int64(8) + ax0_ax1_fused_0 * T.int64(2) + ax0_ax1_fused_1) + T.reads(model_decoder_layers_0_self_attn_out_proj_weight5[v0, v1]) + T.writes(model_decoder_layers_0_self_attn_out_proj_weight5_local[v0, v1]) + model_decoder_layers_0_self_attn_out_proj_weight5_local[v0, v1] = model_decoder_layers_0_self_attn_out_proj_weight5[v0, v1] + for u_fused_ax0_fused_fused_2, ax1_fused_u_fused_2 in T.grid(T.int64(1), T.int64(2)): + for ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1 in T.vectorized(T.int64(4)): + with T.block("NT_matmul_rf_update"): + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused = T.axis.spatial(T.int64(128), ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 * T.int64(4) + ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1) + v0 = T.axis.spatial(T.int64(1280), u_fused_ax0_fused_fused_0 * T.int64(16) + u_fused_ax0_fused_fused_1 + u_fused_ax0_fused_fused_2) + vax1_fused_u_fused_0, vax1_fused_u_fused_2 = T.axis.remap("RR", [ax1_fused_u_fused_0, ax1_fused_u_fused_2]) + T.reads(NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0], reshape1361_shared[T.int64(0), T.int64(0), vax1_fused_u_fused_0 * T.int64(256) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused // T.int64(4) * T.int64(8) + vax1_fused_u_fused_2 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused % T.int64(4)], model_decoder_layers_0_self_attn_out_proj_weight5_local[v0, vax1_fused_u_fused_0 * T.int64(256) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused // T.int64(4) * T.int64(8) + vax1_fused_u_fused_2 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused % T.int64(4)]) + T.writes(NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0]) + NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0] = NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0] + reshape1361_shared[T.int64(0), T.int64(0), vax1_fused_u_fused_0 * T.int64(256) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused // T.int64(4) * T.int64(8) + vax1_fused_u_fused_2 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused % T.int64(4)] * model_decoder_layers_0_self_attn_out_proj_weight5_local[v0, vax1_fused_u_fused_0 * T.int64(256) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused // T.int64(4) * T.int64(8) + vax1_fused_u_fused_2 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused % T.int64(4)] + for ax2_fused_0_ax2_fused_1_fused in T.thread_binding(T.int64(16), thread="threadIdx.y"): + for ax0 in T.thread_binding(T.int64(32), thread="threadIdx.x"): + for ax2_fused_2_0 in T.serial(T.int64(1), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): + for ax2_fused_2_1 in T.vectorized(T.int64(1)): + with T.block("NT_matmul_rf_init"): + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 = T.axis.spatial(T.int64(32), ax0) + v0 = T.axis.spatial(T.int64(1280), u_fused_ax0_fused_fused_0 * T.int64(16) + ax2_fused_0_ax2_fused_1_fused + ax2_fused_2_0 + ax2_fused_2_1) + T.reads() + T.writes(NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0]) + NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0] = T.float16(0) + for ax1 in range(T.int64(4)): + with T.block("NT_matmul_rf_update"): + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1 = T.axis.remap("SR", [ax0, ax1]) + v0 = T.axis.spatial(T.int64(1280), u_fused_ax0_fused_fused_0 * T.int64(16) + ax2_fused_0_ax2_fused_1_fused + ax2_fused_2_0 + ax2_fused_2_1) + T.reads(NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0], NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1, T.int64(0), T.int64(0), v0]) + T.writes(NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0]) + NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0] = NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0] + NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1, T.int64(0), T.int64(0), v0] + for ax1_fused_2 in range(T.int64(1)): + for ax1_fused_0_ax1_fused_1_fused in T.thread_binding(T.int64(16), thread="threadIdx.y"): + for ax0 in T.thread_binding(T.int64(32), thread="threadIdx.x"): + with T.block("NT_matmul"): + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 = T.axis.reduce(T.int64(32), ax0) + v0 = T.axis.spatial(T.int64(1280), u_fused_ax0_fused_fused_0 * T.int64(16) + ax1_fused_0_ax1_fused_1_fused + ax1_fused_2) + T.reads(NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0]) + T.writes(NT_matmul_intermediate_local[T.int64(0), T.int64(0), v0]) + with T.init(): + NT_matmul_intermediate_local[T.int64(0), T.int64(0), v0] = T.float16(0) + NT_matmul_intermediate_local[T.int64(0), T.int64(0), v0] = NT_matmul_intermediate_local[T.int64(0), T.int64(0), v0] + NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0] + for ax0_fused_0_ax0_fused_1_fused in T.thread_binding(T.int64(16), thread="threadIdx.y"): + for ax0_fused_2 in range(T.int64(1)): + with T.block("T_add_1"): + v0 = T.axis.spatial(T.int64(1280), u_fused_ax0_fused_fused_0 * T.int64(16) + ax0_fused_0_ax0_fused_1_fused + ax0_fused_2) + T.reads(add1220[T.int64(0), T.int64(0), v0], NT_matmul_intermediate_local[T.int64(0), T.int64(0), v0], model_decoder_layers_0_self_attn_out_proj_bias5[v0]) + T.writes(T_add_intermediate_1[T.int64(0), T.int64(0), v0]) + T_add_intermediate_1[T.int64(0), T.int64(0), v0] = add1220[T.int64(0), T.int64(0), v0] + (NT_matmul_intermediate_local[T.int64(0), T.int64(0), v0] + model_decoder_layers_0_self_attn_out_proj_bias5[v0]) + + @T.prim_func + def fused_add4_maximum_minimum(p_add4: T.handle, p_lv611: T.handle, p_output0: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + batch_size = T.int64() + add4 = T.match_buffer(p_add4, (batch_size, T.int64(1500), T.int64(1280)), "float16") + lv611 = T.match_buffer(p_lv611, (batch_size, T.int64(1500), T.int64(1280)), "float16") + T_minimum_intermediate = T.match_buffer(p_output0, (batch_size, T.int64(1500), T.int64(1280)), "float16") + # with T.block("root"): + for ax0_ax1_ax2_fused_0 in T.thread_binding(batch_size * T.int64(1875), thread="blockIdx.x"): + for ax0_ax1_ax2_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_minimum"): + v0 = T.axis.spatial(batch_size, (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) // T.int64(1920000)) + v1 = T.axis.spatial(T.int64(1500), (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) % T.int64(1920000) // T.int64(1280)) + v2 = T.axis.spatial(T.int64(1280), (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) % T.int64(1280)) + T.reads(add4[v0, v1, v2], lv611[v0, v1, v2]) + T.writes(T_minimum_intermediate[v0, v1, v2]) + T_minimum_intermediate[v0, v1, v2] = T.min(T.max(add4[v0, v1, v2] + lv611[v0, v1, v2], T.float16(-65504)), T.float16(65504)) + + @T.prim_func + def fused_conv1d1_add2_gelu1(p_gelu: T.handle, model_encoder_conv2_weight: T.Buffer((T.int64(1280), T.int64(1280), T.int64(3)), "float16"), lv3: T.Buffer((T.int64(1), T.int64(1280), T.int64(1)), "float16"), p_output0: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + batch_size = T.int64() + gelu = T.match_buffer(p_gelu, (batch_size, T.int64(1280), T.int64(3000)), "float16") + T_multiply_intermediate = T.match_buffer(p_output0, (batch_size, T.int64(1280), T.int64(1500)), "float16") + # with T.block("root"): + conv1d_ncw_intermediate_shared = T.alloc_buffer((batch_size, T.int64(1280), T.int64(1500)), "float16", scope="shared") + for ax0_ax1_ax2_fused in T.thread_binding(batch_size * T.int64(1920000), thread="blockIdx.x"): + for ax0, ax1, ax2 in T.grid(T.int64(1), T.int64(1), T.int64(1)): + for ax3_ax4_fused_1 in T.thread_binding(T.int64(256), thread="threadIdx.x"): + for ax3_ax4_fused_0 in T.serial(T.int64(15), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): + with T.block("conv1d_ncw"): + v0 = T.axis.spatial(batch_size, ax0_ax1_ax2_fused // T.int64(1920000) + ax0) + v1 = T.axis.spatial(T.int64(1280), ax0_ax1_ax2_fused % T.int64(1920000) // T.int64(1500) + ax1) + v2 = T.axis.spatial(T.int64(1500), ax0_ax1_ax2_fused % T.int64(1500) + ax2) + v3 = T.axis.reduce(T.int64(1280), (ax3_ax4_fused_0 * T.int64(256) + ax3_ax4_fused_1) // T.int64(3)) + v4 = T.axis.reduce(T.int64(3), (ax3_ax4_fused_0 * T.int64(256) + ax3_ax4_fused_1) % T.int64(3)) + T.reads(gelu[v0, v3, v2 * T.int64(2) + v4 - T.int64(1)], model_encoder_conv2_weight[v1, v3, v4]) + T.writes(conv1d_ncw_intermediate_shared[v0, v1, v2]) + with T.init(): + conv1d_ncw_intermediate_shared[v0, v1, v2] = T.float16(0) + conv1d_ncw_intermediate_shared[v0, v1, v2] = conv1d_ncw_intermediate_shared[v0, v1, v2] + T.if_then_else(T.int64(1) <= v2 * T.int64(2) + v4 and v2 * T.int64(2) + v4 < T.int64(3001), gelu[v0, v3, v2 * T.int64(2) + v4 - T.int64(1)], T.float16(0)) * model_encoder_conv2_weight[v1, v3, v4] + for ax3 in range(T.int64(1)): + for ax4_1 in T.thread_binding(T.int64(256), thread="threadIdx.x"): + for ax4_0 in T.serial(T.int64(1), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): + with T.block("T_multiply_2"): + v0 = T.axis.spatial(batch_size, ax0_ax1_ax2_fused // T.int64(1920000)) + v1 = T.axis.spatial(T.int64(1280), ax0_ax1_ax2_fused % T.int64(1920000) // T.int64(1500)) + v2 = T.axis.spatial(T.int64(1500), ax0_ax1_ax2_fused % T.int64(1500)) + v3 = T.axis.spatial(T.int64(1), ax3) + v4 = T.axis.spatial(T.int64(1), ax4_0 * T.int64(256) + ax4_1) + T.where(ax4_0 * T.int64(256) + ax4_1 < T.int64(1)) + T.reads(conv1d_ncw_intermediate_shared[v0, v1, v2], lv3[T.int64(0), v1, T.int64(0)]) + T.writes(T_multiply_intermediate[v0, v1, v2]) + T_multiply_intermediate[v0, v1, v2] = (conv1d_ncw_intermediate_shared[v0, v1, v2] + lv3[T.int64(0), v1, T.int64(0)]) * (T.float16(0.5) + T.Cast("float16", T.erf(T.Cast("float32", (conv1d_ncw_intermediate_shared[v0, v1, v2] + lv3[T.int64(0), v1, T.int64(0)]) * T.float16(0.70710678118654757)))) * T.float16(0.5)) + + @T.prim_func + def fused_conv1d_add1_gelu(p_input_features: T.handle, model_encoder_conv1_weight: T.Buffer((T.int64(1280), T.int64(128), T.int64(3)), "float16"), lv1: T.Buffer((T.int64(1), T.int64(1280), T.int64(1)), "float16"), p_output0: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + batch_size = T.int64() + input_features = T.match_buffer(p_input_features, (batch_size, T.int64(128), T.int64(3000)), "float16") + T_multiply_intermediate = T.match_buffer(p_output0, (batch_size, T.int64(1280), T.int64(3000)), "float16") + # with T.block("root"): + conv1d_ncw_intermediate_shared = T.alloc_buffer((batch_size, T.int64(1280), T.int64(3000)), "float16", scope="shared") + for ax0_ax1_ax2_fused in T.thread_binding(batch_size * T.int64(3840000), thread="blockIdx.x"): + for ax0, ax1, ax2 in T.grid(T.int64(1), T.int64(1), T.int64(1)): + for ax3_ax4_fused_1 in T.thread_binding(T.int64(256), thread="threadIdx.x"): + for ax3_ax4_fused_0 in T.serial(T.int64(2), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): + with T.block("conv1d_ncw"): + v0 = T.axis.spatial(batch_size, ax0_ax1_ax2_fused // T.int64(3840000) + ax0) + v1 = T.axis.spatial(T.int64(1280), ax0_ax1_ax2_fused % T.int64(3840000) // T.int64(3000) + ax1) + v2 = T.axis.spatial(T.int64(3000), ax0_ax1_ax2_fused % T.int64(3000) + ax2) + v3 = T.axis.reduce(T.int64(128), (ax3_ax4_fused_0 * T.int64(256) + ax3_ax4_fused_1) // T.int64(3)) + v4 = T.axis.reduce(T.int64(3), (ax3_ax4_fused_0 * T.int64(256) + ax3_ax4_fused_1) % T.int64(3)) + T.where(ax3_ax4_fused_0 * T.int64(256) + ax3_ax4_fused_1 < T.int64(384)) + T.reads(input_features[v0, v3, v2 + v4 - T.int64(1)], model_encoder_conv1_weight[v1, v3, v4]) + T.writes(conv1d_ncw_intermediate_shared[v0, v1, v2]) + with T.init(): + conv1d_ncw_intermediate_shared[v0, v1, v2] = T.float16(0) + conv1d_ncw_intermediate_shared[v0, v1, v2] = conv1d_ncw_intermediate_shared[v0, v1, v2] + T.if_then_else(T.int64(1) <= v2 + v4 and v2 + v4 < T.int64(3001), input_features[v0, v3, v2 + v4 - T.int64(1)], T.float16(0)) * model_encoder_conv1_weight[v1, v3, v4] + for ax3 in range(T.int64(1)): + for ax4_1 in T.thread_binding(T.int64(256), thread="threadIdx.x"): + for ax4_0 in T.serial(T.int64(1), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): + with T.block("T_multiply_2"): + v0 = T.axis.spatial(batch_size, ax0_ax1_ax2_fused // T.int64(3840000)) + v1 = T.axis.spatial(T.int64(1280), ax0_ax1_ax2_fused % T.int64(3840000) // T.int64(3000)) + v2 = T.axis.spatial(T.int64(3000), ax0_ax1_ax2_fused % T.int64(3000)) + v3 = T.axis.spatial(T.int64(1), ax3) + v4 = T.axis.spatial(T.int64(1), ax4_0 * T.int64(256) + ax4_1) + T.where(ax4_0 * T.int64(256) + ax4_1 < T.int64(1)) + T.reads(conv1d_ncw_intermediate_shared[v0, v1, v2], lv1[T.int64(0), v1, T.int64(0)]) + T.writes(T_multiply_intermediate[v0, v1, v2]) + T_multiply_intermediate[v0, v1, v2] = (conv1d_ncw_intermediate_shared[v0, v1, v2] + lv1[T.int64(0), v1, T.int64(0)]) * (T.float16(0.5) + T.Cast("float16", T.erf(T.Cast("float32", (conv1d_ncw_intermediate_shared[v0, v1, v2] + lv1[T.int64(0), v1, T.int64(0)]) * T.float16(0.70710678118654757)))) * T.float16(0.5)) + + @T.prim_func + def fused_reshape20_reshape20_add6(take7: T.Buffer((T.int64(1), T.int64(1280)), "float16"), take8: T.Buffer((T.int64(1), T.int64(1280)), "float16"), T_add_intermediate: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16")): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + # with T.block("root"): + for ax0_fused_0 in T.thread_binding(T.int64(2), thread="blockIdx.x"): + for ax0_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_add"): + v0 = T.axis.spatial(T.int64(1280), ax0_fused_0 * T.int64(1024) + ax0_fused_1) + T.where(ax0_fused_0 * T.int64(1024) + ax0_fused_1 < T.int64(1280)) + T.reads(take7[T.int64(0), v0], take8[T.int64(0), v0]) + T.writes(T_add_intermediate[T.int64(0), T.int64(0), v0]) + T_add_intermediate[T.int64(0), T.int64(0), v0] = take7[T.int64(0), v0] + take8[T.int64(0), v0] + + @T.prim_func + def fused_reshape21_reshape21_reshape21_concatenate2_reshape22(add1221: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16"), lv1: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16"), add1222: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16"), T_reshape_intermediate_1_2_3: T.Buffer((T.int64(1), T.int64(60), T.int64(64)), "float16")): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + # with T.block("root"): + for ax0_ax1_fused_0 in T.thread_binding(T.int64(4), thread="blockIdx.x"): + for ax0_ax1_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_reshape_3"): + v0 = T.axis.spatial(T.int64(60), (ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1) // T.int64(64)) + v1 = T.axis.spatial(T.int64(64), (ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1) % T.int64(64)) + T.where(ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1 < T.int64(3840)) + T.reads(add1222[T.int64(0), T.int64(0), (v0 - T.int64(40)) * T.int64(64) + v1], lv1[T.int64(0), T.int64(0), (v0 + T.int64(-20)) * T.int64(64) + v1], add1221[T.int64(0), T.int64(0), v0 * T.int64(64) + v1]) + T.writes(T_reshape_intermediate_1_2_3[T.int64(0), v0, v1]) + T_reshape_intermediate_1_2_3[T.int64(0), v0, v1] = T.if_then_else(T.int64(40) <= v0, add1222[T.int64(0), T.int64(0), (v0 - T.int64(40)) * T.int64(64) + v1], T.if_then_else(T.int64(20) <= v0, lv1[T.int64(0), T.int64(0), (v0 + T.int64(-20)) * T.int64(64) + v1], add1221[T.int64(0), T.int64(0), v0 * T.int64(64) + v1])) + + @T.prim_func + def fused_reshape21_reshape25(add1225: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16"), T_reshape_intermediate_1: T.Buffer((T.int64(1), T.int64(20), T.int64(64)), "float16")): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + # with T.block("root"): + for ax0_ax1_fused_0 in T.thread_binding(T.int64(2), thread="blockIdx.x"): + for ax0_ax1_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_reshape_1"): + v0 = T.axis.spatial(T.int64(20), (ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1) // T.int64(64)) + v1 = T.axis.spatial(T.int64(64), (ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1) % T.int64(64)) + T.where(ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1 < T.int64(1280)) + T.reads(add1225[T.int64(0), T.int64(0), v0 * T.int64(64) + v1]) + T.writes(T_reshape_intermediate_1[T.int64(0), v0, v1]) + T_reshape_intermediate_1[T.int64(0), v0, v1] = add1225[T.int64(0), T.int64(0), v0 * T.int64(64) + v1] + + @T.prim_func + def fused_reshape23_reshape24(lv265: T.Buffer((T.int64(1), T.int64(20), T.int64(64)), "float16"), T_reshape_intermediate_1: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16")): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + # with T.block("root"): + for ax0_fused_0 in T.thread_binding(T.int64(2), thread="blockIdx.x"): + for ax0_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_reshape_1"): + v0 = T.axis.spatial(T.int64(1280), ax0_fused_0 * T.int64(1024) + ax0_fused_1) + T.where(ax0_fused_0 * T.int64(1024) + ax0_fused_1 < T.int64(1280)) + T.reads(lv265[T.int64(0), v0 // T.int64(64), v0 % T.int64(64)]) + T.writes(T_reshape_intermediate_1[T.int64(0), T.int64(0), v0]) + T_reshape_intermediate_1[T.int64(0), T.int64(0), v0] = lv265[T.int64(0), v0 // T.int64(64), v0 % T.int64(64)] + + @T.prim_func + def fused_reshape9(packed_params_1: T.Buffer((T.int64(1280),), "float16"), T_reshape_intermediate: T.Buffer((T.int64(1), T.int64(1280), T.int64(1)), "float16")): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + # with T.block("root"): + for ax0_fused_0 in T.thread_binding(T.int64(2), thread="blockIdx.x"): + for ax0_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_reshape"): + v0 = T.axis.spatial(T.int64(1280), ax0_fused_0 * T.int64(1024) + ax0_fused_1) + T.where(ax0_fused_0 * T.int64(1024) + ax0_fused_1 < T.int64(1280)) + T.reads(packed_params_1[v0]) + T.writes(T_reshape_intermediate[T.int64(0), v0, T.int64(0)]) + T_reshape_intermediate[T.int64(0), v0, T.int64(0)] = packed_params_1[v0] + + @T.prim_func + def fused_rope(var_qkv: T.handle, var_position_map: T.handle, var_q: T.handle, var_k: T.handle, var_v: T.handle, apply_rope: T.int32): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + seq_len = T.int64() + qkv = T.match_buffer(var_qkv, (seq_len, 60, 64), "float16") + position_map = T.match_buffer(var_position_map, (seq_len,), "int32", offset_factor=1) + q = T.match_buffer(var_q, (seq_len, 20, 64), "float16") + k = T.match_buffer(var_k, (seq_len, 20, 64), "float16") + v = T.match_buffer(var_v, (seq_len, 20, 64), "float16") + # with T.block("root"): + for iters_0_iters_1_iters_2_fused_0 in T.thread_binding((seq_len * T.int64(3840) + T.int64(1023)) // T.int64(1024), thread="blockIdx.x"): + for iters_0_iters_1_iters_2_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("llama_fused_rope"): + s = T.axis.spatial(seq_len, (iters_0_iters_1_iters_2_fused_0 * T.int64(1024) + iters_0_iters_1_iters_2_fused_1) // T.int64(3840)) + h = T.axis.spatial(60, T.Cast("int32", (iters_0_iters_1_iters_2_fused_0 * T.int64(1024) + iters_0_iters_1_iters_2_fused_1) % T.int64(3840) // T.int64(64))) + d = T.axis.spatial(64, T.Cast("int32", (iters_0_iters_1_iters_2_fused_0 * T.int64(1024) + iters_0_iters_1_iters_2_fused_1) % T.int64(64))) + T.where(iters_0_iters_1_iters_2_fused_0 * T.int64(1024) + iters_0_iters_1_iters_2_fused_1 < seq_len * T.int64(3840)) + T.reads(position_map[s], qkv[s, h, d - 32:d - 32 + 65]) + T.writes(q[s, h, d], k[s, h - 20, d], v[s, h - 40, d]) + if h < 20: + q[s, h, d] = T.if_then_else(apply_rope > 0 and d < 64, T.Cast("float16", T.cos(T.Cast("float32", position_map[s]) / T.pow(T.float32(1), T.Cast("float32", d * 2 % 64) / T.float32(64))) * T.Cast("float32", qkv[s, h, d]) + T.sin(T.Cast("float32", position_map[s]) / T.pow(T.float32(1), T.Cast("float32", d * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(d < 32, qkv[s, h, d + 32] * T.float16(-1), qkv[s, h, d - 32]))), qkv[s, h, d]) + else: + if h < 40: + k[s, h - 20, d] = T.if_then_else(apply_rope > 0 and d < 64, T.Cast("float16", T.cos(T.Cast("float32", position_map[s]) / T.pow(T.float32(1), T.Cast("float32", d * 2 % 64) / T.float32(64))) * T.Cast("float32", qkv[s, h, d]) + T.sin(T.Cast("float32", position_map[s]) / T.pow(T.float32(1), T.Cast("float32", d * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(d < 32, qkv[s, h, d + 32] * T.float16(-1), qkv[s, h, d - 32]))), qkv[s, h, d]) + else: + v[s, h - 40, d] = qkv[s, h, d] + + @T.prim_func + def fused_transpose_add3(packed_params_4: T.Buffer((T.int64(1500), T.int64(1280)), "float16"), p_gelu1: T.handle, p_output0: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + batch_size = T.int64() + gelu1 = T.match_buffer(p_gelu1, (batch_size, T.int64(1280), T.int64(1500)), "float16") + T_add_intermediate = T.match_buffer(p_output0, (batch_size, T.int64(1500), T.int64(1280)), "float16") + # with T.block("root"): + for ax0_ax1_ax2_fused_0 in T.thread_binding(batch_size * T.int64(1875), thread="blockIdx.x"): + for ax0_ax1_ax2_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_add"): + v0 = T.axis.spatial(batch_size, (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) // T.int64(1920000)) + v1 = T.axis.spatial(T.int64(1500), (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) % T.int64(1920000) // T.int64(1280)) + v2 = T.axis.spatial(T.int64(1280), (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) % T.int64(1280)) + T.reads(gelu1[v0, v2, v1], packed_params_4[v1, v2]) + T.writes(T_add_intermediate[v0, v1, v2]) + T_add_intermediate[v0, v1, v2] = gelu1[v0, v2, v1] + packed_params_4[v1, v2] + + @T.prim_func + def gather_probs(var_src: T.handle, var_indices: T.handle, var_dst: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + m, n = T.int32(is_size_var=True), T.int32(is_size_var=True) + src = T.match_buffer(var_src, (m, n)) + batch_size = T.int32(is_size_var=True) + indices = T.match_buffer(var_indices, (batch_size,), "int32") + dst = T.match_buffer(var_dst, (batch_size, n)) + # with T.block("root"): + for ax0_ax1_fused_0 in T.thread_binding((batch_size * n + 1023) // 1024, thread="blockIdx.x"): + for ax0_ax1_fused_1 in T.thread_binding(1024, thread="threadIdx.x"): + with T.block("gather_2d"): + v0 = T.axis.spatial(batch_size, (ax0_ax1_fused_0 * 1024 + ax0_ax1_fused_1) % (n * batch_size) // n) + v1 = T.axis.spatial(n, (ax0_ax1_fused_0 * 1024 + ax0_ax1_fused_1) % n) + T.where(ax0_ax1_fused_0 * 1024 + ax0_ax1_fused_1 < batch_size * n) + T.reads(src[indices[v0], v1], indices[v0]) + T.writes(dst[v0, v1]) + dst[v0, v1] = src[indices[v0], v1] + + @T.prim_func + def get_index_from_sorted(A: T.handle, B: T.handle, C: T.handle, D: T.handle, E: T.handle, F: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) + batch, vocab_size = T.int64(), T.int64() + cumsum_sorted = T.match_buffer(A, (batch, vocab_size)) + indices = T.match_buffer(B, (batch, vocab_size), "int32") + renorm_prob = T.match_buffer(C, (batch, 1)) + out_batch = T.int64() + usample = T.match_buffer(D, (out_batch, 1)) + sample_indices = T.match_buffer(E, (out_batch, 1), "int32") + output_index = T.match_buffer(F, (out_batch, 1), "int32") + # with T.block("root"): + for ax0_ax1_fused_0 in T.thread_binding((out_batch * vocab_size + T.int64(1023)) // T.int64(1024), thread="blockIdx.x"): + for ax0_ax1_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_get_index_from_sorted"): + v0 = T.axis.spatial(out_batch, (ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1) % (vocab_size * out_batch) // vocab_size) + v1 = T.axis.spatial(vocab_size, (ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1) % vocab_size) + T.where(ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1 < out_batch * vocab_size) + T.reads(usample[v0, T.int64(0)], cumsum_sorted[sample_indices[v0, T.int64(0)], v1 - T.int64(1):v1 - T.int64(1) + T.int64(2)], sample_indices[v0, T.int64(0)], renorm_prob[sample_indices[v0, T.int64(0)], 0], indices[sample_indices[v0, T.int64(0)], T.min(T.int64(0), v1):T.min(T.int64(0), v1) + (v1 + T.int64(1))]) + T.writes(output_index[v0, 0]) + if usample[v0, T.int64(0)] < cumsum_sorted[sample_indices[v0, T.int64(0)], v1] / renorm_prob[sample_indices[v0, T.int64(0)], 0] or v1 + T.int64(1) == vocab_size: + if v1 == T.int64(0): + output_index[v0, 0] = indices[sample_indices[v0, T.int64(0)], 0] + else: + if usample[v0, T.int64(0)] >= cumsum_sorted[sample_indices[v0, T.int64(0)], v1 - T.int64(1)] / renorm_prob[sample_indices[v0, T.int64(0)], 0]: + output_index[v0, 0] = indices[sample_indices[v0, T.int64(0)], v1] + + @T.prim_func + def get_renorm_prob(A: T.handle, B: T.handle, C: T.handle, D: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) + batch, vocab_size = T.int64(), T.int64() + cumsum_sorted = T.match_buffer(A, (batch, vocab_size)) + top_p = T.match_buffer(B, (batch, 1)) + top_k = T.match_buffer(C, (batch, 1), "int32") + renorm_prob = T.match_buffer(D, (batch, 1)) + # with T.block("root"): + for ax0_ax1_fused_0 in T.thread_binding((batch * vocab_size + T.int64(1023)) // T.int64(1024), thread="blockIdx.x"): + for ax0_ax1_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_get_renorm_prob"): + v0 = T.axis.spatial(batch, (ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1) % (vocab_size * batch) // vocab_size) + v1 = T.axis.spatial(vocab_size, (ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1) % vocab_size) + T.where(ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1 < batch * vocab_size) + T.reads(cumsum_sorted[v0, T.min(T.min(T.int64(0), v1), v1 + T.int64(1)):T.min(T.min(T.int64(0), v1), v1 + T.int64(1)) + (v1 + T.int64(2))], top_p[v0, 0], top_k[v0, 0]) + T.writes(renorm_prob[v0, 0]) + if not (cumsum_sorted[v0, 0] < top_p[v0, 0] and top_k[v0, 0] > 1): + renorm_prob[v0, 0] = cumsum_sorted[v0, 0] + else: + if cumsum_sorted[v0, v1] < top_p[v0, 0] and v1 + T.int64(1) < T.Cast("int64", top_k[v0, 0]): + if v1 + T.int64(1) == vocab_size: + renorm_prob[v0, 0] = cumsum_sorted[v0, v1] + else: + if not (cumsum_sorted[v0, v1 + T.int64(1)] < top_p[v0, 0] and v1 + T.int64(1) + T.int64(1) < T.Cast("int64", top_k[v0, 0])): + renorm_prob[v0, 0] = cumsum_sorted[v0, v1 + T.int64(1)] + + @T.prim_func + def index(var_layer_norm355: T.handle, index: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16")): + T.func_attr({"target": T.target({"arch": "sm_89", "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + seq_len = T.int64() + layer_norm355 = T.match_buffer(var_layer_norm355, (T.int64(1), seq_len, T.int64(1280)), "float16") + # with T.block("root"): + for ax0_fused_0 in T.thread_binding(T.int64(2), thread="blockIdx.x"): + for ax0_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("index"): + v0 = T.axis.spatial(T.int64(1280), ax0_fused_0 * T.int64(1024) + ax0_fused_1) + T.where(ax0_fused_0 * T.int64(1024) + ax0_fused_1 < T.int64(1280)) + T.reads(layer_norm355[T.int64(0), seq_len - T.int64(1), v0]) + T.writes(index[T.int64(0), T.int64(0), v0]) + index[T.int64(0), T.int64(0), v0] = layer_norm355[T.int64(0), seq_len - T.int64(1), v0] + + @T.prim_func + def layer_norm(var_add578: T.handle, model_decoder_layers_0_self_attn_layer_norm_weight3: T.Buffer((T.int64(1280),), "float16"), model_decoder_layers_0_self_attn_layer_norm_bias3: T.Buffer((T.int64(1280),), "float16"), var_T_layer_norm: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + batch_size = T.int64() + add578 = T.match_buffer(var_add578, (batch_size, T.int64(1), T.int64(1280)), "float16") + T_layer_norm = T.match_buffer(var_T_layer_norm, (batch_size, T.int64(1), T.int64(1280)), "float16") + # with T.block("root"): + add578_red_temp_v0_shared = T.alloc_buffer((batch_size, T.int64(1)), scope="shared") + add578_red_temp_v1_shared = T.alloc_buffer((batch_size, T.int64(1)), scope="shared") + for ax0_fused in T.thread_binding(batch_size, thread="blockIdx.x"): + for ax0 in range(T.int64(1)): + for ax1_fused_1 in T.thread_binding(T.int64(256), thread="threadIdx.x"): + for ax1_fused_0 in T.serial(T.int64(5), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): + with T.block("add578_red_temp"): + v0 = T.axis.spatial(batch_size, ax0_fused + ax0) + v1 = T.axis.reduce(T.int64(1280), ax1_fused_0 * T.int64(256) + ax1_fused_1) + T.reads(add578[v0, T.int64(0), v1]) + T.writes(add578_red_temp_v0_shared[v0, T.int64(0)], add578_red_temp_v1_shared[v0, T.int64(0)]) + with T.init(): + add578_red_temp_v0_shared[v0, T.int64(0)] = T.float32(0) + add578_red_temp_v1_shared[v0, T.int64(0)] = T.float32(0) + v_add578_red_temp_v0: T.float32 = add578_red_temp_v0_shared[v0, T.int64(0)] + T.Cast("float32", add578[v0, T.int64(0), v1]) + v_add578_red_temp_v1: T.float32 = add578_red_temp_v1_shared[v0, T.int64(0)] + T.Cast("float32", add578[v0, T.int64(0), v1]) * T.Cast("float32", add578[v0, T.int64(0), v1]) + add578_red_temp_v0_shared[v0, T.int64(0)] = v_add578_red_temp_v0 + add578_red_temp_v1_shared[v0, T.int64(0)] = v_add578_red_temp_v1 + for ax1_1 in T.thread_binding(T.int64(256), thread="threadIdx.x"): + for ax1_0 in T.serial(T.int64(5), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): + with T.block("T_layer_norm"): + v0 = T.axis.spatial(batch_size, ax0_fused) + v1 = T.axis.spatial(T.int64(1280), ax1_0 * T.int64(256) + ax1_1) + T.reads(add578[v0, T.int64(0), v1], add578_red_temp_v0_shared[v0, T.int64(0)], add578_red_temp_v1_shared[v0, T.int64(0)], model_decoder_layers_0_self_attn_layer_norm_weight3[v1], model_decoder_layers_0_self_attn_layer_norm_bias3[v1]) + T.writes(T_layer_norm[v0, T.int64(0), v1]) + T_layer_norm[v0, T.int64(0), v1] = T.Cast("float16", (T.Cast("float32", add578[v0, T.int64(0), v1]) - add578_red_temp_v0_shared[v0, T.int64(0)] * T.float32(0.00078125000000000004)) * T.rsqrt(add578_red_temp_v1_shared[v0, T.int64(0)] * T.float32(0.00078125000000000004) - add578_red_temp_v0_shared[v0, T.int64(0)] * T.float32(0.00078125000000000004) * (add578_red_temp_v0_shared[v0, T.int64(0)] * T.float32(0.00078125000000000004)) + T.float32(1.0000000000000001e-05))) * model_decoder_layers_0_self_attn_layer_norm_weight3[v1] + model_decoder_layers_0_self_attn_layer_norm_bias3[v1] + + @T.prim_func + def layer_norm1(var_add: T.handle, model_encoder_layers_0_self_attn_layer_norm_weight: T.Buffer((T.int64(1280),), "float16"), model_encoder_layers_0_self_attn_layer_norm_bias: T.Buffer((T.int64(1280),), "float16"), var_T_layer_norm: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + batch_size = T.int64() + add = T.match_buffer(var_add, (batch_size, T.int64(1500), T.int64(1280)), "float16") + T_layer_norm = T.match_buffer(var_T_layer_norm, (batch_size, T.int64(1500), T.int64(1280)), "float16") + # with T.block("root"): + add_red_temp_v0_shared = T.alloc_buffer((batch_size, T.int64(1500)), scope="shared") + add_red_temp_v1_shared = T.alloc_buffer((batch_size, T.int64(1500)), scope="shared") + for ax0_ax1_fused in T.thread_binding(batch_size * T.int64(1500), thread="blockIdx.x"): + for ax0, ax1 in T.grid(T.int64(1), T.int64(1)): + for ax2_fused_1 in T.thread_binding(T.int64(256), thread="threadIdx.x"): + for ax2_fused_0 in T.serial(T.int64(5), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): + with T.block("add_red_temp"): + v0 = T.axis.spatial(batch_size, ax0_ax1_fused // T.int64(1500) + ax0) + v1 = T.axis.spatial(T.int64(1500), ax0_ax1_fused % T.int64(1500) + ax1) + v2 = T.axis.reduce(T.int64(1280), ax2_fused_0 * T.int64(256) + ax2_fused_1) + T.reads(add[v0, v1, v2]) + T.writes(add_red_temp_v0_shared[v0, v1], add_red_temp_v1_shared[v0, v1]) + with T.init(): + add_red_temp_v0_shared[v0, v1] = T.float32(0) + add_red_temp_v1_shared[v0, v1] = T.float32(0) + v_add_red_temp_v0: T.float32 = add_red_temp_v0_shared[v0, v1] + T.Cast("float32", add[v0, v1, v2]) + v_add_red_temp_v1: T.float32 = add_red_temp_v1_shared[v0, v1] + T.Cast("float32", add[v0, v1, v2]) * T.Cast("float32", add[v0, v1, v2]) + add_red_temp_v0_shared[v0, v1] = v_add_red_temp_v0 + add_red_temp_v1_shared[v0, v1] = v_add_red_temp_v1 + for ax2_1 in T.thread_binding(T.int64(256), thread="threadIdx.x"): + for ax2_0 in T.serial(T.int64(5), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): + with T.block("T_layer_norm"): + v0 = T.axis.spatial(batch_size, ax0_ax1_fused // T.int64(1500)) + v1 = T.axis.spatial(T.int64(1500), ax0_ax1_fused % T.int64(1500)) + v2 = T.axis.spatial(T.int64(1280), ax2_0 * T.int64(256) + ax2_1) + T.reads(add[v0, v1, v2], add_red_temp_v0_shared[v0, v1], add_red_temp_v1_shared[v0, v1], model_encoder_layers_0_self_attn_layer_norm_weight[v2], model_encoder_layers_0_self_attn_layer_norm_bias[v2]) + T.writes(T_layer_norm[v0, v1, v2]) + T_layer_norm[v0, v1, v2] = T.Cast("float16", (T.Cast("float32", add[v0, v1, v2]) - add_red_temp_v0_shared[v0, v1] * T.float32(0.00078125000000000004)) * T.rsqrt(add_red_temp_v1_shared[v0, v1] * T.float32(0.00078125000000000004) - add_red_temp_v0_shared[v0, v1] * T.float32(0.00078125000000000004) * (add_red_temp_v0_shared[v0, v1] * T.float32(0.00078125000000000004)) + T.float32(1.0000000000000001e-05))) * model_encoder_layers_0_self_attn_layer_norm_weight[v2] + model_encoder_layers_0_self_attn_layer_norm_bias[v2] + + @T.prim_func + def layer_norm2(var_add257: T.handle, model_decoder_layers_0_self_attn_layer_norm_weight2: T.Buffer((T.int64(1280),), "float16"), model_decoder_layers_0_self_attn_layer_norm_bias2: T.Buffer((T.int64(1280),), "float16"), var_T_layer_norm: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + seq_len = T.int64() + add257 = T.match_buffer(var_add257, (T.int64(1), seq_len, T.int64(1280)), "float16") + T_layer_norm = T.match_buffer(var_T_layer_norm, (T.int64(1), seq_len, T.int64(1280)), "float16") + # with T.block("root"): + add257_red_temp_v0_shared = T.alloc_buffer((T.int64(1), seq_len), scope="shared") + add257_red_temp_v1_shared = T.alloc_buffer((T.int64(1), seq_len), scope="shared") + for ax0_fused in T.thread_binding(seq_len, thread="blockIdx.x"): + for ax0 in range(T.int64(1)): + for ax1_fused_1 in T.thread_binding(T.int64(256), thread="threadIdx.x"): + for ax1_fused_0 in T.serial(T.int64(5), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): + with T.block("add257_red_temp"): + v0 = T.axis.spatial(seq_len, ax0_fused + ax0) + v1 = T.axis.reduce(T.int64(1280), ax1_fused_0 * T.int64(256) + ax1_fused_1) + T.reads(add257[T.int64(0), v0, v1]) + T.writes(add257_red_temp_v0_shared[T.int64(0), v0], add257_red_temp_v1_shared[T.int64(0), v0]) + with T.init(): + add257_red_temp_v0_shared[T.int64(0), v0] = T.float32(0) + add257_red_temp_v1_shared[T.int64(0), v0] = T.float32(0) + v_add257_red_temp_v0: T.float32 = add257_red_temp_v0_shared[T.int64(0), v0] + T.Cast("float32", add257[T.int64(0), v0, v1]) + v_add257_red_temp_v1: T.float32 = add257_red_temp_v1_shared[T.int64(0), v0] + T.Cast("float32", add257[T.int64(0), v0, v1]) * T.Cast("float32", add257[T.int64(0), v0, v1]) + add257_red_temp_v0_shared[T.int64(0), v0] = v_add257_red_temp_v0 + add257_red_temp_v1_shared[T.int64(0), v0] = v_add257_red_temp_v1 + for ax1_1 in T.thread_binding(T.int64(256), thread="threadIdx.x"): + for ax1_0 in T.serial(T.int64(5), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): + with T.block("T_layer_norm"): + v0 = T.axis.spatial(seq_len, ax0_fused) + v1 = T.axis.spatial(T.int64(1280), ax1_0 * T.int64(256) + ax1_1) + T.reads(add257[T.int64(0), v0, v1], add257_red_temp_v0_shared[T.int64(0), v0], add257_red_temp_v1_shared[T.int64(0), v0], model_decoder_layers_0_self_attn_layer_norm_weight2[v1], model_decoder_layers_0_self_attn_layer_norm_bias2[v1]) + T.writes(T_layer_norm[T.int64(0), v0, v1]) + T_layer_norm[T.int64(0), v0, v1] = T.Cast("float16", (T.Cast("float32", add257[T.int64(0), v0, v1]) - add257_red_temp_v0_shared[T.int64(0), v0] * T.float32(0.00078125000000000004)) * T.rsqrt(add257_red_temp_v1_shared[T.int64(0), v0] * T.float32(0.00078125000000000004) - add257_red_temp_v0_shared[T.int64(0), v0] * T.float32(0.00078125000000000004) * (add257_red_temp_v0_shared[T.int64(0), v0] * T.float32(0.00078125000000000004)) + T.float32(1.0000000000000001e-05))) * model_decoder_layers_0_self_attn_layer_norm_weight2[v1] + model_decoder_layers_0_self_attn_layer_norm_bias2[v1] + + @T.prim_func + def layer_norm3(add1220: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16"), model_decoder_layers_0_self_attn_layer_norm_weight5: T.Buffer((T.int64(1280),), "float16"), model_decoder_layers_0_self_attn_layer_norm_bias5: T.Buffer((T.int64(1280),), "float16"), T_layer_norm: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16")): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + # with T.block("root"): + add1220_red_temp_v0_shared = T.alloc_buffer((T.int64(1), T.int64(1)), scope="shared") + add1220_red_temp_v1_shared = T.alloc_buffer((T.int64(1), T.int64(1)), scope="shared") + for ax0_fused in T.thread_binding(T.int64(1), thread="blockIdx.x"): + for ax0 in range(T.int64(1)): + for ax1_fused_1 in T.thread_binding(T.int64(256), thread="threadIdx.x"): + for ax1_fused_0 in T.serial(T.int64(5), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): + with T.block("add1220_red_temp"): + v0 = T.axis.spatial(T.int64(1), ax0) + v1 = T.axis.reduce(T.int64(1280), ax1_fused_0 * T.int64(256) + ax1_fused_1) + T.reads(add1220[T.int64(0), T.int64(0), v1]) + T.writes(add1220_red_temp_v0_shared[T.int64(0), T.int64(0)], add1220_red_temp_v1_shared[T.int64(0), T.int64(0)]) + with T.init(): + add1220_red_temp_v0_shared[T.int64(0), T.int64(0)] = T.float32(0) + add1220_red_temp_v1_shared[T.int64(0), T.int64(0)] = T.float32(0) + v_add1220_red_temp_v0: T.float32 = add1220_red_temp_v0_shared[T.int64(0), T.int64(0)] + T.Cast("float32", add1220[T.int64(0), T.int64(0), v1]) + v_add1220_red_temp_v1: T.float32 = add1220_red_temp_v1_shared[T.int64(0), T.int64(0)] + T.Cast("float32", add1220[T.int64(0), T.int64(0), v1]) * T.Cast("float32", add1220[T.int64(0), T.int64(0), v1]) + add1220_red_temp_v0_shared[T.int64(0), T.int64(0)] = v_add1220_red_temp_v0 + add1220_red_temp_v1_shared[T.int64(0), T.int64(0)] = v_add1220_red_temp_v1 + for ax1_1 in T.thread_binding(T.int64(256), thread="threadIdx.x"): + for ax1_0 in T.serial(T.int64(5), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): + with T.block("T_layer_norm"): + v0 = T.axis.spatial(T.int64(1), T.int64(0)) + v1 = T.axis.spatial(T.int64(1280), ax1_0 * T.int64(256) + ax1_1) + T.reads(add1220[T.int64(0), T.int64(0), v1], add1220_red_temp_v0_shared[T.int64(0), T.int64(0)], add1220_red_temp_v1_shared[T.int64(0), T.int64(0)], model_decoder_layers_0_self_attn_layer_norm_weight5[v1], model_decoder_layers_0_self_attn_layer_norm_bias5[v1]) + T.writes(T_layer_norm[T.int64(0), T.int64(0), v1]) + T_layer_norm[T.int64(0), T.int64(0), v1] = T.Cast("float16", (T.Cast("float32", add1220[T.int64(0), T.int64(0), v1]) - add1220_red_temp_v0_shared[T.int64(0), T.int64(0)] * T.float32(0.00078125000000000004)) * T.rsqrt(add1220_red_temp_v1_shared[T.int64(0), T.int64(0)] * T.float32(0.00078125000000000004) - add1220_red_temp_v0_shared[T.int64(0), T.int64(0)] * T.float32(0.00078125000000000004) * (add1220_red_temp_v0_shared[T.int64(0), T.int64(0)] * T.float32(0.00078125000000000004)) + T.float32(1.0000000000000001e-05))) * model_decoder_layers_0_self_attn_layer_norm_weight5[v1] + model_decoder_layers_0_self_attn_layer_norm_bias5[v1] + + @T.prim_func + def merge_state_inplace(v: T.handle, s: T.handle, v_other: T.handle, s_other: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) + N, H, D = T.int32(is_size_var=True), T.int32(is_size_var=True), T.int32(is_size_var=True) + V = T.match_buffer(v, (N, H, D), "float16") + S = T.match_buffer(s, (N, H)) + V_other = T.match_buffer(v_other, (N, H, D), "float16") + S_other = T.match_buffer(s_other, (N, H)) + # with T.block("root"): + for bx in T.thread_binding(N, thread="blockIdx.x"): + for by in T.thread_binding(1, thread="blockIdx.y"): + for ty in T.thread_binding(20, thread="threadIdx.y"): + for tx in T.thread_binding(16, thread="threadIdx.x"): + with T.block("merge"): + T.reads(S[bx, ty + by * 20], S_other[bx, ty + by * 20], V[bx, ty + by * 20, tx * 4:tx * 4 + 4], V_other[bx, ty + by * 20, tx * 4:tx * 4 + 4]) + T.writes(V[bx, ty + by * 20, tx * 4:tx * 4 + 4], S[bx, ty + by * 20]) + s_val = T.alloc_buffer((1,), scope="local") + s_other_val = T.alloc_buffer((1,), scope="local") + s_max = T.alloc_buffer((1,), scope="local") + scale = T.alloc_buffer((1,), scope="local") + other_scale = T.alloc_buffer((1,), scope="local") + v_vec = T.alloc_buffer((4,), "float16", scope="local") + v_other_vec = T.alloc_buffer((4,), "float16", scope="local") + s_val[0] = S[bx, ty + by * 20] + s_other_val[0] = S_other[bx, ty + by * 20] + s_max[0] = T.max(s_val[0], s_other_val[0]) + s_val[0] = T.exp2(s_val[0] - s_max[0]) + s_other_val[0] = T.exp2(s_other_val[0] - s_max[0]) + scale[0] = s_val[0] / (s_val[0] + s_other_val[0]) + other_scale[0] = s_other_val[0] / (s_val[0] + s_other_val[0]) + for vec in T.vectorized(4): + v_vec[vec] = V[bx, ty + by * 20, tx * 4 + vec] + for vec in T.vectorized(4): + v_other_vec[vec] = V_other[bx, ty + by * 20, tx * 4 + vec] + for vec in range(4): + v_vec[vec] = T.Cast("float16", T.Cast("float32", v_vec[vec]) * scale[0] + T.Cast("float32", v_other_vec[vec]) * other_scale[0]) + for vec in T.vectorized(4): + V[bx, ty + by * 20, tx * 4 + vec] = v_vec[vec] + S[bx, ty + by * 20] = T.log2(s_val[0] + s_other_val[0]) + s_max[0] + + @T.prim_func + def parallel_sampling_from_prob(var_prob: T.handle, var_uniform_samples: T.handle, var_row_indices: T.handle, var_sampled_token_ids: T.handle): + T.func_attr({"tir.is_scheduled": 1}) + n, vocab_size = T.int64(), T.int64() + prob = T.match_buffer(var_prob, (n, vocab_size)) + batch_size = T.int64() + uniform_samples = T.match_buffer(var_uniform_samples, (batch_size, 1)) + row_indices = T.match_buffer(var_row_indices, (batch_size, 1), "int32") + token_ids = T.match_buffer(var_sampled_token_ids, (batch_size, 1), "int32") + # with T.block("root"): + aggregate = T.alloc_buffer((), scope="local") + sample_id_local = T.alloc_buffer((), "int32", scope="local") + step_iter = T.alloc_buffer((), "int32", scope="local") + for bx in T.thread_binding(batch_size, thread="blockIdx.x"): + row_idx: T.int32 = row_indices[bx, 0] + for ty in T.thread_binding(T.int64(4), thread="threadIdx.y"): + for tx in T.thread_binding(T.int64(32), thread="threadIdx.x"): + u: T.float32 = uniform_samples[bx, 0] + aggregate[()] = T.Cast("float32", 0) + step_iter[()] = 0 + while T.tvm_thread_invariant((step_iter[()] == 0 or aggregate[()] < u - T.float32(9.9999999999999995e-07)) and T.Cast("int64", step_iter[()]) < (vocab_size + T.int64(512) - T.int64(1)) // T.int64(512)): + with T.block(""): + T.reads(step_iter[()], prob[row_idx, T.Cast("int64", step_iter[()]) * T.int64(512) + ty * T.int64(128) + tx * T.int64(4):T.Cast("int64", step_iter[()]) * T.int64(512) + ty * T.int64(128) + tx * T.int64(4) + T.int64(4)], aggregate[()]) + T.writes(sample_id_local[()], aggregate[()]) + prob_gt_threshold = T.alloc_buffer((T.int64(4),), scope="local") + cumsum = T.alloc_buffer((T.int64(512),), scope="shared") + greater_than_u = T.alloc_buffer((T.int64(4),), "bool", scope="local") + mask = T.alloc_buffer((T.int64(4),), "bool", scope="local") + valid = T.alloc_buffer((T.int64(4),), "bool", scope="local") + indices = T.alloc_buffer((T.int64(4),), "int32", scope="local") + step_aggregate = T.alloc_buffer((), scope="local") + for v in T.unroll(T.int64(4)): + idx: T.int64 = T.Cast("int64", step_iter[()]) * T.int64(512) + ty * T.int64(128) + tx * T.int64(4) + v + prob_local: T.float32 = T.if_then_else(idx < vocab_size, prob[row_idx, idx], T.Cast("float32", 0)) + prob_gt_threshold[v] = T.if_then_else(prob_local > T.float32(0), prob_local, T.Cast("float32", 0)) + valid[v] = prob_local > T.float32(0) and idx < vocab_size + with T.block(""): + T.reads(prob_gt_threshold[T.int64(0):T.int64(4)]) + T.writes(step_aggregate[()]) + local_sum = T.alloc_buffer((), scope="local") + shared_buf = T.alloc_buffer((T.int64(128),), scope="shared") + idx: T.int64 = ty * T.int64(32) + tx + local_sum[()] = T.Cast("float32", 0) + for i in T.unroll(T.int64(4)): + local_sum[()] = local_sum[()] + prob_gt_threshold[i] + shared_buf[idx] = local_sum[()] + for i in T.unroll(T.int64(7)): + if idx % T.shift_left(T.int64(1), i + T.int64(1)) == T.int64(0): + shared_buf[idx] = shared_buf[idx] + shared_buf[idx + T.shift_left(T.int64(1), i)] + step_aggregate[()] = shared_buf[0] + if T.tvm_thread_invariant(aggregate[()] + step_aggregate[()] >= u - T.float32(9.9999999999999995e-07)): + for i in T.unroll(T.int64(1), T.int64(4)): + prob_gt_threshold[i] = prob_gt_threshold[i] + prob_gt_threshold[i - T.int64(1)] + for i in T.vectorized(T.int64(4)): + cumsum[ty * T.int64(128) + tx * T.int64(4) + i] = prob_gt_threshold[i] + for i in T.unroll(T.int64(5)): + for j in T.vectorized(T.int64(4)): + idx: T.int64 = ty * T.int64(128) + tx * T.int64(4) + if tx >= T.shift_left(T.int64(1), i): + cumsum[idx + j] = cumsum[idx + j] + cumsum[idx - T.shift_left(T.int64(1), i) * T.int64(4) + T.int64(4) - T.int64(1)] + for i in T.unroll(T.int64(1), T.int64(4)): + for j in T.vectorized(T.int64(4)): + if ty == T.int64(0): + idx: T.int64 = i * T.int64(128) + tx * T.int64(4) + cumsum[idx + j] = cumsum[idx + j] + cumsum[i * T.int64(128) - T.int64(1)] + for v in T.unroll(T.int64(4)): + greater_than_u[v] = cumsum[ty * T.int64(128) + tx * T.int64(4) + v] + aggregate[()] >= u - T.float32(9.9999999999999995e-07) + with T.block(""): + T.reads(greater_than_u[T.int64(0):T.int64(4)]) + T.writes(mask[T.int64(0):T.int64(4)]) + shared_buf = T.alloc_buffer((T.int64(128),), "bool", scope="shared") + tx_idx: T.int64 = ty * T.int64(32) + tx + shared_buf[tx_idx] = greater_than_u[T.int64(3)] + mask[0] = T.if_then_else(tx_idx != T.int64(0), T.Cast("int8", greater_than_u[0]) != T.Cast("int8", shared_buf[tx_idx - T.int64(1)]), greater_than_u[0]) + for i in T.unroll(T.int64(1), T.int64(4)): + mask[i] = T.Cast("int8", greater_than_u[i]) != T.Cast("int8", greater_than_u[i - T.int64(1)]) + for v in T.unroll(T.int64(4)): + mask[v] = mask[v] and valid[v] + indices[v] = T.Cast("int32", T.Cast("int64", step_iter[()]) * T.int64(512) + ty * T.int64(128) + tx * T.int64(4) + v) + with T.block(""): + T.reads(mask[T.int64(0):T.int64(4)], indices[T.int64(0):T.int64(4)]) + T.writes(sample_id_local[()]) + local_sum = T.alloc_buffer((), "int32", scope="local") + shared_buf = T.alloc_buffer((T.int64(128),), "int32", scope="shared") + idx: T.int64 = ty * T.int64(32) + tx + local_sum[()] = T.Cast("int32", vocab_size - T.int64(1)) + for i in T.unroll(T.int64(4)): + if mask[i]: + local_sum[()] = T.min(local_sum[()], indices[i]) + shared_buf[idx] = local_sum[()] + for i in T.unroll(T.int64(7)): + if idx % T.shift_left(T.int64(1), i + T.int64(1)) == T.int64(0): + shared_buf[idx] = T.min(shared_buf[idx], shared_buf[idx + T.shift_left(T.int64(1), i)]) + sample_id_local[()] = shared_buf[0] + aggregate[()] = aggregate[()] + step_aggregate[()] + step_iter[()] = step_iter[()] + 1 + if tx == T.int64(0) and ty == T.int64(0): + token_ids[bx, 0] = sample_id_local[()] + + @T.prim_func + def reshape(var_lv: T.handle, var_T_reshape: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + batch_size = T.int64() + lv = T.match_buffer(var_lv, (batch_size, T.int64(1500), T.int64(1280)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (batch_size, T.int64(1500), T.int64(20), T.int64(64)), "float16") + # with T.block("root"): + for ax0_ax1_ax2_ax3_fused_0 in T.thread_binding(batch_size * T.int64(1875), thread="blockIdx.x"): + for ax0_ax1_ax2_ax3_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_reshape"): + v0 = T.axis.spatial(batch_size, (ax0_ax1_ax2_ax3_fused_0 * T.int64(1024) + ax0_ax1_ax2_ax3_fused_1) // T.int64(1920000)) + v1 = T.axis.spatial(T.int64(1500), (ax0_ax1_ax2_ax3_fused_0 * T.int64(1024) + ax0_ax1_ax2_ax3_fused_1) % T.int64(1920000) // T.int64(1280)) + v2 = T.axis.spatial(T.int64(20), (ax0_ax1_ax2_ax3_fused_0 * T.int64(1024) + ax0_ax1_ax2_ax3_fused_1) % T.int64(1280) // T.int64(64)) + v3 = T.axis.spatial(T.int64(64), (ax0_ax1_ax2_ax3_fused_0 * T.int64(1024) + ax0_ax1_ax2_ax3_fused_1) % T.int64(64)) + T.reads(lv[v0, v1, v2 * T.int64(64) + v3]) + T.writes(T_reshape[v0, v1, v2, v3]) + T_reshape[v0, v1, v2, v3] = lv[v0, v1, v2 * T.int64(64) + v3] + + @T.prim_func + def reshape1(var_reshape256: T.handle, var_T_reshape: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + batch_size = T.int64() + reshape256 = T.match_buffer(var_reshape256, (batch_size, T.int64(1500), T.int64(20), T.int64(64)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (batch_size * T.int64(1500), T.int64(20), T.int64(64)), "float16") + # with T.block("root"): + for ax0_ax1_ax2_fused_0 in T.thread_binding(batch_size * T.int64(1875), thread="blockIdx.x"): + for ax0_ax1_ax2_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_reshape"): + v0 = T.axis.spatial(batch_size * T.int64(1500), (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) // T.int64(1280)) + v1 = T.axis.spatial(T.int64(20), (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) % T.int64(1280) // T.int64(64)) + v2 = T.axis.spatial(T.int64(64), (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) % T.int64(64)) + T.reads(reshape256[v0 // T.int64(1500), v0 % T.int64(1500), v1, v2]) + T.writes(T_reshape[v0, v1, v2]) + T_reshape[v0, v1, v2] = reshape256[v0 // T.int64(1500), v0 % T.int64(1500), v1, v2] + + @T.prim_func + def reshape10(var_lv4: T.handle, var_T_reshape: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + batch_size = T.int64() + lv4 = T.match_buffer(var_lv4, (batch_size * T.int64(1500), T.int64(20), T.int64(64)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (batch_size, T.int64(1500), T.int64(20), T.int64(64)), "float16") + # with T.block("root"): + for ax0_ax1_ax2_ax3_fused_0 in T.thread_binding(batch_size * T.int64(1875), thread="blockIdx.x"): + for ax0_ax1_ax2_ax3_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_reshape"): + v0 = T.axis.spatial(batch_size, (ax0_ax1_ax2_ax3_fused_0 * T.int64(1024) + ax0_ax1_ax2_ax3_fused_1) // T.int64(1920000)) + v1 = T.axis.spatial(T.int64(1500), (ax0_ax1_ax2_ax3_fused_0 * T.int64(1024) + ax0_ax1_ax2_ax3_fused_1) % T.int64(1920000) // T.int64(1280)) + v2 = T.axis.spatial(T.int64(20), (ax0_ax1_ax2_ax3_fused_0 * T.int64(1024) + ax0_ax1_ax2_ax3_fused_1) % T.int64(1280) // T.int64(64)) + v3 = T.axis.spatial(T.int64(64), (ax0_ax1_ax2_ax3_fused_0 * T.int64(1024) + ax0_ax1_ax2_ax3_fused_1) % T.int64(64)) + T.reads(lv4[v0 * T.int64(1500) + v1, v2, v3]) + T.writes(T_reshape[v0, v1, v2, v3]) + T_reshape[v0, v1, v2, v3] = lv4[v0 * T.int64(1500) + v1, v2, v3] + + @T.prim_func + def reshape11(var_reshape6: T.handle, var_T_reshape: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + batch_size = T.int64() + reshape6 = T.match_buffer(var_reshape6, (batch_size, T.int64(1500), T.int64(20), T.int64(64)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (batch_size, T.int64(1500), T.int64(1280)), "float16") + # with T.block("root"): + for ax0_ax1_ax2_fused_0 in T.thread_binding(batch_size * T.int64(1875), thread="blockIdx.x"): + for ax0_ax1_ax2_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_reshape"): + v0 = T.axis.spatial(batch_size, (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) // T.int64(1920000)) + v1 = T.axis.spatial(T.int64(1500), (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) % T.int64(1920000) // T.int64(1280)) + v2 = T.axis.spatial(T.int64(1280), (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) % T.int64(1280)) + T.reads(reshape6[v0, v1, v2 // T.int64(64), v2 % T.int64(64)]) + T.writes(T_reshape[v0, v1, v2]) + T_reshape[v0, v1, v2] = reshape6[v0, v1, v2 // T.int64(64), v2 % T.int64(64)] + + @T.prim_func + def reshape12(var_input_ids: T.handle, var_T_reshape: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + seq_len = T.int64() + input_ids = T.match_buffer(var_input_ids, (T.int64(1), seq_len), "int32") + T_reshape = T.match_buffer(var_T_reshape, (seq_len,), "int32") + # with T.block("root"): + for ax0_fused_0 in T.thread_binding((seq_len + T.int64(1023)) // T.int64(1024), thread="blockIdx.x"): + for ax0_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_reshape"): + v0 = T.axis.spatial(seq_len, ax0_fused_0 * T.int64(1024) + ax0_fused_1) + T.where(ax0_fused_0 * T.int64(1024) + ax0_fused_1 < seq_len) + T.reads(input_ids[T.int64(0), v0]) + T.writes(T_reshape[v0]) + T_reshape[v0] = input_ids[T.int64(0), v0] + + @T.prim_func + def reshape13(var_take: T.handle, var_T_reshape: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + seq_len = T.int64() + take = T.match_buffer(var_take, (seq_len, T.int64(1280)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (T.int64(1), seq_len, T.int64(1280)), "float16") + # with T.block("root"): + for ax0_ax1_fused_0 in T.thread_binding((seq_len * T.int64(1280) + T.int64(1023)) // T.int64(1024), thread="blockIdx.x"): + for ax0_ax1_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_reshape"): + v0 = T.axis.spatial(seq_len, (ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1) // T.int64(1280)) + v1 = T.axis.spatial(T.int64(1280), (ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1) % T.int64(1280)) + T.where(ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1 < seq_len * T.int64(1280)) + T.reads(take[v0, v1]) + T.writes(T_reshape[T.int64(0), v0, v1]) + T_reshape[T.int64(0), v0, v1] = take[v0, v1] + + @T.prim_func + def reshape14(var_lv416: T.handle, var_T_reshape: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + seq_len = T.int64() + lv416 = T.match_buffer(var_lv416, (T.int64(1), seq_len, T.int64(1280)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (T.int64(1), seq_len, T.int64(20), T.int64(64)), "float16") + # with T.block("root"): + for ax0_ax1_ax2_fused_0 in T.thread_binding((seq_len * T.int64(1280) + T.int64(1023)) // T.int64(1024), thread="blockIdx.x"): + for ax0_ax1_ax2_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_reshape"): + v0 = T.axis.spatial(seq_len, (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) // T.int64(1280)) + v1 = T.axis.spatial(T.int64(20), (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) % T.int64(1280) // T.int64(64)) + v2 = T.axis.spatial(T.int64(64), (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) % T.int64(64)) + T.where(ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1 < seq_len * T.int64(1280)) + T.reads(lv416[T.int64(0), v0, v1 * T.int64(64) + v2]) + T.writes(T_reshape[T.int64(0), v0, v1, v2]) + T_reshape[T.int64(0), v0, v1, v2] = lv416[T.int64(0), v0, v1 * T.int64(64) + v2] + + @T.prim_func + def reshape15(var_concat: T.handle, var_T_reshape: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + seq_len = T.int64() + concat = T.match_buffer(var_concat, (T.int64(1), seq_len, T.int64(60), T.int64(64)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (seq_len, T.int64(60), T.int64(64)), "float16") + # with T.block("root"): + for ax0_ax1_ax2_fused_0 in T.thread_binding((seq_len * T.int64(3840) + T.int64(1023)) // T.int64(1024), thread="blockIdx.x"): + for ax0_ax1_ax2_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_reshape"): + v0 = T.axis.spatial(seq_len, (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) // T.int64(3840)) + v1 = T.axis.spatial(T.int64(60), (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) % T.int64(3840) // T.int64(64)) + v2 = T.axis.spatial(T.int64(64), (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) % T.int64(64)) + T.where(ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1 < seq_len * T.int64(3840)) + T.reads(concat[T.int64(0), v0, v1, v2]) + T.writes(T_reshape[v0, v1, v2]) + T_reshape[v0, v1, v2] = concat[T.int64(0), v0, v1, v2] + + @T.prim_func + def reshape16(var_lv69: T.handle, var_T_reshape: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + seq_len = T.int64() + lv69 = T.match_buffer(var_lv69, (seq_len, T.int64(20), T.int64(64)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (T.int64(1), seq_len, T.int64(20), T.int64(64)), "float16") + # with T.block("root"): + for ax0_ax1_ax2_fused_0 in T.thread_binding((seq_len * T.int64(1280) + T.int64(1023)) // T.int64(1024), thread="blockIdx.x"): + for ax0_ax1_ax2_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_reshape"): + v0 = T.axis.spatial(seq_len, (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) // T.int64(1280)) + v1 = T.axis.spatial(T.int64(20), (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) % T.int64(1280) // T.int64(64)) + v2 = T.axis.spatial(T.int64(64), (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) % T.int64(64)) + T.where(ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1 < seq_len * T.int64(1280)) + T.reads(lv69[v0, v1, v2]) + T.writes(T_reshape[T.int64(0), v0, v1, v2]) + T_reshape[T.int64(0), v0, v1, v2] = lv69[v0, v1, v2] + + @T.prim_func + def reshape17(var_reshape391: T.handle, var_T_reshape: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + seq_len = T.int64() + reshape391 = T.match_buffer(var_reshape391, (T.int64(1), seq_len, T.int64(20), T.int64(64)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (T.int64(1), seq_len, T.int64(1280)), "float16") + # with T.block("root"): + for ax0_ax1_fused_0 in T.thread_binding((seq_len * T.int64(1280) + T.int64(1023)) // T.int64(1024), thread="blockIdx.x"): + for ax0_ax1_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_reshape"): + v0 = T.axis.spatial(seq_len, (ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1) // T.int64(1280)) + v1 = T.axis.spatial(T.int64(1280), (ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1) % T.int64(1280)) + T.where(ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1 < seq_len * T.int64(1280)) + T.reads(reshape391[T.int64(0), v0, v1 // T.int64(64), v1 % T.int64(64)]) + T.writes(T_reshape[T.int64(0), v0, v1]) + T_reshape[T.int64(0), v0, v1] = reshape391[T.int64(0), v0, v1 // T.int64(64), v1 % T.int64(64)] + + @T.prim_func + def reshape18(var_reshape393: T.handle, var_T_reshape: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + seq_len = T.int64() + reshape393 = T.match_buffer(var_reshape393, (T.int64(1), seq_len, T.int64(20), T.int64(64)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (seq_len, T.int64(20), T.int64(64)), "float16") + # with T.block("root"): + for ax0_ax1_ax2_fused_0 in T.thread_binding((seq_len * T.int64(1280) + T.int64(1023)) // T.int64(1024), thread="blockIdx.x"): + for ax0_ax1_ax2_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_reshape"): + v0 = T.axis.spatial(seq_len, (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) // T.int64(1280)) + v1 = T.axis.spatial(T.int64(20), (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) % T.int64(1280) // T.int64(64)) + v2 = T.axis.spatial(T.int64(64), (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) % T.int64(64)) + T.where(ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1 < seq_len * T.int64(1280)) + T.reads(reshape393[T.int64(0), v0, v1, v2]) + T.writes(T_reshape[v0, v1, v2]) + T_reshape[v0, v1, v2] = reshape393[T.int64(0), v0, v1, v2] + + @T.prim_func + def reshape19(input_ids: T.Buffer((T.int64(1), T.int64(1)), "int32"), T_reshape: T.Buffer((T.int64(1),), "int32")): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + # with T.block("root"): + for ax0_fused_0 in T.thread_binding(T.int64(1), thread="blockIdx.x"): + for ax0_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_reshape"): + v0 = T.axis.spatial(T.int64(1), T.int64(0)) + T.where(ax0_fused_0 * T.int64(1024) + ax0_fused_1 < T.int64(1)) + T.reads(input_ids[T.int64(0), T.int64(0)]) + T.writes(T_reshape[T.int64(0)]) + T_reshape[T.int64(0)] = input_ids[T.int64(0), T.int64(0)] + + @T.prim_func + def reshape2(var_input_ids: T.handle, var_T_reshape: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + batch_size = T.int64() + input_ids = T.match_buffer(var_input_ids, (batch_size, T.int64(1)), "int32") + T_reshape = T.match_buffer(var_T_reshape, (batch_size,), "int32") + # with T.block("root"): + for ax0_fused_0 in T.thread_binding((batch_size + T.int64(1023)) // T.int64(1024), thread="blockIdx.x"): + for ax0_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_reshape"): + v0 = T.axis.spatial(batch_size, ax0_fused_0 * T.int64(1024) + ax0_fused_1) + T.where(ax0_fused_0 * T.int64(1024) + ax0_fused_1 < batch_size) + T.reads(input_ids[v0, T.int64(0)]) + T.writes(T_reshape[v0]) + T_reshape[v0] = input_ids[v0, T.int64(0)] + + @T.prim_func + def reshape3(var_take3: T.handle, var_T_reshape: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + batch_size = T.int64() + take3 = T.match_buffer(var_take3, (batch_size, T.int64(1280)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (batch_size, T.int64(1), T.int64(1280)), "float16") + # with T.block("root"): + for ax0_ax1_fused_0 in T.thread_binding((batch_size * T.int64(1280) + T.int64(1023)) // T.int64(1024), thread="blockIdx.x"): + for ax0_ax1_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_reshape"): + v0 = T.axis.spatial(batch_size, (ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1) // T.int64(1280)) + v1 = T.axis.spatial(T.int64(1280), (ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1) % T.int64(1280)) + T.where(ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1 < batch_size * T.int64(1280)) + T.reads(take3[v0, v1]) + T.writes(T_reshape[v0, T.int64(0), v1]) + T_reshape[v0, T.int64(0), v1] = take3[v0, v1] + + @T.prim_func + def reshape4(var_lv224: T.handle, var_T_reshape: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + batch_size = T.int64() + lv224 = T.match_buffer(var_lv224, (batch_size, T.int64(1), T.int64(1280)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (batch_size, T.int64(1), T.int64(20), T.int64(64)), "float16") + # with T.block("root"): + for ax0_ax1_ax2_fused_0 in T.thread_binding((batch_size * T.int64(1280) + T.int64(1023)) // T.int64(1024), thread="blockIdx.x"): + for ax0_ax1_ax2_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_reshape"): + v0 = T.axis.spatial(batch_size, (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) // T.int64(1280)) + v1 = T.axis.spatial(T.int64(20), (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) % T.int64(1280) // T.int64(64)) + v2 = T.axis.spatial(T.int64(64), (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) % T.int64(64)) + T.where(ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1 < batch_size * T.int64(1280)) + T.reads(lv224[v0, T.int64(0), v1 * T.int64(64) + v2]) + T.writes(T_reshape[v0, T.int64(0), v1, v2]) + T_reshape[v0, T.int64(0), v1, v2] = lv224[v0, T.int64(0), v1 * T.int64(64) + v2] + + @T.prim_func + def reshape5(var_concat32: T.handle, var_T_reshape: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + batch_size = T.int64() + concat32 = T.match_buffer(var_concat32, (batch_size, T.int64(1), T.int64(60), T.int64(64)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (batch_size, T.int64(60), T.int64(64)), "float16") + # with T.block("root"): + for ax0_ax1_ax2_fused_0 in T.thread_binding((batch_size * T.int64(3840) + T.int64(1023)) // T.int64(1024), thread="blockIdx.x"): + for ax0_ax1_ax2_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_reshape"): + v0 = T.axis.spatial(batch_size, (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) // T.int64(3840)) + v1 = T.axis.spatial(T.int64(60), (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) % T.int64(3840) // T.int64(64)) + v2 = T.axis.spatial(T.int64(64), (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) % T.int64(64)) + T.where(ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1 < batch_size * T.int64(3840)) + T.reads(concat32[v0, T.int64(0), v1, v2]) + T.writes(T_reshape[v0, v1, v2]) + T_reshape[v0, v1, v2] = concat32[v0, T.int64(0), v1, v2] + + @T.prim_func + def reshape6(var_lv134: T.handle, var_T_reshape: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + batch_size = T.int64() + lv134 = T.match_buffer(var_lv134, (batch_size, T.int64(20), T.int64(64)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (batch_size, T.int64(1), T.int64(20), T.int64(64)), "float16") + # with T.block("root"): + for ax0_ax1_ax2_fused_0 in T.thread_binding((batch_size * T.int64(1280) + T.int64(1023)) // T.int64(1024), thread="blockIdx.x"): + for ax0_ax1_ax2_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_reshape"): + v0 = T.axis.spatial(batch_size, (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) // T.int64(1280)) + v1 = T.axis.spatial(T.int64(20), (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) % T.int64(1280) // T.int64(64)) + v2 = T.axis.spatial(T.int64(64), (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) % T.int64(64)) + T.where(ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1 < batch_size * T.int64(1280)) + T.reads(lv134[v0, v1, v2]) + T.writes(T_reshape[v0, T.int64(0), v1, v2]) + T_reshape[v0, T.int64(0), v1, v2] = lv134[v0, v1, v2] + + @T.prim_func + def reshape7(var_reshape714: T.handle, var_T_reshape: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + batch_size = T.int64() + reshape714 = T.match_buffer(var_reshape714, (batch_size, T.int64(1), T.int64(20), T.int64(64)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (batch_size, T.int64(1), T.int64(1280)), "float16") + # with T.block("root"): + for ax0_ax1_fused_0 in T.thread_binding((batch_size * T.int64(1280) + T.int64(1023)) // T.int64(1024), thread="blockIdx.x"): + for ax0_ax1_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_reshape"): + v0 = T.axis.spatial(batch_size, (ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1) // T.int64(1280)) + v1 = T.axis.spatial(T.int64(1280), (ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1) % T.int64(1280)) + T.where(ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1 < batch_size * T.int64(1280)) + T.reads(reshape714[v0, T.int64(0), v1 // T.int64(64), v1 % T.int64(64)]) + T.writes(T_reshape[v0, T.int64(0), v1]) + T_reshape[v0, T.int64(0), v1] = reshape714[v0, T.int64(0), v1 // T.int64(64), v1 % T.int64(64)] + + @T.prim_func + def reshape8(var_reshape716: T.handle, var_T_reshape: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + batch_size = T.int64() + reshape716 = T.match_buffer(var_reshape716, (batch_size, T.int64(1), T.int64(20), T.int64(64)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (batch_size, T.int64(20), T.int64(64)), "float16") + # with T.block("root"): + for ax0_ax1_ax2_fused_0 in T.thread_binding((batch_size * T.int64(1280) + T.int64(1023)) // T.int64(1024), thread="blockIdx.x"): + for ax0_ax1_ax2_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_reshape"): + v0 = T.axis.spatial(batch_size, (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) // T.int64(1280)) + v1 = T.axis.spatial(T.int64(20), (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) % T.int64(1280) // T.int64(64)) + v2 = T.axis.spatial(T.int64(64), (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) % T.int64(64)) + T.where(ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1 < batch_size * T.int64(1280)) + T.reads(reshape716[v0, T.int64(0), v1, v2]) + T.writes(T_reshape[v0, v1, v2]) + T_reshape[v0, v1, v2] = reshape716[v0, T.int64(0), v1, v2] + + @T.prim_func + def sampler_take_probs_tir(var_unsorted_probs: T.handle, var_sorted_indices: T.handle, var_sample_indices: T.handle, var_sampling_results: T.handle, var_top_prob_offsets: T.handle, var_sampled_values: T.handle, var_top_prob_probs: T.handle, var_top_prob_indices: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) + batch_size, vocab_size = T.int32(is_size_var=True), T.int32(is_size_var=True) + unsorted_probs = T.match_buffer(var_unsorted_probs, (batch_size, vocab_size)) + sorted_indices = T.match_buffer(var_sorted_indices, (batch_size, vocab_size), "int32") + num_samples = T.int32(is_size_var=True) + sample_indices = T.match_buffer(var_sample_indices, (num_samples,), "int32") + sampling_results = T.match_buffer(var_sampling_results, (num_samples,), "int32") + num_positions = T.int32(is_size_var=True) + top_prob_offsets = T.match_buffer(var_top_prob_offsets, (num_positions,), "int32") + sampled_values = T.match_buffer(var_sampled_values, (num_samples,)) + top_prob_probs = T.match_buffer(var_top_prob_probs, (num_positions,)) + top_prob_indices = T.match_buffer(var_top_prob_indices, (num_positions,), "int32") + # with T.block("root"): + for ax0_fused_0 in T.thread_binding((num_positions + num_samples + 1023) // 1024, thread="blockIdx.x"): + for ax0_fused_1 in T.thread_binding(1024, thread="threadIdx.x"): + with T.block("block"): + v0 = T.axis.spatial(num_positions + num_samples, ax0_fused_0 * 1024 + ax0_fused_1) + T.where(ax0_fused_0 * 1024 + ax0_fused_1 < num_positions + num_samples) + T.reads(top_prob_offsets[v0], sorted_indices[top_prob_offsets[v0] // vocab_size, top_prob_offsets[v0] % vocab_size], unsorted_probs[T.min(top_prob_offsets[v0] // vocab_size, sample_indices[v0 + (0 - num_positions)]):T.min(top_prob_offsets[v0] // vocab_size, sample_indices[v0 + (0 - num_positions)]) + (T.max(top_prob_offsets[v0] // vocab_size, sample_indices[v0 - num_positions]) + 1 - T.min(top_prob_offsets[v0] // vocab_size, sample_indices[v0 - num_positions])), T.min(sorted_indices[top_prob_offsets[v0] // vocab_size, top_prob_offsets[v0] % vocab_size], sampling_results[v0 + (0 - num_positions)]):T.min(sorted_indices[top_prob_offsets[v0] // vocab_size, top_prob_offsets[v0] % vocab_size], sampling_results[v0 + (0 - num_positions)]) + (T.max(sorted_indices[top_prob_offsets[v0] // vocab_size, top_prob_offsets[v0] % vocab_size], sampling_results[v0 - num_positions]) + 1 - T.min(sorted_indices[top_prob_offsets[v0] // vocab_size, top_prob_offsets[v0] % vocab_size], sampling_results[v0 - num_positions]))], sample_indices[v0 + (0 - num_positions)], sampling_results[v0 + (0 - num_positions)]) + T.writes(top_prob_indices[v0], top_prob_probs[v0], sampled_values[v0 + (0 - num_positions)]) + if v0 < num_positions: + row: T.int32 = top_prob_offsets[v0] // vocab_size + col: T.int32 = top_prob_offsets[v0] % vocab_size + top_prob_indices[v0] = sorted_indices[row, col] + top_prob_probs[v0] = unsorted_probs[row, sorted_indices[row, col]] + else: + vj: T.int32 = v0 - num_positions + sampled_values[vj] = unsorted_probs[sample_indices[vj], sampling_results[vj]] + + @T.prim_func + def scatter_probs(var_src: T.handle, var_indices: T.handle, var_dst: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + batch_size, n = T.int32(is_size_var=True), T.int32(is_size_var=True) + src = T.match_buffer(var_src, (batch_size, n)) + indices = T.match_buffer(var_indices, (batch_size,), "int32") + m = T.int32(is_size_var=True) + dst = T.match_buffer(var_dst, (m, n)) + # with T.block("root"): + for ax0_ax1_fused_0 in T.thread_binding((batch_size * n + 1023) // 1024, thread="blockIdx.x"): + for ax0_ax1_fused_1 in T.thread_binding(1024, thread="threadIdx.x"): + with T.block("scatter_2d"): + v0 = T.axis.spatial(batch_size, (ax0_ax1_fused_0 * 1024 + ax0_ax1_fused_1) % (n * batch_size) // n) + v1 = T.axis.spatial(n, (ax0_ax1_fused_0 * 1024 + ax0_ax1_fused_1) % n) + T.where(ax0_ax1_fused_0 * 1024 + ax0_ax1_fused_1 < batch_size * n) + T.reads(src[v0, v1], indices[v0]) + T.writes(dst[indices[v0], v1]) + dst[indices[v0], v1] = src[v0, v1] + + @T.prim_func + def shape_func(H: T.Buffer((T.int64(2),), "int64")): + T.func_attr({"tir.is_host_func": 1}) + H[T.int64(1)] = H[T.int64(0)] * T.int64(1500) + + @T.prim_func + def shape_func1(H: T.Buffer((T.int64(3),), "int64")): + T.func_attr({"tir.is_host_func": 1}) + H[T.int64(1)] = H[T.int64(0)] * T.int64(1500) + + @T.prim_func + def shape_func2(H: T.Buffer((T.int64(5),), "int64")): + T.func_attr({"tir.is_host_func": 1}) + H[T.int64(4)] = T.int64(8) * H[T.int64(1)] * T.int64(4) + H[T.int64(3)] = T.int64(8) * (H[T.int64(0)] * H[T.int64(1)] * T.int64(4)) + T.int64(8388608) + H[T.int64(0)] * H[T.int64(1)] * T.int64(12) + H[T.int64(2)] = T.int64(8) * H[T.int64(1)] * T.int64(4) * T.int64(8) + T.int64(8388608) + T.int64(8) * H[T.int64(1)] * T.int64(12) + + @T.prim_func + def shape_func3(H: T.Buffer((T.int64(6),), "int64")): + T.func_attr({"tir.is_host_func": 1}) + H[T.int64(4)] = T.int64(8) * (H[T.int64(0)] * H[T.int64(1)] * T.int64(4)) + T.int64(8388608) + H[T.int64(0)] * H[T.int64(1)] * T.int64(12) + H[T.int64(3)] = T.int64(8) * H[T.int64(1)] * T.int64(4) * T.int64(8) + T.int64(8388608) + T.int64(8) * H[T.int64(1)] * T.int64(12) + H[T.int64(5)] = T.int64(32) * H[T.int64(1)] + + @T.prim_func + def shape_func4(H: T.Buffer((T.int64(3),), "int64")): + T.func_attr({"tir.is_host_func": 1}) + H[T.int64(2)] = T.int64(8) * H[T.int64(1)] * T.int64(4) + + @T.prim_func + def shape_func5(H: T.Buffer((T.int64(5),), "int64")): + T.func_attr({"tir.is_host_func": 1}) + H[T.int64(2)] = T.int64(32) * ((H[T.int64(1)] + T.int64(4096) - T.int64(1)) // T.int64(4096)) + H[T.int64(4)] = T.int64(32) * H[T.int64(1)] + H[T.int64(3)] = (H[T.int64(1)] + T.int64(4096) - T.int64(1)) // T.int64(4096) + + @T.prim_func + def softmax_with_chunked_sum(var_A: T.handle, var_temperature: T.handle, var_chunked_sum: T.handle, var_chunked_max: T.handle, var_softmax: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + batch_size, vocab_size = T.int64(is_size_var=True), T.int64(is_size_var=True) + A = T.match_buffer(var_A, (batch_size, vocab_size)) + temperature = T.match_buffer(var_temperature, (batch_size,)) + num_chunks = T.int64(is_size_var=True) + chunked_sum = T.match_buffer(var_chunked_sum, (batch_size, num_chunks)) + chunked_max = T.match_buffer(var_chunked_max, (batch_size, num_chunks)) + softmax = T.match_buffer(var_softmax, (batch_size, vocab_size)) + # with T.block("root"): + temp_max_shared = T.alloc_buffer((batch_size,), scope="shared") + temp_sum_shared = T.alloc_buffer((batch_size,), scope="shared") + for l0_l1_fused in T.thread_binding(batch_size * num_chunks, thread="blockIdx.x"): + for ax0_1 in T.thread_binding(T.int64(32), thread="threadIdx.x"): + for ax0_0 in T.serial((num_chunks + T.int64(31)) // T.int64(32), annotations={"pragma_auto_unroll_max_step": 64, "pragma_unroll_explicit": 1}): + with T.block("max"): + v0 = T.axis.spatial(batch_size, l0_l1_fused % (num_chunks * batch_size) // num_chunks) + v1 = T.axis.reduce(num_chunks, ax0_0 * T.int64(32) + ax0_1) + T.where(ax0_0 * T.int64(32) + ax0_1 < num_chunks) + T.reads(chunked_max[v0, v1]) + T.writes(temp_max_shared[v0]) + with T.init(): + temp_max_shared[v0] = T.float32(-3.4028234663852886e+38) + temp_max_shared[v0] = T.max(temp_max_shared[v0], chunked_max[v0, v1]) + for ax0_1 in T.thread_binding(T.int64(32), thread="threadIdx.x"): + for ax0_0 in T.serial((num_chunks + T.int64(31)) // T.int64(32), annotations={"pragma_auto_unroll_max_step": 64, "pragma_unroll_explicit": 1}): + with T.block("sum_exp"): + v0 = T.axis.spatial(batch_size, l0_l1_fused % (num_chunks * batch_size) // num_chunks) + v1 = T.axis.reduce(num_chunks, ax0_0 * T.int64(32) + ax0_1) + T.where(ax0_0 * T.int64(32) + ax0_1 < num_chunks) + T.reads(temperature[v0], chunked_sum[v0, v1], chunked_max[v0, v1], temp_max_shared[v0]) + T.writes(temp_sum_shared[v0]) + with T.init(): + temp_sum_shared[v0] = T.float32(0) + temp_sum_shared[v0] = temp_sum_shared[v0] + T.Select(temperature[v0] > T.float32(1.0000000000000001e-05), T.exp(chunked_sum[v0, v1] + chunked_max[v0, v1] - temp_max_shared[v0]), T.Cast("float32", chunked_max[v0, v1] == temp_max_shared[v0]) * chunked_sum[v0, v1]) + for l2_0 in T.serial(T.int64(4), annotations={"pragma_auto_unroll_max_step": 64, "pragma_unroll_explicit": 1}): + for l2_1 in T.thread_binding(T.int64(32), thread="threadIdx.y"): + for l2_2 in T.thread_binding(T.int64(32), thread="threadIdx.x"): + with T.block("log_pad"): + v0 = T.axis.spatial(batch_size, l0_l1_fused % (num_chunks * batch_size) // num_chunks) + v1 = T.axis.spatial(num_chunks, l0_l1_fused % num_chunks) + v2 = T.axis.spatial(T.int64(4096), l2_0 * T.int64(1024) + l2_1 * T.int64(32) + l2_2) + T.reads(temperature[v0], A[v0, v1 * T.int64(4096) + v2], temp_sum_shared[v0], temp_max_shared[v0]) + T.writes(softmax[v0, v1 * T.int64(4096) + v2]) + if v1 * T.int64(4096) + v2 < vocab_size: + softmax[v0, v1 * T.int64(4096) + v2] = T.if_then_else(temperature[v0] > T.float32(1.0000000000000001e-05), T.exp(A[v0, v1 * T.int64(4096) + v2] / temperature[v0] - (T.log(temp_sum_shared[v0]) + temp_max_shared[v0])), T.Cast("float32", A[v0, v1 * T.int64(4096) + v2] == temp_max_shared[v0]) / temp_sum_shared[v0]) + + @T.prim_func + def take(model_decoder_embed_tokens_weight3: T.Buffer((T.int64(51866), T.int64(1280)), "float16"), var_reshape707: T.handle, var_T_take: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + batch_size = T.int64() + reshape707 = T.match_buffer(var_reshape707, (batch_size,), "int32") + T_take = T.match_buffer(var_T_take, (batch_size, T.int64(1280)), "float16") + # with T.block("root"): + for ax0_ax1_fused_0 in T.thread_binding((batch_size * T.int64(1280) + T.int64(1023)) // T.int64(1024), thread="blockIdx.x"): + for ax0_ax1_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_take"): + v0 = T.axis.spatial(batch_size, (ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1) // T.int64(1280)) + v1 = T.axis.spatial(T.int64(1280), (ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1) % T.int64(1280)) + T.where(ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1 < batch_size * T.int64(1280)) + T.reads(model_decoder_embed_tokens_weight3[reshape707[v0], v1], reshape707[v0]) + T.writes(T_take[v0, v1]) + T_take[v0, v1] = model_decoder_embed_tokens_weight3[reshape707[v0], v1] + + @T.prim_func + def take1(model_decoder_embed_positions_weight3: T.Buffer((T.int64(448), T.int64(1280)), "float16"), var_lv133: T.handle, var_T_take: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + batch_size = T.int64() + lv133 = T.match_buffer(var_lv133, (batch_size,), "int32") + T_take = T.match_buffer(var_T_take, (batch_size, T.int64(1280)), "float16") + # with T.block("root"): + for ax0_ax1_fused_0 in T.thread_binding((batch_size * T.int64(1280) + T.int64(1023)) // T.int64(1024), thread="blockIdx.x"): + for ax0_ax1_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_take"): + v0 = T.axis.spatial(batch_size, (ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1) // T.int64(1280)) + v1 = T.axis.spatial(T.int64(1280), (ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1) % T.int64(1280)) + T.where(ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1 < batch_size * T.int64(1280)) + T.reads(model_decoder_embed_positions_weight3[lv133[v0], v1], lv133[v0]) + T.writes(T_take[v0, v1]) + T_take[v0, v1] = model_decoder_embed_positions_weight3[lv133[v0], v1] + + @T.prim_func + def take2(var_layer_norm161: T.handle, var_logit_positions: T.handle, var_T_take: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + seq_len = T.int64() + layer_norm161 = T.match_buffer(var_layer_norm161, (T.int64(1), seq_len, T.int64(1280)), "float16") + batch_size = T.int64() + logit_positions = T.match_buffer(var_logit_positions, (batch_size,), "int32") + T_take = T.match_buffer(var_T_take, (T.int64(1), batch_size, T.int64(1280)), "float16") + # with T.block("root"): + for ax0_ax1_fused_0 in T.thread_binding((batch_size * T.int64(1280) + T.int64(1023)) // T.int64(1024), thread="blockIdx.x"): + for ax0_ax1_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_take"): + v0 = T.axis.spatial(batch_size, (ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1) // T.int64(1280)) + v1 = T.axis.spatial(T.int64(1280), (ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1) % T.int64(1280)) + T.where(ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1 < batch_size * T.int64(1280)) + T.reads(layer_norm161[T.int64(0), logit_positions[v0], v1], logit_positions[v0]) + T.writes(T_take[T.int64(0), v0, v1]) + T_take[T.int64(0), v0, v1] = layer_norm161[T.int64(0), logit_positions[v0], v1] + + @T.prim_func + def take3(model_decoder_embed_tokens_weight5: T.Buffer((T.int64(51866), T.int64(1280)), "float16"), reshape1353: T.Buffer((T.int64(1),), "int32"), T_take: T.Buffer((T.int64(1), T.int64(1280)), "float16")): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + # with T.block("root"): + for ax0_fused_0 in T.thread_binding(T.int64(2), thread="blockIdx.x"): + for ax0_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_take"): + v0 = T.axis.spatial(T.int64(1280), ax0_fused_0 * T.int64(1024) + ax0_fused_1) + T.where(ax0_fused_0 * T.int64(1024) + ax0_fused_1 < T.int64(1280)) + T.reads(model_decoder_embed_tokens_weight5[reshape1353[T.int64(0)], v0], reshape1353[T.int64(0)]) + T.writes(T_take[T.int64(0), v0]) + T_take[T.int64(0), v0] = model_decoder_embed_tokens_weight5[reshape1353[T.int64(0)], v0] + + @T.prim_func + def take4(model_decoder_embed_positions_weight5: T.Buffer((T.int64(448), T.int64(1280)), "float16"), lv264: T.Buffer((T.int64(1),), "int32"), T_take: T.Buffer((T.int64(1), T.int64(1280)), "float16")): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + # with T.block("root"): + for ax0_fused_0 in T.thread_binding(T.int64(2), thread="blockIdx.x"): + for ax0_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_take"): + v0 = T.axis.spatial(T.int64(1280), ax0_fused_0 * T.int64(1024) + ax0_fused_1) + T.where(ax0_fused_0 * T.int64(1024) + ax0_fused_1 < T.int64(1280)) + T.reads(model_decoder_embed_positions_weight5[lv264[T.int64(0)], v0], lv264[T.int64(0)]) + T.writes(T_take[T.int64(0), v0]) + T_take[T.int64(0), v0] = model_decoder_embed_positions_weight5[lv264[T.int64(0)], v0] + + @T.prim_func + def take_sorted_probs(var_probs: T.handle, var_lv1: T.handle, var_take_sorted_probs: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + batch_size, vocab_size = T.int64(), T.int64() + probs = T.match_buffer(var_probs, (batch_size, vocab_size)) + lv1 = T.match_buffer(var_lv1, (batch_size, vocab_size), "int32") + batch_size_1, vocab_size_1 = T.int64(), T.int64() + take_sorted_probs = T.match_buffer(var_take_sorted_probs, (batch_size_1, vocab_size_1)) + # with T.block("root"): + for ax0_ax1_fused_0 in T.thread_binding((batch_size_1 * vocab_size_1 + T.int64(1023)) // T.int64(1024), thread="blockIdx.x"): + for ax0_ax1_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("take_sorted_probs"): + v0 = T.axis.spatial(batch_size_1, (ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1) % (vocab_size_1 * batch_size_1) // vocab_size_1) + v1 = T.axis.spatial(vocab_size_1, (ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1) % vocab_size_1) + T.where(ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1 < batch_size_1 * vocab_size_1) + T.reads(probs[v0, lv1[v0, v1]], lv1[v0, v1]) + T.writes(take_sorted_probs[v0, v1]) + take_sorted_probs[v0, v1] = probs[v0, lv1[v0, v1]] + + @T.prim_func + def tir_kv_cache_debug_get_kv(var_pages: T.handle, var_position_map: T.handle, var_k_data: T.handle, var_v_data: T.handle, layer_id: T.int64): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + num_pages, page_size = T.int64(), T.int64(is_size_var=True) + pages = T.match_buffer(var_pages, (num_pages, 2, 20, page_size, 64), "float16") + seqlen = T.int64(is_size_var=True) + position_map = T.match_buffer(var_position_map, (seqlen,), "int32", offset_factor=1) + k_data = T.match_buffer(var_k_data, (32, seqlen, 20, 64), "float16") + v_data = T.match_buffer(var_v_data, (32, seqlen, 20, 64), "float16") + # with T.block("root"): + for p_h_d_fused_0 in T.thread_binding((seqlen * T.int64(1280) + T.int64(1023)) // T.int64(1024), thread="blockIdx.x"): + for p_h_d_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("copy0"): + vp = T.axis.spatial(seqlen, (p_h_d_fused_0 * T.int64(1024) + p_h_d_fused_1) // T.int64(1280)) + vh = T.axis.spatial(20, T.Cast("int32", (p_h_d_fused_0 * T.int64(1024) + p_h_d_fused_1) % T.int64(1280) // T.int64(64))) + vd = T.axis.spatial(64, T.Cast("int32", (p_h_d_fused_0 * T.int64(1024) + p_h_d_fused_1) % T.int64(64))) + T.where(p_h_d_fused_0 * T.int64(1024) + p_h_d_fused_1 < seqlen * T.int64(1280)) + T.reads(position_map[vp], pages[T.Cast("int64", position_map[vp]) // page_size, 0:2, vh, T.Cast("int64", position_map[vp]) % page_size, vd]) + T.writes(k_data[layer_id, vp, vh, vd], v_data[layer_id, vp, vh, vd]) + position: T.int32 = position_map[vp] + k_data[layer_id, vp, vh, vd] = pages[T.Cast("int64", position) // page_size, 0, vh, T.Cast("int64", position) % page_size, vd] + v_data[layer_id, vp, vh, vd] = pages[T.Cast("int64", position) // page_size, 1, vh, T.Cast("int64", position) % page_size, vd] + + @T.prim_func + def tir_kv_cache_transpose_append(var_pages: T.handle, var_k_data: T.handle, var_v_data: T.handle, var_position_map: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + num_pages = T.int64() + pages = T.match_buffer(var_pages, (num_pages, 2, 20, 16, 64), "float16") + ntoken = T.int64(is_size_var=True) + k_data = T.match_buffer(var_k_data, (ntoken, 20, 64), "float16") + v_data = T.match_buffer(var_v_data, (ntoken, 20, 64), "float16") + position_map = T.match_buffer(var_position_map, (ntoken,), "int32", offset_factor=1) + # with T.block("root"): + for global_pos_h_f_fused_0 in T.thread_binding((ntoken * T.int64(1280) + T.int64(1023)) // T.int64(1024), thread="blockIdx.x"): + for global_pos_h_f_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + if position_map[(global_pos_h_f_fused_0 * T.int64(1024) + global_pos_h_f_fused_1) // T.int64(1280)] != -1: + with T.block("k_transpose_append"): + vgpos = T.axis.spatial(ntoken, (global_pos_h_f_fused_0 * T.int64(1024) + global_pos_h_f_fused_1) // T.int64(1280)) + vh = T.axis.spatial(20, T.Cast("int32", (global_pos_h_f_fused_0 * T.int64(1024) + global_pos_h_f_fused_1) % T.int64(1280) // T.int64(64))) + vf = T.axis.spatial(64, T.Cast("int32", (global_pos_h_f_fused_0 * T.int64(1024) + global_pos_h_f_fused_1) % T.int64(64))) + T.where(global_pos_h_f_fused_0 * T.int64(1024) + global_pos_h_f_fused_1 < ntoken * T.int64(1280)) + T.reads(position_map[vgpos], k_data[vgpos, vh, vf]) + T.writes(pages[position_map[vgpos] // 16, 0, vh, position_map[vgpos] % 16, vf]) + position: T.int32 = position_map[vgpos] + pages[position // 16, 0, vh, position % 16, vf] = k_data[vgpos, vh, vf] + with T.block("v_transpose_append"): + vgpos = T.axis.spatial(ntoken, (global_pos_h_f_fused_0 * T.int64(1024) + global_pos_h_f_fused_1) // T.int64(1280)) + vh = T.axis.spatial(20, T.Cast("int32", (global_pos_h_f_fused_0 * T.int64(1024) + global_pos_h_f_fused_1) % T.int64(1280) // T.int64(64))) + vf = T.axis.spatial(64, T.Cast("int32", (global_pos_h_f_fused_0 * T.int64(1024) + global_pos_h_f_fused_1) % T.int64(64))) + T.where(global_pos_h_f_fused_0 * T.int64(1024) + global_pos_h_f_fused_1 < ntoken * T.int64(1280)) + T.reads(position_map[vgpos], v_data[vgpos, vh, vf]) + T.writes(pages[position_map[vgpos] // 16, 1, vh, position_map[vgpos] % 16, vf]) + position: T.int32 = position_map[vgpos] + pages[position // 16, 1, vh, position % 16, vf] = v_data[vgpos, vh, vf] + + @T.prim_func + def top_p_pivot_cutoff(var_prob: T.handle, var_top_p_arr: T.handle, var_init_pivots: T.handle, var_final_pivot: T.handle, var_final_lsum: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + B, N = T.int32(), T.int32() + prob = T.match_buffer(var_prob, (B, N)) + top_p_arr = T.match_buffer(var_top_p_arr, (B,)) + init_pivots = T.match_buffer(var_init_pivots, (B, 3)) + final_pivot = T.match_buffer(var_final_pivot, (B,)) + final_lsum = T.match_buffer(var_final_lsum, (B,)) + # with T.block("root"): + pivot = T.alloc_buffer((3,), scope="local") + top_p = T.alloc_buffer((1,), scope="local") + L = T.alloc_buffer((1,), scope="shared") + R_1 = T.alloc_buffer((1,), scope="shared") + L_local = T.alloc_buffer((1,), scope="local") + R_local = T.alloc_buffer((1,), scope="local") + q = T.alloc_buffer((1,), scope="local") + lsum = T.alloc_buffer((3,), scope="local") + lmin_broadcast = T.alloc_buffer((1,), scope="shared") + lmin_broadcast_local = T.alloc_buffer((1,), scope="local") + lmin = T.alloc_buffer((3,), scope="local") + cmin = T.alloc_buffer((3,), "int32", scope="local") + total_sum = T.alloc_buffer((1,), scope="local") + it = T.alloc_buffer((1,), "int32", scope="local") + es_local = T.alloc_buffer((1,), "bool", scope="local") + es = T.alloc_buffer((1,), "bool", scope="shared") + find_pivot_local = T.alloc_buffer((1,), "bool", scope="local") + find_pivot = T.alloc_buffer((1,), "bool", scope="shared") + total_sum_reduce = T.alloc_buffer((1,), scope="local") + lsum_reduce = T.alloc_buffer((1,), scope="local") + lmin_reduce = T.alloc_buffer((1,), scope="local") + cmin_reduce = T.alloc_buffer((1,), "int32", scope="local") + for _bx in T.thread_binding(B, thread="blockIdx.x"): + for _tx in T.thread_binding(1024, thread="threadIdx.x"): + with T.block("CTA"): + b, tx = T.axis.remap("SS", [_bx, _tx]) + T.reads(top_p_arr[b], top_p[0], L[0], R_1[0], init_pivots[b, 0:3], L_local[0], R_local[0], find_pivot_local[0], it[0], es_local[0], prob[b, it[0] * 1024 + tx], total_sum[0], q[0], pivot[T.min(0, it[0]):T.min(0, it[0]) + (T.max(2, it[0]) + 1 - T.min(0, it[0]))], lsum[T.min(0, it[0]):T.min(0, it[0]) + (T.max(2, it[0]) + 1 - T.min(0, it[0]))], lmin[T.min(0, it[0]):T.min(0, it[0]) + (T.max(2, it[0]) + 1 - T.min(0, it[0]))], cmin[T.min(0, it[0]):T.min(0, it[0]) + (T.max(2, it[0]) + 1 - T.min(0, it[0]))], total_sum_reduce[0], es[0], lmin_reduce[0], lmin_broadcast[0], lmin_broadcast_local[0], lsum_reduce[0], cmin_reduce[0], find_pivot[0]) + T.writes(top_p[0], L[0], R_1[0], find_pivot[0], L_local[0], R_local[0], pivot[0:3], find_pivot_local[0], final_lsum[b], final_pivot[b], lsum[0:3], lmin[0:3], cmin[0:3], total_sum[0], it[0], es_local[0], q[0], total_sum_reduce[0], es[0], lsum_reduce[0], lmin_reduce[0], lmin_broadcast[0], lmin_broadcast_local[0], cmin_reduce[0]) + top_p[0] = top_p_arr[b] + if tx == 0: + L[0] = T.float32(1) - top_p[0] + R_1[0] = T.float32(9.9999999999999995e-08) + find_pivot[0] = T.bool(False) + T.tvm_storage_sync("shared") + L_local[0] = L[0] + R_local[0] = R_1[0] + for i in T.unroll(3): + pivot[i] = init_pivots[b, i] + find_pivot_local[0] = T.bool(False) + if L_local[0] - R_local[0] <= T.float32(9.9999999999999995e-08): + if tx == 0: + final_lsum[b] = T.float32(1) + final_pivot[b] = T.float32(0) + find_pivot_local[0] = T.bool(True) + while T.tvm_thread_invariant(L_local[0] - R_local[0] > T.float32(9.9999999999999995e-08) and not find_pivot_local[0]): + T.tvm_storage_sync("shared") + for pidx in T.unroll(3): + lsum[pidx] = T.float32(0) + lmin[pidx] = T.float32(3.4028234663852886e+38) + cmin[pidx] = 0 + total_sum[0] = T.float32(0) + it[0] = 0 + es_local[0] = T.bool(False) + while it[0] < (N + 1024 - 1) // 1024 and not es_local[0]: + q[0] = T.if_then_else(it[0] * 1024 + tx < N, prob[b, it[0] * 1024 + tx], T.float32(0)) + total_sum[0] = total_sum[0] + q[0] + for pidx in T.unroll(3): + if q[0] >= pivot[pidx]: + lsum[pidx] = lsum[pidx] + q[0] + if lmin[pidx] > q[0]: + lmin[pidx] = q[0] + cmin[pidx] = 1 + else: + if lmin[pidx] == q[0]: + cmin[pidx] = cmin[pidx] + 1 + it[0] = it[0] + 1 + if it[0] % 32 == 0: + with T.block("block_cross_thread"): + T.reads(total_sum[0]) + T.writes(total_sum_reduce[0]) + T.attr(T.comm_reducer(lambda x0, y0: x0 + y0, [T.float32(0)]), "reduce_scope", T.reinterpret("handle", T.uint64(0))) + T.tvm_thread_allreduce(T.uint32(1), total_sum[0], T.bool(True), total_sum_reduce[0], tx) + if tx == 0: + es[0] = T.float32(1) - total_sum_reduce[0] < pivot[2] + T.tvm_storage_sync("shared") + es_local[0] = es[0] + T.tvm_storage_sync("shared") + for pidx in range(3): + with T.block("block_cross_thread"): + T.reads(lsum[pidx]) + T.writes(lsum_reduce[0]) + T.attr(T.comm_reducer(lambda x0, y0: x0 + y0, [T.float32(0)]), "reduce_scope", T.reinterpret("handle", T.uint64(0))) + T.tvm_thread_allreduce(T.uint32(1), lsum[pidx], T.bool(True), lsum_reduce[0], tx) + with T.block("block_cross_thread"): + T.reads(lmin[pidx]) + T.writes(lmin_reduce[0]) + T.attr(T.comm_reducer(lambda x0, y0: T.min(x0, y0), [T.float32(0)]), "reduce_scope", T.reinterpret("handle", T.uint64(0))) + T.tvm_thread_allreduce(T.uint32(1), lmin[pidx], T.bool(True), lmin_reduce[0], tx) + if tx == 0: + lmin_broadcast[0] = lmin_reduce[0] + T.tvm_storage_sync("shared") + lmin_broadcast_local[0] = lmin_broadcast[0] + if lmin[pidx] > lmin_broadcast_local[0]: + cmin[pidx] = 0 + if tx == 0: + lsum[pidx] = lsum_reduce[0] + lmin[pidx] = lmin_reduce[0] + with T.block("block_cross_thread"): + T.reads(cmin[pidx]) + T.writes(cmin_reduce[0]) + T.attr(T.comm_reducer(lambda x0, y0: x0 + y0, [0]), "reduce_scope", T.reinterpret("handle", T.uint64(0))) + T.tvm_thread_allreduce(T.uint32(1), cmin[pidx], T.bool(True), cmin_reduce[0], tx) + if tx == 0: + cmin[pidx] = cmin_reduce[0] + T.tvm_storage_sync("shared") + if tx == 0: + it[0] = 0 + while it[0] < 3 and not find_pivot_local[0]: + if lsum[it[0]] >= top_p[0] and top_p[0] > lsum[it[0]] - T.Cast("float32", cmin[it[0]]) * lmin[it[0]]: + find_pivot[0] = T.bool(True) + find_pivot_local[0] = T.bool(True) + final_pivot[b] = pivot[it[0]] + final_lsum[b] = lsum[it[0]] + else: + if lsum[it[0]] - lmin[it[0]] * T.Cast("float32", cmin[it[0]]) >= top_p[0]: + R_1[0] = pivot[it[0]] + final_lsum[b] = lsum[it[0]] + else: + if lsum[it[0]] < top_p[0]: + L[0] = pivot[it[0]] + it[0] = it[0] + 1 + T.tvm_storage_sync("shared") + L_local[0] = L[0] + R_local[0] = R_1[0] + find_pivot_local[0] = find_pivot[0] + for pidx in T.unroll(3): + pivot[pidx] = L[0] - T.Cast("float32", pidx + 1) * (L_local[0] - R_local[0]) / T.float32(4) + if tx == 0: + if not find_pivot_local[0]: + final_pivot[b] = R_local[0] + if R_local[0] == T.float32(9.9999999999999995e-08): + final_lsum[b] = lsum[2] + + @T.prim_func + def top_p_renorm_after_cutoff(var_prob: T.handle, var_final_pivot: T.handle, var_final_lsum: T.handle, var_renorm_prob: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + B, N = T.int32(), T.int32() + prob = T.match_buffer(var_prob, (B, N)) + final_pivot = T.match_buffer(var_final_pivot, (B,)) + final_lsum = T.match_buffer(var_final_lsum, (B,)) + renorm_prob = T.match_buffer(var_renorm_prob, (B, N)) + # with T.block("root"): + pivot = T.alloc_buffer((1,), scope="local") + lsum = T.alloc_buffer((1,), scope="local") + for _by in T.thread_binding(B, thread="blockIdx.y"): + for _bx in T.thread_binding((B + 511) // B, thread="blockIdx.x"): + for _tx in T.thread_binding(1024, thread="threadIdx.x"): + with T.block("CTA"): + by, bx, tx = T.axis.remap("SSS", [_by, _bx, _tx]) + T.reads(final_pivot[by], final_lsum[by], prob[by, T.Select(0 <= (B + 511) // B, 0, (((B + 511) // B * 1024 + N - 1) // ((B + 511) // B * 1024) - 1) * ((B + 511) // B)) * 1024 + bx * 1024 + tx:T.Select(0 <= (B + 511) // B, 0, (((B + 511) // B * 1024 + N - 1) // ((B + 511) // B * 1024) - 1) * ((B + 511) // B)) * 1024 + bx * 1024 + tx + (T.Select(0 <= (B + 511) // B, (N - 1) // ((B + 511) // B * 1024) * ((B + 511) // B), 0 - (((B + 511) // B * 1024 + N - 1) // ((B + 511) // B * 1024) - 1) * ((B + 511) // B)) * 1024 + 1)], pivot[0], lsum[0]) + T.writes(pivot[0], lsum[0], renorm_prob[by, T.Select(0 <= (B + 511) // B, 0, (((B + 511) // B * 1024 + N - 1) // ((B + 511) // B * 1024) - 1) * ((B + 511) // B)) * 1024 + bx * 1024 + tx:T.Select(0 <= (B + 511) // B, 0, (((B + 511) // B * 1024 + N - 1) // ((B + 511) // B * 1024) - 1) * ((B + 511) // B)) * 1024 + bx * 1024 + tx + (T.Select(0 <= (B + 511) // B, (N - 1) // ((B + 511) // B * 1024) * ((B + 511) // B), 0 - (((B + 511) // B * 1024 + N - 1) // ((B + 511) // B * 1024) - 1) * ((B + 511) // B)) * 1024 + 1)]) + pivot[0] = final_pivot[by] + lsum[0] = final_lsum[by] + for i in range(((B + 511) // B * 1024 + N - 1) // ((B + 511) // B * 1024)): + if i * ((512 + B - 1) // B) * 1024 + bx * 1024 + tx < N: + renorm_prob[by, i * ((512 + B - 1) // B) * 1024 + bx * 1024 + tx] = T.if_then_else(prob[by, i * ((512 + B - 1) // B) * 1024 + bx * 1024 + tx] >= pivot[0], prob[by, i * ((512 + B - 1) // B) * 1024 + bx * 1024 + tx] / lsum[0], T.float32(0)) + + @R.function + def _metadata() -> R.Object: + shape_heap: R.Object = R.null_value() + return R.str("{\"model_type\": \"whisper\", \"quantization\": \"q0f16\", \"context_window_size\": 1500, \"sliding_window_size\": -1, \"attention_sink_size\": -1, \"prefill_chunk_size\": 15000, \"tensor_parallel_shards\": 1, \"kv_state_kind\": \"kv_cache\", \"max_batch_size\": 8, \"params\": [{\"name\": \"model.encoder.conv1.weight\", \"shape\": [1280, 128, 3], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.encoder.conv1.bias\", \"shape\": [1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.encoder.conv2.weight\", \"shape\": [1280, 1280, 3], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.encoder.conv2.bias\", \"shape\": [1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.encoder.embed_positions.weight\", \"shape\": [1500, 1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.encoder.layers.0.self_attn.k_proj.weight\", \"shape\": [1280, 1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.encoder.layers.0.self_attn.v_proj.weight\", \"shape\": [1280, 1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.encoder.layers.0.self_attn.v_proj.bias\", \"shape\": [1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.encoder.layers.0.self_attn.q_proj.weight\", \"shape\": [1280, 1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.encoder.layers.0.self_attn.q_proj.bias\", \"shape\": [1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.encoder.layers.0.self_attn.out_proj.weight\", \"shape\": [1280, 1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.encoder.layers.0.self_attn.out_proj.bias\", \"shape\": [1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.encoder.layers.0.self_attn_layer_norm.weight\", \"shape\": [1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.encoder.layers.0.self_attn_layer_norm.bias\", \"shape\": [1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.encoder.layers.0.fc1.weight\", \"shape\": [5120, 1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.encoder.layers.0.fc1.bias\", \"shape\": [5120], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.encoder.layers.0.fc2.weight\", \"shape\": [1280, 5120], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.encoder.layers.0.fc2.bias\", \"shape\": [1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.encoder.layers.0.final_layer_norm.weight\", \"shape\": [1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.encoder.layers.0.final_layer_norm.bias\", \"shape\": [1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.encoder.layers.1.self_attn.k_proj.weight\", \"shape\": [1280, 1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.encoder.layers.1.self_attn.v_proj.weight\", \"shape\": [1280, 1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.encoder.layers.1.self_attn.v_proj.bias\", \"shape\": [1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.encoder.layers.1.self_attn.q_proj.weight\", \"shape\": [1280, 1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.encoder.layers.1.self_attn.q_proj.bias\", \"shape\": [1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.encoder.layers.1.self_attn.out_proj.weight\", \"shape\": [1280, 1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.encoder.layers.1.self_attn.out_proj.bias\", \"shape\": [1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.encoder.layers.1.self_attn_layer_norm.weight\", \"shape\": [1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.encoder.layers.1.self_attn_layer_norm.bias\", \"shape\": [1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.encoder.layers.1.fc1.weight\", \"shape\": [5120, 1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.encoder.layers.1.fc1.bias\", \"shape\": [5120], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": 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5120], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.decoder.layers.29.fc2.bias\", \"shape\": [1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.decoder.layers.29.final_layer_norm.weight\", \"shape\": [1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.decoder.layers.29.final_layer_norm.bias\", \"shape\": [1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.decoder.layers.30.self_attn.k_proj.weight\", \"shape\": [1280, 1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.decoder.layers.30.self_attn.v_proj.weight\", \"shape\": [1280, 1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.decoder.layers.30.self_attn.v_proj.bias\", \"shape\": [1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.decoder.layers.30.self_attn.q_proj.weight\", \"shape\": [1280, 1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.decoder.layers.30.self_attn.q_proj.bias\", \"shape\": [1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.decoder.layers.30.self_attn.out_proj.weight\", \"shape\": [1280, 1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.decoder.layers.30.self_attn.out_proj.bias\", \"shape\": [1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.decoder.layers.30.self_attn_layer_norm.weight\", \"shape\": [1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.decoder.layers.30.self_attn_layer_norm.bias\", \"shape\": [1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.decoder.layers.30.encoder_attn.k_proj.weight\", \"shape\": [1280, 1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.decoder.layers.30.encoder_attn.v_proj.weight\", \"shape\": [1280, 1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.decoder.layers.30.encoder_attn.v_proj.bias\", \"shape\": [1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.decoder.layers.30.encoder_attn.q_proj.weight\", \"shape\": [1280, 1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.decoder.layers.30.encoder_attn.q_proj.bias\", \"shape\": [1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.decoder.layers.30.encoder_attn.out_proj.weight\", \"shape\": [1280, 1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.decoder.layers.30.encoder_attn.out_proj.bias\", \"shape\": [1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.decoder.layers.30.encoder_attn_layer_norm.weight\", \"shape\": [1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.decoder.layers.30.encoder_attn_layer_norm.bias\", \"shape\": [1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.decoder.layers.30.fc1.weight\", \"shape\": [5120, 1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.decoder.layers.30.fc1.bias\", \"shape\": [5120], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.decoder.layers.30.fc2.weight\", \"shape\": [1280, 5120], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.decoder.layers.30.fc2.bias\", \"shape\": [1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.decoder.layers.30.final_layer_norm.weight\", \"shape\": [1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.decoder.layers.30.final_layer_norm.bias\", \"shape\": [1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.decoder.layers.31.self_attn.k_proj.weight\", \"shape\": [1280, 1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.decoder.layers.31.self_attn.v_proj.weight\", \"shape\": [1280, 1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.decoder.layers.31.self_attn.v_proj.bias\", \"shape\": [1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.decoder.layers.31.self_attn.q_proj.weight\", \"shape\": [1280, 1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.decoder.layers.31.self_attn.q_proj.bias\", \"shape\": [1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.decoder.layers.31.self_attn.out_proj.weight\", \"shape\": [1280, 1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.decoder.layers.31.self_attn.out_proj.bias\", \"shape\": [1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.decoder.layers.31.self_attn_layer_norm.weight\", \"shape\": [1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.decoder.layers.31.self_attn_layer_norm.bias\", \"shape\": [1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.decoder.layers.31.encoder_attn.k_proj.weight\", \"shape\": [1280, 1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.decoder.layers.31.encoder_attn.v_proj.weight\", \"shape\": [1280, 1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.decoder.layers.31.encoder_attn.v_proj.bias\", \"shape\": [1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.decoder.layers.31.encoder_attn.q_proj.weight\", \"shape\": [1280, 1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.decoder.layers.31.encoder_attn.q_proj.bias\", \"shape\": [1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.decoder.layers.31.encoder_attn.out_proj.weight\", \"shape\": [1280, 1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.decoder.layers.31.encoder_attn.out_proj.bias\", \"shape\": [1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.decoder.layers.31.encoder_attn_layer_norm.weight\", \"shape\": [1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.decoder.layers.31.encoder_attn_layer_norm.bias\", \"shape\": [1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.decoder.layers.31.fc1.weight\", \"shape\": [5120, 1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.decoder.layers.31.fc1.bias\", \"shape\": [5120], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.decoder.layers.31.fc2.weight\", \"shape\": [1280, 5120], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.decoder.layers.31.fc2.bias\", \"shape\": [1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.decoder.layers.31.final_layer_norm.weight\", \"shape\": [1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.decoder.layers.31.final_layer_norm.bias\", \"shape\": [1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.decoder.layer_norm.weight\", \"shape\": [1280], \"dtype\": \"float16\", \"preprocs\": []}, {\"name\": \"model.decoder.layer_norm.bias\", \"shape\": [1280], \"dtype\": \"float16\", \"preprocs\": []}], \"kv_cache\": {\"num_hidden_layers\": 32, \"num_attention_heads\": 20, \"num_key_value_heads\": 20, \"head_dim\": 64}, \"memory_usage\": {\"argsort_probs\": 0, \"batch_compute_cross_attn_kv\": 61440000, \"batch_decode\": 1987392, \"batch_encode\": 276480000, \"batch_prefill\": 616080192, \"create_tir_paged_kv_cache\": 0, \"decode\": 243304, \"multinomial_from_uniform\": 32, \"prefill\": 614610024, \"renormalize_by_top_p\": 64, \"sample_with_top_p\": 64, \"sampler_take_probs\": 416, \"sampler_verify_draft_tokens\": 0, \"softmax_with_temperature\": 0}}") + + @R.function + def argsort_probs(probs: R.Tensor(("batch_size", "vocab_size"), dtype="float32")) -> R.Tuple(R.Tensor(("batch_size", "vocab_size"), dtype="float32"), R.Tensor(("batch_size", "vocab_size"), dtype="int32")): + batch_size = T.int64() + vocab_size = T.int64() + R.func_attr({"relax.force_pure": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "num_positions": 48, "num_samples": 8}}) + cls = Module + shape_heap: R.Tensor(dtype="int64", ndim=1) = R.call_builtin_with_ctx("vm.builtin.alloc_shape_heap", (R.prim_value(5),), sinfo_args=(R.Tensor(dtype="int64", ndim=1),)) + R.call_packed("vm.builtin.check_tensor_info", probs, R.prim_value(2), R.dtype("float32"), R.str("ErrorContext(fn=argsort_probs, loc=param[0], param=probs, annotation=R.Tensor((batch_size, vocab_size), dtype=\"float32\")) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.match_shape", probs, shape_heap, R.prim_value(2), R.prim_value(1), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.str("ErrorContext(fn=argsort_probs, loc=param[0], param=probs, annotation=R.Tensor((batch_size, vocab_size), dtype=\"float32\")) "), sinfo_args=(R.Tuple,)) + cls.shape_func2(shape_heap) + gv2560: R.Shape(ndim=1) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(1), R.prim_value(1), R.prim_value(2), sinfo_args=(R.Shape(ndim=1),)) + storage30: R.Object = R.vm.alloc_storage(gv2560, R.prim_value(0), R.dtype("uint8"), R.str("global")) + gv2561: R.Shape(ndim=1) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(1), R.prim_value(1), R.prim_value(3), sinfo_args=(R.Shape(ndim=1),)) + lv: R.Tensor(dtype="uint8", ndim=1) = R.vm.alloc_tensor(storage30, R.prim_value(0), gv2561, R.dtype("uint8")) + R.vm.kill_object(storage30) + gv2562: R.Shape(ndim=1) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(1), R.prim_value(1), R.prim_value(4), sinfo_args=(R.Shape(ndim=1),)) + storage31: R.Object = R.vm.alloc_storage(gv2562, R.prim_value(0), R.dtype("uint8"), R.str("global")) + gv2563: R.Shape(ndim=2) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(2), R.prim_value(1), R.prim_value(0), R.prim_value(1), R.prim_value(1), sinfo_args=(R.Shape(ndim=2),)) + alloc1976: R.Tensor(dtype="int32", ndim=2) = R.vm.alloc_tensor(storage31, R.prim_value(0), gv2563, R.dtype("int32")) + R.vm.kill_object(storage31) + cls.argsort_thrust(probs, lv, alloc1976) + R.vm.kill_object(lv) + gv2564: R.Shape(ndim=1) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(1), R.prim_value(1), R.prim_value(4), sinfo_args=(R.Shape(ndim=1),)) + storage32: R.Object = R.vm.alloc_storage(gv2564, R.prim_value(0), R.dtype("uint8"), R.str("global")) + gv2565: R.Shape(ndim=2) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(2), R.prim_value(1), R.prim_value(0), R.prim_value(1), R.prim_value(1), sinfo_args=(R.Shape(ndim=2),)) + alloc1977: R.Tensor(dtype="float32", ndim=2) = R.vm.alloc_tensor(storage32, R.prim_value(0), gv2565, R.dtype("float32")) + R.vm.kill_object(storage32) + cls.take_sorted_probs(probs, alloc1976, alloc1977) + gv1: R.Tuple(R.Tensor(dtype="float32", ndim=2), R.Tensor(dtype="int32", ndim=2)) = alloc1977, alloc1976 + R.vm.kill_object(alloc1976) + R.vm.kill_object(alloc1977) + gv2566: R.Tensor(dtype="float32", ndim=2) = gv1[0] + R.call_packed("vm.builtin.match_shape", gv2566, shape_heap, R.prim_value(2), R.prim_value(3), R.prim_value(0), R.prim_value(3), R.prim_value(1), R.str("ErrorContext(fn=argsort_probs, loc=return, annotation=R.Tuple(R.Tensor((batch_size, vocab_size), dtype=\"float32\"), R.Tensor((batch_size, vocab_size), dtype=\"int32\"))) "), sinfo_args=(R.Tuple,)) + gv2567: R.Tensor(dtype="int32", ndim=2) = gv1[1] + R.call_packed("vm.builtin.match_shape", gv2567, shape_heap, R.prim_value(2), R.prim_value(3), R.prim_value(0), R.prim_value(3), R.prim_value(1), R.str("ErrorContext(fn=argsort_probs, loc=return, annotation=R.Tuple(R.Tensor((batch_size, vocab_size), dtype=\"float32\"), R.Tensor((batch_size, vocab_size), dtype=\"int32\"))) "), sinfo_args=(R.Tuple,)) + return gv1 + + @R.function + def batch_compute_cross_attn_kv(encoder_hidden_states: R.Tensor(("batch_size", 1500, 1280), dtype="float16"), paged_kv_cache: R.Object, packed_params: R.Tuple(R.Tensor((1280, 128, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1500, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), 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R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"))) -> R.Object: + batch_size = T.int64() + R.func_attr({"num_input": 2, "relax.force_pure": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "seq_len": 15000, "total_seq_len": 1500}}) + cls = Module + shape_heap: R.Tensor(dtype="int64", ndim=1) = R.call_builtin_with_ctx("vm.builtin.alloc_shape_heap", (R.prim_value(2),), sinfo_args=(R.Tensor(dtype="int64", ndim=1),)) + R.call_packed("vm.builtin.check_tensor_info", encoder_hidden_states, R.prim_value(3), R.dtype("float16"), R.str("ErrorContext(fn=batch_compute_cross_attn_kv, loc=param[0], param=encoder_hidden_states, annotation=R.Tensor((batch_size, 1500, 1280), dtype=\"float16\")) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.check_tuple_info", packed_params, R.prim_value(1259), R.str("ErrorContext(fn=batch_compute_cross_attn_kv, loc=param[2], param=packed_params, 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R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((5120, 1280), dtype=\"float16\"), R.Tensor((5120,), dtype=\"float16\"), R.Tensor((1280, 5120), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((5120, 1280), dtype=\"float16\"), R.Tensor((5120,), dtype=\"float16\"), R.Tensor((1280, 5120), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"))) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.match_shape", encoder_hidden_states, shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), R.str("ErrorContext(fn=batch_compute_cross_attn_kv, loc=param[0], param=encoder_hidden_states, annotation=R.Tensor((batch_size, 1500, 1280), dtype=\"float16\")) "), sinfo_args=(R.Tuple,)) + cls.shape_func(shape_heap) + model_decoder_layers_0_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[498] + storage11: R.Object = R.vm.alloc_storage(R.shape([30720000]), R.prim_value(0), R.dtype("uint8"), R.str("global")) + gv883: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc554: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage11, R.prim_value(0), gv883, R.dtype("float16")) + _552: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_decoder_layers_0_encoder_attn_k_proj_weight1, encoder_hidden_states, alloc554) + R.vm.kill_object(model_decoder_layers_0_encoder_attn_k_proj_weight1) + gv884: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape256: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc554, gv884, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc554) + model_decoder_layers_0_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[499] + model_decoder_layers_0_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[500] + storage12: R.Object = R.vm.alloc_storage(R.shape([30720000]), R.prim_value(0), R.dtype("uint8"), R.str("global")) + gv885: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc555: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage12, R.prim_value(0), gv885, R.dtype("float16")) + _553: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_decoder_layers_0_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_0_encoder_attn_v_proj_bias1, alloc555) + R.vm.kill_object(model_decoder_layers_0_encoder_attn_v_proj_weight1) + R.vm.kill_object(model_decoder_layers_0_encoder_attn_v_proj_bias1) + gv886: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape257: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc555, gv886, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc555) + gv887: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape258: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape256, gv887, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape256) + gv888: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape259: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape257, gv888, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape257) + lv36: R.Object = R.call_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", paged_kv_cache, R.prim_value(0), reshape258, reshape259, sinfo_args=(R.Object,)) + R.vm.kill_object(reshape258) + R.vm.kill_object(reshape259) + model_decoder_layers_1_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[522] + gv889: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc556: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage11, R.prim_value(0), gv889, R.dtype("float16")) + _554: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_decoder_layers_1_encoder_attn_k_proj_weight1, encoder_hidden_states, alloc556) + R.vm.kill_object(model_decoder_layers_1_encoder_attn_k_proj_weight1) + gv890: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape260: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc556, gv890, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc556) + model_decoder_layers_1_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[523] + model_decoder_layers_1_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[524] + gv891: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc557: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage12, R.prim_value(0), gv891, R.dtype("float16")) + _555: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_decoder_layers_1_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_1_encoder_attn_v_proj_bias1, alloc557) + R.vm.kill_object(model_decoder_layers_1_encoder_attn_v_proj_weight1) + R.vm.kill_object(model_decoder_layers_1_encoder_attn_v_proj_bias1) + gv892: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape261: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc557, gv892, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc557) + gv893: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape262: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape260, gv893, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape260) + gv894: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape263: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape261, gv894, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape261) + lv37: R.Object = R.call_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv36, R.prim_value(1), reshape262, reshape263, sinfo_args=(R.Object,)) + R.vm.kill_object(reshape262) + R.vm.kill_object(reshape263) + R.vm.kill_object(lv36) + model_decoder_layers_2_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[546] + gv895: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc558: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage11, R.prim_value(0), gv895, R.dtype("float16")) + _556: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_decoder_layers_2_encoder_attn_k_proj_weight1, encoder_hidden_states, alloc558) + R.vm.kill_object(model_decoder_layers_2_encoder_attn_k_proj_weight1) + gv896: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape264: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc558, gv896, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc558) + model_decoder_layers_2_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[547] + model_decoder_layers_2_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[548] + gv897: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc559: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage12, R.prim_value(0), gv897, R.dtype("float16")) + _557: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_decoder_layers_2_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_2_encoder_attn_v_proj_bias1, alloc559) + R.vm.kill_object(model_decoder_layers_2_encoder_attn_v_proj_weight1) + R.vm.kill_object(model_decoder_layers_2_encoder_attn_v_proj_bias1) + gv898: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape265: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc559, gv898, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc559) + gv899: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape266: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape264, gv899, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape264) + gv900: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape267: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape265, gv900, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape265) + lv38: R.Object = R.call_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv37, R.prim_value(2), reshape266, reshape267, sinfo_args=(R.Object,)) + R.vm.kill_object(reshape266) + R.vm.kill_object(reshape267) + R.vm.kill_object(lv37) + model_decoder_layers_3_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[570] + gv901: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc560: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage11, R.prim_value(0), gv901, R.dtype("float16")) + _558: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_decoder_layers_3_encoder_attn_k_proj_weight1, encoder_hidden_states, alloc560) + R.vm.kill_object(model_decoder_layers_3_encoder_attn_k_proj_weight1) + gv902: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape268: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc560, gv902, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc560) + model_decoder_layers_3_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[571] + model_decoder_layers_3_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[572] + gv903: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc561: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage12, R.prim_value(0), gv903, R.dtype("float16")) + _559: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_decoder_layers_3_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_3_encoder_attn_v_proj_bias1, alloc561) + R.vm.kill_object(model_decoder_layers_3_encoder_attn_v_proj_weight1) + R.vm.kill_object(model_decoder_layers_3_encoder_attn_v_proj_bias1) + gv904: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape269: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc561, gv904, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc561) + gv905: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape270: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape268, gv905, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape268) + gv906: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape271: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape269, gv906, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape269) + lv39: R.Object = R.call_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv38, R.prim_value(3), reshape270, reshape271, sinfo_args=(R.Object,)) + R.vm.kill_object(reshape270) + R.vm.kill_object(reshape271) + R.vm.kill_object(lv38) + model_decoder_layers_4_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[594] + gv907: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc562: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage11, R.prim_value(0), gv907, R.dtype("float16")) + _560: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_decoder_layers_4_encoder_attn_k_proj_weight1, encoder_hidden_states, alloc562) + R.vm.kill_object(model_decoder_layers_4_encoder_attn_k_proj_weight1) + gv908: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape272: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc562, gv908, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc562) + model_decoder_layers_4_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[595] + model_decoder_layers_4_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[596] + gv909: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc563: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage12, R.prim_value(0), gv909, R.dtype("float16")) + _561: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_decoder_layers_4_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_4_encoder_attn_v_proj_bias1, alloc563) + R.vm.kill_object(model_decoder_layers_4_encoder_attn_v_proj_weight1) + R.vm.kill_object(model_decoder_layers_4_encoder_attn_v_proj_bias1) + gv910: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape273: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc563, gv910, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc563) + gv911: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape274: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape272, gv911, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape272) + gv912: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape275: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape273, gv912, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape273) + lv40: R.Object = R.call_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv39, R.prim_value(4), reshape274, reshape275, sinfo_args=(R.Object,)) + R.vm.kill_object(reshape274) + R.vm.kill_object(reshape275) + R.vm.kill_object(lv39) + model_decoder_layers_5_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[618] + gv913: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc564: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage11, R.prim_value(0), gv913, R.dtype("float16")) + _562: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_decoder_layers_5_encoder_attn_k_proj_weight1, encoder_hidden_states, alloc564) + R.vm.kill_object(model_decoder_layers_5_encoder_attn_k_proj_weight1) + gv914: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape276: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc564, gv914, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc564) + model_decoder_layers_5_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[619] + model_decoder_layers_5_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[620] + gv915: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc565: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage12, R.prim_value(0), gv915, R.dtype("float16")) + _563: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_decoder_layers_5_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_5_encoder_attn_v_proj_bias1, alloc565) + R.vm.kill_object(model_decoder_layers_5_encoder_attn_v_proj_weight1) + R.vm.kill_object(model_decoder_layers_5_encoder_attn_v_proj_bias1) + gv916: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape277: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc565, gv916, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc565) + gv917: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape278: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape276, gv917, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape276) + gv918: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape279: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape277, gv918, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape277) + lv41: R.Object = R.call_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv40, R.prim_value(5), reshape278, reshape279, sinfo_args=(R.Object,)) + R.vm.kill_object(reshape278) + R.vm.kill_object(reshape279) + R.vm.kill_object(lv40) + model_decoder_layers_6_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[642] + gv919: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc566: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage11, R.prim_value(0), gv919, R.dtype("float16")) + _564: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_decoder_layers_6_encoder_attn_k_proj_weight1, encoder_hidden_states, alloc566) + R.vm.kill_object(model_decoder_layers_6_encoder_attn_k_proj_weight1) + gv920: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape280: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc566, gv920, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc566) + model_decoder_layers_6_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[643] + model_decoder_layers_6_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[644] + gv921: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc567: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage12, R.prim_value(0), gv921, R.dtype("float16")) + _565: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_decoder_layers_6_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_6_encoder_attn_v_proj_bias1, alloc567) + R.vm.kill_object(model_decoder_layers_6_encoder_attn_v_proj_weight1) + R.vm.kill_object(model_decoder_layers_6_encoder_attn_v_proj_bias1) + gv922: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape281: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc567, gv922, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc567) + gv923: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape282: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape280, gv923, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape280) + gv924: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape283: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape281, gv924, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape281) + lv42: R.Object = R.call_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv41, R.prim_value(6), reshape282, reshape283, sinfo_args=(R.Object,)) + R.vm.kill_object(reshape282) + R.vm.kill_object(reshape283) + R.vm.kill_object(lv41) + model_decoder_layers_7_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[666] + gv925: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc568: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage11, R.prim_value(0), gv925, R.dtype("float16")) + _566: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_decoder_layers_7_encoder_attn_k_proj_weight1, encoder_hidden_states, alloc568) + R.vm.kill_object(model_decoder_layers_7_encoder_attn_k_proj_weight1) + gv926: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape284: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc568, gv926, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc568) + model_decoder_layers_7_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[667] + model_decoder_layers_7_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[668] + gv927: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc569: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage12, R.prim_value(0), gv927, R.dtype("float16")) + _567: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_decoder_layers_7_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_7_encoder_attn_v_proj_bias1, alloc569) + R.vm.kill_object(model_decoder_layers_7_encoder_attn_v_proj_weight1) + R.vm.kill_object(model_decoder_layers_7_encoder_attn_v_proj_bias1) + gv928: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape285: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc569, gv928, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc569) + gv929: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape286: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape284, gv929, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape284) + gv930: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape287: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape285, gv930, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape285) + lv43: R.Object = R.call_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv42, R.prim_value(7), reshape286, reshape287, sinfo_args=(R.Object,)) + R.vm.kill_object(reshape286) + R.vm.kill_object(reshape287) + R.vm.kill_object(lv42) + model_decoder_layers_8_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[690] + gv931: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc570: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage11, R.prim_value(0), gv931, R.dtype("float16")) + _568: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_decoder_layers_8_encoder_attn_k_proj_weight1, encoder_hidden_states, alloc570) + R.vm.kill_object(model_decoder_layers_8_encoder_attn_k_proj_weight1) + gv932: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape288: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc570, gv932, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc570) + model_decoder_layers_8_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[691] + model_decoder_layers_8_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[692] + gv933: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc571: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage12, R.prim_value(0), gv933, R.dtype("float16")) + _569: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_decoder_layers_8_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_8_encoder_attn_v_proj_bias1, alloc571) + R.vm.kill_object(model_decoder_layers_8_encoder_attn_v_proj_weight1) + R.vm.kill_object(model_decoder_layers_8_encoder_attn_v_proj_bias1) + gv934: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape289: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc571, gv934, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc571) + gv935: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape290: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape288, gv935, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape288) + gv936: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape291: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape289, gv936, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape289) + lv44: R.Object = R.call_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv43, R.prim_value(8), reshape290, reshape291, sinfo_args=(R.Object,)) + R.vm.kill_object(reshape290) + R.vm.kill_object(reshape291) + R.vm.kill_object(lv43) + model_decoder_layers_9_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[714] + gv937: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc572: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage11, R.prim_value(0), gv937, R.dtype("float16")) + _570: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_decoder_layers_9_encoder_attn_k_proj_weight1, encoder_hidden_states, alloc572) + R.vm.kill_object(model_decoder_layers_9_encoder_attn_k_proj_weight1) + gv938: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape292: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc572, gv938, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc572) + model_decoder_layers_9_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[715] + model_decoder_layers_9_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[716] + gv939: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc573: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage12, R.prim_value(0), gv939, R.dtype("float16")) + _571: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_decoder_layers_9_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_9_encoder_attn_v_proj_bias1, alloc573) + R.vm.kill_object(model_decoder_layers_9_encoder_attn_v_proj_weight1) + R.vm.kill_object(model_decoder_layers_9_encoder_attn_v_proj_bias1) + gv940: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape293: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc573, gv940, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc573) + gv941: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape294: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape292, gv941, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape292) + gv942: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape295: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape293, gv942, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape293) + lv45: R.Object = R.call_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv44, R.prim_value(9), reshape294, reshape295, sinfo_args=(R.Object,)) + R.vm.kill_object(reshape294) + R.vm.kill_object(reshape295) + R.vm.kill_object(lv44) + model_decoder_layers_10_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[738] + gv943: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc574: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage11, R.prim_value(0), gv943, R.dtype("float16")) + _572: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_decoder_layers_10_encoder_attn_k_proj_weight1, encoder_hidden_states, alloc574) + R.vm.kill_object(model_decoder_layers_10_encoder_attn_k_proj_weight1) + gv944: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape296: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc574, gv944, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc574) + model_decoder_layers_10_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[739] + model_decoder_layers_10_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[740] + gv945: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc575: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage12, R.prim_value(0), gv945, R.dtype("float16")) + _573: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_decoder_layers_10_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_10_encoder_attn_v_proj_bias1, alloc575) + R.vm.kill_object(model_decoder_layers_10_encoder_attn_v_proj_weight1) + R.vm.kill_object(model_decoder_layers_10_encoder_attn_v_proj_bias1) + gv946: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape297: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc575, gv946, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc575) + gv947: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape298: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape296, gv947, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape296) + gv948: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape299: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape297, gv948, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape297) + lv46: R.Object = R.call_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv45, R.prim_value(10), reshape298, reshape299, sinfo_args=(R.Object,)) + R.vm.kill_object(reshape298) + R.vm.kill_object(reshape299) + R.vm.kill_object(lv45) + model_decoder_layers_11_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[762] + gv949: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc576: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage11, R.prim_value(0), gv949, R.dtype("float16")) + _574: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_decoder_layers_11_encoder_attn_k_proj_weight1, encoder_hidden_states, alloc576) + R.vm.kill_object(model_decoder_layers_11_encoder_attn_k_proj_weight1) + gv950: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape300: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc576, gv950, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc576) + model_decoder_layers_11_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[763] + model_decoder_layers_11_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[764] + gv951: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc577: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage12, R.prim_value(0), gv951, R.dtype("float16")) + _575: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_decoder_layers_11_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_11_encoder_attn_v_proj_bias1, alloc577) + R.vm.kill_object(model_decoder_layers_11_encoder_attn_v_proj_weight1) + R.vm.kill_object(model_decoder_layers_11_encoder_attn_v_proj_bias1) + gv952: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape301: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc577, gv952, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc577) + gv953: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape302: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape300, gv953, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape300) + gv954: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape303: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape301, gv954, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape301) + lv47: R.Object = R.call_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv46, R.prim_value(11), reshape302, reshape303, sinfo_args=(R.Object,)) + R.vm.kill_object(reshape302) + R.vm.kill_object(reshape303) + R.vm.kill_object(lv46) + model_decoder_layers_12_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[786] + gv955: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc578: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage11, R.prim_value(0), gv955, R.dtype("float16")) + _576: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_decoder_layers_12_encoder_attn_k_proj_weight1, encoder_hidden_states, alloc578) + R.vm.kill_object(model_decoder_layers_12_encoder_attn_k_proj_weight1) + gv956: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape304: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc578, gv956, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc578) + model_decoder_layers_12_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[787] + model_decoder_layers_12_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[788] + gv957: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc579: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage12, R.prim_value(0), gv957, R.dtype("float16")) + _577: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_decoder_layers_12_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_12_encoder_attn_v_proj_bias1, alloc579) + R.vm.kill_object(model_decoder_layers_12_encoder_attn_v_proj_weight1) + R.vm.kill_object(model_decoder_layers_12_encoder_attn_v_proj_bias1) + gv958: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape305: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc579, gv958, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc579) + gv959: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape306: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape304, gv959, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape304) + gv960: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape307: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape305, gv960, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape305) + lv48: R.Object = R.call_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv47, R.prim_value(12), reshape306, reshape307, sinfo_args=(R.Object,)) + R.vm.kill_object(reshape306) + R.vm.kill_object(reshape307) + R.vm.kill_object(lv47) + model_decoder_layers_13_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[810] + gv961: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc580: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage11, R.prim_value(0), gv961, R.dtype("float16")) + _578: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_decoder_layers_13_encoder_attn_k_proj_weight1, encoder_hidden_states, alloc580) + R.vm.kill_object(model_decoder_layers_13_encoder_attn_k_proj_weight1) + gv962: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape308: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc580, gv962, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc580) + model_decoder_layers_13_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[811] + model_decoder_layers_13_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[812] + gv963: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc581: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage12, R.prim_value(0), gv963, R.dtype("float16")) + _579: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_decoder_layers_13_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_13_encoder_attn_v_proj_bias1, alloc581) + R.vm.kill_object(model_decoder_layers_13_encoder_attn_v_proj_weight1) + R.vm.kill_object(model_decoder_layers_13_encoder_attn_v_proj_bias1) + gv964: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape309: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc581, gv964, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc581) + gv965: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape310: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape308, gv965, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape308) + gv966: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape311: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape309, gv966, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape309) + lv49: R.Object = R.call_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv48, R.prim_value(13), reshape310, reshape311, sinfo_args=(R.Object,)) + R.vm.kill_object(reshape310) + R.vm.kill_object(reshape311) + R.vm.kill_object(lv48) + model_decoder_layers_14_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[834] + gv967: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc582: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage11, R.prim_value(0), gv967, R.dtype("float16")) + _580: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_decoder_layers_14_encoder_attn_k_proj_weight1, encoder_hidden_states, alloc582) + R.vm.kill_object(model_decoder_layers_14_encoder_attn_k_proj_weight1) + gv968: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape312: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc582, gv968, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc582) + model_decoder_layers_14_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[835] + model_decoder_layers_14_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[836] + gv969: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc583: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage12, R.prim_value(0), gv969, R.dtype("float16")) + _581: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_decoder_layers_14_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_14_encoder_attn_v_proj_bias1, alloc583) + R.vm.kill_object(model_decoder_layers_14_encoder_attn_v_proj_weight1) + R.vm.kill_object(model_decoder_layers_14_encoder_attn_v_proj_bias1) + gv970: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape313: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc583, gv970, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc583) + gv971: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape314: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape312, gv971, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape312) + gv972: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape315: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape313, gv972, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape313) + lv50: R.Object = R.call_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv49, R.prim_value(14), reshape314, reshape315, sinfo_args=(R.Object,)) + R.vm.kill_object(reshape314) + R.vm.kill_object(reshape315) + R.vm.kill_object(lv49) + model_decoder_layers_15_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[858] + gv973: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc584: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage11, R.prim_value(0), gv973, R.dtype("float16")) + _582: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_decoder_layers_15_encoder_attn_k_proj_weight1, encoder_hidden_states, alloc584) + R.vm.kill_object(model_decoder_layers_15_encoder_attn_k_proj_weight1) + gv974: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape316: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc584, gv974, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc584) + model_decoder_layers_15_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[859] + model_decoder_layers_15_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[860] + gv975: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc585: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage12, R.prim_value(0), gv975, R.dtype("float16")) + _583: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_decoder_layers_15_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_15_encoder_attn_v_proj_bias1, alloc585) + R.vm.kill_object(model_decoder_layers_15_encoder_attn_v_proj_weight1) + R.vm.kill_object(model_decoder_layers_15_encoder_attn_v_proj_bias1) + gv976: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape317: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc585, gv976, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc585) + gv977: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape318: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape316, gv977, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape316) + gv978: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape319: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape317, gv978, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape317) + lv51: R.Object = R.call_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv50, R.prim_value(15), reshape318, reshape319, sinfo_args=(R.Object,)) + R.vm.kill_object(reshape318) + R.vm.kill_object(reshape319) + R.vm.kill_object(lv50) + model_decoder_layers_16_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[882] + gv979: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc586: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage11, R.prim_value(0), gv979, R.dtype("float16")) + _584: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_decoder_layers_16_encoder_attn_k_proj_weight1, encoder_hidden_states, alloc586) + R.vm.kill_object(model_decoder_layers_16_encoder_attn_k_proj_weight1) + gv980: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape320: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc586, gv980, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc586) + model_decoder_layers_16_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[883] + model_decoder_layers_16_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[884] + gv981: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc587: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage12, R.prim_value(0), gv981, R.dtype("float16")) + _585: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_decoder_layers_16_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_16_encoder_attn_v_proj_bias1, alloc587) + R.vm.kill_object(model_decoder_layers_16_encoder_attn_v_proj_weight1) + R.vm.kill_object(model_decoder_layers_16_encoder_attn_v_proj_bias1) + gv982: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape321: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc587, gv982, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc587) + gv983: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape322: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape320, gv983, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape320) + gv984: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape323: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape321, gv984, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape321) + lv52: R.Object = R.call_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv51, R.prim_value(16), reshape322, reshape323, sinfo_args=(R.Object,)) + R.vm.kill_object(reshape322) + R.vm.kill_object(reshape323) + R.vm.kill_object(lv51) + model_decoder_layers_17_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[906] + gv985: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc588: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage11, R.prim_value(0), gv985, R.dtype("float16")) + _586: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_decoder_layers_17_encoder_attn_k_proj_weight1, encoder_hidden_states, alloc588) + R.vm.kill_object(model_decoder_layers_17_encoder_attn_k_proj_weight1) + gv986: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape324: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc588, gv986, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc588) + model_decoder_layers_17_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[907] + model_decoder_layers_17_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[908] + gv987: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc589: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage12, R.prim_value(0), gv987, R.dtype("float16")) + _587: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_decoder_layers_17_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_17_encoder_attn_v_proj_bias1, alloc589) + R.vm.kill_object(model_decoder_layers_17_encoder_attn_v_proj_weight1) + R.vm.kill_object(model_decoder_layers_17_encoder_attn_v_proj_bias1) + gv988: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape325: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc589, gv988, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc589) + gv989: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape326: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape324, gv989, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape324) + gv990: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape327: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape325, gv990, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape325) + lv53: R.Object = R.call_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv52, R.prim_value(17), reshape326, reshape327, sinfo_args=(R.Object,)) + R.vm.kill_object(reshape326) + R.vm.kill_object(reshape327) + R.vm.kill_object(lv52) + model_decoder_layers_18_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[930] + gv991: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc590: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage11, R.prim_value(0), gv991, R.dtype("float16")) + _588: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_decoder_layers_18_encoder_attn_k_proj_weight1, encoder_hidden_states, alloc590) + R.vm.kill_object(model_decoder_layers_18_encoder_attn_k_proj_weight1) + gv992: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape328: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc590, gv992, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc590) + model_decoder_layers_18_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[931] + model_decoder_layers_18_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[932] + gv993: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc591: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage12, R.prim_value(0), gv993, R.dtype("float16")) + _589: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_decoder_layers_18_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_18_encoder_attn_v_proj_bias1, alloc591) + R.vm.kill_object(model_decoder_layers_18_encoder_attn_v_proj_weight1) + R.vm.kill_object(model_decoder_layers_18_encoder_attn_v_proj_bias1) + gv994: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape329: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc591, gv994, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc591) + gv995: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape330: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape328, gv995, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape328) + gv996: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape331: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape329, gv996, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape329) + lv54: R.Object = R.call_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv53, R.prim_value(18), reshape330, reshape331, sinfo_args=(R.Object,)) + R.vm.kill_object(reshape330) + R.vm.kill_object(reshape331) + R.vm.kill_object(lv53) + model_decoder_layers_19_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[954] + gv997: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc592: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage11, R.prim_value(0), gv997, R.dtype("float16")) + _590: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_decoder_layers_19_encoder_attn_k_proj_weight1, encoder_hidden_states, alloc592) + R.vm.kill_object(model_decoder_layers_19_encoder_attn_k_proj_weight1) + gv998: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape332: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc592, gv998, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc592) + model_decoder_layers_19_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[955] + model_decoder_layers_19_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[956] + gv999: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc593: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage12, R.prim_value(0), gv999, R.dtype("float16")) + _591: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_decoder_layers_19_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_19_encoder_attn_v_proj_bias1, alloc593) + R.vm.kill_object(model_decoder_layers_19_encoder_attn_v_proj_weight1) + R.vm.kill_object(model_decoder_layers_19_encoder_attn_v_proj_bias1) + gv1000: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape333: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc593, gv1000, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc593) + gv1001: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape334: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape332, gv1001, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape332) + gv1002: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape335: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape333, gv1002, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape333) + lv55: R.Object = R.call_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv54, R.prim_value(19), reshape334, reshape335, sinfo_args=(R.Object,)) + R.vm.kill_object(reshape334) + R.vm.kill_object(reshape335) + R.vm.kill_object(lv54) + model_decoder_layers_20_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[978] + gv1003: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc594: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage11, R.prim_value(0), gv1003, R.dtype("float16")) + _592: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_decoder_layers_20_encoder_attn_k_proj_weight1, encoder_hidden_states, alloc594) + R.vm.kill_object(model_decoder_layers_20_encoder_attn_k_proj_weight1) + gv1004: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape336: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc594, gv1004, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc594) + model_decoder_layers_20_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[979] + model_decoder_layers_20_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[980] + gv1005: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc595: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage12, R.prim_value(0), gv1005, R.dtype("float16")) + _593: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_decoder_layers_20_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_20_encoder_attn_v_proj_bias1, alloc595) + R.vm.kill_object(model_decoder_layers_20_encoder_attn_v_proj_weight1) + R.vm.kill_object(model_decoder_layers_20_encoder_attn_v_proj_bias1) + gv1006: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape337: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc595, gv1006, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc595) + gv1007: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape338: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape336, gv1007, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape336) + gv1008: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape339: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape337, gv1008, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape337) + lv56: R.Object = R.call_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv55, R.prim_value(20), reshape338, reshape339, sinfo_args=(R.Object,)) + R.vm.kill_object(reshape338) + R.vm.kill_object(reshape339) + R.vm.kill_object(lv55) + model_decoder_layers_21_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1002] + gv1009: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc596: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage11, R.prim_value(0), gv1009, R.dtype("float16")) + _594: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_decoder_layers_21_encoder_attn_k_proj_weight1, encoder_hidden_states, alloc596) + R.vm.kill_object(model_decoder_layers_21_encoder_attn_k_proj_weight1) + gv1010: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape340: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc596, gv1010, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc596) + model_decoder_layers_21_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1003] + model_decoder_layers_21_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1004] + gv1011: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc597: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage12, R.prim_value(0), gv1011, R.dtype("float16")) + _595: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_decoder_layers_21_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_21_encoder_attn_v_proj_bias1, alloc597) + R.vm.kill_object(model_decoder_layers_21_encoder_attn_v_proj_weight1) + R.vm.kill_object(model_decoder_layers_21_encoder_attn_v_proj_bias1) + gv1012: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape341: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc597, gv1012, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc597) + gv1013: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape342: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape340, gv1013, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape340) + gv1014: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape343: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape341, gv1014, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape341) + lv57: R.Object = R.call_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv56, R.prim_value(21), reshape342, reshape343, sinfo_args=(R.Object,)) + R.vm.kill_object(reshape342) + R.vm.kill_object(reshape343) + R.vm.kill_object(lv56) + model_decoder_layers_22_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1026] + gv1015: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc598: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage11, R.prim_value(0), gv1015, R.dtype("float16")) + _596: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_decoder_layers_22_encoder_attn_k_proj_weight1, encoder_hidden_states, alloc598) + R.vm.kill_object(model_decoder_layers_22_encoder_attn_k_proj_weight1) + gv1016: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape344: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc598, gv1016, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc598) + model_decoder_layers_22_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1027] + model_decoder_layers_22_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1028] + gv1017: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc599: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage12, R.prim_value(0), gv1017, R.dtype("float16")) + _597: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_decoder_layers_22_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_22_encoder_attn_v_proj_bias1, alloc599) + R.vm.kill_object(model_decoder_layers_22_encoder_attn_v_proj_weight1) + R.vm.kill_object(model_decoder_layers_22_encoder_attn_v_proj_bias1) + gv1018: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape345: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc599, gv1018, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc599) + gv1019: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape346: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape344, gv1019, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape344) + gv1020: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape347: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape345, gv1020, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape345) + lv58: R.Object = R.call_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv57, R.prim_value(22), reshape346, reshape347, sinfo_args=(R.Object,)) + R.vm.kill_object(reshape346) + R.vm.kill_object(reshape347) + R.vm.kill_object(lv57) + model_decoder_layers_23_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1050] + gv1021: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc600: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage11, R.prim_value(0), gv1021, R.dtype("float16")) + _598: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_decoder_layers_23_encoder_attn_k_proj_weight1, encoder_hidden_states, alloc600) + R.vm.kill_object(model_decoder_layers_23_encoder_attn_k_proj_weight1) + gv1022: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape348: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc600, gv1022, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc600) + model_decoder_layers_23_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1051] + model_decoder_layers_23_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1052] + gv1023: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc601: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage12, R.prim_value(0), gv1023, R.dtype("float16")) + _599: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_decoder_layers_23_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_23_encoder_attn_v_proj_bias1, alloc601) + R.vm.kill_object(model_decoder_layers_23_encoder_attn_v_proj_weight1) + R.vm.kill_object(model_decoder_layers_23_encoder_attn_v_proj_bias1) + gv1024: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape349: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc601, gv1024, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc601) + gv1025: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape350: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape348, gv1025, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape348) + gv1026: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape351: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape349, gv1026, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape349) + lv59: R.Object = R.call_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv58, R.prim_value(23), reshape350, reshape351, sinfo_args=(R.Object,)) + R.vm.kill_object(reshape350) + R.vm.kill_object(reshape351) + R.vm.kill_object(lv58) + model_decoder_layers_24_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1074] + gv1027: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc602: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage11, R.prim_value(0), gv1027, R.dtype("float16")) + _600: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_decoder_layers_24_encoder_attn_k_proj_weight1, encoder_hidden_states, alloc602) + R.vm.kill_object(model_decoder_layers_24_encoder_attn_k_proj_weight1) + gv1028: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape352: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc602, gv1028, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc602) + model_decoder_layers_24_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1075] + model_decoder_layers_24_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1076] + gv1029: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc603: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage12, R.prim_value(0), gv1029, R.dtype("float16")) + _601: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_decoder_layers_24_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_24_encoder_attn_v_proj_bias1, alloc603) + R.vm.kill_object(model_decoder_layers_24_encoder_attn_v_proj_weight1) + R.vm.kill_object(model_decoder_layers_24_encoder_attn_v_proj_bias1) + gv1030: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape353: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc603, gv1030, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc603) + gv1031: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape354: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape352, gv1031, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape352) + gv1032: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape355: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape353, gv1032, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape353) + lv60: R.Object = R.call_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv59, R.prim_value(24), reshape354, reshape355, sinfo_args=(R.Object,)) + R.vm.kill_object(reshape354) + R.vm.kill_object(reshape355) + R.vm.kill_object(lv59) + model_decoder_layers_25_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1098] + gv1033: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc604: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage11, R.prim_value(0), gv1033, R.dtype("float16")) + _602: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_decoder_layers_25_encoder_attn_k_proj_weight1, encoder_hidden_states, alloc604) + R.vm.kill_object(model_decoder_layers_25_encoder_attn_k_proj_weight1) + gv1034: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape356: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc604, gv1034, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc604) + model_decoder_layers_25_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1099] + model_decoder_layers_25_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1100] + gv1035: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc605: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage12, R.prim_value(0), gv1035, R.dtype("float16")) + _603: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_decoder_layers_25_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_25_encoder_attn_v_proj_bias1, alloc605) + R.vm.kill_object(model_decoder_layers_25_encoder_attn_v_proj_weight1) + R.vm.kill_object(model_decoder_layers_25_encoder_attn_v_proj_bias1) + gv1036: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape357: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc605, gv1036, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc605) + gv1037: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape358: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape356, gv1037, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape356) + gv1038: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape359: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape357, gv1038, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape357) + lv61: R.Object = R.call_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv60, R.prim_value(25), reshape358, reshape359, sinfo_args=(R.Object,)) + R.vm.kill_object(reshape358) + R.vm.kill_object(reshape359) + R.vm.kill_object(lv60) + model_decoder_layers_26_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1122] + gv1039: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc606: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage11, R.prim_value(0), gv1039, R.dtype("float16")) + _604: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_decoder_layers_26_encoder_attn_k_proj_weight1, encoder_hidden_states, alloc606) + R.vm.kill_object(model_decoder_layers_26_encoder_attn_k_proj_weight1) + gv1040: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape360: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc606, gv1040, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc606) + model_decoder_layers_26_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1123] + model_decoder_layers_26_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1124] + gv1041: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc607: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage12, R.prim_value(0), gv1041, R.dtype("float16")) + _605: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_decoder_layers_26_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_26_encoder_attn_v_proj_bias1, alloc607) + R.vm.kill_object(model_decoder_layers_26_encoder_attn_v_proj_weight1) + R.vm.kill_object(model_decoder_layers_26_encoder_attn_v_proj_bias1) + gv1042: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape361: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc607, gv1042, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc607) + gv1043: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape362: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape360, gv1043, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape360) + gv1044: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape363: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape361, gv1044, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape361) + lv62: R.Object = R.call_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv61, R.prim_value(26), reshape362, reshape363, sinfo_args=(R.Object,)) + R.vm.kill_object(reshape362) + R.vm.kill_object(reshape363) + R.vm.kill_object(lv61) + model_decoder_layers_27_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1146] + gv1045: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc608: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage11, R.prim_value(0), gv1045, R.dtype("float16")) + _606: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_decoder_layers_27_encoder_attn_k_proj_weight1, encoder_hidden_states, alloc608) + R.vm.kill_object(model_decoder_layers_27_encoder_attn_k_proj_weight1) + gv1046: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape364: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc608, gv1046, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc608) + model_decoder_layers_27_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1147] + model_decoder_layers_27_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1148] + gv1047: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc609: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage12, R.prim_value(0), gv1047, R.dtype("float16")) + _607: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_decoder_layers_27_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_27_encoder_attn_v_proj_bias1, alloc609) + R.vm.kill_object(model_decoder_layers_27_encoder_attn_v_proj_weight1) + R.vm.kill_object(model_decoder_layers_27_encoder_attn_v_proj_bias1) + gv1048: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape365: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc609, gv1048, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc609) + gv1049: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape366: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape364, gv1049, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape364) + gv1050: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape367: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape365, gv1050, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape365) + lv63: R.Object = R.call_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv62, R.prim_value(27), reshape366, reshape367, sinfo_args=(R.Object,)) + R.vm.kill_object(reshape366) + R.vm.kill_object(reshape367) + R.vm.kill_object(lv62) + model_decoder_layers_28_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1170] + gv1051: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc610: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage11, R.prim_value(0), gv1051, R.dtype("float16")) + _608: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_decoder_layers_28_encoder_attn_k_proj_weight1, encoder_hidden_states, alloc610) + R.vm.kill_object(model_decoder_layers_28_encoder_attn_k_proj_weight1) + gv1052: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape368: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc610, gv1052, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc610) + model_decoder_layers_28_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1171] + model_decoder_layers_28_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1172] + gv1053: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc611: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage12, R.prim_value(0), gv1053, R.dtype("float16")) + _609: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_decoder_layers_28_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_28_encoder_attn_v_proj_bias1, alloc611) + R.vm.kill_object(model_decoder_layers_28_encoder_attn_v_proj_weight1) + R.vm.kill_object(model_decoder_layers_28_encoder_attn_v_proj_bias1) + gv1054: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape369: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc611, gv1054, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc611) + gv1055: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape370: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape368, gv1055, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape368) + gv1056: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape371: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape369, gv1056, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape369) + lv64: R.Object = R.call_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv63, R.prim_value(28), reshape370, reshape371, sinfo_args=(R.Object,)) + R.vm.kill_object(reshape370) + R.vm.kill_object(reshape371) + R.vm.kill_object(lv63) + model_decoder_layers_29_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1194] + gv1057: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc612: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage11, R.prim_value(0), gv1057, R.dtype("float16")) + _610: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_decoder_layers_29_encoder_attn_k_proj_weight1, encoder_hidden_states, alloc612) + R.vm.kill_object(model_decoder_layers_29_encoder_attn_k_proj_weight1) + gv1058: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape372: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc612, gv1058, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc612) + model_decoder_layers_29_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1195] + model_decoder_layers_29_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1196] + gv1059: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc613: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage12, R.prim_value(0), gv1059, R.dtype("float16")) + _611: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_decoder_layers_29_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_29_encoder_attn_v_proj_bias1, alloc613) + R.vm.kill_object(model_decoder_layers_29_encoder_attn_v_proj_weight1) + R.vm.kill_object(model_decoder_layers_29_encoder_attn_v_proj_bias1) + gv1060: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape373: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc613, gv1060, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc613) + gv1061: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape374: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape372, gv1061, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape372) + gv1062: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape375: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape373, gv1062, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape373) + lv65: R.Object = R.call_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv64, R.prim_value(29), reshape374, reshape375, sinfo_args=(R.Object,)) + R.vm.kill_object(reshape374) + R.vm.kill_object(reshape375) + R.vm.kill_object(lv64) + model_decoder_layers_30_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1218] + gv1063: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc614: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage11, R.prim_value(0), gv1063, R.dtype("float16")) + _612: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_decoder_layers_30_encoder_attn_k_proj_weight1, encoder_hidden_states, alloc614) + R.vm.kill_object(model_decoder_layers_30_encoder_attn_k_proj_weight1) + gv1064: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape376: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc614, gv1064, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc614) + model_decoder_layers_30_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1219] + model_decoder_layers_30_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1220] + gv1065: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc615: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage12, R.prim_value(0), gv1065, R.dtype("float16")) + _613: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_decoder_layers_30_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_30_encoder_attn_v_proj_bias1, alloc615) + R.vm.kill_object(model_decoder_layers_30_encoder_attn_v_proj_weight1) + R.vm.kill_object(model_decoder_layers_30_encoder_attn_v_proj_bias1) + gv1066: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape377: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc615, gv1066, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc615) + gv1067: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape378: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape376, gv1067, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape376) + gv1068: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape379: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape377, gv1068, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape377) + lv66: R.Object = R.call_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv65, R.prim_value(30), reshape378, reshape379, sinfo_args=(R.Object,)) + R.vm.kill_object(reshape378) + R.vm.kill_object(reshape379) + R.vm.kill_object(lv65) + model_decoder_layers_31_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1242] + gv1069: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc616: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage11, R.prim_value(0), gv1069, R.dtype("float16")) + R.vm.kill_object(storage11) + _614: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_decoder_layers_31_encoder_attn_k_proj_weight1, encoder_hidden_states, alloc616) + R.vm.kill_object(model_decoder_layers_31_encoder_attn_k_proj_weight1) + gv1070: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape380: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc616, gv1070, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc616) + model_decoder_layers_31_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1243] + model_decoder_layers_31_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1244] + gv1071: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc617: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage12, R.prim_value(0), gv1071, R.dtype("float16")) + R.vm.kill_object(storage12) + _615: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_decoder_layers_31_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_31_encoder_attn_v_proj_bias1, alloc617) + R.vm.kill_object(model_decoder_layers_31_encoder_attn_v_proj_weight1) + R.vm.kill_object(model_decoder_layers_31_encoder_attn_v_proj_bias1) + gv1072: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape381: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc617, gv1072, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc617) + gv1073: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape382: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape380, gv1073, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape380) + gv1074: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape383: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape381, gv1074, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape381) + gv1: R.Object = R.call_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv66, R.prim_value(31), reshape382, reshape383, sinfo_args=(R.Object,)) + R.vm.kill_object(reshape382) + R.vm.kill_object(reshape383) + R.vm.kill_object(lv66) + return gv1 + + @R.function + def batch_decode(input_ids: R.Tensor(("batch_size", 1), dtype="int32"), paged_kv_cache: R.Object, packed_params: R.Tuple(R.Tensor((1280, 128, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1500, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), 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1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((5120, 1280), dtype=\"float16\"), R.Tensor((5120,), dtype=\"float16\"), R.Tensor((1280, 5120), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((5120, 1280), dtype=\"float16\"), R.Tensor((5120,), dtype=\"float16\"), R.Tensor((1280, 5120), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((5120, 1280), dtype=\"float16\"), R.Tensor((5120,), dtype=\"float16\"), R.Tensor((1280, 5120), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((5120, 1280), dtype=\"float16\"), R.Tensor((5120,), dtype=\"float16\"), R.Tensor((1280, 5120), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((5120, 1280), dtype=\"float16\"), R.Tensor((5120,), dtype=\"float16\"), R.Tensor((1280, 5120), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"))) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.match_shape", input_ids, shape_heap, R.prim_value(2), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.str("ErrorContext(fn=batch_decode, loc=param[0], param=input_ids, annotation=R.Tensor((batch_size, 1), dtype=\"int32\")) "), sinfo_args=(R.Tuple,)) + model_decoder_embed_tokens_weight3: R.Tensor((51866, 1280), dtype="float16") = packed_params[487] + gv1075: R.Shape(ndim=1) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(1), R.prim_value(1), R.prim_value(0), sinfo_args=(R.Shape(ndim=1),)) + reshape707: R.Tensor((batch_size,), dtype="int32") = R.call_packed("vm.builtin.reshape", input_ids, gv1075, sinfo_args=(R.Tensor((batch_size,), dtype="int32"),)) + model_decoder_embed_tokens_weight3_1: R.Tensor((51866, 1280), dtype="float16") = packed_params[487] + storage13: R.Object = R.vm.alloc_storage(R.shape([81920]), R.prim_value(0), R.dtype("uint8"), R.str("global")) + gv1076: R.Shape(ndim=2) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(2), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=2),)) + alloc618: R.Tensor(dtype="float16", ndim=2) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1076, R.dtype("float16")) + cls.take(model_decoder_embed_tokens_weight3_1, reshape707, alloc618) + R.vm.kill_object(reshape707) + R.vm.kill_object(model_decoder_embed_tokens_weight3_1) + gv1077: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape708: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc618, gv1077, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc618) + lv133: R.Tensor((batch_size,), dtype="int32") = R.call_packed("vm.builtin.attention_kv_cache_get_query_positions", paged_kv_cache, sinfo_args=(R.Tensor((batch_size,), dtype="int32"),)) + model_decoder_embed_positions_weight3: R.Tensor((448, 1280), dtype="float16") = packed_params[488] + storage14: R.Object = R.vm.alloc_storage(R.shape([61440]), R.prim_value(0), R.dtype("uint8"), R.str("global")) + gv1078: R.Shape(ndim=2) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(2), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=2),)) + alloc619: R.Tensor(dtype="float16", ndim=2) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1078, R.dtype("float16")) + cls.take1(model_decoder_embed_positions_weight3, lv133, alloc619) + R.vm.kill_object(lv133) + R.vm.kill_object(model_decoder_embed_positions_weight3) + gv1079: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape709: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc619, gv1079, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc619) + storage15: R.Object = R.vm.alloc_storage(R.shape([61440]), R.prim_value(0), R.dtype("uint8"), R.str("global")) + gv1080: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc620: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1080, R.dtype("float16")) + cls.add(reshape708, reshape709, alloc620) + R.vm.kill_object(reshape708) + R.vm.kill_object(reshape709) + model_decoder_layers_0_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[496] + model_decoder_layers_0_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[497] + gv1081: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc621: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1081, R.dtype("float16")) + cls.layer_norm(alloc620, model_decoder_layers_0_self_attn_layer_norm_weight3, model_decoder_layers_0_self_attn_layer_norm_bias3, alloc621) + R.vm.kill_object(model_decoder_layers_0_self_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_0_self_attn_layer_norm_bias3) + model_decoder_layers_0_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[492] + model_decoder_layers_0_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[493] + gv1082: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc622: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1082, R.dtype("float16")) + _620: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_0_self_attn_q_proj_weight3, alloc621, model_decoder_layers_0_self_attn_q_proj_bias3, alloc622) + R.vm.kill_object(model_decoder_layers_0_self_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_0_self_attn_q_proj_bias3) + gv1083: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape710: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc622, gv1083, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc622) + model_decoder_layers_0_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[489] + storage16: R.Object = R.vm.alloc_storage(R.shape([61440]), R.prim_value(0), R.dtype("uint8"), R.str("global")) + gv1084: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc623: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1084, R.dtype("float16")) + _621: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul3_cublas", model_decoder_layers_0_self_attn_k_proj_weight3, alloc621, alloc623) + R.vm.kill_object(model_decoder_layers_0_self_attn_k_proj_weight3) + gv1085: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape711: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc623, gv1085, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc623) + model_decoder_layers_0_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[490] + model_decoder_layers_0_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[491] + storage17: R.Object = R.vm.alloc_storage(R.shape([61440]), R.prim_value(0), R.dtype("uint8"), R.str("global")) + gv1086: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc624: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1086, R.dtype("float16")) + _622: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_0_self_attn_v_proj_weight3, alloc621, model_decoder_layers_0_self_attn_v_proj_bias3, alloc624) + R.vm.kill_object(alloc621) + R.vm.kill_object(model_decoder_layers_0_self_attn_v_proj_weight3) + R.vm.kill_object(model_decoder_layers_0_self_attn_v_proj_bias3) + gv1087: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape712: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc624, gv1087, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc624) + gv1088: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc625: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1088, R.dtype("float16")) + cls.concatenate(reshape710, reshape711, reshape712, alloc625) + R.vm.kill_object(reshape710) + R.vm.kill_object(reshape711) + R.vm.kill_object(reshape712) + gv1089: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape713: R.Tensor((batch_size, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc625, gv1089, sinfo_args=(R.Tensor((batch_size, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc625) + gv1090: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc626: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1090, R.dtype("float16")) + _624: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), reshape713, alloc626) + R.vm.kill_object(reshape713) + gv1091: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape714: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc626, gv1091, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc626) + gv1092: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape715: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape714, gv1092, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape714) + model_decoder_layers_0_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[494] + model_decoder_layers_0_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[495] + gv1093: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc627: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1093, R.dtype("float16")) + _625: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_0_self_attn_out_proj_weight3, reshape715, model_decoder_layers_0_self_attn_out_proj_bias3, alloc627) + R.vm.kill_object(reshape715) + R.vm.kill_object(model_decoder_layers_0_self_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_0_self_attn_out_proj_bias3) + gv1094: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc628: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1094, R.dtype("float16")) + cls.add(alloc620, alloc627, alloc628) + R.vm.kill_object(alloc620) + R.vm.kill_object(alloc627) + model_decoder_layers_0_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[505] + model_decoder_layers_0_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[506] + gv1095: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc629: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1095, R.dtype("float16")) + cls.layer_norm(alloc628, model_decoder_layers_0_encoder_attn_layer_norm_weight3, model_decoder_layers_0_encoder_attn_layer_norm_bias3, alloc629) + R.vm.kill_object(model_decoder_layers_0_encoder_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_0_encoder_attn_layer_norm_bias3) + model_decoder_layers_0_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[501] + model_decoder_layers_0_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[502] + gv1096: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc630: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1096, R.dtype("float16")) + _628: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_0_encoder_attn_q_proj_weight3, alloc629, model_decoder_layers_0_encoder_attn_q_proj_bias3, alloc630) + R.vm.kill_object(alloc629) + R.vm.kill_object(model_decoder_layers_0_encoder_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_0_encoder_attn_q_proj_bias3) + gv1097: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape716: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc630, gv1097, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc630) + gv1098: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape717: R.Tensor((batch_size, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape716, gv1098, sinfo_args=(R.Tensor((batch_size, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape716) + gv1099: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc631: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1099, R.dtype("float16")) + _629: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), reshape717, alloc631) + R.vm.kill_object(reshape717) + gv1100: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape718: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc631, gv1100, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc631) + gv1101: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape719: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape718, gv1101, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape718) + model_decoder_layers_0_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[503] + model_decoder_layers_0_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[504] + gv1102: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc632: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1102, R.dtype("float16")) + _630: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_0_encoder_attn_out_proj_weight3, reshape719, model_decoder_layers_0_encoder_attn_out_proj_bias3, alloc632) + R.vm.kill_object(reshape719) + R.vm.kill_object(model_decoder_layers_0_encoder_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_0_encoder_attn_out_proj_bias3) + gv1103: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc633: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1103, R.dtype("float16")) + cls.add(alloc628, alloc632, alloc633) + R.vm.kill_object(alloc628) + R.vm.kill_object(alloc632) + model_decoder_layers_0_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[511] + model_decoder_layers_0_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[512] + gv1104: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc634: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1104, R.dtype("float16")) + cls.layer_norm(alloc633, model_decoder_layers_0_final_layer_norm_weight3, model_decoder_layers_0_final_layer_norm_bias3, alloc634) + R.vm.kill_object(model_decoder_layers_0_final_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_0_final_layer_norm_bias3) + model_decoder_layers_0_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[507] + model_decoder_layers_0_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[508] + gv1105: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc635: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1105, R.dtype("float16")) + _633: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", model_decoder_layers_0_fc1_weight3, alloc634, model_decoder_layers_0_fc1_bias3, alloc635) + R.vm.kill_object(alloc634) + R.vm.kill_object(model_decoder_layers_0_fc1_weight3) + R.vm.kill_object(model_decoder_layers_0_fc1_bias3) + model_decoder_layers_0_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[509] + model_decoder_layers_0_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[510] + gv1106: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc636: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1106, R.dtype("float16")) + _634: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", model_decoder_layers_0_fc2_weight3, alloc635, model_decoder_layers_0_fc2_bias3, alloc636) + R.vm.kill_object(alloc635) + R.vm.kill_object(model_decoder_layers_0_fc2_weight3) + R.vm.kill_object(model_decoder_layers_0_fc2_bias3) + gv1107: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc637: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1107, R.dtype("float16")) + cls.add(alloc633, alloc636, alloc637) + R.vm.kill_object(alloc633) + R.vm.kill_object(alloc636) + model_decoder_layers_1_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[520] + model_decoder_layers_1_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[521] + gv1108: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc638: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1108, R.dtype("float16")) + cls.layer_norm(alloc637, model_decoder_layers_1_self_attn_layer_norm_weight3, model_decoder_layers_1_self_attn_layer_norm_bias3, alloc638) + R.vm.kill_object(model_decoder_layers_1_self_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_1_self_attn_layer_norm_bias3) + model_decoder_layers_1_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[516] + model_decoder_layers_1_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[517] + gv1109: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc639: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1109, R.dtype("float16")) + _637: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_1_self_attn_q_proj_weight3, alloc638, model_decoder_layers_1_self_attn_q_proj_bias3, alloc639) + R.vm.kill_object(model_decoder_layers_1_self_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_1_self_attn_q_proj_bias3) + gv1110: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape720: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc639, gv1110, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc639) + model_decoder_layers_1_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[513] + gv1111: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc640: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1111, R.dtype("float16")) + _638: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul3_cublas", model_decoder_layers_1_self_attn_k_proj_weight3, alloc638, alloc640) + R.vm.kill_object(model_decoder_layers_1_self_attn_k_proj_weight3) + gv1112: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape721: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc640, gv1112, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc640) + model_decoder_layers_1_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[514] + model_decoder_layers_1_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[515] + gv1113: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc641: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1113, R.dtype("float16")) + _639: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_1_self_attn_v_proj_weight3, alloc638, model_decoder_layers_1_self_attn_v_proj_bias3, alloc641) + R.vm.kill_object(alloc638) + R.vm.kill_object(model_decoder_layers_1_self_attn_v_proj_weight3) + R.vm.kill_object(model_decoder_layers_1_self_attn_v_proj_bias3) + gv1114: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape722: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc641, gv1114, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc641) + gv1115: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc642: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1115, R.dtype("float16")) + cls.concatenate(reshape720, reshape721, reshape722, alloc642) + R.vm.kill_object(reshape720) + R.vm.kill_object(reshape721) + R.vm.kill_object(reshape722) + gv1116: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape723: R.Tensor((batch_size, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc642, gv1116, sinfo_args=(R.Tensor((batch_size, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc642) + gv1117: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc643: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1117, R.dtype("float16")) + _641: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), reshape723, alloc643) + R.vm.kill_object(reshape723) + gv1118: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape724: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc643, gv1118, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc643) + gv1119: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape725: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape724, gv1119, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape724) + model_decoder_layers_1_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[518] + model_decoder_layers_1_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[519] + gv1120: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc644: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1120, R.dtype("float16")) + _642: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_1_self_attn_out_proj_weight3, reshape725, model_decoder_layers_1_self_attn_out_proj_bias3, alloc644) + R.vm.kill_object(reshape725) + R.vm.kill_object(model_decoder_layers_1_self_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_1_self_attn_out_proj_bias3) + gv1121: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc645: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1121, R.dtype("float16")) + cls.add(alloc637, alloc644, alloc645) + R.vm.kill_object(alloc637) + R.vm.kill_object(alloc644) + model_decoder_layers_1_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[529] + model_decoder_layers_1_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[530] + gv1122: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc646: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1122, R.dtype("float16")) + cls.layer_norm(alloc645, model_decoder_layers_1_encoder_attn_layer_norm_weight3, model_decoder_layers_1_encoder_attn_layer_norm_bias3, alloc646) + R.vm.kill_object(model_decoder_layers_1_encoder_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_1_encoder_attn_layer_norm_bias3) + model_decoder_layers_1_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[525] + model_decoder_layers_1_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[526] + gv1123: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc647: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1123, R.dtype("float16")) + _645: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_1_encoder_attn_q_proj_weight3, alloc646, model_decoder_layers_1_encoder_attn_q_proj_bias3, alloc647) + R.vm.kill_object(alloc646) + R.vm.kill_object(model_decoder_layers_1_encoder_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_1_encoder_attn_q_proj_bias3) + gv1124: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape726: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc647, gv1124, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc647) + gv1125: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape727: R.Tensor((batch_size, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape726, gv1125, sinfo_args=(R.Tensor((batch_size, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape726) + gv1126: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc648: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1126, R.dtype("float16")) + _646: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), reshape727, alloc648) + R.vm.kill_object(reshape727) + gv1127: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape728: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc648, gv1127, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc648) + gv1128: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape729: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape728, gv1128, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape728) + model_decoder_layers_1_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[527] + model_decoder_layers_1_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[528] + gv1129: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc649: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1129, R.dtype("float16")) + _647: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_1_encoder_attn_out_proj_weight3, reshape729, model_decoder_layers_1_encoder_attn_out_proj_bias3, alloc649) + R.vm.kill_object(reshape729) + R.vm.kill_object(model_decoder_layers_1_encoder_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_1_encoder_attn_out_proj_bias3) + gv1130: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc650: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1130, R.dtype("float16")) + cls.add(alloc645, alloc649, alloc650) + R.vm.kill_object(alloc645) + R.vm.kill_object(alloc649) + model_decoder_layers_1_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[535] + model_decoder_layers_1_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[536] + gv1131: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc651: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1131, R.dtype("float16")) + cls.layer_norm(alloc650, model_decoder_layers_1_final_layer_norm_weight3, model_decoder_layers_1_final_layer_norm_bias3, alloc651) + R.vm.kill_object(model_decoder_layers_1_final_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_1_final_layer_norm_bias3) + model_decoder_layers_1_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[531] + model_decoder_layers_1_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[532] + gv1132: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc652: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1132, R.dtype("float16")) + _650: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", model_decoder_layers_1_fc1_weight3, alloc651, model_decoder_layers_1_fc1_bias3, alloc652) + R.vm.kill_object(alloc651) + R.vm.kill_object(model_decoder_layers_1_fc1_weight3) + R.vm.kill_object(model_decoder_layers_1_fc1_bias3) + model_decoder_layers_1_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[533] + model_decoder_layers_1_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[534] + gv1133: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc653: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1133, R.dtype("float16")) + _651: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", model_decoder_layers_1_fc2_weight3, alloc652, model_decoder_layers_1_fc2_bias3, alloc653) + R.vm.kill_object(alloc652) + R.vm.kill_object(model_decoder_layers_1_fc2_weight3) + R.vm.kill_object(model_decoder_layers_1_fc2_bias3) + gv1134: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc654: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1134, R.dtype("float16")) + cls.add(alloc650, alloc653, alloc654) + R.vm.kill_object(alloc650) + R.vm.kill_object(alloc653) + model_decoder_layers_2_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[544] + model_decoder_layers_2_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[545] + gv1135: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc655: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1135, R.dtype("float16")) + cls.layer_norm(alloc654, model_decoder_layers_2_self_attn_layer_norm_weight3, model_decoder_layers_2_self_attn_layer_norm_bias3, alloc655) + R.vm.kill_object(model_decoder_layers_2_self_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_2_self_attn_layer_norm_bias3) + model_decoder_layers_2_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[540] + model_decoder_layers_2_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[541] + gv1136: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc656: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1136, R.dtype("float16")) + _654: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_2_self_attn_q_proj_weight3, alloc655, model_decoder_layers_2_self_attn_q_proj_bias3, alloc656) + R.vm.kill_object(model_decoder_layers_2_self_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_2_self_attn_q_proj_bias3) + gv1137: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape730: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc656, gv1137, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc656) + model_decoder_layers_2_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[537] + gv1138: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc657: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1138, R.dtype("float16")) + _655: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul3_cublas", model_decoder_layers_2_self_attn_k_proj_weight3, alloc655, alloc657) + R.vm.kill_object(model_decoder_layers_2_self_attn_k_proj_weight3) + gv1139: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape731: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc657, gv1139, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc657) + model_decoder_layers_2_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[538] + model_decoder_layers_2_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[539] + gv1140: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc658: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1140, R.dtype("float16")) + _656: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_2_self_attn_v_proj_weight3, alloc655, model_decoder_layers_2_self_attn_v_proj_bias3, alloc658) + R.vm.kill_object(alloc655) + R.vm.kill_object(model_decoder_layers_2_self_attn_v_proj_weight3) + R.vm.kill_object(model_decoder_layers_2_self_attn_v_proj_bias3) + gv1141: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape732: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc658, gv1141, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc658) + gv1142: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc659: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1142, R.dtype("float16")) + cls.concatenate(reshape730, reshape731, reshape732, alloc659) + R.vm.kill_object(reshape730) + R.vm.kill_object(reshape731) + R.vm.kill_object(reshape732) + gv1143: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape733: R.Tensor((batch_size, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc659, gv1143, sinfo_args=(R.Tensor((batch_size, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc659) + gv1144: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc660: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1144, R.dtype("float16")) + _658: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), reshape733, alloc660) + R.vm.kill_object(reshape733) + gv1145: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape734: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc660, gv1145, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc660) + gv1146: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape735: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape734, gv1146, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape734) + model_decoder_layers_2_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[542] + model_decoder_layers_2_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[543] + gv1147: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc661: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1147, R.dtype("float16")) + _659: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_2_self_attn_out_proj_weight3, reshape735, model_decoder_layers_2_self_attn_out_proj_bias3, alloc661) + R.vm.kill_object(reshape735) + R.vm.kill_object(model_decoder_layers_2_self_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_2_self_attn_out_proj_bias3) + gv1148: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc662: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1148, R.dtype("float16")) + cls.add(alloc654, alloc661, alloc662) + R.vm.kill_object(alloc654) + R.vm.kill_object(alloc661) + model_decoder_layers_2_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[553] + model_decoder_layers_2_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[554] + gv1149: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc663: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1149, R.dtype("float16")) + cls.layer_norm(alloc662, model_decoder_layers_2_encoder_attn_layer_norm_weight3, model_decoder_layers_2_encoder_attn_layer_norm_bias3, alloc663) + R.vm.kill_object(model_decoder_layers_2_encoder_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_2_encoder_attn_layer_norm_bias3) + model_decoder_layers_2_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[549] + model_decoder_layers_2_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[550] + gv1150: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc664: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1150, R.dtype("float16")) + _662: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_2_encoder_attn_q_proj_weight3, alloc663, model_decoder_layers_2_encoder_attn_q_proj_bias3, alloc664) + R.vm.kill_object(alloc663) + R.vm.kill_object(model_decoder_layers_2_encoder_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_2_encoder_attn_q_proj_bias3) + gv1151: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape736: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc664, gv1151, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc664) + gv1152: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape737: R.Tensor((batch_size, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape736, gv1152, sinfo_args=(R.Tensor((batch_size, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape736) + gv1153: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc665: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1153, R.dtype("float16")) + _663: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), reshape737, alloc665) + R.vm.kill_object(reshape737) + gv1154: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape738: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc665, gv1154, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc665) + gv1155: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape739: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape738, gv1155, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape738) + model_decoder_layers_2_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[551] + model_decoder_layers_2_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[552] + gv1156: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc666: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1156, R.dtype("float16")) + _664: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_2_encoder_attn_out_proj_weight3, reshape739, model_decoder_layers_2_encoder_attn_out_proj_bias3, alloc666) + R.vm.kill_object(reshape739) + R.vm.kill_object(model_decoder_layers_2_encoder_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_2_encoder_attn_out_proj_bias3) + gv1157: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc667: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1157, R.dtype("float16")) + cls.add(alloc662, alloc666, alloc667) + R.vm.kill_object(alloc662) + R.vm.kill_object(alloc666) + model_decoder_layers_2_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[559] + model_decoder_layers_2_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[560] + gv1158: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc668: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1158, R.dtype("float16")) + cls.layer_norm(alloc667, model_decoder_layers_2_final_layer_norm_weight3, model_decoder_layers_2_final_layer_norm_bias3, alloc668) + R.vm.kill_object(model_decoder_layers_2_final_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_2_final_layer_norm_bias3) + model_decoder_layers_2_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[555] + model_decoder_layers_2_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[556] + gv1159: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc669: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1159, R.dtype("float16")) + _667: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", model_decoder_layers_2_fc1_weight3, alloc668, model_decoder_layers_2_fc1_bias3, alloc669) + R.vm.kill_object(alloc668) + R.vm.kill_object(model_decoder_layers_2_fc1_weight3) + R.vm.kill_object(model_decoder_layers_2_fc1_bias3) + model_decoder_layers_2_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[557] + model_decoder_layers_2_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[558] + gv1160: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc670: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1160, R.dtype("float16")) + _668: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", model_decoder_layers_2_fc2_weight3, alloc669, model_decoder_layers_2_fc2_bias3, alloc670) + R.vm.kill_object(alloc669) + R.vm.kill_object(model_decoder_layers_2_fc2_weight3) + R.vm.kill_object(model_decoder_layers_2_fc2_bias3) + gv1161: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc671: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1161, R.dtype("float16")) + cls.add(alloc667, alloc670, alloc671) + R.vm.kill_object(alloc667) + R.vm.kill_object(alloc670) + model_decoder_layers_3_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[568] + model_decoder_layers_3_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[569] + gv1162: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc672: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1162, R.dtype("float16")) + cls.layer_norm(alloc671, model_decoder_layers_3_self_attn_layer_norm_weight3, model_decoder_layers_3_self_attn_layer_norm_bias3, alloc672) + R.vm.kill_object(model_decoder_layers_3_self_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_3_self_attn_layer_norm_bias3) + model_decoder_layers_3_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[564] + model_decoder_layers_3_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[565] + gv1163: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc673: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1163, R.dtype("float16")) + _671: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_3_self_attn_q_proj_weight3, alloc672, model_decoder_layers_3_self_attn_q_proj_bias3, alloc673) + R.vm.kill_object(model_decoder_layers_3_self_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_3_self_attn_q_proj_bias3) + gv1164: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape740: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc673, gv1164, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc673) + model_decoder_layers_3_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[561] + gv1165: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc674: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1165, R.dtype("float16")) + _672: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul3_cublas", model_decoder_layers_3_self_attn_k_proj_weight3, alloc672, alloc674) + R.vm.kill_object(model_decoder_layers_3_self_attn_k_proj_weight3) + gv1166: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape741: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc674, gv1166, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc674) + model_decoder_layers_3_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[562] + model_decoder_layers_3_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[563] + gv1167: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc675: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1167, R.dtype("float16")) + _673: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_3_self_attn_v_proj_weight3, alloc672, model_decoder_layers_3_self_attn_v_proj_bias3, alloc675) + R.vm.kill_object(alloc672) + R.vm.kill_object(model_decoder_layers_3_self_attn_v_proj_weight3) + R.vm.kill_object(model_decoder_layers_3_self_attn_v_proj_bias3) + gv1168: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape742: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc675, gv1168, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc675) + gv1169: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc676: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1169, R.dtype("float16")) + cls.concatenate(reshape740, reshape741, reshape742, alloc676) + R.vm.kill_object(reshape740) + R.vm.kill_object(reshape741) + R.vm.kill_object(reshape742) + gv1170: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape743: R.Tensor((batch_size, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc676, gv1170, sinfo_args=(R.Tensor((batch_size, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc676) + gv1171: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc677: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1171, R.dtype("float16")) + _675: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), reshape743, alloc677) + R.vm.kill_object(reshape743) + gv1172: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape744: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc677, gv1172, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc677) + gv1173: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape745: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape744, gv1173, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape744) + model_decoder_layers_3_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[566] + model_decoder_layers_3_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[567] + gv1174: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc678: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1174, R.dtype("float16")) + _676: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_3_self_attn_out_proj_weight3, reshape745, model_decoder_layers_3_self_attn_out_proj_bias3, alloc678) + R.vm.kill_object(reshape745) + R.vm.kill_object(model_decoder_layers_3_self_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_3_self_attn_out_proj_bias3) + gv1175: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc679: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1175, R.dtype("float16")) + cls.add(alloc671, alloc678, alloc679) + R.vm.kill_object(alloc671) + R.vm.kill_object(alloc678) + model_decoder_layers_3_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[577] + model_decoder_layers_3_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[578] + gv1176: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc680: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1176, R.dtype("float16")) + cls.layer_norm(alloc679, model_decoder_layers_3_encoder_attn_layer_norm_weight3, model_decoder_layers_3_encoder_attn_layer_norm_bias3, alloc680) + R.vm.kill_object(model_decoder_layers_3_encoder_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_3_encoder_attn_layer_norm_bias3) + model_decoder_layers_3_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[573] + model_decoder_layers_3_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[574] + gv1177: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc681: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1177, R.dtype("float16")) + _679: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_3_encoder_attn_q_proj_weight3, alloc680, model_decoder_layers_3_encoder_attn_q_proj_bias3, alloc681) + R.vm.kill_object(alloc680) + R.vm.kill_object(model_decoder_layers_3_encoder_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_3_encoder_attn_q_proj_bias3) + gv1178: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape746: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc681, gv1178, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc681) + gv1179: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape747: R.Tensor((batch_size, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape746, gv1179, sinfo_args=(R.Tensor((batch_size, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape746) + gv1180: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc682: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1180, R.dtype("float16")) + _680: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), reshape747, alloc682) + R.vm.kill_object(reshape747) + gv1181: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape748: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc682, gv1181, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc682) + gv1182: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape749: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape748, gv1182, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape748) + model_decoder_layers_3_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[575] + model_decoder_layers_3_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[576] + gv1183: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc683: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1183, R.dtype("float16")) + _681: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_3_encoder_attn_out_proj_weight3, reshape749, model_decoder_layers_3_encoder_attn_out_proj_bias3, alloc683) + R.vm.kill_object(reshape749) + R.vm.kill_object(model_decoder_layers_3_encoder_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_3_encoder_attn_out_proj_bias3) + gv1184: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc684: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1184, R.dtype("float16")) + cls.add(alloc679, alloc683, alloc684) + R.vm.kill_object(alloc679) + R.vm.kill_object(alloc683) + model_decoder_layers_3_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[583] + model_decoder_layers_3_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[584] + gv1185: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc685: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1185, R.dtype("float16")) + cls.layer_norm(alloc684, model_decoder_layers_3_final_layer_norm_weight3, model_decoder_layers_3_final_layer_norm_bias3, alloc685) + R.vm.kill_object(model_decoder_layers_3_final_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_3_final_layer_norm_bias3) + model_decoder_layers_3_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[579] + model_decoder_layers_3_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[580] + gv1186: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc686: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1186, R.dtype("float16")) + _684: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", model_decoder_layers_3_fc1_weight3, alloc685, model_decoder_layers_3_fc1_bias3, alloc686) + R.vm.kill_object(alloc685) + R.vm.kill_object(model_decoder_layers_3_fc1_weight3) + R.vm.kill_object(model_decoder_layers_3_fc1_bias3) + model_decoder_layers_3_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[581] + model_decoder_layers_3_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[582] + gv1187: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc687: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1187, R.dtype("float16")) + _685: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", model_decoder_layers_3_fc2_weight3, alloc686, model_decoder_layers_3_fc2_bias3, alloc687) + R.vm.kill_object(alloc686) + R.vm.kill_object(model_decoder_layers_3_fc2_weight3) + R.vm.kill_object(model_decoder_layers_3_fc2_bias3) + gv1188: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc688: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1188, R.dtype("float16")) + cls.add(alloc684, alloc687, alloc688) + R.vm.kill_object(alloc684) + R.vm.kill_object(alloc687) + model_decoder_layers_4_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[592] + model_decoder_layers_4_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[593] + gv1189: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc689: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1189, R.dtype("float16")) + cls.layer_norm(alloc688, model_decoder_layers_4_self_attn_layer_norm_weight3, model_decoder_layers_4_self_attn_layer_norm_bias3, alloc689) + R.vm.kill_object(model_decoder_layers_4_self_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_4_self_attn_layer_norm_bias3) + model_decoder_layers_4_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[588] + model_decoder_layers_4_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[589] + gv1190: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc690: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1190, R.dtype("float16")) + _688: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_4_self_attn_q_proj_weight3, alloc689, model_decoder_layers_4_self_attn_q_proj_bias3, alloc690) + R.vm.kill_object(model_decoder_layers_4_self_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_4_self_attn_q_proj_bias3) + gv1191: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape750: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc690, gv1191, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc690) + model_decoder_layers_4_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[585] + gv1192: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc691: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1192, R.dtype("float16")) + _689: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul3_cublas", model_decoder_layers_4_self_attn_k_proj_weight3, alloc689, alloc691) + R.vm.kill_object(model_decoder_layers_4_self_attn_k_proj_weight3) + gv1193: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape751: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc691, gv1193, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc691) + model_decoder_layers_4_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[586] + model_decoder_layers_4_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[587] + gv1194: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc692: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1194, R.dtype("float16")) + _690: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_4_self_attn_v_proj_weight3, alloc689, model_decoder_layers_4_self_attn_v_proj_bias3, alloc692) + R.vm.kill_object(alloc689) + R.vm.kill_object(model_decoder_layers_4_self_attn_v_proj_weight3) + R.vm.kill_object(model_decoder_layers_4_self_attn_v_proj_bias3) + gv1195: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape752: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc692, gv1195, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc692) + gv1196: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc693: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1196, R.dtype("float16")) + cls.concatenate(reshape750, reshape751, reshape752, alloc693) + R.vm.kill_object(reshape750) + R.vm.kill_object(reshape751) + R.vm.kill_object(reshape752) + gv1197: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape753: R.Tensor((batch_size, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc693, gv1197, sinfo_args=(R.Tensor((batch_size, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc693) + gv1198: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc694: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1198, R.dtype("float16")) + _692: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), reshape753, alloc694) + R.vm.kill_object(reshape753) + gv1199: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape754: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc694, gv1199, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc694) + gv1200: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape755: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape754, gv1200, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape754) + model_decoder_layers_4_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[590] + model_decoder_layers_4_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[591] + gv1201: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc695: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1201, R.dtype("float16")) + _693: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_4_self_attn_out_proj_weight3, reshape755, model_decoder_layers_4_self_attn_out_proj_bias3, alloc695) + R.vm.kill_object(reshape755) + R.vm.kill_object(model_decoder_layers_4_self_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_4_self_attn_out_proj_bias3) + gv1202: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc696: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1202, R.dtype("float16")) + cls.add(alloc688, alloc695, alloc696) + R.vm.kill_object(alloc688) + R.vm.kill_object(alloc695) + model_decoder_layers_4_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[601] + model_decoder_layers_4_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[602] + gv1203: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc697: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1203, R.dtype("float16")) + cls.layer_norm(alloc696, model_decoder_layers_4_encoder_attn_layer_norm_weight3, model_decoder_layers_4_encoder_attn_layer_norm_bias3, alloc697) + R.vm.kill_object(model_decoder_layers_4_encoder_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_4_encoder_attn_layer_norm_bias3) + model_decoder_layers_4_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[597] + model_decoder_layers_4_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[598] + gv1204: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc698: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1204, R.dtype("float16")) + _696: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_4_encoder_attn_q_proj_weight3, alloc697, model_decoder_layers_4_encoder_attn_q_proj_bias3, alloc698) + R.vm.kill_object(alloc697) + R.vm.kill_object(model_decoder_layers_4_encoder_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_4_encoder_attn_q_proj_bias3) + gv1205: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape756: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc698, gv1205, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc698) + gv1206: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape757: R.Tensor((batch_size, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape756, gv1206, sinfo_args=(R.Tensor((batch_size, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape756) + gv1207: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc699: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1207, R.dtype("float16")) + _697: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), reshape757, alloc699) + R.vm.kill_object(reshape757) + gv1208: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape758: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc699, gv1208, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc699) + gv1209: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape759: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape758, gv1209, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape758) + model_decoder_layers_4_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[599] + model_decoder_layers_4_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[600] + gv1210: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc700: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1210, R.dtype("float16")) + _698: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_4_encoder_attn_out_proj_weight3, reshape759, model_decoder_layers_4_encoder_attn_out_proj_bias3, alloc700) + R.vm.kill_object(reshape759) + R.vm.kill_object(model_decoder_layers_4_encoder_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_4_encoder_attn_out_proj_bias3) + gv1211: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc701: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1211, R.dtype("float16")) + cls.add(alloc696, alloc700, alloc701) + R.vm.kill_object(alloc696) + R.vm.kill_object(alloc700) + model_decoder_layers_4_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[607] + model_decoder_layers_4_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[608] + gv1212: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc702: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1212, R.dtype("float16")) + cls.layer_norm(alloc701, model_decoder_layers_4_final_layer_norm_weight3, model_decoder_layers_4_final_layer_norm_bias3, alloc702) + R.vm.kill_object(model_decoder_layers_4_final_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_4_final_layer_norm_bias3) + model_decoder_layers_4_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[603] + model_decoder_layers_4_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[604] + gv1213: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc703: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1213, R.dtype("float16")) + _701: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", model_decoder_layers_4_fc1_weight3, alloc702, model_decoder_layers_4_fc1_bias3, alloc703) + R.vm.kill_object(alloc702) + R.vm.kill_object(model_decoder_layers_4_fc1_weight3) + R.vm.kill_object(model_decoder_layers_4_fc1_bias3) + model_decoder_layers_4_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[605] + model_decoder_layers_4_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[606] + gv1214: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc704: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1214, R.dtype("float16")) + _702: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", model_decoder_layers_4_fc2_weight3, alloc703, model_decoder_layers_4_fc2_bias3, alloc704) + R.vm.kill_object(alloc703) + R.vm.kill_object(model_decoder_layers_4_fc2_weight3) + R.vm.kill_object(model_decoder_layers_4_fc2_bias3) + gv1215: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc705: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1215, R.dtype("float16")) + cls.add(alloc701, alloc704, alloc705) + R.vm.kill_object(alloc701) + R.vm.kill_object(alloc704) + model_decoder_layers_5_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[616] + model_decoder_layers_5_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[617] + gv1216: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc706: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1216, R.dtype("float16")) + cls.layer_norm(alloc705, model_decoder_layers_5_self_attn_layer_norm_weight3, model_decoder_layers_5_self_attn_layer_norm_bias3, alloc706) + R.vm.kill_object(model_decoder_layers_5_self_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_5_self_attn_layer_norm_bias3) + model_decoder_layers_5_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[612] + model_decoder_layers_5_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[613] + gv1217: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc707: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1217, R.dtype("float16")) + _705: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_5_self_attn_q_proj_weight3, alloc706, model_decoder_layers_5_self_attn_q_proj_bias3, alloc707) + R.vm.kill_object(model_decoder_layers_5_self_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_5_self_attn_q_proj_bias3) + gv1218: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape760: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc707, gv1218, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc707) + model_decoder_layers_5_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[609] + gv1219: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc708: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1219, R.dtype("float16")) + _706: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul3_cublas", model_decoder_layers_5_self_attn_k_proj_weight3, alloc706, alloc708) + R.vm.kill_object(model_decoder_layers_5_self_attn_k_proj_weight3) + gv1220: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape761: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc708, gv1220, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc708) + model_decoder_layers_5_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[610] + model_decoder_layers_5_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[611] + gv1221: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc709: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1221, R.dtype("float16")) + _707: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_5_self_attn_v_proj_weight3, alloc706, model_decoder_layers_5_self_attn_v_proj_bias3, alloc709) + R.vm.kill_object(alloc706) + R.vm.kill_object(model_decoder_layers_5_self_attn_v_proj_weight3) + R.vm.kill_object(model_decoder_layers_5_self_attn_v_proj_bias3) + gv1222: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape762: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc709, gv1222, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc709) + gv1223: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc710: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1223, R.dtype("float16")) + cls.concatenate(reshape760, reshape761, reshape762, alloc710) + R.vm.kill_object(reshape760) + R.vm.kill_object(reshape761) + R.vm.kill_object(reshape762) + gv1224: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape763: R.Tensor((batch_size, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc710, gv1224, sinfo_args=(R.Tensor((batch_size, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc710) + gv1225: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc711: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1225, R.dtype("float16")) + _709: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), reshape763, alloc711) + R.vm.kill_object(reshape763) + gv1226: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape764: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc711, gv1226, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc711) + gv1227: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape765: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape764, gv1227, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape764) + model_decoder_layers_5_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[614] + model_decoder_layers_5_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[615] + gv1228: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc712: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1228, R.dtype("float16")) + _710: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_5_self_attn_out_proj_weight3, reshape765, model_decoder_layers_5_self_attn_out_proj_bias3, alloc712) + R.vm.kill_object(reshape765) + R.vm.kill_object(model_decoder_layers_5_self_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_5_self_attn_out_proj_bias3) + gv1229: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc713: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1229, R.dtype("float16")) + cls.add(alloc705, alloc712, alloc713) + R.vm.kill_object(alloc705) + R.vm.kill_object(alloc712) + model_decoder_layers_5_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[625] + model_decoder_layers_5_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[626] + gv1230: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc714: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1230, R.dtype("float16")) + cls.layer_norm(alloc713, model_decoder_layers_5_encoder_attn_layer_norm_weight3, model_decoder_layers_5_encoder_attn_layer_norm_bias3, alloc714) + R.vm.kill_object(model_decoder_layers_5_encoder_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_5_encoder_attn_layer_norm_bias3) + model_decoder_layers_5_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[621] + model_decoder_layers_5_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[622] + gv1231: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc715: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1231, R.dtype("float16")) + _713: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_5_encoder_attn_q_proj_weight3, alloc714, model_decoder_layers_5_encoder_attn_q_proj_bias3, alloc715) + R.vm.kill_object(alloc714) + R.vm.kill_object(model_decoder_layers_5_encoder_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_5_encoder_attn_q_proj_bias3) + gv1232: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape766: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc715, gv1232, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc715) + gv1233: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape767: R.Tensor((batch_size, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape766, gv1233, sinfo_args=(R.Tensor((batch_size, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape766) + gv1234: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc716: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1234, R.dtype("float16")) + _714: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), reshape767, alloc716) + R.vm.kill_object(reshape767) + gv1235: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape768: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc716, gv1235, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc716) + gv1236: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape769: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape768, gv1236, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape768) + model_decoder_layers_5_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[623] + model_decoder_layers_5_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[624] + gv1237: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc717: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1237, R.dtype("float16")) + _715: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_5_encoder_attn_out_proj_weight3, reshape769, model_decoder_layers_5_encoder_attn_out_proj_bias3, alloc717) + R.vm.kill_object(reshape769) + R.vm.kill_object(model_decoder_layers_5_encoder_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_5_encoder_attn_out_proj_bias3) + gv1238: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc718: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1238, R.dtype("float16")) + cls.add(alloc713, alloc717, alloc718) + R.vm.kill_object(alloc713) + R.vm.kill_object(alloc717) + model_decoder_layers_5_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[631] + model_decoder_layers_5_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[632] + gv1239: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc719: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1239, R.dtype("float16")) + cls.layer_norm(alloc718, model_decoder_layers_5_final_layer_norm_weight3, model_decoder_layers_5_final_layer_norm_bias3, alloc719) + R.vm.kill_object(model_decoder_layers_5_final_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_5_final_layer_norm_bias3) + model_decoder_layers_5_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[627] + model_decoder_layers_5_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[628] + gv1240: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc720: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1240, R.dtype("float16")) + _718: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", model_decoder_layers_5_fc1_weight3, alloc719, model_decoder_layers_5_fc1_bias3, alloc720) + R.vm.kill_object(alloc719) + R.vm.kill_object(model_decoder_layers_5_fc1_weight3) + R.vm.kill_object(model_decoder_layers_5_fc1_bias3) + model_decoder_layers_5_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[629] + model_decoder_layers_5_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[630] + gv1241: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc721: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1241, R.dtype("float16")) + _719: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", model_decoder_layers_5_fc2_weight3, alloc720, model_decoder_layers_5_fc2_bias3, alloc721) + R.vm.kill_object(alloc720) + R.vm.kill_object(model_decoder_layers_5_fc2_weight3) + R.vm.kill_object(model_decoder_layers_5_fc2_bias3) + gv1242: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc722: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1242, R.dtype("float16")) + cls.add(alloc718, alloc721, alloc722) + R.vm.kill_object(alloc718) + R.vm.kill_object(alloc721) + model_decoder_layers_6_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[640] + model_decoder_layers_6_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[641] + gv1243: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc723: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1243, R.dtype("float16")) + cls.layer_norm(alloc722, model_decoder_layers_6_self_attn_layer_norm_weight3, model_decoder_layers_6_self_attn_layer_norm_bias3, alloc723) + R.vm.kill_object(model_decoder_layers_6_self_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_6_self_attn_layer_norm_bias3) + model_decoder_layers_6_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[636] + model_decoder_layers_6_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[637] + gv1244: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc724: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1244, R.dtype("float16")) + _722: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_6_self_attn_q_proj_weight3, alloc723, model_decoder_layers_6_self_attn_q_proj_bias3, alloc724) + R.vm.kill_object(model_decoder_layers_6_self_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_6_self_attn_q_proj_bias3) + gv1245: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape770: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc724, gv1245, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc724) + model_decoder_layers_6_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[633] + gv1246: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc725: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1246, R.dtype("float16")) + _723: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul3_cublas", model_decoder_layers_6_self_attn_k_proj_weight3, alloc723, alloc725) + R.vm.kill_object(model_decoder_layers_6_self_attn_k_proj_weight3) + gv1247: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape771: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc725, gv1247, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc725) + model_decoder_layers_6_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[634] + model_decoder_layers_6_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[635] + gv1248: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc726: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1248, R.dtype("float16")) + _724: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_6_self_attn_v_proj_weight3, alloc723, model_decoder_layers_6_self_attn_v_proj_bias3, alloc726) + R.vm.kill_object(alloc723) + R.vm.kill_object(model_decoder_layers_6_self_attn_v_proj_weight3) + R.vm.kill_object(model_decoder_layers_6_self_attn_v_proj_bias3) + gv1249: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape772: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc726, gv1249, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc726) + gv1250: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc727: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1250, R.dtype("float16")) + cls.concatenate(reshape770, reshape771, reshape772, alloc727) + R.vm.kill_object(reshape770) + R.vm.kill_object(reshape771) + R.vm.kill_object(reshape772) + gv1251: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape773: R.Tensor((batch_size, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc727, gv1251, sinfo_args=(R.Tensor((batch_size, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc727) + gv1252: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc728: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1252, R.dtype("float16")) + _726: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), reshape773, alloc728) + R.vm.kill_object(reshape773) + gv1253: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape774: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc728, gv1253, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc728) + gv1254: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape775: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape774, gv1254, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape774) + model_decoder_layers_6_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[638] + model_decoder_layers_6_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[639] + gv1255: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc729: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1255, R.dtype("float16")) + _727: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_6_self_attn_out_proj_weight3, reshape775, model_decoder_layers_6_self_attn_out_proj_bias3, alloc729) + R.vm.kill_object(reshape775) + R.vm.kill_object(model_decoder_layers_6_self_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_6_self_attn_out_proj_bias3) + gv1256: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc730: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1256, R.dtype("float16")) + cls.add(alloc722, alloc729, alloc730) + R.vm.kill_object(alloc722) + R.vm.kill_object(alloc729) + model_decoder_layers_6_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[649] + model_decoder_layers_6_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[650] + gv1257: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc731: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1257, R.dtype("float16")) + cls.layer_norm(alloc730, model_decoder_layers_6_encoder_attn_layer_norm_weight3, model_decoder_layers_6_encoder_attn_layer_norm_bias3, alloc731) + R.vm.kill_object(model_decoder_layers_6_encoder_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_6_encoder_attn_layer_norm_bias3) + model_decoder_layers_6_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[645] + model_decoder_layers_6_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[646] + gv1258: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc732: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1258, R.dtype("float16")) + _730: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_6_encoder_attn_q_proj_weight3, alloc731, model_decoder_layers_6_encoder_attn_q_proj_bias3, alloc732) + R.vm.kill_object(alloc731) + R.vm.kill_object(model_decoder_layers_6_encoder_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_6_encoder_attn_q_proj_bias3) + gv1259: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape776: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc732, gv1259, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc732) + gv1260: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape777: R.Tensor((batch_size, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape776, gv1260, sinfo_args=(R.Tensor((batch_size, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape776) + gv1261: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc733: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1261, R.dtype("float16")) + _731: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), reshape777, alloc733) + R.vm.kill_object(reshape777) + gv1262: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape778: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc733, gv1262, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc733) + gv1263: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape779: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape778, gv1263, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape778) + model_decoder_layers_6_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[647] + model_decoder_layers_6_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[648] + gv1264: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc734: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1264, R.dtype("float16")) + _732: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_6_encoder_attn_out_proj_weight3, reshape779, model_decoder_layers_6_encoder_attn_out_proj_bias3, alloc734) + R.vm.kill_object(reshape779) + R.vm.kill_object(model_decoder_layers_6_encoder_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_6_encoder_attn_out_proj_bias3) + gv1265: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc735: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1265, R.dtype("float16")) + cls.add(alloc730, alloc734, alloc735) + R.vm.kill_object(alloc730) + R.vm.kill_object(alloc734) + model_decoder_layers_6_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[655] + model_decoder_layers_6_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[656] + gv1266: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc736: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1266, R.dtype("float16")) + cls.layer_norm(alloc735, model_decoder_layers_6_final_layer_norm_weight3, model_decoder_layers_6_final_layer_norm_bias3, alloc736) + R.vm.kill_object(model_decoder_layers_6_final_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_6_final_layer_norm_bias3) + model_decoder_layers_6_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[651] + model_decoder_layers_6_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[652] + gv1267: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc737: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1267, R.dtype("float16")) + _735: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", model_decoder_layers_6_fc1_weight3, alloc736, model_decoder_layers_6_fc1_bias3, alloc737) + R.vm.kill_object(alloc736) + R.vm.kill_object(model_decoder_layers_6_fc1_weight3) + R.vm.kill_object(model_decoder_layers_6_fc1_bias3) + model_decoder_layers_6_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[653] + model_decoder_layers_6_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[654] + gv1268: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc738: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1268, R.dtype("float16")) + _736: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", model_decoder_layers_6_fc2_weight3, alloc737, model_decoder_layers_6_fc2_bias3, alloc738) + R.vm.kill_object(alloc737) + R.vm.kill_object(model_decoder_layers_6_fc2_weight3) + R.vm.kill_object(model_decoder_layers_6_fc2_bias3) + gv1269: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc739: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1269, R.dtype("float16")) + cls.add(alloc735, alloc738, alloc739) + R.vm.kill_object(alloc735) + R.vm.kill_object(alloc738) + model_decoder_layers_7_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[664] + model_decoder_layers_7_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[665] + gv1270: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc740: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1270, R.dtype("float16")) + cls.layer_norm(alloc739, model_decoder_layers_7_self_attn_layer_norm_weight3, model_decoder_layers_7_self_attn_layer_norm_bias3, alloc740) + R.vm.kill_object(model_decoder_layers_7_self_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_7_self_attn_layer_norm_bias3) + model_decoder_layers_7_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[660] + model_decoder_layers_7_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[661] + gv1271: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc741: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1271, R.dtype("float16")) + _739: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_7_self_attn_q_proj_weight3, alloc740, model_decoder_layers_7_self_attn_q_proj_bias3, alloc741) + R.vm.kill_object(model_decoder_layers_7_self_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_7_self_attn_q_proj_bias3) + gv1272: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape780: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc741, gv1272, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc741) + model_decoder_layers_7_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[657] + gv1273: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc742: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1273, R.dtype("float16")) + _740: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul3_cublas", model_decoder_layers_7_self_attn_k_proj_weight3, alloc740, alloc742) + R.vm.kill_object(model_decoder_layers_7_self_attn_k_proj_weight3) + gv1274: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape781: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc742, gv1274, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc742) + model_decoder_layers_7_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[658] + model_decoder_layers_7_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[659] + gv1275: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc743: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1275, R.dtype("float16")) + _741: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_7_self_attn_v_proj_weight3, alloc740, model_decoder_layers_7_self_attn_v_proj_bias3, alloc743) + R.vm.kill_object(alloc740) + R.vm.kill_object(model_decoder_layers_7_self_attn_v_proj_weight3) + R.vm.kill_object(model_decoder_layers_7_self_attn_v_proj_bias3) + gv1276: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape782: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc743, gv1276, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc743) + gv1277: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc744: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1277, R.dtype("float16")) + cls.concatenate(reshape780, reshape781, reshape782, alloc744) + R.vm.kill_object(reshape780) + R.vm.kill_object(reshape781) + R.vm.kill_object(reshape782) + gv1278: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape783: R.Tensor((batch_size, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc744, gv1278, sinfo_args=(R.Tensor((batch_size, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc744) + gv1279: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc745: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1279, R.dtype("float16")) + _743: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), reshape783, alloc745) + R.vm.kill_object(reshape783) + gv1280: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape784: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc745, gv1280, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc745) + gv1281: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape785: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape784, gv1281, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape784) + model_decoder_layers_7_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[662] + model_decoder_layers_7_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[663] + gv1282: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc746: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1282, R.dtype("float16")) + _744: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_7_self_attn_out_proj_weight3, reshape785, model_decoder_layers_7_self_attn_out_proj_bias3, alloc746) + R.vm.kill_object(reshape785) + R.vm.kill_object(model_decoder_layers_7_self_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_7_self_attn_out_proj_bias3) + gv1283: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc747: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1283, R.dtype("float16")) + cls.add(alloc739, alloc746, alloc747) + R.vm.kill_object(alloc739) + R.vm.kill_object(alloc746) + model_decoder_layers_7_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[673] + model_decoder_layers_7_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[674] + gv1284: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc748: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1284, R.dtype("float16")) + cls.layer_norm(alloc747, model_decoder_layers_7_encoder_attn_layer_norm_weight3, model_decoder_layers_7_encoder_attn_layer_norm_bias3, alloc748) + R.vm.kill_object(model_decoder_layers_7_encoder_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_7_encoder_attn_layer_norm_bias3) + model_decoder_layers_7_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[669] + model_decoder_layers_7_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[670] + gv1285: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc749: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1285, R.dtype("float16")) + _747: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_7_encoder_attn_q_proj_weight3, alloc748, model_decoder_layers_7_encoder_attn_q_proj_bias3, alloc749) + R.vm.kill_object(alloc748) + R.vm.kill_object(model_decoder_layers_7_encoder_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_7_encoder_attn_q_proj_bias3) + gv1286: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape786: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc749, gv1286, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc749) + gv1287: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape787: R.Tensor((batch_size, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape786, gv1287, sinfo_args=(R.Tensor((batch_size, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape786) + gv1288: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc750: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1288, R.dtype("float16")) + _748: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), reshape787, alloc750) + R.vm.kill_object(reshape787) + gv1289: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape788: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc750, gv1289, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc750) + gv1290: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape789: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape788, gv1290, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape788) + model_decoder_layers_7_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[671] + model_decoder_layers_7_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[672] + gv1291: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc751: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1291, R.dtype("float16")) + _749: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_7_encoder_attn_out_proj_weight3, reshape789, model_decoder_layers_7_encoder_attn_out_proj_bias3, alloc751) + R.vm.kill_object(reshape789) + R.vm.kill_object(model_decoder_layers_7_encoder_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_7_encoder_attn_out_proj_bias3) + gv1292: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc752: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1292, R.dtype("float16")) + cls.add(alloc747, alloc751, alloc752) + R.vm.kill_object(alloc747) + R.vm.kill_object(alloc751) + model_decoder_layers_7_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[679] + model_decoder_layers_7_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[680] + gv1293: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc753: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1293, R.dtype("float16")) + cls.layer_norm(alloc752, model_decoder_layers_7_final_layer_norm_weight3, model_decoder_layers_7_final_layer_norm_bias3, alloc753) + R.vm.kill_object(model_decoder_layers_7_final_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_7_final_layer_norm_bias3) + model_decoder_layers_7_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[675] + model_decoder_layers_7_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[676] + gv1294: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc754: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1294, R.dtype("float16")) + _752: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", model_decoder_layers_7_fc1_weight3, alloc753, model_decoder_layers_7_fc1_bias3, alloc754) + R.vm.kill_object(alloc753) + R.vm.kill_object(model_decoder_layers_7_fc1_weight3) + R.vm.kill_object(model_decoder_layers_7_fc1_bias3) + model_decoder_layers_7_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[677] + model_decoder_layers_7_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[678] + gv1295: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc755: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1295, R.dtype("float16")) + _753: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", model_decoder_layers_7_fc2_weight3, alloc754, model_decoder_layers_7_fc2_bias3, alloc755) + R.vm.kill_object(alloc754) + R.vm.kill_object(model_decoder_layers_7_fc2_weight3) + R.vm.kill_object(model_decoder_layers_7_fc2_bias3) + gv1296: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc756: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1296, R.dtype("float16")) + cls.add(alloc752, alloc755, alloc756) + R.vm.kill_object(alloc752) + R.vm.kill_object(alloc755) + model_decoder_layers_8_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[688] + model_decoder_layers_8_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[689] + gv1297: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc757: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1297, R.dtype("float16")) + cls.layer_norm(alloc756, model_decoder_layers_8_self_attn_layer_norm_weight3, model_decoder_layers_8_self_attn_layer_norm_bias3, alloc757) + R.vm.kill_object(model_decoder_layers_8_self_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_8_self_attn_layer_norm_bias3) + model_decoder_layers_8_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[684] + model_decoder_layers_8_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[685] + gv1298: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc758: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1298, R.dtype("float16")) + _756: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_8_self_attn_q_proj_weight3, alloc757, model_decoder_layers_8_self_attn_q_proj_bias3, alloc758) + R.vm.kill_object(model_decoder_layers_8_self_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_8_self_attn_q_proj_bias3) + gv1299: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape790: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc758, gv1299, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc758) + model_decoder_layers_8_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[681] + gv1300: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc759: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1300, R.dtype("float16")) + _757: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul3_cublas", model_decoder_layers_8_self_attn_k_proj_weight3, alloc757, alloc759) + R.vm.kill_object(model_decoder_layers_8_self_attn_k_proj_weight3) + gv1301: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape791: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc759, gv1301, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc759) + model_decoder_layers_8_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[682] + model_decoder_layers_8_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[683] + gv1302: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc760: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1302, R.dtype("float16")) + _758: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_8_self_attn_v_proj_weight3, alloc757, model_decoder_layers_8_self_attn_v_proj_bias3, alloc760) + R.vm.kill_object(alloc757) + R.vm.kill_object(model_decoder_layers_8_self_attn_v_proj_weight3) + R.vm.kill_object(model_decoder_layers_8_self_attn_v_proj_bias3) + gv1303: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape792: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc760, gv1303, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc760) + gv1304: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc761: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1304, R.dtype("float16")) + cls.concatenate(reshape790, reshape791, reshape792, alloc761) + R.vm.kill_object(reshape790) + R.vm.kill_object(reshape791) + R.vm.kill_object(reshape792) + gv1305: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape793: R.Tensor((batch_size, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc761, gv1305, sinfo_args=(R.Tensor((batch_size, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc761) + gv1306: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc762: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1306, R.dtype("float16")) + _760: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), reshape793, alloc762) + R.vm.kill_object(reshape793) + gv1307: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape794: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc762, gv1307, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc762) + gv1308: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape795: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape794, gv1308, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape794) + model_decoder_layers_8_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[686] + model_decoder_layers_8_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[687] + gv1309: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc763: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1309, R.dtype("float16")) + _761: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_8_self_attn_out_proj_weight3, reshape795, model_decoder_layers_8_self_attn_out_proj_bias3, alloc763) + R.vm.kill_object(reshape795) + R.vm.kill_object(model_decoder_layers_8_self_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_8_self_attn_out_proj_bias3) + gv1310: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc764: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1310, R.dtype("float16")) + cls.add(alloc756, alloc763, alloc764) + R.vm.kill_object(alloc756) + R.vm.kill_object(alloc763) + model_decoder_layers_8_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[697] + model_decoder_layers_8_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[698] + gv1311: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc765: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1311, R.dtype("float16")) + cls.layer_norm(alloc764, model_decoder_layers_8_encoder_attn_layer_norm_weight3, model_decoder_layers_8_encoder_attn_layer_norm_bias3, alloc765) + R.vm.kill_object(model_decoder_layers_8_encoder_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_8_encoder_attn_layer_norm_bias3) + model_decoder_layers_8_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[693] + model_decoder_layers_8_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[694] + gv1312: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc766: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1312, R.dtype("float16")) + _764: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_8_encoder_attn_q_proj_weight3, alloc765, model_decoder_layers_8_encoder_attn_q_proj_bias3, alloc766) + R.vm.kill_object(alloc765) + R.vm.kill_object(model_decoder_layers_8_encoder_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_8_encoder_attn_q_proj_bias3) + gv1313: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape796: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc766, gv1313, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc766) + gv1314: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape797: R.Tensor((batch_size, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape796, gv1314, sinfo_args=(R.Tensor((batch_size, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape796) + gv1315: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc767: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1315, R.dtype("float16")) + _765: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), reshape797, alloc767) + R.vm.kill_object(reshape797) + gv1316: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape798: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc767, gv1316, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc767) + gv1317: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape799: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape798, gv1317, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape798) + model_decoder_layers_8_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[695] + model_decoder_layers_8_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[696] + gv1318: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc768: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1318, R.dtype("float16")) + _766: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_8_encoder_attn_out_proj_weight3, reshape799, model_decoder_layers_8_encoder_attn_out_proj_bias3, alloc768) + R.vm.kill_object(reshape799) + R.vm.kill_object(model_decoder_layers_8_encoder_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_8_encoder_attn_out_proj_bias3) + gv1319: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc769: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1319, R.dtype("float16")) + cls.add(alloc764, alloc768, alloc769) + R.vm.kill_object(alloc764) + R.vm.kill_object(alloc768) + model_decoder_layers_8_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[703] + model_decoder_layers_8_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[704] + gv1320: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc770: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1320, R.dtype("float16")) + cls.layer_norm(alloc769, model_decoder_layers_8_final_layer_norm_weight3, model_decoder_layers_8_final_layer_norm_bias3, alloc770) + R.vm.kill_object(model_decoder_layers_8_final_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_8_final_layer_norm_bias3) + model_decoder_layers_8_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[699] + model_decoder_layers_8_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[700] + gv1321: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc771: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1321, R.dtype("float16")) + _769: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", model_decoder_layers_8_fc1_weight3, alloc770, model_decoder_layers_8_fc1_bias3, alloc771) + R.vm.kill_object(alloc770) + R.vm.kill_object(model_decoder_layers_8_fc1_weight3) + R.vm.kill_object(model_decoder_layers_8_fc1_bias3) + model_decoder_layers_8_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[701] + model_decoder_layers_8_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[702] + gv1322: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc772: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1322, R.dtype("float16")) + _770: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", model_decoder_layers_8_fc2_weight3, alloc771, model_decoder_layers_8_fc2_bias3, alloc772) + R.vm.kill_object(alloc771) + R.vm.kill_object(model_decoder_layers_8_fc2_weight3) + R.vm.kill_object(model_decoder_layers_8_fc2_bias3) + gv1323: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc773: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1323, R.dtype("float16")) + cls.add(alloc769, alloc772, alloc773) + R.vm.kill_object(alloc769) + R.vm.kill_object(alloc772) + model_decoder_layers_9_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[712] + model_decoder_layers_9_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[713] + gv1324: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc774: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1324, R.dtype("float16")) + cls.layer_norm(alloc773, model_decoder_layers_9_self_attn_layer_norm_weight3, model_decoder_layers_9_self_attn_layer_norm_bias3, alloc774) + R.vm.kill_object(model_decoder_layers_9_self_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_9_self_attn_layer_norm_bias3) + model_decoder_layers_9_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[708] + model_decoder_layers_9_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[709] + gv1325: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc775: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1325, R.dtype("float16")) + _773: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_9_self_attn_q_proj_weight3, alloc774, model_decoder_layers_9_self_attn_q_proj_bias3, alloc775) + R.vm.kill_object(model_decoder_layers_9_self_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_9_self_attn_q_proj_bias3) + gv1326: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape800: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc775, gv1326, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc775) + model_decoder_layers_9_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[705] + gv1327: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc776: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1327, R.dtype("float16")) + _774: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul3_cublas", model_decoder_layers_9_self_attn_k_proj_weight3, alloc774, alloc776) + R.vm.kill_object(model_decoder_layers_9_self_attn_k_proj_weight3) + gv1328: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape801: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc776, gv1328, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc776) + model_decoder_layers_9_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[706] + model_decoder_layers_9_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[707] + gv1329: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc777: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1329, R.dtype("float16")) + _775: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_9_self_attn_v_proj_weight3, alloc774, model_decoder_layers_9_self_attn_v_proj_bias3, alloc777) + R.vm.kill_object(alloc774) + R.vm.kill_object(model_decoder_layers_9_self_attn_v_proj_weight3) + R.vm.kill_object(model_decoder_layers_9_self_attn_v_proj_bias3) + gv1330: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape802: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc777, gv1330, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc777) + gv1331: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc778: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1331, R.dtype("float16")) + cls.concatenate(reshape800, reshape801, reshape802, alloc778) + R.vm.kill_object(reshape800) + R.vm.kill_object(reshape801) + R.vm.kill_object(reshape802) + gv1332: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape803: R.Tensor((batch_size, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc778, gv1332, sinfo_args=(R.Tensor((batch_size, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc778) + gv1333: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc779: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1333, R.dtype("float16")) + _777: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), reshape803, alloc779) + R.vm.kill_object(reshape803) + gv1334: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape804: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc779, gv1334, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc779) + gv1335: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape805: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape804, gv1335, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape804) + model_decoder_layers_9_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[710] + model_decoder_layers_9_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[711] + gv1336: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc780: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1336, R.dtype("float16")) + _778: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_9_self_attn_out_proj_weight3, reshape805, model_decoder_layers_9_self_attn_out_proj_bias3, alloc780) + R.vm.kill_object(reshape805) + R.vm.kill_object(model_decoder_layers_9_self_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_9_self_attn_out_proj_bias3) + gv1337: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc781: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1337, R.dtype("float16")) + cls.add(alloc773, alloc780, alloc781) + R.vm.kill_object(alloc773) + R.vm.kill_object(alloc780) + model_decoder_layers_9_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[721] + model_decoder_layers_9_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[722] + gv1338: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc782: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1338, R.dtype("float16")) + cls.layer_norm(alloc781, model_decoder_layers_9_encoder_attn_layer_norm_weight3, model_decoder_layers_9_encoder_attn_layer_norm_bias3, alloc782) + R.vm.kill_object(model_decoder_layers_9_encoder_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_9_encoder_attn_layer_norm_bias3) + model_decoder_layers_9_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[717] + model_decoder_layers_9_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[718] + gv1339: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc783: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1339, R.dtype("float16")) + _781: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_9_encoder_attn_q_proj_weight3, alloc782, model_decoder_layers_9_encoder_attn_q_proj_bias3, alloc783) + R.vm.kill_object(alloc782) + R.vm.kill_object(model_decoder_layers_9_encoder_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_9_encoder_attn_q_proj_bias3) + gv1340: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape806: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc783, gv1340, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc783) + gv1341: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape807: R.Tensor((batch_size, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape806, gv1341, sinfo_args=(R.Tensor((batch_size, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape806) + gv1342: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc784: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1342, R.dtype("float16")) + _782: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), reshape807, alloc784) + R.vm.kill_object(reshape807) + gv1343: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape808: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc784, gv1343, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc784) + gv1344: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape809: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape808, gv1344, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape808) + model_decoder_layers_9_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[719] + model_decoder_layers_9_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[720] + gv1345: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc785: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1345, R.dtype("float16")) + _783: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_9_encoder_attn_out_proj_weight3, reshape809, model_decoder_layers_9_encoder_attn_out_proj_bias3, alloc785) + R.vm.kill_object(reshape809) + R.vm.kill_object(model_decoder_layers_9_encoder_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_9_encoder_attn_out_proj_bias3) + gv1346: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc786: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1346, R.dtype("float16")) + cls.add(alloc781, alloc785, alloc786) + R.vm.kill_object(alloc781) + R.vm.kill_object(alloc785) + model_decoder_layers_9_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[727] + model_decoder_layers_9_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[728] + gv1347: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc787: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1347, R.dtype("float16")) + cls.layer_norm(alloc786, model_decoder_layers_9_final_layer_norm_weight3, model_decoder_layers_9_final_layer_norm_bias3, alloc787) + R.vm.kill_object(model_decoder_layers_9_final_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_9_final_layer_norm_bias3) + model_decoder_layers_9_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[723] + model_decoder_layers_9_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[724] + gv1348: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc788: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1348, R.dtype("float16")) + _786: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", model_decoder_layers_9_fc1_weight3, alloc787, model_decoder_layers_9_fc1_bias3, alloc788) + R.vm.kill_object(alloc787) + R.vm.kill_object(model_decoder_layers_9_fc1_weight3) + R.vm.kill_object(model_decoder_layers_9_fc1_bias3) + model_decoder_layers_9_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[725] + model_decoder_layers_9_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[726] + gv1349: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc789: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1349, R.dtype("float16")) + _787: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", model_decoder_layers_9_fc2_weight3, alloc788, model_decoder_layers_9_fc2_bias3, alloc789) + R.vm.kill_object(alloc788) + R.vm.kill_object(model_decoder_layers_9_fc2_weight3) + R.vm.kill_object(model_decoder_layers_9_fc2_bias3) + gv1350: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc790: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1350, R.dtype("float16")) + cls.add(alloc786, alloc789, alloc790) + R.vm.kill_object(alloc786) + R.vm.kill_object(alloc789) + model_decoder_layers_10_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[736] + model_decoder_layers_10_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[737] + gv1351: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc791: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1351, R.dtype("float16")) + cls.layer_norm(alloc790, model_decoder_layers_10_self_attn_layer_norm_weight3, model_decoder_layers_10_self_attn_layer_norm_bias3, alloc791) + R.vm.kill_object(model_decoder_layers_10_self_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_10_self_attn_layer_norm_bias3) + model_decoder_layers_10_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[732] + model_decoder_layers_10_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[733] + gv1352: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc792: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1352, R.dtype("float16")) + _790: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_10_self_attn_q_proj_weight3, alloc791, model_decoder_layers_10_self_attn_q_proj_bias3, alloc792) + R.vm.kill_object(model_decoder_layers_10_self_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_10_self_attn_q_proj_bias3) + gv1353: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape810: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc792, gv1353, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc792) + model_decoder_layers_10_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[729] + gv1354: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc793: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1354, R.dtype("float16")) + _791: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul3_cublas", model_decoder_layers_10_self_attn_k_proj_weight3, alloc791, alloc793) + R.vm.kill_object(model_decoder_layers_10_self_attn_k_proj_weight3) + gv1355: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape811: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc793, gv1355, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc793) + model_decoder_layers_10_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[730] + model_decoder_layers_10_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[731] + gv1356: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc794: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1356, R.dtype("float16")) + _792: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_10_self_attn_v_proj_weight3, alloc791, model_decoder_layers_10_self_attn_v_proj_bias3, alloc794) + R.vm.kill_object(alloc791) + R.vm.kill_object(model_decoder_layers_10_self_attn_v_proj_weight3) + R.vm.kill_object(model_decoder_layers_10_self_attn_v_proj_bias3) + gv1357: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape812: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc794, gv1357, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc794) + gv1358: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc795: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1358, R.dtype("float16")) + cls.concatenate(reshape810, reshape811, reshape812, alloc795) + R.vm.kill_object(reshape810) + R.vm.kill_object(reshape811) + R.vm.kill_object(reshape812) + gv1359: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape813: R.Tensor((batch_size, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc795, gv1359, sinfo_args=(R.Tensor((batch_size, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc795) + gv1360: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc796: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1360, R.dtype("float16")) + _794: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), reshape813, alloc796) + R.vm.kill_object(reshape813) + gv1361: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape814: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc796, gv1361, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc796) + gv1362: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape815: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape814, gv1362, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape814) + model_decoder_layers_10_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[734] + model_decoder_layers_10_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[735] + gv1363: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc797: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1363, R.dtype("float16")) + _795: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_10_self_attn_out_proj_weight3, reshape815, model_decoder_layers_10_self_attn_out_proj_bias3, alloc797) + R.vm.kill_object(reshape815) + R.vm.kill_object(model_decoder_layers_10_self_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_10_self_attn_out_proj_bias3) + gv1364: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc798: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1364, R.dtype("float16")) + cls.add(alloc790, alloc797, alloc798) + R.vm.kill_object(alloc790) + R.vm.kill_object(alloc797) + model_decoder_layers_10_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[745] + model_decoder_layers_10_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[746] + gv1365: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc799: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1365, R.dtype("float16")) + cls.layer_norm(alloc798, model_decoder_layers_10_encoder_attn_layer_norm_weight3, model_decoder_layers_10_encoder_attn_layer_norm_bias3, alloc799) + R.vm.kill_object(model_decoder_layers_10_encoder_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_10_encoder_attn_layer_norm_bias3) + model_decoder_layers_10_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[741] + model_decoder_layers_10_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[742] + gv1366: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc800: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1366, R.dtype("float16")) + _798: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_10_encoder_attn_q_proj_weight3, alloc799, model_decoder_layers_10_encoder_attn_q_proj_bias3, alloc800) + R.vm.kill_object(alloc799) + R.vm.kill_object(model_decoder_layers_10_encoder_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_10_encoder_attn_q_proj_bias3) + gv1367: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape816: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc800, gv1367, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc800) + gv1368: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape817: R.Tensor((batch_size, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape816, gv1368, sinfo_args=(R.Tensor((batch_size, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape816) + gv1369: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc801: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1369, R.dtype("float16")) + _799: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), reshape817, alloc801) + R.vm.kill_object(reshape817) + gv1370: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape818: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc801, gv1370, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc801) + gv1371: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape819: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape818, gv1371, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape818) + model_decoder_layers_10_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[743] + model_decoder_layers_10_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[744] + gv1372: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc802: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1372, R.dtype("float16")) + _800: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_10_encoder_attn_out_proj_weight3, reshape819, model_decoder_layers_10_encoder_attn_out_proj_bias3, alloc802) + R.vm.kill_object(reshape819) + R.vm.kill_object(model_decoder_layers_10_encoder_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_10_encoder_attn_out_proj_bias3) + gv1373: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc803: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1373, R.dtype("float16")) + cls.add(alloc798, alloc802, alloc803) + R.vm.kill_object(alloc798) + R.vm.kill_object(alloc802) + model_decoder_layers_10_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[751] + model_decoder_layers_10_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[752] + gv1374: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc804: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1374, R.dtype("float16")) + cls.layer_norm(alloc803, model_decoder_layers_10_final_layer_norm_weight3, model_decoder_layers_10_final_layer_norm_bias3, alloc804) + R.vm.kill_object(model_decoder_layers_10_final_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_10_final_layer_norm_bias3) + model_decoder_layers_10_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[747] + model_decoder_layers_10_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[748] + gv1375: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc805: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1375, R.dtype("float16")) + _803: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", model_decoder_layers_10_fc1_weight3, alloc804, model_decoder_layers_10_fc1_bias3, alloc805) + R.vm.kill_object(alloc804) + R.vm.kill_object(model_decoder_layers_10_fc1_weight3) + R.vm.kill_object(model_decoder_layers_10_fc1_bias3) + model_decoder_layers_10_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[749] + model_decoder_layers_10_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[750] + gv1376: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc806: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1376, R.dtype("float16")) + _804: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", model_decoder_layers_10_fc2_weight3, alloc805, model_decoder_layers_10_fc2_bias3, alloc806) + R.vm.kill_object(alloc805) + R.vm.kill_object(model_decoder_layers_10_fc2_weight3) + R.vm.kill_object(model_decoder_layers_10_fc2_bias3) + gv1377: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc807: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1377, R.dtype("float16")) + cls.add(alloc803, alloc806, alloc807) + R.vm.kill_object(alloc803) + R.vm.kill_object(alloc806) + model_decoder_layers_11_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[760] + model_decoder_layers_11_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[761] + gv1378: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc808: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1378, R.dtype("float16")) + cls.layer_norm(alloc807, model_decoder_layers_11_self_attn_layer_norm_weight3, model_decoder_layers_11_self_attn_layer_norm_bias3, alloc808) + R.vm.kill_object(model_decoder_layers_11_self_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_11_self_attn_layer_norm_bias3) + model_decoder_layers_11_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[756] + model_decoder_layers_11_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[757] + gv1379: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc809: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1379, R.dtype("float16")) + _807: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_11_self_attn_q_proj_weight3, alloc808, model_decoder_layers_11_self_attn_q_proj_bias3, alloc809) + R.vm.kill_object(model_decoder_layers_11_self_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_11_self_attn_q_proj_bias3) + gv1380: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape820: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc809, gv1380, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc809) + model_decoder_layers_11_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[753] + gv1381: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc810: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1381, R.dtype("float16")) + _808: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul3_cublas", model_decoder_layers_11_self_attn_k_proj_weight3, alloc808, alloc810) + R.vm.kill_object(model_decoder_layers_11_self_attn_k_proj_weight3) + gv1382: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape821: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc810, gv1382, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc810) + model_decoder_layers_11_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[754] + model_decoder_layers_11_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[755] + gv1383: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc811: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1383, R.dtype("float16")) + _809: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_11_self_attn_v_proj_weight3, alloc808, model_decoder_layers_11_self_attn_v_proj_bias3, alloc811) + R.vm.kill_object(alloc808) + R.vm.kill_object(model_decoder_layers_11_self_attn_v_proj_weight3) + R.vm.kill_object(model_decoder_layers_11_self_attn_v_proj_bias3) + gv1384: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape822: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc811, gv1384, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc811) + gv1385: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc812: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1385, R.dtype("float16")) + cls.concatenate(reshape820, reshape821, reshape822, alloc812) + R.vm.kill_object(reshape820) + R.vm.kill_object(reshape821) + R.vm.kill_object(reshape822) + gv1386: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape823: R.Tensor((batch_size, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc812, gv1386, sinfo_args=(R.Tensor((batch_size, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc812) + gv1387: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc813: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1387, R.dtype("float16")) + _811: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), reshape823, alloc813) + R.vm.kill_object(reshape823) + gv1388: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape824: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc813, gv1388, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc813) + gv1389: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape825: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape824, gv1389, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape824) + model_decoder_layers_11_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[758] + model_decoder_layers_11_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[759] + gv1390: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc814: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1390, R.dtype("float16")) + _812: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_11_self_attn_out_proj_weight3, reshape825, model_decoder_layers_11_self_attn_out_proj_bias3, alloc814) + R.vm.kill_object(reshape825) + R.vm.kill_object(model_decoder_layers_11_self_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_11_self_attn_out_proj_bias3) + gv1391: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc815: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1391, R.dtype("float16")) + cls.add(alloc807, alloc814, alloc815) + R.vm.kill_object(alloc807) + R.vm.kill_object(alloc814) + model_decoder_layers_11_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[769] + model_decoder_layers_11_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[770] + gv1392: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc816: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1392, R.dtype("float16")) + cls.layer_norm(alloc815, model_decoder_layers_11_encoder_attn_layer_norm_weight3, model_decoder_layers_11_encoder_attn_layer_norm_bias3, alloc816) + R.vm.kill_object(model_decoder_layers_11_encoder_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_11_encoder_attn_layer_norm_bias3) + model_decoder_layers_11_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[765] + model_decoder_layers_11_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[766] + gv1393: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc817: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1393, R.dtype("float16")) + _815: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_11_encoder_attn_q_proj_weight3, alloc816, model_decoder_layers_11_encoder_attn_q_proj_bias3, alloc817) + R.vm.kill_object(alloc816) + R.vm.kill_object(model_decoder_layers_11_encoder_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_11_encoder_attn_q_proj_bias3) + gv1394: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape826: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc817, gv1394, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc817) + gv1395: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape827: R.Tensor((batch_size, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape826, gv1395, sinfo_args=(R.Tensor((batch_size, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape826) + gv1396: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc818: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1396, R.dtype("float16")) + _816: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), reshape827, alloc818) + R.vm.kill_object(reshape827) + gv1397: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape828: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc818, gv1397, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc818) + gv1398: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape829: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape828, gv1398, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape828) + model_decoder_layers_11_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[767] + model_decoder_layers_11_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[768] + gv1399: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc819: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1399, R.dtype("float16")) + _817: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_11_encoder_attn_out_proj_weight3, reshape829, model_decoder_layers_11_encoder_attn_out_proj_bias3, alloc819) + R.vm.kill_object(reshape829) + R.vm.kill_object(model_decoder_layers_11_encoder_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_11_encoder_attn_out_proj_bias3) + gv1400: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc820: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1400, R.dtype("float16")) + cls.add(alloc815, alloc819, alloc820) + R.vm.kill_object(alloc815) + R.vm.kill_object(alloc819) + model_decoder_layers_11_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[775] + model_decoder_layers_11_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[776] + gv1401: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc821: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1401, R.dtype("float16")) + cls.layer_norm(alloc820, model_decoder_layers_11_final_layer_norm_weight3, model_decoder_layers_11_final_layer_norm_bias3, alloc821) + R.vm.kill_object(model_decoder_layers_11_final_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_11_final_layer_norm_bias3) + model_decoder_layers_11_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[771] + model_decoder_layers_11_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[772] + gv1402: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc822: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1402, R.dtype("float16")) + _820: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", model_decoder_layers_11_fc1_weight3, alloc821, model_decoder_layers_11_fc1_bias3, alloc822) + R.vm.kill_object(alloc821) + R.vm.kill_object(model_decoder_layers_11_fc1_weight3) + R.vm.kill_object(model_decoder_layers_11_fc1_bias3) + model_decoder_layers_11_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[773] + model_decoder_layers_11_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[774] + gv1403: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc823: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1403, R.dtype("float16")) + _821: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", model_decoder_layers_11_fc2_weight3, alloc822, model_decoder_layers_11_fc2_bias3, alloc823) + R.vm.kill_object(alloc822) + R.vm.kill_object(model_decoder_layers_11_fc2_weight3) + R.vm.kill_object(model_decoder_layers_11_fc2_bias3) + gv1404: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc824: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1404, R.dtype("float16")) + cls.add(alloc820, alloc823, alloc824) + R.vm.kill_object(alloc820) + R.vm.kill_object(alloc823) + model_decoder_layers_12_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[784] + model_decoder_layers_12_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[785] + gv1405: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc825: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1405, R.dtype("float16")) + cls.layer_norm(alloc824, model_decoder_layers_12_self_attn_layer_norm_weight3, model_decoder_layers_12_self_attn_layer_norm_bias3, alloc825) + R.vm.kill_object(model_decoder_layers_12_self_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_12_self_attn_layer_norm_bias3) + model_decoder_layers_12_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[780] + model_decoder_layers_12_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[781] + gv1406: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc826: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1406, R.dtype("float16")) + _824: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_12_self_attn_q_proj_weight3, alloc825, model_decoder_layers_12_self_attn_q_proj_bias3, alloc826) + R.vm.kill_object(model_decoder_layers_12_self_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_12_self_attn_q_proj_bias3) + gv1407: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape830: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc826, gv1407, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc826) + model_decoder_layers_12_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[777] + gv1408: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc827: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1408, R.dtype("float16")) + _825: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul3_cublas", model_decoder_layers_12_self_attn_k_proj_weight3, alloc825, alloc827) + R.vm.kill_object(model_decoder_layers_12_self_attn_k_proj_weight3) + gv1409: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape831: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc827, gv1409, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc827) + model_decoder_layers_12_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[778] + model_decoder_layers_12_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[779] + gv1410: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc828: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1410, R.dtype("float16")) + _826: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_12_self_attn_v_proj_weight3, alloc825, model_decoder_layers_12_self_attn_v_proj_bias3, alloc828) + R.vm.kill_object(alloc825) + R.vm.kill_object(model_decoder_layers_12_self_attn_v_proj_weight3) + R.vm.kill_object(model_decoder_layers_12_self_attn_v_proj_bias3) + gv1411: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape832: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc828, gv1411, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc828) + gv1412: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc829: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1412, R.dtype("float16")) + cls.concatenate(reshape830, reshape831, reshape832, alloc829) + R.vm.kill_object(reshape830) + R.vm.kill_object(reshape831) + R.vm.kill_object(reshape832) + gv1413: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape833: R.Tensor((batch_size, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc829, gv1413, sinfo_args=(R.Tensor((batch_size, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc829) + gv1414: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc830: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1414, R.dtype("float16")) + _828: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), reshape833, alloc830) + R.vm.kill_object(reshape833) + gv1415: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape834: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc830, gv1415, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc830) + gv1416: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape835: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape834, gv1416, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape834) + model_decoder_layers_12_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[782] + model_decoder_layers_12_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[783] + gv1417: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc831: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1417, R.dtype("float16")) + _829: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_12_self_attn_out_proj_weight3, reshape835, model_decoder_layers_12_self_attn_out_proj_bias3, alloc831) + R.vm.kill_object(reshape835) + R.vm.kill_object(model_decoder_layers_12_self_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_12_self_attn_out_proj_bias3) + gv1418: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc832: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1418, R.dtype("float16")) + cls.add(alloc824, alloc831, alloc832) + R.vm.kill_object(alloc824) + R.vm.kill_object(alloc831) + model_decoder_layers_12_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[793] + model_decoder_layers_12_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[794] + gv1419: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc833: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1419, R.dtype("float16")) + cls.layer_norm(alloc832, model_decoder_layers_12_encoder_attn_layer_norm_weight3, model_decoder_layers_12_encoder_attn_layer_norm_bias3, alloc833) + R.vm.kill_object(model_decoder_layers_12_encoder_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_12_encoder_attn_layer_norm_bias3) + model_decoder_layers_12_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[789] + model_decoder_layers_12_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[790] + gv1420: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc834: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1420, R.dtype("float16")) + _832: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_12_encoder_attn_q_proj_weight3, alloc833, model_decoder_layers_12_encoder_attn_q_proj_bias3, alloc834) + R.vm.kill_object(alloc833) + R.vm.kill_object(model_decoder_layers_12_encoder_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_12_encoder_attn_q_proj_bias3) + gv1421: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape836: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc834, gv1421, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc834) + gv1422: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape837: R.Tensor((batch_size, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape836, gv1422, sinfo_args=(R.Tensor((batch_size, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape836) + gv1423: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc835: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1423, R.dtype("float16")) + _833: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), reshape837, alloc835) + R.vm.kill_object(reshape837) + gv1424: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape838: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc835, gv1424, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc835) + gv1425: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape839: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape838, gv1425, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape838) + model_decoder_layers_12_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[791] + model_decoder_layers_12_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[792] + gv1426: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc836: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1426, R.dtype("float16")) + _834: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_12_encoder_attn_out_proj_weight3, reshape839, model_decoder_layers_12_encoder_attn_out_proj_bias3, alloc836) + R.vm.kill_object(reshape839) + R.vm.kill_object(model_decoder_layers_12_encoder_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_12_encoder_attn_out_proj_bias3) + gv1427: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc837: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1427, R.dtype("float16")) + cls.add(alloc832, alloc836, alloc837) + R.vm.kill_object(alloc832) + R.vm.kill_object(alloc836) + model_decoder_layers_12_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[799] + model_decoder_layers_12_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[800] + gv1428: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc838: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1428, R.dtype("float16")) + cls.layer_norm(alloc837, model_decoder_layers_12_final_layer_norm_weight3, model_decoder_layers_12_final_layer_norm_bias3, alloc838) + R.vm.kill_object(model_decoder_layers_12_final_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_12_final_layer_norm_bias3) + model_decoder_layers_12_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[795] + model_decoder_layers_12_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[796] + gv1429: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc839: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1429, R.dtype("float16")) + _837: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", model_decoder_layers_12_fc1_weight3, alloc838, model_decoder_layers_12_fc1_bias3, alloc839) + R.vm.kill_object(alloc838) + R.vm.kill_object(model_decoder_layers_12_fc1_weight3) + R.vm.kill_object(model_decoder_layers_12_fc1_bias3) + model_decoder_layers_12_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[797] + model_decoder_layers_12_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[798] + gv1430: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc840: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1430, R.dtype("float16")) + _838: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", model_decoder_layers_12_fc2_weight3, alloc839, model_decoder_layers_12_fc2_bias3, alloc840) + R.vm.kill_object(alloc839) + R.vm.kill_object(model_decoder_layers_12_fc2_weight3) + R.vm.kill_object(model_decoder_layers_12_fc2_bias3) + gv1431: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc841: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1431, R.dtype("float16")) + cls.add(alloc837, alloc840, alloc841) + R.vm.kill_object(alloc837) + R.vm.kill_object(alloc840) + model_decoder_layers_13_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[808] + model_decoder_layers_13_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[809] + gv1432: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc842: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1432, R.dtype("float16")) + cls.layer_norm(alloc841, model_decoder_layers_13_self_attn_layer_norm_weight3, model_decoder_layers_13_self_attn_layer_norm_bias3, alloc842) + R.vm.kill_object(model_decoder_layers_13_self_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_13_self_attn_layer_norm_bias3) + model_decoder_layers_13_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[804] + model_decoder_layers_13_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[805] + gv1433: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc843: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1433, R.dtype("float16")) + _841: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_13_self_attn_q_proj_weight3, alloc842, model_decoder_layers_13_self_attn_q_proj_bias3, alloc843) + R.vm.kill_object(model_decoder_layers_13_self_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_13_self_attn_q_proj_bias3) + gv1434: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape840: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc843, gv1434, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc843) + model_decoder_layers_13_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[801] + gv1435: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc844: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1435, R.dtype("float16")) + _842: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul3_cublas", model_decoder_layers_13_self_attn_k_proj_weight3, alloc842, alloc844) + R.vm.kill_object(model_decoder_layers_13_self_attn_k_proj_weight3) + gv1436: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape841: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc844, gv1436, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc844) + model_decoder_layers_13_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[802] + model_decoder_layers_13_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[803] + gv1437: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc845: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1437, R.dtype("float16")) + _843: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_13_self_attn_v_proj_weight3, alloc842, model_decoder_layers_13_self_attn_v_proj_bias3, alloc845) + R.vm.kill_object(alloc842) + R.vm.kill_object(model_decoder_layers_13_self_attn_v_proj_weight3) + R.vm.kill_object(model_decoder_layers_13_self_attn_v_proj_bias3) + gv1438: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape842: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc845, gv1438, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc845) + gv1439: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc846: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1439, R.dtype("float16")) + cls.concatenate(reshape840, reshape841, reshape842, alloc846) + R.vm.kill_object(reshape840) + R.vm.kill_object(reshape841) + R.vm.kill_object(reshape842) + gv1440: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape843: R.Tensor((batch_size, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc846, gv1440, sinfo_args=(R.Tensor((batch_size, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc846) + gv1441: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc847: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1441, R.dtype("float16")) + _845: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), reshape843, alloc847) + R.vm.kill_object(reshape843) + gv1442: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape844: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc847, gv1442, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc847) + gv1443: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape845: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape844, gv1443, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape844) + model_decoder_layers_13_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[806] + model_decoder_layers_13_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[807] + gv1444: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc848: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1444, R.dtype("float16")) + _846: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_13_self_attn_out_proj_weight3, reshape845, model_decoder_layers_13_self_attn_out_proj_bias3, alloc848) + R.vm.kill_object(reshape845) + R.vm.kill_object(model_decoder_layers_13_self_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_13_self_attn_out_proj_bias3) + gv1445: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc849: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1445, R.dtype("float16")) + cls.add(alloc841, alloc848, alloc849) + R.vm.kill_object(alloc841) + R.vm.kill_object(alloc848) + model_decoder_layers_13_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[817] + model_decoder_layers_13_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[818] + gv1446: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc850: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1446, R.dtype("float16")) + cls.layer_norm(alloc849, model_decoder_layers_13_encoder_attn_layer_norm_weight3, model_decoder_layers_13_encoder_attn_layer_norm_bias3, alloc850) + R.vm.kill_object(model_decoder_layers_13_encoder_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_13_encoder_attn_layer_norm_bias3) + model_decoder_layers_13_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[813] + model_decoder_layers_13_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[814] + gv1447: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc851: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1447, R.dtype("float16")) + _849: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_13_encoder_attn_q_proj_weight3, alloc850, model_decoder_layers_13_encoder_attn_q_proj_bias3, alloc851) + R.vm.kill_object(alloc850) + R.vm.kill_object(model_decoder_layers_13_encoder_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_13_encoder_attn_q_proj_bias3) + gv1448: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape846: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc851, gv1448, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc851) + gv1449: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape847: R.Tensor((batch_size, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape846, gv1449, sinfo_args=(R.Tensor((batch_size, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape846) + gv1450: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc852: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1450, R.dtype("float16")) + _850: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), reshape847, alloc852) + R.vm.kill_object(reshape847) + gv1451: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape848: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc852, gv1451, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc852) + gv1452: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape849: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape848, gv1452, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape848) + model_decoder_layers_13_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[815] + model_decoder_layers_13_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[816] + gv1453: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc853: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1453, R.dtype("float16")) + _851: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_13_encoder_attn_out_proj_weight3, reshape849, model_decoder_layers_13_encoder_attn_out_proj_bias3, alloc853) + R.vm.kill_object(reshape849) + R.vm.kill_object(model_decoder_layers_13_encoder_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_13_encoder_attn_out_proj_bias3) + gv1454: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc854: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1454, R.dtype("float16")) + cls.add(alloc849, alloc853, alloc854) + R.vm.kill_object(alloc849) + R.vm.kill_object(alloc853) + model_decoder_layers_13_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[823] + model_decoder_layers_13_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[824] + gv1455: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc855: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1455, R.dtype("float16")) + cls.layer_norm(alloc854, model_decoder_layers_13_final_layer_norm_weight3, model_decoder_layers_13_final_layer_norm_bias3, alloc855) + R.vm.kill_object(model_decoder_layers_13_final_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_13_final_layer_norm_bias3) + model_decoder_layers_13_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[819] + model_decoder_layers_13_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[820] + gv1456: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc856: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1456, R.dtype("float16")) + _854: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", model_decoder_layers_13_fc1_weight3, alloc855, model_decoder_layers_13_fc1_bias3, alloc856) + R.vm.kill_object(alloc855) + R.vm.kill_object(model_decoder_layers_13_fc1_weight3) + R.vm.kill_object(model_decoder_layers_13_fc1_bias3) + model_decoder_layers_13_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[821] + model_decoder_layers_13_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[822] + gv1457: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc857: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1457, R.dtype("float16")) + _855: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", model_decoder_layers_13_fc2_weight3, alloc856, model_decoder_layers_13_fc2_bias3, alloc857) + R.vm.kill_object(alloc856) + R.vm.kill_object(model_decoder_layers_13_fc2_weight3) + R.vm.kill_object(model_decoder_layers_13_fc2_bias3) + gv1458: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc858: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1458, R.dtype("float16")) + cls.add(alloc854, alloc857, alloc858) + R.vm.kill_object(alloc854) + R.vm.kill_object(alloc857) + model_decoder_layers_14_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[832] + model_decoder_layers_14_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[833] + gv1459: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc859: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1459, R.dtype("float16")) + cls.layer_norm(alloc858, model_decoder_layers_14_self_attn_layer_norm_weight3, model_decoder_layers_14_self_attn_layer_norm_bias3, alloc859) + R.vm.kill_object(model_decoder_layers_14_self_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_14_self_attn_layer_norm_bias3) + model_decoder_layers_14_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[828] + model_decoder_layers_14_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[829] + gv1460: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc860: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1460, R.dtype("float16")) + _858: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_14_self_attn_q_proj_weight3, alloc859, model_decoder_layers_14_self_attn_q_proj_bias3, alloc860) + R.vm.kill_object(model_decoder_layers_14_self_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_14_self_attn_q_proj_bias3) + gv1461: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape850: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc860, gv1461, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc860) + model_decoder_layers_14_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[825] + gv1462: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc861: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1462, R.dtype("float16")) + _859: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul3_cublas", model_decoder_layers_14_self_attn_k_proj_weight3, alloc859, alloc861) + R.vm.kill_object(model_decoder_layers_14_self_attn_k_proj_weight3) + gv1463: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape851: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc861, gv1463, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc861) + model_decoder_layers_14_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[826] + model_decoder_layers_14_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[827] + gv1464: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc862: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1464, R.dtype("float16")) + _860: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_14_self_attn_v_proj_weight3, alloc859, model_decoder_layers_14_self_attn_v_proj_bias3, alloc862) + R.vm.kill_object(alloc859) + R.vm.kill_object(model_decoder_layers_14_self_attn_v_proj_weight3) + R.vm.kill_object(model_decoder_layers_14_self_attn_v_proj_bias3) + gv1465: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape852: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc862, gv1465, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc862) + gv1466: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc863: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1466, R.dtype("float16")) + cls.concatenate(reshape850, reshape851, reshape852, alloc863) + R.vm.kill_object(reshape850) + R.vm.kill_object(reshape851) + R.vm.kill_object(reshape852) + gv1467: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape853: R.Tensor((batch_size, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc863, gv1467, sinfo_args=(R.Tensor((batch_size, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc863) + gv1468: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc864: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1468, R.dtype("float16")) + _862: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), reshape853, alloc864) + R.vm.kill_object(reshape853) + gv1469: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape854: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc864, gv1469, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc864) + gv1470: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape855: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape854, gv1470, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape854) + model_decoder_layers_14_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[830] + model_decoder_layers_14_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[831] + gv1471: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc865: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1471, R.dtype("float16")) + _863: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_14_self_attn_out_proj_weight3, reshape855, model_decoder_layers_14_self_attn_out_proj_bias3, alloc865) + R.vm.kill_object(reshape855) + R.vm.kill_object(model_decoder_layers_14_self_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_14_self_attn_out_proj_bias3) + gv1472: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc866: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1472, R.dtype("float16")) + cls.add(alloc858, alloc865, alloc866) + R.vm.kill_object(alloc858) + R.vm.kill_object(alloc865) + model_decoder_layers_14_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[841] + model_decoder_layers_14_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[842] + gv1473: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc867: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1473, R.dtype("float16")) + cls.layer_norm(alloc866, model_decoder_layers_14_encoder_attn_layer_norm_weight3, model_decoder_layers_14_encoder_attn_layer_norm_bias3, alloc867) + R.vm.kill_object(model_decoder_layers_14_encoder_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_14_encoder_attn_layer_norm_bias3) + model_decoder_layers_14_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[837] + model_decoder_layers_14_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[838] + gv1474: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc868: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1474, R.dtype("float16")) + _866: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_14_encoder_attn_q_proj_weight3, alloc867, model_decoder_layers_14_encoder_attn_q_proj_bias3, alloc868) + R.vm.kill_object(alloc867) + R.vm.kill_object(model_decoder_layers_14_encoder_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_14_encoder_attn_q_proj_bias3) + gv1475: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape856: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc868, gv1475, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc868) + gv1476: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape857: R.Tensor((batch_size, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape856, gv1476, sinfo_args=(R.Tensor((batch_size, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape856) + gv1477: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc869: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1477, R.dtype("float16")) + _867: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), reshape857, alloc869) + R.vm.kill_object(reshape857) + gv1478: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape858: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc869, gv1478, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc869) + gv1479: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape859: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape858, gv1479, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape858) + model_decoder_layers_14_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[839] + model_decoder_layers_14_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[840] + gv1480: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc870: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1480, R.dtype("float16")) + _868: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_14_encoder_attn_out_proj_weight3, reshape859, model_decoder_layers_14_encoder_attn_out_proj_bias3, alloc870) + R.vm.kill_object(reshape859) + R.vm.kill_object(model_decoder_layers_14_encoder_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_14_encoder_attn_out_proj_bias3) + gv1481: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc871: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1481, R.dtype("float16")) + cls.add(alloc866, alloc870, alloc871) + R.vm.kill_object(alloc866) + R.vm.kill_object(alloc870) + model_decoder_layers_14_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[847] + model_decoder_layers_14_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[848] + gv1482: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc872: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1482, R.dtype("float16")) + cls.layer_norm(alloc871, model_decoder_layers_14_final_layer_norm_weight3, model_decoder_layers_14_final_layer_norm_bias3, alloc872) + R.vm.kill_object(model_decoder_layers_14_final_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_14_final_layer_norm_bias3) + model_decoder_layers_14_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[843] + model_decoder_layers_14_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[844] + gv1483: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc873: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1483, R.dtype("float16")) + _871: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", model_decoder_layers_14_fc1_weight3, alloc872, model_decoder_layers_14_fc1_bias3, alloc873) + R.vm.kill_object(alloc872) + R.vm.kill_object(model_decoder_layers_14_fc1_weight3) + R.vm.kill_object(model_decoder_layers_14_fc1_bias3) + model_decoder_layers_14_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[845] + model_decoder_layers_14_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[846] + gv1484: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc874: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1484, R.dtype("float16")) + _872: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", model_decoder_layers_14_fc2_weight3, alloc873, model_decoder_layers_14_fc2_bias3, alloc874) + R.vm.kill_object(alloc873) + R.vm.kill_object(model_decoder_layers_14_fc2_weight3) + R.vm.kill_object(model_decoder_layers_14_fc2_bias3) + gv1485: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc875: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1485, R.dtype("float16")) + cls.add(alloc871, alloc874, alloc875) + R.vm.kill_object(alloc871) + R.vm.kill_object(alloc874) + model_decoder_layers_15_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[856] + model_decoder_layers_15_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[857] + gv1486: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc876: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1486, R.dtype("float16")) + cls.layer_norm(alloc875, model_decoder_layers_15_self_attn_layer_norm_weight3, model_decoder_layers_15_self_attn_layer_norm_bias3, alloc876) + R.vm.kill_object(model_decoder_layers_15_self_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_15_self_attn_layer_norm_bias3) + model_decoder_layers_15_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[852] + model_decoder_layers_15_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[853] + gv1487: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc877: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1487, R.dtype("float16")) + _875: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_15_self_attn_q_proj_weight3, alloc876, model_decoder_layers_15_self_attn_q_proj_bias3, alloc877) + R.vm.kill_object(model_decoder_layers_15_self_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_15_self_attn_q_proj_bias3) + gv1488: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape860: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc877, gv1488, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc877) + model_decoder_layers_15_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[849] + gv1489: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc878: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1489, R.dtype("float16")) + _876: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul3_cublas", model_decoder_layers_15_self_attn_k_proj_weight3, alloc876, alloc878) + R.vm.kill_object(model_decoder_layers_15_self_attn_k_proj_weight3) + gv1490: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape861: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc878, gv1490, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc878) + model_decoder_layers_15_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[850] + model_decoder_layers_15_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[851] + gv1491: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc879: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1491, R.dtype("float16")) + _877: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_15_self_attn_v_proj_weight3, alloc876, model_decoder_layers_15_self_attn_v_proj_bias3, alloc879) + R.vm.kill_object(alloc876) + R.vm.kill_object(model_decoder_layers_15_self_attn_v_proj_weight3) + R.vm.kill_object(model_decoder_layers_15_self_attn_v_proj_bias3) + gv1492: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape862: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc879, gv1492, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc879) + gv1493: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc880: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1493, R.dtype("float16")) + cls.concatenate(reshape860, reshape861, reshape862, alloc880) + R.vm.kill_object(reshape860) + R.vm.kill_object(reshape861) + R.vm.kill_object(reshape862) + gv1494: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape863: R.Tensor((batch_size, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc880, gv1494, sinfo_args=(R.Tensor((batch_size, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc880) + gv1495: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc881: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1495, R.dtype("float16")) + _879: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), reshape863, alloc881) + R.vm.kill_object(reshape863) + gv1496: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape864: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc881, gv1496, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc881) + gv1497: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape865: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape864, gv1497, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape864) + model_decoder_layers_15_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[854] + model_decoder_layers_15_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[855] + gv1498: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc882: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1498, R.dtype("float16")) + _880: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_15_self_attn_out_proj_weight3, reshape865, model_decoder_layers_15_self_attn_out_proj_bias3, alloc882) + R.vm.kill_object(reshape865) + R.vm.kill_object(model_decoder_layers_15_self_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_15_self_attn_out_proj_bias3) + gv1499: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc883: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1499, R.dtype("float16")) + cls.add(alloc875, alloc882, alloc883) + R.vm.kill_object(alloc875) + R.vm.kill_object(alloc882) + model_decoder_layers_15_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[865] + model_decoder_layers_15_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[866] + gv1500: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc884: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1500, R.dtype("float16")) + cls.layer_norm(alloc883, model_decoder_layers_15_encoder_attn_layer_norm_weight3, model_decoder_layers_15_encoder_attn_layer_norm_bias3, alloc884) + R.vm.kill_object(model_decoder_layers_15_encoder_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_15_encoder_attn_layer_norm_bias3) + model_decoder_layers_15_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[861] + model_decoder_layers_15_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[862] + gv1501: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc885: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1501, R.dtype("float16")) + _883: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_15_encoder_attn_q_proj_weight3, alloc884, model_decoder_layers_15_encoder_attn_q_proj_bias3, alloc885) + R.vm.kill_object(alloc884) + R.vm.kill_object(model_decoder_layers_15_encoder_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_15_encoder_attn_q_proj_bias3) + gv1502: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape866: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc885, gv1502, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc885) + gv1503: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape867: R.Tensor((batch_size, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape866, gv1503, sinfo_args=(R.Tensor((batch_size, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape866) + gv1504: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc886: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1504, R.dtype("float16")) + _884: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), reshape867, alloc886) + R.vm.kill_object(reshape867) + gv1505: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape868: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc886, gv1505, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc886) + gv1506: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape869: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape868, gv1506, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape868) + model_decoder_layers_15_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[863] + model_decoder_layers_15_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[864] + gv1507: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc887: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1507, R.dtype("float16")) + _885: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_15_encoder_attn_out_proj_weight3, reshape869, model_decoder_layers_15_encoder_attn_out_proj_bias3, alloc887) + R.vm.kill_object(reshape869) + R.vm.kill_object(model_decoder_layers_15_encoder_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_15_encoder_attn_out_proj_bias3) + gv1508: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc888: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1508, R.dtype("float16")) + cls.add(alloc883, alloc887, alloc888) + R.vm.kill_object(alloc883) + R.vm.kill_object(alloc887) + model_decoder_layers_15_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[871] + model_decoder_layers_15_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[872] + gv1509: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc889: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1509, R.dtype("float16")) + cls.layer_norm(alloc888, model_decoder_layers_15_final_layer_norm_weight3, model_decoder_layers_15_final_layer_norm_bias3, alloc889) + R.vm.kill_object(model_decoder_layers_15_final_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_15_final_layer_norm_bias3) + model_decoder_layers_15_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[867] + model_decoder_layers_15_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[868] + gv1510: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc890: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1510, R.dtype("float16")) + _888: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", model_decoder_layers_15_fc1_weight3, alloc889, model_decoder_layers_15_fc1_bias3, alloc890) + R.vm.kill_object(alloc889) + R.vm.kill_object(model_decoder_layers_15_fc1_weight3) + R.vm.kill_object(model_decoder_layers_15_fc1_bias3) + model_decoder_layers_15_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[869] + model_decoder_layers_15_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[870] + gv1511: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc891: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1511, R.dtype("float16")) + _889: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", model_decoder_layers_15_fc2_weight3, alloc890, model_decoder_layers_15_fc2_bias3, alloc891) + R.vm.kill_object(alloc890) + R.vm.kill_object(model_decoder_layers_15_fc2_weight3) + R.vm.kill_object(model_decoder_layers_15_fc2_bias3) + gv1512: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc892: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1512, R.dtype("float16")) + cls.add(alloc888, alloc891, alloc892) + R.vm.kill_object(alloc888) + R.vm.kill_object(alloc891) + model_decoder_layers_16_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[880] + model_decoder_layers_16_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[881] + gv1513: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc893: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1513, R.dtype("float16")) + cls.layer_norm(alloc892, model_decoder_layers_16_self_attn_layer_norm_weight3, model_decoder_layers_16_self_attn_layer_norm_bias3, alloc893) + R.vm.kill_object(model_decoder_layers_16_self_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_16_self_attn_layer_norm_bias3) + model_decoder_layers_16_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[876] + model_decoder_layers_16_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[877] + gv1514: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc894: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1514, R.dtype("float16")) + _892: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_16_self_attn_q_proj_weight3, alloc893, model_decoder_layers_16_self_attn_q_proj_bias3, alloc894) + R.vm.kill_object(model_decoder_layers_16_self_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_16_self_attn_q_proj_bias3) + gv1515: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape870: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc894, gv1515, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc894) + model_decoder_layers_16_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[873] + gv1516: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc895: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1516, R.dtype("float16")) + _893: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul3_cublas", model_decoder_layers_16_self_attn_k_proj_weight3, alloc893, alloc895) + R.vm.kill_object(model_decoder_layers_16_self_attn_k_proj_weight3) + gv1517: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape871: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc895, gv1517, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc895) + model_decoder_layers_16_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[874] + model_decoder_layers_16_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[875] + gv1518: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc896: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1518, R.dtype("float16")) + _894: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_16_self_attn_v_proj_weight3, alloc893, model_decoder_layers_16_self_attn_v_proj_bias3, alloc896) + R.vm.kill_object(alloc893) + R.vm.kill_object(model_decoder_layers_16_self_attn_v_proj_weight3) + R.vm.kill_object(model_decoder_layers_16_self_attn_v_proj_bias3) + gv1519: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape872: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc896, gv1519, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc896) + gv1520: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc897: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1520, R.dtype("float16")) + cls.concatenate(reshape870, reshape871, reshape872, alloc897) + R.vm.kill_object(reshape870) + R.vm.kill_object(reshape871) + R.vm.kill_object(reshape872) + gv1521: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape873: R.Tensor((batch_size, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc897, gv1521, sinfo_args=(R.Tensor((batch_size, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc897) + gv1522: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc898: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1522, R.dtype("float16")) + _896: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), reshape873, alloc898) + R.vm.kill_object(reshape873) + gv1523: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape874: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc898, gv1523, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc898) + gv1524: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape875: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape874, gv1524, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape874) + model_decoder_layers_16_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[878] + model_decoder_layers_16_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[879] + gv1525: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc899: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1525, R.dtype("float16")) + _897: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_16_self_attn_out_proj_weight3, reshape875, model_decoder_layers_16_self_attn_out_proj_bias3, alloc899) + R.vm.kill_object(reshape875) + R.vm.kill_object(model_decoder_layers_16_self_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_16_self_attn_out_proj_bias3) + gv1526: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc900: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1526, R.dtype("float16")) + cls.add(alloc892, alloc899, alloc900) + R.vm.kill_object(alloc892) + R.vm.kill_object(alloc899) + model_decoder_layers_16_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[889] + model_decoder_layers_16_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[890] + gv1527: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc901: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1527, R.dtype("float16")) + cls.layer_norm(alloc900, model_decoder_layers_16_encoder_attn_layer_norm_weight3, model_decoder_layers_16_encoder_attn_layer_norm_bias3, alloc901) + R.vm.kill_object(model_decoder_layers_16_encoder_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_16_encoder_attn_layer_norm_bias3) + model_decoder_layers_16_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[885] + model_decoder_layers_16_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[886] + gv1528: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc902: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1528, R.dtype("float16")) + _900: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_16_encoder_attn_q_proj_weight3, alloc901, model_decoder_layers_16_encoder_attn_q_proj_bias3, alloc902) + R.vm.kill_object(alloc901) + R.vm.kill_object(model_decoder_layers_16_encoder_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_16_encoder_attn_q_proj_bias3) + gv1529: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape876: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc902, gv1529, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc902) + gv1530: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape877: R.Tensor((batch_size, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape876, gv1530, sinfo_args=(R.Tensor((batch_size, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape876) + gv1531: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc903: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1531, R.dtype("float16")) + _901: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), reshape877, alloc903) + R.vm.kill_object(reshape877) + gv1532: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape878: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc903, gv1532, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc903) + gv1533: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape879: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape878, gv1533, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape878) + model_decoder_layers_16_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[887] + model_decoder_layers_16_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[888] + gv1534: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc904: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1534, R.dtype("float16")) + _902: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_16_encoder_attn_out_proj_weight3, reshape879, model_decoder_layers_16_encoder_attn_out_proj_bias3, alloc904) + R.vm.kill_object(reshape879) + R.vm.kill_object(model_decoder_layers_16_encoder_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_16_encoder_attn_out_proj_bias3) + gv1535: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc905: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1535, R.dtype("float16")) + cls.add(alloc900, alloc904, alloc905) + R.vm.kill_object(alloc900) + R.vm.kill_object(alloc904) + model_decoder_layers_16_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[895] + model_decoder_layers_16_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[896] + gv1536: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc906: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1536, R.dtype("float16")) + cls.layer_norm(alloc905, model_decoder_layers_16_final_layer_norm_weight3, model_decoder_layers_16_final_layer_norm_bias3, alloc906) + R.vm.kill_object(model_decoder_layers_16_final_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_16_final_layer_norm_bias3) + model_decoder_layers_16_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[891] + model_decoder_layers_16_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[892] + gv1537: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc907: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1537, R.dtype("float16")) + _905: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", model_decoder_layers_16_fc1_weight3, alloc906, model_decoder_layers_16_fc1_bias3, alloc907) + R.vm.kill_object(alloc906) + R.vm.kill_object(model_decoder_layers_16_fc1_weight3) + R.vm.kill_object(model_decoder_layers_16_fc1_bias3) + model_decoder_layers_16_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[893] + model_decoder_layers_16_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[894] + gv1538: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc908: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1538, R.dtype("float16")) + _906: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", model_decoder_layers_16_fc2_weight3, alloc907, model_decoder_layers_16_fc2_bias3, alloc908) + R.vm.kill_object(alloc907) + R.vm.kill_object(model_decoder_layers_16_fc2_weight3) + R.vm.kill_object(model_decoder_layers_16_fc2_bias3) + gv1539: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc909: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1539, R.dtype("float16")) + cls.add(alloc905, alloc908, alloc909) + R.vm.kill_object(alloc905) + R.vm.kill_object(alloc908) + model_decoder_layers_17_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[904] + model_decoder_layers_17_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[905] + gv1540: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc910: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1540, R.dtype("float16")) + cls.layer_norm(alloc909, model_decoder_layers_17_self_attn_layer_norm_weight3, model_decoder_layers_17_self_attn_layer_norm_bias3, alloc910) + R.vm.kill_object(model_decoder_layers_17_self_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_17_self_attn_layer_norm_bias3) + model_decoder_layers_17_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[900] + model_decoder_layers_17_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[901] + gv1541: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc911: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1541, R.dtype("float16")) + _909: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_17_self_attn_q_proj_weight3, alloc910, model_decoder_layers_17_self_attn_q_proj_bias3, alloc911) + R.vm.kill_object(model_decoder_layers_17_self_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_17_self_attn_q_proj_bias3) + gv1542: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape880: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc911, gv1542, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc911) + model_decoder_layers_17_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[897] + gv1543: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc912: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1543, R.dtype("float16")) + _910: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul3_cublas", model_decoder_layers_17_self_attn_k_proj_weight3, alloc910, alloc912) + R.vm.kill_object(model_decoder_layers_17_self_attn_k_proj_weight3) + gv1544: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape881: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc912, gv1544, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc912) + model_decoder_layers_17_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[898] + model_decoder_layers_17_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[899] + gv1545: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc913: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1545, R.dtype("float16")) + _911: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_17_self_attn_v_proj_weight3, alloc910, model_decoder_layers_17_self_attn_v_proj_bias3, alloc913) + R.vm.kill_object(alloc910) + R.vm.kill_object(model_decoder_layers_17_self_attn_v_proj_weight3) + R.vm.kill_object(model_decoder_layers_17_self_attn_v_proj_bias3) + gv1546: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape882: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc913, gv1546, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc913) + gv1547: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc914: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1547, R.dtype("float16")) + cls.concatenate(reshape880, reshape881, reshape882, alloc914) + R.vm.kill_object(reshape880) + R.vm.kill_object(reshape881) + R.vm.kill_object(reshape882) + gv1548: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape883: R.Tensor((batch_size, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc914, gv1548, sinfo_args=(R.Tensor((batch_size, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc914) + gv1549: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc915: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1549, R.dtype("float16")) + _913: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), reshape883, alloc915) + R.vm.kill_object(reshape883) + gv1550: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape884: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc915, gv1550, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc915) + gv1551: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape885: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape884, gv1551, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape884) + model_decoder_layers_17_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[902] + model_decoder_layers_17_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[903] + gv1552: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc916: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1552, R.dtype("float16")) + _914: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_17_self_attn_out_proj_weight3, reshape885, model_decoder_layers_17_self_attn_out_proj_bias3, alloc916) + R.vm.kill_object(reshape885) + R.vm.kill_object(model_decoder_layers_17_self_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_17_self_attn_out_proj_bias3) + gv1553: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc917: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1553, R.dtype("float16")) + cls.add(alloc909, alloc916, alloc917) + R.vm.kill_object(alloc909) + R.vm.kill_object(alloc916) + model_decoder_layers_17_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[913] + model_decoder_layers_17_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[914] + gv1554: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc918: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1554, R.dtype("float16")) + cls.layer_norm(alloc917, model_decoder_layers_17_encoder_attn_layer_norm_weight3, model_decoder_layers_17_encoder_attn_layer_norm_bias3, alloc918) + R.vm.kill_object(model_decoder_layers_17_encoder_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_17_encoder_attn_layer_norm_bias3) + model_decoder_layers_17_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[909] + model_decoder_layers_17_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[910] + gv1555: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc919: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1555, R.dtype("float16")) + _917: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_17_encoder_attn_q_proj_weight3, alloc918, model_decoder_layers_17_encoder_attn_q_proj_bias3, alloc919) + R.vm.kill_object(alloc918) + R.vm.kill_object(model_decoder_layers_17_encoder_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_17_encoder_attn_q_proj_bias3) + gv1556: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape886: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc919, gv1556, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc919) + gv1557: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape887: R.Tensor((batch_size, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape886, gv1557, sinfo_args=(R.Tensor((batch_size, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape886) + gv1558: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc920: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1558, R.dtype("float16")) + _918: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), reshape887, alloc920) + R.vm.kill_object(reshape887) + gv1559: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape888: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc920, gv1559, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc920) + gv1560: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape889: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape888, gv1560, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape888) + model_decoder_layers_17_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[911] + model_decoder_layers_17_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[912] + gv1561: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc921: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1561, R.dtype("float16")) + _919: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_17_encoder_attn_out_proj_weight3, reshape889, model_decoder_layers_17_encoder_attn_out_proj_bias3, alloc921) + R.vm.kill_object(reshape889) + R.vm.kill_object(model_decoder_layers_17_encoder_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_17_encoder_attn_out_proj_bias3) + gv1562: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc922: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1562, R.dtype("float16")) + cls.add(alloc917, alloc921, alloc922) + R.vm.kill_object(alloc917) + R.vm.kill_object(alloc921) + model_decoder_layers_17_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[919] + model_decoder_layers_17_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[920] + gv1563: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc923: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1563, R.dtype("float16")) + cls.layer_norm(alloc922, model_decoder_layers_17_final_layer_norm_weight3, model_decoder_layers_17_final_layer_norm_bias3, alloc923) + R.vm.kill_object(model_decoder_layers_17_final_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_17_final_layer_norm_bias3) + model_decoder_layers_17_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[915] + model_decoder_layers_17_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[916] + gv1564: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc924: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1564, R.dtype("float16")) + _922: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", model_decoder_layers_17_fc1_weight3, alloc923, model_decoder_layers_17_fc1_bias3, alloc924) + R.vm.kill_object(alloc923) + R.vm.kill_object(model_decoder_layers_17_fc1_weight3) + R.vm.kill_object(model_decoder_layers_17_fc1_bias3) + model_decoder_layers_17_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[917] + model_decoder_layers_17_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[918] + gv1565: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc925: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1565, R.dtype("float16")) + _923: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", model_decoder_layers_17_fc2_weight3, alloc924, model_decoder_layers_17_fc2_bias3, alloc925) + R.vm.kill_object(alloc924) + R.vm.kill_object(model_decoder_layers_17_fc2_weight3) + R.vm.kill_object(model_decoder_layers_17_fc2_bias3) + gv1566: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc926: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1566, R.dtype("float16")) + cls.add(alloc922, alloc925, alloc926) + R.vm.kill_object(alloc922) + R.vm.kill_object(alloc925) + model_decoder_layers_18_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[928] + model_decoder_layers_18_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[929] + gv1567: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc927: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1567, R.dtype("float16")) + cls.layer_norm(alloc926, model_decoder_layers_18_self_attn_layer_norm_weight3, model_decoder_layers_18_self_attn_layer_norm_bias3, alloc927) + R.vm.kill_object(model_decoder_layers_18_self_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_18_self_attn_layer_norm_bias3) + model_decoder_layers_18_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[924] + model_decoder_layers_18_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[925] + gv1568: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc928: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1568, R.dtype("float16")) + _926: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_18_self_attn_q_proj_weight3, alloc927, model_decoder_layers_18_self_attn_q_proj_bias3, alloc928) + R.vm.kill_object(model_decoder_layers_18_self_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_18_self_attn_q_proj_bias3) + gv1569: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape890: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc928, gv1569, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc928) + model_decoder_layers_18_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[921] + gv1570: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc929: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1570, R.dtype("float16")) + _927: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul3_cublas", model_decoder_layers_18_self_attn_k_proj_weight3, alloc927, alloc929) + R.vm.kill_object(model_decoder_layers_18_self_attn_k_proj_weight3) + gv1571: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape891: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc929, gv1571, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc929) + model_decoder_layers_18_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[922] + model_decoder_layers_18_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[923] + gv1572: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc930: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1572, R.dtype("float16")) + _928: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_18_self_attn_v_proj_weight3, alloc927, model_decoder_layers_18_self_attn_v_proj_bias3, alloc930) + R.vm.kill_object(alloc927) + R.vm.kill_object(model_decoder_layers_18_self_attn_v_proj_weight3) + R.vm.kill_object(model_decoder_layers_18_self_attn_v_proj_bias3) + gv1573: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape892: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc930, gv1573, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc930) + gv1574: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc931: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1574, R.dtype("float16")) + cls.concatenate(reshape890, reshape891, reshape892, alloc931) + R.vm.kill_object(reshape890) + R.vm.kill_object(reshape891) + R.vm.kill_object(reshape892) + gv1575: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape893: R.Tensor((batch_size, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc931, gv1575, sinfo_args=(R.Tensor((batch_size, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc931) + gv1576: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc932: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1576, R.dtype("float16")) + _930: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), reshape893, alloc932) + R.vm.kill_object(reshape893) + gv1577: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape894: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc932, gv1577, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc932) + gv1578: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape895: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape894, gv1578, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape894) + model_decoder_layers_18_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[926] + model_decoder_layers_18_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[927] + gv1579: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc933: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1579, R.dtype("float16")) + _931: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_18_self_attn_out_proj_weight3, reshape895, model_decoder_layers_18_self_attn_out_proj_bias3, alloc933) + R.vm.kill_object(reshape895) + R.vm.kill_object(model_decoder_layers_18_self_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_18_self_attn_out_proj_bias3) + gv1580: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc934: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1580, R.dtype("float16")) + cls.add(alloc926, alloc933, alloc934) + R.vm.kill_object(alloc926) + R.vm.kill_object(alloc933) + model_decoder_layers_18_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[937] + model_decoder_layers_18_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[938] + gv1581: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc935: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1581, R.dtype("float16")) + cls.layer_norm(alloc934, model_decoder_layers_18_encoder_attn_layer_norm_weight3, model_decoder_layers_18_encoder_attn_layer_norm_bias3, alloc935) + R.vm.kill_object(model_decoder_layers_18_encoder_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_18_encoder_attn_layer_norm_bias3) + model_decoder_layers_18_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[933] + model_decoder_layers_18_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[934] + gv1582: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc936: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1582, R.dtype("float16")) + _934: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_18_encoder_attn_q_proj_weight3, alloc935, model_decoder_layers_18_encoder_attn_q_proj_bias3, alloc936) + R.vm.kill_object(alloc935) + R.vm.kill_object(model_decoder_layers_18_encoder_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_18_encoder_attn_q_proj_bias3) + gv1583: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape896: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc936, gv1583, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc936) + gv1584: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape897: R.Tensor((batch_size, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape896, gv1584, sinfo_args=(R.Tensor((batch_size, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape896) + gv1585: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc937: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1585, R.dtype("float16")) + _935: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), reshape897, alloc937) + R.vm.kill_object(reshape897) + gv1586: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape898: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc937, gv1586, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc937) + gv1587: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape899: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape898, gv1587, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape898) + model_decoder_layers_18_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[935] + model_decoder_layers_18_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[936] + gv1588: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc938: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1588, R.dtype("float16")) + _936: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_18_encoder_attn_out_proj_weight3, reshape899, model_decoder_layers_18_encoder_attn_out_proj_bias3, alloc938) + R.vm.kill_object(reshape899) + R.vm.kill_object(model_decoder_layers_18_encoder_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_18_encoder_attn_out_proj_bias3) + gv1589: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc939: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1589, R.dtype("float16")) + cls.add(alloc934, alloc938, alloc939) + R.vm.kill_object(alloc934) + R.vm.kill_object(alloc938) + model_decoder_layers_18_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[943] + model_decoder_layers_18_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[944] + gv1590: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc940: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1590, R.dtype("float16")) + cls.layer_norm(alloc939, model_decoder_layers_18_final_layer_norm_weight3, model_decoder_layers_18_final_layer_norm_bias3, alloc940) + R.vm.kill_object(model_decoder_layers_18_final_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_18_final_layer_norm_bias3) + model_decoder_layers_18_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[939] + model_decoder_layers_18_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[940] + gv1591: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc941: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1591, R.dtype("float16")) + _939: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", model_decoder_layers_18_fc1_weight3, alloc940, model_decoder_layers_18_fc1_bias3, alloc941) + R.vm.kill_object(alloc940) + R.vm.kill_object(model_decoder_layers_18_fc1_weight3) + R.vm.kill_object(model_decoder_layers_18_fc1_bias3) + model_decoder_layers_18_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[941] + model_decoder_layers_18_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[942] + gv1592: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc942: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1592, R.dtype("float16")) + _940: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", model_decoder_layers_18_fc2_weight3, alloc941, model_decoder_layers_18_fc2_bias3, alloc942) + R.vm.kill_object(alloc941) + R.vm.kill_object(model_decoder_layers_18_fc2_weight3) + R.vm.kill_object(model_decoder_layers_18_fc2_bias3) + gv1593: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc943: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1593, R.dtype("float16")) + cls.add(alloc939, alloc942, alloc943) + R.vm.kill_object(alloc939) + R.vm.kill_object(alloc942) + model_decoder_layers_19_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[952] + model_decoder_layers_19_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[953] + gv1594: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc944: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1594, R.dtype("float16")) + cls.layer_norm(alloc943, model_decoder_layers_19_self_attn_layer_norm_weight3, model_decoder_layers_19_self_attn_layer_norm_bias3, alloc944) + R.vm.kill_object(model_decoder_layers_19_self_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_19_self_attn_layer_norm_bias3) + model_decoder_layers_19_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[948] + model_decoder_layers_19_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[949] + gv1595: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc945: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1595, R.dtype("float16")) + _943: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_19_self_attn_q_proj_weight3, alloc944, model_decoder_layers_19_self_attn_q_proj_bias3, alloc945) + R.vm.kill_object(model_decoder_layers_19_self_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_19_self_attn_q_proj_bias3) + gv1596: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape900: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc945, gv1596, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc945) + model_decoder_layers_19_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[945] + gv1597: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc946: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1597, R.dtype("float16")) + _944: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul3_cublas", model_decoder_layers_19_self_attn_k_proj_weight3, alloc944, alloc946) + R.vm.kill_object(model_decoder_layers_19_self_attn_k_proj_weight3) + gv1598: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape901: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc946, gv1598, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc946) + model_decoder_layers_19_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[946] + model_decoder_layers_19_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[947] + gv1599: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc947: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1599, R.dtype("float16")) + _945: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_19_self_attn_v_proj_weight3, alloc944, model_decoder_layers_19_self_attn_v_proj_bias3, alloc947) + R.vm.kill_object(alloc944) + R.vm.kill_object(model_decoder_layers_19_self_attn_v_proj_weight3) + R.vm.kill_object(model_decoder_layers_19_self_attn_v_proj_bias3) + gv1600: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape902: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc947, gv1600, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc947) + gv1601: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc948: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1601, R.dtype("float16")) + cls.concatenate(reshape900, reshape901, reshape902, alloc948) + R.vm.kill_object(reshape900) + R.vm.kill_object(reshape901) + R.vm.kill_object(reshape902) + gv1602: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape903: R.Tensor((batch_size, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc948, gv1602, sinfo_args=(R.Tensor((batch_size, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc948) + gv1603: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc949: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1603, R.dtype("float16")) + _947: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), reshape903, alloc949) + R.vm.kill_object(reshape903) + gv1604: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape904: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc949, gv1604, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc949) + gv1605: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape905: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape904, gv1605, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape904) + model_decoder_layers_19_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[950] + model_decoder_layers_19_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[951] + gv1606: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc950: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1606, R.dtype("float16")) + _948: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_19_self_attn_out_proj_weight3, reshape905, model_decoder_layers_19_self_attn_out_proj_bias3, alloc950) + R.vm.kill_object(reshape905) + R.vm.kill_object(model_decoder_layers_19_self_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_19_self_attn_out_proj_bias3) + gv1607: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc951: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1607, R.dtype("float16")) + cls.add(alloc943, alloc950, alloc951) + R.vm.kill_object(alloc943) + R.vm.kill_object(alloc950) + model_decoder_layers_19_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[961] + model_decoder_layers_19_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[962] + gv1608: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc952: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1608, R.dtype("float16")) + cls.layer_norm(alloc951, model_decoder_layers_19_encoder_attn_layer_norm_weight3, model_decoder_layers_19_encoder_attn_layer_norm_bias3, alloc952) + R.vm.kill_object(model_decoder_layers_19_encoder_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_19_encoder_attn_layer_norm_bias3) + model_decoder_layers_19_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[957] + model_decoder_layers_19_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[958] + gv1609: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc953: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1609, R.dtype("float16")) + _951: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_19_encoder_attn_q_proj_weight3, alloc952, model_decoder_layers_19_encoder_attn_q_proj_bias3, alloc953) + R.vm.kill_object(alloc952) + R.vm.kill_object(model_decoder_layers_19_encoder_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_19_encoder_attn_q_proj_bias3) + gv1610: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape906: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc953, gv1610, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc953) + gv1611: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape907: R.Tensor((batch_size, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape906, gv1611, sinfo_args=(R.Tensor((batch_size, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape906) + gv1612: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc954: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1612, R.dtype("float16")) + _952: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), reshape907, alloc954) + R.vm.kill_object(reshape907) + gv1613: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape908: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc954, gv1613, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc954) + gv1614: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape909: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape908, gv1614, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape908) + model_decoder_layers_19_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[959] + model_decoder_layers_19_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[960] + gv1615: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc955: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1615, R.dtype("float16")) + _953: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_19_encoder_attn_out_proj_weight3, reshape909, model_decoder_layers_19_encoder_attn_out_proj_bias3, alloc955) + R.vm.kill_object(reshape909) + R.vm.kill_object(model_decoder_layers_19_encoder_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_19_encoder_attn_out_proj_bias3) + gv1616: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc956: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1616, R.dtype("float16")) + cls.add(alloc951, alloc955, alloc956) + R.vm.kill_object(alloc951) + R.vm.kill_object(alloc955) + model_decoder_layers_19_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[967] + model_decoder_layers_19_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[968] + gv1617: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc957: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1617, R.dtype("float16")) + cls.layer_norm(alloc956, model_decoder_layers_19_final_layer_norm_weight3, model_decoder_layers_19_final_layer_norm_bias3, alloc957) + R.vm.kill_object(model_decoder_layers_19_final_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_19_final_layer_norm_bias3) + model_decoder_layers_19_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[963] + model_decoder_layers_19_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[964] + gv1618: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc958: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1618, R.dtype("float16")) + _956: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", model_decoder_layers_19_fc1_weight3, alloc957, model_decoder_layers_19_fc1_bias3, alloc958) + R.vm.kill_object(alloc957) + R.vm.kill_object(model_decoder_layers_19_fc1_weight3) + R.vm.kill_object(model_decoder_layers_19_fc1_bias3) + model_decoder_layers_19_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[965] + model_decoder_layers_19_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[966] + gv1619: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc959: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1619, R.dtype("float16")) + _957: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", model_decoder_layers_19_fc2_weight3, alloc958, model_decoder_layers_19_fc2_bias3, alloc959) + R.vm.kill_object(alloc958) + R.vm.kill_object(model_decoder_layers_19_fc2_weight3) + R.vm.kill_object(model_decoder_layers_19_fc2_bias3) + gv1620: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc960: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1620, R.dtype("float16")) + cls.add(alloc956, alloc959, alloc960) + R.vm.kill_object(alloc956) + R.vm.kill_object(alloc959) + model_decoder_layers_20_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[976] + model_decoder_layers_20_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[977] + gv1621: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc961: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1621, R.dtype("float16")) + cls.layer_norm(alloc960, model_decoder_layers_20_self_attn_layer_norm_weight3, model_decoder_layers_20_self_attn_layer_norm_bias3, alloc961) + R.vm.kill_object(model_decoder_layers_20_self_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_20_self_attn_layer_norm_bias3) + model_decoder_layers_20_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[972] + model_decoder_layers_20_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[973] + gv1622: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc962: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1622, R.dtype("float16")) + _960: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_20_self_attn_q_proj_weight3, alloc961, model_decoder_layers_20_self_attn_q_proj_bias3, alloc962) + R.vm.kill_object(model_decoder_layers_20_self_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_20_self_attn_q_proj_bias3) + gv1623: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape910: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc962, gv1623, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc962) + model_decoder_layers_20_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[969] + gv1624: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc963: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1624, R.dtype("float16")) + _961: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul3_cublas", model_decoder_layers_20_self_attn_k_proj_weight3, alloc961, alloc963) + R.vm.kill_object(model_decoder_layers_20_self_attn_k_proj_weight3) + gv1625: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape911: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc963, gv1625, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc963) + model_decoder_layers_20_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[970] + model_decoder_layers_20_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[971] + gv1626: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc964: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1626, R.dtype("float16")) + _962: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_20_self_attn_v_proj_weight3, alloc961, model_decoder_layers_20_self_attn_v_proj_bias3, alloc964) + R.vm.kill_object(alloc961) + R.vm.kill_object(model_decoder_layers_20_self_attn_v_proj_weight3) + R.vm.kill_object(model_decoder_layers_20_self_attn_v_proj_bias3) + gv1627: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape912: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc964, gv1627, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc964) + gv1628: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc965: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1628, R.dtype("float16")) + cls.concatenate(reshape910, reshape911, reshape912, alloc965) + R.vm.kill_object(reshape910) + R.vm.kill_object(reshape911) + R.vm.kill_object(reshape912) + gv1629: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape913: R.Tensor((batch_size, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc965, gv1629, sinfo_args=(R.Tensor((batch_size, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc965) + gv1630: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc966: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1630, R.dtype("float16")) + _964: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), reshape913, alloc966) + R.vm.kill_object(reshape913) + gv1631: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape914: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc966, gv1631, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc966) + gv1632: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape915: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape914, gv1632, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape914) + model_decoder_layers_20_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[974] + model_decoder_layers_20_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[975] + gv1633: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc967: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1633, R.dtype("float16")) + _965: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_20_self_attn_out_proj_weight3, reshape915, model_decoder_layers_20_self_attn_out_proj_bias3, alloc967) + R.vm.kill_object(reshape915) + R.vm.kill_object(model_decoder_layers_20_self_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_20_self_attn_out_proj_bias3) + gv1634: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc968: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1634, R.dtype("float16")) + cls.add(alloc960, alloc967, alloc968) + R.vm.kill_object(alloc960) + R.vm.kill_object(alloc967) + model_decoder_layers_20_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[985] + model_decoder_layers_20_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[986] + gv1635: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc969: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1635, R.dtype("float16")) + cls.layer_norm(alloc968, model_decoder_layers_20_encoder_attn_layer_norm_weight3, model_decoder_layers_20_encoder_attn_layer_norm_bias3, alloc969) + R.vm.kill_object(model_decoder_layers_20_encoder_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_20_encoder_attn_layer_norm_bias3) + model_decoder_layers_20_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[981] + model_decoder_layers_20_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[982] + gv1636: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc970: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1636, R.dtype("float16")) + _968: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_20_encoder_attn_q_proj_weight3, alloc969, model_decoder_layers_20_encoder_attn_q_proj_bias3, alloc970) + R.vm.kill_object(alloc969) + R.vm.kill_object(model_decoder_layers_20_encoder_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_20_encoder_attn_q_proj_bias3) + gv1637: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape916: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc970, gv1637, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc970) + gv1638: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape917: R.Tensor((batch_size, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape916, gv1638, sinfo_args=(R.Tensor((batch_size, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape916) + gv1639: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc971: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1639, R.dtype("float16")) + _969: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), reshape917, alloc971) + R.vm.kill_object(reshape917) + gv1640: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape918: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc971, gv1640, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc971) + gv1641: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape919: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape918, gv1641, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape918) + model_decoder_layers_20_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[983] + model_decoder_layers_20_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[984] + gv1642: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc972: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1642, R.dtype("float16")) + _970: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_20_encoder_attn_out_proj_weight3, reshape919, model_decoder_layers_20_encoder_attn_out_proj_bias3, alloc972) + R.vm.kill_object(reshape919) + R.vm.kill_object(model_decoder_layers_20_encoder_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_20_encoder_attn_out_proj_bias3) + gv1643: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc973: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1643, R.dtype("float16")) + cls.add(alloc968, alloc972, alloc973) + R.vm.kill_object(alloc968) + R.vm.kill_object(alloc972) + model_decoder_layers_20_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[991] + model_decoder_layers_20_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[992] + gv1644: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc974: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1644, R.dtype("float16")) + cls.layer_norm(alloc973, model_decoder_layers_20_final_layer_norm_weight3, model_decoder_layers_20_final_layer_norm_bias3, alloc974) + R.vm.kill_object(model_decoder_layers_20_final_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_20_final_layer_norm_bias3) + model_decoder_layers_20_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[987] + model_decoder_layers_20_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[988] + gv1645: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc975: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1645, R.dtype("float16")) + _973: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", model_decoder_layers_20_fc1_weight3, alloc974, model_decoder_layers_20_fc1_bias3, alloc975) + R.vm.kill_object(alloc974) + R.vm.kill_object(model_decoder_layers_20_fc1_weight3) + R.vm.kill_object(model_decoder_layers_20_fc1_bias3) + model_decoder_layers_20_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[989] + model_decoder_layers_20_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[990] + gv1646: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc976: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1646, R.dtype("float16")) + _974: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", model_decoder_layers_20_fc2_weight3, alloc975, model_decoder_layers_20_fc2_bias3, alloc976) + R.vm.kill_object(alloc975) + R.vm.kill_object(model_decoder_layers_20_fc2_weight3) + R.vm.kill_object(model_decoder_layers_20_fc2_bias3) + gv1647: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc977: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1647, R.dtype("float16")) + cls.add(alloc973, alloc976, alloc977) + R.vm.kill_object(alloc973) + R.vm.kill_object(alloc976) + model_decoder_layers_21_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1000] + model_decoder_layers_21_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1001] + gv1648: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc978: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1648, R.dtype("float16")) + cls.layer_norm(alloc977, model_decoder_layers_21_self_attn_layer_norm_weight3, model_decoder_layers_21_self_attn_layer_norm_bias3, alloc978) + R.vm.kill_object(model_decoder_layers_21_self_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_21_self_attn_layer_norm_bias3) + model_decoder_layers_21_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[996] + model_decoder_layers_21_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[997] + gv1649: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc979: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1649, R.dtype("float16")) + _977: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_21_self_attn_q_proj_weight3, alloc978, model_decoder_layers_21_self_attn_q_proj_bias3, alloc979) + R.vm.kill_object(model_decoder_layers_21_self_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_21_self_attn_q_proj_bias3) + gv1650: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape920: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc979, gv1650, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc979) + model_decoder_layers_21_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[993] + gv1651: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc980: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1651, R.dtype("float16")) + _978: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul3_cublas", model_decoder_layers_21_self_attn_k_proj_weight3, alloc978, alloc980) + R.vm.kill_object(model_decoder_layers_21_self_attn_k_proj_weight3) + gv1652: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape921: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc980, gv1652, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc980) + model_decoder_layers_21_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[994] + model_decoder_layers_21_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[995] + gv1653: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc981: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1653, R.dtype("float16")) + _979: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_21_self_attn_v_proj_weight3, alloc978, model_decoder_layers_21_self_attn_v_proj_bias3, alloc981) + R.vm.kill_object(alloc978) + R.vm.kill_object(model_decoder_layers_21_self_attn_v_proj_weight3) + R.vm.kill_object(model_decoder_layers_21_self_attn_v_proj_bias3) + gv1654: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape922: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc981, gv1654, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc981) + gv1655: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc982: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1655, R.dtype("float16")) + cls.concatenate(reshape920, reshape921, reshape922, alloc982) + R.vm.kill_object(reshape920) + R.vm.kill_object(reshape921) + R.vm.kill_object(reshape922) + gv1656: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape923: R.Tensor((batch_size, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc982, gv1656, sinfo_args=(R.Tensor((batch_size, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc982) + gv1657: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc983: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1657, R.dtype("float16")) + _981: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), reshape923, alloc983) + R.vm.kill_object(reshape923) + gv1658: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape924: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc983, gv1658, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc983) + gv1659: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape925: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape924, gv1659, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape924) + model_decoder_layers_21_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[998] + model_decoder_layers_21_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[999] + gv1660: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc984: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1660, R.dtype("float16")) + _982: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_21_self_attn_out_proj_weight3, reshape925, model_decoder_layers_21_self_attn_out_proj_bias3, alloc984) + R.vm.kill_object(reshape925) + R.vm.kill_object(model_decoder_layers_21_self_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_21_self_attn_out_proj_bias3) + gv1661: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc985: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1661, R.dtype("float16")) + cls.add(alloc977, alloc984, alloc985) + R.vm.kill_object(alloc977) + R.vm.kill_object(alloc984) + model_decoder_layers_21_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1009] + model_decoder_layers_21_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1010] + gv1662: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc986: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1662, R.dtype("float16")) + cls.layer_norm(alloc985, model_decoder_layers_21_encoder_attn_layer_norm_weight3, model_decoder_layers_21_encoder_attn_layer_norm_bias3, alloc986) + R.vm.kill_object(model_decoder_layers_21_encoder_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_21_encoder_attn_layer_norm_bias3) + model_decoder_layers_21_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1005] + model_decoder_layers_21_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1006] + gv1663: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc987: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1663, R.dtype("float16")) + _985: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_21_encoder_attn_q_proj_weight3, alloc986, model_decoder_layers_21_encoder_attn_q_proj_bias3, alloc987) + R.vm.kill_object(alloc986) + R.vm.kill_object(model_decoder_layers_21_encoder_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_21_encoder_attn_q_proj_bias3) + gv1664: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape926: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc987, gv1664, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc987) + gv1665: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape927: R.Tensor((batch_size, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape926, gv1665, sinfo_args=(R.Tensor((batch_size, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape926) + gv1666: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc988: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1666, R.dtype("float16")) + _986: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), reshape927, alloc988) + R.vm.kill_object(reshape927) + gv1667: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape928: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc988, gv1667, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc988) + gv1668: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape929: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape928, gv1668, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape928) + model_decoder_layers_21_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1007] + model_decoder_layers_21_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1008] + gv1669: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc989: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1669, R.dtype("float16")) + _987: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_21_encoder_attn_out_proj_weight3, reshape929, model_decoder_layers_21_encoder_attn_out_proj_bias3, alloc989) + R.vm.kill_object(reshape929) + R.vm.kill_object(model_decoder_layers_21_encoder_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_21_encoder_attn_out_proj_bias3) + gv1670: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc990: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1670, R.dtype("float16")) + cls.add(alloc985, alloc989, alloc990) + R.vm.kill_object(alloc985) + R.vm.kill_object(alloc989) + model_decoder_layers_21_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1015] + model_decoder_layers_21_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1016] + gv1671: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc991: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1671, R.dtype("float16")) + cls.layer_norm(alloc990, model_decoder_layers_21_final_layer_norm_weight3, model_decoder_layers_21_final_layer_norm_bias3, alloc991) + R.vm.kill_object(model_decoder_layers_21_final_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_21_final_layer_norm_bias3) + model_decoder_layers_21_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1011] + model_decoder_layers_21_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1012] + gv1672: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc992: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1672, R.dtype("float16")) + _990: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", model_decoder_layers_21_fc1_weight3, alloc991, model_decoder_layers_21_fc1_bias3, alloc992) + R.vm.kill_object(alloc991) + R.vm.kill_object(model_decoder_layers_21_fc1_weight3) + R.vm.kill_object(model_decoder_layers_21_fc1_bias3) + model_decoder_layers_21_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1013] + model_decoder_layers_21_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1014] + gv1673: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc993: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1673, R.dtype("float16")) + _991: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", model_decoder_layers_21_fc2_weight3, alloc992, model_decoder_layers_21_fc2_bias3, alloc993) + R.vm.kill_object(alloc992) + R.vm.kill_object(model_decoder_layers_21_fc2_weight3) + R.vm.kill_object(model_decoder_layers_21_fc2_bias3) + gv1674: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc994: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1674, R.dtype("float16")) + cls.add(alloc990, alloc993, alloc994) + R.vm.kill_object(alloc990) + R.vm.kill_object(alloc993) + model_decoder_layers_22_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1024] + model_decoder_layers_22_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1025] + gv1675: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc995: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1675, R.dtype("float16")) + cls.layer_norm(alloc994, model_decoder_layers_22_self_attn_layer_norm_weight3, model_decoder_layers_22_self_attn_layer_norm_bias3, alloc995) + R.vm.kill_object(model_decoder_layers_22_self_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_22_self_attn_layer_norm_bias3) + model_decoder_layers_22_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1020] + model_decoder_layers_22_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1021] + gv1676: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc996: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1676, R.dtype("float16")) + _994: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_22_self_attn_q_proj_weight3, alloc995, model_decoder_layers_22_self_attn_q_proj_bias3, alloc996) + R.vm.kill_object(model_decoder_layers_22_self_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_22_self_attn_q_proj_bias3) + gv1677: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape930: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc996, gv1677, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc996) + model_decoder_layers_22_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1017] + gv1678: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc997: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1678, R.dtype("float16")) + _995: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul3_cublas", model_decoder_layers_22_self_attn_k_proj_weight3, alloc995, alloc997) + R.vm.kill_object(model_decoder_layers_22_self_attn_k_proj_weight3) + gv1679: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape931: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc997, gv1679, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc997) + model_decoder_layers_22_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1018] + model_decoder_layers_22_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1019] + gv1680: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc998: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1680, R.dtype("float16")) + _996: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_22_self_attn_v_proj_weight3, alloc995, model_decoder_layers_22_self_attn_v_proj_bias3, alloc998) + R.vm.kill_object(alloc995) + R.vm.kill_object(model_decoder_layers_22_self_attn_v_proj_weight3) + R.vm.kill_object(model_decoder_layers_22_self_attn_v_proj_bias3) + gv1681: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape932: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc998, gv1681, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc998) + gv1682: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc999: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1682, R.dtype("float16")) + cls.concatenate(reshape930, reshape931, reshape932, alloc999) + R.vm.kill_object(reshape930) + R.vm.kill_object(reshape931) + R.vm.kill_object(reshape932) + gv1683: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape933: R.Tensor((batch_size, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc999, gv1683, sinfo_args=(R.Tensor((batch_size, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc999) + gv1684: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc1000: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1684, R.dtype("float16")) + _998: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), reshape933, alloc1000) + R.vm.kill_object(reshape933) + gv1685: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape934: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1000, gv1685, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1000) + gv1686: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape935: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape934, gv1686, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape934) + model_decoder_layers_22_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1022] + model_decoder_layers_22_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1023] + gv1687: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1001: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1687, R.dtype("float16")) + _999: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_22_self_attn_out_proj_weight3, reshape935, model_decoder_layers_22_self_attn_out_proj_bias3, alloc1001) + R.vm.kill_object(reshape935) + R.vm.kill_object(model_decoder_layers_22_self_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_22_self_attn_out_proj_bias3) + gv1688: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1002: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1688, R.dtype("float16")) + cls.add(alloc994, alloc1001, alloc1002) + R.vm.kill_object(alloc994) + R.vm.kill_object(alloc1001) + model_decoder_layers_22_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1033] + model_decoder_layers_22_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1034] + gv1689: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1003: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1689, R.dtype("float16")) + cls.layer_norm(alloc1002, model_decoder_layers_22_encoder_attn_layer_norm_weight3, model_decoder_layers_22_encoder_attn_layer_norm_bias3, alloc1003) + R.vm.kill_object(model_decoder_layers_22_encoder_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_22_encoder_attn_layer_norm_bias3) + model_decoder_layers_22_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1029] + model_decoder_layers_22_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1030] + gv1690: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1004: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1690, R.dtype("float16")) + _1002: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_22_encoder_attn_q_proj_weight3, alloc1003, model_decoder_layers_22_encoder_attn_q_proj_bias3, alloc1004) + R.vm.kill_object(alloc1003) + R.vm.kill_object(model_decoder_layers_22_encoder_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_22_encoder_attn_q_proj_bias3) + gv1691: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape936: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1004, gv1691, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1004) + gv1692: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape937: R.Tensor((batch_size, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape936, gv1692, sinfo_args=(R.Tensor((batch_size, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape936) + gv1693: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc1005: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1693, R.dtype("float16")) + _1003: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), reshape937, alloc1005) + R.vm.kill_object(reshape937) + gv1694: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape938: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1005, gv1694, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1005) + gv1695: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape939: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape938, gv1695, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape938) + model_decoder_layers_22_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1031] + model_decoder_layers_22_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1032] + gv1696: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1006: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1696, R.dtype("float16")) + _1004: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_22_encoder_attn_out_proj_weight3, reshape939, model_decoder_layers_22_encoder_attn_out_proj_bias3, alloc1006) + R.vm.kill_object(reshape939) + R.vm.kill_object(model_decoder_layers_22_encoder_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_22_encoder_attn_out_proj_bias3) + gv1697: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1007: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1697, R.dtype("float16")) + cls.add(alloc1002, alloc1006, alloc1007) + R.vm.kill_object(alloc1002) + R.vm.kill_object(alloc1006) + model_decoder_layers_22_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1039] + model_decoder_layers_22_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1040] + gv1698: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1008: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1698, R.dtype("float16")) + cls.layer_norm(alloc1007, model_decoder_layers_22_final_layer_norm_weight3, model_decoder_layers_22_final_layer_norm_bias3, alloc1008) + R.vm.kill_object(model_decoder_layers_22_final_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_22_final_layer_norm_bias3) + model_decoder_layers_22_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1035] + model_decoder_layers_22_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1036] + gv1699: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc1009: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1699, R.dtype("float16")) + _1007: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", model_decoder_layers_22_fc1_weight3, alloc1008, model_decoder_layers_22_fc1_bias3, alloc1009) + R.vm.kill_object(alloc1008) + R.vm.kill_object(model_decoder_layers_22_fc1_weight3) + R.vm.kill_object(model_decoder_layers_22_fc1_bias3) + model_decoder_layers_22_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1037] + model_decoder_layers_22_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1038] + gv1700: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1010: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1700, R.dtype("float16")) + _1008: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", model_decoder_layers_22_fc2_weight3, alloc1009, model_decoder_layers_22_fc2_bias3, alloc1010) + R.vm.kill_object(alloc1009) + R.vm.kill_object(model_decoder_layers_22_fc2_weight3) + R.vm.kill_object(model_decoder_layers_22_fc2_bias3) + gv1701: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1011: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1701, R.dtype("float16")) + cls.add(alloc1007, alloc1010, alloc1011) + R.vm.kill_object(alloc1007) + R.vm.kill_object(alloc1010) + model_decoder_layers_23_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1048] + model_decoder_layers_23_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1049] + gv1702: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1012: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1702, R.dtype("float16")) + cls.layer_norm(alloc1011, model_decoder_layers_23_self_attn_layer_norm_weight3, model_decoder_layers_23_self_attn_layer_norm_bias3, alloc1012) + R.vm.kill_object(model_decoder_layers_23_self_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_23_self_attn_layer_norm_bias3) + model_decoder_layers_23_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1044] + model_decoder_layers_23_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1045] + gv1703: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1013: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1703, R.dtype("float16")) + _1011: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_23_self_attn_q_proj_weight3, alloc1012, model_decoder_layers_23_self_attn_q_proj_bias3, alloc1013) + R.vm.kill_object(model_decoder_layers_23_self_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_23_self_attn_q_proj_bias3) + gv1704: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape940: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1013, gv1704, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1013) + model_decoder_layers_23_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1041] + gv1705: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1014: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1705, R.dtype("float16")) + _1012: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul3_cublas", model_decoder_layers_23_self_attn_k_proj_weight3, alloc1012, alloc1014) + R.vm.kill_object(model_decoder_layers_23_self_attn_k_proj_weight3) + gv1706: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape941: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1014, gv1706, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1014) + model_decoder_layers_23_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1042] + model_decoder_layers_23_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1043] + gv1707: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1015: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1707, R.dtype("float16")) + _1013: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_23_self_attn_v_proj_weight3, alloc1012, model_decoder_layers_23_self_attn_v_proj_bias3, alloc1015) + R.vm.kill_object(alloc1012) + R.vm.kill_object(model_decoder_layers_23_self_attn_v_proj_weight3) + R.vm.kill_object(model_decoder_layers_23_self_attn_v_proj_bias3) + gv1708: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape942: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1015, gv1708, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1015) + gv1709: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc1016: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1709, R.dtype("float16")) + cls.concatenate(reshape940, reshape941, reshape942, alloc1016) + R.vm.kill_object(reshape940) + R.vm.kill_object(reshape941) + R.vm.kill_object(reshape942) + gv1710: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape943: R.Tensor((batch_size, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1016, gv1710, sinfo_args=(R.Tensor((batch_size, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc1016) + gv1711: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc1017: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1711, R.dtype("float16")) + _1015: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), reshape943, alloc1017) + R.vm.kill_object(reshape943) + gv1712: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape944: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1017, gv1712, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1017) + gv1713: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape945: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape944, gv1713, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape944) + model_decoder_layers_23_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1046] + model_decoder_layers_23_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1047] + gv1714: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1018: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1714, R.dtype("float16")) + _1016: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_23_self_attn_out_proj_weight3, reshape945, model_decoder_layers_23_self_attn_out_proj_bias3, alloc1018) + R.vm.kill_object(reshape945) + R.vm.kill_object(model_decoder_layers_23_self_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_23_self_attn_out_proj_bias3) + gv1715: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1019: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1715, R.dtype("float16")) + cls.add(alloc1011, alloc1018, alloc1019) + R.vm.kill_object(alloc1011) + R.vm.kill_object(alloc1018) + model_decoder_layers_23_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1057] + model_decoder_layers_23_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1058] + gv1716: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1020: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1716, R.dtype("float16")) + cls.layer_norm(alloc1019, model_decoder_layers_23_encoder_attn_layer_norm_weight3, model_decoder_layers_23_encoder_attn_layer_norm_bias3, alloc1020) + R.vm.kill_object(model_decoder_layers_23_encoder_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_23_encoder_attn_layer_norm_bias3) + model_decoder_layers_23_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1053] + model_decoder_layers_23_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1054] + gv1717: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1021: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1717, R.dtype("float16")) + _1019: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_23_encoder_attn_q_proj_weight3, alloc1020, model_decoder_layers_23_encoder_attn_q_proj_bias3, alloc1021) + R.vm.kill_object(alloc1020) + R.vm.kill_object(model_decoder_layers_23_encoder_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_23_encoder_attn_q_proj_bias3) + gv1718: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape946: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1021, gv1718, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1021) + gv1719: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape947: R.Tensor((batch_size, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape946, gv1719, sinfo_args=(R.Tensor((batch_size, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape946) + gv1720: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc1022: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1720, R.dtype("float16")) + _1020: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), reshape947, alloc1022) + R.vm.kill_object(reshape947) + gv1721: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape948: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1022, gv1721, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1022) + gv1722: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape949: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape948, gv1722, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape948) + model_decoder_layers_23_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1055] + model_decoder_layers_23_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1056] + gv1723: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1023: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1723, R.dtype("float16")) + _1021: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_23_encoder_attn_out_proj_weight3, reshape949, model_decoder_layers_23_encoder_attn_out_proj_bias3, alloc1023) + R.vm.kill_object(reshape949) + R.vm.kill_object(model_decoder_layers_23_encoder_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_23_encoder_attn_out_proj_bias3) + gv1724: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1024: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1724, R.dtype("float16")) + cls.add(alloc1019, alloc1023, alloc1024) + R.vm.kill_object(alloc1019) + R.vm.kill_object(alloc1023) + model_decoder_layers_23_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1063] + model_decoder_layers_23_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1064] + gv1725: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1025: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1725, R.dtype("float16")) + cls.layer_norm(alloc1024, model_decoder_layers_23_final_layer_norm_weight3, model_decoder_layers_23_final_layer_norm_bias3, alloc1025) + R.vm.kill_object(model_decoder_layers_23_final_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_23_final_layer_norm_bias3) + model_decoder_layers_23_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1059] + model_decoder_layers_23_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1060] + gv1726: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc1026: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1726, R.dtype("float16")) + _1024: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", model_decoder_layers_23_fc1_weight3, alloc1025, model_decoder_layers_23_fc1_bias3, alloc1026) + R.vm.kill_object(alloc1025) + R.vm.kill_object(model_decoder_layers_23_fc1_weight3) + R.vm.kill_object(model_decoder_layers_23_fc1_bias3) + model_decoder_layers_23_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1061] + model_decoder_layers_23_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1062] + gv1727: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1027: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1727, R.dtype("float16")) + _1025: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", model_decoder_layers_23_fc2_weight3, alloc1026, model_decoder_layers_23_fc2_bias3, alloc1027) + R.vm.kill_object(alloc1026) + R.vm.kill_object(model_decoder_layers_23_fc2_weight3) + R.vm.kill_object(model_decoder_layers_23_fc2_bias3) + gv1728: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1028: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1728, R.dtype("float16")) + cls.add(alloc1024, alloc1027, alloc1028) + R.vm.kill_object(alloc1024) + R.vm.kill_object(alloc1027) + model_decoder_layers_24_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1072] + model_decoder_layers_24_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1073] + gv1729: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1029: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1729, R.dtype("float16")) + cls.layer_norm(alloc1028, model_decoder_layers_24_self_attn_layer_norm_weight3, model_decoder_layers_24_self_attn_layer_norm_bias3, alloc1029) + R.vm.kill_object(model_decoder_layers_24_self_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_24_self_attn_layer_norm_bias3) + model_decoder_layers_24_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1068] + model_decoder_layers_24_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1069] + gv1730: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1030: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1730, R.dtype("float16")) + _1028: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_24_self_attn_q_proj_weight3, alloc1029, model_decoder_layers_24_self_attn_q_proj_bias3, alloc1030) + R.vm.kill_object(model_decoder_layers_24_self_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_24_self_attn_q_proj_bias3) + gv1731: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape950: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1030, gv1731, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1030) + model_decoder_layers_24_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1065] + gv1732: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1031: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1732, R.dtype("float16")) + _1029: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul3_cublas", model_decoder_layers_24_self_attn_k_proj_weight3, alloc1029, alloc1031) + R.vm.kill_object(model_decoder_layers_24_self_attn_k_proj_weight3) + gv1733: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape951: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1031, gv1733, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1031) + model_decoder_layers_24_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1066] + model_decoder_layers_24_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1067] + gv1734: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1032: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1734, R.dtype("float16")) + _1030: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_24_self_attn_v_proj_weight3, alloc1029, model_decoder_layers_24_self_attn_v_proj_bias3, alloc1032) + R.vm.kill_object(alloc1029) + R.vm.kill_object(model_decoder_layers_24_self_attn_v_proj_weight3) + R.vm.kill_object(model_decoder_layers_24_self_attn_v_proj_bias3) + gv1735: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape952: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1032, gv1735, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1032) + gv1736: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc1033: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1736, R.dtype("float16")) + cls.concatenate(reshape950, reshape951, reshape952, alloc1033) + R.vm.kill_object(reshape950) + R.vm.kill_object(reshape951) + R.vm.kill_object(reshape952) + gv1737: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape953: R.Tensor((batch_size, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1033, gv1737, sinfo_args=(R.Tensor((batch_size, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc1033) + gv1738: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc1034: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1738, R.dtype("float16")) + _1032: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), reshape953, alloc1034) + R.vm.kill_object(reshape953) + gv1739: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape954: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1034, gv1739, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1034) + gv1740: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape955: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape954, gv1740, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape954) + model_decoder_layers_24_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1070] + model_decoder_layers_24_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1071] + gv1741: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1035: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1741, R.dtype("float16")) + _1033: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_24_self_attn_out_proj_weight3, reshape955, model_decoder_layers_24_self_attn_out_proj_bias3, alloc1035) + R.vm.kill_object(reshape955) + R.vm.kill_object(model_decoder_layers_24_self_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_24_self_attn_out_proj_bias3) + gv1742: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1036: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1742, R.dtype("float16")) + cls.add(alloc1028, alloc1035, alloc1036) + R.vm.kill_object(alloc1028) + R.vm.kill_object(alloc1035) + model_decoder_layers_24_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1081] + model_decoder_layers_24_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1082] + gv1743: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1037: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1743, R.dtype("float16")) + cls.layer_norm(alloc1036, model_decoder_layers_24_encoder_attn_layer_norm_weight3, model_decoder_layers_24_encoder_attn_layer_norm_bias3, alloc1037) + R.vm.kill_object(model_decoder_layers_24_encoder_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_24_encoder_attn_layer_norm_bias3) + model_decoder_layers_24_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1077] + model_decoder_layers_24_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1078] + gv1744: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1038: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1744, R.dtype("float16")) + _1036: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_24_encoder_attn_q_proj_weight3, alloc1037, model_decoder_layers_24_encoder_attn_q_proj_bias3, alloc1038) + R.vm.kill_object(alloc1037) + R.vm.kill_object(model_decoder_layers_24_encoder_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_24_encoder_attn_q_proj_bias3) + gv1745: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape956: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1038, gv1745, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1038) + gv1746: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape957: R.Tensor((batch_size, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape956, gv1746, sinfo_args=(R.Tensor((batch_size, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape956) + gv1747: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc1039: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1747, R.dtype("float16")) + _1037: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), reshape957, alloc1039) + R.vm.kill_object(reshape957) + gv1748: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape958: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1039, gv1748, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1039) + gv1749: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape959: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape958, gv1749, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape958) + model_decoder_layers_24_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1079] + model_decoder_layers_24_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1080] + gv1750: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1040: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1750, R.dtype("float16")) + _1038: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_24_encoder_attn_out_proj_weight3, reshape959, model_decoder_layers_24_encoder_attn_out_proj_bias3, alloc1040) + R.vm.kill_object(reshape959) + R.vm.kill_object(model_decoder_layers_24_encoder_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_24_encoder_attn_out_proj_bias3) + gv1751: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1041: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1751, R.dtype("float16")) + cls.add(alloc1036, alloc1040, alloc1041) + R.vm.kill_object(alloc1036) + R.vm.kill_object(alloc1040) + model_decoder_layers_24_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1087] + model_decoder_layers_24_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1088] + gv1752: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1042: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1752, R.dtype("float16")) + cls.layer_norm(alloc1041, model_decoder_layers_24_final_layer_norm_weight3, model_decoder_layers_24_final_layer_norm_bias3, alloc1042) + R.vm.kill_object(model_decoder_layers_24_final_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_24_final_layer_norm_bias3) + model_decoder_layers_24_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1083] + model_decoder_layers_24_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1084] + gv1753: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc1043: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1753, R.dtype("float16")) + _1041: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", model_decoder_layers_24_fc1_weight3, alloc1042, model_decoder_layers_24_fc1_bias3, alloc1043) + R.vm.kill_object(alloc1042) + R.vm.kill_object(model_decoder_layers_24_fc1_weight3) + R.vm.kill_object(model_decoder_layers_24_fc1_bias3) + model_decoder_layers_24_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1085] + model_decoder_layers_24_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1086] + gv1754: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1044: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1754, R.dtype("float16")) + _1042: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", model_decoder_layers_24_fc2_weight3, alloc1043, model_decoder_layers_24_fc2_bias3, alloc1044) + R.vm.kill_object(alloc1043) + R.vm.kill_object(model_decoder_layers_24_fc2_weight3) + R.vm.kill_object(model_decoder_layers_24_fc2_bias3) + gv1755: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1045: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1755, R.dtype("float16")) + cls.add(alloc1041, alloc1044, alloc1045) + R.vm.kill_object(alloc1041) + R.vm.kill_object(alloc1044) + model_decoder_layers_25_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1096] + model_decoder_layers_25_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1097] + gv1756: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1046: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1756, R.dtype("float16")) + cls.layer_norm(alloc1045, model_decoder_layers_25_self_attn_layer_norm_weight3, model_decoder_layers_25_self_attn_layer_norm_bias3, alloc1046) + R.vm.kill_object(model_decoder_layers_25_self_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_25_self_attn_layer_norm_bias3) + model_decoder_layers_25_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1092] + model_decoder_layers_25_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1093] + gv1757: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1047: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1757, R.dtype("float16")) + _1045: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_25_self_attn_q_proj_weight3, alloc1046, model_decoder_layers_25_self_attn_q_proj_bias3, alloc1047) + R.vm.kill_object(model_decoder_layers_25_self_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_25_self_attn_q_proj_bias3) + gv1758: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape960: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1047, gv1758, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1047) + model_decoder_layers_25_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1089] + gv1759: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1048: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1759, R.dtype("float16")) + _1046: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul3_cublas", model_decoder_layers_25_self_attn_k_proj_weight3, alloc1046, alloc1048) + R.vm.kill_object(model_decoder_layers_25_self_attn_k_proj_weight3) + gv1760: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape961: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1048, gv1760, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1048) + model_decoder_layers_25_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1090] + model_decoder_layers_25_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1091] + gv1761: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1049: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1761, R.dtype("float16")) + _1047: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_25_self_attn_v_proj_weight3, alloc1046, model_decoder_layers_25_self_attn_v_proj_bias3, alloc1049) + R.vm.kill_object(alloc1046) + R.vm.kill_object(model_decoder_layers_25_self_attn_v_proj_weight3) + R.vm.kill_object(model_decoder_layers_25_self_attn_v_proj_bias3) + gv1762: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape962: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1049, gv1762, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1049) + gv1763: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc1050: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1763, R.dtype("float16")) + cls.concatenate(reshape960, reshape961, reshape962, alloc1050) + R.vm.kill_object(reshape960) + R.vm.kill_object(reshape961) + R.vm.kill_object(reshape962) + gv1764: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape963: R.Tensor((batch_size, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1050, gv1764, sinfo_args=(R.Tensor((batch_size, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc1050) + gv1765: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc1051: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1765, R.dtype("float16")) + _1049: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), reshape963, alloc1051) + R.vm.kill_object(reshape963) + gv1766: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape964: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1051, gv1766, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1051) + gv1767: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape965: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape964, gv1767, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape964) + model_decoder_layers_25_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1094] + model_decoder_layers_25_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1095] + gv1768: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1052: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1768, R.dtype("float16")) + _1050: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_25_self_attn_out_proj_weight3, reshape965, model_decoder_layers_25_self_attn_out_proj_bias3, alloc1052) + R.vm.kill_object(reshape965) + R.vm.kill_object(model_decoder_layers_25_self_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_25_self_attn_out_proj_bias3) + gv1769: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1053: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1769, R.dtype("float16")) + cls.add(alloc1045, alloc1052, alloc1053) + R.vm.kill_object(alloc1045) + R.vm.kill_object(alloc1052) + model_decoder_layers_25_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1105] + model_decoder_layers_25_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1106] + gv1770: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1054: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1770, R.dtype("float16")) + cls.layer_norm(alloc1053, model_decoder_layers_25_encoder_attn_layer_norm_weight3, model_decoder_layers_25_encoder_attn_layer_norm_bias3, alloc1054) + R.vm.kill_object(model_decoder_layers_25_encoder_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_25_encoder_attn_layer_norm_bias3) + model_decoder_layers_25_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1101] + model_decoder_layers_25_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1102] + gv1771: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1055: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1771, R.dtype("float16")) + _1053: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_25_encoder_attn_q_proj_weight3, alloc1054, model_decoder_layers_25_encoder_attn_q_proj_bias3, alloc1055) + R.vm.kill_object(alloc1054) + R.vm.kill_object(model_decoder_layers_25_encoder_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_25_encoder_attn_q_proj_bias3) + gv1772: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape966: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1055, gv1772, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1055) + gv1773: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape967: R.Tensor((batch_size, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape966, gv1773, sinfo_args=(R.Tensor((batch_size, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape966) + gv1774: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc1056: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1774, R.dtype("float16")) + _1054: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), reshape967, alloc1056) + R.vm.kill_object(reshape967) + gv1775: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape968: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1056, gv1775, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1056) + gv1776: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape969: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape968, gv1776, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape968) + model_decoder_layers_25_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1103] + model_decoder_layers_25_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1104] + gv1777: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1057: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1777, R.dtype("float16")) + _1055: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_25_encoder_attn_out_proj_weight3, reshape969, model_decoder_layers_25_encoder_attn_out_proj_bias3, alloc1057) + R.vm.kill_object(reshape969) + R.vm.kill_object(model_decoder_layers_25_encoder_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_25_encoder_attn_out_proj_bias3) + gv1778: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1058: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1778, R.dtype("float16")) + cls.add(alloc1053, alloc1057, alloc1058) + R.vm.kill_object(alloc1053) + R.vm.kill_object(alloc1057) + model_decoder_layers_25_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1111] + model_decoder_layers_25_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1112] + gv1779: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1059: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1779, R.dtype("float16")) + cls.layer_norm(alloc1058, model_decoder_layers_25_final_layer_norm_weight3, model_decoder_layers_25_final_layer_norm_bias3, alloc1059) + R.vm.kill_object(model_decoder_layers_25_final_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_25_final_layer_norm_bias3) + model_decoder_layers_25_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1107] + model_decoder_layers_25_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1108] + gv1780: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc1060: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1780, R.dtype("float16")) + _1058: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", model_decoder_layers_25_fc1_weight3, alloc1059, model_decoder_layers_25_fc1_bias3, alloc1060) + R.vm.kill_object(alloc1059) + R.vm.kill_object(model_decoder_layers_25_fc1_weight3) + R.vm.kill_object(model_decoder_layers_25_fc1_bias3) + model_decoder_layers_25_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1109] + model_decoder_layers_25_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1110] + gv1781: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1061: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1781, R.dtype("float16")) + _1059: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", model_decoder_layers_25_fc2_weight3, alloc1060, model_decoder_layers_25_fc2_bias3, alloc1061) + R.vm.kill_object(alloc1060) + R.vm.kill_object(model_decoder_layers_25_fc2_weight3) + R.vm.kill_object(model_decoder_layers_25_fc2_bias3) + gv1782: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1062: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1782, R.dtype("float16")) + cls.add(alloc1058, alloc1061, alloc1062) + R.vm.kill_object(alloc1058) + R.vm.kill_object(alloc1061) + model_decoder_layers_26_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1120] + model_decoder_layers_26_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1121] + gv1783: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1063: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1783, R.dtype("float16")) + cls.layer_norm(alloc1062, model_decoder_layers_26_self_attn_layer_norm_weight3, model_decoder_layers_26_self_attn_layer_norm_bias3, alloc1063) + R.vm.kill_object(model_decoder_layers_26_self_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_26_self_attn_layer_norm_bias3) + model_decoder_layers_26_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1116] + model_decoder_layers_26_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1117] + gv1784: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1064: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1784, R.dtype("float16")) + _1062: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_26_self_attn_q_proj_weight3, alloc1063, model_decoder_layers_26_self_attn_q_proj_bias3, alloc1064) + R.vm.kill_object(model_decoder_layers_26_self_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_26_self_attn_q_proj_bias3) + gv1785: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape970: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1064, gv1785, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1064) + model_decoder_layers_26_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1113] + gv1786: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1065: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1786, R.dtype("float16")) + _1063: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul3_cublas", model_decoder_layers_26_self_attn_k_proj_weight3, alloc1063, alloc1065) + R.vm.kill_object(model_decoder_layers_26_self_attn_k_proj_weight3) + gv1787: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape971: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1065, gv1787, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1065) + model_decoder_layers_26_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1114] + model_decoder_layers_26_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1115] + gv1788: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1066: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1788, R.dtype("float16")) + _1064: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_26_self_attn_v_proj_weight3, alloc1063, model_decoder_layers_26_self_attn_v_proj_bias3, alloc1066) + R.vm.kill_object(alloc1063) + R.vm.kill_object(model_decoder_layers_26_self_attn_v_proj_weight3) + R.vm.kill_object(model_decoder_layers_26_self_attn_v_proj_bias3) + gv1789: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape972: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1066, gv1789, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1066) + gv1790: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc1067: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1790, R.dtype("float16")) + cls.concatenate(reshape970, reshape971, reshape972, alloc1067) + R.vm.kill_object(reshape970) + R.vm.kill_object(reshape971) + R.vm.kill_object(reshape972) + gv1791: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape973: R.Tensor((batch_size, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1067, gv1791, sinfo_args=(R.Tensor((batch_size, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc1067) + gv1792: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc1068: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1792, R.dtype("float16")) + _1066: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), reshape973, alloc1068) + R.vm.kill_object(reshape973) + gv1793: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape974: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1068, gv1793, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1068) + gv1794: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape975: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape974, gv1794, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape974) + model_decoder_layers_26_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1118] + model_decoder_layers_26_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1119] + gv1795: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1069: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1795, R.dtype("float16")) + _1067: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_26_self_attn_out_proj_weight3, reshape975, model_decoder_layers_26_self_attn_out_proj_bias3, alloc1069) + R.vm.kill_object(reshape975) + R.vm.kill_object(model_decoder_layers_26_self_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_26_self_attn_out_proj_bias3) + gv1796: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1070: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1796, R.dtype("float16")) + cls.add(alloc1062, alloc1069, alloc1070) + R.vm.kill_object(alloc1062) + R.vm.kill_object(alloc1069) + model_decoder_layers_26_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1129] + model_decoder_layers_26_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1130] + gv1797: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1071: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1797, R.dtype("float16")) + cls.layer_norm(alloc1070, model_decoder_layers_26_encoder_attn_layer_norm_weight3, model_decoder_layers_26_encoder_attn_layer_norm_bias3, alloc1071) + R.vm.kill_object(model_decoder_layers_26_encoder_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_26_encoder_attn_layer_norm_bias3) + model_decoder_layers_26_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1125] + model_decoder_layers_26_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1126] + gv1798: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1072: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1798, R.dtype("float16")) + _1070: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_26_encoder_attn_q_proj_weight3, alloc1071, model_decoder_layers_26_encoder_attn_q_proj_bias3, alloc1072) + R.vm.kill_object(alloc1071) + R.vm.kill_object(model_decoder_layers_26_encoder_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_26_encoder_attn_q_proj_bias3) + gv1799: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape976: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1072, gv1799, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1072) + gv1800: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape977: R.Tensor((batch_size, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape976, gv1800, sinfo_args=(R.Tensor((batch_size, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape976) + gv1801: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc1073: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1801, R.dtype("float16")) + _1071: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), reshape977, alloc1073) + R.vm.kill_object(reshape977) + gv1802: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape978: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1073, gv1802, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1073) + gv1803: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape979: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape978, gv1803, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape978) + model_decoder_layers_26_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1127] + model_decoder_layers_26_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1128] + gv1804: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1074: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1804, R.dtype("float16")) + _1072: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_26_encoder_attn_out_proj_weight3, reshape979, model_decoder_layers_26_encoder_attn_out_proj_bias3, alloc1074) + R.vm.kill_object(reshape979) + R.vm.kill_object(model_decoder_layers_26_encoder_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_26_encoder_attn_out_proj_bias3) + gv1805: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1075: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1805, R.dtype("float16")) + cls.add(alloc1070, alloc1074, alloc1075) + R.vm.kill_object(alloc1070) + R.vm.kill_object(alloc1074) + model_decoder_layers_26_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1135] + model_decoder_layers_26_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1136] + gv1806: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1076: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1806, R.dtype("float16")) + cls.layer_norm(alloc1075, model_decoder_layers_26_final_layer_norm_weight3, model_decoder_layers_26_final_layer_norm_bias3, alloc1076) + R.vm.kill_object(model_decoder_layers_26_final_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_26_final_layer_norm_bias3) + model_decoder_layers_26_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1131] + model_decoder_layers_26_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1132] + gv1807: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc1077: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1807, R.dtype("float16")) + _1075: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", model_decoder_layers_26_fc1_weight3, alloc1076, model_decoder_layers_26_fc1_bias3, alloc1077) + R.vm.kill_object(alloc1076) + R.vm.kill_object(model_decoder_layers_26_fc1_weight3) + R.vm.kill_object(model_decoder_layers_26_fc1_bias3) + model_decoder_layers_26_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1133] + model_decoder_layers_26_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1134] + gv1808: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1078: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1808, R.dtype("float16")) + _1076: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", model_decoder_layers_26_fc2_weight3, alloc1077, model_decoder_layers_26_fc2_bias3, alloc1078) + R.vm.kill_object(alloc1077) + R.vm.kill_object(model_decoder_layers_26_fc2_weight3) + R.vm.kill_object(model_decoder_layers_26_fc2_bias3) + gv1809: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1079: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1809, R.dtype("float16")) + cls.add(alloc1075, alloc1078, alloc1079) + R.vm.kill_object(alloc1075) + R.vm.kill_object(alloc1078) + model_decoder_layers_27_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1144] + model_decoder_layers_27_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1145] + gv1810: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1080: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1810, R.dtype("float16")) + cls.layer_norm(alloc1079, model_decoder_layers_27_self_attn_layer_norm_weight3, model_decoder_layers_27_self_attn_layer_norm_bias3, alloc1080) + R.vm.kill_object(model_decoder_layers_27_self_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_27_self_attn_layer_norm_bias3) + model_decoder_layers_27_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1140] + model_decoder_layers_27_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1141] + gv1811: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1081: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1811, R.dtype("float16")) + _1079: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_27_self_attn_q_proj_weight3, alloc1080, model_decoder_layers_27_self_attn_q_proj_bias3, alloc1081) + R.vm.kill_object(model_decoder_layers_27_self_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_27_self_attn_q_proj_bias3) + gv1812: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape980: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1081, gv1812, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1081) + model_decoder_layers_27_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1137] + gv1813: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1082: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1813, R.dtype("float16")) + _1080: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul3_cublas", model_decoder_layers_27_self_attn_k_proj_weight3, alloc1080, alloc1082) + R.vm.kill_object(model_decoder_layers_27_self_attn_k_proj_weight3) + gv1814: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape981: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1082, gv1814, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1082) + model_decoder_layers_27_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1138] + model_decoder_layers_27_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1139] + gv1815: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1083: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1815, R.dtype("float16")) + _1081: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_27_self_attn_v_proj_weight3, alloc1080, model_decoder_layers_27_self_attn_v_proj_bias3, alloc1083) + R.vm.kill_object(alloc1080) + R.vm.kill_object(model_decoder_layers_27_self_attn_v_proj_weight3) + R.vm.kill_object(model_decoder_layers_27_self_attn_v_proj_bias3) + gv1816: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape982: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1083, gv1816, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1083) + gv1817: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc1084: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1817, R.dtype("float16")) + cls.concatenate(reshape980, reshape981, reshape982, alloc1084) + R.vm.kill_object(reshape980) + R.vm.kill_object(reshape981) + R.vm.kill_object(reshape982) + gv1818: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape983: R.Tensor((batch_size, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1084, gv1818, sinfo_args=(R.Tensor((batch_size, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc1084) + gv1819: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc1085: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1819, R.dtype("float16")) + _1083: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), reshape983, alloc1085) + R.vm.kill_object(reshape983) + gv1820: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape984: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1085, gv1820, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1085) + gv1821: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape985: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape984, gv1821, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape984) + model_decoder_layers_27_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1142] + model_decoder_layers_27_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1143] + gv1822: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1086: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1822, R.dtype("float16")) + _1084: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_27_self_attn_out_proj_weight3, reshape985, model_decoder_layers_27_self_attn_out_proj_bias3, alloc1086) + R.vm.kill_object(reshape985) + R.vm.kill_object(model_decoder_layers_27_self_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_27_self_attn_out_proj_bias3) + gv1823: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1087: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1823, R.dtype("float16")) + cls.add(alloc1079, alloc1086, alloc1087) + R.vm.kill_object(alloc1079) + R.vm.kill_object(alloc1086) + model_decoder_layers_27_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1153] + model_decoder_layers_27_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1154] + gv1824: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1088: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1824, R.dtype("float16")) + cls.layer_norm(alloc1087, model_decoder_layers_27_encoder_attn_layer_norm_weight3, model_decoder_layers_27_encoder_attn_layer_norm_bias3, alloc1088) + R.vm.kill_object(model_decoder_layers_27_encoder_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_27_encoder_attn_layer_norm_bias3) + model_decoder_layers_27_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1149] + model_decoder_layers_27_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1150] + gv1825: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1089: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1825, R.dtype("float16")) + _1087: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_27_encoder_attn_q_proj_weight3, alloc1088, model_decoder_layers_27_encoder_attn_q_proj_bias3, alloc1089) + R.vm.kill_object(alloc1088) + R.vm.kill_object(model_decoder_layers_27_encoder_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_27_encoder_attn_q_proj_bias3) + gv1826: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape986: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1089, gv1826, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1089) + gv1827: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape987: R.Tensor((batch_size, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape986, gv1827, sinfo_args=(R.Tensor((batch_size, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape986) + gv1828: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc1090: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1828, R.dtype("float16")) + _1088: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), reshape987, alloc1090) + R.vm.kill_object(reshape987) + gv1829: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape988: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1090, gv1829, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1090) + gv1830: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape989: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape988, gv1830, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape988) + model_decoder_layers_27_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1151] + model_decoder_layers_27_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1152] + gv1831: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1091: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1831, R.dtype("float16")) + _1089: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_27_encoder_attn_out_proj_weight3, reshape989, model_decoder_layers_27_encoder_attn_out_proj_bias3, alloc1091) + R.vm.kill_object(reshape989) + R.vm.kill_object(model_decoder_layers_27_encoder_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_27_encoder_attn_out_proj_bias3) + gv1832: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1092: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1832, R.dtype("float16")) + cls.add(alloc1087, alloc1091, alloc1092) + R.vm.kill_object(alloc1087) + R.vm.kill_object(alloc1091) + model_decoder_layers_27_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1159] + model_decoder_layers_27_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1160] + gv1833: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1093: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1833, R.dtype("float16")) + cls.layer_norm(alloc1092, model_decoder_layers_27_final_layer_norm_weight3, model_decoder_layers_27_final_layer_norm_bias3, alloc1093) + R.vm.kill_object(model_decoder_layers_27_final_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_27_final_layer_norm_bias3) + model_decoder_layers_27_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1155] + model_decoder_layers_27_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1156] + gv1834: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc1094: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1834, R.dtype("float16")) + _1092: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", model_decoder_layers_27_fc1_weight3, alloc1093, model_decoder_layers_27_fc1_bias3, alloc1094) + R.vm.kill_object(alloc1093) + R.vm.kill_object(model_decoder_layers_27_fc1_weight3) + R.vm.kill_object(model_decoder_layers_27_fc1_bias3) + model_decoder_layers_27_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1157] + model_decoder_layers_27_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1158] + gv1835: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1095: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1835, R.dtype("float16")) + _1093: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", model_decoder_layers_27_fc2_weight3, alloc1094, model_decoder_layers_27_fc2_bias3, alloc1095) + R.vm.kill_object(alloc1094) + R.vm.kill_object(model_decoder_layers_27_fc2_weight3) + R.vm.kill_object(model_decoder_layers_27_fc2_bias3) + gv1836: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1096: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1836, R.dtype("float16")) + cls.add(alloc1092, alloc1095, alloc1096) + R.vm.kill_object(alloc1092) + R.vm.kill_object(alloc1095) + model_decoder_layers_28_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1168] + model_decoder_layers_28_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1169] + gv1837: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1097: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1837, R.dtype("float16")) + cls.layer_norm(alloc1096, model_decoder_layers_28_self_attn_layer_norm_weight3, model_decoder_layers_28_self_attn_layer_norm_bias3, alloc1097) + R.vm.kill_object(model_decoder_layers_28_self_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_28_self_attn_layer_norm_bias3) + model_decoder_layers_28_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1164] + model_decoder_layers_28_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1165] + gv1838: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1098: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1838, R.dtype("float16")) + _1096: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_28_self_attn_q_proj_weight3, alloc1097, model_decoder_layers_28_self_attn_q_proj_bias3, alloc1098) + R.vm.kill_object(model_decoder_layers_28_self_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_28_self_attn_q_proj_bias3) + gv1839: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape990: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1098, gv1839, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1098) + model_decoder_layers_28_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1161] + gv1840: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1099: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1840, R.dtype("float16")) + _1097: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul3_cublas", model_decoder_layers_28_self_attn_k_proj_weight3, alloc1097, alloc1099) + R.vm.kill_object(model_decoder_layers_28_self_attn_k_proj_weight3) + gv1841: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape991: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1099, gv1841, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1099) + model_decoder_layers_28_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1162] + model_decoder_layers_28_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1163] + gv1842: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1100: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1842, R.dtype("float16")) + _1098: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_28_self_attn_v_proj_weight3, alloc1097, model_decoder_layers_28_self_attn_v_proj_bias3, alloc1100) + R.vm.kill_object(alloc1097) + R.vm.kill_object(model_decoder_layers_28_self_attn_v_proj_weight3) + R.vm.kill_object(model_decoder_layers_28_self_attn_v_proj_bias3) + gv1843: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape992: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1100, gv1843, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1100) + gv1844: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc1101: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1844, R.dtype("float16")) + cls.concatenate(reshape990, reshape991, reshape992, alloc1101) + R.vm.kill_object(reshape990) + R.vm.kill_object(reshape991) + R.vm.kill_object(reshape992) + gv1845: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape993: R.Tensor((batch_size, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1101, gv1845, sinfo_args=(R.Tensor((batch_size, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc1101) + gv1846: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc1102: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1846, R.dtype("float16")) + _1100: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), reshape993, alloc1102) + R.vm.kill_object(reshape993) + gv1847: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape994: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1102, gv1847, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1102) + gv1848: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape995: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape994, gv1848, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape994) + model_decoder_layers_28_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1166] + model_decoder_layers_28_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1167] + gv1849: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1103: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1849, R.dtype("float16")) + _1101: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_28_self_attn_out_proj_weight3, reshape995, model_decoder_layers_28_self_attn_out_proj_bias3, alloc1103) + R.vm.kill_object(reshape995) + R.vm.kill_object(model_decoder_layers_28_self_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_28_self_attn_out_proj_bias3) + gv1850: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1104: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1850, R.dtype("float16")) + cls.add(alloc1096, alloc1103, alloc1104) + R.vm.kill_object(alloc1096) + R.vm.kill_object(alloc1103) + model_decoder_layers_28_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1177] + model_decoder_layers_28_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1178] + gv1851: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1105: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1851, R.dtype("float16")) + cls.layer_norm(alloc1104, model_decoder_layers_28_encoder_attn_layer_norm_weight3, model_decoder_layers_28_encoder_attn_layer_norm_bias3, alloc1105) + R.vm.kill_object(model_decoder_layers_28_encoder_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_28_encoder_attn_layer_norm_bias3) + model_decoder_layers_28_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1173] + model_decoder_layers_28_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1174] + gv1852: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1106: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1852, R.dtype("float16")) + _1104: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_28_encoder_attn_q_proj_weight3, alloc1105, model_decoder_layers_28_encoder_attn_q_proj_bias3, alloc1106) + R.vm.kill_object(alloc1105) + R.vm.kill_object(model_decoder_layers_28_encoder_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_28_encoder_attn_q_proj_bias3) + gv1853: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape996: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1106, gv1853, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1106) + gv1854: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape997: R.Tensor((batch_size, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape996, gv1854, sinfo_args=(R.Tensor((batch_size, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape996) + gv1855: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc1107: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1855, R.dtype("float16")) + _1105: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), reshape997, alloc1107) + R.vm.kill_object(reshape997) + gv1856: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape998: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1107, gv1856, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1107) + gv1857: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape999: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape998, gv1857, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape998) + model_decoder_layers_28_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1175] + model_decoder_layers_28_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1176] + gv1858: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1108: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1858, R.dtype("float16")) + _1106: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_28_encoder_attn_out_proj_weight3, reshape999, model_decoder_layers_28_encoder_attn_out_proj_bias3, alloc1108) + R.vm.kill_object(reshape999) + R.vm.kill_object(model_decoder_layers_28_encoder_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_28_encoder_attn_out_proj_bias3) + gv1859: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1109: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1859, R.dtype("float16")) + cls.add(alloc1104, alloc1108, alloc1109) + R.vm.kill_object(alloc1104) + R.vm.kill_object(alloc1108) + model_decoder_layers_28_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1183] + model_decoder_layers_28_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1184] + gv1860: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1110: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1860, R.dtype("float16")) + cls.layer_norm(alloc1109, model_decoder_layers_28_final_layer_norm_weight3, model_decoder_layers_28_final_layer_norm_bias3, alloc1110) + R.vm.kill_object(model_decoder_layers_28_final_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_28_final_layer_norm_bias3) + model_decoder_layers_28_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1179] + model_decoder_layers_28_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1180] + gv1861: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc1111: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1861, R.dtype("float16")) + _1109: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", model_decoder_layers_28_fc1_weight3, alloc1110, model_decoder_layers_28_fc1_bias3, alloc1111) + R.vm.kill_object(alloc1110) + R.vm.kill_object(model_decoder_layers_28_fc1_weight3) + R.vm.kill_object(model_decoder_layers_28_fc1_bias3) + model_decoder_layers_28_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1181] + model_decoder_layers_28_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1182] + gv1862: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1112: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1862, R.dtype("float16")) + _1110: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", model_decoder_layers_28_fc2_weight3, alloc1111, model_decoder_layers_28_fc2_bias3, alloc1112) + R.vm.kill_object(alloc1111) + R.vm.kill_object(model_decoder_layers_28_fc2_weight3) + R.vm.kill_object(model_decoder_layers_28_fc2_bias3) + gv1863: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1113: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1863, R.dtype("float16")) + cls.add(alloc1109, alloc1112, alloc1113) + R.vm.kill_object(alloc1109) + R.vm.kill_object(alloc1112) + model_decoder_layers_29_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1192] + model_decoder_layers_29_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1193] + gv1864: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1114: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1864, R.dtype("float16")) + cls.layer_norm(alloc1113, model_decoder_layers_29_self_attn_layer_norm_weight3, model_decoder_layers_29_self_attn_layer_norm_bias3, alloc1114) + R.vm.kill_object(model_decoder_layers_29_self_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_29_self_attn_layer_norm_bias3) + model_decoder_layers_29_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1188] + model_decoder_layers_29_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1189] + gv1865: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1115: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1865, R.dtype("float16")) + _1113: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_29_self_attn_q_proj_weight3, alloc1114, model_decoder_layers_29_self_attn_q_proj_bias3, alloc1115) + R.vm.kill_object(model_decoder_layers_29_self_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_29_self_attn_q_proj_bias3) + gv1866: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1000: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1115, gv1866, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1115) + model_decoder_layers_29_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1185] + gv1867: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1116: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1867, R.dtype("float16")) + _1114: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul3_cublas", model_decoder_layers_29_self_attn_k_proj_weight3, alloc1114, alloc1116) + R.vm.kill_object(model_decoder_layers_29_self_attn_k_proj_weight3) + gv1868: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1001: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1116, gv1868, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1116) + model_decoder_layers_29_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1186] + model_decoder_layers_29_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1187] + gv1869: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1117: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1869, R.dtype("float16")) + _1115: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_29_self_attn_v_proj_weight3, alloc1114, model_decoder_layers_29_self_attn_v_proj_bias3, alloc1117) + R.vm.kill_object(alloc1114) + R.vm.kill_object(model_decoder_layers_29_self_attn_v_proj_weight3) + R.vm.kill_object(model_decoder_layers_29_self_attn_v_proj_bias3) + gv1870: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1002: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1117, gv1870, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1117) + gv1871: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc1118: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1871, R.dtype("float16")) + cls.concatenate(reshape1000, reshape1001, reshape1002, alloc1118) + R.vm.kill_object(reshape1000) + R.vm.kill_object(reshape1001) + R.vm.kill_object(reshape1002) + gv1872: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1003: R.Tensor((batch_size, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1118, gv1872, sinfo_args=(R.Tensor((batch_size, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc1118) + gv1873: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc1119: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1873, R.dtype("float16")) + _1117: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), reshape1003, alloc1119) + R.vm.kill_object(reshape1003) + gv1874: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1004: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1119, gv1874, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1119) + gv1875: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1005: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1004, gv1875, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1004) + model_decoder_layers_29_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1190] + model_decoder_layers_29_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1191] + gv1876: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1120: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1876, R.dtype("float16")) + _1118: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_29_self_attn_out_proj_weight3, reshape1005, model_decoder_layers_29_self_attn_out_proj_bias3, alloc1120) + R.vm.kill_object(reshape1005) + R.vm.kill_object(model_decoder_layers_29_self_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_29_self_attn_out_proj_bias3) + gv1877: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1121: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1877, R.dtype("float16")) + cls.add(alloc1113, alloc1120, alloc1121) + R.vm.kill_object(alloc1113) + R.vm.kill_object(alloc1120) + model_decoder_layers_29_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1201] + model_decoder_layers_29_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1202] + gv1878: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1122: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1878, R.dtype("float16")) + cls.layer_norm(alloc1121, model_decoder_layers_29_encoder_attn_layer_norm_weight3, model_decoder_layers_29_encoder_attn_layer_norm_bias3, alloc1122) + R.vm.kill_object(model_decoder_layers_29_encoder_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_29_encoder_attn_layer_norm_bias3) + model_decoder_layers_29_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1197] + model_decoder_layers_29_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1198] + gv1879: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1123: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1879, R.dtype("float16")) + _1121: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_29_encoder_attn_q_proj_weight3, alloc1122, model_decoder_layers_29_encoder_attn_q_proj_bias3, alloc1123) + R.vm.kill_object(alloc1122) + R.vm.kill_object(model_decoder_layers_29_encoder_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_29_encoder_attn_q_proj_bias3) + gv1880: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1006: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1123, gv1880, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1123) + gv1881: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1007: R.Tensor((batch_size, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1006, gv1881, sinfo_args=(R.Tensor((batch_size, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape1006) + gv1882: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc1124: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1882, R.dtype("float16")) + _1122: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), reshape1007, alloc1124) + R.vm.kill_object(reshape1007) + gv1883: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1008: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1124, gv1883, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1124) + gv1884: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1009: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1008, gv1884, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1008) + model_decoder_layers_29_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1199] + model_decoder_layers_29_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1200] + gv1885: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1125: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1885, R.dtype("float16")) + _1123: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_29_encoder_attn_out_proj_weight3, reshape1009, model_decoder_layers_29_encoder_attn_out_proj_bias3, alloc1125) + R.vm.kill_object(reshape1009) + R.vm.kill_object(model_decoder_layers_29_encoder_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_29_encoder_attn_out_proj_bias3) + gv1886: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1126: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1886, R.dtype("float16")) + cls.add(alloc1121, alloc1125, alloc1126) + R.vm.kill_object(alloc1121) + R.vm.kill_object(alloc1125) + model_decoder_layers_29_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1207] + model_decoder_layers_29_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1208] + gv1887: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1127: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1887, R.dtype("float16")) + cls.layer_norm(alloc1126, model_decoder_layers_29_final_layer_norm_weight3, model_decoder_layers_29_final_layer_norm_bias3, alloc1127) + R.vm.kill_object(model_decoder_layers_29_final_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_29_final_layer_norm_bias3) + model_decoder_layers_29_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1203] + model_decoder_layers_29_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1204] + gv1888: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc1128: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1888, R.dtype("float16")) + _1126: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", model_decoder_layers_29_fc1_weight3, alloc1127, model_decoder_layers_29_fc1_bias3, alloc1128) + R.vm.kill_object(alloc1127) + R.vm.kill_object(model_decoder_layers_29_fc1_weight3) + R.vm.kill_object(model_decoder_layers_29_fc1_bias3) + model_decoder_layers_29_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1205] + model_decoder_layers_29_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1206] + gv1889: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1129: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1889, R.dtype("float16")) + _1127: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", model_decoder_layers_29_fc2_weight3, alloc1128, model_decoder_layers_29_fc2_bias3, alloc1129) + R.vm.kill_object(alloc1128) + R.vm.kill_object(model_decoder_layers_29_fc2_weight3) + R.vm.kill_object(model_decoder_layers_29_fc2_bias3) + gv1890: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1130: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1890, R.dtype("float16")) + cls.add(alloc1126, alloc1129, alloc1130) + R.vm.kill_object(alloc1126) + R.vm.kill_object(alloc1129) + model_decoder_layers_30_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1216] + model_decoder_layers_30_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1217] + gv1891: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1131: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1891, R.dtype("float16")) + cls.layer_norm(alloc1130, model_decoder_layers_30_self_attn_layer_norm_weight3, model_decoder_layers_30_self_attn_layer_norm_bias3, alloc1131) + R.vm.kill_object(model_decoder_layers_30_self_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_30_self_attn_layer_norm_bias3) + model_decoder_layers_30_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1212] + model_decoder_layers_30_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1213] + gv1892: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1132: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1892, R.dtype("float16")) + _1130: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_30_self_attn_q_proj_weight3, alloc1131, model_decoder_layers_30_self_attn_q_proj_bias3, alloc1132) + R.vm.kill_object(model_decoder_layers_30_self_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_30_self_attn_q_proj_bias3) + gv1893: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1010: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1132, gv1893, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1132) + model_decoder_layers_30_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1209] + gv1894: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1133: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1894, R.dtype("float16")) + _1131: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul3_cublas", model_decoder_layers_30_self_attn_k_proj_weight3, alloc1131, alloc1133) + R.vm.kill_object(model_decoder_layers_30_self_attn_k_proj_weight3) + gv1895: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1011: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1133, gv1895, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1133) + model_decoder_layers_30_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1210] + model_decoder_layers_30_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1211] + gv1896: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1134: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1896, R.dtype("float16")) + _1132: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_30_self_attn_v_proj_weight3, alloc1131, model_decoder_layers_30_self_attn_v_proj_bias3, alloc1134) + R.vm.kill_object(alloc1131) + R.vm.kill_object(model_decoder_layers_30_self_attn_v_proj_weight3) + R.vm.kill_object(model_decoder_layers_30_self_attn_v_proj_bias3) + gv1897: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1012: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1134, gv1897, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1134) + gv1898: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc1135: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1898, R.dtype("float16")) + cls.concatenate(reshape1010, reshape1011, reshape1012, alloc1135) + R.vm.kill_object(reshape1010) + R.vm.kill_object(reshape1011) + R.vm.kill_object(reshape1012) + gv1899: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1013: R.Tensor((batch_size, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1135, gv1899, sinfo_args=(R.Tensor((batch_size, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc1135) + gv1900: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc1136: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1900, R.dtype("float16")) + _1134: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), reshape1013, alloc1136) + R.vm.kill_object(reshape1013) + gv1901: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1014: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1136, gv1901, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1136) + gv1902: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1015: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1014, gv1902, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1014) + model_decoder_layers_30_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1214] + model_decoder_layers_30_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1215] + gv1903: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1137: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1903, R.dtype("float16")) + _1135: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_30_self_attn_out_proj_weight3, reshape1015, model_decoder_layers_30_self_attn_out_proj_bias3, alloc1137) + R.vm.kill_object(reshape1015) + R.vm.kill_object(model_decoder_layers_30_self_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_30_self_attn_out_proj_bias3) + gv1904: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1138: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1904, R.dtype("float16")) + cls.add(alloc1130, alloc1137, alloc1138) + R.vm.kill_object(alloc1130) + R.vm.kill_object(alloc1137) + model_decoder_layers_30_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1225] + model_decoder_layers_30_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1226] + gv1905: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1139: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1905, R.dtype("float16")) + cls.layer_norm(alloc1138, model_decoder_layers_30_encoder_attn_layer_norm_weight3, model_decoder_layers_30_encoder_attn_layer_norm_bias3, alloc1139) + R.vm.kill_object(model_decoder_layers_30_encoder_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_30_encoder_attn_layer_norm_bias3) + model_decoder_layers_30_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1221] + model_decoder_layers_30_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1222] + gv1906: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1140: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1906, R.dtype("float16")) + _1138: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_30_encoder_attn_q_proj_weight3, alloc1139, model_decoder_layers_30_encoder_attn_q_proj_bias3, alloc1140) + R.vm.kill_object(alloc1139) + R.vm.kill_object(model_decoder_layers_30_encoder_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_30_encoder_attn_q_proj_bias3) + gv1907: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1016: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1140, gv1907, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1140) + gv1908: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1017: R.Tensor((batch_size, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1016, gv1908, sinfo_args=(R.Tensor((batch_size, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape1016) + gv1909: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc1141: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1909, R.dtype("float16")) + _1139: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), reshape1017, alloc1141) + R.vm.kill_object(reshape1017) + gv1910: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1018: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1141, gv1910, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1141) + gv1911: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1019: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1018, gv1911, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1018) + model_decoder_layers_30_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1223] + model_decoder_layers_30_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1224] + gv1912: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1142: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1912, R.dtype("float16")) + _1140: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_30_encoder_attn_out_proj_weight3, reshape1019, model_decoder_layers_30_encoder_attn_out_proj_bias3, alloc1142) + R.vm.kill_object(reshape1019) + R.vm.kill_object(model_decoder_layers_30_encoder_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_30_encoder_attn_out_proj_bias3) + gv1913: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1143: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1913, R.dtype("float16")) + cls.add(alloc1138, alloc1142, alloc1143) + R.vm.kill_object(alloc1138) + R.vm.kill_object(alloc1142) + model_decoder_layers_30_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1231] + model_decoder_layers_30_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1232] + gv1914: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1144: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1914, R.dtype("float16")) + cls.layer_norm(alloc1143, model_decoder_layers_30_final_layer_norm_weight3, model_decoder_layers_30_final_layer_norm_bias3, alloc1144) + R.vm.kill_object(model_decoder_layers_30_final_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_30_final_layer_norm_bias3) + model_decoder_layers_30_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1227] + model_decoder_layers_30_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1228] + gv1915: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc1145: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1915, R.dtype("float16")) + _1143: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", model_decoder_layers_30_fc1_weight3, alloc1144, model_decoder_layers_30_fc1_bias3, alloc1145) + R.vm.kill_object(alloc1144) + R.vm.kill_object(model_decoder_layers_30_fc1_weight3) + R.vm.kill_object(model_decoder_layers_30_fc1_bias3) + model_decoder_layers_30_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1229] + model_decoder_layers_30_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1230] + gv1916: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1146: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1916, R.dtype("float16")) + _1144: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", model_decoder_layers_30_fc2_weight3, alloc1145, model_decoder_layers_30_fc2_bias3, alloc1146) + R.vm.kill_object(alloc1145) + R.vm.kill_object(model_decoder_layers_30_fc2_weight3) + R.vm.kill_object(model_decoder_layers_30_fc2_bias3) + gv1917: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1147: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1917, R.dtype("float16")) + cls.add(alloc1143, alloc1146, alloc1147) + R.vm.kill_object(alloc1143) + R.vm.kill_object(alloc1146) + model_decoder_layers_31_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1240] + model_decoder_layers_31_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1241] + gv1918: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1148: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1918, R.dtype("float16")) + cls.layer_norm(alloc1147, model_decoder_layers_31_self_attn_layer_norm_weight3, model_decoder_layers_31_self_attn_layer_norm_bias3, alloc1148) + R.vm.kill_object(model_decoder_layers_31_self_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_31_self_attn_layer_norm_bias3) + model_decoder_layers_31_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1236] + model_decoder_layers_31_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1237] + gv1919: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1149: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1919, R.dtype("float16")) + _1147: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_31_self_attn_q_proj_weight3, alloc1148, model_decoder_layers_31_self_attn_q_proj_bias3, alloc1149) + R.vm.kill_object(model_decoder_layers_31_self_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_31_self_attn_q_proj_bias3) + gv1920: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1020: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1149, gv1920, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1149) + model_decoder_layers_31_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1233] + gv1921: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1150: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1921, R.dtype("float16")) + _1148: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul3_cublas", model_decoder_layers_31_self_attn_k_proj_weight3, alloc1148, alloc1150) + R.vm.kill_object(model_decoder_layers_31_self_attn_k_proj_weight3) + gv1922: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1021: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1150, gv1922, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1150) + model_decoder_layers_31_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1234] + model_decoder_layers_31_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1235] + gv1923: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1151: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1923, R.dtype("float16")) + _1149: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_31_self_attn_v_proj_weight3, alloc1148, model_decoder_layers_31_self_attn_v_proj_bias3, alloc1151) + R.vm.kill_object(alloc1148) + R.vm.kill_object(model_decoder_layers_31_self_attn_v_proj_weight3) + R.vm.kill_object(model_decoder_layers_31_self_attn_v_proj_bias3) + gv1924: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1022: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1151, gv1924, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1151) + gv1925: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc1152: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1925, R.dtype("float16")) + cls.concatenate(reshape1020, reshape1021, reshape1022, alloc1152) + R.vm.kill_object(reshape1020) + R.vm.kill_object(reshape1021) + R.vm.kill_object(reshape1022) + gv1926: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1023: R.Tensor((batch_size, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1152, gv1926, sinfo_args=(R.Tensor((batch_size, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc1152) + gv1927: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc1153: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1927, R.dtype("float16")) + _1151: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), reshape1023, alloc1153) + R.vm.kill_object(reshape1023) + gv1928: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1024: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1153, gv1928, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1153) + gv1929: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1025: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1024, gv1929, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1024) + model_decoder_layers_31_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1238] + model_decoder_layers_31_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1239] + gv1930: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1154: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1930, R.dtype("float16")) + _1152: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_31_self_attn_out_proj_weight3, reshape1025, model_decoder_layers_31_self_attn_out_proj_bias3, alloc1154) + R.vm.kill_object(reshape1025) + R.vm.kill_object(model_decoder_layers_31_self_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_31_self_attn_out_proj_bias3) + gv1931: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1155: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1931, R.dtype("float16")) + cls.add(alloc1147, alloc1154, alloc1155) + R.vm.kill_object(alloc1147) + R.vm.kill_object(alloc1154) + model_decoder_layers_31_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1249] + model_decoder_layers_31_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1250] + gv1932: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1156: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1932, R.dtype("float16")) + cls.layer_norm(alloc1155, model_decoder_layers_31_encoder_attn_layer_norm_weight3, model_decoder_layers_31_encoder_attn_layer_norm_bias3, alloc1156) + R.vm.kill_object(model_decoder_layers_31_encoder_attn_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_31_encoder_attn_layer_norm_bias3) + model_decoder_layers_31_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1245] + model_decoder_layers_31_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1246] + gv1933: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1157: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1933, R.dtype("float16")) + _1155: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_31_encoder_attn_q_proj_weight3, alloc1156, model_decoder_layers_31_encoder_attn_q_proj_bias3, alloc1157) + R.vm.kill_object(alloc1156) + R.vm.kill_object(model_decoder_layers_31_encoder_attn_q_proj_weight3) + R.vm.kill_object(model_decoder_layers_31_encoder_attn_q_proj_bias3) + gv1934: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1026: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1157, gv1934, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1157) + gv1935: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1027: R.Tensor((batch_size, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1026, gv1935, sinfo_args=(R.Tensor((batch_size, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape1026) + gv1936: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc1158: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1936, R.dtype("float16")) + _1156: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), reshape1027, alloc1158) + R.vm.kill_object(reshape1027) + gv1937: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1028: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1158, gv1937, sinfo_args=(R.Tensor((batch_size, 1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1158) + gv1938: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1029: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1028, gv1938, sinfo_args=(R.Tensor((batch_size, 1, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1028) + model_decoder_layers_31_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1247] + model_decoder_layers_31_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1248] + gv1939: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1159: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1939, R.dtype("float16")) + _1157: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", model_decoder_layers_31_encoder_attn_out_proj_weight3, reshape1029, model_decoder_layers_31_encoder_attn_out_proj_bias3, alloc1159) + R.vm.kill_object(reshape1029) + R.vm.kill_object(model_decoder_layers_31_encoder_attn_out_proj_weight3) + R.vm.kill_object(model_decoder_layers_31_encoder_attn_out_proj_bias3) + gv1940: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1160: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage15, R.prim_value(0), gv1940, R.dtype("float16")) + R.vm.kill_object(storage15) + cls.add(alloc1155, alloc1159, alloc1160) + R.vm.kill_object(alloc1155) + R.vm.kill_object(alloc1159) + model_decoder_layers_31_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1255] + model_decoder_layers_31_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1256] + gv1941: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1161: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1941, R.dtype("float16")) + cls.layer_norm(alloc1160, model_decoder_layers_31_final_layer_norm_weight3, model_decoder_layers_31_final_layer_norm_bias3, alloc1161) + R.vm.kill_object(model_decoder_layers_31_final_layer_norm_weight3) + R.vm.kill_object(model_decoder_layers_31_final_layer_norm_bias3) + model_decoder_layers_31_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1251] + model_decoder_layers_31_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1252] + gv1942: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc1162: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage13, R.prim_value(0), gv1942, R.dtype("float16")) + R.vm.kill_object(storage13) + _1160: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", model_decoder_layers_31_fc1_weight3, alloc1161, model_decoder_layers_31_fc1_bias3, alloc1162) + R.vm.kill_object(alloc1161) + R.vm.kill_object(model_decoder_layers_31_fc1_weight3) + R.vm.kill_object(model_decoder_layers_31_fc1_bias3) + model_decoder_layers_31_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1253] + model_decoder_layers_31_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1254] + gv1943: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1163: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage14, R.prim_value(0), gv1943, R.dtype("float16")) + R.vm.kill_object(storage14) + _1161: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", model_decoder_layers_31_fc2_weight3, alloc1162, model_decoder_layers_31_fc2_bias3, alloc1163) + R.vm.kill_object(alloc1162) + R.vm.kill_object(model_decoder_layers_31_fc2_weight3) + R.vm.kill_object(model_decoder_layers_31_fc2_bias3) + gv1944: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1164: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage16, R.prim_value(0), gv1944, R.dtype("float16")) + R.vm.kill_object(storage16) + cls.add(alloc1160, alloc1163, alloc1164) + R.vm.kill_object(alloc1160) + R.vm.kill_object(alloc1163) + model_decoder_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1257] + model_decoder_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1258] + gv1945: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1165: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage17, R.prim_value(0), gv1945, R.dtype("float16")) + R.vm.kill_object(storage17) + cls.layer_norm(alloc1164, model_decoder_layer_norm_weight3, model_decoder_layer_norm_bias3, alloc1165) + R.vm.kill_object(alloc1164) + R.vm.kill_object(model_decoder_layer_norm_weight3) + R.vm.kill_object(model_decoder_layer_norm_bias3) + storage18: R.Object = R.vm.alloc_storage(R.shape([1659712]), R.prim_value(0), R.dtype("uint8"), R.str("global")) + gv1946: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(51866), sinfo_args=(R.Shape(ndim=3),)) + alloc1166: R.Tensor(dtype="float32", ndim=3) = R.vm.alloc_tensor(storage18, R.prim_value(0), gv1946, R.dtype("float32")) + R.vm.kill_object(storage18) + _1164: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul4_cublas", model_decoder_embed_tokens_weight3, alloc1165, alloc1166) + R.vm.kill_object(model_decoder_embed_tokens_weight3) + R.vm.kill_object(alloc1165) + R.call_packed("vm.builtin.match_shape", alloc1166, shape_heap, R.prim_value(3), R.prim_value(3), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(51866), R.str("ErrorContext(fn=batch_decode, loc=return, annotation=R.Tensor((batch_size, 1, 51866), dtype=\"float32\")) "), sinfo_args=(R.Tuple,)) + return alloc1166 + + @R.function + def batch_encode(input_features: R.Tensor(("batch_size", 128, 3000), dtype="float16"), paged_kv_cache: R.Object, packed_params: R.Tuple(R.Tensor((1280, 128, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1500, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), 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R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), 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dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((5120, 1280), dtype=\"float16\"), R.Tensor((5120,), dtype=\"float16\"), R.Tensor((1280, 5120), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((5120, 1280), dtype=\"float16\"), R.Tensor((5120,), dtype=\"float16\"), R.Tensor((1280, 5120), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"))) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.match_shape", input_features, shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(128), R.prim_value(0), R.prim_value(3000), R.str("ErrorContext(fn=batch_encode, loc=param[0], param=input_features, annotation=R.Tensor((batch_size, 128, 3000), dtype=\"float16\")) "), sinfo_args=(R.Tuple,)) + cls.shape_func1(shape_heap) + lv: R.Tensor((1280,), dtype="float16") = packed_params[1] + lv1: R.Tensor((1, 1280, 1), dtype="float16") = R.call_packed("vm.builtin.reshape", lv, R.shape([1, 1280, 1]), sinfo_args=(R.Tensor((1, 1280, 1), dtype="float16"),)) + R.vm.kill_object(lv) + lv2: R.Tensor((1280,), dtype="float16") = packed_params[3] + lv3: R.Tensor((1, 1280, 1), dtype="float16") = R.call_packed("vm.builtin.reshape", lv2, R.shape([1, 1280, 1]), sinfo_args=(R.Tensor((1, 1280, 1), dtype="float16"),)) + R.vm.kill_object(lv2) + model_encoder_conv1_weight: R.Tensor((1280, 128, 3), dtype="float16") = packed_params[0] + storage24: R.Object = R.vm.alloc_storage(R.shape([122880000]), R.prim_value(0), R.dtype("uint8"), R.str("global")) + gv1947: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), R.prim_value(0), R.prim_value(3000), sinfo_args=(R.Shape(ndim=3),)) + alloc1620: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv1947, R.dtype("float16")) + cls.fused_conv1d_add1_gelu(input_features, model_encoder_conv1_weight, lv1, alloc1620) + R.vm.kill_object(lv1) + R.vm.kill_object(model_encoder_conv1_weight) + model_encoder_conv2_weight: R.Tensor((1280, 1280, 3), dtype="float16") = packed_params[2] + storage25: R.Object = R.vm.alloc_storage(R.shape([30720000]), R.prim_value(0), R.dtype("uint8"), R.str("global")) + gv1948: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), R.prim_value(0), R.prim_value(1500), sinfo_args=(R.Shape(ndim=3),)) + alloc1621: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv1948, R.dtype("float16")) + cls.fused_conv1d1_add2_gelu1(alloc1620, model_encoder_conv2_weight, lv3, alloc1621) + R.vm.kill_object(lv3) + R.vm.kill_object(alloc1620) + R.vm.kill_object(model_encoder_conv2_weight) + lv6: R.Tensor((1500, 1280), dtype="float16") = packed_params[4] + gv1949: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1622: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv1949, R.dtype("float16")) + cls.fused_transpose_add3(lv6, alloc1621, alloc1622) + R.vm.kill_object(alloc1621) + R.vm.kill_object(lv6) + model_encoder_layers_0_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[12] + model_encoder_layers_0_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[13] + gv1950: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1623: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv1950, R.dtype("float16")) + cls.layer_norm1(alloc1622, model_encoder_layers_0_self_attn_layer_norm_weight, model_encoder_layers_0_self_attn_layer_norm_bias, alloc1623) + R.vm.kill_object(model_encoder_layers_0_self_attn_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_0_self_attn_layer_norm_bias) + model_encoder_layers_0_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[8] + model_encoder_layers_0_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[9] + storage26: R.Object = R.vm.alloc_storage(R.shape([30720000]), R.prim_value(0), R.dtype("uint8"), R.str("global")) + gv1951: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1624: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv1951, R.dtype("float16")) + _1622: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_0_self_attn_q_proj_weight, alloc1623, model_encoder_layers_0_self_attn_q_proj_bias, alloc1624) + R.vm.kill_object(model_encoder_layers_0_self_attn_q_proj_weight) + R.vm.kill_object(model_encoder_layers_0_self_attn_q_proj_bias) + gv1952: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1624, gv1952, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1624) + model_encoder_layers_0_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[5] + storage27: R.Object = R.vm.alloc_storage(R.shape([30720000]), R.prim_value(0), R.dtype("uint8"), R.str("global")) + gv1953: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1625: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv1953, R.dtype("float16")) + _1623: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_encoder_layers_0_self_attn_k_proj_weight, alloc1623, alloc1625) + R.vm.kill_object(model_encoder_layers_0_self_attn_k_proj_weight) + gv1954: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1625, gv1954, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1625) + model_encoder_layers_0_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[6] + model_encoder_layers_0_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[7] + storage28: R.Object = R.vm.alloc_storage(R.shape([30720000]), R.prim_value(0), R.dtype("uint8"), R.str("global")) + gv1955: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1626: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv1955, R.dtype("float16")) + _1624: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_0_self_attn_v_proj_weight, alloc1623, model_encoder_layers_0_self_attn_v_proj_bias, alloc1626) + R.vm.kill_object(alloc1623) + R.vm.kill_object(model_encoder_layers_0_self_attn_v_proj_weight) + R.vm.kill_object(model_encoder_layers_0_self_attn_v_proj_bias) + gv1956: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape2: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1626, gv1956, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1626) + gv1957: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape3: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape, gv1957, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape) + gv1958: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape4: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1, gv1958, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape1) + gv1959: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape5: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape2, gv1959, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape2) + gv1960: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc1627: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv1960, R.dtype("float16")) + _1625: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_no_append", paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), reshape3, reshape4, reshape5, alloc1627) + R.vm.kill_object(reshape3) + R.vm.kill_object(reshape4) + R.vm.kill_object(reshape5) + gv1961: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape6: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1627, gv1961, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1627) + gv1962: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape7: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape6, gv1962, sinfo_args=(R.Tensor((batch_size, 1500, 1280), dtype="float16"),)) + R.vm.kill_object(reshape6) + model_encoder_layers_0_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[10] + model_encoder_layers_0_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[11] + gv1963: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1628: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv1963, R.dtype("float16")) + _1626: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_0_self_attn_out_proj_weight, reshape7, model_encoder_layers_0_self_attn_out_proj_bias, alloc1628) + R.vm.kill_object(reshape7) + R.vm.kill_object(model_encoder_layers_0_self_attn_out_proj_weight) + R.vm.kill_object(model_encoder_layers_0_self_attn_out_proj_bias) + gv1964: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1629: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv1964, R.dtype("float16")) + cls.add4(alloc1622, alloc1628, alloc1629) + R.vm.kill_object(alloc1622) + R.vm.kill_object(alloc1628) + model_encoder_layers_0_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[18] + model_encoder_layers_0_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[19] + gv1965: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1630: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv1965, R.dtype("float16")) + cls.layer_norm1(alloc1629, model_encoder_layers_0_final_layer_norm_weight, model_encoder_layers_0_final_layer_norm_bias, alloc1630) + R.vm.kill_object(model_encoder_layers_0_final_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_0_final_layer_norm_bias) + model_encoder_layers_0_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[14] + model_encoder_layers_0_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[15] + gv1966: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc1631: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv1966, R.dtype("float16")) + _1629: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", model_encoder_layers_0_fc1_weight, alloc1630, model_encoder_layers_0_fc1_bias, alloc1631) + R.vm.kill_object(alloc1630) + R.vm.kill_object(model_encoder_layers_0_fc1_weight) + R.vm.kill_object(model_encoder_layers_0_fc1_bias) + model_encoder_layers_0_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[16] + model_encoder_layers_0_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[17] + gv1967: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1632: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv1967, R.dtype("float16")) + _1630: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", model_encoder_layers_0_fc2_weight, alloc1631, model_encoder_layers_0_fc2_bias, alloc1632) + R.vm.kill_object(alloc1631) + R.vm.kill_object(model_encoder_layers_0_fc2_weight) + R.vm.kill_object(model_encoder_layers_0_fc2_bias) + gv1968: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1633: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv1968, R.dtype("float16")) + cls.fused_add4_maximum_minimum(alloc1629, alloc1632, alloc1633) + R.vm.kill_object(alloc1629) + R.vm.kill_object(alloc1632) + model_encoder_layers_1_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[27] + model_encoder_layers_1_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[28] + gv1969: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1634: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv1969, R.dtype("float16")) + cls.layer_norm1(alloc1633, model_encoder_layers_1_self_attn_layer_norm_weight, model_encoder_layers_1_self_attn_layer_norm_bias, alloc1634) + R.vm.kill_object(model_encoder_layers_1_self_attn_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_1_self_attn_layer_norm_bias) + model_encoder_layers_1_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[23] + model_encoder_layers_1_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[24] + gv1970: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1635: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv1970, R.dtype("float16")) + _1633: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_1_self_attn_q_proj_weight, alloc1634, model_encoder_layers_1_self_attn_q_proj_bias, alloc1635) + R.vm.kill_object(model_encoder_layers_1_self_attn_q_proj_weight) + R.vm.kill_object(model_encoder_layers_1_self_attn_q_proj_bias) + gv1971: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape8: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1635, gv1971, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1635) + model_encoder_layers_1_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[20] + gv1972: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1636: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv1972, R.dtype("float16")) + _1634: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_encoder_layers_1_self_attn_k_proj_weight, alloc1634, alloc1636) + R.vm.kill_object(model_encoder_layers_1_self_attn_k_proj_weight) + gv1973: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape9: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1636, gv1973, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1636) + model_encoder_layers_1_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[21] + model_encoder_layers_1_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[22] + gv1974: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1637: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv1974, R.dtype("float16")) + _1635: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_1_self_attn_v_proj_weight, alloc1634, model_encoder_layers_1_self_attn_v_proj_bias, alloc1637) + R.vm.kill_object(alloc1634) + R.vm.kill_object(model_encoder_layers_1_self_attn_v_proj_weight) + R.vm.kill_object(model_encoder_layers_1_self_attn_v_proj_bias) + gv1975: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape10: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1637, gv1975, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1637) + gv1976: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape11: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape8, gv1976, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape8) + gv1977: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape12: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape9, gv1977, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape9) + gv1978: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape13: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape10, gv1978, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape10) + gv1979: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc1638: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv1979, R.dtype("float16")) + _1636: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_no_append", paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), reshape11, reshape12, reshape13, alloc1638) + R.vm.kill_object(reshape11) + R.vm.kill_object(reshape12) + R.vm.kill_object(reshape13) + gv1980: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape14: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1638, gv1980, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1638) + gv1981: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape15: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape14, gv1981, sinfo_args=(R.Tensor((batch_size, 1500, 1280), dtype="float16"),)) + R.vm.kill_object(reshape14) + model_encoder_layers_1_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[25] + model_encoder_layers_1_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[26] + gv1982: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1639: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv1982, R.dtype("float16")) + _1637: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_1_self_attn_out_proj_weight, reshape15, model_encoder_layers_1_self_attn_out_proj_bias, alloc1639) + R.vm.kill_object(reshape15) + R.vm.kill_object(model_encoder_layers_1_self_attn_out_proj_weight) + R.vm.kill_object(model_encoder_layers_1_self_attn_out_proj_bias) + gv1983: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1640: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv1983, R.dtype("float16")) + cls.add4(alloc1633, alloc1639, alloc1640) + R.vm.kill_object(alloc1633) + R.vm.kill_object(alloc1639) + model_encoder_layers_1_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[33] + model_encoder_layers_1_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[34] + gv1984: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1641: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv1984, R.dtype("float16")) + cls.layer_norm1(alloc1640, model_encoder_layers_1_final_layer_norm_weight, model_encoder_layers_1_final_layer_norm_bias, alloc1641) + R.vm.kill_object(model_encoder_layers_1_final_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_1_final_layer_norm_bias) + model_encoder_layers_1_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[29] + model_encoder_layers_1_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[30] + gv1985: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc1642: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv1985, R.dtype("float16")) + _1640: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", model_encoder_layers_1_fc1_weight, alloc1641, model_encoder_layers_1_fc1_bias, alloc1642) + R.vm.kill_object(alloc1641) + R.vm.kill_object(model_encoder_layers_1_fc1_weight) + R.vm.kill_object(model_encoder_layers_1_fc1_bias) + model_encoder_layers_1_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[31] + model_encoder_layers_1_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[32] + gv1986: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1643: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv1986, R.dtype("float16")) + _1641: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", model_encoder_layers_1_fc2_weight, alloc1642, model_encoder_layers_1_fc2_bias, alloc1643) + R.vm.kill_object(alloc1642) + R.vm.kill_object(model_encoder_layers_1_fc2_weight) + R.vm.kill_object(model_encoder_layers_1_fc2_bias) + gv1987: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1644: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv1987, R.dtype("float16")) + cls.fused_add4_maximum_minimum(alloc1640, alloc1643, alloc1644) + R.vm.kill_object(alloc1640) + R.vm.kill_object(alloc1643) + model_encoder_layers_2_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[42] + model_encoder_layers_2_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[43] + gv1988: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1645: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv1988, R.dtype("float16")) + cls.layer_norm1(alloc1644, model_encoder_layers_2_self_attn_layer_norm_weight, model_encoder_layers_2_self_attn_layer_norm_bias, alloc1645) + R.vm.kill_object(model_encoder_layers_2_self_attn_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_2_self_attn_layer_norm_bias) + model_encoder_layers_2_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[38] + model_encoder_layers_2_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[39] + gv1989: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1646: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv1989, R.dtype("float16")) + _1644: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_2_self_attn_q_proj_weight, alloc1645, model_encoder_layers_2_self_attn_q_proj_bias, alloc1646) + R.vm.kill_object(model_encoder_layers_2_self_attn_q_proj_weight) + R.vm.kill_object(model_encoder_layers_2_self_attn_q_proj_bias) + gv1990: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape16: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1646, gv1990, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1646) + model_encoder_layers_2_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[35] + gv1991: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1647: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv1991, R.dtype("float16")) + _1645: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_encoder_layers_2_self_attn_k_proj_weight, alloc1645, alloc1647) + R.vm.kill_object(model_encoder_layers_2_self_attn_k_proj_weight) + gv1992: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape17: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1647, gv1992, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1647) + model_encoder_layers_2_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[36] + model_encoder_layers_2_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[37] + gv1993: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1648: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv1993, R.dtype("float16")) + _1646: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_2_self_attn_v_proj_weight, alloc1645, model_encoder_layers_2_self_attn_v_proj_bias, alloc1648) + R.vm.kill_object(alloc1645) + R.vm.kill_object(model_encoder_layers_2_self_attn_v_proj_weight) + R.vm.kill_object(model_encoder_layers_2_self_attn_v_proj_bias) + gv1994: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape18: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1648, gv1994, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1648) + gv1995: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape19: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape16, gv1995, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape16) + gv1996: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape20: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape17, gv1996, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape17) + gv1997: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape21: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape18, gv1997, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape18) + gv1998: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc1649: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv1998, R.dtype("float16")) + _1647: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_no_append", paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), reshape19, reshape20, reshape21, alloc1649) + R.vm.kill_object(reshape19) + R.vm.kill_object(reshape20) + R.vm.kill_object(reshape21) + gv1999: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape22: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1649, gv1999, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1649) + gv2000: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape23: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape22, gv2000, sinfo_args=(R.Tensor((batch_size, 1500, 1280), dtype="float16"),)) + R.vm.kill_object(reshape22) + model_encoder_layers_2_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[40] + model_encoder_layers_2_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[41] + gv2001: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1650: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv2001, R.dtype("float16")) + _1648: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_2_self_attn_out_proj_weight, reshape23, model_encoder_layers_2_self_attn_out_proj_bias, alloc1650) + R.vm.kill_object(reshape23) + R.vm.kill_object(model_encoder_layers_2_self_attn_out_proj_weight) + R.vm.kill_object(model_encoder_layers_2_self_attn_out_proj_bias) + gv2002: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1651: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv2002, R.dtype("float16")) + cls.add4(alloc1644, alloc1650, alloc1651) + R.vm.kill_object(alloc1644) + R.vm.kill_object(alloc1650) + model_encoder_layers_2_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[48] + model_encoder_layers_2_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[49] + gv2003: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1652: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2003, R.dtype("float16")) + cls.layer_norm1(alloc1651, model_encoder_layers_2_final_layer_norm_weight, model_encoder_layers_2_final_layer_norm_bias, alloc1652) + R.vm.kill_object(model_encoder_layers_2_final_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_2_final_layer_norm_bias) + model_encoder_layers_2_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[44] + model_encoder_layers_2_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[45] + gv2004: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc1653: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv2004, R.dtype("float16")) + _1651: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", model_encoder_layers_2_fc1_weight, alloc1652, model_encoder_layers_2_fc1_bias, alloc1653) + R.vm.kill_object(alloc1652) + R.vm.kill_object(model_encoder_layers_2_fc1_weight) + R.vm.kill_object(model_encoder_layers_2_fc1_bias) + model_encoder_layers_2_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[46] + model_encoder_layers_2_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[47] + gv2005: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1654: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv2005, R.dtype("float16")) + _1652: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", model_encoder_layers_2_fc2_weight, alloc1653, model_encoder_layers_2_fc2_bias, alloc1654) + R.vm.kill_object(alloc1653) + R.vm.kill_object(model_encoder_layers_2_fc2_weight) + R.vm.kill_object(model_encoder_layers_2_fc2_bias) + gv2006: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1655: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv2006, R.dtype("float16")) + cls.fused_add4_maximum_minimum(alloc1651, alloc1654, alloc1655) + R.vm.kill_object(alloc1651) + R.vm.kill_object(alloc1654) + model_encoder_layers_3_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[57] + model_encoder_layers_3_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[58] + gv2007: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1656: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2007, R.dtype("float16")) + cls.layer_norm1(alloc1655, model_encoder_layers_3_self_attn_layer_norm_weight, model_encoder_layers_3_self_attn_layer_norm_bias, alloc1656) + R.vm.kill_object(model_encoder_layers_3_self_attn_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_3_self_attn_layer_norm_bias) + model_encoder_layers_3_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[53] + model_encoder_layers_3_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[54] + gv2008: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1657: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv2008, R.dtype("float16")) + _1655: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_3_self_attn_q_proj_weight, alloc1656, model_encoder_layers_3_self_attn_q_proj_bias, alloc1657) + R.vm.kill_object(model_encoder_layers_3_self_attn_q_proj_weight) + R.vm.kill_object(model_encoder_layers_3_self_attn_q_proj_bias) + gv2009: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape24: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1657, gv2009, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1657) + model_encoder_layers_3_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[50] + gv2010: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1658: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv2010, R.dtype("float16")) + _1656: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_encoder_layers_3_self_attn_k_proj_weight, alloc1656, alloc1658) + R.vm.kill_object(model_encoder_layers_3_self_attn_k_proj_weight) + gv2011: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape25: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1658, gv2011, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1658) + model_encoder_layers_3_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[51] + model_encoder_layers_3_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[52] + gv2012: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1659: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv2012, R.dtype("float16")) + _1657: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_3_self_attn_v_proj_weight, alloc1656, model_encoder_layers_3_self_attn_v_proj_bias, alloc1659) + R.vm.kill_object(alloc1656) + R.vm.kill_object(model_encoder_layers_3_self_attn_v_proj_weight) + R.vm.kill_object(model_encoder_layers_3_self_attn_v_proj_bias) + gv2013: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape26: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1659, gv2013, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1659) + gv2014: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape27: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape24, gv2014, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape24) + gv2015: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape28: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape25, gv2015, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape25) + gv2016: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape29: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape26, gv2016, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape26) + gv2017: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc1660: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2017, R.dtype("float16")) + _1658: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_no_append", paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), reshape27, reshape28, reshape29, alloc1660) + R.vm.kill_object(reshape27) + R.vm.kill_object(reshape28) + R.vm.kill_object(reshape29) + gv2018: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape30: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1660, gv2018, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1660) + gv2019: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape31: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape30, gv2019, sinfo_args=(R.Tensor((batch_size, 1500, 1280), dtype="float16"),)) + R.vm.kill_object(reshape30) + model_encoder_layers_3_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[55] + model_encoder_layers_3_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[56] + gv2020: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1661: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv2020, R.dtype("float16")) + _1659: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_3_self_attn_out_proj_weight, reshape31, model_encoder_layers_3_self_attn_out_proj_bias, alloc1661) + R.vm.kill_object(reshape31) + R.vm.kill_object(model_encoder_layers_3_self_attn_out_proj_weight) + R.vm.kill_object(model_encoder_layers_3_self_attn_out_proj_bias) + gv2021: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1662: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv2021, R.dtype("float16")) + cls.add4(alloc1655, alloc1661, alloc1662) + R.vm.kill_object(alloc1655) + R.vm.kill_object(alloc1661) + model_encoder_layers_3_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[63] + model_encoder_layers_3_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[64] + gv2022: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1663: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2022, R.dtype("float16")) + cls.layer_norm1(alloc1662, model_encoder_layers_3_final_layer_norm_weight, model_encoder_layers_3_final_layer_norm_bias, alloc1663) + R.vm.kill_object(model_encoder_layers_3_final_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_3_final_layer_norm_bias) + model_encoder_layers_3_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[59] + model_encoder_layers_3_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[60] + gv2023: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc1664: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv2023, R.dtype("float16")) + _1662: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", model_encoder_layers_3_fc1_weight, alloc1663, model_encoder_layers_3_fc1_bias, alloc1664) + R.vm.kill_object(alloc1663) + R.vm.kill_object(model_encoder_layers_3_fc1_weight) + R.vm.kill_object(model_encoder_layers_3_fc1_bias) + model_encoder_layers_3_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[61] + model_encoder_layers_3_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[62] + gv2024: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1665: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv2024, R.dtype("float16")) + _1663: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", model_encoder_layers_3_fc2_weight, alloc1664, model_encoder_layers_3_fc2_bias, alloc1665) + R.vm.kill_object(alloc1664) + R.vm.kill_object(model_encoder_layers_3_fc2_weight) + R.vm.kill_object(model_encoder_layers_3_fc2_bias) + gv2025: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1666: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv2025, R.dtype("float16")) + cls.fused_add4_maximum_minimum(alloc1662, alloc1665, alloc1666) + R.vm.kill_object(alloc1662) + R.vm.kill_object(alloc1665) + model_encoder_layers_4_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[72] + model_encoder_layers_4_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[73] + gv2026: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1667: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2026, R.dtype("float16")) + cls.layer_norm1(alloc1666, model_encoder_layers_4_self_attn_layer_norm_weight, model_encoder_layers_4_self_attn_layer_norm_bias, alloc1667) + R.vm.kill_object(model_encoder_layers_4_self_attn_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_4_self_attn_layer_norm_bias) + model_encoder_layers_4_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[68] + model_encoder_layers_4_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[69] + gv2027: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1668: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv2027, R.dtype("float16")) + _1666: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_4_self_attn_q_proj_weight, alloc1667, model_encoder_layers_4_self_attn_q_proj_bias, alloc1668) + R.vm.kill_object(model_encoder_layers_4_self_attn_q_proj_weight) + R.vm.kill_object(model_encoder_layers_4_self_attn_q_proj_bias) + gv2028: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape32: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1668, gv2028, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1668) + model_encoder_layers_4_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[65] + gv2029: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1669: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv2029, R.dtype("float16")) + _1667: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_encoder_layers_4_self_attn_k_proj_weight, alloc1667, alloc1669) + R.vm.kill_object(model_encoder_layers_4_self_attn_k_proj_weight) + gv2030: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape33: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1669, gv2030, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1669) + model_encoder_layers_4_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[66] + model_encoder_layers_4_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[67] + gv2031: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1670: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv2031, R.dtype("float16")) + _1668: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_4_self_attn_v_proj_weight, alloc1667, model_encoder_layers_4_self_attn_v_proj_bias, alloc1670) + R.vm.kill_object(alloc1667) + R.vm.kill_object(model_encoder_layers_4_self_attn_v_proj_weight) + R.vm.kill_object(model_encoder_layers_4_self_attn_v_proj_bias) + gv2032: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape34: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1670, gv2032, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1670) + gv2033: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape35: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape32, gv2033, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape32) + gv2034: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape36: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape33, gv2034, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape33) + gv2035: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape37: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape34, gv2035, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape34) + gv2036: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc1671: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2036, R.dtype("float16")) + _1669: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_no_append", paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), reshape35, reshape36, reshape37, alloc1671) + R.vm.kill_object(reshape35) + R.vm.kill_object(reshape36) + R.vm.kill_object(reshape37) + gv2037: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape38: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1671, gv2037, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1671) + gv2038: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape39: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape38, gv2038, sinfo_args=(R.Tensor((batch_size, 1500, 1280), dtype="float16"),)) + R.vm.kill_object(reshape38) + model_encoder_layers_4_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[70] + model_encoder_layers_4_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[71] + gv2039: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1672: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv2039, R.dtype("float16")) + _1670: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_4_self_attn_out_proj_weight, reshape39, model_encoder_layers_4_self_attn_out_proj_bias, alloc1672) + R.vm.kill_object(reshape39) + R.vm.kill_object(model_encoder_layers_4_self_attn_out_proj_weight) + R.vm.kill_object(model_encoder_layers_4_self_attn_out_proj_bias) + gv2040: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1673: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv2040, R.dtype("float16")) + cls.add4(alloc1666, alloc1672, alloc1673) + R.vm.kill_object(alloc1666) + R.vm.kill_object(alloc1672) + model_encoder_layers_4_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[78] + model_encoder_layers_4_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[79] + gv2041: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1674: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2041, R.dtype("float16")) + cls.layer_norm1(alloc1673, model_encoder_layers_4_final_layer_norm_weight, model_encoder_layers_4_final_layer_norm_bias, alloc1674) + R.vm.kill_object(model_encoder_layers_4_final_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_4_final_layer_norm_bias) + model_encoder_layers_4_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[74] + model_encoder_layers_4_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[75] + gv2042: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc1675: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv2042, R.dtype("float16")) + _1673: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", model_encoder_layers_4_fc1_weight, alloc1674, model_encoder_layers_4_fc1_bias, alloc1675) + R.vm.kill_object(alloc1674) + R.vm.kill_object(model_encoder_layers_4_fc1_weight) + R.vm.kill_object(model_encoder_layers_4_fc1_bias) + model_encoder_layers_4_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[76] + model_encoder_layers_4_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[77] + gv2043: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1676: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv2043, R.dtype("float16")) + _1674: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", model_encoder_layers_4_fc2_weight, alloc1675, model_encoder_layers_4_fc2_bias, alloc1676) + R.vm.kill_object(alloc1675) + R.vm.kill_object(model_encoder_layers_4_fc2_weight) + R.vm.kill_object(model_encoder_layers_4_fc2_bias) + gv2044: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1677: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv2044, R.dtype("float16")) + cls.fused_add4_maximum_minimum(alloc1673, alloc1676, alloc1677) + R.vm.kill_object(alloc1673) + R.vm.kill_object(alloc1676) + model_encoder_layers_5_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[87] + model_encoder_layers_5_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[88] + gv2045: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1678: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2045, R.dtype("float16")) + cls.layer_norm1(alloc1677, model_encoder_layers_5_self_attn_layer_norm_weight, model_encoder_layers_5_self_attn_layer_norm_bias, alloc1678) + R.vm.kill_object(model_encoder_layers_5_self_attn_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_5_self_attn_layer_norm_bias) + model_encoder_layers_5_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[83] + model_encoder_layers_5_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[84] + gv2046: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1679: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv2046, R.dtype("float16")) + _1677: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_5_self_attn_q_proj_weight, alloc1678, model_encoder_layers_5_self_attn_q_proj_bias, alloc1679) + R.vm.kill_object(model_encoder_layers_5_self_attn_q_proj_weight) + R.vm.kill_object(model_encoder_layers_5_self_attn_q_proj_bias) + gv2047: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape40: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1679, gv2047, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1679) + model_encoder_layers_5_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[80] + gv2048: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1680: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv2048, R.dtype("float16")) + _1678: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_encoder_layers_5_self_attn_k_proj_weight, alloc1678, alloc1680) + R.vm.kill_object(model_encoder_layers_5_self_attn_k_proj_weight) + gv2049: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape41: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1680, gv2049, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1680) + model_encoder_layers_5_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[81] + model_encoder_layers_5_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[82] + gv2050: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1681: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv2050, R.dtype("float16")) + _1679: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_5_self_attn_v_proj_weight, alloc1678, model_encoder_layers_5_self_attn_v_proj_bias, alloc1681) + R.vm.kill_object(alloc1678) + R.vm.kill_object(model_encoder_layers_5_self_attn_v_proj_weight) + R.vm.kill_object(model_encoder_layers_5_self_attn_v_proj_bias) + gv2051: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape42: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1681, gv2051, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1681) + gv2052: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape43: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape40, gv2052, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape40) + gv2053: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape44: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape41, gv2053, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape41) + gv2054: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape45: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape42, gv2054, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape42) + gv2055: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc1682: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2055, R.dtype("float16")) + _1680: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_no_append", paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), reshape43, reshape44, reshape45, alloc1682) + R.vm.kill_object(reshape43) + R.vm.kill_object(reshape44) + R.vm.kill_object(reshape45) + gv2056: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape46: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1682, gv2056, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1682) + gv2057: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape47: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape46, gv2057, sinfo_args=(R.Tensor((batch_size, 1500, 1280), dtype="float16"),)) + R.vm.kill_object(reshape46) + model_encoder_layers_5_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[85] + model_encoder_layers_5_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[86] + gv2058: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1683: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv2058, R.dtype("float16")) + _1681: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_5_self_attn_out_proj_weight, reshape47, model_encoder_layers_5_self_attn_out_proj_bias, alloc1683) + R.vm.kill_object(reshape47) + R.vm.kill_object(model_encoder_layers_5_self_attn_out_proj_weight) + R.vm.kill_object(model_encoder_layers_5_self_attn_out_proj_bias) + gv2059: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1684: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv2059, R.dtype("float16")) + cls.add4(alloc1677, alloc1683, alloc1684) + R.vm.kill_object(alloc1677) + R.vm.kill_object(alloc1683) + model_encoder_layers_5_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[93] + model_encoder_layers_5_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[94] + gv2060: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1685: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2060, R.dtype("float16")) + cls.layer_norm1(alloc1684, model_encoder_layers_5_final_layer_norm_weight, model_encoder_layers_5_final_layer_norm_bias, alloc1685) + R.vm.kill_object(model_encoder_layers_5_final_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_5_final_layer_norm_bias) + model_encoder_layers_5_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[89] + model_encoder_layers_5_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[90] + gv2061: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc1686: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv2061, R.dtype("float16")) + _1684: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", model_encoder_layers_5_fc1_weight, alloc1685, model_encoder_layers_5_fc1_bias, alloc1686) + R.vm.kill_object(alloc1685) + R.vm.kill_object(model_encoder_layers_5_fc1_weight) + R.vm.kill_object(model_encoder_layers_5_fc1_bias) + model_encoder_layers_5_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[91] + model_encoder_layers_5_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[92] + gv2062: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1687: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv2062, R.dtype("float16")) + _1685: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", model_encoder_layers_5_fc2_weight, alloc1686, model_encoder_layers_5_fc2_bias, alloc1687) + R.vm.kill_object(alloc1686) + R.vm.kill_object(model_encoder_layers_5_fc2_weight) + R.vm.kill_object(model_encoder_layers_5_fc2_bias) + gv2063: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1688: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv2063, R.dtype("float16")) + cls.fused_add4_maximum_minimum(alloc1684, alloc1687, alloc1688) + R.vm.kill_object(alloc1684) + R.vm.kill_object(alloc1687) + model_encoder_layers_6_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[102] + model_encoder_layers_6_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[103] + gv2064: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1689: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2064, R.dtype("float16")) + cls.layer_norm1(alloc1688, model_encoder_layers_6_self_attn_layer_norm_weight, model_encoder_layers_6_self_attn_layer_norm_bias, alloc1689) + R.vm.kill_object(model_encoder_layers_6_self_attn_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_6_self_attn_layer_norm_bias) + model_encoder_layers_6_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[98] + model_encoder_layers_6_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[99] + gv2065: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1690: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv2065, R.dtype("float16")) + _1688: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_6_self_attn_q_proj_weight, alloc1689, model_encoder_layers_6_self_attn_q_proj_bias, alloc1690) + R.vm.kill_object(model_encoder_layers_6_self_attn_q_proj_weight) + R.vm.kill_object(model_encoder_layers_6_self_attn_q_proj_bias) + gv2066: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape48: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1690, gv2066, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1690) + model_encoder_layers_6_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[95] + gv2067: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1691: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv2067, R.dtype("float16")) + _1689: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_encoder_layers_6_self_attn_k_proj_weight, alloc1689, alloc1691) + R.vm.kill_object(model_encoder_layers_6_self_attn_k_proj_weight) + gv2068: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape49: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1691, gv2068, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1691) + model_encoder_layers_6_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[96] + model_encoder_layers_6_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[97] + gv2069: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1692: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv2069, R.dtype("float16")) + _1690: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_6_self_attn_v_proj_weight, alloc1689, model_encoder_layers_6_self_attn_v_proj_bias, alloc1692) + R.vm.kill_object(alloc1689) + R.vm.kill_object(model_encoder_layers_6_self_attn_v_proj_weight) + R.vm.kill_object(model_encoder_layers_6_self_attn_v_proj_bias) + gv2070: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape50: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1692, gv2070, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1692) + gv2071: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape51: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape48, gv2071, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape48) + gv2072: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape52: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape49, gv2072, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape49) + gv2073: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape53: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape50, gv2073, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape50) + gv2074: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc1693: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2074, R.dtype("float16")) + _1691: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_no_append", paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), reshape51, reshape52, reshape53, alloc1693) + R.vm.kill_object(reshape51) + R.vm.kill_object(reshape52) + R.vm.kill_object(reshape53) + gv2075: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape54: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1693, gv2075, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1693) + gv2076: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape55: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape54, gv2076, sinfo_args=(R.Tensor((batch_size, 1500, 1280), dtype="float16"),)) + R.vm.kill_object(reshape54) + model_encoder_layers_6_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[100] + model_encoder_layers_6_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[101] + gv2077: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1694: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv2077, R.dtype("float16")) + _1692: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_6_self_attn_out_proj_weight, reshape55, model_encoder_layers_6_self_attn_out_proj_bias, alloc1694) + R.vm.kill_object(reshape55) + R.vm.kill_object(model_encoder_layers_6_self_attn_out_proj_weight) + R.vm.kill_object(model_encoder_layers_6_self_attn_out_proj_bias) + gv2078: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1695: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv2078, R.dtype("float16")) + cls.add4(alloc1688, alloc1694, alloc1695) + R.vm.kill_object(alloc1688) + R.vm.kill_object(alloc1694) + model_encoder_layers_6_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[108] + model_encoder_layers_6_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[109] + gv2079: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1696: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2079, R.dtype("float16")) + cls.layer_norm1(alloc1695, model_encoder_layers_6_final_layer_norm_weight, model_encoder_layers_6_final_layer_norm_bias, alloc1696) + R.vm.kill_object(model_encoder_layers_6_final_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_6_final_layer_norm_bias) + model_encoder_layers_6_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[104] + model_encoder_layers_6_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[105] + gv2080: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc1697: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv2080, R.dtype("float16")) + _1695: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", model_encoder_layers_6_fc1_weight, alloc1696, model_encoder_layers_6_fc1_bias, alloc1697) + R.vm.kill_object(alloc1696) + R.vm.kill_object(model_encoder_layers_6_fc1_weight) + R.vm.kill_object(model_encoder_layers_6_fc1_bias) + model_encoder_layers_6_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[106] + model_encoder_layers_6_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[107] + gv2081: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1698: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv2081, R.dtype("float16")) + _1696: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", model_encoder_layers_6_fc2_weight, alloc1697, model_encoder_layers_6_fc2_bias, alloc1698) + R.vm.kill_object(alloc1697) + R.vm.kill_object(model_encoder_layers_6_fc2_weight) + R.vm.kill_object(model_encoder_layers_6_fc2_bias) + gv2082: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1699: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv2082, R.dtype("float16")) + cls.fused_add4_maximum_minimum(alloc1695, alloc1698, alloc1699) + R.vm.kill_object(alloc1695) + R.vm.kill_object(alloc1698) + model_encoder_layers_7_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[117] + model_encoder_layers_7_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[118] + gv2083: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1700: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2083, R.dtype("float16")) + cls.layer_norm1(alloc1699, model_encoder_layers_7_self_attn_layer_norm_weight, model_encoder_layers_7_self_attn_layer_norm_bias, alloc1700) + R.vm.kill_object(model_encoder_layers_7_self_attn_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_7_self_attn_layer_norm_bias) + model_encoder_layers_7_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[113] + model_encoder_layers_7_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[114] + gv2084: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1701: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv2084, R.dtype("float16")) + _1699: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_7_self_attn_q_proj_weight, alloc1700, model_encoder_layers_7_self_attn_q_proj_bias, alloc1701) + R.vm.kill_object(model_encoder_layers_7_self_attn_q_proj_weight) + R.vm.kill_object(model_encoder_layers_7_self_attn_q_proj_bias) + gv2085: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape56: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1701, gv2085, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1701) + model_encoder_layers_7_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[110] + gv2086: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1702: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv2086, R.dtype("float16")) + _1700: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_encoder_layers_7_self_attn_k_proj_weight, alloc1700, alloc1702) + R.vm.kill_object(model_encoder_layers_7_self_attn_k_proj_weight) + gv2087: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape57: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1702, gv2087, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1702) + model_encoder_layers_7_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[111] + model_encoder_layers_7_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[112] + gv2088: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1703: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv2088, R.dtype("float16")) + _1701: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_7_self_attn_v_proj_weight, alloc1700, model_encoder_layers_7_self_attn_v_proj_bias, alloc1703) + R.vm.kill_object(alloc1700) + R.vm.kill_object(model_encoder_layers_7_self_attn_v_proj_weight) + R.vm.kill_object(model_encoder_layers_7_self_attn_v_proj_bias) + gv2089: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape58: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1703, gv2089, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1703) + gv2090: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape59: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape56, gv2090, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape56) + gv2091: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape60: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape57, gv2091, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape57) + gv2092: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape61: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape58, gv2092, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape58) + gv2093: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc1704: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2093, R.dtype("float16")) + _1702: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_no_append", paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), reshape59, reshape60, reshape61, alloc1704) + R.vm.kill_object(reshape59) + R.vm.kill_object(reshape60) + R.vm.kill_object(reshape61) + gv2094: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape62: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1704, gv2094, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1704) + gv2095: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape63: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape62, gv2095, sinfo_args=(R.Tensor((batch_size, 1500, 1280), dtype="float16"),)) + R.vm.kill_object(reshape62) + model_encoder_layers_7_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[115] + model_encoder_layers_7_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[116] + gv2096: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1705: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv2096, R.dtype("float16")) + _1703: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_7_self_attn_out_proj_weight, reshape63, model_encoder_layers_7_self_attn_out_proj_bias, alloc1705) + R.vm.kill_object(reshape63) + R.vm.kill_object(model_encoder_layers_7_self_attn_out_proj_weight) + R.vm.kill_object(model_encoder_layers_7_self_attn_out_proj_bias) + gv2097: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1706: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv2097, R.dtype("float16")) + cls.add4(alloc1699, alloc1705, alloc1706) + R.vm.kill_object(alloc1699) + R.vm.kill_object(alloc1705) + model_encoder_layers_7_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[123] + model_encoder_layers_7_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[124] + gv2098: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1707: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2098, R.dtype("float16")) + cls.layer_norm1(alloc1706, model_encoder_layers_7_final_layer_norm_weight, model_encoder_layers_7_final_layer_norm_bias, alloc1707) + R.vm.kill_object(model_encoder_layers_7_final_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_7_final_layer_norm_bias) + model_encoder_layers_7_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[119] + model_encoder_layers_7_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[120] + gv2099: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc1708: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv2099, R.dtype("float16")) + _1706: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", model_encoder_layers_7_fc1_weight, alloc1707, model_encoder_layers_7_fc1_bias, alloc1708) + R.vm.kill_object(alloc1707) + R.vm.kill_object(model_encoder_layers_7_fc1_weight) + R.vm.kill_object(model_encoder_layers_7_fc1_bias) + model_encoder_layers_7_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[121] + model_encoder_layers_7_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[122] + gv2100: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1709: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv2100, R.dtype("float16")) + _1707: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", model_encoder_layers_7_fc2_weight, alloc1708, model_encoder_layers_7_fc2_bias, alloc1709) + R.vm.kill_object(alloc1708) + R.vm.kill_object(model_encoder_layers_7_fc2_weight) + R.vm.kill_object(model_encoder_layers_7_fc2_bias) + gv2101: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1710: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv2101, R.dtype("float16")) + cls.fused_add4_maximum_minimum(alloc1706, alloc1709, alloc1710) + R.vm.kill_object(alloc1706) + R.vm.kill_object(alloc1709) + model_encoder_layers_8_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[132] + model_encoder_layers_8_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[133] + gv2102: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1711: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2102, R.dtype("float16")) + cls.layer_norm1(alloc1710, model_encoder_layers_8_self_attn_layer_norm_weight, model_encoder_layers_8_self_attn_layer_norm_bias, alloc1711) + R.vm.kill_object(model_encoder_layers_8_self_attn_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_8_self_attn_layer_norm_bias) + model_encoder_layers_8_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[128] + model_encoder_layers_8_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[129] + gv2103: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1712: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv2103, R.dtype("float16")) + _1710: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_8_self_attn_q_proj_weight, alloc1711, model_encoder_layers_8_self_attn_q_proj_bias, alloc1712) + R.vm.kill_object(model_encoder_layers_8_self_attn_q_proj_weight) + R.vm.kill_object(model_encoder_layers_8_self_attn_q_proj_bias) + gv2104: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape64: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1712, gv2104, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1712) + model_encoder_layers_8_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[125] + gv2105: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1713: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv2105, R.dtype("float16")) + _1711: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_encoder_layers_8_self_attn_k_proj_weight, alloc1711, alloc1713) + R.vm.kill_object(model_encoder_layers_8_self_attn_k_proj_weight) + gv2106: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape65: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1713, gv2106, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1713) + model_encoder_layers_8_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[126] + model_encoder_layers_8_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[127] + gv2107: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1714: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv2107, R.dtype("float16")) + _1712: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_8_self_attn_v_proj_weight, alloc1711, model_encoder_layers_8_self_attn_v_proj_bias, alloc1714) + R.vm.kill_object(alloc1711) + R.vm.kill_object(model_encoder_layers_8_self_attn_v_proj_weight) + R.vm.kill_object(model_encoder_layers_8_self_attn_v_proj_bias) + gv2108: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape66: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1714, gv2108, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1714) + gv2109: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape67: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape64, gv2109, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape64) + gv2110: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape68: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape65, gv2110, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape65) + gv2111: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape69: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape66, gv2111, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape66) + gv2112: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc1715: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2112, R.dtype("float16")) + _1713: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_no_append", paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), reshape67, reshape68, reshape69, alloc1715) + R.vm.kill_object(reshape67) + R.vm.kill_object(reshape68) + R.vm.kill_object(reshape69) + gv2113: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape70: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1715, gv2113, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1715) + gv2114: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape71: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape70, gv2114, sinfo_args=(R.Tensor((batch_size, 1500, 1280), dtype="float16"),)) + R.vm.kill_object(reshape70) + model_encoder_layers_8_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[130] + model_encoder_layers_8_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[131] + gv2115: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1716: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv2115, R.dtype("float16")) + _1714: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_8_self_attn_out_proj_weight, reshape71, model_encoder_layers_8_self_attn_out_proj_bias, alloc1716) + R.vm.kill_object(reshape71) + R.vm.kill_object(model_encoder_layers_8_self_attn_out_proj_weight) + R.vm.kill_object(model_encoder_layers_8_self_attn_out_proj_bias) + gv2116: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1717: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv2116, R.dtype("float16")) + cls.add4(alloc1710, alloc1716, alloc1717) + R.vm.kill_object(alloc1710) + R.vm.kill_object(alloc1716) + model_encoder_layers_8_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[138] + model_encoder_layers_8_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[139] + gv2117: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1718: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2117, R.dtype("float16")) + cls.layer_norm1(alloc1717, model_encoder_layers_8_final_layer_norm_weight, model_encoder_layers_8_final_layer_norm_bias, alloc1718) + R.vm.kill_object(model_encoder_layers_8_final_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_8_final_layer_norm_bias) + model_encoder_layers_8_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[134] + model_encoder_layers_8_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[135] + gv2118: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc1719: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv2118, R.dtype("float16")) + _1717: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", model_encoder_layers_8_fc1_weight, alloc1718, model_encoder_layers_8_fc1_bias, alloc1719) + R.vm.kill_object(alloc1718) + R.vm.kill_object(model_encoder_layers_8_fc1_weight) + R.vm.kill_object(model_encoder_layers_8_fc1_bias) + model_encoder_layers_8_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[136] + model_encoder_layers_8_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[137] + gv2119: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1720: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv2119, R.dtype("float16")) + _1718: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", model_encoder_layers_8_fc2_weight, alloc1719, model_encoder_layers_8_fc2_bias, alloc1720) + R.vm.kill_object(alloc1719) + R.vm.kill_object(model_encoder_layers_8_fc2_weight) + R.vm.kill_object(model_encoder_layers_8_fc2_bias) + gv2120: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1721: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv2120, R.dtype("float16")) + cls.fused_add4_maximum_minimum(alloc1717, alloc1720, alloc1721) + R.vm.kill_object(alloc1717) + R.vm.kill_object(alloc1720) + model_encoder_layers_9_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[147] + model_encoder_layers_9_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[148] + gv2121: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1722: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2121, R.dtype("float16")) + cls.layer_norm1(alloc1721, model_encoder_layers_9_self_attn_layer_norm_weight, model_encoder_layers_9_self_attn_layer_norm_bias, alloc1722) + R.vm.kill_object(model_encoder_layers_9_self_attn_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_9_self_attn_layer_norm_bias) + model_encoder_layers_9_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[143] + model_encoder_layers_9_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[144] + gv2122: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1723: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv2122, R.dtype("float16")) + _1721: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_9_self_attn_q_proj_weight, alloc1722, model_encoder_layers_9_self_attn_q_proj_bias, alloc1723) + R.vm.kill_object(model_encoder_layers_9_self_attn_q_proj_weight) + R.vm.kill_object(model_encoder_layers_9_self_attn_q_proj_bias) + gv2123: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape72: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1723, gv2123, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1723) + model_encoder_layers_9_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[140] + gv2124: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1724: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv2124, R.dtype("float16")) + _1722: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_encoder_layers_9_self_attn_k_proj_weight, alloc1722, alloc1724) + R.vm.kill_object(model_encoder_layers_9_self_attn_k_proj_weight) + gv2125: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape73: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1724, gv2125, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1724) + model_encoder_layers_9_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[141] + model_encoder_layers_9_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[142] + gv2126: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1725: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv2126, R.dtype("float16")) + _1723: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_9_self_attn_v_proj_weight, alloc1722, model_encoder_layers_9_self_attn_v_proj_bias, alloc1725) + R.vm.kill_object(alloc1722) + R.vm.kill_object(model_encoder_layers_9_self_attn_v_proj_weight) + R.vm.kill_object(model_encoder_layers_9_self_attn_v_proj_bias) + gv2127: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape74: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1725, gv2127, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1725) + gv2128: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape75: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape72, gv2128, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape72) + gv2129: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape76: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape73, gv2129, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape73) + gv2130: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape77: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape74, gv2130, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape74) + gv2131: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc1726: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2131, R.dtype("float16")) + _1724: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_no_append", paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), reshape75, reshape76, reshape77, alloc1726) + R.vm.kill_object(reshape75) + R.vm.kill_object(reshape76) + R.vm.kill_object(reshape77) + gv2132: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape78: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1726, gv2132, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1726) + gv2133: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape79: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape78, gv2133, sinfo_args=(R.Tensor((batch_size, 1500, 1280), dtype="float16"),)) + R.vm.kill_object(reshape78) + model_encoder_layers_9_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[145] + model_encoder_layers_9_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[146] + gv2134: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1727: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv2134, R.dtype("float16")) + _1725: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_9_self_attn_out_proj_weight, reshape79, model_encoder_layers_9_self_attn_out_proj_bias, alloc1727) + R.vm.kill_object(reshape79) + R.vm.kill_object(model_encoder_layers_9_self_attn_out_proj_weight) + R.vm.kill_object(model_encoder_layers_9_self_attn_out_proj_bias) + gv2135: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1728: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv2135, R.dtype("float16")) + cls.add4(alloc1721, alloc1727, alloc1728) + R.vm.kill_object(alloc1721) + R.vm.kill_object(alloc1727) + model_encoder_layers_9_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[153] + model_encoder_layers_9_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[154] + gv2136: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1729: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2136, R.dtype("float16")) + cls.layer_norm1(alloc1728, model_encoder_layers_9_final_layer_norm_weight, model_encoder_layers_9_final_layer_norm_bias, alloc1729) + R.vm.kill_object(model_encoder_layers_9_final_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_9_final_layer_norm_bias) + model_encoder_layers_9_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[149] + model_encoder_layers_9_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[150] + gv2137: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc1730: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv2137, R.dtype("float16")) + _1728: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", model_encoder_layers_9_fc1_weight, alloc1729, model_encoder_layers_9_fc1_bias, alloc1730) + R.vm.kill_object(alloc1729) + R.vm.kill_object(model_encoder_layers_9_fc1_weight) + R.vm.kill_object(model_encoder_layers_9_fc1_bias) + model_encoder_layers_9_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[151] + model_encoder_layers_9_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[152] + gv2138: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1731: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv2138, R.dtype("float16")) + _1729: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", model_encoder_layers_9_fc2_weight, alloc1730, model_encoder_layers_9_fc2_bias, alloc1731) + R.vm.kill_object(alloc1730) + R.vm.kill_object(model_encoder_layers_9_fc2_weight) + R.vm.kill_object(model_encoder_layers_9_fc2_bias) + gv2139: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1732: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv2139, R.dtype("float16")) + cls.fused_add4_maximum_minimum(alloc1728, alloc1731, alloc1732) + R.vm.kill_object(alloc1728) + R.vm.kill_object(alloc1731) + model_encoder_layers_10_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[162] + model_encoder_layers_10_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[163] + gv2140: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1733: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2140, R.dtype("float16")) + cls.layer_norm1(alloc1732, model_encoder_layers_10_self_attn_layer_norm_weight, model_encoder_layers_10_self_attn_layer_norm_bias, alloc1733) + R.vm.kill_object(model_encoder_layers_10_self_attn_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_10_self_attn_layer_norm_bias) + model_encoder_layers_10_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[158] + model_encoder_layers_10_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[159] + gv2141: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1734: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv2141, R.dtype("float16")) + _1732: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_10_self_attn_q_proj_weight, alloc1733, model_encoder_layers_10_self_attn_q_proj_bias, alloc1734) + R.vm.kill_object(model_encoder_layers_10_self_attn_q_proj_weight) + R.vm.kill_object(model_encoder_layers_10_self_attn_q_proj_bias) + gv2142: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape80: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1734, gv2142, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1734) + model_encoder_layers_10_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[155] + gv2143: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1735: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv2143, R.dtype("float16")) + _1733: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_encoder_layers_10_self_attn_k_proj_weight, alloc1733, alloc1735) + R.vm.kill_object(model_encoder_layers_10_self_attn_k_proj_weight) + gv2144: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape81: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1735, gv2144, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1735) + model_encoder_layers_10_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[156] + model_encoder_layers_10_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[157] + gv2145: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1736: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv2145, R.dtype("float16")) + _1734: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_10_self_attn_v_proj_weight, alloc1733, model_encoder_layers_10_self_attn_v_proj_bias, alloc1736) + R.vm.kill_object(alloc1733) + R.vm.kill_object(model_encoder_layers_10_self_attn_v_proj_weight) + R.vm.kill_object(model_encoder_layers_10_self_attn_v_proj_bias) + gv2146: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape82: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1736, gv2146, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1736) + gv2147: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape83: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape80, gv2147, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape80) + gv2148: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape84: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape81, gv2148, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape81) + gv2149: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape85: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape82, gv2149, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape82) + gv2150: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc1737: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2150, R.dtype("float16")) + _1735: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_no_append", paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), reshape83, reshape84, reshape85, alloc1737) + R.vm.kill_object(reshape83) + R.vm.kill_object(reshape84) + R.vm.kill_object(reshape85) + gv2151: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape86: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1737, gv2151, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1737) + gv2152: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape87: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape86, gv2152, sinfo_args=(R.Tensor((batch_size, 1500, 1280), dtype="float16"),)) + R.vm.kill_object(reshape86) + model_encoder_layers_10_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[160] + model_encoder_layers_10_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[161] + gv2153: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1738: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv2153, R.dtype("float16")) + _1736: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_10_self_attn_out_proj_weight, reshape87, model_encoder_layers_10_self_attn_out_proj_bias, alloc1738) + R.vm.kill_object(reshape87) + R.vm.kill_object(model_encoder_layers_10_self_attn_out_proj_weight) + R.vm.kill_object(model_encoder_layers_10_self_attn_out_proj_bias) + gv2154: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1739: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv2154, R.dtype("float16")) + cls.add4(alloc1732, alloc1738, alloc1739) + R.vm.kill_object(alloc1732) + R.vm.kill_object(alloc1738) + model_encoder_layers_10_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[168] + model_encoder_layers_10_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[169] + gv2155: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1740: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2155, R.dtype("float16")) + cls.layer_norm1(alloc1739, model_encoder_layers_10_final_layer_norm_weight, model_encoder_layers_10_final_layer_norm_bias, alloc1740) + R.vm.kill_object(model_encoder_layers_10_final_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_10_final_layer_norm_bias) + model_encoder_layers_10_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[164] + model_encoder_layers_10_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[165] + gv2156: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc1741: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv2156, R.dtype("float16")) + _1739: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", model_encoder_layers_10_fc1_weight, alloc1740, model_encoder_layers_10_fc1_bias, alloc1741) + R.vm.kill_object(alloc1740) + R.vm.kill_object(model_encoder_layers_10_fc1_weight) + R.vm.kill_object(model_encoder_layers_10_fc1_bias) + model_encoder_layers_10_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[166] + model_encoder_layers_10_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[167] + gv2157: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1742: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv2157, R.dtype("float16")) + _1740: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", model_encoder_layers_10_fc2_weight, alloc1741, model_encoder_layers_10_fc2_bias, alloc1742) + R.vm.kill_object(alloc1741) + R.vm.kill_object(model_encoder_layers_10_fc2_weight) + R.vm.kill_object(model_encoder_layers_10_fc2_bias) + gv2158: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1743: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv2158, R.dtype("float16")) + cls.fused_add4_maximum_minimum(alloc1739, alloc1742, alloc1743) + R.vm.kill_object(alloc1739) + R.vm.kill_object(alloc1742) + model_encoder_layers_11_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[177] + model_encoder_layers_11_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[178] + gv2159: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1744: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2159, R.dtype("float16")) + cls.layer_norm1(alloc1743, model_encoder_layers_11_self_attn_layer_norm_weight, model_encoder_layers_11_self_attn_layer_norm_bias, alloc1744) + R.vm.kill_object(model_encoder_layers_11_self_attn_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_11_self_attn_layer_norm_bias) + model_encoder_layers_11_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[173] + model_encoder_layers_11_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[174] + gv2160: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1745: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv2160, R.dtype("float16")) + _1743: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_11_self_attn_q_proj_weight, alloc1744, model_encoder_layers_11_self_attn_q_proj_bias, alloc1745) + R.vm.kill_object(model_encoder_layers_11_self_attn_q_proj_weight) + R.vm.kill_object(model_encoder_layers_11_self_attn_q_proj_bias) + gv2161: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape88: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1745, gv2161, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1745) + model_encoder_layers_11_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[170] + gv2162: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1746: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv2162, R.dtype("float16")) + _1744: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_encoder_layers_11_self_attn_k_proj_weight, alloc1744, alloc1746) + R.vm.kill_object(model_encoder_layers_11_self_attn_k_proj_weight) + gv2163: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape89: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1746, gv2163, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1746) + model_encoder_layers_11_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[171] + model_encoder_layers_11_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[172] + gv2164: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1747: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv2164, R.dtype("float16")) + _1745: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_11_self_attn_v_proj_weight, alloc1744, model_encoder_layers_11_self_attn_v_proj_bias, alloc1747) + R.vm.kill_object(alloc1744) + R.vm.kill_object(model_encoder_layers_11_self_attn_v_proj_weight) + R.vm.kill_object(model_encoder_layers_11_self_attn_v_proj_bias) + gv2165: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape90: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1747, gv2165, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1747) + gv2166: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape91: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape88, gv2166, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape88) + gv2167: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape92: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape89, gv2167, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape89) + gv2168: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape93: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape90, gv2168, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape90) + gv2169: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc1748: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2169, R.dtype("float16")) + _1746: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_no_append", paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), reshape91, reshape92, reshape93, alloc1748) + R.vm.kill_object(reshape91) + R.vm.kill_object(reshape92) + R.vm.kill_object(reshape93) + gv2170: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape94: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1748, gv2170, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1748) + gv2171: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape95: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape94, gv2171, sinfo_args=(R.Tensor((batch_size, 1500, 1280), dtype="float16"),)) + R.vm.kill_object(reshape94) + model_encoder_layers_11_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[175] + model_encoder_layers_11_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[176] + gv2172: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1749: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv2172, R.dtype("float16")) + _1747: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_11_self_attn_out_proj_weight, reshape95, model_encoder_layers_11_self_attn_out_proj_bias, alloc1749) + R.vm.kill_object(reshape95) + R.vm.kill_object(model_encoder_layers_11_self_attn_out_proj_weight) + R.vm.kill_object(model_encoder_layers_11_self_attn_out_proj_bias) + gv2173: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1750: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv2173, R.dtype("float16")) + cls.add4(alloc1743, alloc1749, alloc1750) + R.vm.kill_object(alloc1743) + R.vm.kill_object(alloc1749) + model_encoder_layers_11_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[183] + model_encoder_layers_11_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[184] + gv2174: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1751: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2174, R.dtype("float16")) + cls.layer_norm1(alloc1750, model_encoder_layers_11_final_layer_norm_weight, model_encoder_layers_11_final_layer_norm_bias, alloc1751) + R.vm.kill_object(model_encoder_layers_11_final_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_11_final_layer_norm_bias) + model_encoder_layers_11_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[179] + model_encoder_layers_11_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[180] + gv2175: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc1752: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv2175, R.dtype("float16")) + _1750: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", model_encoder_layers_11_fc1_weight, alloc1751, model_encoder_layers_11_fc1_bias, alloc1752) + R.vm.kill_object(alloc1751) + R.vm.kill_object(model_encoder_layers_11_fc1_weight) + R.vm.kill_object(model_encoder_layers_11_fc1_bias) + model_encoder_layers_11_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[181] + model_encoder_layers_11_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[182] + gv2176: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1753: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv2176, R.dtype("float16")) + _1751: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", model_encoder_layers_11_fc2_weight, alloc1752, model_encoder_layers_11_fc2_bias, alloc1753) + R.vm.kill_object(alloc1752) + R.vm.kill_object(model_encoder_layers_11_fc2_weight) + R.vm.kill_object(model_encoder_layers_11_fc2_bias) + gv2177: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1754: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv2177, R.dtype("float16")) + cls.fused_add4_maximum_minimum(alloc1750, alloc1753, alloc1754) + R.vm.kill_object(alloc1750) + R.vm.kill_object(alloc1753) + model_encoder_layers_12_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[192] + model_encoder_layers_12_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[193] + gv2178: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1755: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2178, R.dtype("float16")) + cls.layer_norm1(alloc1754, model_encoder_layers_12_self_attn_layer_norm_weight, model_encoder_layers_12_self_attn_layer_norm_bias, alloc1755) + R.vm.kill_object(model_encoder_layers_12_self_attn_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_12_self_attn_layer_norm_bias) + model_encoder_layers_12_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[188] + model_encoder_layers_12_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[189] + gv2179: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1756: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv2179, R.dtype("float16")) + _1754: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_12_self_attn_q_proj_weight, alloc1755, model_encoder_layers_12_self_attn_q_proj_bias, alloc1756) + R.vm.kill_object(model_encoder_layers_12_self_attn_q_proj_weight) + R.vm.kill_object(model_encoder_layers_12_self_attn_q_proj_bias) + gv2180: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape96: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1756, gv2180, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1756) + model_encoder_layers_12_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[185] + gv2181: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1757: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv2181, R.dtype("float16")) + _1755: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_encoder_layers_12_self_attn_k_proj_weight, alloc1755, alloc1757) + R.vm.kill_object(model_encoder_layers_12_self_attn_k_proj_weight) + gv2182: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape97: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1757, gv2182, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1757) + model_encoder_layers_12_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[186] + model_encoder_layers_12_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[187] + gv2183: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1758: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv2183, R.dtype("float16")) + _1756: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_12_self_attn_v_proj_weight, alloc1755, model_encoder_layers_12_self_attn_v_proj_bias, alloc1758) + R.vm.kill_object(alloc1755) + R.vm.kill_object(model_encoder_layers_12_self_attn_v_proj_weight) + R.vm.kill_object(model_encoder_layers_12_self_attn_v_proj_bias) + gv2184: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape98: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1758, gv2184, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1758) + gv2185: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape99: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape96, gv2185, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape96) + gv2186: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape100: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape97, gv2186, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape97) + gv2187: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape101: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape98, gv2187, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape98) + gv2188: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc1759: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2188, R.dtype("float16")) + _1757: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_no_append", paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), reshape99, reshape100, reshape101, alloc1759) + R.vm.kill_object(reshape99) + R.vm.kill_object(reshape100) + R.vm.kill_object(reshape101) + gv2189: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape102: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1759, gv2189, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1759) + gv2190: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape103: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape102, gv2190, sinfo_args=(R.Tensor((batch_size, 1500, 1280), dtype="float16"),)) + R.vm.kill_object(reshape102) + model_encoder_layers_12_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[190] + model_encoder_layers_12_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[191] + gv2191: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1760: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv2191, R.dtype("float16")) + _1758: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_12_self_attn_out_proj_weight, reshape103, model_encoder_layers_12_self_attn_out_proj_bias, alloc1760) + R.vm.kill_object(reshape103) + R.vm.kill_object(model_encoder_layers_12_self_attn_out_proj_weight) + R.vm.kill_object(model_encoder_layers_12_self_attn_out_proj_bias) + gv2192: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1761: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv2192, R.dtype("float16")) + cls.add4(alloc1754, alloc1760, alloc1761) + R.vm.kill_object(alloc1754) + R.vm.kill_object(alloc1760) + model_encoder_layers_12_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[198] + model_encoder_layers_12_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[199] + gv2193: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1762: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2193, R.dtype("float16")) + cls.layer_norm1(alloc1761, model_encoder_layers_12_final_layer_norm_weight, model_encoder_layers_12_final_layer_norm_bias, alloc1762) + R.vm.kill_object(model_encoder_layers_12_final_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_12_final_layer_norm_bias) + model_encoder_layers_12_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[194] + model_encoder_layers_12_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[195] + gv2194: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc1763: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv2194, R.dtype("float16")) + _1761: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", model_encoder_layers_12_fc1_weight, alloc1762, model_encoder_layers_12_fc1_bias, alloc1763) + R.vm.kill_object(alloc1762) + R.vm.kill_object(model_encoder_layers_12_fc1_weight) + R.vm.kill_object(model_encoder_layers_12_fc1_bias) + model_encoder_layers_12_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[196] + model_encoder_layers_12_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[197] + gv2195: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1764: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv2195, R.dtype("float16")) + _1762: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", model_encoder_layers_12_fc2_weight, alloc1763, model_encoder_layers_12_fc2_bias, alloc1764) + R.vm.kill_object(alloc1763) + R.vm.kill_object(model_encoder_layers_12_fc2_weight) + R.vm.kill_object(model_encoder_layers_12_fc2_bias) + gv2196: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1765: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv2196, R.dtype("float16")) + cls.fused_add4_maximum_minimum(alloc1761, alloc1764, alloc1765) + R.vm.kill_object(alloc1761) + R.vm.kill_object(alloc1764) + model_encoder_layers_13_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[207] + model_encoder_layers_13_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[208] + gv2197: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1766: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2197, R.dtype("float16")) + cls.layer_norm1(alloc1765, model_encoder_layers_13_self_attn_layer_norm_weight, model_encoder_layers_13_self_attn_layer_norm_bias, alloc1766) + R.vm.kill_object(model_encoder_layers_13_self_attn_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_13_self_attn_layer_norm_bias) + model_encoder_layers_13_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[203] + model_encoder_layers_13_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[204] + gv2198: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1767: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv2198, R.dtype("float16")) + _1765: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_13_self_attn_q_proj_weight, alloc1766, model_encoder_layers_13_self_attn_q_proj_bias, alloc1767) + R.vm.kill_object(model_encoder_layers_13_self_attn_q_proj_weight) + R.vm.kill_object(model_encoder_layers_13_self_attn_q_proj_bias) + gv2199: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape104: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1767, gv2199, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1767) + model_encoder_layers_13_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[200] + gv2200: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1768: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv2200, R.dtype("float16")) + _1766: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_encoder_layers_13_self_attn_k_proj_weight, alloc1766, alloc1768) + R.vm.kill_object(model_encoder_layers_13_self_attn_k_proj_weight) + gv2201: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape105: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1768, gv2201, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1768) + model_encoder_layers_13_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[201] + model_encoder_layers_13_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[202] + gv2202: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1769: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv2202, R.dtype("float16")) + _1767: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_13_self_attn_v_proj_weight, alloc1766, model_encoder_layers_13_self_attn_v_proj_bias, alloc1769) + R.vm.kill_object(alloc1766) + R.vm.kill_object(model_encoder_layers_13_self_attn_v_proj_weight) + R.vm.kill_object(model_encoder_layers_13_self_attn_v_proj_bias) + gv2203: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape106: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1769, gv2203, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1769) + gv2204: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape107: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape104, gv2204, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape104) + gv2205: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape108: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape105, gv2205, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape105) + gv2206: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape109: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape106, gv2206, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape106) + gv2207: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc1770: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2207, R.dtype("float16")) + _1768: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_no_append", paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), reshape107, reshape108, reshape109, alloc1770) + R.vm.kill_object(reshape107) + R.vm.kill_object(reshape108) + R.vm.kill_object(reshape109) + gv2208: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape110: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1770, gv2208, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1770) + gv2209: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape111: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape110, gv2209, sinfo_args=(R.Tensor((batch_size, 1500, 1280), dtype="float16"),)) + R.vm.kill_object(reshape110) + model_encoder_layers_13_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[205] + model_encoder_layers_13_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[206] + gv2210: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1771: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv2210, R.dtype("float16")) + _1769: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_13_self_attn_out_proj_weight, reshape111, model_encoder_layers_13_self_attn_out_proj_bias, alloc1771) + R.vm.kill_object(reshape111) + R.vm.kill_object(model_encoder_layers_13_self_attn_out_proj_weight) + R.vm.kill_object(model_encoder_layers_13_self_attn_out_proj_bias) + gv2211: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1772: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv2211, R.dtype("float16")) + cls.add4(alloc1765, alloc1771, alloc1772) + R.vm.kill_object(alloc1765) + R.vm.kill_object(alloc1771) + model_encoder_layers_13_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[213] + model_encoder_layers_13_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[214] + gv2212: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1773: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2212, R.dtype("float16")) + cls.layer_norm1(alloc1772, model_encoder_layers_13_final_layer_norm_weight, model_encoder_layers_13_final_layer_norm_bias, alloc1773) + R.vm.kill_object(model_encoder_layers_13_final_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_13_final_layer_norm_bias) + model_encoder_layers_13_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[209] + model_encoder_layers_13_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[210] + gv2213: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc1774: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv2213, R.dtype("float16")) + _1772: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", model_encoder_layers_13_fc1_weight, alloc1773, model_encoder_layers_13_fc1_bias, alloc1774) + R.vm.kill_object(alloc1773) + R.vm.kill_object(model_encoder_layers_13_fc1_weight) + R.vm.kill_object(model_encoder_layers_13_fc1_bias) + model_encoder_layers_13_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[211] + model_encoder_layers_13_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[212] + gv2214: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1775: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv2214, R.dtype("float16")) + _1773: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", model_encoder_layers_13_fc2_weight, alloc1774, model_encoder_layers_13_fc2_bias, alloc1775) + R.vm.kill_object(alloc1774) + R.vm.kill_object(model_encoder_layers_13_fc2_weight) + R.vm.kill_object(model_encoder_layers_13_fc2_bias) + gv2215: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1776: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv2215, R.dtype("float16")) + cls.fused_add4_maximum_minimum(alloc1772, alloc1775, alloc1776) + R.vm.kill_object(alloc1772) + R.vm.kill_object(alloc1775) + model_encoder_layers_14_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[222] + model_encoder_layers_14_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[223] + gv2216: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1777: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2216, R.dtype("float16")) + cls.layer_norm1(alloc1776, model_encoder_layers_14_self_attn_layer_norm_weight, model_encoder_layers_14_self_attn_layer_norm_bias, alloc1777) + R.vm.kill_object(model_encoder_layers_14_self_attn_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_14_self_attn_layer_norm_bias) + model_encoder_layers_14_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[218] + model_encoder_layers_14_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[219] + gv2217: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1778: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv2217, R.dtype("float16")) + _1776: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_14_self_attn_q_proj_weight, alloc1777, model_encoder_layers_14_self_attn_q_proj_bias, alloc1778) + R.vm.kill_object(model_encoder_layers_14_self_attn_q_proj_weight) + R.vm.kill_object(model_encoder_layers_14_self_attn_q_proj_bias) + gv2218: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape112: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1778, gv2218, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1778) + model_encoder_layers_14_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[215] + gv2219: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1779: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv2219, R.dtype("float16")) + _1777: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_encoder_layers_14_self_attn_k_proj_weight, alloc1777, alloc1779) + R.vm.kill_object(model_encoder_layers_14_self_attn_k_proj_weight) + gv2220: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape113: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1779, gv2220, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1779) + model_encoder_layers_14_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[216] + model_encoder_layers_14_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[217] + gv2221: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1780: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv2221, R.dtype("float16")) + _1778: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_14_self_attn_v_proj_weight, alloc1777, model_encoder_layers_14_self_attn_v_proj_bias, alloc1780) + R.vm.kill_object(alloc1777) + R.vm.kill_object(model_encoder_layers_14_self_attn_v_proj_weight) + R.vm.kill_object(model_encoder_layers_14_self_attn_v_proj_bias) + gv2222: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape114: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1780, gv2222, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1780) + gv2223: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape115: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape112, gv2223, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape112) + gv2224: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape116: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape113, gv2224, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape113) + gv2225: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape117: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape114, gv2225, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape114) + gv2226: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc1781: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2226, R.dtype("float16")) + _1779: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_no_append", paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), reshape115, reshape116, reshape117, alloc1781) + R.vm.kill_object(reshape115) + R.vm.kill_object(reshape116) + R.vm.kill_object(reshape117) + gv2227: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape118: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1781, gv2227, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1781) + gv2228: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape119: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape118, gv2228, sinfo_args=(R.Tensor((batch_size, 1500, 1280), dtype="float16"),)) + R.vm.kill_object(reshape118) + model_encoder_layers_14_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[220] + model_encoder_layers_14_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[221] + gv2229: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1782: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv2229, R.dtype("float16")) + _1780: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_14_self_attn_out_proj_weight, reshape119, model_encoder_layers_14_self_attn_out_proj_bias, alloc1782) + R.vm.kill_object(reshape119) + R.vm.kill_object(model_encoder_layers_14_self_attn_out_proj_weight) + R.vm.kill_object(model_encoder_layers_14_self_attn_out_proj_bias) + gv2230: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1783: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv2230, R.dtype("float16")) + cls.add4(alloc1776, alloc1782, alloc1783) + R.vm.kill_object(alloc1776) + R.vm.kill_object(alloc1782) + model_encoder_layers_14_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[228] + model_encoder_layers_14_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[229] + gv2231: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1784: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2231, R.dtype("float16")) + cls.layer_norm1(alloc1783, model_encoder_layers_14_final_layer_norm_weight, model_encoder_layers_14_final_layer_norm_bias, alloc1784) + R.vm.kill_object(model_encoder_layers_14_final_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_14_final_layer_norm_bias) + model_encoder_layers_14_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[224] + model_encoder_layers_14_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[225] + gv2232: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc1785: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv2232, R.dtype("float16")) + _1783: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", model_encoder_layers_14_fc1_weight, alloc1784, model_encoder_layers_14_fc1_bias, alloc1785) + R.vm.kill_object(alloc1784) + R.vm.kill_object(model_encoder_layers_14_fc1_weight) + R.vm.kill_object(model_encoder_layers_14_fc1_bias) + model_encoder_layers_14_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[226] + model_encoder_layers_14_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[227] + gv2233: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1786: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv2233, R.dtype("float16")) + _1784: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", model_encoder_layers_14_fc2_weight, alloc1785, model_encoder_layers_14_fc2_bias, alloc1786) + R.vm.kill_object(alloc1785) + R.vm.kill_object(model_encoder_layers_14_fc2_weight) + R.vm.kill_object(model_encoder_layers_14_fc2_bias) + gv2234: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1787: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv2234, R.dtype("float16")) + cls.fused_add4_maximum_minimum(alloc1783, alloc1786, alloc1787) + R.vm.kill_object(alloc1783) + R.vm.kill_object(alloc1786) + model_encoder_layers_15_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[237] + model_encoder_layers_15_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[238] + gv2235: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1788: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2235, R.dtype("float16")) + cls.layer_norm1(alloc1787, model_encoder_layers_15_self_attn_layer_norm_weight, model_encoder_layers_15_self_attn_layer_norm_bias, alloc1788) + R.vm.kill_object(model_encoder_layers_15_self_attn_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_15_self_attn_layer_norm_bias) + model_encoder_layers_15_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[233] + model_encoder_layers_15_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[234] + gv2236: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1789: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv2236, R.dtype("float16")) + _1787: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_15_self_attn_q_proj_weight, alloc1788, model_encoder_layers_15_self_attn_q_proj_bias, alloc1789) + R.vm.kill_object(model_encoder_layers_15_self_attn_q_proj_weight) + R.vm.kill_object(model_encoder_layers_15_self_attn_q_proj_bias) + gv2237: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape120: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1789, gv2237, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1789) + model_encoder_layers_15_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[230] + gv2238: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1790: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv2238, R.dtype("float16")) + _1788: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_encoder_layers_15_self_attn_k_proj_weight, alloc1788, alloc1790) + R.vm.kill_object(model_encoder_layers_15_self_attn_k_proj_weight) + gv2239: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape121: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1790, gv2239, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1790) + model_encoder_layers_15_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[231] + model_encoder_layers_15_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[232] + gv2240: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1791: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv2240, R.dtype("float16")) + _1789: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_15_self_attn_v_proj_weight, alloc1788, model_encoder_layers_15_self_attn_v_proj_bias, alloc1791) + R.vm.kill_object(alloc1788) + R.vm.kill_object(model_encoder_layers_15_self_attn_v_proj_weight) + R.vm.kill_object(model_encoder_layers_15_self_attn_v_proj_bias) + gv2241: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape122: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1791, gv2241, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1791) + gv2242: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape123: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape120, gv2242, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape120) + gv2243: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape124: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape121, gv2243, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape121) + gv2244: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape125: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape122, gv2244, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape122) + gv2245: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc1792: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2245, R.dtype("float16")) + _1790: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_no_append", paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), reshape123, reshape124, reshape125, alloc1792) + R.vm.kill_object(reshape123) + R.vm.kill_object(reshape124) + R.vm.kill_object(reshape125) + gv2246: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape126: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1792, gv2246, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1792) + gv2247: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape127: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape126, gv2247, sinfo_args=(R.Tensor((batch_size, 1500, 1280), dtype="float16"),)) + R.vm.kill_object(reshape126) + model_encoder_layers_15_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[235] + model_encoder_layers_15_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[236] + gv2248: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1793: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv2248, R.dtype("float16")) + _1791: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_15_self_attn_out_proj_weight, reshape127, model_encoder_layers_15_self_attn_out_proj_bias, alloc1793) + R.vm.kill_object(reshape127) + R.vm.kill_object(model_encoder_layers_15_self_attn_out_proj_weight) + R.vm.kill_object(model_encoder_layers_15_self_attn_out_proj_bias) + gv2249: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1794: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv2249, R.dtype("float16")) + cls.add4(alloc1787, alloc1793, alloc1794) + R.vm.kill_object(alloc1787) + R.vm.kill_object(alloc1793) + model_encoder_layers_15_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[243] + model_encoder_layers_15_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[244] + gv2250: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1795: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2250, R.dtype("float16")) + cls.layer_norm1(alloc1794, model_encoder_layers_15_final_layer_norm_weight, model_encoder_layers_15_final_layer_norm_bias, alloc1795) + R.vm.kill_object(model_encoder_layers_15_final_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_15_final_layer_norm_bias) + model_encoder_layers_15_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[239] + model_encoder_layers_15_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[240] + gv2251: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc1796: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv2251, R.dtype("float16")) + _1794: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", model_encoder_layers_15_fc1_weight, alloc1795, model_encoder_layers_15_fc1_bias, alloc1796) + R.vm.kill_object(alloc1795) + R.vm.kill_object(model_encoder_layers_15_fc1_weight) + R.vm.kill_object(model_encoder_layers_15_fc1_bias) + model_encoder_layers_15_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[241] + model_encoder_layers_15_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[242] + gv2252: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1797: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv2252, R.dtype("float16")) + _1795: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", model_encoder_layers_15_fc2_weight, alloc1796, model_encoder_layers_15_fc2_bias, alloc1797) + R.vm.kill_object(alloc1796) + R.vm.kill_object(model_encoder_layers_15_fc2_weight) + R.vm.kill_object(model_encoder_layers_15_fc2_bias) + gv2253: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1798: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv2253, R.dtype("float16")) + cls.fused_add4_maximum_minimum(alloc1794, alloc1797, alloc1798) + R.vm.kill_object(alloc1794) + R.vm.kill_object(alloc1797) + model_encoder_layers_16_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[252] + model_encoder_layers_16_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[253] + gv2254: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1799: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2254, R.dtype("float16")) + cls.layer_norm1(alloc1798, model_encoder_layers_16_self_attn_layer_norm_weight, model_encoder_layers_16_self_attn_layer_norm_bias, alloc1799) + R.vm.kill_object(model_encoder_layers_16_self_attn_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_16_self_attn_layer_norm_bias) + model_encoder_layers_16_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[248] + model_encoder_layers_16_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[249] + gv2255: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1800: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv2255, R.dtype("float16")) + _1798: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_16_self_attn_q_proj_weight, alloc1799, model_encoder_layers_16_self_attn_q_proj_bias, alloc1800) + R.vm.kill_object(model_encoder_layers_16_self_attn_q_proj_weight) + R.vm.kill_object(model_encoder_layers_16_self_attn_q_proj_bias) + gv2256: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape128: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1800, gv2256, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1800) + model_encoder_layers_16_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[245] + gv2257: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1801: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv2257, R.dtype("float16")) + _1799: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_encoder_layers_16_self_attn_k_proj_weight, alloc1799, alloc1801) + R.vm.kill_object(model_encoder_layers_16_self_attn_k_proj_weight) + gv2258: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape129: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1801, gv2258, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1801) + model_encoder_layers_16_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[246] + model_encoder_layers_16_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[247] + gv2259: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1802: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv2259, R.dtype("float16")) + _1800: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_16_self_attn_v_proj_weight, alloc1799, model_encoder_layers_16_self_attn_v_proj_bias, alloc1802) + R.vm.kill_object(alloc1799) + R.vm.kill_object(model_encoder_layers_16_self_attn_v_proj_weight) + R.vm.kill_object(model_encoder_layers_16_self_attn_v_proj_bias) + gv2260: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape130: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1802, gv2260, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1802) + gv2261: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape131: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape128, gv2261, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape128) + gv2262: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape132: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape129, gv2262, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape129) + gv2263: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape133: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape130, gv2263, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape130) + gv2264: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc1803: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2264, R.dtype("float16")) + _1801: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_no_append", paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), reshape131, reshape132, reshape133, alloc1803) + R.vm.kill_object(reshape131) + R.vm.kill_object(reshape132) + R.vm.kill_object(reshape133) + gv2265: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape134: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1803, gv2265, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1803) + gv2266: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape135: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape134, gv2266, sinfo_args=(R.Tensor((batch_size, 1500, 1280), dtype="float16"),)) + R.vm.kill_object(reshape134) + model_encoder_layers_16_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[250] + model_encoder_layers_16_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[251] + gv2267: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1804: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv2267, R.dtype("float16")) + _1802: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_16_self_attn_out_proj_weight, reshape135, model_encoder_layers_16_self_attn_out_proj_bias, alloc1804) + R.vm.kill_object(reshape135) + R.vm.kill_object(model_encoder_layers_16_self_attn_out_proj_weight) + R.vm.kill_object(model_encoder_layers_16_self_attn_out_proj_bias) + gv2268: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1805: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv2268, R.dtype("float16")) + cls.add4(alloc1798, alloc1804, alloc1805) + R.vm.kill_object(alloc1798) + R.vm.kill_object(alloc1804) + model_encoder_layers_16_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[258] + model_encoder_layers_16_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[259] + gv2269: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1806: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2269, R.dtype("float16")) + cls.layer_norm1(alloc1805, model_encoder_layers_16_final_layer_norm_weight, model_encoder_layers_16_final_layer_norm_bias, alloc1806) + R.vm.kill_object(model_encoder_layers_16_final_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_16_final_layer_norm_bias) + model_encoder_layers_16_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[254] + model_encoder_layers_16_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[255] + gv2270: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc1807: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv2270, R.dtype("float16")) + _1805: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", model_encoder_layers_16_fc1_weight, alloc1806, model_encoder_layers_16_fc1_bias, alloc1807) + R.vm.kill_object(alloc1806) + R.vm.kill_object(model_encoder_layers_16_fc1_weight) + R.vm.kill_object(model_encoder_layers_16_fc1_bias) + model_encoder_layers_16_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[256] + model_encoder_layers_16_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[257] + gv2271: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1808: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv2271, R.dtype("float16")) + _1806: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", model_encoder_layers_16_fc2_weight, alloc1807, model_encoder_layers_16_fc2_bias, alloc1808) + R.vm.kill_object(alloc1807) + R.vm.kill_object(model_encoder_layers_16_fc2_weight) + R.vm.kill_object(model_encoder_layers_16_fc2_bias) + gv2272: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1809: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv2272, R.dtype("float16")) + cls.fused_add4_maximum_minimum(alloc1805, alloc1808, alloc1809) + R.vm.kill_object(alloc1805) + R.vm.kill_object(alloc1808) + model_encoder_layers_17_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[267] + model_encoder_layers_17_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[268] + gv2273: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1810: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2273, R.dtype("float16")) + cls.layer_norm1(alloc1809, model_encoder_layers_17_self_attn_layer_norm_weight, model_encoder_layers_17_self_attn_layer_norm_bias, alloc1810) + R.vm.kill_object(model_encoder_layers_17_self_attn_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_17_self_attn_layer_norm_bias) + model_encoder_layers_17_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[263] + model_encoder_layers_17_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[264] + gv2274: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1811: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv2274, R.dtype("float16")) + _1809: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_17_self_attn_q_proj_weight, alloc1810, model_encoder_layers_17_self_attn_q_proj_bias, alloc1811) + R.vm.kill_object(model_encoder_layers_17_self_attn_q_proj_weight) + R.vm.kill_object(model_encoder_layers_17_self_attn_q_proj_bias) + gv2275: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape136: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1811, gv2275, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1811) + model_encoder_layers_17_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[260] + gv2276: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1812: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv2276, R.dtype("float16")) + _1810: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_encoder_layers_17_self_attn_k_proj_weight, alloc1810, alloc1812) + R.vm.kill_object(model_encoder_layers_17_self_attn_k_proj_weight) + gv2277: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape137: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1812, gv2277, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1812) + model_encoder_layers_17_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[261] + model_encoder_layers_17_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[262] + gv2278: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1813: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv2278, R.dtype("float16")) + _1811: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_17_self_attn_v_proj_weight, alloc1810, model_encoder_layers_17_self_attn_v_proj_bias, alloc1813) + R.vm.kill_object(alloc1810) + R.vm.kill_object(model_encoder_layers_17_self_attn_v_proj_weight) + R.vm.kill_object(model_encoder_layers_17_self_attn_v_proj_bias) + gv2279: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape138: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1813, gv2279, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1813) + gv2280: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape139: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape136, gv2280, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape136) + gv2281: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape140: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape137, gv2281, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape137) + gv2282: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape141: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape138, gv2282, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape138) + gv2283: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc1814: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2283, R.dtype("float16")) + _1812: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_no_append", paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), reshape139, reshape140, reshape141, alloc1814) + R.vm.kill_object(reshape139) + R.vm.kill_object(reshape140) + R.vm.kill_object(reshape141) + gv2284: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape142: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1814, gv2284, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1814) + gv2285: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape143: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape142, gv2285, sinfo_args=(R.Tensor((batch_size, 1500, 1280), dtype="float16"),)) + R.vm.kill_object(reshape142) + model_encoder_layers_17_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[265] + model_encoder_layers_17_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[266] + gv2286: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1815: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv2286, R.dtype("float16")) + _1813: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_17_self_attn_out_proj_weight, reshape143, model_encoder_layers_17_self_attn_out_proj_bias, alloc1815) + R.vm.kill_object(reshape143) + R.vm.kill_object(model_encoder_layers_17_self_attn_out_proj_weight) + R.vm.kill_object(model_encoder_layers_17_self_attn_out_proj_bias) + gv2287: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1816: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv2287, R.dtype("float16")) + cls.add4(alloc1809, alloc1815, alloc1816) + R.vm.kill_object(alloc1809) + R.vm.kill_object(alloc1815) + model_encoder_layers_17_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[273] + model_encoder_layers_17_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[274] + gv2288: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1817: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2288, R.dtype("float16")) + cls.layer_norm1(alloc1816, model_encoder_layers_17_final_layer_norm_weight, model_encoder_layers_17_final_layer_norm_bias, alloc1817) + R.vm.kill_object(model_encoder_layers_17_final_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_17_final_layer_norm_bias) + model_encoder_layers_17_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[269] + model_encoder_layers_17_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[270] + gv2289: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc1818: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv2289, R.dtype("float16")) + _1816: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", model_encoder_layers_17_fc1_weight, alloc1817, model_encoder_layers_17_fc1_bias, alloc1818) + R.vm.kill_object(alloc1817) + R.vm.kill_object(model_encoder_layers_17_fc1_weight) + R.vm.kill_object(model_encoder_layers_17_fc1_bias) + model_encoder_layers_17_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[271] + model_encoder_layers_17_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[272] + gv2290: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1819: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv2290, R.dtype("float16")) + _1817: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", model_encoder_layers_17_fc2_weight, alloc1818, model_encoder_layers_17_fc2_bias, alloc1819) + R.vm.kill_object(alloc1818) + R.vm.kill_object(model_encoder_layers_17_fc2_weight) + R.vm.kill_object(model_encoder_layers_17_fc2_bias) + gv2291: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1820: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv2291, R.dtype("float16")) + cls.fused_add4_maximum_minimum(alloc1816, alloc1819, alloc1820) + R.vm.kill_object(alloc1816) + R.vm.kill_object(alloc1819) + model_encoder_layers_18_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[282] + model_encoder_layers_18_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[283] + gv2292: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1821: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2292, R.dtype("float16")) + cls.layer_norm1(alloc1820, model_encoder_layers_18_self_attn_layer_norm_weight, model_encoder_layers_18_self_attn_layer_norm_bias, alloc1821) + R.vm.kill_object(model_encoder_layers_18_self_attn_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_18_self_attn_layer_norm_bias) + model_encoder_layers_18_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[278] + model_encoder_layers_18_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[279] + gv2293: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1822: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv2293, R.dtype("float16")) + _1820: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_18_self_attn_q_proj_weight, alloc1821, model_encoder_layers_18_self_attn_q_proj_bias, alloc1822) + R.vm.kill_object(model_encoder_layers_18_self_attn_q_proj_weight) + R.vm.kill_object(model_encoder_layers_18_self_attn_q_proj_bias) + gv2294: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape144: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1822, gv2294, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1822) + model_encoder_layers_18_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[275] + gv2295: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1823: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv2295, R.dtype("float16")) + _1821: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_encoder_layers_18_self_attn_k_proj_weight, alloc1821, alloc1823) + R.vm.kill_object(model_encoder_layers_18_self_attn_k_proj_weight) + gv2296: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape145: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1823, gv2296, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1823) + model_encoder_layers_18_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[276] + model_encoder_layers_18_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[277] + gv2297: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1824: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv2297, R.dtype("float16")) + _1822: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_18_self_attn_v_proj_weight, alloc1821, model_encoder_layers_18_self_attn_v_proj_bias, alloc1824) + R.vm.kill_object(alloc1821) + R.vm.kill_object(model_encoder_layers_18_self_attn_v_proj_weight) + R.vm.kill_object(model_encoder_layers_18_self_attn_v_proj_bias) + gv2298: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape146: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1824, gv2298, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1824) + gv2299: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape147: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape144, gv2299, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape144) + gv2300: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape148: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape145, gv2300, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape145) + gv2301: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape149: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape146, gv2301, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape146) + gv2302: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc1825: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2302, R.dtype("float16")) + _1823: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_no_append", paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), reshape147, reshape148, reshape149, alloc1825) + R.vm.kill_object(reshape147) + R.vm.kill_object(reshape148) + R.vm.kill_object(reshape149) + gv2303: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape150: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1825, gv2303, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1825) + gv2304: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape151: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape150, gv2304, sinfo_args=(R.Tensor((batch_size, 1500, 1280), dtype="float16"),)) + R.vm.kill_object(reshape150) + model_encoder_layers_18_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[280] + model_encoder_layers_18_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[281] + gv2305: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1826: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv2305, R.dtype("float16")) + _1824: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_18_self_attn_out_proj_weight, reshape151, model_encoder_layers_18_self_attn_out_proj_bias, alloc1826) + R.vm.kill_object(reshape151) + R.vm.kill_object(model_encoder_layers_18_self_attn_out_proj_weight) + R.vm.kill_object(model_encoder_layers_18_self_attn_out_proj_bias) + gv2306: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1827: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv2306, R.dtype("float16")) + cls.add4(alloc1820, alloc1826, alloc1827) + R.vm.kill_object(alloc1820) + R.vm.kill_object(alloc1826) + model_encoder_layers_18_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[288] + model_encoder_layers_18_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[289] + gv2307: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1828: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2307, R.dtype("float16")) + cls.layer_norm1(alloc1827, model_encoder_layers_18_final_layer_norm_weight, model_encoder_layers_18_final_layer_norm_bias, alloc1828) + R.vm.kill_object(model_encoder_layers_18_final_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_18_final_layer_norm_bias) + model_encoder_layers_18_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[284] + model_encoder_layers_18_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[285] + gv2308: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc1829: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv2308, R.dtype("float16")) + _1827: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", model_encoder_layers_18_fc1_weight, alloc1828, model_encoder_layers_18_fc1_bias, alloc1829) + R.vm.kill_object(alloc1828) + R.vm.kill_object(model_encoder_layers_18_fc1_weight) + R.vm.kill_object(model_encoder_layers_18_fc1_bias) + model_encoder_layers_18_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[286] + model_encoder_layers_18_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[287] + gv2309: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1830: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv2309, R.dtype("float16")) + _1828: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", model_encoder_layers_18_fc2_weight, alloc1829, model_encoder_layers_18_fc2_bias, alloc1830) + R.vm.kill_object(alloc1829) + R.vm.kill_object(model_encoder_layers_18_fc2_weight) + R.vm.kill_object(model_encoder_layers_18_fc2_bias) + gv2310: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1831: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv2310, R.dtype("float16")) + cls.fused_add4_maximum_minimum(alloc1827, alloc1830, alloc1831) + R.vm.kill_object(alloc1827) + R.vm.kill_object(alloc1830) + model_encoder_layers_19_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[297] + model_encoder_layers_19_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[298] + gv2311: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1832: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2311, R.dtype("float16")) + cls.layer_norm1(alloc1831, model_encoder_layers_19_self_attn_layer_norm_weight, model_encoder_layers_19_self_attn_layer_norm_bias, alloc1832) + R.vm.kill_object(model_encoder_layers_19_self_attn_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_19_self_attn_layer_norm_bias) + model_encoder_layers_19_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[293] + model_encoder_layers_19_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[294] + gv2312: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1833: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv2312, R.dtype("float16")) + _1831: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_19_self_attn_q_proj_weight, alloc1832, model_encoder_layers_19_self_attn_q_proj_bias, alloc1833) + R.vm.kill_object(model_encoder_layers_19_self_attn_q_proj_weight) + R.vm.kill_object(model_encoder_layers_19_self_attn_q_proj_bias) + gv2313: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape152: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1833, gv2313, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1833) + model_encoder_layers_19_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[290] + gv2314: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1834: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv2314, R.dtype("float16")) + _1832: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_encoder_layers_19_self_attn_k_proj_weight, alloc1832, alloc1834) + R.vm.kill_object(model_encoder_layers_19_self_attn_k_proj_weight) + gv2315: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape153: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1834, gv2315, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1834) + model_encoder_layers_19_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[291] + model_encoder_layers_19_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[292] + gv2316: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1835: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv2316, R.dtype("float16")) + _1833: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_19_self_attn_v_proj_weight, alloc1832, model_encoder_layers_19_self_attn_v_proj_bias, alloc1835) + R.vm.kill_object(alloc1832) + R.vm.kill_object(model_encoder_layers_19_self_attn_v_proj_weight) + R.vm.kill_object(model_encoder_layers_19_self_attn_v_proj_bias) + gv2317: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape154: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1835, gv2317, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1835) + gv2318: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape155: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape152, gv2318, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape152) + gv2319: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape156: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape153, gv2319, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape153) + gv2320: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape157: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape154, gv2320, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape154) + gv2321: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc1836: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2321, R.dtype("float16")) + _1834: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_no_append", paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), reshape155, reshape156, reshape157, alloc1836) + R.vm.kill_object(reshape155) + R.vm.kill_object(reshape156) + R.vm.kill_object(reshape157) + gv2322: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape158: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1836, gv2322, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1836) + gv2323: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape159: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape158, gv2323, sinfo_args=(R.Tensor((batch_size, 1500, 1280), dtype="float16"),)) + R.vm.kill_object(reshape158) + model_encoder_layers_19_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[295] + model_encoder_layers_19_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[296] + gv2324: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1837: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv2324, R.dtype("float16")) + _1835: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_19_self_attn_out_proj_weight, reshape159, model_encoder_layers_19_self_attn_out_proj_bias, alloc1837) + R.vm.kill_object(reshape159) + R.vm.kill_object(model_encoder_layers_19_self_attn_out_proj_weight) + R.vm.kill_object(model_encoder_layers_19_self_attn_out_proj_bias) + gv2325: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1838: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv2325, R.dtype("float16")) + cls.add4(alloc1831, alloc1837, alloc1838) + R.vm.kill_object(alloc1831) + R.vm.kill_object(alloc1837) + model_encoder_layers_19_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[303] + model_encoder_layers_19_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[304] + gv2326: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1839: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2326, R.dtype("float16")) + cls.layer_norm1(alloc1838, model_encoder_layers_19_final_layer_norm_weight, model_encoder_layers_19_final_layer_norm_bias, alloc1839) + R.vm.kill_object(model_encoder_layers_19_final_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_19_final_layer_norm_bias) + model_encoder_layers_19_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[299] + model_encoder_layers_19_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[300] + gv2327: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc1840: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv2327, R.dtype("float16")) + _1838: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", model_encoder_layers_19_fc1_weight, alloc1839, model_encoder_layers_19_fc1_bias, alloc1840) + R.vm.kill_object(alloc1839) + R.vm.kill_object(model_encoder_layers_19_fc1_weight) + R.vm.kill_object(model_encoder_layers_19_fc1_bias) + model_encoder_layers_19_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[301] + model_encoder_layers_19_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[302] + gv2328: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1841: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv2328, R.dtype("float16")) + _1839: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", model_encoder_layers_19_fc2_weight, alloc1840, model_encoder_layers_19_fc2_bias, alloc1841) + R.vm.kill_object(alloc1840) + R.vm.kill_object(model_encoder_layers_19_fc2_weight) + R.vm.kill_object(model_encoder_layers_19_fc2_bias) + gv2329: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1842: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv2329, R.dtype("float16")) + cls.fused_add4_maximum_minimum(alloc1838, alloc1841, alloc1842) + R.vm.kill_object(alloc1838) + R.vm.kill_object(alloc1841) + model_encoder_layers_20_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[312] + model_encoder_layers_20_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[313] + gv2330: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1843: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2330, R.dtype("float16")) + cls.layer_norm1(alloc1842, model_encoder_layers_20_self_attn_layer_norm_weight, model_encoder_layers_20_self_attn_layer_norm_bias, alloc1843) + R.vm.kill_object(model_encoder_layers_20_self_attn_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_20_self_attn_layer_norm_bias) + model_encoder_layers_20_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[308] + model_encoder_layers_20_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[309] + gv2331: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1844: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv2331, R.dtype("float16")) + _1842: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_20_self_attn_q_proj_weight, alloc1843, model_encoder_layers_20_self_attn_q_proj_bias, alloc1844) + R.vm.kill_object(model_encoder_layers_20_self_attn_q_proj_weight) + R.vm.kill_object(model_encoder_layers_20_self_attn_q_proj_bias) + gv2332: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape160: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1844, gv2332, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1844) + model_encoder_layers_20_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[305] + gv2333: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1845: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv2333, R.dtype("float16")) + _1843: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_encoder_layers_20_self_attn_k_proj_weight, alloc1843, alloc1845) + R.vm.kill_object(model_encoder_layers_20_self_attn_k_proj_weight) + gv2334: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape161: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1845, gv2334, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1845) + model_encoder_layers_20_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[306] + model_encoder_layers_20_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[307] + gv2335: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1846: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv2335, R.dtype("float16")) + _1844: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_20_self_attn_v_proj_weight, alloc1843, model_encoder_layers_20_self_attn_v_proj_bias, alloc1846) + R.vm.kill_object(alloc1843) + R.vm.kill_object(model_encoder_layers_20_self_attn_v_proj_weight) + R.vm.kill_object(model_encoder_layers_20_self_attn_v_proj_bias) + gv2336: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape162: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1846, gv2336, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1846) + gv2337: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape163: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape160, gv2337, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape160) + gv2338: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape164: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape161, gv2338, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape161) + gv2339: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape165: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape162, gv2339, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape162) + gv2340: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc1847: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2340, R.dtype("float16")) + _1845: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_no_append", paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), reshape163, reshape164, reshape165, alloc1847) + R.vm.kill_object(reshape163) + R.vm.kill_object(reshape164) + R.vm.kill_object(reshape165) + gv2341: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape166: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1847, gv2341, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1847) + gv2342: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape167: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape166, gv2342, sinfo_args=(R.Tensor((batch_size, 1500, 1280), dtype="float16"),)) + R.vm.kill_object(reshape166) + model_encoder_layers_20_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[310] + model_encoder_layers_20_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[311] + gv2343: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1848: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv2343, R.dtype("float16")) + _1846: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_20_self_attn_out_proj_weight, reshape167, model_encoder_layers_20_self_attn_out_proj_bias, alloc1848) + R.vm.kill_object(reshape167) + R.vm.kill_object(model_encoder_layers_20_self_attn_out_proj_weight) + R.vm.kill_object(model_encoder_layers_20_self_attn_out_proj_bias) + gv2344: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1849: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv2344, R.dtype("float16")) + cls.add4(alloc1842, alloc1848, alloc1849) + R.vm.kill_object(alloc1842) + R.vm.kill_object(alloc1848) + model_encoder_layers_20_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[318] + model_encoder_layers_20_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[319] + gv2345: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1850: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2345, R.dtype("float16")) + cls.layer_norm1(alloc1849, model_encoder_layers_20_final_layer_norm_weight, model_encoder_layers_20_final_layer_norm_bias, alloc1850) + R.vm.kill_object(model_encoder_layers_20_final_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_20_final_layer_norm_bias) + model_encoder_layers_20_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[314] + model_encoder_layers_20_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[315] + gv2346: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc1851: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv2346, R.dtype("float16")) + _1849: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", model_encoder_layers_20_fc1_weight, alloc1850, model_encoder_layers_20_fc1_bias, alloc1851) + R.vm.kill_object(alloc1850) + R.vm.kill_object(model_encoder_layers_20_fc1_weight) + R.vm.kill_object(model_encoder_layers_20_fc1_bias) + model_encoder_layers_20_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[316] + model_encoder_layers_20_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[317] + gv2347: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1852: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv2347, R.dtype("float16")) + _1850: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", model_encoder_layers_20_fc2_weight, alloc1851, model_encoder_layers_20_fc2_bias, alloc1852) + R.vm.kill_object(alloc1851) + R.vm.kill_object(model_encoder_layers_20_fc2_weight) + R.vm.kill_object(model_encoder_layers_20_fc2_bias) + gv2348: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1853: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv2348, R.dtype("float16")) + cls.fused_add4_maximum_minimum(alloc1849, alloc1852, alloc1853) + R.vm.kill_object(alloc1849) + R.vm.kill_object(alloc1852) + model_encoder_layers_21_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[327] + model_encoder_layers_21_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[328] + gv2349: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1854: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2349, R.dtype("float16")) + cls.layer_norm1(alloc1853, model_encoder_layers_21_self_attn_layer_norm_weight, model_encoder_layers_21_self_attn_layer_norm_bias, alloc1854) + R.vm.kill_object(model_encoder_layers_21_self_attn_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_21_self_attn_layer_norm_bias) + model_encoder_layers_21_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[323] + model_encoder_layers_21_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[324] + gv2350: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1855: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv2350, R.dtype("float16")) + _1853: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_21_self_attn_q_proj_weight, alloc1854, model_encoder_layers_21_self_attn_q_proj_bias, alloc1855) + R.vm.kill_object(model_encoder_layers_21_self_attn_q_proj_weight) + R.vm.kill_object(model_encoder_layers_21_self_attn_q_proj_bias) + gv2351: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape168: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1855, gv2351, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1855) + model_encoder_layers_21_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[320] + gv2352: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1856: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv2352, R.dtype("float16")) + _1854: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_encoder_layers_21_self_attn_k_proj_weight, alloc1854, alloc1856) + R.vm.kill_object(model_encoder_layers_21_self_attn_k_proj_weight) + gv2353: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape169: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1856, gv2353, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1856) + model_encoder_layers_21_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[321] + model_encoder_layers_21_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[322] + gv2354: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1857: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv2354, R.dtype("float16")) + _1855: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_21_self_attn_v_proj_weight, alloc1854, model_encoder_layers_21_self_attn_v_proj_bias, alloc1857) + R.vm.kill_object(alloc1854) + R.vm.kill_object(model_encoder_layers_21_self_attn_v_proj_weight) + R.vm.kill_object(model_encoder_layers_21_self_attn_v_proj_bias) + gv2355: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape170: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1857, gv2355, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1857) + gv2356: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape171: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape168, gv2356, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape168) + gv2357: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape172: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape169, gv2357, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape169) + gv2358: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape173: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape170, gv2358, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape170) + gv2359: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc1858: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2359, R.dtype("float16")) + _1856: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_no_append", paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), reshape171, reshape172, reshape173, alloc1858) + R.vm.kill_object(reshape171) + R.vm.kill_object(reshape172) + R.vm.kill_object(reshape173) + gv2360: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape174: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1858, gv2360, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1858) + gv2361: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape175: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape174, gv2361, sinfo_args=(R.Tensor((batch_size, 1500, 1280), dtype="float16"),)) + R.vm.kill_object(reshape174) + model_encoder_layers_21_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[325] + model_encoder_layers_21_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[326] + gv2362: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1859: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv2362, R.dtype("float16")) + _1857: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_21_self_attn_out_proj_weight, reshape175, model_encoder_layers_21_self_attn_out_proj_bias, alloc1859) + R.vm.kill_object(reshape175) + R.vm.kill_object(model_encoder_layers_21_self_attn_out_proj_weight) + R.vm.kill_object(model_encoder_layers_21_self_attn_out_proj_bias) + gv2363: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1860: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv2363, R.dtype("float16")) + cls.add4(alloc1853, alloc1859, alloc1860) + R.vm.kill_object(alloc1853) + R.vm.kill_object(alloc1859) + model_encoder_layers_21_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[333] + model_encoder_layers_21_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[334] + gv2364: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1861: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2364, R.dtype("float16")) + cls.layer_norm1(alloc1860, model_encoder_layers_21_final_layer_norm_weight, model_encoder_layers_21_final_layer_norm_bias, alloc1861) + R.vm.kill_object(model_encoder_layers_21_final_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_21_final_layer_norm_bias) + model_encoder_layers_21_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[329] + model_encoder_layers_21_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[330] + gv2365: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc1862: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv2365, R.dtype("float16")) + _1860: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", model_encoder_layers_21_fc1_weight, alloc1861, model_encoder_layers_21_fc1_bias, alloc1862) + R.vm.kill_object(alloc1861) + R.vm.kill_object(model_encoder_layers_21_fc1_weight) + R.vm.kill_object(model_encoder_layers_21_fc1_bias) + model_encoder_layers_21_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[331] + model_encoder_layers_21_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[332] + gv2366: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1863: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv2366, R.dtype("float16")) + _1861: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", model_encoder_layers_21_fc2_weight, alloc1862, model_encoder_layers_21_fc2_bias, alloc1863) + R.vm.kill_object(alloc1862) + R.vm.kill_object(model_encoder_layers_21_fc2_weight) + R.vm.kill_object(model_encoder_layers_21_fc2_bias) + gv2367: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1864: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv2367, R.dtype("float16")) + cls.fused_add4_maximum_minimum(alloc1860, alloc1863, alloc1864) + R.vm.kill_object(alloc1860) + R.vm.kill_object(alloc1863) + model_encoder_layers_22_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[342] + model_encoder_layers_22_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[343] + gv2368: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1865: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2368, R.dtype("float16")) + cls.layer_norm1(alloc1864, model_encoder_layers_22_self_attn_layer_norm_weight, model_encoder_layers_22_self_attn_layer_norm_bias, alloc1865) + R.vm.kill_object(model_encoder_layers_22_self_attn_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_22_self_attn_layer_norm_bias) + model_encoder_layers_22_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[338] + model_encoder_layers_22_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[339] + gv2369: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1866: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv2369, R.dtype("float16")) + _1864: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_22_self_attn_q_proj_weight, alloc1865, model_encoder_layers_22_self_attn_q_proj_bias, alloc1866) + R.vm.kill_object(model_encoder_layers_22_self_attn_q_proj_weight) + R.vm.kill_object(model_encoder_layers_22_self_attn_q_proj_bias) + gv2370: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape176: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1866, gv2370, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1866) + model_encoder_layers_22_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[335] + gv2371: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1867: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv2371, R.dtype("float16")) + _1865: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_encoder_layers_22_self_attn_k_proj_weight, alloc1865, alloc1867) + R.vm.kill_object(model_encoder_layers_22_self_attn_k_proj_weight) + gv2372: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape177: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1867, gv2372, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1867) + model_encoder_layers_22_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[336] + model_encoder_layers_22_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[337] + gv2373: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1868: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv2373, R.dtype("float16")) + _1866: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_22_self_attn_v_proj_weight, alloc1865, model_encoder_layers_22_self_attn_v_proj_bias, alloc1868) + R.vm.kill_object(alloc1865) + R.vm.kill_object(model_encoder_layers_22_self_attn_v_proj_weight) + R.vm.kill_object(model_encoder_layers_22_self_attn_v_proj_bias) + gv2374: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape178: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1868, gv2374, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1868) + gv2375: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape179: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape176, gv2375, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape176) + gv2376: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape180: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape177, gv2376, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape177) + gv2377: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape181: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape178, gv2377, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape178) + gv2378: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc1869: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2378, R.dtype("float16")) + _1867: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_no_append", paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), reshape179, reshape180, reshape181, alloc1869) + R.vm.kill_object(reshape179) + R.vm.kill_object(reshape180) + R.vm.kill_object(reshape181) + gv2379: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape182: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1869, gv2379, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1869) + gv2380: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape183: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape182, gv2380, sinfo_args=(R.Tensor((batch_size, 1500, 1280), dtype="float16"),)) + R.vm.kill_object(reshape182) + model_encoder_layers_22_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[340] + model_encoder_layers_22_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[341] + gv2381: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1870: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv2381, R.dtype("float16")) + _1868: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_22_self_attn_out_proj_weight, reshape183, model_encoder_layers_22_self_attn_out_proj_bias, alloc1870) + R.vm.kill_object(reshape183) + R.vm.kill_object(model_encoder_layers_22_self_attn_out_proj_weight) + R.vm.kill_object(model_encoder_layers_22_self_attn_out_proj_bias) + gv2382: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1871: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv2382, R.dtype("float16")) + cls.add4(alloc1864, alloc1870, alloc1871) + R.vm.kill_object(alloc1864) + R.vm.kill_object(alloc1870) + model_encoder_layers_22_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[348] + model_encoder_layers_22_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[349] + gv2383: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1872: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2383, R.dtype("float16")) + cls.layer_norm1(alloc1871, model_encoder_layers_22_final_layer_norm_weight, model_encoder_layers_22_final_layer_norm_bias, alloc1872) + R.vm.kill_object(model_encoder_layers_22_final_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_22_final_layer_norm_bias) + model_encoder_layers_22_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[344] + model_encoder_layers_22_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[345] + gv2384: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc1873: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv2384, R.dtype("float16")) + _1871: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", model_encoder_layers_22_fc1_weight, alloc1872, model_encoder_layers_22_fc1_bias, alloc1873) + R.vm.kill_object(alloc1872) + R.vm.kill_object(model_encoder_layers_22_fc1_weight) + R.vm.kill_object(model_encoder_layers_22_fc1_bias) + model_encoder_layers_22_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[346] + model_encoder_layers_22_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[347] + gv2385: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1874: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv2385, R.dtype("float16")) + _1872: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", model_encoder_layers_22_fc2_weight, alloc1873, model_encoder_layers_22_fc2_bias, alloc1874) + R.vm.kill_object(alloc1873) + R.vm.kill_object(model_encoder_layers_22_fc2_weight) + R.vm.kill_object(model_encoder_layers_22_fc2_bias) + gv2386: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1875: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv2386, R.dtype("float16")) + cls.fused_add4_maximum_minimum(alloc1871, alloc1874, alloc1875) + R.vm.kill_object(alloc1871) + R.vm.kill_object(alloc1874) + model_encoder_layers_23_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[357] + model_encoder_layers_23_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[358] + gv2387: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1876: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2387, R.dtype("float16")) + cls.layer_norm1(alloc1875, model_encoder_layers_23_self_attn_layer_norm_weight, model_encoder_layers_23_self_attn_layer_norm_bias, alloc1876) + R.vm.kill_object(model_encoder_layers_23_self_attn_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_23_self_attn_layer_norm_bias) + model_encoder_layers_23_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[353] + model_encoder_layers_23_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[354] + gv2388: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1877: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv2388, R.dtype("float16")) + _1875: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_23_self_attn_q_proj_weight, alloc1876, model_encoder_layers_23_self_attn_q_proj_bias, alloc1877) + R.vm.kill_object(model_encoder_layers_23_self_attn_q_proj_weight) + R.vm.kill_object(model_encoder_layers_23_self_attn_q_proj_bias) + gv2389: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape184: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1877, gv2389, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1877) + model_encoder_layers_23_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[350] + gv2390: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1878: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv2390, R.dtype("float16")) + _1876: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_encoder_layers_23_self_attn_k_proj_weight, alloc1876, alloc1878) + R.vm.kill_object(model_encoder_layers_23_self_attn_k_proj_weight) + gv2391: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape185: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1878, gv2391, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1878) + model_encoder_layers_23_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[351] + model_encoder_layers_23_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[352] + gv2392: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1879: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv2392, R.dtype("float16")) + _1877: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_23_self_attn_v_proj_weight, alloc1876, model_encoder_layers_23_self_attn_v_proj_bias, alloc1879) + R.vm.kill_object(alloc1876) + R.vm.kill_object(model_encoder_layers_23_self_attn_v_proj_weight) + R.vm.kill_object(model_encoder_layers_23_self_attn_v_proj_bias) + gv2393: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape186: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1879, gv2393, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1879) + gv2394: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape187: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape184, gv2394, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape184) + gv2395: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape188: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape185, gv2395, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape185) + gv2396: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape189: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape186, gv2396, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape186) + gv2397: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc1880: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2397, R.dtype("float16")) + _1878: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_no_append", paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), reshape187, reshape188, reshape189, alloc1880) + R.vm.kill_object(reshape187) + R.vm.kill_object(reshape188) + R.vm.kill_object(reshape189) + gv2398: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape190: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1880, gv2398, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1880) + gv2399: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape191: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape190, gv2399, sinfo_args=(R.Tensor((batch_size, 1500, 1280), dtype="float16"),)) + R.vm.kill_object(reshape190) + model_encoder_layers_23_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[355] + model_encoder_layers_23_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[356] + gv2400: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1881: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv2400, R.dtype("float16")) + _1879: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_23_self_attn_out_proj_weight, reshape191, model_encoder_layers_23_self_attn_out_proj_bias, alloc1881) + R.vm.kill_object(reshape191) + R.vm.kill_object(model_encoder_layers_23_self_attn_out_proj_weight) + R.vm.kill_object(model_encoder_layers_23_self_attn_out_proj_bias) + gv2401: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1882: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv2401, R.dtype("float16")) + cls.add4(alloc1875, alloc1881, alloc1882) + R.vm.kill_object(alloc1875) + R.vm.kill_object(alloc1881) + model_encoder_layers_23_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[363] + model_encoder_layers_23_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[364] + gv2402: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1883: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2402, R.dtype("float16")) + cls.layer_norm1(alloc1882, model_encoder_layers_23_final_layer_norm_weight, model_encoder_layers_23_final_layer_norm_bias, alloc1883) + R.vm.kill_object(model_encoder_layers_23_final_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_23_final_layer_norm_bias) + model_encoder_layers_23_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[359] + model_encoder_layers_23_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[360] + gv2403: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc1884: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv2403, R.dtype("float16")) + _1882: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", model_encoder_layers_23_fc1_weight, alloc1883, model_encoder_layers_23_fc1_bias, alloc1884) + R.vm.kill_object(alloc1883) + R.vm.kill_object(model_encoder_layers_23_fc1_weight) + R.vm.kill_object(model_encoder_layers_23_fc1_bias) + model_encoder_layers_23_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[361] + model_encoder_layers_23_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[362] + gv2404: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1885: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv2404, R.dtype("float16")) + _1883: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", model_encoder_layers_23_fc2_weight, alloc1884, model_encoder_layers_23_fc2_bias, alloc1885) + R.vm.kill_object(alloc1884) + R.vm.kill_object(model_encoder_layers_23_fc2_weight) + R.vm.kill_object(model_encoder_layers_23_fc2_bias) + gv2405: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1886: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv2405, R.dtype("float16")) + cls.fused_add4_maximum_minimum(alloc1882, alloc1885, alloc1886) + R.vm.kill_object(alloc1882) + R.vm.kill_object(alloc1885) + model_encoder_layers_24_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[372] + model_encoder_layers_24_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[373] + gv2406: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1887: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2406, R.dtype("float16")) + cls.layer_norm1(alloc1886, model_encoder_layers_24_self_attn_layer_norm_weight, model_encoder_layers_24_self_attn_layer_norm_bias, alloc1887) + R.vm.kill_object(model_encoder_layers_24_self_attn_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_24_self_attn_layer_norm_bias) + model_encoder_layers_24_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[368] + model_encoder_layers_24_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[369] + gv2407: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1888: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv2407, R.dtype("float16")) + _1886: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_24_self_attn_q_proj_weight, alloc1887, model_encoder_layers_24_self_attn_q_proj_bias, alloc1888) + R.vm.kill_object(model_encoder_layers_24_self_attn_q_proj_weight) + R.vm.kill_object(model_encoder_layers_24_self_attn_q_proj_bias) + gv2408: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape192: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1888, gv2408, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1888) + model_encoder_layers_24_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[365] + gv2409: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1889: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv2409, R.dtype("float16")) + _1887: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_encoder_layers_24_self_attn_k_proj_weight, alloc1887, alloc1889) + R.vm.kill_object(model_encoder_layers_24_self_attn_k_proj_weight) + gv2410: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape193: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1889, gv2410, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1889) + model_encoder_layers_24_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[366] + model_encoder_layers_24_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[367] + gv2411: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1890: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv2411, R.dtype("float16")) + _1888: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_24_self_attn_v_proj_weight, alloc1887, model_encoder_layers_24_self_attn_v_proj_bias, alloc1890) + R.vm.kill_object(alloc1887) + R.vm.kill_object(model_encoder_layers_24_self_attn_v_proj_weight) + R.vm.kill_object(model_encoder_layers_24_self_attn_v_proj_bias) + gv2412: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape194: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1890, gv2412, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1890) + gv2413: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape195: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape192, gv2413, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape192) + gv2414: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape196: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape193, gv2414, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape193) + gv2415: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape197: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape194, gv2415, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape194) + gv2416: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc1891: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2416, R.dtype("float16")) + _1889: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_no_append", paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), reshape195, reshape196, reshape197, alloc1891) + R.vm.kill_object(reshape195) + R.vm.kill_object(reshape196) + R.vm.kill_object(reshape197) + gv2417: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape198: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1891, gv2417, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1891) + gv2418: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape199: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape198, gv2418, sinfo_args=(R.Tensor((batch_size, 1500, 1280), dtype="float16"),)) + R.vm.kill_object(reshape198) + model_encoder_layers_24_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[370] + model_encoder_layers_24_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[371] + gv2419: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1892: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv2419, R.dtype("float16")) + _1890: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_24_self_attn_out_proj_weight, reshape199, model_encoder_layers_24_self_attn_out_proj_bias, alloc1892) + R.vm.kill_object(reshape199) + R.vm.kill_object(model_encoder_layers_24_self_attn_out_proj_weight) + R.vm.kill_object(model_encoder_layers_24_self_attn_out_proj_bias) + gv2420: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1893: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv2420, R.dtype("float16")) + cls.add4(alloc1886, alloc1892, alloc1893) + R.vm.kill_object(alloc1886) + R.vm.kill_object(alloc1892) + model_encoder_layers_24_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[378] + model_encoder_layers_24_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[379] + gv2421: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1894: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2421, R.dtype("float16")) + cls.layer_norm1(alloc1893, model_encoder_layers_24_final_layer_norm_weight, model_encoder_layers_24_final_layer_norm_bias, alloc1894) + R.vm.kill_object(model_encoder_layers_24_final_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_24_final_layer_norm_bias) + model_encoder_layers_24_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[374] + model_encoder_layers_24_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[375] + gv2422: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc1895: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv2422, R.dtype("float16")) + _1893: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", model_encoder_layers_24_fc1_weight, alloc1894, model_encoder_layers_24_fc1_bias, alloc1895) + R.vm.kill_object(alloc1894) + R.vm.kill_object(model_encoder_layers_24_fc1_weight) + R.vm.kill_object(model_encoder_layers_24_fc1_bias) + model_encoder_layers_24_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[376] + model_encoder_layers_24_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[377] + gv2423: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1896: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv2423, R.dtype("float16")) + _1894: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", model_encoder_layers_24_fc2_weight, alloc1895, model_encoder_layers_24_fc2_bias, alloc1896) + R.vm.kill_object(alloc1895) + R.vm.kill_object(model_encoder_layers_24_fc2_weight) + R.vm.kill_object(model_encoder_layers_24_fc2_bias) + gv2424: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1897: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv2424, R.dtype("float16")) + cls.fused_add4_maximum_minimum(alloc1893, alloc1896, alloc1897) + R.vm.kill_object(alloc1893) + R.vm.kill_object(alloc1896) + model_encoder_layers_25_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[387] + model_encoder_layers_25_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[388] + gv2425: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1898: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2425, R.dtype("float16")) + cls.layer_norm1(alloc1897, model_encoder_layers_25_self_attn_layer_norm_weight, model_encoder_layers_25_self_attn_layer_norm_bias, alloc1898) + R.vm.kill_object(model_encoder_layers_25_self_attn_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_25_self_attn_layer_norm_bias) + model_encoder_layers_25_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[383] + model_encoder_layers_25_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[384] + gv2426: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1899: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv2426, R.dtype("float16")) + _1897: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_25_self_attn_q_proj_weight, alloc1898, model_encoder_layers_25_self_attn_q_proj_bias, alloc1899) + R.vm.kill_object(model_encoder_layers_25_self_attn_q_proj_weight) + R.vm.kill_object(model_encoder_layers_25_self_attn_q_proj_bias) + gv2427: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape200: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1899, gv2427, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1899) + model_encoder_layers_25_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[380] + gv2428: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1900: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv2428, R.dtype("float16")) + _1898: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_encoder_layers_25_self_attn_k_proj_weight, alloc1898, alloc1900) + R.vm.kill_object(model_encoder_layers_25_self_attn_k_proj_weight) + gv2429: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape201: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1900, gv2429, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1900) + model_encoder_layers_25_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[381] + model_encoder_layers_25_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[382] + gv2430: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1901: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv2430, R.dtype("float16")) + _1899: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_25_self_attn_v_proj_weight, alloc1898, model_encoder_layers_25_self_attn_v_proj_bias, alloc1901) + R.vm.kill_object(alloc1898) + R.vm.kill_object(model_encoder_layers_25_self_attn_v_proj_weight) + R.vm.kill_object(model_encoder_layers_25_self_attn_v_proj_bias) + gv2431: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape202: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1901, gv2431, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1901) + gv2432: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape203: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape200, gv2432, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape200) + gv2433: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape204: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape201, gv2433, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape201) + gv2434: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape205: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape202, gv2434, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape202) + gv2435: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc1902: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2435, R.dtype("float16")) + _1900: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_no_append", paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), reshape203, reshape204, reshape205, alloc1902) + R.vm.kill_object(reshape203) + R.vm.kill_object(reshape204) + R.vm.kill_object(reshape205) + gv2436: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape206: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1902, gv2436, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1902) + gv2437: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape207: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape206, gv2437, sinfo_args=(R.Tensor((batch_size, 1500, 1280), dtype="float16"),)) + R.vm.kill_object(reshape206) + model_encoder_layers_25_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[385] + model_encoder_layers_25_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[386] + gv2438: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1903: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv2438, R.dtype("float16")) + _1901: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_25_self_attn_out_proj_weight, reshape207, model_encoder_layers_25_self_attn_out_proj_bias, alloc1903) + R.vm.kill_object(reshape207) + R.vm.kill_object(model_encoder_layers_25_self_attn_out_proj_weight) + R.vm.kill_object(model_encoder_layers_25_self_attn_out_proj_bias) + gv2439: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1904: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv2439, R.dtype("float16")) + cls.add4(alloc1897, alloc1903, alloc1904) + R.vm.kill_object(alloc1897) + R.vm.kill_object(alloc1903) + model_encoder_layers_25_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[393] + model_encoder_layers_25_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[394] + gv2440: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1905: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2440, R.dtype("float16")) + cls.layer_norm1(alloc1904, model_encoder_layers_25_final_layer_norm_weight, model_encoder_layers_25_final_layer_norm_bias, alloc1905) + R.vm.kill_object(model_encoder_layers_25_final_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_25_final_layer_norm_bias) + model_encoder_layers_25_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[389] + model_encoder_layers_25_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[390] + gv2441: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc1906: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv2441, R.dtype("float16")) + _1904: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", model_encoder_layers_25_fc1_weight, alloc1905, model_encoder_layers_25_fc1_bias, alloc1906) + R.vm.kill_object(alloc1905) + R.vm.kill_object(model_encoder_layers_25_fc1_weight) + R.vm.kill_object(model_encoder_layers_25_fc1_bias) + model_encoder_layers_25_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[391] + model_encoder_layers_25_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[392] + gv2442: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1907: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv2442, R.dtype("float16")) + _1905: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", model_encoder_layers_25_fc2_weight, alloc1906, model_encoder_layers_25_fc2_bias, alloc1907) + R.vm.kill_object(alloc1906) + R.vm.kill_object(model_encoder_layers_25_fc2_weight) + R.vm.kill_object(model_encoder_layers_25_fc2_bias) + gv2443: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1908: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv2443, R.dtype("float16")) + cls.fused_add4_maximum_minimum(alloc1904, alloc1907, alloc1908) + R.vm.kill_object(alloc1904) + R.vm.kill_object(alloc1907) + model_encoder_layers_26_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[402] + model_encoder_layers_26_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[403] + gv2444: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1909: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2444, R.dtype("float16")) + cls.layer_norm1(alloc1908, model_encoder_layers_26_self_attn_layer_norm_weight, model_encoder_layers_26_self_attn_layer_norm_bias, alloc1909) + R.vm.kill_object(model_encoder_layers_26_self_attn_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_26_self_attn_layer_norm_bias) + model_encoder_layers_26_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[398] + model_encoder_layers_26_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[399] + gv2445: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1910: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv2445, R.dtype("float16")) + _1908: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_26_self_attn_q_proj_weight, alloc1909, model_encoder_layers_26_self_attn_q_proj_bias, alloc1910) + R.vm.kill_object(model_encoder_layers_26_self_attn_q_proj_weight) + R.vm.kill_object(model_encoder_layers_26_self_attn_q_proj_bias) + gv2446: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape208: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1910, gv2446, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1910) + model_encoder_layers_26_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[395] + gv2447: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1911: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv2447, R.dtype("float16")) + _1909: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_encoder_layers_26_self_attn_k_proj_weight, alloc1909, alloc1911) + R.vm.kill_object(model_encoder_layers_26_self_attn_k_proj_weight) + gv2448: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape209: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1911, gv2448, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1911) + model_encoder_layers_26_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[396] + model_encoder_layers_26_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[397] + gv2449: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1912: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv2449, R.dtype("float16")) + _1910: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_26_self_attn_v_proj_weight, alloc1909, model_encoder_layers_26_self_attn_v_proj_bias, alloc1912) + R.vm.kill_object(alloc1909) + R.vm.kill_object(model_encoder_layers_26_self_attn_v_proj_weight) + R.vm.kill_object(model_encoder_layers_26_self_attn_v_proj_bias) + gv2450: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape210: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1912, gv2450, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1912) + gv2451: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape211: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape208, gv2451, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape208) + gv2452: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape212: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape209, gv2452, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape209) + gv2453: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape213: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape210, gv2453, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape210) + gv2454: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc1913: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2454, R.dtype("float16")) + _1911: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_no_append", paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), reshape211, reshape212, reshape213, alloc1913) + R.vm.kill_object(reshape211) + R.vm.kill_object(reshape212) + R.vm.kill_object(reshape213) + gv2455: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape214: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1913, gv2455, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1913) + gv2456: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape215: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape214, gv2456, sinfo_args=(R.Tensor((batch_size, 1500, 1280), dtype="float16"),)) + R.vm.kill_object(reshape214) + model_encoder_layers_26_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[400] + model_encoder_layers_26_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[401] + gv2457: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1914: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv2457, R.dtype("float16")) + _1912: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_26_self_attn_out_proj_weight, reshape215, model_encoder_layers_26_self_attn_out_proj_bias, alloc1914) + R.vm.kill_object(reshape215) + R.vm.kill_object(model_encoder_layers_26_self_attn_out_proj_weight) + R.vm.kill_object(model_encoder_layers_26_self_attn_out_proj_bias) + gv2458: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1915: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv2458, R.dtype("float16")) + cls.add4(alloc1908, alloc1914, alloc1915) + R.vm.kill_object(alloc1908) + R.vm.kill_object(alloc1914) + model_encoder_layers_26_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[408] + model_encoder_layers_26_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[409] + gv2459: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1916: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2459, R.dtype("float16")) + cls.layer_norm1(alloc1915, model_encoder_layers_26_final_layer_norm_weight, model_encoder_layers_26_final_layer_norm_bias, alloc1916) + R.vm.kill_object(model_encoder_layers_26_final_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_26_final_layer_norm_bias) + model_encoder_layers_26_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[404] + model_encoder_layers_26_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[405] + gv2460: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc1917: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv2460, R.dtype("float16")) + _1915: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", model_encoder_layers_26_fc1_weight, alloc1916, model_encoder_layers_26_fc1_bias, alloc1917) + R.vm.kill_object(alloc1916) + R.vm.kill_object(model_encoder_layers_26_fc1_weight) + R.vm.kill_object(model_encoder_layers_26_fc1_bias) + model_encoder_layers_26_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[406] + model_encoder_layers_26_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[407] + gv2461: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1918: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv2461, R.dtype("float16")) + _1916: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", model_encoder_layers_26_fc2_weight, alloc1917, model_encoder_layers_26_fc2_bias, alloc1918) + R.vm.kill_object(alloc1917) + R.vm.kill_object(model_encoder_layers_26_fc2_weight) + R.vm.kill_object(model_encoder_layers_26_fc2_bias) + gv2462: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1919: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv2462, R.dtype("float16")) + cls.fused_add4_maximum_minimum(alloc1915, alloc1918, alloc1919) + R.vm.kill_object(alloc1915) + R.vm.kill_object(alloc1918) + model_encoder_layers_27_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[417] + model_encoder_layers_27_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[418] + gv2463: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1920: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2463, R.dtype("float16")) + cls.layer_norm1(alloc1919, model_encoder_layers_27_self_attn_layer_norm_weight, model_encoder_layers_27_self_attn_layer_norm_bias, alloc1920) + R.vm.kill_object(model_encoder_layers_27_self_attn_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_27_self_attn_layer_norm_bias) + model_encoder_layers_27_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[413] + model_encoder_layers_27_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[414] + gv2464: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1921: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv2464, R.dtype("float16")) + _1919: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_27_self_attn_q_proj_weight, alloc1920, model_encoder_layers_27_self_attn_q_proj_bias, alloc1921) + R.vm.kill_object(model_encoder_layers_27_self_attn_q_proj_weight) + R.vm.kill_object(model_encoder_layers_27_self_attn_q_proj_bias) + gv2465: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape216: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1921, gv2465, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1921) + model_encoder_layers_27_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[410] + gv2466: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1922: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv2466, R.dtype("float16")) + _1920: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_encoder_layers_27_self_attn_k_proj_weight, alloc1920, alloc1922) + R.vm.kill_object(model_encoder_layers_27_self_attn_k_proj_weight) + gv2467: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape217: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1922, gv2467, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1922) + model_encoder_layers_27_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[411] + model_encoder_layers_27_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[412] + gv2468: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1923: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv2468, R.dtype("float16")) + _1921: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_27_self_attn_v_proj_weight, alloc1920, model_encoder_layers_27_self_attn_v_proj_bias, alloc1923) + R.vm.kill_object(alloc1920) + R.vm.kill_object(model_encoder_layers_27_self_attn_v_proj_weight) + R.vm.kill_object(model_encoder_layers_27_self_attn_v_proj_bias) + gv2469: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape218: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1923, gv2469, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1923) + gv2470: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape219: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape216, gv2470, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape216) + gv2471: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape220: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape217, gv2471, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape217) + gv2472: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape221: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape218, gv2472, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape218) + gv2473: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc1924: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2473, R.dtype("float16")) + _1922: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_no_append", paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), reshape219, reshape220, reshape221, alloc1924) + R.vm.kill_object(reshape219) + R.vm.kill_object(reshape220) + R.vm.kill_object(reshape221) + gv2474: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape222: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1924, gv2474, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1924) + gv2475: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape223: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape222, gv2475, sinfo_args=(R.Tensor((batch_size, 1500, 1280), dtype="float16"),)) + R.vm.kill_object(reshape222) + model_encoder_layers_27_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[415] + model_encoder_layers_27_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[416] + gv2476: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1925: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv2476, R.dtype("float16")) + _1923: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_27_self_attn_out_proj_weight, reshape223, model_encoder_layers_27_self_attn_out_proj_bias, alloc1925) + R.vm.kill_object(reshape223) + R.vm.kill_object(model_encoder_layers_27_self_attn_out_proj_weight) + R.vm.kill_object(model_encoder_layers_27_self_attn_out_proj_bias) + gv2477: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1926: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv2477, R.dtype("float16")) + cls.add4(alloc1919, alloc1925, alloc1926) + R.vm.kill_object(alloc1919) + R.vm.kill_object(alloc1925) + model_encoder_layers_27_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[423] + model_encoder_layers_27_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[424] + gv2478: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1927: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2478, R.dtype("float16")) + cls.layer_norm1(alloc1926, model_encoder_layers_27_final_layer_norm_weight, model_encoder_layers_27_final_layer_norm_bias, alloc1927) + R.vm.kill_object(model_encoder_layers_27_final_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_27_final_layer_norm_bias) + model_encoder_layers_27_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[419] + model_encoder_layers_27_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[420] + gv2479: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc1928: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv2479, R.dtype("float16")) + _1926: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", model_encoder_layers_27_fc1_weight, alloc1927, model_encoder_layers_27_fc1_bias, alloc1928) + R.vm.kill_object(alloc1927) + R.vm.kill_object(model_encoder_layers_27_fc1_weight) + R.vm.kill_object(model_encoder_layers_27_fc1_bias) + model_encoder_layers_27_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[421] + model_encoder_layers_27_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[422] + gv2480: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1929: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv2480, R.dtype("float16")) + _1927: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", model_encoder_layers_27_fc2_weight, alloc1928, model_encoder_layers_27_fc2_bias, alloc1929) + R.vm.kill_object(alloc1928) + R.vm.kill_object(model_encoder_layers_27_fc2_weight) + R.vm.kill_object(model_encoder_layers_27_fc2_bias) + gv2481: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1930: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv2481, R.dtype("float16")) + cls.fused_add4_maximum_minimum(alloc1926, alloc1929, alloc1930) + R.vm.kill_object(alloc1926) + R.vm.kill_object(alloc1929) + model_encoder_layers_28_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[432] + model_encoder_layers_28_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[433] + gv2482: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1931: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2482, R.dtype("float16")) + cls.layer_norm1(alloc1930, model_encoder_layers_28_self_attn_layer_norm_weight, model_encoder_layers_28_self_attn_layer_norm_bias, alloc1931) + R.vm.kill_object(model_encoder_layers_28_self_attn_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_28_self_attn_layer_norm_bias) + model_encoder_layers_28_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[428] + model_encoder_layers_28_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[429] + gv2483: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1932: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv2483, R.dtype("float16")) + _1930: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_28_self_attn_q_proj_weight, alloc1931, model_encoder_layers_28_self_attn_q_proj_bias, alloc1932) + R.vm.kill_object(model_encoder_layers_28_self_attn_q_proj_weight) + R.vm.kill_object(model_encoder_layers_28_self_attn_q_proj_bias) + gv2484: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape224: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1932, gv2484, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1932) + model_encoder_layers_28_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[425] + gv2485: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1933: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv2485, R.dtype("float16")) + _1931: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_encoder_layers_28_self_attn_k_proj_weight, alloc1931, alloc1933) + R.vm.kill_object(model_encoder_layers_28_self_attn_k_proj_weight) + gv2486: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape225: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1933, gv2486, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1933) + model_encoder_layers_28_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[426] + model_encoder_layers_28_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[427] + gv2487: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1934: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv2487, R.dtype("float16")) + _1932: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_28_self_attn_v_proj_weight, alloc1931, model_encoder_layers_28_self_attn_v_proj_bias, alloc1934) + R.vm.kill_object(alloc1931) + R.vm.kill_object(model_encoder_layers_28_self_attn_v_proj_weight) + R.vm.kill_object(model_encoder_layers_28_self_attn_v_proj_bias) + gv2488: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape226: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1934, gv2488, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1934) + gv2489: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape227: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape224, gv2489, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape224) + gv2490: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape228: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape225, gv2490, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape225) + gv2491: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape229: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape226, gv2491, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape226) + gv2492: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc1935: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2492, R.dtype("float16")) + _1933: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_no_append", paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), reshape227, reshape228, reshape229, alloc1935) + R.vm.kill_object(reshape227) + R.vm.kill_object(reshape228) + R.vm.kill_object(reshape229) + gv2493: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape230: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1935, gv2493, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1935) + gv2494: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape231: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape230, gv2494, sinfo_args=(R.Tensor((batch_size, 1500, 1280), dtype="float16"),)) + R.vm.kill_object(reshape230) + model_encoder_layers_28_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[430] + model_encoder_layers_28_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[431] + gv2495: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1936: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv2495, R.dtype("float16")) + _1934: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_28_self_attn_out_proj_weight, reshape231, model_encoder_layers_28_self_attn_out_proj_bias, alloc1936) + R.vm.kill_object(reshape231) + R.vm.kill_object(model_encoder_layers_28_self_attn_out_proj_weight) + R.vm.kill_object(model_encoder_layers_28_self_attn_out_proj_bias) + gv2496: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1937: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv2496, R.dtype("float16")) + cls.add4(alloc1930, alloc1936, alloc1937) + R.vm.kill_object(alloc1930) + R.vm.kill_object(alloc1936) + model_encoder_layers_28_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[438] + model_encoder_layers_28_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[439] + gv2497: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1938: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2497, R.dtype("float16")) + cls.layer_norm1(alloc1937, model_encoder_layers_28_final_layer_norm_weight, model_encoder_layers_28_final_layer_norm_bias, alloc1938) + R.vm.kill_object(model_encoder_layers_28_final_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_28_final_layer_norm_bias) + model_encoder_layers_28_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[434] + model_encoder_layers_28_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[435] + gv2498: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc1939: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv2498, R.dtype("float16")) + _1937: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", model_encoder_layers_28_fc1_weight, alloc1938, model_encoder_layers_28_fc1_bias, alloc1939) + R.vm.kill_object(alloc1938) + R.vm.kill_object(model_encoder_layers_28_fc1_weight) + R.vm.kill_object(model_encoder_layers_28_fc1_bias) + model_encoder_layers_28_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[436] + model_encoder_layers_28_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[437] + gv2499: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1940: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv2499, R.dtype("float16")) + _1938: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", model_encoder_layers_28_fc2_weight, alloc1939, model_encoder_layers_28_fc2_bias, alloc1940) + R.vm.kill_object(alloc1939) + R.vm.kill_object(model_encoder_layers_28_fc2_weight) + R.vm.kill_object(model_encoder_layers_28_fc2_bias) + gv2500: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1941: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv2500, R.dtype("float16")) + cls.fused_add4_maximum_minimum(alloc1937, alloc1940, alloc1941) + R.vm.kill_object(alloc1937) + R.vm.kill_object(alloc1940) + model_encoder_layers_29_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[447] + model_encoder_layers_29_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[448] + gv2501: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1942: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2501, R.dtype("float16")) + cls.layer_norm1(alloc1941, model_encoder_layers_29_self_attn_layer_norm_weight, model_encoder_layers_29_self_attn_layer_norm_bias, alloc1942) + R.vm.kill_object(model_encoder_layers_29_self_attn_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_29_self_attn_layer_norm_bias) + model_encoder_layers_29_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[443] + model_encoder_layers_29_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[444] + gv2502: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1943: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv2502, R.dtype("float16")) + _1941: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_29_self_attn_q_proj_weight, alloc1942, model_encoder_layers_29_self_attn_q_proj_bias, alloc1943) + R.vm.kill_object(model_encoder_layers_29_self_attn_q_proj_weight) + R.vm.kill_object(model_encoder_layers_29_self_attn_q_proj_bias) + gv2503: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape232: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1943, gv2503, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1943) + model_encoder_layers_29_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[440] + gv2504: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1944: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv2504, R.dtype("float16")) + _1942: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_encoder_layers_29_self_attn_k_proj_weight, alloc1942, alloc1944) + R.vm.kill_object(model_encoder_layers_29_self_attn_k_proj_weight) + gv2505: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape233: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1944, gv2505, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1944) + model_encoder_layers_29_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[441] + model_encoder_layers_29_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[442] + gv2506: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1945: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv2506, R.dtype("float16")) + _1943: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_29_self_attn_v_proj_weight, alloc1942, model_encoder_layers_29_self_attn_v_proj_bias, alloc1945) + R.vm.kill_object(alloc1942) + R.vm.kill_object(model_encoder_layers_29_self_attn_v_proj_weight) + R.vm.kill_object(model_encoder_layers_29_self_attn_v_proj_bias) + gv2507: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape234: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1945, gv2507, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1945) + gv2508: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape235: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape232, gv2508, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape232) + gv2509: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape236: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape233, gv2509, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape233) + gv2510: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape237: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape234, gv2510, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape234) + gv2511: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc1946: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2511, R.dtype("float16")) + _1944: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_no_append", paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), reshape235, reshape236, reshape237, alloc1946) + R.vm.kill_object(reshape235) + R.vm.kill_object(reshape236) + R.vm.kill_object(reshape237) + gv2512: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape238: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1946, gv2512, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1946) + gv2513: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape239: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape238, gv2513, sinfo_args=(R.Tensor((batch_size, 1500, 1280), dtype="float16"),)) + R.vm.kill_object(reshape238) + model_encoder_layers_29_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[445] + model_encoder_layers_29_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[446] + gv2514: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1947: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv2514, R.dtype("float16")) + _1945: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_29_self_attn_out_proj_weight, reshape239, model_encoder_layers_29_self_attn_out_proj_bias, alloc1947) + R.vm.kill_object(reshape239) + R.vm.kill_object(model_encoder_layers_29_self_attn_out_proj_weight) + R.vm.kill_object(model_encoder_layers_29_self_attn_out_proj_bias) + gv2515: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1948: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv2515, R.dtype("float16")) + cls.add4(alloc1941, alloc1947, alloc1948) + R.vm.kill_object(alloc1941) + R.vm.kill_object(alloc1947) + model_encoder_layers_29_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[453] + model_encoder_layers_29_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[454] + gv2516: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1949: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2516, R.dtype("float16")) + cls.layer_norm1(alloc1948, model_encoder_layers_29_final_layer_norm_weight, model_encoder_layers_29_final_layer_norm_bias, alloc1949) + R.vm.kill_object(model_encoder_layers_29_final_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_29_final_layer_norm_bias) + model_encoder_layers_29_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[449] + model_encoder_layers_29_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[450] + gv2517: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc1950: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv2517, R.dtype("float16")) + _1948: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", model_encoder_layers_29_fc1_weight, alloc1949, model_encoder_layers_29_fc1_bias, alloc1950) + R.vm.kill_object(alloc1949) + R.vm.kill_object(model_encoder_layers_29_fc1_weight) + R.vm.kill_object(model_encoder_layers_29_fc1_bias) + model_encoder_layers_29_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[451] + model_encoder_layers_29_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[452] + gv2518: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1951: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv2518, R.dtype("float16")) + _1949: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", model_encoder_layers_29_fc2_weight, alloc1950, model_encoder_layers_29_fc2_bias, alloc1951) + R.vm.kill_object(alloc1950) + R.vm.kill_object(model_encoder_layers_29_fc2_weight) + R.vm.kill_object(model_encoder_layers_29_fc2_bias) + gv2519: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1952: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv2519, R.dtype("float16")) + cls.fused_add4_maximum_minimum(alloc1948, alloc1951, alloc1952) + R.vm.kill_object(alloc1948) + R.vm.kill_object(alloc1951) + model_encoder_layers_30_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[462] + model_encoder_layers_30_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[463] + gv2520: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1953: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2520, R.dtype("float16")) + cls.layer_norm1(alloc1952, model_encoder_layers_30_self_attn_layer_norm_weight, model_encoder_layers_30_self_attn_layer_norm_bias, alloc1953) + R.vm.kill_object(model_encoder_layers_30_self_attn_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_30_self_attn_layer_norm_bias) + model_encoder_layers_30_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[458] + model_encoder_layers_30_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[459] + gv2521: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1954: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv2521, R.dtype("float16")) + _1952: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_30_self_attn_q_proj_weight, alloc1953, model_encoder_layers_30_self_attn_q_proj_bias, alloc1954) + R.vm.kill_object(model_encoder_layers_30_self_attn_q_proj_weight) + R.vm.kill_object(model_encoder_layers_30_self_attn_q_proj_bias) + gv2522: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape240: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1954, gv2522, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1954) + model_encoder_layers_30_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[455] + gv2523: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1955: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv2523, R.dtype("float16")) + _1953: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_encoder_layers_30_self_attn_k_proj_weight, alloc1953, alloc1955) + R.vm.kill_object(model_encoder_layers_30_self_attn_k_proj_weight) + gv2524: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape241: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1955, gv2524, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1955) + model_encoder_layers_30_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[456] + model_encoder_layers_30_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[457] + gv2525: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1956: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv2525, R.dtype("float16")) + _1954: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_30_self_attn_v_proj_weight, alloc1953, model_encoder_layers_30_self_attn_v_proj_bias, alloc1956) + R.vm.kill_object(alloc1953) + R.vm.kill_object(model_encoder_layers_30_self_attn_v_proj_weight) + R.vm.kill_object(model_encoder_layers_30_self_attn_v_proj_bias) + gv2526: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape242: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1956, gv2526, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1956) + gv2527: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape243: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape240, gv2527, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape240) + gv2528: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape244: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape241, gv2528, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape241) + gv2529: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape245: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape242, gv2529, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape242) + gv2530: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc1957: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2530, R.dtype("float16")) + _1955: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_no_append", paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), reshape243, reshape244, reshape245, alloc1957) + R.vm.kill_object(reshape243) + R.vm.kill_object(reshape244) + R.vm.kill_object(reshape245) + gv2531: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape246: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1957, gv2531, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1957) + gv2532: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape247: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape246, gv2532, sinfo_args=(R.Tensor((batch_size, 1500, 1280), dtype="float16"),)) + R.vm.kill_object(reshape246) + model_encoder_layers_30_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[460] + model_encoder_layers_30_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[461] + gv2533: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1958: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv2533, R.dtype("float16")) + _1956: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_30_self_attn_out_proj_weight, reshape247, model_encoder_layers_30_self_attn_out_proj_bias, alloc1958) + R.vm.kill_object(reshape247) + R.vm.kill_object(model_encoder_layers_30_self_attn_out_proj_weight) + R.vm.kill_object(model_encoder_layers_30_self_attn_out_proj_bias) + gv2534: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1959: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv2534, R.dtype("float16")) + cls.add4(alloc1952, alloc1958, alloc1959) + R.vm.kill_object(alloc1952) + R.vm.kill_object(alloc1958) + model_encoder_layers_30_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[468] + model_encoder_layers_30_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[469] + gv2535: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1960: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2535, R.dtype("float16")) + cls.layer_norm1(alloc1959, model_encoder_layers_30_final_layer_norm_weight, model_encoder_layers_30_final_layer_norm_bias, alloc1960) + R.vm.kill_object(model_encoder_layers_30_final_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_30_final_layer_norm_bias) + model_encoder_layers_30_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[464] + model_encoder_layers_30_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[465] + gv2536: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc1961: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv2536, R.dtype("float16")) + _1959: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", model_encoder_layers_30_fc1_weight, alloc1960, model_encoder_layers_30_fc1_bias, alloc1961) + R.vm.kill_object(alloc1960) + R.vm.kill_object(model_encoder_layers_30_fc1_weight) + R.vm.kill_object(model_encoder_layers_30_fc1_bias) + model_encoder_layers_30_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[466] + model_encoder_layers_30_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[467] + gv2537: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1962: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv2537, R.dtype("float16")) + _1960: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", model_encoder_layers_30_fc2_weight, alloc1961, model_encoder_layers_30_fc2_bias, alloc1962) + R.vm.kill_object(alloc1961) + R.vm.kill_object(model_encoder_layers_30_fc2_weight) + R.vm.kill_object(model_encoder_layers_30_fc2_bias) + gv2538: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1963: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv2538, R.dtype("float16")) + cls.fused_add4_maximum_minimum(alloc1959, alloc1962, alloc1963) + R.vm.kill_object(alloc1959) + R.vm.kill_object(alloc1962) + model_encoder_layers_31_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[477] + model_encoder_layers_31_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[478] + gv2539: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1964: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2539, R.dtype("float16")) + cls.layer_norm1(alloc1963, model_encoder_layers_31_self_attn_layer_norm_weight, model_encoder_layers_31_self_attn_layer_norm_bias, alloc1964) + R.vm.kill_object(model_encoder_layers_31_self_attn_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_31_self_attn_layer_norm_bias) + model_encoder_layers_31_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[473] + model_encoder_layers_31_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[474] + gv2540: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1965: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv2540, R.dtype("float16")) + _1963: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_31_self_attn_q_proj_weight, alloc1964, model_encoder_layers_31_self_attn_q_proj_bias, alloc1965) + R.vm.kill_object(model_encoder_layers_31_self_attn_q_proj_weight) + R.vm.kill_object(model_encoder_layers_31_self_attn_q_proj_bias) + gv2541: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape248: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1965, gv2541, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1965) + model_encoder_layers_31_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[470] + gv2542: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1966: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv2542, R.dtype("float16")) + _1964: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_cublas", model_encoder_layers_31_self_attn_k_proj_weight, alloc1964, alloc1966) + R.vm.kill_object(model_encoder_layers_31_self_attn_k_proj_weight) + gv2543: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape249: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1966, gv2543, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1966) + model_encoder_layers_31_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[471] + model_encoder_layers_31_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[472] + gv2544: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1967: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv2544, R.dtype("float16")) + _1965: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_31_self_attn_v_proj_weight, alloc1964, model_encoder_layers_31_self_attn_v_proj_bias, alloc1967) + R.vm.kill_object(alloc1964) + R.vm.kill_object(model_encoder_layers_31_self_attn_v_proj_weight) + R.vm.kill_object(model_encoder_layers_31_self_attn_v_proj_bias) + gv2545: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape250: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1967, gv2545, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1967) + gv2546: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape251: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape248, gv2546, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape248) + gv2547: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape252: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape249, gv2547, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape249) + gv2548: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape253: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape250, gv2548, sinfo_args=(R.Tensor((batch_size * 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape250) + gv2549: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc1968: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2549, R.dtype("float16")) + _1966: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_no_append", paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), reshape251, reshape252, reshape253, alloc1968) + R.vm.kill_object(reshape251) + R.vm.kill_object(reshape252) + R.vm.kill_object(reshape253) + gv2550: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape254: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1968, gv2550, sinfo_args=(R.Tensor((batch_size, 1500, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1968) + gv2551: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape255: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape254, gv2551, sinfo_args=(R.Tensor((batch_size, 1500, 1280), dtype="float16"),)) + R.vm.kill_object(reshape254) + model_encoder_layers_31_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[475] + model_encoder_layers_31_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[476] + gv2552: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1969: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv2552, R.dtype("float16")) + _1967: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", model_encoder_layers_31_self_attn_out_proj_weight, reshape255, model_encoder_layers_31_self_attn_out_proj_bias, alloc1969) + R.vm.kill_object(reshape255) + R.vm.kill_object(model_encoder_layers_31_self_attn_out_proj_weight) + R.vm.kill_object(model_encoder_layers_31_self_attn_out_proj_bias) + gv2553: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1970: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage25, R.prim_value(0), gv2553, R.dtype("float16")) + R.vm.kill_object(storage25) + cls.add4(alloc1963, alloc1969, alloc1970) + R.vm.kill_object(alloc1963) + R.vm.kill_object(alloc1969) + model_encoder_layers_31_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[483] + model_encoder_layers_31_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[484] + gv2554: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1971: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage28, R.prim_value(0), gv2554, R.dtype("float16")) + R.vm.kill_object(storage28) + cls.layer_norm1(alloc1970, model_encoder_layers_31_final_layer_norm_weight, model_encoder_layers_31_final_layer_norm_bias, alloc1971) + R.vm.kill_object(model_encoder_layers_31_final_layer_norm_weight) + R.vm.kill_object(model_encoder_layers_31_final_layer_norm_bias) + model_encoder_layers_31_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[479] + model_encoder_layers_31_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[480] + gv2555: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc1972: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage24, R.prim_value(0), gv2555, R.dtype("float16")) + R.vm.kill_object(storage24) + _1970: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", model_encoder_layers_31_fc1_weight, alloc1971, model_encoder_layers_31_fc1_bias, alloc1972) + R.vm.kill_object(alloc1971) + R.vm.kill_object(model_encoder_layers_31_fc1_weight) + R.vm.kill_object(model_encoder_layers_31_fc1_bias) + model_encoder_layers_31_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[481] + model_encoder_layers_31_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[482] + gv2556: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1973: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage26, R.prim_value(0), gv2556, R.dtype("float16")) + R.vm.kill_object(storage26) + _1971: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", model_encoder_layers_31_fc2_weight, alloc1972, model_encoder_layers_31_fc2_bias, alloc1973) + R.vm.kill_object(alloc1972) + R.vm.kill_object(model_encoder_layers_31_fc2_weight) + R.vm.kill_object(model_encoder_layers_31_fc2_bias) + gv2557: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1974: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage27, R.prim_value(0), gv2557, R.dtype("float16")) + R.vm.kill_object(storage27) + cls.fused_add4_maximum_minimum(alloc1970, alloc1973, alloc1974) + R.vm.kill_object(alloc1970) + R.vm.kill_object(alloc1973) + model_encoder_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[485] + model_encoder_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[486] + storage29: R.Object = R.vm.alloc_storage(R.shape([30720000]), R.prim_value(0), R.dtype("uint8"), R.str("global")) + gv2558: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1975: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage29, R.prim_value(0), gv2558, R.dtype("float16")) + R.vm.kill_object(storage29) + cls.layer_norm1(alloc1974, model_encoder_layer_norm_weight, model_encoder_layer_norm_bias, alloc1975) + R.vm.kill_object(alloc1974) + R.vm.kill_object(model_encoder_layer_norm_weight) + R.vm.kill_object(model_encoder_layer_norm_bias) + R.call_packed("vm.builtin.match_shape", alloc1975, shape_heap, R.prim_value(3), R.prim_value(3), R.prim_value(0), R.prim_value(0), R.prim_value(1500), R.prim_value(0), R.prim_value(1280), R.str("ErrorContext(fn=batch_encode, loc=return, annotation=R.Tensor((batch_size, 1500, 1280), dtype=\"float16\")) "), sinfo_args=(R.Tuple,)) + return alloc1975 + + @R.function + def batch_prefill(input_ids: R.Tensor((1, "seq_len"), dtype="int32"), logit_positions: R.Tensor(("batch_size",), dtype="int32"), paged_kv_cache: R.Object, packed_params: R.Tuple(R.Tensor((1280, 128, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1500, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), 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dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((5120, 1280), dtype=\"float16\"), R.Tensor((5120,), dtype=\"float16\"), R.Tensor((1280, 5120), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((5120, 1280), dtype=\"float16\"), R.Tensor((5120,), dtype=\"float16\"), R.Tensor((1280, 5120), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((5120, 1280), dtype=\"float16\"), R.Tensor((5120,), dtype=\"float16\"), R.Tensor((1280, 5120), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"))) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.match_shape", input_ids, shape_heap, R.prim_value(2), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.str("ErrorContext(fn=batch_prefill, loc=param[0], param=input_ids, annotation=R.Tensor((1, seq_len), dtype=\"int32\")) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.match_shape", logit_positions, shape_heap, R.prim_value(1), R.prim_value(1), R.prim_value(1), R.str("ErrorContext(fn=batch_prefill, loc=param[1], param=logit_positions, annotation=R.Tensor((batch_size,), dtype=\"int32\")) "), sinfo_args=(R.Tuple,)) + model_decoder_embed_tokens_weight2: R.Tensor((51866, 1280), dtype="float16") = packed_params[487] + gv10: R.Shape(ndim=1) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(1), R.prim_value(1), R.prim_value(0), sinfo_args=(R.Shape(ndim=1),)) + reshape384: R.Tensor((seq_len,), dtype="int32") = R.call_packed("vm.builtin.reshape", input_ids, gv10, sinfo_args=(R.Tensor((seq_len,), dtype="int32"),)) + model_decoder_embed_tokens_weight2_1: R.Tensor((51866, 1280), dtype="float16") = packed_params[487] + storage4: R.Object = R.vm.alloc_storage(R.shape([153600000]), R.prim_value(0), R.dtype("uint8"), R.str("global")) + gv11: R.Shape(ndim=2) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(2), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=2),)) + alloc4: R.Tensor(dtype="float16", ndim=2) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv11, R.dtype("float16")) + cls.take(model_decoder_embed_tokens_weight2_1, reshape384, alloc4) + R.vm.kill_object(reshape384) + R.vm.kill_object(model_decoder_embed_tokens_weight2_1) + gv12: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape385: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc4, gv12, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(alloc4) + lv68: R.Tensor((seq_len,), dtype="int32") = R.call_packed("vm.builtin.attention_kv_cache_get_query_positions", paged_kv_cache, sinfo_args=(R.Tensor((seq_len,), dtype="int32"),)) + model_decoder_embed_positions_weight2: R.Tensor((448, 1280), dtype="float16") = packed_params[488] + storage5: R.Object = R.vm.alloc_storage(R.shape([115200000]), R.prim_value(0), R.dtype("uint8"), R.str("global")) + gv13: R.Shape(ndim=2) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(2), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=2),)) + alloc5: R.Tensor(dtype="float16", ndim=2) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv13, R.dtype("float16")) + cls.take1(model_decoder_embed_positions_weight2, lv68, alloc5) + R.vm.kill_object(lv68) + R.vm.kill_object(model_decoder_embed_positions_weight2) + gv14: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape386: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc5, gv14, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(alloc5) + storage6: R.Object = R.vm.alloc_storage(R.shape([115200000]), R.prim_value(0), R.dtype("uint8"), R.str("global")) + gv15: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc6: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv15, R.dtype("float16")) + cls.add5(reshape385, reshape386, alloc6) + R.vm.kill_object(reshape385) + R.vm.kill_object(reshape386) + model_decoder_layers_0_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[496] + model_decoder_layers_0_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[497] + gv16: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc7: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv16, R.dtype("float16")) + cls.layer_norm2(alloc6, model_decoder_layers_0_self_attn_layer_norm_weight2, model_decoder_layers_0_self_attn_layer_norm_bias2, alloc7) + R.vm.kill_object(model_decoder_layers_0_self_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_0_self_attn_layer_norm_bias2) + model_decoder_layers_0_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[492] + model_decoder_layers_0_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[493] + gv17: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc8: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv17, R.dtype("float16")) + _6: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_0_self_attn_q_proj_weight2, alloc7, model_decoder_layers_0_self_attn_q_proj_bias2, alloc8) + R.vm.kill_object(model_decoder_layers_0_self_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_0_self_attn_q_proj_bias2) + gv18: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape387: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc8, gv18, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc8) + model_decoder_layers_0_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[489] + storage7: R.Object = R.vm.alloc_storage(R.shape([115200000]), R.prim_value(0), R.dtype("uint8"), R.str("global")) + gv19: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc9: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv19, R.dtype("float16")) + _7: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_0_self_attn_k_proj_weight2, alloc7, alloc9) + R.vm.kill_object(model_decoder_layers_0_self_attn_k_proj_weight2) + gv20: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape388: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc9, gv20, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc9) + model_decoder_layers_0_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[490] + model_decoder_layers_0_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[491] + storage8: R.Object = R.vm.alloc_storage(R.shape([115200000]), R.prim_value(0), R.dtype("uint8"), R.str("global")) + gv21: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc10: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv21, R.dtype("float16")) + _8: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_0_self_attn_v_proj_weight2, alloc7, model_decoder_layers_0_self_attn_v_proj_bias2, alloc10) + R.vm.kill_object(alloc7) + R.vm.kill_object(model_decoder_layers_0_self_attn_v_proj_weight2) + R.vm.kill_object(model_decoder_layers_0_self_attn_v_proj_bias2) + gv22: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape389: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc10, gv22, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc10) + gv23: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc11: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv23, R.dtype("float16")) + cls.concatenate1(reshape387, reshape388, reshape389, alloc11) + R.vm.kill_object(reshape387) + R.vm.kill_object(reshape388) + R.vm.kill_object(reshape389) + gv24: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape390: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc11, gv24, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc11) + gv25: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc12: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv25, R.dtype("float16")) + _10: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), reshape390, alloc12) + R.vm.kill_object(reshape390) + gv26: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape391: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc12, gv26, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc12) + gv27: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape392: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape391, gv27, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape391) + model_decoder_layers_0_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[494] + model_decoder_layers_0_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[495] + gv28: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc13: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv28, R.dtype("float16")) + _11: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_0_self_attn_out_proj_weight2, reshape392, model_decoder_layers_0_self_attn_out_proj_bias2, alloc13) + R.vm.kill_object(reshape392) + R.vm.kill_object(model_decoder_layers_0_self_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_0_self_attn_out_proj_bias2) + gv29: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc14: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv29, R.dtype("float16")) + cls.add5(alloc6, alloc13, alloc14) + R.vm.kill_object(alloc6) + R.vm.kill_object(alloc13) + model_decoder_layers_0_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[505] + model_decoder_layers_0_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[506] + gv30: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc15: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv30, R.dtype("float16")) + cls.layer_norm2(alloc14, model_decoder_layers_0_encoder_attn_layer_norm_weight2, model_decoder_layers_0_encoder_attn_layer_norm_bias2, alloc15) + R.vm.kill_object(model_decoder_layers_0_encoder_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_0_encoder_attn_layer_norm_bias2) + model_decoder_layers_0_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[501] + model_decoder_layers_0_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[502] + gv31: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc16: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv31, R.dtype("float16")) + _14: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_0_encoder_attn_q_proj_weight2, alloc15, model_decoder_layers_0_encoder_attn_q_proj_bias2, alloc16) + R.vm.kill_object(alloc15) + R.vm.kill_object(model_decoder_layers_0_encoder_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_0_encoder_attn_q_proj_bias2) + gv32: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape393: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc16, gv32, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc16) + gv33: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape394: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape393, gv33, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape393) + gv34: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc17: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv34, R.dtype("float16")) + _15: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), reshape394, alloc17) + R.vm.kill_object(reshape394) + gv35: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape395: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc17, gv35, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc17) + gv36: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape396: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape395, gv36, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape395) + model_decoder_layers_0_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[503] + model_decoder_layers_0_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[504] + gv37: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc18: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv37, R.dtype("float16")) + _16: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_0_encoder_attn_out_proj_weight2, reshape396, model_decoder_layers_0_encoder_attn_out_proj_bias2, alloc18) + R.vm.kill_object(reshape396) + R.vm.kill_object(model_decoder_layers_0_encoder_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_0_encoder_attn_out_proj_bias2) + gv38: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc19: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv38, R.dtype("float16")) + cls.add5(alloc14, alloc18, alloc19) + R.vm.kill_object(alloc14) + R.vm.kill_object(alloc18) + model_decoder_layers_0_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[511] + model_decoder_layers_0_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[512] + gv39: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc20: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv39, R.dtype("float16")) + cls.layer_norm2(alloc19, model_decoder_layers_0_final_layer_norm_weight2, model_decoder_layers_0_final_layer_norm_bias2, alloc20) + R.vm.kill_object(model_decoder_layers_0_final_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_0_final_layer_norm_bias2) + model_decoder_layers_0_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[507] + model_decoder_layers_0_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[508] + gv40: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc21: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv40, R.dtype("float16")) + _19: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_0_fc1_weight2, alloc20, model_decoder_layers_0_fc1_bias2, alloc21) + R.vm.kill_object(alloc20) + R.vm.kill_object(model_decoder_layers_0_fc1_weight2) + R.vm.kill_object(model_decoder_layers_0_fc1_bias2) + model_decoder_layers_0_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[509] + model_decoder_layers_0_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[510] + gv41: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc22: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv41, R.dtype("float16")) + _20: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_0_fc2_weight2, alloc21, model_decoder_layers_0_fc2_bias2, alloc22) + R.vm.kill_object(alloc21) + R.vm.kill_object(model_decoder_layers_0_fc2_weight2) + R.vm.kill_object(model_decoder_layers_0_fc2_bias2) + gv42: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc23: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv42, R.dtype("float16")) + cls.add5(alloc19, alloc22, alloc23) + R.vm.kill_object(alloc19) + R.vm.kill_object(alloc22) + model_decoder_layers_1_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[520] + model_decoder_layers_1_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[521] + gv43: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc24: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv43, R.dtype("float16")) + cls.layer_norm2(alloc23, model_decoder_layers_1_self_attn_layer_norm_weight2, model_decoder_layers_1_self_attn_layer_norm_bias2, alloc24) + R.vm.kill_object(model_decoder_layers_1_self_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_1_self_attn_layer_norm_bias2) + model_decoder_layers_1_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[516] + model_decoder_layers_1_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[517] + gv44: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc25: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv44, R.dtype("float16")) + _23: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_1_self_attn_q_proj_weight2, alloc24, model_decoder_layers_1_self_attn_q_proj_bias2, alloc25) + R.vm.kill_object(model_decoder_layers_1_self_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_1_self_attn_q_proj_bias2) + gv45: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape397: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc25, gv45, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc25) + model_decoder_layers_1_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[513] + gv46: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc26: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv46, R.dtype("float16")) + _24: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_1_self_attn_k_proj_weight2, alloc24, alloc26) + R.vm.kill_object(model_decoder_layers_1_self_attn_k_proj_weight2) + gv47: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape398: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc26, gv47, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc26) + model_decoder_layers_1_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[514] + model_decoder_layers_1_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[515] + gv48: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc27: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv48, R.dtype("float16")) + _25: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_1_self_attn_v_proj_weight2, alloc24, model_decoder_layers_1_self_attn_v_proj_bias2, alloc27) + R.vm.kill_object(alloc24) + R.vm.kill_object(model_decoder_layers_1_self_attn_v_proj_weight2) + R.vm.kill_object(model_decoder_layers_1_self_attn_v_proj_bias2) + gv49: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape399: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc27, gv49, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc27) + gv50: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc28: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv50, R.dtype("float16")) + cls.concatenate1(reshape397, reshape398, reshape399, alloc28) + R.vm.kill_object(reshape397) + R.vm.kill_object(reshape398) + R.vm.kill_object(reshape399) + gv51: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape400: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc28, gv51, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc28) + gv52: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc29: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv52, R.dtype("float16")) + _27: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), reshape400, alloc29) + R.vm.kill_object(reshape400) + gv53: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape401: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc29, gv53, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc29) + gv54: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape402: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape401, gv54, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape401) + model_decoder_layers_1_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[518] + model_decoder_layers_1_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[519] + gv55: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc30: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv55, R.dtype("float16")) + _28: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_1_self_attn_out_proj_weight2, reshape402, model_decoder_layers_1_self_attn_out_proj_bias2, alloc30) + R.vm.kill_object(reshape402) + R.vm.kill_object(model_decoder_layers_1_self_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_1_self_attn_out_proj_bias2) + gv56: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc31: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv56, R.dtype("float16")) + cls.add5(alloc23, alloc30, alloc31) + R.vm.kill_object(alloc23) + R.vm.kill_object(alloc30) + model_decoder_layers_1_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[529] + model_decoder_layers_1_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[530] + gv57: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc32: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv57, R.dtype("float16")) + cls.layer_norm2(alloc31, model_decoder_layers_1_encoder_attn_layer_norm_weight2, model_decoder_layers_1_encoder_attn_layer_norm_bias2, alloc32) + R.vm.kill_object(model_decoder_layers_1_encoder_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_1_encoder_attn_layer_norm_bias2) + model_decoder_layers_1_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[525] + model_decoder_layers_1_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[526] + gv58: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc33: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv58, R.dtype("float16")) + _31: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_1_encoder_attn_q_proj_weight2, alloc32, model_decoder_layers_1_encoder_attn_q_proj_bias2, alloc33) + R.vm.kill_object(alloc32) + R.vm.kill_object(model_decoder_layers_1_encoder_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_1_encoder_attn_q_proj_bias2) + gv59: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape403: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc33, gv59, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc33) + gv60: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape404: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape403, gv60, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape403) + gv61: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc34: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv61, R.dtype("float16")) + _32: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), reshape404, alloc34) + R.vm.kill_object(reshape404) + gv62: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape405: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc34, gv62, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc34) + gv63: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape406: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape405, gv63, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape405) + model_decoder_layers_1_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[527] + model_decoder_layers_1_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[528] + gv64: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc35: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv64, R.dtype("float16")) + _33: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_1_encoder_attn_out_proj_weight2, reshape406, model_decoder_layers_1_encoder_attn_out_proj_bias2, alloc35) + R.vm.kill_object(reshape406) + R.vm.kill_object(model_decoder_layers_1_encoder_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_1_encoder_attn_out_proj_bias2) + gv65: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc36: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv65, R.dtype("float16")) + cls.add5(alloc31, alloc35, alloc36) + R.vm.kill_object(alloc31) + R.vm.kill_object(alloc35) + model_decoder_layers_1_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[535] + model_decoder_layers_1_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[536] + gv66: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc37: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv66, R.dtype("float16")) + cls.layer_norm2(alloc36, model_decoder_layers_1_final_layer_norm_weight2, model_decoder_layers_1_final_layer_norm_bias2, alloc37) + R.vm.kill_object(model_decoder_layers_1_final_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_1_final_layer_norm_bias2) + model_decoder_layers_1_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[531] + model_decoder_layers_1_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[532] + gv67: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc38: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv67, R.dtype("float16")) + _36: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_1_fc1_weight2, alloc37, model_decoder_layers_1_fc1_bias2, alloc38) + R.vm.kill_object(alloc37) + R.vm.kill_object(model_decoder_layers_1_fc1_weight2) + R.vm.kill_object(model_decoder_layers_1_fc1_bias2) + model_decoder_layers_1_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[533] + model_decoder_layers_1_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[534] + gv68: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc39: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv68, R.dtype("float16")) + _37: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_1_fc2_weight2, alloc38, model_decoder_layers_1_fc2_bias2, alloc39) + R.vm.kill_object(alloc38) + R.vm.kill_object(model_decoder_layers_1_fc2_weight2) + R.vm.kill_object(model_decoder_layers_1_fc2_bias2) + gv69: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc40: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv69, R.dtype("float16")) + cls.add5(alloc36, alloc39, alloc40) + R.vm.kill_object(alloc36) + R.vm.kill_object(alloc39) + model_decoder_layers_2_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[544] + model_decoder_layers_2_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[545] + gv70: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc41: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv70, R.dtype("float16")) + cls.layer_norm2(alloc40, model_decoder_layers_2_self_attn_layer_norm_weight2, model_decoder_layers_2_self_attn_layer_norm_bias2, alloc41) + R.vm.kill_object(model_decoder_layers_2_self_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_2_self_attn_layer_norm_bias2) + model_decoder_layers_2_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[540] + model_decoder_layers_2_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[541] + gv71: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc42: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv71, R.dtype("float16")) + _40: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_2_self_attn_q_proj_weight2, alloc41, model_decoder_layers_2_self_attn_q_proj_bias2, alloc42) + R.vm.kill_object(model_decoder_layers_2_self_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_2_self_attn_q_proj_bias2) + gv72: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape407: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc42, gv72, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc42) + model_decoder_layers_2_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[537] + gv73: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc43: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv73, R.dtype("float16")) + _41: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_2_self_attn_k_proj_weight2, alloc41, alloc43) + R.vm.kill_object(model_decoder_layers_2_self_attn_k_proj_weight2) + gv74: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape408: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc43, gv74, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc43) + model_decoder_layers_2_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[538] + model_decoder_layers_2_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[539] + gv75: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc44: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv75, R.dtype("float16")) + _42: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_2_self_attn_v_proj_weight2, alloc41, model_decoder_layers_2_self_attn_v_proj_bias2, alloc44) + R.vm.kill_object(alloc41) + R.vm.kill_object(model_decoder_layers_2_self_attn_v_proj_weight2) + R.vm.kill_object(model_decoder_layers_2_self_attn_v_proj_bias2) + gv76: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape409: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc44, gv76, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc44) + gv77: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc45: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv77, R.dtype("float16")) + cls.concatenate1(reshape407, reshape408, reshape409, alloc45) + R.vm.kill_object(reshape407) + R.vm.kill_object(reshape408) + R.vm.kill_object(reshape409) + gv78: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape410: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc45, gv78, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc45) + gv79: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc46: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv79, R.dtype("float16")) + _44: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), reshape410, alloc46) + R.vm.kill_object(reshape410) + gv80: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape411: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc46, gv80, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc46) + gv81: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape412: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape411, gv81, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape411) + model_decoder_layers_2_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[542] + model_decoder_layers_2_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[543] + gv82: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc47: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv82, R.dtype("float16")) + _45: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_2_self_attn_out_proj_weight2, reshape412, model_decoder_layers_2_self_attn_out_proj_bias2, alloc47) + R.vm.kill_object(reshape412) + R.vm.kill_object(model_decoder_layers_2_self_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_2_self_attn_out_proj_bias2) + gv83: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc48: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv83, R.dtype("float16")) + cls.add5(alloc40, alloc47, alloc48) + R.vm.kill_object(alloc40) + R.vm.kill_object(alloc47) + model_decoder_layers_2_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[553] + model_decoder_layers_2_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[554] + gv84: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc49: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv84, R.dtype("float16")) + cls.layer_norm2(alloc48, model_decoder_layers_2_encoder_attn_layer_norm_weight2, model_decoder_layers_2_encoder_attn_layer_norm_bias2, alloc49) + R.vm.kill_object(model_decoder_layers_2_encoder_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_2_encoder_attn_layer_norm_bias2) + model_decoder_layers_2_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[549] + model_decoder_layers_2_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[550] + gv85: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc50: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv85, R.dtype("float16")) + _48: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_2_encoder_attn_q_proj_weight2, alloc49, model_decoder_layers_2_encoder_attn_q_proj_bias2, alloc50) + R.vm.kill_object(alloc49) + R.vm.kill_object(model_decoder_layers_2_encoder_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_2_encoder_attn_q_proj_bias2) + gv86: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape413: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc50, gv86, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc50) + gv87: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape414: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape413, gv87, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape413) + gv88: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc51: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv88, R.dtype("float16")) + _49: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), reshape414, alloc51) + R.vm.kill_object(reshape414) + gv89: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape415: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc51, gv89, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc51) + gv90: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape416: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape415, gv90, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape415) + model_decoder_layers_2_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[551] + model_decoder_layers_2_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[552] + gv91: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc52: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv91, R.dtype("float16")) + _50: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_2_encoder_attn_out_proj_weight2, reshape416, model_decoder_layers_2_encoder_attn_out_proj_bias2, alloc52) + R.vm.kill_object(reshape416) + R.vm.kill_object(model_decoder_layers_2_encoder_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_2_encoder_attn_out_proj_bias2) + gv92: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc53: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv92, R.dtype("float16")) + cls.add5(alloc48, alloc52, alloc53) + R.vm.kill_object(alloc48) + R.vm.kill_object(alloc52) + model_decoder_layers_2_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[559] + model_decoder_layers_2_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[560] + gv93: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc54: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv93, R.dtype("float16")) + cls.layer_norm2(alloc53, model_decoder_layers_2_final_layer_norm_weight2, model_decoder_layers_2_final_layer_norm_bias2, alloc54) + R.vm.kill_object(model_decoder_layers_2_final_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_2_final_layer_norm_bias2) + model_decoder_layers_2_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[555] + model_decoder_layers_2_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[556] + gv94: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc55: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv94, R.dtype("float16")) + _53: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_2_fc1_weight2, alloc54, model_decoder_layers_2_fc1_bias2, alloc55) + R.vm.kill_object(alloc54) + R.vm.kill_object(model_decoder_layers_2_fc1_weight2) + R.vm.kill_object(model_decoder_layers_2_fc1_bias2) + model_decoder_layers_2_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[557] + model_decoder_layers_2_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[558] + gv95: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc56: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv95, R.dtype("float16")) + _54: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_2_fc2_weight2, alloc55, model_decoder_layers_2_fc2_bias2, alloc56) + R.vm.kill_object(alloc55) + R.vm.kill_object(model_decoder_layers_2_fc2_weight2) + R.vm.kill_object(model_decoder_layers_2_fc2_bias2) + gv96: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc57: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv96, R.dtype("float16")) + cls.add5(alloc53, alloc56, alloc57) + R.vm.kill_object(alloc53) + R.vm.kill_object(alloc56) + model_decoder_layers_3_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[568] + model_decoder_layers_3_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[569] + gv97: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc58: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv97, R.dtype("float16")) + cls.layer_norm2(alloc57, model_decoder_layers_3_self_attn_layer_norm_weight2, model_decoder_layers_3_self_attn_layer_norm_bias2, alloc58) + R.vm.kill_object(model_decoder_layers_3_self_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_3_self_attn_layer_norm_bias2) + model_decoder_layers_3_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[564] + model_decoder_layers_3_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[565] + gv98: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc59: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv98, R.dtype("float16")) + _57: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_3_self_attn_q_proj_weight2, alloc58, model_decoder_layers_3_self_attn_q_proj_bias2, alloc59) + R.vm.kill_object(model_decoder_layers_3_self_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_3_self_attn_q_proj_bias2) + gv99: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape417: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc59, gv99, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc59) + model_decoder_layers_3_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[561] + gv100: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc60: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv100, R.dtype("float16")) + _58: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_3_self_attn_k_proj_weight2, alloc58, alloc60) + R.vm.kill_object(model_decoder_layers_3_self_attn_k_proj_weight2) + gv101: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape418: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc60, gv101, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc60) + model_decoder_layers_3_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[562] + model_decoder_layers_3_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[563] + gv102: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc61: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv102, R.dtype("float16")) + _59: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_3_self_attn_v_proj_weight2, alloc58, model_decoder_layers_3_self_attn_v_proj_bias2, alloc61) + R.vm.kill_object(alloc58) + R.vm.kill_object(model_decoder_layers_3_self_attn_v_proj_weight2) + R.vm.kill_object(model_decoder_layers_3_self_attn_v_proj_bias2) + gv103: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape419: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc61, gv103, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc61) + gv104: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc62: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv104, R.dtype("float16")) + cls.concatenate1(reshape417, reshape418, reshape419, alloc62) + R.vm.kill_object(reshape417) + R.vm.kill_object(reshape418) + R.vm.kill_object(reshape419) + gv105: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape420: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc62, gv105, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc62) + gv106: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc63: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv106, R.dtype("float16")) + _61: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), reshape420, alloc63) + R.vm.kill_object(reshape420) + gv107: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape421: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc63, gv107, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc63) + gv108: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape422: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape421, gv108, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape421) + model_decoder_layers_3_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[566] + model_decoder_layers_3_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[567] + gv109: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc64: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv109, R.dtype("float16")) + _62: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_3_self_attn_out_proj_weight2, reshape422, model_decoder_layers_3_self_attn_out_proj_bias2, alloc64) + R.vm.kill_object(reshape422) + R.vm.kill_object(model_decoder_layers_3_self_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_3_self_attn_out_proj_bias2) + gv110: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc65: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv110, R.dtype("float16")) + cls.add5(alloc57, alloc64, alloc65) + R.vm.kill_object(alloc57) + R.vm.kill_object(alloc64) + model_decoder_layers_3_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[577] + model_decoder_layers_3_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[578] + gv111: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc66: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv111, R.dtype("float16")) + cls.layer_norm2(alloc65, model_decoder_layers_3_encoder_attn_layer_norm_weight2, model_decoder_layers_3_encoder_attn_layer_norm_bias2, alloc66) + R.vm.kill_object(model_decoder_layers_3_encoder_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_3_encoder_attn_layer_norm_bias2) + model_decoder_layers_3_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[573] + model_decoder_layers_3_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[574] + gv112: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc67: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv112, R.dtype("float16")) + _65: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_3_encoder_attn_q_proj_weight2, alloc66, model_decoder_layers_3_encoder_attn_q_proj_bias2, alloc67) + R.vm.kill_object(alloc66) + R.vm.kill_object(model_decoder_layers_3_encoder_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_3_encoder_attn_q_proj_bias2) + gv113: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape423: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc67, gv113, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc67) + gv114: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape424: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape423, gv114, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape423) + gv115: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc68: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv115, R.dtype("float16")) + _66: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), reshape424, alloc68) + R.vm.kill_object(reshape424) + gv116: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape425: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc68, gv116, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc68) + gv117: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape426: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape425, gv117, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape425) + model_decoder_layers_3_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[575] + model_decoder_layers_3_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[576] + gv118: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc69: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv118, R.dtype("float16")) + _67: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_3_encoder_attn_out_proj_weight2, reshape426, model_decoder_layers_3_encoder_attn_out_proj_bias2, alloc69) + R.vm.kill_object(reshape426) + R.vm.kill_object(model_decoder_layers_3_encoder_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_3_encoder_attn_out_proj_bias2) + gv119: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc70: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv119, R.dtype("float16")) + cls.add5(alloc65, alloc69, alloc70) + R.vm.kill_object(alloc65) + R.vm.kill_object(alloc69) + model_decoder_layers_3_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[583] + model_decoder_layers_3_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[584] + gv120: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc71: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv120, R.dtype("float16")) + cls.layer_norm2(alloc70, model_decoder_layers_3_final_layer_norm_weight2, model_decoder_layers_3_final_layer_norm_bias2, alloc71) + R.vm.kill_object(model_decoder_layers_3_final_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_3_final_layer_norm_bias2) + model_decoder_layers_3_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[579] + model_decoder_layers_3_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[580] + gv121: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc72: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv121, R.dtype("float16")) + _70: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_3_fc1_weight2, alloc71, model_decoder_layers_3_fc1_bias2, alloc72) + R.vm.kill_object(alloc71) + R.vm.kill_object(model_decoder_layers_3_fc1_weight2) + R.vm.kill_object(model_decoder_layers_3_fc1_bias2) + model_decoder_layers_3_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[581] + model_decoder_layers_3_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[582] + gv122: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc73: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv122, R.dtype("float16")) + _71: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_3_fc2_weight2, alloc72, model_decoder_layers_3_fc2_bias2, alloc73) + R.vm.kill_object(alloc72) + R.vm.kill_object(model_decoder_layers_3_fc2_weight2) + R.vm.kill_object(model_decoder_layers_3_fc2_bias2) + gv123: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc74: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv123, R.dtype("float16")) + cls.add5(alloc70, alloc73, alloc74) + R.vm.kill_object(alloc70) + R.vm.kill_object(alloc73) + model_decoder_layers_4_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[592] + model_decoder_layers_4_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[593] + gv124: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc75: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv124, R.dtype("float16")) + cls.layer_norm2(alloc74, model_decoder_layers_4_self_attn_layer_norm_weight2, model_decoder_layers_4_self_attn_layer_norm_bias2, alloc75) + R.vm.kill_object(model_decoder_layers_4_self_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_4_self_attn_layer_norm_bias2) + model_decoder_layers_4_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[588] + model_decoder_layers_4_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[589] + gv125: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc76: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv125, R.dtype("float16")) + _74: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_4_self_attn_q_proj_weight2, alloc75, model_decoder_layers_4_self_attn_q_proj_bias2, alloc76) + R.vm.kill_object(model_decoder_layers_4_self_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_4_self_attn_q_proj_bias2) + gv126: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape427: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc76, gv126, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc76) + model_decoder_layers_4_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[585] + gv127: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc77: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv127, R.dtype("float16")) + _75: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_4_self_attn_k_proj_weight2, alloc75, alloc77) + R.vm.kill_object(model_decoder_layers_4_self_attn_k_proj_weight2) + gv128: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape428: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc77, gv128, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc77) + model_decoder_layers_4_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[586] + model_decoder_layers_4_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[587] + gv129: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc78: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv129, R.dtype("float16")) + _76: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_4_self_attn_v_proj_weight2, alloc75, model_decoder_layers_4_self_attn_v_proj_bias2, alloc78) + R.vm.kill_object(alloc75) + R.vm.kill_object(model_decoder_layers_4_self_attn_v_proj_weight2) + R.vm.kill_object(model_decoder_layers_4_self_attn_v_proj_bias2) + gv130: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape429: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc78, gv130, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc78) + gv131: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc79: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv131, R.dtype("float16")) + cls.concatenate1(reshape427, reshape428, reshape429, alloc79) + R.vm.kill_object(reshape427) + R.vm.kill_object(reshape428) + R.vm.kill_object(reshape429) + gv132: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape430: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc79, gv132, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc79) + gv133: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc80: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv133, R.dtype("float16")) + _78: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), reshape430, alloc80) + R.vm.kill_object(reshape430) + gv134: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape431: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc80, gv134, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc80) + gv135: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape432: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape431, gv135, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape431) + model_decoder_layers_4_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[590] + model_decoder_layers_4_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[591] + gv136: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc81: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv136, R.dtype("float16")) + _79: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_4_self_attn_out_proj_weight2, reshape432, model_decoder_layers_4_self_attn_out_proj_bias2, alloc81) + R.vm.kill_object(reshape432) + R.vm.kill_object(model_decoder_layers_4_self_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_4_self_attn_out_proj_bias2) + gv137: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc82: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv137, R.dtype("float16")) + cls.add5(alloc74, alloc81, alloc82) + R.vm.kill_object(alloc74) + R.vm.kill_object(alloc81) + model_decoder_layers_4_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[601] + model_decoder_layers_4_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[602] + gv138: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc83: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv138, R.dtype("float16")) + cls.layer_norm2(alloc82, model_decoder_layers_4_encoder_attn_layer_norm_weight2, model_decoder_layers_4_encoder_attn_layer_norm_bias2, alloc83) + R.vm.kill_object(model_decoder_layers_4_encoder_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_4_encoder_attn_layer_norm_bias2) + model_decoder_layers_4_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[597] + model_decoder_layers_4_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[598] + gv139: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc84: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv139, R.dtype("float16")) + _82: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_4_encoder_attn_q_proj_weight2, alloc83, model_decoder_layers_4_encoder_attn_q_proj_bias2, alloc84) + R.vm.kill_object(alloc83) + R.vm.kill_object(model_decoder_layers_4_encoder_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_4_encoder_attn_q_proj_bias2) + gv140: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape433: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc84, gv140, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc84) + gv141: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape434: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape433, gv141, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape433) + gv142: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc85: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv142, R.dtype("float16")) + _83: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), reshape434, alloc85) + R.vm.kill_object(reshape434) + gv143: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape435: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc85, gv143, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc85) + gv144: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape436: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape435, gv144, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape435) + model_decoder_layers_4_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[599] + model_decoder_layers_4_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[600] + gv145: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc86: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv145, R.dtype("float16")) + _84: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_4_encoder_attn_out_proj_weight2, reshape436, model_decoder_layers_4_encoder_attn_out_proj_bias2, alloc86) + R.vm.kill_object(reshape436) + R.vm.kill_object(model_decoder_layers_4_encoder_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_4_encoder_attn_out_proj_bias2) + gv146: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc87: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv146, R.dtype("float16")) + cls.add5(alloc82, alloc86, alloc87) + R.vm.kill_object(alloc82) + R.vm.kill_object(alloc86) + model_decoder_layers_4_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[607] + model_decoder_layers_4_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[608] + gv147: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc88: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv147, R.dtype("float16")) + cls.layer_norm2(alloc87, model_decoder_layers_4_final_layer_norm_weight2, model_decoder_layers_4_final_layer_norm_bias2, alloc88) + R.vm.kill_object(model_decoder_layers_4_final_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_4_final_layer_norm_bias2) + model_decoder_layers_4_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[603] + model_decoder_layers_4_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[604] + gv148: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc89: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv148, R.dtype("float16")) + _87: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_4_fc1_weight2, alloc88, model_decoder_layers_4_fc1_bias2, alloc89) + R.vm.kill_object(alloc88) + R.vm.kill_object(model_decoder_layers_4_fc1_weight2) + R.vm.kill_object(model_decoder_layers_4_fc1_bias2) + model_decoder_layers_4_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[605] + model_decoder_layers_4_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[606] + gv149: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc90: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv149, R.dtype("float16")) + _88: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_4_fc2_weight2, alloc89, model_decoder_layers_4_fc2_bias2, alloc90) + R.vm.kill_object(alloc89) + R.vm.kill_object(model_decoder_layers_4_fc2_weight2) + R.vm.kill_object(model_decoder_layers_4_fc2_bias2) + gv150: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc91: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv150, R.dtype("float16")) + cls.add5(alloc87, alloc90, alloc91) + R.vm.kill_object(alloc87) + R.vm.kill_object(alloc90) + model_decoder_layers_5_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[616] + model_decoder_layers_5_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[617] + gv151: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc92: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv151, R.dtype("float16")) + cls.layer_norm2(alloc91, model_decoder_layers_5_self_attn_layer_norm_weight2, model_decoder_layers_5_self_attn_layer_norm_bias2, alloc92) + R.vm.kill_object(model_decoder_layers_5_self_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_5_self_attn_layer_norm_bias2) + model_decoder_layers_5_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[612] + model_decoder_layers_5_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[613] + gv152: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc93: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv152, R.dtype("float16")) + _91: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_5_self_attn_q_proj_weight2, alloc92, model_decoder_layers_5_self_attn_q_proj_bias2, alloc93) + R.vm.kill_object(model_decoder_layers_5_self_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_5_self_attn_q_proj_bias2) + gv153: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape437: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc93, gv153, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc93) + model_decoder_layers_5_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[609] + gv154: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc94: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv154, R.dtype("float16")) + _92: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_5_self_attn_k_proj_weight2, alloc92, alloc94) + R.vm.kill_object(model_decoder_layers_5_self_attn_k_proj_weight2) + gv155: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape438: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc94, gv155, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc94) + model_decoder_layers_5_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[610] + model_decoder_layers_5_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[611] + gv156: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc95: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv156, R.dtype("float16")) + _93: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_5_self_attn_v_proj_weight2, alloc92, model_decoder_layers_5_self_attn_v_proj_bias2, alloc95) + R.vm.kill_object(alloc92) + R.vm.kill_object(model_decoder_layers_5_self_attn_v_proj_weight2) + R.vm.kill_object(model_decoder_layers_5_self_attn_v_proj_bias2) + gv157: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape439: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc95, gv157, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc95) + gv158: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc96: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv158, R.dtype("float16")) + cls.concatenate1(reshape437, reshape438, reshape439, alloc96) + R.vm.kill_object(reshape437) + R.vm.kill_object(reshape438) + R.vm.kill_object(reshape439) + gv159: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape440: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc96, gv159, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc96) + gv160: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc97: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv160, R.dtype("float16")) + _95: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), reshape440, alloc97) + R.vm.kill_object(reshape440) + gv161: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape441: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc97, gv161, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc97) + gv162: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape442: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape441, gv162, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape441) + model_decoder_layers_5_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[614] + model_decoder_layers_5_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[615] + gv163: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc98: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv163, R.dtype("float16")) + _96: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_5_self_attn_out_proj_weight2, reshape442, model_decoder_layers_5_self_attn_out_proj_bias2, alloc98) + R.vm.kill_object(reshape442) + R.vm.kill_object(model_decoder_layers_5_self_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_5_self_attn_out_proj_bias2) + gv164: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc99: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv164, R.dtype("float16")) + cls.add5(alloc91, alloc98, alloc99) + R.vm.kill_object(alloc91) + R.vm.kill_object(alloc98) + model_decoder_layers_5_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[625] + model_decoder_layers_5_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[626] + gv165: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc100: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv165, R.dtype("float16")) + cls.layer_norm2(alloc99, model_decoder_layers_5_encoder_attn_layer_norm_weight2, model_decoder_layers_5_encoder_attn_layer_norm_bias2, alloc100) + R.vm.kill_object(model_decoder_layers_5_encoder_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_5_encoder_attn_layer_norm_bias2) + model_decoder_layers_5_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[621] + model_decoder_layers_5_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[622] + gv166: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc101: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv166, R.dtype("float16")) + _99: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_5_encoder_attn_q_proj_weight2, alloc100, model_decoder_layers_5_encoder_attn_q_proj_bias2, alloc101) + R.vm.kill_object(alloc100) + R.vm.kill_object(model_decoder_layers_5_encoder_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_5_encoder_attn_q_proj_bias2) + gv167: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape443: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc101, gv167, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc101) + gv168: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape444: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape443, gv168, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape443) + gv169: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc102: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv169, R.dtype("float16")) + _100: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), reshape444, alloc102) + R.vm.kill_object(reshape444) + gv170: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape445: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc102, gv170, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc102) + gv171: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape446: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape445, gv171, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape445) + model_decoder_layers_5_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[623] + model_decoder_layers_5_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[624] + gv172: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc103: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv172, R.dtype("float16")) + _101: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_5_encoder_attn_out_proj_weight2, reshape446, model_decoder_layers_5_encoder_attn_out_proj_bias2, alloc103) + R.vm.kill_object(reshape446) + R.vm.kill_object(model_decoder_layers_5_encoder_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_5_encoder_attn_out_proj_bias2) + gv173: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc104: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv173, R.dtype("float16")) + cls.add5(alloc99, alloc103, alloc104) + R.vm.kill_object(alloc99) + R.vm.kill_object(alloc103) + model_decoder_layers_5_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[631] + model_decoder_layers_5_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[632] + gv174: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc105: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv174, R.dtype("float16")) + cls.layer_norm2(alloc104, model_decoder_layers_5_final_layer_norm_weight2, model_decoder_layers_5_final_layer_norm_bias2, alloc105) + R.vm.kill_object(model_decoder_layers_5_final_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_5_final_layer_norm_bias2) + model_decoder_layers_5_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[627] + model_decoder_layers_5_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[628] + gv175: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc106: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv175, R.dtype("float16")) + _104: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_5_fc1_weight2, alloc105, model_decoder_layers_5_fc1_bias2, alloc106) + R.vm.kill_object(alloc105) + R.vm.kill_object(model_decoder_layers_5_fc1_weight2) + R.vm.kill_object(model_decoder_layers_5_fc1_bias2) + model_decoder_layers_5_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[629] + model_decoder_layers_5_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[630] + gv176: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc107: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv176, R.dtype("float16")) + _105: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_5_fc2_weight2, alloc106, model_decoder_layers_5_fc2_bias2, alloc107) + R.vm.kill_object(alloc106) + R.vm.kill_object(model_decoder_layers_5_fc2_weight2) + R.vm.kill_object(model_decoder_layers_5_fc2_bias2) + gv177: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc108: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv177, R.dtype("float16")) + cls.add5(alloc104, alloc107, alloc108) + R.vm.kill_object(alloc104) + R.vm.kill_object(alloc107) + model_decoder_layers_6_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[640] + model_decoder_layers_6_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[641] + gv178: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc109: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv178, R.dtype("float16")) + cls.layer_norm2(alloc108, model_decoder_layers_6_self_attn_layer_norm_weight2, model_decoder_layers_6_self_attn_layer_norm_bias2, alloc109) + R.vm.kill_object(model_decoder_layers_6_self_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_6_self_attn_layer_norm_bias2) + model_decoder_layers_6_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[636] + model_decoder_layers_6_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[637] + gv179: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc110: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv179, R.dtype("float16")) + _108: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_6_self_attn_q_proj_weight2, alloc109, model_decoder_layers_6_self_attn_q_proj_bias2, alloc110) + R.vm.kill_object(model_decoder_layers_6_self_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_6_self_attn_q_proj_bias2) + gv180: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape447: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc110, gv180, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc110) + model_decoder_layers_6_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[633] + gv181: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc111: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv181, R.dtype("float16")) + _109: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_6_self_attn_k_proj_weight2, alloc109, alloc111) + R.vm.kill_object(model_decoder_layers_6_self_attn_k_proj_weight2) + gv182: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape448: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc111, gv182, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc111) + model_decoder_layers_6_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[634] + model_decoder_layers_6_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[635] + gv183: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc112: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv183, R.dtype("float16")) + _110: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_6_self_attn_v_proj_weight2, alloc109, model_decoder_layers_6_self_attn_v_proj_bias2, alloc112) + R.vm.kill_object(alloc109) + R.vm.kill_object(model_decoder_layers_6_self_attn_v_proj_weight2) + R.vm.kill_object(model_decoder_layers_6_self_attn_v_proj_bias2) + gv184: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape449: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc112, gv184, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc112) + gv185: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc113: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv185, R.dtype("float16")) + cls.concatenate1(reshape447, reshape448, reshape449, alloc113) + R.vm.kill_object(reshape447) + R.vm.kill_object(reshape448) + R.vm.kill_object(reshape449) + gv186: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape450: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc113, gv186, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc113) + gv187: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc114: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv187, R.dtype("float16")) + _112: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), reshape450, alloc114) + R.vm.kill_object(reshape450) + gv188: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape451: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc114, gv188, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc114) + gv189: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape452: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape451, gv189, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape451) + model_decoder_layers_6_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[638] + model_decoder_layers_6_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[639] + gv190: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc115: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv190, R.dtype("float16")) + _113: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_6_self_attn_out_proj_weight2, reshape452, model_decoder_layers_6_self_attn_out_proj_bias2, alloc115) + R.vm.kill_object(reshape452) + R.vm.kill_object(model_decoder_layers_6_self_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_6_self_attn_out_proj_bias2) + gv191: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc116: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv191, R.dtype("float16")) + cls.add5(alloc108, alloc115, alloc116) + R.vm.kill_object(alloc108) + R.vm.kill_object(alloc115) + model_decoder_layers_6_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[649] + model_decoder_layers_6_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[650] + gv192: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc117: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv192, R.dtype("float16")) + cls.layer_norm2(alloc116, model_decoder_layers_6_encoder_attn_layer_norm_weight2, model_decoder_layers_6_encoder_attn_layer_norm_bias2, alloc117) + R.vm.kill_object(model_decoder_layers_6_encoder_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_6_encoder_attn_layer_norm_bias2) + model_decoder_layers_6_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[645] + model_decoder_layers_6_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[646] + gv193: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc118: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv193, R.dtype("float16")) + _116: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_6_encoder_attn_q_proj_weight2, alloc117, model_decoder_layers_6_encoder_attn_q_proj_bias2, alloc118) + R.vm.kill_object(alloc117) + R.vm.kill_object(model_decoder_layers_6_encoder_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_6_encoder_attn_q_proj_bias2) + gv194: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape453: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc118, gv194, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc118) + gv195: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape454: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape453, gv195, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape453) + gv196: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc119: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv196, R.dtype("float16")) + _117: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), reshape454, alloc119) + R.vm.kill_object(reshape454) + gv197: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape455: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc119, gv197, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc119) + gv198: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape456: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape455, gv198, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape455) + model_decoder_layers_6_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[647] + model_decoder_layers_6_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[648] + gv199: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc120: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv199, R.dtype("float16")) + _118: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_6_encoder_attn_out_proj_weight2, reshape456, model_decoder_layers_6_encoder_attn_out_proj_bias2, alloc120) + R.vm.kill_object(reshape456) + R.vm.kill_object(model_decoder_layers_6_encoder_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_6_encoder_attn_out_proj_bias2) + gv200: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc121: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv200, R.dtype("float16")) + cls.add5(alloc116, alloc120, alloc121) + R.vm.kill_object(alloc116) + R.vm.kill_object(alloc120) + model_decoder_layers_6_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[655] + model_decoder_layers_6_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[656] + gv201: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc122: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv201, R.dtype("float16")) + cls.layer_norm2(alloc121, model_decoder_layers_6_final_layer_norm_weight2, model_decoder_layers_6_final_layer_norm_bias2, alloc122) + R.vm.kill_object(model_decoder_layers_6_final_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_6_final_layer_norm_bias2) + model_decoder_layers_6_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[651] + model_decoder_layers_6_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[652] + gv202: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc123: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv202, R.dtype("float16")) + _121: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_6_fc1_weight2, alloc122, model_decoder_layers_6_fc1_bias2, alloc123) + R.vm.kill_object(alloc122) + R.vm.kill_object(model_decoder_layers_6_fc1_weight2) + R.vm.kill_object(model_decoder_layers_6_fc1_bias2) + model_decoder_layers_6_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[653] + model_decoder_layers_6_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[654] + gv203: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc124: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv203, R.dtype("float16")) + _122: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_6_fc2_weight2, alloc123, model_decoder_layers_6_fc2_bias2, alloc124) + R.vm.kill_object(alloc123) + R.vm.kill_object(model_decoder_layers_6_fc2_weight2) + R.vm.kill_object(model_decoder_layers_6_fc2_bias2) + gv204: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc125: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv204, R.dtype("float16")) + cls.add5(alloc121, alloc124, alloc125) + R.vm.kill_object(alloc121) + R.vm.kill_object(alloc124) + model_decoder_layers_7_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[664] + model_decoder_layers_7_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[665] + gv205: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc126: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv205, R.dtype("float16")) + cls.layer_norm2(alloc125, model_decoder_layers_7_self_attn_layer_norm_weight2, model_decoder_layers_7_self_attn_layer_norm_bias2, alloc126) + R.vm.kill_object(model_decoder_layers_7_self_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_7_self_attn_layer_norm_bias2) + model_decoder_layers_7_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[660] + model_decoder_layers_7_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[661] + gv206: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc127: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv206, R.dtype("float16")) + _125: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_7_self_attn_q_proj_weight2, alloc126, model_decoder_layers_7_self_attn_q_proj_bias2, alloc127) + R.vm.kill_object(model_decoder_layers_7_self_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_7_self_attn_q_proj_bias2) + gv207: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape457: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc127, gv207, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc127) + model_decoder_layers_7_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[657] + gv208: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc128: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv208, R.dtype("float16")) + _126: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_7_self_attn_k_proj_weight2, alloc126, alloc128) + R.vm.kill_object(model_decoder_layers_7_self_attn_k_proj_weight2) + gv209: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape458: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc128, gv209, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc128) + model_decoder_layers_7_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[658] + model_decoder_layers_7_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[659] + gv210: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc129: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv210, R.dtype("float16")) + _127: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_7_self_attn_v_proj_weight2, alloc126, model_decoder_layers_7_self_attn_v_proj_bias2, alloc129) + R.vm.kill_object(alloc126) + R.vm.kill_object(model_decoder_layers_7_self_attn_v_proj_weight2) + R.vm.kill_object(model_decoder_layers_7_self_attn_v_proj_bias2) + gv211: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape459: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc129, gv211, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc129) + gv212: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc130: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv212, R.dtype("float16")) + cls.concatenate1(reshape457, reshape458, reshape459, alloc130) + R.vm.kill_object(reshape457) + R.vm.kill_object(reshape458) + R.vm.kill_object(reshape459) + gv213: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape460: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc130, gv213, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc130) + gv214: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc131: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv214, R.dtype("float16")) + _129: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), reshape460, alloc131) + R.vm.kill_object(reshape460) + gv215: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape461: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc131, gv215, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc131) + gv216: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape462: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape461, gv216, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape461) + model_decoder_layers_7_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[662] + model_decoder_layers_7_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[663] + gv217: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc132: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv217, R.dtype("float16")) + _130: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_7_self_attn_out_proj_weight2, reshape462, model_decoder_layers_7_self_attn_out_proj_bias2, alloc132) + R.vm.kill_object(reshape462) + R.vm.kill_object(model_decoder_layers_7_self_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_7_self_attn_out_proj_bias2) + gv218: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc133: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv218, R.dtype("float16")) + cls.add5(alloc125, alloc132, alloc133) + R.vm.kill_object(alloc125) + R.vm.kill_object(alloc132) + model_decoder_layers_7_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[673] + model_decoder_layers_7_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[674] + gv219: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc134: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv219, R.dtype("float16")) + cls.layer_norm2(alloc133, model_decoder_layers_7_encoder_attn_layer_norm_weight2, model_decoder_layers_7_encoder_attn_layer_norm_bias2, alloc134) + R.vm.kill_object(model_decoder_layers_7_encoder_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_7_encoder_attn_layer_norm_bias2) + model_decoder_layers_7_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[669] + model_decoder_layers_7_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[670] + gv220: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc135: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv220, R.dtype("float16")) + _133: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_7_encoder_attn_q_proj_weight2, alloc134, model_decoder_layers_7_encoder_attn_q_proj_bias2, alloc135) + R.vm.kill_object(alloc134) + R.vm.kill_object(model_decoder_layers_7_encoder_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_7_encoder_attn_q_proj_bias2) + gv221: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape463: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc135, gv221, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc135) + gv222: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape464: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape463, gv222, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape463) + gv223: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc136: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv223, R.dtype("float16")) + _134: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), reshape464, alloc136) + R.vm.kill_object(reshape464) + gv224: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape465: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc136, gv224, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc136) + gv225: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape466: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape465, gv225, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape465) + model_decoder_layers_7_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[671] + model_decoder_layers_7_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[672] + gv226: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc137: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv226, R.dtype("float16")) + _135: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_7_encoder_attn_out_proj_weight2, reshape466, model_decoder_layers_7_encoder_attn_out_proj_bias2, alloc137) + R.vm.kill_object(reshape466) + R.vm.kill_object(model_decoder_layers_7_encoder_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_7_encoder_attn_out_proj_bias2) + gv227: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc138: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv227, R.dtype("float16")) + cls.add5(alloc133, alloc137, alloc138) + R.vm.kill_object(alloc133) + R.vm.kill_object(alloc137) + model_decoder_layers_7_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[679] + model_decoder_layers_7_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[680] + gv228: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc139: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv228, R.dtype("float16")) + cls.layer_norm2(alloc138, model_decoder_layers_7_final_layer_norm_weight2, model_decoder_layers_7_final_layer_norm_bias2, alloc139) + R.vm.kill_object(model_decoder_layers_7_final_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_7_final_layer_norm_bias2) + model_decoder_layers_7_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[675] + model_decoder_layers_7_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[676] + gv229: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc140: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv229, R.dtype("float16")) + _138: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_7_fc1_weight2, alloc139, model_decoder_layers_7_fc1_bias2, alloc140) + R.vm.kill_object(alloc139) + R.vm.kill_object(model_decoder_layers_7_fc1_weight2) + R.vm.kill_object(model_decoder_layers_7_fc1_bias2) + model_decoder_layers_7_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[677] + model_decoder_layers_7_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[678] + gv230: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc141: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv230, R.dtype("float16")) + _139: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_7_fc2_weight2, alloc140, model_decoder_layers_7_fc2_bias2, alloc141) + R.vm.kill_object(alloc140) + R.vm.kill_object(model_decoder_layers_7_fc2_weight2) + R.vm.kill_object(model_decoder_layers_7_fc2_bias2) + gv231: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc142: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv231, R.dtype("float16")) + cls.add5(alloc138, alloc141, alloc142) + R.vm.kill_object(alloc138) + R.vm.kill_object(alloc141) + model_decoder_layers_8_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[688] + model_decoder_layers_8_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[689] + gv232: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc143: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv232, R.dtype("float16")) + cls.layer_norm2(alloc142, model_decoder_layers_8_self_attn_layer_norm_weight2, model_decoder_layers_8_self_attn_layer_norm_bias2, alloc143) + R.vm.kill_object(model_decoder_layers_8_self_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_8_self_attn_layer_norm_bias2) + model_decoder_layers_8_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[684] + model_decoder_layers_8_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[685] + gv233: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc144: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv233, R.dtype("float16")) + _142: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_8_self_attn_q_proj_weight2, alloc143, model_decoder_layers_8_self_attn_q_proj_bias2, alloc144) + R.vm.kill_object(model_decoder_layers_8_self_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_8_self_attn_q_proj_bias2) + gv234: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape467: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc144, gv234, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc144) + model_decoder_layers_8_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[681] + gv235: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc145: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv235, R.dtype("float16")) + _143: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_8_self_attn_k_proj_weight2, alloc143, alloc145) + R.vm.kill_object(model_decoder_layers_8_self_attn_k_proj_weight2) + gv236: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape468: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc145, gv236, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc145) + model_decoder_layers_8_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[682] + model_decoder_layers_8_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[683] + gv237: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc146: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv237, R.dtype("float16")) + _144: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_8_self_attn_v_proj_weight2, alloc143, model_decoder_layers_8_self_attn_v_proj_bias2, alloc146) + R.vm.kill_object(alloc143) + R.vm.kill_object(model_decoder_layers_8_self_attn_v_proj_weight2) + R.vm.kill_object(model_decoder_layers_8_self_attn_v_proj_bias2) + gv238: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape469: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc146, gv238, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc146) + gv239: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc147: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv239, R.dtype("float16")) + cls.concatenate1(reshape467, reshape468, reshape469, alloc147) + R.vm.kill_object(reshape467) + R.vm.kill_object(reshape468) + R.vm.kill_object(reshape469) + gv240: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape470: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc147, gv240, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc147) + gv241: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc148: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv241, R.dtype("float16")) + _146: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), reshape470, alloc148) + R.vm.kill_object(reshape470) + gv242: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape471: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc148, gv242, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc148) + gv243: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape472: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape471, gv243, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape471) + model_decoder_layers_8_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[686] + model_decoder_layers_8_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[687] + gv244: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc149: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv244, R.dtype("float16")) + _147: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_8_self_attn_out_proj_weight2, reshape472, model_decoder_layers_8_self_attn_out_proj_bias2, alloc149) + R.vm.kill_object(reshape472) + R.vm.kill_object(model_decoder_layers_8_self_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_8_self_attn_out_proj_bias2) + gv245: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc150: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv245, R.dtype("float16")) + cls.add5(alloc142, alloc149, alloc150) + R.vm.kill_object(alloc142) + R.vm.kill_object(alloc149) + model_decoder_layers_8_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[697] + model_decoder_layers_8_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[698] + gv246: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc151: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv246, R.dtype("float16")) + cls.layer_norm2(alloc150, model_decoder_layers_8_encoder_attn_layer_norm_weight2, model_decoder_layers_8_encoder_attn_layer_norm_bias2, alloc151) + R.vm.kill_object(model_decoder_layers_8_encoder_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_8_encoder_attn_layer_norm_bias2) + model_decoder_layers_8_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[693] + model_decoder_layers_8_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[694] + gv247: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc152: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv247, R.dtype("float16")) + _150: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_8_encoder_attn_q_proj_weight2, alloc151, model_decoder_layers_8_encoder_attn_q_proj_bias2, alloc152) + R.vm.kill_object(alloc151) + R.vm.kill_object(model_decoder_layers_8_encoder_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_8_encoder_attn_q_proj_bias2) + gv248: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape473: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc152, gv248, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc152) + gv249: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape474: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape473, gv249, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape473) + gv250: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc153: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv250, R.dtype("float16")) + _151: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), reshape474, alloc153) + R.vm.kill_object(reshape474) + gv251: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape475: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc153, gv251, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc153) + gv252: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape476: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape475, gv252, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape475) + model_decoder_layers_8_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[695] + model_decoder_layers_8_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[696] + gv253: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc154: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv253, R.dtype("float16")) + _152: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_8_encoder_attn_out_proj_weight2, reshape476, model_decoder_layers_8_encoder_attn_out_proj_bias2, alloc154) + R.vm.kill_object(reshape476) + R.vm.kill_object(model_decoder_layers_8_encoder_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_8_encoder_attn_out_proj_bias2) + gv254: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc155: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv254, R.dtype("float16")) + cls.add5(alloc150, alloc154, alloc155) + R.vm.kill_object(alloc150) + R.vm.kill_object(alloc154) + model_decoder_layers_8_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[703] + model_decoder_layers_8_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[704] + gv255: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc156: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv255, R.dtype("float16")) + cls.layer_norm2(alloc155, model_decoder_layers_8_final_layer_norm_weight2, model_decoder_layers_8_final_layer_norm_bias2, alloc156) + R.vm.kill_object(model_decoder_layers_8_final_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_8_final_layer_norm_bias2) + model_decoder_layers_8_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[699] + model_decoder_layers_8_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[700] + gv256: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc157: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv256, R.dtype("float16")) + _155: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_8_fc1_weight2, alloc156, model_decoder_layers_8_fc1_bias2, alloc157) + R.vm.kill_object(alloc156) + R.vm.kill_object(model_decoder_layers_8_fc1_weight2) + R.vm.kill_object(model_decoder_layers_8_fc1_bias2) + model_decoder_layers_8_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[701] + model_decoder_layers_8_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[702] + gv257: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc158: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv257, R.dtype("float16")) + _156: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_8_fc2_weight2, alloc157, model_decoder_layers_8_fc2_bias2, alloc158) + R.vm.kill_object(alloc157) + R.vm.kill_object(model_decoder_layers_8_fc2_weight2) + R.vm.kill_object(model_decoder_layers_8_fc2_bias2) + gv258: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc159: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv258, R.dtype("float16")) + cls.add5(alloc155, alloc158, alloc159) + R.vm.kill_object(alloc155) + R.vm.kill_object(alloc158) + model_decoder_layers_9_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[712] + model_decoder_layers_9_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[713] + gv259: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc160: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv259, R.dtype("float16")) + cls.layer_norm2(alloc159, model_decoder_layers_9_self_attn_layer_norm_weight2, model_decoder_layers_9_self_attn_layer_norm_bias2, alloc160) + R.vm.kill_object(model_decoder_layers_9_self_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_9_self_attn_layer_norm_bias2) + model_decoder_layers_9_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[708] + model_decoder_layers_9_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[709] + gv260: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc161: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv260, R.dtype("float16")) + _159: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_9_self_attn_q_proj_weight2, alloc160, model_decoder_layers_9_self_attn_q_proj_bias2, alloc161) + R.vm.kill_object(model_decoder_layers_9_self_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_9_self_attn_q_proj_bias2) + gv261: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape477: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc161, gv261, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc161) + model_decoder_layers_9_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[705] + gv262: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc162: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv262, R.dtype("float16")) + _160: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_9_self_attn_k_proj_weight2, alloc160, alloc162) + R.vm.kill_object(model_decoder_layers_9_self_attn_k_proj_weight2) + gv263: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape478: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc162, gv263, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc162) + model_decoder_layers_9_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[706] + model_decoder_layers_9_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[707] + gv264: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc163: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv264, R.dtype("float16")) + _161: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_9_self_attn_v_proj_weight2, alloc160, model_decoder_layers_9_self_attn_v_proj_bias2, alloc163) + R.vm.kill_object(alloc160) + R.vm.kill_object(model_decoder_layers_9_self_attn_v_proj_weight2) + R.vm.kill_object(model_decoder_layers_9_self_attn_v_proj_bias2) + gv265: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape479: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc163, gv265, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc163) + gv266: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc164: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv266, R.dtype("float16")) + cls.concatenate1(reshape477, reshape478, reshape479, alloc164) + R.vm.kill_object(reshape477) + R.vm.kill_object(reshape478) + R.vm.kill_object(reshape479) + gv267: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape480: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc164, gv267, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc164) + gv268: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc165: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv268, R.dtype("float16")) + _163: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), reshape480, alloc165) + R.vm.kill_object(reshape480) + gv269: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape481: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc165, gv269, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc165) + gv270: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape482: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape481, gv270, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape481) + model_decoder_layers_9_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[710] + model_decoder_layers_9_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[711] + gv271: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc166: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv271, R.dtype("float16")) + _164: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_9_self_attn_out_proj_weight2, reshape482, model_decoder_layers_9_self_attn_out_proj_bias2, alloc166) + R.vm.kill_object(reshape482) + R.vm.kill_object(model_decoder_layers_9_self_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_9_self_attn_out_proj_bias2) + gv272: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc167: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv272, R.dtype("float16")) + cls.add5(alloc159, alloc166, alloc167) + R.vm.kill_object(alloc159) + R.vm.kill_object(alloc166) + model_decoder_layers_9_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[721] + model_decoder_layers_9_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[722] + gv273: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc168: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv273, R.dtype("float16")) + cls.layer_norm2(alloc167, model_decoder_layers_9_encoder_attn_layer_norm_weight2, model_decoder_layers_9_encoder_attn_layer_norm_bias2, alloc168) + R.vm.kill_object(model_decoder_layers_9_encoder_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_9_encoder_attn_layer_norm_bias2) + model_decoder_layers_9_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[717] + model_decoder_layers_9_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[718] + gv274: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc169: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv274, R.dtype("float16")) + _167: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_9_encoder_attn_q_proj_weight2, alloc168, model_decoder_layers_9_encoder_attn_q_proj_bias2, alloc169) + R.vm.kill_object(alloc168) + R.vm.kill_object(model_decoder_layers_9_encoder_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_9_encoder_attn_q_proj_bias2) + gv275: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape483: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc169, gv275, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc169) + gv276: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape484: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape483, gv276, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape483) + gv277: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc170: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv277, R.dtype("float16")) + _168: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), reshape484, alloc170) + R.vm.kill_object(reshape484) + gv278: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape485: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc170, gv278, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc170) + gv279: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape486: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape485, gv279, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape485) + model_decoder_layers_9_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[719] + model_decoder_layers_9_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[720] + gv280: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc171: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv280, R.dtype("float16")) + _169: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_9_encoder_attn_out_proj_weight2, reshape486, model_decoder_layers_9_encoder_attn_out_proj_bias2, alloc171) + R.vm.kill_object(reshape486) + R.vm.kill_object(model_decoder_layers_9_encoder_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_9_encoder_attn_out_proj_bias2) + gv281: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc172: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv281, R.dtype("float16")) + cls.add5(alloc167, alloc171, alloc172) + R.vm.kill_object(alloc167) + R.vm.kill_object(alloc171) + model_decoder_layers_9_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[727] + model_decoder_layers_9_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[728] + gv282: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc173: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv282, R.dtype("float16")) + cls.layer_norm2(alloc172, model_decoder_layers_9_final_layer_norm_weight2, model_decoder_layers_9_final_layer_norm_bias2, alloc173) + R.vm.kill_object(model_decoder_layers_9_final_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_9_final_layer_norm_bias2) + model_decoder_layers_9_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[723] + model_decoder_layers_9_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[724] + gv283: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc174: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv283, R.dtype("float16")) + _172: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_9_fc1_weight2, alloc173, model_decoder_layers_9_fc1_bias2, alloc174) + R.vm.kill_object(alloc173) + R.vm.kill_object(model_decoder_layers_9_fc1_weight2) + R.vm.kill_object(model_decoder_layers_9_fc1_bias2) + model_decoder_layers_9_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[725] + model_decoder_layers_9_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[726] + gv284: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc175: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv284, R.dtype("float16")) + _173: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_9_fc2_weight2, alloc174, model_decoder_layers_9_fc2_bias2, alloc175) + R.vm.kill_object(alloc174) + R.vm.kill_object(model_decoder_layers_9_fc2_weight2) + R.vm.kill_object(model_decoder_layers_9_fc2_bias2) + gv285: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc176: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv285, R.dtype("float16")) + cls.add5(alloc172, alloc175, alloc176) + R.vm.kill_object(alloc172) + R.vm.kill_object(alloc175) + model_decoder_layers_10_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[736] + model_decoder_layers_10_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[737] + gv286: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc177: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv286, R.dtype("float16")) + cls.layer_norm2(alloc176, model_decoder_layers_10_self_attn_layer_norm_weight2, model_decoder_layers_10_self_attn_layer_norm_bias2, alloc177) + R.vm.kill_object(model_decoder_layers_10_self_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_10_self_attn_layer_norm_bias2) + model_decoder_layers_10_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[732] + model_decoder_layers_10_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[733] + gv287: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc178: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv287, R.dtype("float16")) + _176: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_10_self_attn_q_proj_weight2, alloc177, model_decoder_layers_10_self_attn_q_proj_bias2, alloc178) + R.vm.kill_object(model_decoder_layers_10_self_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_10_self_attn_q_proj_bias2) + gv288: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape487: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc178, gv288, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc178) + model_decoder_layers_10_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[729] + gv289: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc179: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv289, R.dtype("float16")) + _177: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_10_self_attn_k_proj_weight2, alloc177, alloc179) + R.vm.kill_object(model_decoder_layers_10_self_attn_k_proj_weight2) + gv290: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape488: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc179, gv290, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc179) + model_decoder_layers_10_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[730] + model_decoder_layers_10_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[731] + gv291: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc180: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv291, R.dtype("float16")) + _178: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_10_self_attn_v_proj_weight2, alloc177, model_decoder_layers_10_self_attn_v_proj_bias2, alloc180) + R.vm.kill_object(alloc177) + R.vm.kill_object(model_decoder_layers_10_self_attn_v_proj_weight2) + R.vm.kill_object(model_decoder_layers_10_self_attn_v_proj_bias2) + gv292: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape489: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc180, gv292, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc180) + gv293: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc181: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv293, R.dtype("float16")) + cls.concatenate1(reshape487, reshape488, reshape489, alloc181) + R.vm.kill_object(reshape487) + R.vm.kill_object(reshape488) + R.vm.kill_object(reshape489) + gv294: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape490: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc181, gv294, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc181) + gv295: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc182: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv295, R.dtype("float16")) + _180: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), reshape490, alloc182) + R.vm.kill_object(reshape490) + gv296: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape491: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc182, gv296, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc182) + gv297: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape492: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape491, gv297, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape491) + model_decoder_layers_10_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[734] + model_decoder_layers_10_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[735] + gv298: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc183: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv298, R.dtype("float16")) + _181: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_10_self_attn_out_proj_weight2, reshape492, model_decoder_layers_10_self_attn_out_proj_bias2, alloc183) + R.vm.kill_object(reshape492) + R.vm.kill_object(model_decoder_layers_10_self_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_10_self_attn_out_proj_bias2) + gv299: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc184: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv299, R.dtype("float16")) + cls.add5(alloc176, alloc183, alloc184) + R.vm.kill_object(alloc176) + R.vm.kill_object(alloc183) + model_decoder_layers_10_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[745] + model_decoder_layers_10_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[746] + gv300: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc185: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv300, R.dtype("float16")) + cls.layer_norm2(alloc184, model_decoder_layers_10_encoder_attn_layer_norm_weight2, model_decoder_layers_10_encoder_attn_layer_norm_bias2, alloc185) + R.vm.kill_object(model_decoder_layers_10_encoder_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_10_encoder_attn_layer_norm_bias2) + model_decoder_layers_10_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[741] + model_decoder_layers_10_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[742] + gv301: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc186: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv301, R.dtype("float16")) + _184: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_10_encoder_attn_q_proj_weight2, alloc185, model_decoder_layers_10_encoder_attn_q_proj_bias2, alloc186) + R.vm.kill_object(alloc185) + R.vm.kill_object(model_decoder_layers_10_encoder_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_10_encoder_attn_q_proj_bias2) + gv302: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape493: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc186, gv302, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc186) + gv303: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape494: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape493, gv303, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape493) + gv304: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc187: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv304, R.dtype("float16")) + _185: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), reshape494, alloc187) + R.vm.kill_object(reshape494) + gv305: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape495: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc187, gv305, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc187) + gv306: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape496: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape495, gv306, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape495) + model_decoder_layers_10_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[743] + model_decoder_layers_10_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[744] + gv307: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc188: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv307, R.dtype("float16")) + _186: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_10_encoder_attn_out_proj_weight2, reshape496, model_decoder_layers_10_encoder_attn_out_proj_bias2, alloc188) + R.vm.kill_object(reshape496) + R.vm.kill_object(model_decoder_layers_10_encoder_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_10_encoder_attn_out_proj_bias2) + gv308: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc189: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv308, R.dtype("float16")) + cls.add5(alloc184, alloc188, alloc189) + R.vm.kill_object(alloc184) + R.vm.kill_object(alloc188) + model_decoder_layers_10_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[751] + model_decoder_layers_10_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[752] + gv309: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc190: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv309, R.dtype("float16")) + cls.layer_norm2(alloc189, model_decoder_layers_10_final_layer_norm_weight2, model_decoder_layers_10_final_layer_norm_bias2, alloc190) + R.vm.kill_object(model_decoder_layers_10_final_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_10_final_layer_norm_bias2) + model_decoder_layers_10_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[747] + model_decoder_layers_10_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[748] + gv310: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc191: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv310, R.dtype("float16")) + _189: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_10_fc1_weight2, alloc190, model_decoder_layers_10_fc1_bias2, alloc191) + R.vm.kill_object(alloc190) + R.vm.kill_object(model_decoder_layers_10_fc1_weight2) + R.vm.kill_object(model_decoder_layers_10_fc1_bias2) + model_decoder_layers_10_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[749] + model_decoder_layers_10_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[750] + gv311: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc192: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv311, R.dtype("float16")) + _190: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_10_fc2_weight2, alloc191, model_decoder_layers_10_fc2_bias2, alloc192) + R.vm.kill_object(alloc191) + R.vm.kill_object(model_decoder_layers_10_fc2_weight2) + R.vm.kill_object(model_decoder_layers_10_fc2_bias2) + gv312: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc193: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv312, R.dtype("float16")) + cls.add5(alloc189, alloc192, alloc193) + R.vm.kill_object(alloc189) + R.vm.kill_object(alloc192) + model_decoder_layers_11_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[760] + model_decoder_layers_11_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[761] + gv313: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc194: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv313, R.dtype("float16")) + cls.layer_norm2(alloc193, model_decoder_layers_11_self_attn_layer_norm_weight2, model_decoder_layers_11_self_attn_layer_norm_bias2, alloc194) + R.vm.kill_object(model_decoder_layers_11_self_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_11_self_attn_layer_norm_bias2) + model_decoder_layers_11_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[756] + model_decoder_layers_11_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[757] + gv314: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc195: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv314, R.dtype("float16")) + _193: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_11_self_attn_q_proj_weight2, alloc194, model_decoder_layers_11_self_attn_q_proj_bias2, alloc195) + R.vm.kill_object(model_decoder_layers_11_self_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_11_self_attn_q_proj_bias2) + gv315: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape497: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc195, gv315, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc195) + model_decoder_layers_11_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[753] + gv316: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc196: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv316, R.dtype("float16")) + _194: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_11_self_attn_k_proj_weight2, alloc194, alloc196) + R.vm.kill_object(model_decoder_layers_11_self_attn_k_proj_weight2) + gv317: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape498: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc196, gv317, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc196) + model_decoder_layers_11_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[754] + model_decoder_layers_11_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[755] + gv318: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc197: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv318, R.dtype("float16")) + _195: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_11_self_attn_v_proj_weight2, alloc194, model_decoder_layers_11_self_attn_v_proj_bias2, alloc197) + R.vm.kill_object(alloc194) + R.vm.kill_object(model_decoder_layers_11_self_attn_v_proj_weight2) + R.vm.kill_object(model_decoder_layers_11_self_attn_v_proj_bias2) + gv319: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape499: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc197, gv319, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc197) + gv320: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc198: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv320, R.dtype("float16")) + cls.concatenate1(reshape497, reshape498, reshape499, alloc198) + R.vm.kill_object(reshape497) + R.vm.kill_object(reshape498) + R.vm.kill_object(reshape499) + gv321: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape500: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc198, gv321, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc198) + gv322: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc199: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv322, R.dtype("float16")) + _197: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), reshape500, alloc199) + R.vm.kill_object(reshape500) + gv323: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape501: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc199, gv323, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc199) + gv324: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape502: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape501, gv324, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape501) + model_decoder_layers_11_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[758] + model_decoder_layers_11_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[759] + gv325: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc200: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv325, R.dtype("float16")) + _198: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_11_self_attn_out_proj_weight2, reshape502, model_decoder_layers_11_self_attn_out_proj_bias2, alloc200) + R.vm.kill_object(reshape502) + R.vm.kill_object(model_decoder_layers_11_self_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_11_self_attn_out_proj_bias2) + gv326: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc201: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv326, R.dtype("float16")) + cls.add5(alloc193, alloc200, alloc201) + R.vm.kill_object(alloc193) + R.vm.kill_object(alloc200) + model_decoder_layers_11_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[769] + model_decoder_layers_11_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[770] + gv327: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc202: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv327, R.dtype("float16")) + cls.layer_norm2(alloc201, model_decoder_layers_11_encoder_attn_layer_norm_weight2, model_decoder_layers_11_encoder_attn_layer_norm_bias2, alloc202) + R.vm.kill_object(model_decoder_layers_11_encoder_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_11_encoder_attn_layer_norm_bias2) + model_decoder_layers_11_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[765] + model_decoder_layers_11_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[766] + gv328: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc203: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv328, R.dtype("float16")) + _201: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_11_encoder_attn_q_proj_weight2, alloc202, model_decoder_layers_11_encoder_attn_q_proj_bias2, alloc203) + R.vm.kill_object(alloc202) + R.vm.kill_object(model_decoder_layers_11_encoder_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_11_encoder_attn_q_proj_bias2) + gv329: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape503: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc203, gv329, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc203) + gv330: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape504: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape503, gv330, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape503) + gv331: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc204: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv331, R.dtype("float16")) + _202: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), reshape504, alloc204) + R.vm.kill_object(reshape504) + gv332: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape505: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc204, gv332, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc204) + gv333: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape506: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape505, gv333, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape505) + model_decoder_layers_11_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[767] + model_decoder_layers_11_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[768] + gv334: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc205: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv334, R.dtype("float16")) + _203: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_11_encoder_attn_out_proj_weight2, reshape506, model_decoder_layers_11_encoder_attn_out_proj_bias2, alloc205) + R.vm.kill_object(reshape506) + R.vm.kill_object(model_decoder_layers_11_encoder_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_11_encoder_attn_out_proj_bias2) + gv335: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc206: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv335, R.dtype("float16")) + cls.add5(alloc201, alloc205, alloc206) + R.vm.kill_object(alloc201) + R.vm.kill_object(alloc205) + model_decoder_layers_11_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[775] + model_decoder_layers_11_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[776] + gv336: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc207: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv336, R.dtype("float16")) + cls.layer_norm2(alloc206, model_decoder_layers_11_final_layer_norm_weight2, model_decoder_layers_11_final_layer_norm_bias2, alloc207) + R.vm.kill_object(model_decoder_layers_11_final_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_11_final_layer_norm_bias2) + model_decoder_layers_11_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[771] + model_decoder_layers_11_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[772] + gv337: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc208: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv337, R.dtype("float16")) + _206: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_11_fc1_weight2, alloc207, model_decoder_layers_11_fc1_bias2, alloc208) + R.vm.kill_object(alloc207) + R.vm.kill_object(model_decoder_layers_11_fc1_weight2) + R.vm.kill_object(model_decoder_layers_11_fc1_bias2) + model_decoder_layers_11_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[773] + model_decoder_layers_11_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[774] + gv338: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc209: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv338, R.dtype("float16")) + _207: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_11_fc2_weight2, alloc208, model_decoder_layers_11_fc2_bias2, alloc209) + R.vm.kill_object(alloc208) + R.vm.kill_object(model_decoder_layers_11_fc2_weight2) + R.vm.kill_object(model_decoder_layers_11_fc2_bias2) + gv339: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc210: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv339, R.dtype("float16")) + cls.add5(alloc206, alloc209, alloc210) + R.vm.kill_object(alloc206) + R.vm.kill_object(alloc209) + model_decoder_layers_12_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[784] + model_decoder_layers_12_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[785] + gv340: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc211: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv340, R.dtype("float16")) + cls.layer_norm2(alloc210, model_decoder_layers_12_self_attn_layer_norm_weight2, model_decoder_layers_12_self_attn_layer_norm_bias2, alloc211) + R.vm.kill_object(model_decoder_layers_12_self_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_12_self_attn_layer_norm_bias2) + model_decoder_layers_12_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[780] + model_decoder_layers_12_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[781] + gv341: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc212: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv341, R.dtype("float16")) + _210: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_12_self_attn_q_proj_weight2, alloc211, model_decoder_layers_12_self_attn_q_proj_bias2, alloc212) + R.vm.kill_object(model_decoder_layers_12_self_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_12_self_attn_q_proj_bias2) + gv342: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape507: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc212, gv342, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc212) + model_decoder_layers_12_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[777] + gv343: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc213: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv343, R.dtype("float16")) + _211: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_12_self_attn_k_proj_weight2, alloc211, alloc213) + R.vm.kill_object(model_decoder_layers_12_self_attn_k_proj_weight2) + gv344: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape508: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc213, gv344, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc213) + model_decoder_layers_12_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[778] + model_decoder_layers_12_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[779] + gv345: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc214: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv345, R.dtype("float16")) + _212: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_12_self_attn_v_proj_weight2, alloc211, model_decoder_layers_12_self_attn_v_proj_bias2, alloc214) + R.vm.kill_object(alloc211) + R.vm.kill_object(model_decoder_layers_12_self_attn_v_proj_weight2) + R.vm.kill_object(model_decoder_layers_12_self_attn_v_proj_bias2) + gv346: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape509: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc214, gv346, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc214) + gv347: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc215: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv347, R.dtype("float16")) + cls.concatenate1(reshape507, reshape508, reshape509, alloc215) + R.vm.kill_object(reshape507) + R.vm.kill_object(reshape508) + R.vm.kill_object(reshape509) + gv348: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape510: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc215, gv348, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc215) + gv349: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc216: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv349, R.dtype("float16")) + _214: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), reshape510, alloc216) + R.vm.kill_object(reshape510) + gv350: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape511: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc216, gv350, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc216) + gv351: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape512: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape511, gv351, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape511) + model_decoder_layers_12_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[782] + model_decoder_layers_12_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[783] + gv352: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc217: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv352, R.dtype("float16")) + _215: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_12_self_attn_out_proj_weight2, reshape512, model_decoder_layers_12_self_attn_out_proj_bias2, alloc217) + R.vm.kill_object(reshape512) + R.vm.kill_object(model_decoder_layers_12_self_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_12_self_attn_out_proj_bias2) + gv353: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc218: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv353, R.dtype("float16")) + cls.add5(alloc210, alloc217, alloc218) + R.vm.kill_object(alloc210) + R.vm.kill_object(alloc217) + model_decoder_layers_12_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[793] + model_decoder_layers_12_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[794] + gv354: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc219: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv354, R.dtype("float16")) + cls.layer_norm2(alloc218, model_decoder_layers_12_encoder_attn_layer_norm_weight2, model_decoder_layers_12_encoder_attn_layer_norm_bias2, alloc219) + R.vm.kill_object(model_decoder_layers_12_encoder_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_12_encoder_attn_layer_norm_bias2) + model_decoder_layers_12_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[789] + model_decoder_layers_12_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[790] + gv355: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc220: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv355, R.dtype("float16")) + _218: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_12_encoder_attn_q_proj_weight2, alloc219, model_decoder_layers_12_encoder_attn_q_proj_bias2, alloc220) + R.vm.kill_object(alloc219) + R.vm.kill_object(model_decoder_layers_12_encoder_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_12_encoder_attn_q_proj_bias2) + gv356: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape513: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc220, gv356, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc220) + gv357: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape514: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape513, gv357, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape513) + gv358: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc221: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv358, R.dtype("float16")) + _219: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), reshape514, alloc221) + R.vm.kill_object(reshape514) + gv359: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape515: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc221, gv359, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc221) + gv360: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape516: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape515, gv360, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape515) + model_decoder_layers_12_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[791] + model_decoder_layers_12_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[792] + gv361: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc222: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv361, R.dtype("float16")) + _220: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_12_encoder_attn_out_proj_weight2, reshape516, model_decoder_layers_12_encoder_attn_out_proj_bias2, alloc222) + R.vm.kill_object(reshape516) + R.vm.kill_object(model_decoder_layers_12_encoder_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_12_encoder_attn_out_proj_bias2) + gv362: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc223: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv362, R.dtype("float16")) + cls.add5(alloc218, alloc222, alloc223) + R.vm.kill_object(alloc218) + R.vm.kill_object(alloc222) + model_decoder_layers_12_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[799] + model_decoder_layers_12_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[800] + gv363: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc224: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv363, R.dtype("float16")) + cls.layer_norm2(alloc223, model_decoder_layers_12_final_layer_norm_weight2, model_decoder_layers_12_final_layer_norm_bias2, alloc224) + R.vm.kill_object(model_decoder_layers_12_final_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_12_final_layer_norm_bias2) + model_decoder_layers_12_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[795] + model_decoder_layers_12_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[796] + gv364: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc225: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv364, R.dtype("float16")) + _223: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_12_fc1_weight2, alloc224, model_decoder_layers_12_fc1_bias2, alloc225) + R.vm.kill_object(alloc224) + R.vm.kill_object(model_decoder_layers_12_fc1_weight2) + R.vm.kill_object(model_decoder_layers_12_fc1_bias2) + model_decoder_layers_12_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[797] + model_decoder_layers_12_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[798] + gv365: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc226: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv365, R.dtype("float16")) + _224: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_12_fc2_weight2, alloc225, model_decoder_layers_12_fc2_bias2, alloc226) + R.vm.kill_object(alloc225) + R.vm.kill_object(model_decoder_layers_12_fc2_weight2) + R.vm.kill_object(model_decoder_layers_12_fc2_bias2) + gv366: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc227: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv366, R.dtype("float16")) + cls.add5(alloc223, alloc226, alloc227) + R.vm.kill_object(alloc223) + R.vm.kill_object(alloc226) + model_decoder_layers_13_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[808] + model_decoder_layers_13_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[809] + gv367: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc228: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv367, R.dtype("float16")) + cls.layer_norm2(alloc227, model_decoder_layers_13_self_attn_layer_norm_weight2, model_decoder_layers_13_self_attn_layer_norm_bias2, alloc228) + R.vm.kill_object(model_decoder_layers_13_self_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_13_self_attn_layer_norm_bias2) + model_decoder_layers_13_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[804] + model_decoder_layers_13_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[805] + gv368: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc229: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv368, R.dtype("float16")) + _227: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_13_self_attn_q_proj_weight2, alloc228, model_decoder_layers_13_self_attn_q_proj_bias2, alloc229) + R.vm.kill_object(model_decoder_layers_13_self_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_13_self_attn_q_proj_bias2) + gv369: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape517: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc229, gv369, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc229) + model_decoder_layers_13_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[801] + gv370: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc230: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv370, R.dtype("float16")) + _228: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_13_self_attn_k_proj_weight2, alloc228, alloc230) + R.vm.kill_object(model_decoder_layers_13_self_attn_k_proj_weight2) + gv371: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape518: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc230, gv371, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc230) + model_decoder_layers_13_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[802] + model_decoder_layers_13_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[803] + gv372: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc231: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv372, R.dtype("float16")) + _229: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_13_self_attn_v_proj_weight2, alloc228, model_decoder_layers_13_self_attn_v_proj_bias2, alloc231) + R.vm.kill_object(alloc228) + R.vm.kill_object(model_decoder_layers_13_self_attn_v_proj_weight2) + R.vm.kill_object(model_decoder_layers_13_self_attn_v_proj_bias2) + gv373: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape519: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc231, gv373, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc231) + gv374: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc232: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv374, R.dtype("float16")) + cls.concatenate1(reshape517, reshape518, reshape519, alloc232) + R.vm.kill_object(reshape517) + R.vm.kill_object(reshape518) + R.vm.kill_object(reshape519) + gv375: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape520: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc232, gv375, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc232) + gv376: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc233: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv376, R.dtype("float16")) + _231: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), reshape520, alloc233) + R.vm.kill_object(reshape520) + gv377: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape521: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc233, gv377, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc233) + gv378: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape522: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape521, gv378, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape521) + model_decoder_layers_13_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[806] + model_decoder_layers_13_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[807] + gv379: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc234: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv379, R.dtype("float16")) + _232: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_13_self_attn_out_proj_weight2, reshape522, model_decoder_layers_13_self_attn_out_proj_bias2, alloc234) + R.vm.kill_object(reshape522) + R.vm.kill_object(model_decoder_layers_13_self_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_13_self_attn_out_proj_bias2) + gv380: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc235: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv380, R.dtype("float16")) + cls.add5(alloc227, alloc234, alloc235) + R.vm.kill_object(alloc227) + R.vm.kill_object(alloc234) + model_decoder_layers_13_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[817] + model_decoder_layers_13_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[818] + gv381: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc236: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv381, R.dtype("float16")) + cls.layer_norm2(alloc235, model_decoder_layers_13_encoder_attn_layer_norm_weight2, model_decoder_layers_13_encoder_attn_layer_norm_bias2, alloc236) + R.vm.kill_object(model_decoder_layers_13_encoder_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_13_encoder_attn_layer_norm_bias2) + model_decoder_layers_13_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[813] + model_decoder_layers_13_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[814] + gv382: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc237: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv382, R.dtype("float16")) + _235: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_13_encoder_attn_q_proj_weight2, alloc236, model_decoder_layers_13_encoder_attn_q_proj_bias2, alloc237) + R.vm.kill_object(alloc236) + R.vm.kill_object(model_decoder_layers_13_encoder_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_13_encoder_attn_q_proj_bias2) + gv383: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape523: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc237, gv383, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc237) + gv384: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape524: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape523, gv384, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape523) + gv385: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc238: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv385, R.dtype("float16")) + _236: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), reshape524, alloc238) + R.vm.kill_object(reshape524) + gv386: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape525: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc238, gv386, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc238) + gv387: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape526: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape525, gv387, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape525) + model_decoder_layers_13_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[815] + model_decoder_layers_13_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[816] + gv388: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc239: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv388, R.dtype("float16")) + _237: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_13_encoder_attn_out_proj_weight2, reshape526, model_decoder_layers_13_encoder_attn_out_proj_bias2, alloc239) + R.vm.kill_object(reshape526) + R.vm.kill_object(model_decoder_layers_13_encoder_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_13_encoder_attn_out_proj_bias2) + gv389: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc240: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv389, R.dtype("float16")) + cls.add5(alloc235, alloc239, alloc240) + R.vm.kill_object(alloc235) + R.vm.kill_object(alloc239) + model_decoder_layers_13_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[823] + model_decoder_layers_13_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[824] + gv390: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc241: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv390, R.dtype("float16")) + cls.layer_norm2(alloc240, model_decoder_layers_13_final_layer_norm_weight2, model_decoder_layers_13_final_layer_norm_bias2, alloc241) + R.vm.kill_object(model_decoder_layers_13_final_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_13_final_layer_norm_bias2) + model_decoder_layers_13_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[819] + model_decoder_layers_13_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[820] + gv391: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc242: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv391, R.dtype("float16")) + _240: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_13_fc1_weight2, alloc241, model_decoder_layers_13_fc1_bias2, alloc242) + R.vm.kill_object(alloc241) + R.vm.kill_object(model_decoder_layers_13_fc1_weight2) + R.vm.kill_object(model_decoder_layers_13_fc1_bias2) + model_decoder_layers_13_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[821] + model_decoder_layers_13_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[822] + gv392: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc243: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv392, R.dtype("float16")) + _241: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_13_fc2_weight2, alloc242, model_decoder_layers_13_fc2_bias2, alloc243) + R.vm.kill_object(alloc242) + R.vm.kill_object(model_decoder_layers_13_fc2_weight2) + R.vm.kill_object(model_decoder_layers_13_fc2_bias2) + gv393: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc244: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv393, R.dtype("float16")) + cls.add5(alloc240, alloc243, alloc244) + R.vm.kill_object(alloc240) + R.vm.kill_object(alloc243) + model_decoder_layers_14_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[832] + model_decoder_layers_14_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[833] + gv394: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc245: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv394, R.dtype("float16")) + cls.layer_norm2(alloc244, model_decoder_layers_14_self_attn_layer_norm_weight2, model_decoder_layers_14_self_attn_layer_norm_bias2, alloc245) + R.vm.kill_object(model_decoder_layers_14_self_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_14_self_attn_layer_norm_bias2) + model_decoder_layers_14_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[828] + model_decoder_layers_14_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[829] + gv395: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc246: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv395, R.dtype("float16")) + _244: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_14_self_attn_q_proj_weight2, alloc245, model_decoder_layers_14_self_attn_q_proj_bias2, alloc246) + R.vm.kill_object(model_decoder_layers_14_self_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_14_self_attn_q_proj_bias2) + gv396: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape527: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc246, gv396, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc246) + model_decoder_layers_14_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[825] + gv397: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc247: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv397, R.dtype("float16")) + _245: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_14_self_attn_k_proj_weight2, alloc245, alloc247) + R.vm.kill_object(model_decoder_layers_14_self_attn_k_proj_weight2) + gv398: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape528: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc247, gv398, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc247) + model_decoder_layers_14_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[826] + model_decoder_layers_14_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[827] + gv399: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc248: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv399, R.dtype("float16")) + _246: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_14_self_attn_v_proj_weight2, alloc245, model_decoder_layers_14_self_attn_v_proj_bias2, alloc248) + R.vm.kill_object(alloc245) + R.vm.kill_object(model_decoder_layers_14_self_attn_v_proj_weight2) + R.vm.kill_object(model_decoder_layers_14_self_attn_v_proj_bias2) + gv400: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape529: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc248, gv400, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc248) + gv401: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc249: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv401, R.dtype("float16")) + cls.concatenate1(reshape527, reshape528, reshape529, alloc249) + R.vm.kill_object(reshape527) + R.vm.kill_object(reshape528) + R.vm.kill_object(reshape529) + gv402: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape530: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc249, gv402, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc249) + gv403: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc250: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv403, R.dtype("float16")) + _248: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), reshape530, alloc250) + R.vm.kill_object(reshape530) + gv404: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape531: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc250, gv404, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc250) + gv405: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape532: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape531, gv405, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape531) + model_decoder_layers_14_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[830] + model_decoder_layers_14_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[831] + gv406: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc251: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv406, R.dtype("float16")) + _249: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_14_self_attn_out_proj_weight2, reshape532, model_decoder_layers_14_self_attn_out_proj_bias2, alloc251) + R.vm.kill_object(reshape532) + R.vm.kill_object(model_decoder_layers_14_self_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_14_self_attn_out_proj_bias2) + gv407: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc252: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv407, R.dtype("float16")) + cls.add5(alloc244, alloc251, alloc252) + R.vm.kill_object(alloc244) + R.vm.kill_object(alloc251) + model_decoder_layers_14_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[841] + model_decoder_layers_14_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[842] + gv408: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc253: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv408, R.dtype("float16")) + cls.layer_norm2(alloc252, model_decoder_layers_14_encoder_attn_layer_norm_weight2, model_decoder_layers_14_encoder_attn_layer_norm_bias2, alloc253) + R.vm.kill_object(model_decoder_layers_14_encoder_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_14_encoder_attn_layer_norm_bias2) + model_decoder_layers_14_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[837] + model_decoder_layers_14_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[838] + gv409: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc254: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv409, R.dtype("float16")) + _252: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_14_encoder_attn_q_proj_weight2, alloc253, model_decoder_layers_14_encoder_attn_q_proj_bias2, alloc254) + R.vm.kill_object(alloc253) + R.vm.kill_object(model_decoder_layers_14_encoder_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_14_encoder_attn_q_proj_bias2) + gv410: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape533: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc254, gv410, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc254) + gv411: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape534: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape533, gv411, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape533) + gv412: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc255: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv412, R.dtype("float16")) + _253: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), reshape534, alloc255) + R.vm.kill_object(reshape534) + gv413: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape535: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc255, gv413, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc255) + gv414: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape536: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape535, gv414, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape535) + model_decoder_layers_14_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[839] + model_decoder_layers_14_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[840] + gv415: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc256: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv415, R.dtype("float16")) + _254: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_14_encoder_attn_out_proj_weight2, reshape536, model_decoder_layers_14_encoder_attn_out_proj_bias2, alloc256) + R.vm.kill_object(reshape536) + R.vm.kill_object(model_decoder_layers_14_encoder_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_14_encoder_attn_out_proj_bias2) + gv416: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc257: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv416, R.dtype("float16")) + cls.add5(alloc252, alloc256, alloc257) + R.vm.kill_object(alloc252) + R.vm.kill_object(alloc256) + model_decoder_layers_14_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[847] + model_decoder_layers_14_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[848] + gv417: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc258: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv417, R.dtype("float16")) + cls.layer_norm2(alloc257, model_decoder_layers_14_final_layer_norm_weight2, model_decoder_layers_14_final_layer_norm_bias2, alloc258) + R.vm.kill_object(model_decoder_layers_14_final_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_14_final_layer_norm_bias2) + model_decoder_layers_14_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[843] + model_decoder_layers_14_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[844] + gv418: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc259: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv418, R.dtype("float16")) + _257: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_14_fc1_weight2, alloc258, model_decoder_layers_14_fc1_bias2, alloc259) + R.vm.kill_object(alloc258) + R.vm.kill_object(model_decoder_layers_14_fc1_weight2) + R.vm.kill_object(model_decoder_layers_14_fc1_bias2) + model_decoder_layers_14_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[845] + model_decoder_layers_14_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[846] + gv419: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc260: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv419, R.dtype("float16")) + _258: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_14_fc2_weight2, alloc259, model_decoder_layers_14_fc2_bias2, alloc260) + R.vm.kill_object(alloc259) + R.vm.kill_object(model_decoder_layers_14_fc2_weight2) + R.vm.kill_object(model_decoder_layers_14_fc2_bias2) + gv420: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc261: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv420, R.dtype("float16")) + cls.add5(alloc257, alloc260, alloc261) + R.vm.kill_object(alloc257) + R.vm.kill_object(alloc260) + model_decoder_layers_15_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[856] + model_decoder_layers_15_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[857] + gv421: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc262: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv421, R.dtype("float16")) + cls.layer_norm2(alloc261, model_decoder_layers_15_self_attn_layer_norm_weight2, model_decoder_layers_15_self_attn_layer_norm_bias2, alloc262) + R.vm.kill_object(model_decoder_layers_15_self_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_15_self_attn_layer_norm_bias2) + model_decoder_layers_15_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[852] + model_decoder_layers_15_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[853] + gv422: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc263: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv422, R.dtype("float16")) + _261: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_15_self_attn_q_proj_weight2, alloc262, model_decoder_layers_15_self_attn_q_proj_bias2, alloc263) + R.vm.kill_object(model_decoder_layers_15_self_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_15_self_attn_q_proj_bias2) + gv423: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape537: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc263, gv423, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc263) + model_decoder_layers_15_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[849] + gv424: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc264: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv424, R.dtype("float16")) + _262: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_15_self_attn_k_proj_weight2, alloc262, alloc264) + R.vm.kill_object(model_decoder_layers_15_self_attn_k_proj_weight2) + gv425: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape538: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc264, gv425, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc264) + model_decoder_layers_15_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[850] + model_decoder_layers_15_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[851] + gv426: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc265: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv426, R.dtype("float16")) + _263: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_15_self_attn_v_proj_weight2, alloc262, model_decoder_layers_15_self_attn_v_proj_bias2, alloc265) + R.vm.kill_object(alloc262) + R.vm.kill_object(model_decoder_layers_15_self_attn_v_proj_weight2) + R.vm.kill_object(model_decoder_layers_15_self_attn_v_proj_bias2) + gv427: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape539: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc265, gv427, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc265) + gv428: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc266: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv428, R.dtype("float16")) + cls.concatenate1(reshape537, reshape538, reshape539, alloc266) + R.vm.kill_object(reshape537) + R.vm.kill_object(reshape538) + R.vm.kill_object(reshape539) + gv429: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape540: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc266, gv429, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc266) + gv430: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc267: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv430, R.dtype("float16")) + _265: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), reshape540, alloc267) + R.vm.kill_object(reshape540) + gv431: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape541: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc267, gv431, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc267) + gv432: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape542: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape541, gv432, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape541) + model_decoder_layers_15_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[854] + model_decoder_layers_15_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[855] + gv433: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc268: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv433, R.dtype("float16")) + _266: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_15_self_attn_out_proj_weight2, reshape542, model_decoder_layers_15_self_attn_out_proj_bias2, alloc268) + R.vm.kill_object(reshape542) + R.vm.kill_object(model_decoder_layers_15_self_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_15_self_attn_out_proj_bias2) + gv434: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc269: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv434, R.dtype("float16")) + cls.add5(alloc261, alloc268, alloc269) + R.vm.kill_object(alloc261) + R.vm.kill_object(alloc268) + model_decoder_layers_15_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[865] + model_decoder_layers_15_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[866] + gv435: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc270: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv435, R.dtype("float16")) + cls.layer_norm2(alloc269, model_decoder_layers_15_encoder_attn_layer_norm_weight2, model_decoder_layers_15_encoder_attn_layer_norm_bias2, alloc270) + R.vm.kill_object(model_decoder_layers_15_encoder_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_15_encoder_attn_layer_norm_bias2) + model_decoder_layers_15_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[861] + model_decoder_layers_15_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[862] + gv436: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc271: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv436, R.dtype("float16")) + _269: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_15_encoder_attn_q_proj_weight2, alloc270, model_decoder_layers_15_encoder_attn_q_proj_bias2, alloc271) + R.vm.kill_object(alloc270) + R.vm.kill_object(model_decoder_layers_15_encoder_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_15_encoder_attn_q_proj_bias2) + gv437: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape543: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc271, gv437, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc271) + gv438: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape544: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape543, gv438, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape543) + gv439: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc272: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv439, R.dtype("float16")) + _270: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), reshape544, alloc272) + R.vm.kill_object(reshape544) + gv440: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape545: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc272, gv440, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc272) + gv441: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape546: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape545, gv441, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape545) + model_decoder_layers_15_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[863] + model_decoder_layers_15_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[864] + gv442: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc273: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv442, R.dtype("float16")) + _271: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_15_encoder_attn_out_proj_weight2, reshape546, model_decoder_layers_15_encoder_attn_out_proj_bias2, alloc273) + R.vm.kill_object(reshape546) + R.vm.kill_object(model_decoder_layers_15_encoder_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_15_encoder_attn_out_proj_bias2) + gv443: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc274: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv443, R.dtype("float16")) + cls.add5(alloc269, alloc273, alloc274) + R.vm.kill_object(alloc269) + R.vm.kill_object(alloc273) + model_decoder_layers_15_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[871] + model_decoder_layers_15_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[872] + gv444: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc275: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv444, R.dtype("float16")) + cls.layer_norm2(alloc274, model_decoder_layers_15_final_layer_norm_weight2, model_decoder_layers_15_final_layer_norm_bias2, alloc275) + R.vm.kill_object(model_decoder_layers_15_final_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_15_final_layer_norm_bias2) + model_decoder_layers_15_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[867] + model_decoder_layers_15_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[868] + gv445: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc276: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv445, R.dtype("float16")) + _274: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_15_fc1_weight2, alloc275, model_decoder_layers_15_fc1_bias2, alloc276) + R.vm.kill_object(alloc275) + R.vm.kill_object(model_decoder_layers_15_fc1_weight2) + R.vm.kill_object(model_decoder_layers_15_fc1_bias2) + model_decoder_layers_15_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[869] + model_decoder_layers_15_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[870] + gv446: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc277: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv446, R.dtype("float16")) + _275: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_15_fc2_weight2, alloc276, model_decoder_layers_15_fc2_bias2, alloc277) + R.vm.kill_object(alloc276) + R.vm.kill_object(model_decoder_layers_15_fc2_weight2) + R.vm.kill_object(model_decoder_layers_15_fc2_bias2) + gv447: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc278: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv447, R.dtype("float16")) + cls.add5(alloc274, alloc277, alloc278) + R.vm.kill_object(alloc274) + R.vm.kill_object(alloc277) + model_decoder_layers_16_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[880] + model_decoder_layers_16_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[881] + gv448: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc279: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv448, R.dtype("float16")) + cls.layer_norm2(alloc278, model_decoder_layers_16_self_attn_layer_norm_weight2, model_decoder_layers_16_self_attn_layer_norm_bias2, alloc279) + R.vm.kill_object(model_decoder_layers_16_self_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_16_self_attn_layer_norm_bias2) + model_decoder_layers_16_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[876] + model_decoder_layers_16_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[877] + gv449: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc280: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv449, R.dtype("float16")) + _278: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_16_self_attn_q_proj_weight2, alloc279, model_decoder_layers_16_self_attn_q_proj_bias2, alloc280) + R.vm.kill_object(model_decoder_layers_16_self_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_16_self_attn_q_proj_bias2) + gv450: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape547: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc280, gv450, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc280) + model_decoder_layers_16_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[873] + gv451: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc281: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv451, R.dtype("float16")) + _279: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_16_self_attn_k_proj_weight2, alloc279, alloc281) + R.vm.kill_object(model_decoder_layers_16_self_attn_k_proj_weight2) + gv452: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape548: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc281, gv452, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc281) + model_decoder_layers_16_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[874] + model_decoder_layers_16_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[875] + gv453: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc282: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv453, R.dtype("float16")) + _280: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_16_self_attn_v_proj_weight2, alloc279, model_decoder_layers_16_self_attn_v_proj_bias2, alloc282) + R.vm.kill_object(alloc279) + R.vm.kill_object(model_decoder_layers_16_self_attn_v_proj_weight2) + R.vm.kill_object(model_decoder_layers_16_self_attn_v_proj_bias2) + gv454: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape549: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc282, gv454, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc282) + gv455: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc283: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv455, R.dtype("float16")) + cls.concatenate1(reshape547, reshape548, reshape549, alloc283) + R.vm.kill_object(reshape547) + R.vm.kill_object(reshape548) + R.vm.kill_object(reshape549) + gv456: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape550: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc283, gv456, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc283) + gv457: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc284: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv457, R.dtype("float16")) + _282: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), reshape550, alloc284) + R.vm.kill_object(reshape550) + gv458: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape551: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc284, gv458, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc284) + gv459: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape552: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape551, gv459, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape551) + model_decoder_layers_16_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[878] + model_decoder_layers_16_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[879] + gv460: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc285: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv460, R.dtype("float16")) + _283: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_16_self_attn_out_proj_weight2, reshape552, model_decoder_layers_16_self_attn_out_proj_bias2, alloc285) + R.vm.kill_object(reshape552) + R.vm.kill_object(model_decoder_layers_16_self_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_16_self_attn_out_proj_bias2) + gv461: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc286: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv461, R.dtype("float16")) + cls.add5(alloc278, alloc285, alloc286) + R.vm.kill_object(alloc278) + R.vm.kill_object(alloc285) + model_decoder_layers_16_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[889] + model_decoder_layers_16_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[890] + gv462: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc287: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv462, R.dtype("float16")) + cls.layer_norm2(alloc286, model_decoder_layers_16_encoder_attn_layer_norm_weight2, model_decoder_layers_16_encoder_attn_layer_norm_bias2, alloc287) + R.vm.kill_object(model_decoder_layers_16_encoder_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_16_encoder_attn_layer_norm_bias2) + model_decoder_layers_16_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[885] + model_decoder_layers_16_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[886] + gv463: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc288: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv463, R.dtype("float16")) + _286: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_16_encoder_attn_q_proj_weight2, alloc287, model_decoder_layers_16_encoder_attn_q_proj_bias2, alloc288) + R.vm.kill_object(alloc287) + R.vm.kill_object(model_decoder_layers_16_encoder_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_16_encoder_attn_q_proj_bias2) + gv464: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape553: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc288, gv464, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc288) + gv465: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape554: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape553, gv465, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape553) + gv466: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc289: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv466, R.dtype("float16")) + _287: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), reshape554, alloc289) + R.vm.kill_object(reshape554) + gv467: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape555: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc289, gv467, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc289) + gv468: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape556: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape555, gv468, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape555) + model_decoder_layers_16_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[887] + model_decoder_layers_16_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[888] + gv469: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc290: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv469, R.dtype("float16")) + _288: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_16_encoder_attn_out_proj_weight2, reshape556, model_decoder_layers_16_encoder_attn_out_proj_bias2, alloc290) + R.vm.kill_object(reshape556) + R.vm.kill_object(model_decoder_layers_16_encoder_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_16_encoder_attn_out_proj_bias2) + gv470: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc291: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv470, R.dtype("float16")) + cls.add5(alloc286, alloc290, alloc291) + R.vm.kill_object(alloc286) + R.vm.kill_object(alloc290) + model_decoder_layers_16_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[895] + model_decoder_layers_16_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[896] + gv471: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc292: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv471, R.dtype("float16")) + cls.layer_norm2(alloc291, model_decoder_layers_16_final_layer_norm_weight2, model_decoder_layers_16_final_layer_norm_bias2, alloc292) + R.vm.kill_object(model_decoder_layers_16_final_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_16_final_layer_norm_bias2) + model_decoder_layers_16_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[891] + model_decoder_layers_16_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[892] + gv472: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc293: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv472, R.dtype("float16")) + _291: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_16_fc1_weight2, alloc292, model_decoder_layers_16_fc1_bias2, alloc293) + R.vm.kill_object(alloc292) + R.vm.kill_object(model_decoder_layers_16_fc1_weight2) + R.vm.kill_object(model_decoder_layers_16_fc1_bias2) + model_decoder_layers_16_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[893] + model_decoder_layers_16_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[894] + gv473: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc294: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv473, R.dtype("float16")) + _292: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_16_fc2_weight2, alloc293, model_decoder_layers_16_fc2_bias2, alloc294) + R.vm.kill_object(alloc293) + R.vm.kill_object(model_decoder_layers_16_fc2_weight2) + R.vm.kill_object(model_decoder_layers_16_fc2_bias2) + gv474: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc295: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv474, R.dtype("float16")) + cls.add5(alloc291, alloc294, alloc295) + R.vm.kill_object(alloc291) + R.vm.kill_object(alloc294) + model_decoder_layers_17_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[904] + model_decoder_layers_17_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[905] + gv475: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc296: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv475, R.dtype("float16")) + cls.layer_norm2(alloc295, model_decoder_layers_17_self_attn_layer_norm_weight2, model_decoder_layers_17_self_attn_layer_norm_bias2, alloc296) + R.vm.kill_object(model_decoder_layers_17_self_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_17_self_attn_layer_norm_bias2) + model_decoder_layers_17_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[900] + model_decoder_layers_17_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[901] + gv476: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc297: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv476, R.dtype("float16")) + _295: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_17_self_attn_q_proj_weight2, alloc296, model_decoder_layers_17_self_attn_q_proj_bias2, alloc297) + R.vm.kill_object(model_decoder_layers_17_self_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_17_self_attn_q_proj_bias2) + gv477: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape557: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc297, gv477, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc297) + model_decoder_layers_17_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[897] + gv478: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc298: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv478, R.dtype("float16")) + _296: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_17_self_attn_k_proj_weight2, alloc296, alloc298) + R.vm.kill_object(model_decoder_layers_17_self_attn_k_proj_weight2) + gv479: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape558: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc298, gv479, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc298) + model_decoder_layers_17_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[898] + model_decoder_layers_17_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[899] + gv480: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc299: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv480, R.dtype("float16")) + _297: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_17_self_attn_v_proj_weight2, alloc296, model_decoder_layers_17_self_attn_v_proj_bias2, alloc299) + R.vm.kill_object(alloc296) + R.vm.kill_object(model_decoder_layers_17_self_attn_v_proj_weight2) + R.vm.kill_object(model_decoder_layers_17_self_attn_v_proj_bias2) + gv481: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape559: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc299, gv481, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc299) + gv482: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc300: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv482, R.dtype("float16")) + cls.concatenate1(reshape557, reshape558, reshape559, alloc300) + R.vm.kill_object(reshape557) + R.vm.kill_object(reshape558) + R.vm.kill_object(reshape559) + gv483: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape560: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc300, gv483, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc300) + gv484: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc301: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv484, R.dtype("float16")) + _299: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), reshape560, alloc301) + R.vm.kill_object(reshape560) + gv485: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape561: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc301, gv485, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc301) + gv486: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape562: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape561, gv486, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape561) + model_decoder_layers_17_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[902] + model_decoder_layers_17_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[903] + gv487: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc302: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv487, R.dtype("float16")) + _300: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_17_self_attn_out_proj_weight2, reshape562, model_decoder_layers_17_self_attn_out_proj_bias2, alloc302) + R.vm.kill_object(reshape562) + R.vm.kill_object(model_decoder_layers_17_self_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_17_self_attn_out_proj_bias2) + gv488: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc303: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv488, R.dtype("float16")) + cls.add5(alloc295, alloc302, alloc303) + R.vm.kill_object(alloc295) + R.vm.kill_object(alloc302) + model_decoder_layers_17_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[913] + model_decoder_layers_17_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[914] + gv489: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc304: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv489, R.dtype("float16")) + cls.layer_norm2(alloc303, model_decoder_layers_17_encoder_attn_layer_norm_weight2, model_decoder_layers_17_encoder_attn_layer_norm_bias2, alloc304) + R.vm.kill_object(model_decoder_layers_17_encoder_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_17_encoder_attn_layer_norm_bias2) + model_decoder_layers_17_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[909] + model_decoder_layers_17_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[910] + gv490: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc305: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv490, R.dtype("float16")) + _303: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_17_encoder_attn_q_proj_weight2, alloc304, model_decoder_layers_17_encoder_attn_q_proj_bias2, alloc305) + R.vm.kill_object(alloc304) + R.vm.kill_object(model_decoder_layers_17_encoder_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_17_encoder_attn_q_proj_bias2) + gv491: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape563: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc305, gv491, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc305) + gv492: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape564: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape563, gv492, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape563) + gv493: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc306: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv493, R.dtype("float16")) + _304: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), reshape564, alloc306) + R.vm.kill_object(reshape564) + gv494: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape565: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc306, gv494, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc306) + gv495: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape566: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape565, gv495, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape565) + model_decoder_layers_17_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[911] + model_decoder_layers_17_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[912] + gv496: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc307: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv496, R.dtype("float16")) + _305: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_17_encoder_attn_out_proj_weight2, reshape566, model_decoder_layers_17_encoder_attn_out_proj_bias2, alloc307) + R.vm.kill_object(reshape566) + R.vm.kill_object(model_decoder_layers_17_encoder_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_17_encoder_attn_out_proj_bias2) + gv497: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc308: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv497, R.dtype("float16")) + cls.add5(alloc303, alloc307, alloc308) + R.vm.kill_object(alloc303) + R.vm.kill_object(alloc307) + model_decoder_layers_17_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[919] + model_decoder_layers_17_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[920] + gv498: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc309: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv498, R.dtype("float16")) + cls.layer_norm2(alloc308, model_decoder_layers_17_final_layer_norm_weight2, model_decoder_layers_17_final_layer_norm_bias2, alloc309) + R.vm.kill_object(model_decoder_layers_17_final_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_17_final_layer_norm_bias2) + model_decoder_layers_17_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[915] + model_decoder_layers_17_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[916] + gv499: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc310: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv499, R.dtype("float16")) + _308: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_17_fc1_weight2, alloc309, model_decoder_layers_17_fc1_bias2, alloc310) + R.vm.kill_object(alloc309) + R.vm.kill_object(model_decoder_layers_17_fc1_weight2) + R.vm.kill_object(model_decoder_layers_17_fc1_bias2) + model_decoder_layers_17_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[917] + model_decoder_layers_17_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[918] + gv500: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc311: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv500, R.dtype("float16")) + _309: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_17_fc2_weight2, alloc310, model_decoder_layers_17_fc2_bias2, alloc311) + R.vm.kill_object(alloc310) + R.vm.kill_object(model_decoder_layers_17_fc2_weight2) + R.vm.kill_object(model_decoder_layers_17_fc2_bias2) + gv501: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc312: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv501, R.dtype("float16")) + cls.add5(alloc308, alloc311, alloc312) + R.vm.kill_object(alloc308) + R.vm.kill_object(alloc311) + model_decoder_layers_18_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[928] + model_decoder_layers_18_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[929] + gv502: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc313: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv502, R.dtype("float16")) + cls.layer_norm2(alloc312, model_decoder_layers_18_self_attn_layer_norm_weight2, model_decoder_layers_18_self_attn_layer_norm_bias2, alloc313) + R.vm.kill_object(model_decoder_layers_18_self_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_18_self_attn_layer_norm_bias2) + model_decoder_layers_18_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[924] + model_decoder_layers_18_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[925] + gv503: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc314: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv503, R.dtype("float16")) + _312: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_18_self_attn_q_proj_weight2, alloc313, model_decoder_layers_18_self_attn_q_proj_bias2, alloc314) + R.vm.kill_object(model_decoder_layers_18_self_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_18_self_attn_q_proj_bias2) + gv504: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape567: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc314, gv504, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc314) + model_decoder_layers_18_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[921] + gv505: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc315: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv505, R.dtype("float16")) + _313: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_18_self_attn_k_proj_weight2, alloc313, alloc315) + R.vm.kill_object(model_decoder_layers_18_self_attn_k_proj_weight2) + gv506: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape568: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc315, gv506, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc315) + model_decoder_layers_18_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[922] + model_decoder_layers_18_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[923] + gv507: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc316: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv507, R.dtype("float16")) + _314: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_18_self_attn_v_proj_weight2, alloc313, model_decoder_layers_18_self_attn_v_proj_bias2, alloc316) + R.vm.kill_object(alloc313) + R.vm.kill_object(model_decoder_layers_18_self_attn_v_proj_weight2) + R.vm.kill_object(model_decoder_layers_18_self_attn_v_proj_bias2) + gv508: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape569: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc316, gv508, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc316) + gv509: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc317: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv509, R.dtype("float16")) + cls.concatenate1(reshape567, reshape568, reshape569, alloc317) + R.vm.kill_object(reshape567) + R.vm.kill_object(reshape568) + R.vm.kill_object(reshape569) + gv510: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape570: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc317, gv510, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc317) + gv511: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc318: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv511, R.dtype("float16")) + _316: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), reshape570, alloc318) + R.vm.kill_object(reshape570) + gv512: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape571: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc318, gv512, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc318) + gv513: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape572: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape571, gv513, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape571) + model_decoder_layers_18_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[926] + model_decoder_layers_18_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[927] + gv514: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc319: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv514, R.dtype("float16")) + _317: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_18_self_attn_out_proj_weight2, reshape572, model_decoder_layers_18_self_attn_out_proj_bias2, alloc319) + R.vm.kill_object(reshape572) + R.vm.kill_object(model_decoder_layers_18_self_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_18_self_attn_out_proj_bias2) + gv515: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc320: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv515, R.dtype("float16")) + cls.add5(alloc312, alloc319, alloc320) + R.vm.kill_object(alloc312) + R.vm.kill_object(alloc319) + model_decoder_layers_18_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[937] + model_decoder_layers_18_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[938] + gv516: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc321: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv516, R.dtype("float16")) + cls.layer_norm2(alloc320, model_decoder_layers_18_encoder_attn_layer_norm_weight2, model_decoder_layers_18_encoder_attn_layer_norm_bias2, alloc321) + R.vm.kill_object(model_decoder_layers_18_encoder_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_18_encoder_attn_layer_norm_bias2) + model_decoder_layers_18_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[933] + model_decoder_layers_18_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[934] + gv517: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc322: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv517, R.dtype("float16")) + _320: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_18_encoder_attn_q_proj_weight2, alloc321, model_decoder_layers_18_encoder_attn_q_proj_bias2, alloc322) + R.vm.kill_object(alloc321) + R.vm.kill_object(model_decoder_layers_18_encoder_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_18_encoder_attn_q_proj_bias2) + gv518: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape573: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc322, gv518, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc322) + gv519: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape574: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape573, gv519, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape573) + gv520: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc323: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv520, R.dtype("float16")) + _321: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), reshape574, alloc323) + R.vm.kill_object(reshape574) + gv521: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape575: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc323, gv521, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc323) + gv522: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape576: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape575, gv522, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape575) + model_decoder_layers_18_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[935] + model_decoder_layers_18_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[936] + gv523: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc324: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv523, R.dtype("float16")) + _322: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_18_encoder_attn_out_proj_weight2, reshape576, model_decoder_layers_18_encoder_attn_out_proj_bias2, alloc324) + R.vm.kill_object(reshape576) + R.vm.kill_object(model_decoder_layers_18_encoder_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_18_encoder_attn_out_proj_bias2) + gv524: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc325: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv524, R.dtype("float16")) + cls.add5(alloc320, alloc324, alloc325) + R.vm.kill_object(alloc320) + R.vm.kill_object(alloc324) + model_decoder_layers_18_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[943] + model_decoder_layers_18_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[944] + gv525: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc326: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv525, R.dtype("float16")) + cls.layer_norm2(alloc325, model_decoder_layers_18_final_layer_norm_weight2, model_decoder_layers_18_final_layer_norm_bias2, alloc326) + R.vm.kill_object(model_decoder_layers_18_final_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_18_final_layer_norm_bias2) + model_decoder_layers_18_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[939] + model_decoder_layers_18_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[940] + gv526: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc327: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv526, R.dtype("float16")) + _325: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_18_fc1_weight2, alloc326, model_decoder_layers_18_fc1_bias2, alloc327) + R.vm.kill_object(alloc326) + R.vm.kill_object(model_decoder_layers_18_fc1_weight2) + R.vm.kill_object(model_decoder_layers_18_fc1_bias2) + model_decoder_layers_18_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[941] + model_decoder_layers_18_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[942] + gv527: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc328: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv527, R.dtype("float16")) + _326: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_18_fc2_weight2, alloc327, model_decoder_layers_18_fc2_bias2, alloc328) + R.vm.kill_object(alloc327) + R.vm.kill_object(model_decoder_layers_18_fc2_weight2) + R.vm.kill_object(model_decoder_layers_18_fc2_bias2) + gv528: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc329: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv528, R.dtype("float16")) + cls.add5(alloc325, alloc328, alloc329) + R.vm.kill_object(alloc325) + R.vm.kill_object(alloc328) + model_decoder_layers_19_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[952] + model_decoder_layers_19_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[953] + gv529: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc330: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv529, R.dtype("float16")) + cls.layer_norm2(alloc329, model_decoder_layers_19_self_attn_layer_norm_weight2, model_decoder_layers_19_self_attn_layer_norm_bias2, alloc330) + R.vm.kill_object(model_decoder_layers_19_self_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_19_self_attn_layer_norm_bias2) + model_decoder_layers_19_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[948] + model_decoder_layers_19_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[949] + gv530: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc331: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv530, R.dtype("float16")) + _329: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_19_self_attn_q_proj_weight2, alloc330, model_decoder_layers_19_self_attn_q_proj_bias2, alloc331) + R.vm.kill_object(model_decoder_layers_19_self_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_19_self_attn_q_proj_bias2) + gv531: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape577: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc331, gv531, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc331) + model_decoder_layers_19_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[945] + gv532: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc332: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv532, R.dtype("float16")) + _330: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_19_self_attn_k_proj_weight2, alloc330, alloc332) + R.vm.kill_object(model_decoder_layers_19_self_attn_k_proj_weight2) + gv533: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape578: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc332, gv533, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc332) + model_decoder_layers_19_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[946] + model_decoder_layers_19_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[947] + gv534: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc333: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv534, R.dtype("float16")) + _331: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_19_self_attn_v_proj_weight2, alloc330, model_decoder_layers_19_self_attn_v_proj_bias2, alloc333) + R.vm.kill_object(alloc330) + R.vm.kill_object(model_decoder_layers_19_self_attn_v_proj_weight2) + R.vm.kill_object(model_decoder_layers_19_self_attn_v_proj_bias2) + gv535: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape579: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc333, gv535, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc333) + gv536: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc334: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv536, R.dtype("float16")) + cls.concatenate1(reshape577, reshape578, reshape579, alloc334) + R.vm.kill_object(reshape577) + R.vm.kill_object(reshape578) + R.vm.kill_object(reshape579) + gv537: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape580: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc334, gv537, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc334) + gv538: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc335: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv538, R.dtype("float16")) + _333: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), reshape580, alloc335) + R.vm.kill_object(reshape580) + gv539: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape581: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc335, gv539, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc335) + gv540: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape582: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape581, gv540, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape581) + model_decoder_layers_19_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[950] + model_decoder_layers_19_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[951] + gv541: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc336: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv541, R.dtype("float16")) + _334: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_19_self_attn_out_proj_weight2, reshape582, model_decoder_layers_19_self_attn_out_proj_bias2, alloc336) + R.vm.kill_object(reshape582) + R.vm.kill_object(model_decoder_layers_19_self_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_19_self_attn_out_proj_bias2) + gv542: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc337: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv542, R.dtype("float16")) + cls.add5(alloc329, alloc336, alloc337) + R.vm.kill_object(alloc329) + R.vm.kill_object(alloc336) + model_decoder_layers_19_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[961] + model_decoder_layers_19_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[962] + gv543: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc338: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv543, R.dtype("float16")) + cls.layer_norm2(alloc337, model_decoder_layers_19_encoder_attn_layer_norm_weight2, model_decoder_layers_19_encoder_attn_layer_norm_bias2, alloc338) + R.vm.kill_object(model_decoder_layers_19_encoder_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_19_encoder_attn_layer_norm_bias2) + model_decoder_layers_19_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[957] + model_decoder_layers_19_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[958] + gv544: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc339: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv544, R.dtype("float16")) + _337: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_19_encoder_attn_q_proj_weight2, alloc338, model_decoder_layers_19_encoder_attn_q_proj_bias2, alloc339) + R.vm.kill_object(alloc338) + R.vm.kill_object(model_decoder_layers_19_encoder_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_19_encoder_attn_q_proj_bias2) + gv545: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape583: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc339, gv545, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc339) + gv546: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape584: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape583, gv546, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape583) + gv547: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc340: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv547, R.dtype("float16")) + _338: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), reshape584, alloc340) + R.vm.kill_object(reshape584) + gv548: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape585: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc340, gv548, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc340) + gv549: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape586: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape585, gv549, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape585) + model_decoder_layers_19_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[959] + model_decoder_layers_19_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[960] + gv550: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc341: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv550, R.dtype("float16")) + _339: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_19_encoder_attn_out_proj_weight2, reshape586, model_decoder_layers_19_encoder_attn_out_proj_bias2, alloc341) + R.vm.kill_object(reshape586) + R.vm.kill_object(model_decoder_layers_19_encoder_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_19_encoder_attn_out_proj_bias2) + gv551: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc342: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv551, R.dtype("float16")) + cls.add5(alloc337, alloc341, alloc342) + R.vm.kill_object(alloc337) + R.vm.kill_object(alloc341) + model_decoder_layers_19_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[967] + model_decoder_layers_19_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[968] + gv552: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc343: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv552, R.dtype("float16")) + cls.layer_norm2(alloc342, model_decoder_layers_19_final_layer_norm_weight2, model_decoder_layers_19_final_layer_norm_bias2, alloc343) + R.vm.kill_object(model_decoder_layers_19_final_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_19_final_layer_norm_bias2) + model_decoder_layers_19_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[963] + model_decoder_layers_19_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[964] + gv553: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc344: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv553, R.dtype("float16")) + _342: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_19_fc1_weight2, alloc343, model_decoder_layers_19_fc1_bias2, alloc344) + R.vm.kill_object(alloc343) + R.vm.kill_object(model_decoder_layers_19_fc1_weight2) + R.vm.kill_object(model_decoder_layers_19_fc1_bias2) + model_decoder_layers_19_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[965] + model_decoder_layers_19_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[966] + gv554: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc345: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv554, R.dtype("float16")) + _343: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_19_fc2_weight2, alloc344, model_decoder_layers_19_fc2_bias2, alloc345) + R.vm.kill_object(alloc344) + R.vm.kill_object(model_decoder_layers_19_fc2_weight2) + R.vm.kill_object(model_decoder_layers_19_fc2_bias2) + gv555: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc346: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv555, R.dtype("float16")) + cls.add5(alloc342, alloc345, alloc346) + R.vm.kill_object(alloc342) + R.vm.kill_object(alloc345) + model_decoder_layers_20_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[976] + model_decoder_layers_20_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[977] + gv556: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc347: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv556, R.dtype("float16")) + cls.layer_norm2(alloc346, model_decoder_layers_20_self_attn_layer_norm_weight2, model_decoder_layers_20_self_attn_layer_norm_bias2, alloc347) + R.vm.kill_object(model_decoder_layers_20_self_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_20_self_attn_layer_norm_bias2) + model_decoder_layers_20_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[972] + model_decoder_layers_20_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[973] + gv557: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc348: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv557, R.dtype("float16")) + _346: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_20_self_attn_q_proj_weight2, alloc347, model_decoder_layers_20_self_attn_q_proj_bias2, alloc348) + R.vm.kill_object(model_decoder_layers_20_self_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_20_self_attn_q_proj_bias2) + gv558: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape587: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc348, gv558, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc348) + model_decoder_layers_20_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[969] + gv559: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc349: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv559, R.dtype("float16")) + _347: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_20_self_attn_k_proj_weight2, alloc347, alloc349) + R.vm.kill_object(model_decoder_layers_20_self_attn_k_proj_weight2) + gv560: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape588: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc349, gv560, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc349) + model_decoder_layers_20_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[970] + model_decoder_layers_20_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[971] + gv561: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc350: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv561, R.dtype("float16")) + _348: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_20_self_attn_v_proj_weight2, alloc347, model_decoder_layers_20_self_attn_v_proj_bias2, alloc350) + R.vm.kill_object(alloc347) + R.vm.kill_object(model_decoder_layers_20_self_attn_v_proj_weight2) + R.vm.kill_object(model_decoder_layers_20_self_attn_v_proj_bias2) + gv562: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape589: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc350, gv562, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc350) + gv563: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc351: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv563, R.dtype("float16")) + cls.concatenate1(reshape587, reshape588, reshape589, alloc351) + R.vm.kill_object(reshape587) + R.vm.kill_object(reshape588) + R.vm.kill_object(reshape589) + gv564: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape590: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc351, gv564, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc351) + gv565: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc352: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv565, R.dtype("float16")) + _350: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), reshape590, alloc352) + R.vm.kill_object(reshape590) + gv566: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape591: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc352, gv566, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc352) + gv567: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape592: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape591, gv567, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape591) + model_decoder_layers_20_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[974] + model_decoder_layers_20_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[975] + gv568: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc353: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv568, R.dtype("float16")) + _351: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_20_self_attn_out_proj_weight2, reshape592, model_decoder_layers_20_self_attn_out_proj_bias2, alloc353) + R.vm.kill_object(reshape592) + R.vm.kill_object(model_decoder_layers_20_self_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_20_self_attn_out_proj_bias2) + gv569: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc354: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv569, R.dtype("float16")) + cls.add5(alloc346, alloc353, alloc354) + R.vm.kill_object(alloc346) + R.vm.kill_object(alloc353) + model_decoder_layers_20_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[985] + model_decoder_layers_20_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[986] + gv570: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc355: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv570, R.dtype("float16")) + cls.layer_norm2(alloc354, model_decoder_layers_20_encoder_attn_layer_norm_weight2, model_decoder_layers_20_encoder_attn_layer_norm_bias2, alloc355) + R.vm.kill_object(model_decoder_layers_20_encoder_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_20_encoder_attn_layer_norm_bias2) + model_decoder_layers_20_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[981] + model_decoder_layers_20_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[982] + gv571: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc356: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv571, R.dtype("float16")) + _354: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_20_encoder_attn_q_proj_weight2, alloc355, model_decoder_layers_20_encoder_attn_q_proj_bias2, alloc356) + R.vm.kill_object(alloc355) + R.vm.kill_object(model_decoder_layers_20_encoder_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_20_encoder_attn_q_proj_bias2) + gv572: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape593: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc356, gv572, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc356) + gv573: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape594: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape593, gv573, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape593) + gv574: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc357: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv574, R.dtype("float16")) + _355: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), reshape594, alloc357) + R.vm.kill_object(reshape594) + gv575: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape595: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc357, gv575, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc357) + gv576: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape596: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape595, gv576, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape595) + model_decoder_layers_20_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[983] + model_decoder_layers_20_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[984] + gv577: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc358: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv577, R.dtype("float16")) + _356: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_20_encoder_attn_out_proj_weight2, reshape596, model_decoder_layers_20_encoder_attn_out_proj_bias2, alloc358) + R.vm.kill_object(reshape596) + R.vm.kill_object(model_decoder_layers_20_encoder_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_20_encoder_attn_out_proj_bias2) + gv578: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc359: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv578, R.dtype("float16")) + cls.add5(alloc354, alloc358, alloc359) + R.vm.kill_object(alloc354) + R.vm.kill_object(alloc358) + model_decoder_layers_20_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[991] + model_decoder_layers_20_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[992] + gv579: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc360: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv579, R.dtype("float16")) + cls.layer_norm2(alloc359, model_decoder_layers_20_final_layer_norm_weight2, model_decoder_layers_20_final_layer_norm_bias2, alloc360) + R.vm.kill_object(model_decoder_layers_20_final_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_20_final_layer_norm_bias2) + model_decoder_layers_20_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[987] + model_decoder_layers_20_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[988] + gv580: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc361: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv580, R.dtype("float16")) + _359: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_20_fc1_weight2, alloc360, model_decoder_layers_20_fc1_bias2, alloc361) + R.vm.kill_object(alloc360) + R.vm.kill_object(model_decoder_layers_20_fc1_weight2) + R.vm.kill_object(model_decoder_layers_20_fc1_bias2) + model_decoder_layers_20_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[989] + model_decoder_layers_20_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[990] + gv581: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc362: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv581, R.dtype("float16")) + _360: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_20_fc2_weight2, alloc361, model_decoder_layers_20_fc2_bias2, alloc362) + R.vm.kill_object(alloc361) + R.vm.kill_object(model_decoder_layers_20_fc2_weight2) + R.vm.kill_object(model_decoder_layers_20_fc2_bias2) + gv582: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc363: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv582, R.dtype("float16")) + cls.add5(alloc359, alloc362, alloc363) + R.vm.kill_object(alloc359) + R.vm.kill_object(alloc362) + model_decoder_layers_21_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1000] + model_decoder_layers_21_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1001] + gv583: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc364: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv583, R.dtype("float16")) + cls.layer_norm2(alloc363, model_decoder_layers_21_self_attn_layer_norm_weight2, model_decoder_layers_21_self_attn_layer_norm_bias2, alloc364) + R.vm.kill_object(model_decoder_layers_21_self_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_21_self_attn_layer_norm_bias2) + model_decoder_layers_21_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[996] + model_decoder_layers_21_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[997] + gv584: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc365: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv584, R.dtype("float16")) + _363: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_21_self_attn_q_proj_weight2, alloc364, model_decoder_layers_21_self_attn_q_proj_bias2, alloc365) + R.vm.kill_object(model_decoder_layers_21_self_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_21_self_attn_q_proj_bias2) + gv585: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape597: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc365, gv585, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc365) + model_decoder_layers_21_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[993] + gv586: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc366: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv586, R.dtype("float16")) + _364: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_21_self_attn_k_proj_weight2, alloc364, alloc366) + R.vm.kill_object(model_decoder_layers_21_self_attn_k_proj_weight2) + gv587: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape598: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc366, gv587, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc366) + model_decoder_layers_21_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[994] + model_decoder_layers_21_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[995] + gv588: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc367: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv588, R.dtype("float16")) + _365: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_21_self_attn_v_proj_weight2, alloc364, model_decoder_layers_21_self_attn_v_proj_bias2, alloc367) + R.vm.kill_object(alloc364) + R.vm.kill_object(model_decoder_layers_21_self_attn_v_proj_weight2) + R.vm.kill_object(model_decoder_layers_21_self_attn_v_proj_bias2) + gv589: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape599: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc367, gv589, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc367) + gv590: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc368: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv590, R.dtype("float16")) + cls.concatenate1(reshape597, reshape598, reshape599, alloc368) + R.vm.kill_object(reshape597) + R.vm.kill_object(reshape598) + R.vm.kill_object(reshape599) + gv591: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape600: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc368, gv591, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc368) + gv592: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc369: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv592, R.dtype("float16")) + _367: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), reshape600, alloc369) + R.vm.kill_object(reshape600) + gv593: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape601: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc369, gv593, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc369) + gv594: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape602: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape601, gv594, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape601) + model_decoder_layers_21_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[998] + model_decoder_layers_21_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[999] + gv595: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc370: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv595, R.dtype("float16")) + _368: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_21_self_attn_out_proj_weight2, reshape602, model_decoder_layers_21_self_attn_out_proj_bias2, alloc370) + R.vm.kill_object(reshape602) + R.vm.kill_object(model_decoder_layers_21_self_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_21_self_attn_out_proj_bias2) + gv596: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc371: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv596, R.dtype("float16")) + cls.add5(alloc363, alloc370, alloc371) + R.vm.kill_object(alloc363) + R.vm.kill_object(alloc370) + model_decoder_layers_21_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1009] + model_decoder_layers_21_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1010] + gv597: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc372: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv597, R.dtype("float16")) + cls.layer_norm2(alloc371, model_decoder_layers_21_encoder_attn_layer_norm_weight2, model_decoder_layers_21_encoder_attn_layer_norm_bias2, alloc372) + R.vm.kill_object(model_decoder_layers_21_encoder_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_21_encoder_attn_layer_norm_bias2) + model_decoder_layers_21_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1005] + model_decoder_layers_21_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1006] + gv598: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc373: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv598, R.dtype("float16")) + _371: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_21_encoder_attn_q_proj_weight2, alloc372, model_decoder_layers_21_encoder_attn_q_proj_bias2, alloc373) + R.vm.kill_object(alloc372) + R.vm.kill_object(model_decoder_layers_21_encoder_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_21_encoder_attn_q_proj_bias2) + gv599: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape603: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc373, gv599, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc373) + gv600: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape604: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape603, gv600, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape603) + gv601: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc374: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv601, R.dtype("float16")) + _372: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), reshape604, alloc374) + R.vm.kill_object(reshape604) + gv602: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape605: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc374, gv602, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc374) + gv603: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape606: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape605, gv603, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape605) + model_decoder_layers_21_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1007] + model_decoder_layers_21_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1008] + gv604: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc375: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv604, R.dtype("float16")) + _373: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_21_encoder_attn_out_proj_weight2, reshape606, model_decoder_layers_21_encoder_attn_out_proj_bias2, alloc375) + R.vm.kill_object(reshape606) + R.vm.kill_object(model_decoder_layers_21_encoder_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_21_encoder_attn_out_proj_bias2) + gv605: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc376: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv605, R.dtype("float16")) + cls.add5(alloc371, alloc375, alloc376) + R.vm.kill_object(alloc371) + R.vm.kill_object(alloc375) + model_decoder_layers_21_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1015] + model_decoder_layers_21_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1016] + gv606: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc377: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv606, R.dtype("float16")) + cls.layer_norm2(alloc376, model_decoder_layers_21_final_layer_norm_weight2, model_decoder_layers_21_final_layer_norm_bias2, alloc377) + R.vm.kill_object(model_decoder_layers_21_final_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_21_final_layer_norm_bias2) + model_decoder_layers_21_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1011] + model_decoder_layers_21_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1012] + gv607: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc378: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv607, R.dtype("float16")) + _376: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_21_fc1_weight2, alloc377, model_decoder_layers_21_fc1_bias2, alloc378) + R.vm.kill_object(alloc377) + R.vm.kill_object(model_decoder_layers_21_fc1_weight2) + R.vm.kill_object(model_decoder_layers_21_fc1_bias2) + model_decoder_layers_21_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1013] + model_decoder_layers_21_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1014] + gv608: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc379: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv608, R.dtype("float16")) + _377: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_21_fc2_weight2, alloc378, model_decoder_layers_21_fc2_bias2, alloc379) + R.vm.kill_object(alloc378) + R.vm.kill_object(model_decoder_layers_21_fc2_weight2) + R.vm.kill_object(model_decoder_layers_21_fc2_bias2) + gv609: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc380: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv609, R.dtype("float16")) + cls.add5(alloc376, alloc379, alloc380) + R.vm.kill_object(alloc376) + R.vm.kill_object(alloc379) + model_decoder_layers_22_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1024] + model_decoder_layers_22_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1025] + gv610: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc381: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv610, R.dtype("float16")) + cls.layer_norm2(alloc380, model_decoder_layers_22_self_attn_layer_norm_weight2, model_decoder_layers_22_self_attn_layer_norm_bias2, alloc381) + R.vm.kill_object(model_decoder_layers_22_self_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_22_self_attn_layer_norm_bias2) + model_decoder_layers_22_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1020] + model_decoder_layers_22_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1021] + gv611: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc382: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv611, R.dtype("float16")) + _380: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_22_self_attn_q_proj_weight2, alloc381, model_decoder_layers_22_self_attn_q_proj_bias2, alloc382) + R.vm.kill_object(model_decoder_layers_22_self_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_22_self_attn_q_proj_bias2) + gv612: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape607: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc382, gv612, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc382) + model_decoder_layers_22_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1017] + gv613: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc383: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv613, R.dtype("float16")) + _381: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_22_self_attn_k_proj_weight2, alloc381, alloc383) + R.vm.kill_object(model_decoder_layers_22_self_attn_k_proj_weight2) + gv614: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape608: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc383, gv614, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc383) + model_decoder_layers_22_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1018] + model_decoder_layers_22_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1019] + gv615: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc384: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv615, R.dtype("float16")) + _382: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_22_self_attn_v_proj_weight2, alloc381, model_decoder_layers_22_self_attn_v_proj_bias2, alloc384) + R.vm.kill_object(alloc381) + R.vm.kill_object(model_decoder_layers_22_self_attn_v_proj_weight2) + R.vm.kill_object(model_decoder_layers_22_self_attn_v_proj_bias2) + gv616: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape609: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc384, gv616, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc384) + gv617: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc385: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv617, R.dtype("float16")) + cls.concatenate1(reshape607, reshape608, reshape609, alloc385) + R.vm.kill_object(reshape607) + R.vm.kill_object(reshape608) + R.vm.kill_object(reshape609) + gv618: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape610: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc385, gv618, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc385) + gv619: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc386: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv619, R.dtype("float16")) + _384: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), reshape610, alloc386) + R.vm.kill_object(reshape610) + gv620: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape611: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc386, gv620, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc386) + gv621: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape612: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape611, gv621, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape611) + model_decoder_layers_22_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1022] + model_decoder_layers_22_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1023] + gv622: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc387: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv622, R.dtype("float16")) + _385: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_22_self_attn_out_proj_weight2, reshape612, model_decoder_layers_22_self_attn_out_proj_bias2, alloc387) + R.vm.kill_object(reshape612) + R.vm.kill_object(model_decoder_layers_22_self_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_22_self_attn_out_proj_bias2) + gv623: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc388: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv623, R.dtype("float16")) + cls.add5(alloc380, alloc387, alloc388) + R.vm.kill_object(alloc380) + R.vm.kill_object(alloc387) + model_decoder_layers_22_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1033] + model_decoder_layers_22_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1034] + gv624: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc389: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv624, R.dtype("float16")) + cls.layer_norm2(alloc388, model_decoder_layers_22_encoder_attn_layer_norm_weight2, model_decoder_layers_22_encoder_attn_layer_norm_bias2, alloc389) + R.vm.kill_object(model_decoder_layers_22_encoder_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_22_encoder_attn_layer_norm_bias2) + model_decoder_layers_22_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1029] + model_decoder_layers_22_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1030] + gv625: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc390: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv625, R.dtype("float16")) + _388: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_22_encoder_attn_q_proj_weight2, alloc389, model_decoder_layers_22_encoder_attn_q_proj_bias2, alloc390) + R.vm.kill_object(alloc389) + R.vm.kill_object(model_decoder_layers_22_encoder_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_22_encoder_attn_q_proj_bias2) + gv626: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape613: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc390, gv626, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc390) + gv627: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape614: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape613, gv627, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape613) + gv628: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc391: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv628, R.dtype("float16")) + _389: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), reshape614, alloc391) + R.vm.kill_object(reshape614) + gv629: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape615: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc391, gv629, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc391) + gv630: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape616: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape615, gv630, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape615) + model_decoder_layers_22_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1031] + model_decoder_layers_22_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1032] + gv631: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc392: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv631, R.dtype("float16")) + _390: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_22_encoder_attn_out_proj_weight2, reshape616, model_decoder_layers_22_encoder_attn_out_proj_bias2, alloc392) + R.vm.kill_object(reshape616) + R.vm.kill_object(model_decoder_layers_22_encoder_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_22_encoder_attn_out_proj_bias2) + gv632: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc393: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv632, R.dtype("float16")) + cls.add5(alloc388, alloc392, alloc393) + R.vm.kill_object(alloc388) + R.vm.kill_object(alloc392) + model_decoder_layers_22_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1039] + model_decoder_layers_22_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1040] + gv633: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc394: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv633, R.dtype("float16")) + cls.layer_norm2(alloc393, model_decoder_layers_22_final_layer_norm_weight2, model_decoder_layers_22_final_layer_norm_bias2, alloc394) + R.vm.kill_object(model_decoder_layers_22_final_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_22_final_layer_norm_bias2) + model_decoder_layers_22_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1035] + model_decoder_layers_22_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1036] + gv634: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc395: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv634, R.dtype("float16")) + _393: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_22_fc1_weight2, alloc394, model_decoder_layers_22_fc1_bias2, alloc395) + R.vm.kill_object(alloc394) + R.vm.kill_object(model_decoder_layers_22_fc1_weight2) + R.vm.kill_object(model_decoder_layers_22_fc1_bias2) + model_decoder_layers_22_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1037] + model_decoder_layers_22_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1038] + gv635: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc396: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv635, R.dtype("float16")) + _394: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_22_fc2_weight2, alloc395, model_decoder_layers_22_fc2_bias2, alloc396) + R.vm.kill_object(alloc395) + R.vm.kill_object(model_decoder_layers_22_fc2_weight2) + R.vm.kill_object(model_decoder_layers_22_fc2_bias2) + gv636: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc397: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv636, R.dtype("float16")) + cls.add5(alloc393, alloc396, alloc397) + R.vm.kill_object(alloc393) + R.vm.kill_object(alloc396) + model_decoder_layers_23_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1048] + model_decoder_layers_23_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1049] + gv637: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc398: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv637, R.dtype("float16")) + cls.layer_norm2(alloc397, model_decoder_layers_23_self_attn_layer_norm_weight2, model_decoder_layers_23_self_attn_layer_norm_bias2, alloc398) + R.vm.kill_object(model_decoder_layers_23_self_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_23_self_attn_layer_norm_bias2) + model_decoder_layers_23_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1044] + model_decoder_layers_23_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1045] + gv638: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc399: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv638, R.dtype("float16")) + _397: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_23_self_attn_q_proj_weight2, alloc398, model_decoder_layers_23_self_attn_q_proj_bias2, alloc399) + R.vm.kill_object(model_decoder_layers_23_self_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_23_self_attn_q_proj_bias2) + gv639: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape617: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc399, gv639, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc399) + model_decoder_layers_23_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1041] + gv640: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc400: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv640, R.dtype("float16")) + _398: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_23_self_attn_k_proj_weight2, alloc398, alloc400) + R.vm.kill_object(model_decoder_layers_23_self_attn_k_proj_weight2) + gv641: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape618: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc400, gv641, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc400) + model_decoder_layers_23_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1042] + model_decoder_layers_23_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1043] + gv642: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc401: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv642, R.dtype("float16")) + _399: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_23_self_attn_v_proj_weight2, alloc398, model_decoder_layers_23_self_attn_v_proj_bias2, alloc401) + R.vm.kill_object(alloc398) + R.vm.kill_object(model_decoder_layers_23_self_attn_v_proj_weight2) + R.vm.kill_object(model_decoder_layers_23_self_attn_v_proj_bias2) + gv643: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape619: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc401, gv643, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc401) + gv644: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc402: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv644, R.dtype("float16")) + cls.concatenate1(reshape617, reshape618, reshape619, alloc402) + R.vm.kill_object(reshape617) + R.vm.kill_object(reshape618) + R.vm.kill_object(reshape619) + gv645: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape620: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc402, gv645, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc402) + gv646: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc403: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv646, R.dtype("float16")) + _401: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), reshape620, alloc403) + R.vm.kill_object(reshape620) + gv647: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape621: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc403, gv647, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc403) + gv648: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape622: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape621, gv648, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape621) + model_decoder_layers_23_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1046] + model_decoder_layers_23_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1047] + gv649: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc404: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv649, R.dtype("float16")) + _402: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_23_self_attn_out_proj_weight2, reshape622, model_decoder_layers_23_self_attn_out_proj_bias2, alloc404) + R.vm.kill_object(reshape622) + R.vm.kill_object(model_decoder_layers_23_self_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_23_self_attn_out_proj_bias2) + gv650: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc405: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv650, R.dtype("float16")) + cls.add5(alloc397, alloc404, alloc405) + R.vm.kill_object(alloc397) + R.vm.kill_object(alloc404) + model_decoder_layers_23_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1057] + model_decoder_layers_23_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1058] + gv651: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc406: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv651, R.dtype("float16")) + cls.layer_norm2(alloc405, model_decoder_layers_23_encoder_attn_layer_norm_weight2, model_decoder_layers_23_encoder_attn_layer_norm_bias2, alloc406) + R.vm.kill_object(model_decoder_layers_23_encoder_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_23_encoder_attn_layer_norm_bias2) + model_decoder_layers_23_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1053] + model_decoder_layers_23_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1054] + gv652: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc407: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv652, R.dtype("float16")) + _405: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_23_encoder_attn_q_proj_weight2, alloc406, model_decoder_layers_23_encoder_attn_q_proj_bias2, alloc407) + R.vm.kill_object(alloc406) + R.vm.kill_object(model_decoder_layers_23_encoder_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_23_encoder_attn_q_proj_bias2) + gv653: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape623: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc407, gv653, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc407) + gv654: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape624: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape623, gv654, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape623) + gv655: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc408: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv655, R.dtype("float16")) + _406: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), reshape624, alloc408) + R.vm.kill_object(reshape624) + gv656: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape625: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc408, gv656, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc408) + gv657: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape626: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape625, gv657, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape625) + model_decoder_layers_23_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1055] + model_decoder_layers_23_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1056] + gv658: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc409: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv658, R.dtype("float16")) + _407: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_23_encoder_attn_out_proj_weight2, reshape626, model_decoder_layers_23_encoder_attn_out_proj_bias2, alloc409) + R.vm.kill_object(reshape626) + R.vm.kill_object(model_decoder_layers_23_encoder_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_23_encoder_attn_out_proj_bias2) + gv659: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc410: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv659, R.dtype("float16")) + cls.add5(alloc405, alloc409, alloc410) + R.vm.kill_object(alloc405) + R.vm.kill_object(alloc409) + model_decoder_layers_23_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1063] + model_decoder_layers_23_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1064] + gv660: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc411: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv660, R.dtype("float16")) + cls.layer_norm2(alloc410, model_decoder_layers_23_final_layer_norm_weight2, model_decoder_layers_23_final_layer_norm_bias2, alloc411) + R.vm.kill_object(model_decoder_layers_23_final_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_23_final_layer_norm_bias2) + model_decoder_layers_23_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1059] + model_decoder_layers_23_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1060] + gv661: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc412: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv661, R.dtype("float16")) + _410: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_23_fc1_weight2, alloc411, model_decoder_layers_23_fc1_bias2, alloc412) + R.vm.kill_object(alloc411) + R.vm.kill_object(model_decoder_layers_23_fc1_weight2) + R.vm.kill_object(model_decoder_layers_23_fc1_bias2) + model_decoder_layers_23_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1061] + model_decoder_layers_23_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1062] + gv662: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc413: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv662, R.dtype("float16")) + _411: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_23_fc2_weight2, alloc412, model_decoder_layers_23_fc2_bias2, alloc413) + R.vm.kill_object(alloc412) + R.vm.kill_object(model_decoder_layers_23_fc2_weight2) + R.vm.kill_object(model_decoder_layers_23_fc2_bias2) + gv663: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc414: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv663, R.dtype("float16")) + cls.add5(alloc410, alloc413, alloc414) + R.vm.kill_object(alloc410) + R.vm.kill_object(alloc413) + model_decoder_layers_24_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1072] + model_decoder_layers_24_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1073] + gv664: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc415: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv664, R.dtype("float16")) + cls.layer_norm2(alloc414, model_decoder_layers_24_self_attn_layer_norm_weight2, model_decoder_layers_24_self_attn_layer_norm_bias2, alloc415) + R.vm.kill_object(model_decoder_layers_24_self_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_24_self_attn_layer_norm_bias2) + model_decoder_layers_24_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1068] + model_decoder_layers_24_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1069] + gv665: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc416: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv665, R.dtype("float16")) + _414: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_24_self_attn_q_proj_weight2, alloc415, model_decoder_layers_24_self_attn_q_proj_bias2, alloc416) + R.vm.kill_object(model_decoder_layers_24_self_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_24_self_attn_q_proj_bias2) + gv666: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape627: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc416, gv666, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc416) + model_decoder_layers_24_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1065] + gv667: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc417: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv667, R.dtype("float16")) + _415: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_24_self_attn_k_proj_weight2, alloc415, alloc417) + R.vm.kill_object(model_decoder_layers_24_self_attn_k_proj_weight2) + gv668: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape628: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc417, gv668, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc417) + model_decoder_layers_24_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1066] + model_decoder_layers_24_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1067] + gv669: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc418: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv669, R.dtype("float16")) + _416: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_24_self_attn_v_proj_weight2, alloc415, model_decoder_layers_24_self_attn_v_proj_bias2, alloc418) + R.vm.kill_object(alloc415) + R.vm.kill_object(model_decoder_layers_24_self_attn_v_proj_weight2) + R.vm.kill_object(model_decoder_layers_24_self_attn_v_proj_bias2) + gv670: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape629: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc418, gv670, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc418) + gv671: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc419: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv671, R.dtype("float16")) + cls.concatenate1(reshape627, reshape628, reshape629, alloc419) + R.vm.kill_object(reshape627) + R.vm.kill_object(reshape628) + R.vm.kill_object(reshape629) + gv672: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape630: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc419, gv672, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc419) + gv673: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc420: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv673, R.dtype("float16")) + _418: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), reshape630, alloc420) + R.vm.kill_object(reshape630) + gv674: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape631: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc420, gv674, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc420) + gv675: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape632: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape631, gv675, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape631) + model_decoder_layers_24_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1070] + model_decoder_layers_24_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1071] + gv676: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc421: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv676, R.dtype("float16")) + _419: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_24_self_attn_out_proj_weight2, reshape632, model_decoder_layers_24_self_attn_out_proj_bias2, alloc421) + R.vm.kill_object(reshape632) + R.vm.kill_object(model_decoder_layers_24_self_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_24_self_attn_out_proj_bias2) + gv677: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc422: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv677, R.dtype("float16")) + cls.add5(alloc414, alloc421, alloc422) + R.vm.kill_object(alloc414) + R.vm.kill_object(alloc421) + model_decoder_layers_24_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1081] + model_decoder_layers_24_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1082] + gv678: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc423: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv678, R.dtype("float16")) + cls.layer_norm2(alloc422, model_decoder_layers_24_encoder_attn_layer_norm_weight2, model_decoder_layers_24_encoder_attn_layer_norm_bias2, alloc423) + R.vm.kill_object(model_decoder_layers_24_encoder_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_24_encoder_attn_layer_norm_bias2) + model_decoder_layers_24_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1077] + model_decoder_layers_24_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1078] + gv679: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc424: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv679, R.dtype("float16")) + _422: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_24_encoder_attn_q_proj_weight2, alloc423, model_decoder_layers_24_encoder_attn_q_proj_bias2, alloc424) + R.vm.kill_object(alloc423) + R.vm.kill_object(model_decoder_layers_24_encoder_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_24_encoder_attn_q_proj_bias2) + gv680: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape633: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc424, gv680, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc424) + gv681: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape634: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape633, gv681, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape633) + gv682: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc425: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv682, R.dtype("float16")) + _423: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), reshape634, alloc425) + R.vm.kill_object(reshape634) + gv683: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape635: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc425, gv683, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc425) + gv684: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape636: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape635, gv684, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape635) + model_decoder_layers_24_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1079] + model_decoder_layers_24_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1080] + gv685: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc426: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv685, R.dtype("float16")) + _424: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_24_encoder_attn_out_proj_weight2, reshape636, model_decoder_layers_24_encoder_attn_out_proj_bias2, alloc426) + R.vm.kill_object(reshape636) + R.vm.kill_object(model_decoder_layers_24_encoder_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_24_encoder_attn_out_proj_bias2) + gv686: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc427: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv686, R.dtype("float16")) + cls.add5(alloc422, alloc426, alloc427) + R.vm.kill_object(alloc422) + R.vm.kill_object(alloc426) + model_decoder_layers_24_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1087] + model_decoder_layers_24_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1088] + gv687: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc428: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv687, R.dtype("float16")) + cls.layer_norm2(alloc427, model_decoder_layers_24_final_layer_norm_weight2, model_decoder_layers_24_final_layer_norm_bias2, alloc428) + R.vm.kill_object(model_decoder_layers_24_final_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_24_final_layer_norm_bias2) + model_decoder_layers_24_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1083] + model_decoder_layers_24_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1084] + gv688: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc429: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv688, R.dtype("float16")) + _427: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_24_fc1_weight2, alloc428, model_decoder_layers_24_fc1_bias2, alloc429) + R.vm.kill_object(alloc428) + R.vm.kill_object(model_decoder_layers_24_fc1_weight2) + R.vm.kill_object(model_decoder_layers_24_fc1_bias2) + model_decoder_layers_24_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1085] + model_decoder_layers_24_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1086] + gv689: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc430: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv689, R.dtype("float16")) + _428: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_24_fc2_weight2, alloc429, model_decoder_layers_24_fc2_bias2, alloc430) + R.vm.kill_object(alloc429) + R.vm.kill_object(model_decoder_layers_24_fc2_weight2) + R.vm.kill_object(model_decoder_layers_24_fc2_bias2) + gv690: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc431: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv690, R.dtype("float16")) + cls.add5(alloc427, alloc430, alloc431) + R.vm.kill_object(alloc427) + R.vm.kill_object(alloc430) + model_decoder_layers_25_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1096] + model_decoder_layers_25_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1097] + gv691: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc432: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv691, R.dtype("float16")) + cls.layer_norm2(alloc431, model_decoder_layers_25_self_attn_layer_norm_weight2, model_decoder_layers_25_self_attn_layer_norm_bias2, alloc432) + R.vm.kill_object(model_decoder_layers_25_self_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_25_self_attn_layer_norm_bias2) + model_decoder_layers_25_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1092] + model_decoder_layers_25_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1093] + gv692: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc433: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv692, R.dtype("float16")) + _431: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_25_self_attn_q_proj_weight2, alloc432, model_decoder_layers_25_self_attn_q_proj_bias2, alloc433) + R.vm.kill_object(model_decoder_layers_25_self_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_25_self_attn_q_proj_bias2) + gv693: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape637: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc433, gv693, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc433) + model_decoder_layers_25_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1089] + gv694: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc434: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv694, R.dtype("float16")) + _432: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_25_self_attn_k_proj_weight2, alloc432, alloc434) + R.vm.kill_object(model_decoder_layers_25_self_attn_k_proj_weight2) + gv695: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape638: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc434, gv695, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc434) + model_decoder_layers_25_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1090] + model_decoder_layers_25_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1091] + gv696: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc435: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv696, R.dtype("float16")) + _433: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_25_self_attn_v_proj_weight2, alloc432, model_decoder_layers_25_self_attn_v_proj_bias2, alloc435) + R.vm.kill_object(alloc432) + R.vm.kill_object(model_decoder_layers_25_self_attn_v_proj_weight2) + R.vm.kill_object(model_decoder_layers_25_self_attn_v_proj_bias2) + gv697: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape639: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc435, gv697, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc435) + gv698: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc436: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv698, R.dtype("float16")) + cls.concatenate1(reshape637, reshape638, reshape639, alloc436) + R.vm.kill_object(reshape637) + R.vm.kill_object(reshape638) + R.vm.kill_object(reshape639) + gv699: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape640: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc436, gv699, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc436) + gv700: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc437: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv700, R.dtype("float16")) + _435: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), reshape640, alloc437) + R.vm.kill_object(reshape640) + gv701: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape641: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc437, gv701, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc437) + gv702: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape642: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape641, gv702, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape641) + model_decoder_layers_25_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1094] + model_decoder_layers_25_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1095] + gv703: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc438: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv703, R.dtype("float16")) + _436: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_25_self_attn_out_proj_weight2, reshape642, model_decoder_layers_25_self_attn_out_proj_bias2, alloc438) + R.vm.kill_object(reshape642) + R.vm.kill_object(model_decoder_layers_25_self_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_25_self_attn_out_proj_bias2) + gv704: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc439: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv704, R.dtype("float16")) + cls.add5(alloc431, alloc438, alloc439) + R.vm.kill_object(alloc431) + R.vm.kill_object(alloc438) + model_decoder_layers_25_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1105] + model_decoder_layers_25_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1106] + gv705: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc440: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv705, R.dtype("float16")) + cls.layer_norm2(alloc439, model_decoder_layers_25_encoder_attn_layer_norm_weight2, model_decoder_layers_25_encoder_attn_layer_norm_bias2, alloc440) + R.vm.kill_object(model_decoder_layers_25_encoder_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_25_encoder_attn_layer_norm_bias2) + model_decoder_layers_25_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1101] + model_decoder_layers_25_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1102] + gv706: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc441: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv706, R.dtype("float16")) + _439: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_25_encoder_attn_q_proj_weight2, alloc440, model_decoder_layers_25_encoder_attn_q_proj_bias2, alloc441) + R.vm.kill_object(alloc440) + R.vm.kill_object(model_decoder_layers_25_encoder_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_25_encoder_attn_q_proj_bias2) + gv707: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape643: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc441, gv707, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc441) + gv708: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape644: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape643, gv708, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape643) + gv709: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc442: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv709, R.dtype("float16")) + _440: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), reshape644, alloc442) + R.vm.kill_object(reshape644) + gv710: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape645: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc442, gv710, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc442) + gv711: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape646: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape645, gv711, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape645) + model_decoder_layers_25_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1103] + model_decoder_layers_25_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1104] + gv712: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc443: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv712, R.dtype("float16")) + _441: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_25_encoder_attn_out_proj_weight2, reshape646, model_decoder_layers_25_encoder_attn_out_proj_bias2, alloc443) + R.vm.kill_object(reshape646) + R.vm.kill_object(model_decoder_layers_25_encoder_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_25_encoder_attn_out_proj_bias2) + gv713: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc444: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv713, R.dtype("float16")) + cls.add5(alloc439, alloc443, alloc444) + R.vm.kill_object(alloc439) + R.vm.kill_object(alloc443) + model_decoder_layers_25_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1111] + model_decoder_layers_25_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1112] + gv714: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc445: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv714, R.dtype("float16")) + cls.layer_norm2(alloc444, model_decoder_layers_25_final_layer_norm_weight2, model_decoder_layers_25_final_layer_norm_bias2, alloc445) + R.vm.kill_object(model_decoder_layers_25_final_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_25_final_layer_norm_bias2) + model_decoder_layers_25_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1107] + model_decoder_layers_25_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1108] + gv715: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc446: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv715, R.dtype("float16")) + _444: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_25_fc1_weight2, alloc445, model_decoder_layers_25_fc1_bias2, alloc446) + R.vm.kill_object(alloc445) + R.vm.kill_object(model_decoder_layers_25_fc1_weight2) + R.vm.kill_object(model_decoder_layers_25_fc1_bias2) + model_decoder_layers_25_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1109] + model_decoder_layers_25_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1110] + gv716: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc447: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv716, R.dtype("float16")) + _445: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_25_fc2_weight2, alloc446, model_decoder_layers_25_fc2_bias2, alloc447) + R.vm.kill_object(alloc446) + R.vm.kill_object(model_decoder_layers_25_fc2_weight2) + R.vm.kill_object(model_decoder_layers_25_fc2_bias2) + gv717: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc448: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv717, R.dtype("float16")) + cls.add5(alloc444, alloc447, alloc448) + R.vm.kill_object(alloc444) + R.vm.kill_object(alloc447) + model_decoder_layers_26_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1120] + model_decoder_layers_26_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1121] + gv718: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc449: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv718, R.dtype("float16")) + cls.layer_norm2(alloc448, model_decoder_layers_26_self_attn_layer_norm_weight2, model_decoder_layers_26_self_attn_layer_norm_bias2, alloc449) + R.vm.kill_object(model_decoder_layers_26_self_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_26_self_attn_layer_norm_bias2) + model_decoder_layers_26_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1116] + model_decoder_layers_26_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1117] + gv719: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc450: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv719, R.dtype("float16")) + _448: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_26_self_attn_q_proj_weight2, alloc449, model_decoder_layers_26_self_attn_q_proj_bias2, alloc450) + R.vm.kill_object(model_decoder_layers_26_self_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_26_self_attn_q_proj_bias2) + gv720: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape647: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc450, gv720, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc450) + model_decoder_layers_26_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1113] + gv721: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc451: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv721, R.dtype("float16")) + _449: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_26_self_attn_k_proj_weight2, alloc449, alloc451) + R.vm.kill_object(model_decoder_layers_26_self_attn_k_proj_weight2) + gv722: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape648: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc451, gv722, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc451) + model_decoder_layers_26_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1114] + model_decoder_layers_26_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1115] + gv723: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc452: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv723, R.dtype("float16")) + _450: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_26_self_attn_v_proj_weight2, alloc449, model_decoder_layers_26_self_attn_v_proj_bias2, alloc452) + R.vm.kill_object(alloc449) + R.vm.kill_object(model_decoder_layers_26_self_attn_v_proj_weight2) + R.vm.kill_object(model_decoder_layers_26_self_attn_v_proj_bias2) + gv724: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape649: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc452, gv724, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc452) + gv725: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc453: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv725, R.dtype("float16")) + cls.concatenate1(reshape647, reshape648, reshape649, alloc453) + R.vm.kill_object(reshape647) + R.vm.kill_object(reshape648) + R.vm.kill_object(reshape649) + gv726: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape650: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc453, gv726, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc453) + gv727: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc454: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv727, R.dtype("float16")) + _452: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), reshape650, alloc454) + R.vm.kill_object(reshape650) + gv728: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape651: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc454, gv728, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc454) + gv729: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape652: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape651, gv729, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape651) + model_decoder_layers_26_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1118] + model_decoder_layers_26_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1119] + gv730: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc455: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv730, R.dtype("float16")) + _453: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_26_self_attn_out_proj_weight2, reshape652, model_decoder_layers_26_self_attn_out_proj_bias2, alloc455) + R.vm.kill_object(reshape652) + R.vm.kill_object(model_decoder_layers_26_self_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_26_self_attn_out_proj_bias2) + gv731: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc456: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv731, R.dtype("float16")) + cls.add5(alloc448, alloc455, alloc456) + R.vm.kill_object(alloc448) + R.vm.kill_object(alloc455) + model_decoder_layers_26_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1129] + model_decoder_layers_26_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1130] + gv732: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc457: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv732, R.dtype("float16")) + cls.layer_norm2(alloc456, model_decoder_layers_26_encoder_attn_layer_norm_weight2, model_decoder_layers_26_encoder_attn_layer_norm_bias2, alloc457) + R.vm.kill_object(model_decoder_layers_26_encoder_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_26_encoder_attn_layer_norm_bias2) + model_decoder_layers_26_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1125] + model_decoder_layers_26_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1126] + gv733: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc458: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv733, R.dtype("float16")) + _456: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_26_encoder_attn_q_proj_weight2, alloc457, model_decoder_layers_26_encoder_attn_q_proj_bias2, alloc458) + R.vm.kill_object(alloc457) + R.vm.kill_object(model_decoder_layers_26_encoder_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_26_encoder_attn_q_proj_bias2) + gv734: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape653: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc458, gv734, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc458) + gv735: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape654: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape653, gv735, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape653) + gv736: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc459: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv736, R.dtype("float16")) + _457: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), reshape654, alloc459) + R.vm.kill_object(reshape654) + gv737: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape655: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc459, gv737, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc459) + gv738: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape656: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape655, gv738, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape655) + model_decoder_layers_26_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1127] + model_decoder_layers_26_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1128] + gv739: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc460: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv739, R.dtype("float16")) + _458: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_26_encoder_attn_out_proj_weight2, reshape656, model_decoder_layers_26_encoder_attn_out_proj_bias2, alloc460) + R.vm.kill_object(reshape656) + R.vm.kill_object(model_decoder_layers_26_encoder_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_26_encoder_attn_out_proj_bias2) + gv740: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc461: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv740, R.dtype("float16")) + cls.add5(alloc456, alloc460, alloc461) + R.vm.kill_object(alloc456) + R.vm.kill_object(alloc460) + model_decoder_layers_26_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1135] + model_decoder_layers_26_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1136] + gv741: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc462: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv741, R.dtype("float16")) + cls.layer_norm2(alloc461, model_decoder_layers_26_final_layer_norm_weight2, model_decoder_layers_26_final_layer_norm_bias2, alloc462) + R.vm.kill_object(model_decoder_layers_26_final_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_26_final_layer_norm_bias2) + model_decoder_layers_26_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1131] + model_decoder_layers_26_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1132] + gv742: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc463: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv742, R.dtype("float16")) + _461: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_26_fc1_weight2, alloc462, model_decoder_layers_26_fc1_bias2, alloc463) + R.vm.kill_object(alloc462) + R.vm.kill_object(model_decoder_layers_26_fc1_weight2) + R.vm.kill_object(model_decoder_layers_26_fc1_bias2) + model_decoder_layers_26_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1133] + model_decoder_layers_26_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1134] + gv743: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc464: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv743, R.dtype("float16")) + _462: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_26_fc2_weight2, alloc463, model_decoder_layers_26_fc2_bias2, alloc464) + R.vm.kill_object(alloc463) + R.vm.kill_object(model_decoder_layers_26_fc2_weight2) + R.vm.kill_object(model_decoder_layers_26_fc2_bias2) + gv744: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc465: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv744, R.dtype("float16")) + cls.add5(alloc461, alloc464, alloc465) + R.vm.kill_object(alloc461) + R.vm.kill_object(alloc464) + model_decoder_layers_27_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1144] + model_decoder_layers_27_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1145] + gv745: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc466: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv745, R.dtype("float16")) + cls.layer_norm2(alloc465, model_decoder_layers_27_self_attn_layer_norm_weight2, model_decoder_layers_27_self_attn_layer_norm_bias2, alloc466) + R.vm.kill_object(model_decoder_layers_27_self_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_27_self_attn_layer_norm_bias2) + model_decoder_layers_27_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1140] + model_decoder_layers_27_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1141] + gv746: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc467: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv746, R.dtype("float16")) + _465: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_27_self_attn_q_proj_weight2, alloc466, model_decoder_layers_27_self_attn_q_proj_bias2, alloc467) + R.vm.kill_object(model_decoder_layers_27_self_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_27_self_attn_q_proj_bias2) + gv747: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape657: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc467, gv747, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc467) + model_decoder_layers_27_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1137] + gv748: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc468: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv748, R.dtype("float16")) + _466: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_27_self_attn_k_proj_weight2, alloc466, alloc468) + R.vm.kill_object(model_decoder_layers_27_self_attn_k_proj_weight2) + gv749: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape658: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc468, gv749, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc468) + model_decoder_layers_27_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1138] + model_decoder_layers_27_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1139] + gv750: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc469: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv750, R.dtype("float16")) + _467: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_27_self_attn_v_proj_weight2, alloc466, model_decoder_layers_27_self_attn_v_proj_bias2, alloc469) + R.vm.kill_object(alloc466) + R.vm.kill_object(model_decoder_layers_27_self_attn_v_proj_weight2) + R.vm.kill_object(model_decoder_layers_27_self_attn_v_proj_bias2) + gv751: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape659: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc469, gv751, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc469) + gv752: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc470: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv752, R.dtype("float16")) + cls.concatenate1(reshape657, reshape658, reshape659, alloc470) + R.vm.kill_object(reshape657) + R.vm.kill_object(reshape658) + R.vm.kill_object(reshape659) + gv753: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape660: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc470, gv753, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc470) + gv754: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc471: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv754, R.dtype("float16")) + _469: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), reshape660, alloc471) + R.vm.kill_object(reshape660) + gv755: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape661: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc471, gv755, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc471) + gv756: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape662: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape661, gv756, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape661) + model_decoder_layers_27_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1142] + model_decoder_layers_27_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1143] + gv757: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc472: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv757, R.dtype("float16")) + _470: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_27_self_attn_out_proj_weight2, reshape662, model_decoder_layers_27_self_attn_out_proj_bias2, alloc472) + R.vm.kill_object(reshape662) + R.vm.kill_object(model_decoder_layers_27_self_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_27_self_attn_out_proj_bias2) + gv758: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc473: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv758, R.dtype("float16")) + cls.add5(alloc465, alloc472, alloc473) + R.vm.kill_object(alloc465) + R.vm.kill_object(alloc472) + model_decoder_layers_27_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1153] + model_decoder_layers_27_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1154] + gv759: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc474: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv759, R.dtype("float16")) + cls.layer_norm2(alloc473, model_decoder_layers_27_encoder_attn_layer_norm_weight2, model_decoder_layers_27_encoder_attn_layer_norm_bias2, alloc474) + R.vm.kill_object(model_decoder_layers_27_encoder_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_27_encoder_attn_layer_norm_bias2) + model_decoder_layers_27_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1149] + model_decoder_layers_27_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1150] + gv760: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc475: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv760, R.dtype("float16")) + _473: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_27_encoder_attn_q_proj_weight2, alloc474, model_decoder_layers_27_encoder_attn_q_proj_bias2, alloc475) + R.vm.kill_object(alloc474) + R.vm.kill_object(model_decoder_layers_27_encoder_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_27_encoder_attn_q_proj_bias2) + gv761: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape663: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc475, gv761, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc475) + gv762: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape664: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape663, gv762, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape663) + gv763: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc476: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv763, R.dtype("float16")) + _474: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), reshape664, alloc476) + R.vm.kill_object(reshape664) + gv764: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape665: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc476, gv764, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc476) + gv765: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape666: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape665, gv765, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape665) + model_decoder_layers_27_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1151] + model_decoder_layers_27_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1152] + gv766: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc477: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv766, R.dtype("float16")) + _475: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_27_encoder_attn_out_proj_weight2, reshape666, model_decoder_layers_27_encoder_attn_out_proj_bias2, alloc477) + R.vm.kill_object(reshape666) + R.vm.kill_object(model_decoder_layers_27_encoder_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_27_encoder_attn_out_proj_bias2) + gv767: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc478: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv767, R.dtype("float16")) + cls.add5(alloc473, alloc477, alloc478) + R.vm.kill_object(alloc473) + R.vm.kill_object(alloc477) + model_decoder_layers_27_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1159] + model_decoder_layers_27_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1160] + gv768: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc479: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv768, R.dtype("float16")) + cls.layer_norm2(alloc478, model_decoder_layers_27_final_layer_norm_weight2, model_decoder_layers_27_final_layer_norm_bias2, alloc479) + R.vm.kill_object(model_decoder_layers_27_final_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_27_final_layer_norm_bias2) + model_decoder_layers_27_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1155] + model_decoder_layers_27_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1156] + gv769: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc480: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv769, R.dtype("float16")) + _478: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_27_fc1_weight2, alloc479, model_decoder_layers_27_fc1_bias2, alloc480) + R.vm.kill_object(alloc479) + R.vm.kill_object(model_decoder_layers_27_fc1_weight2) + R.vm.kill_object(model_decoder_layers_27_fc1_bias2) + model_decoder_layers_27_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1157] + model_decoder_layers_27_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1158] + gv770: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc481: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv770, R.dtype("float16")) + _479: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_27_fc2_weight2, alloc480, model_decoder_layers_27_fc2_bias2, alloc481) + R.vm.kill_object(alloc480) + R.vm.kill_object(model_decoder_layers_27_fc2_weight2) + R.vm.kill_object(model_decoder_layers_27_fc2_bias2) + gv771: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc482: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv771, R.dtype("float16")) + cls.add5(alloc478, alloc481, alloc482) + R.vm.kill_object(alloc478) + R.vm.kill_object(alloc481) + model_decoder_layers_28_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1168] + model_decoder_layers_28_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1169] + gv772: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc483: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv772, R.dtype("float16")) + cls.layer_norm2(alloc482, model_decoder_layers_28_self_attn_layer_norm_weight2, model_decoder_layers_28_self_attn_layer_norm_bias2, alloc483) + R.vm.kill_object(model_decoder_layers_28_self_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_28_self_attn_layer_norm_bias2) + model_decoder_layers_28_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1164] + model_decoder_layers_28_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1165] + gv773: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc484: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv773, R.dtype("float16")) + _482: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_28_self_attn_q_proj_weight2, alloc483, model_decoder_layers_28_self_attn_q_proj_bias2, alloc484) + R.vm.kill_object(model_decoder_layers_28_self_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_28_self_attn_q_proj_bias2) + gv774: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape667: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc484, gv774, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc484) + model_decoder_layers_28_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1161] + gv775: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc485: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv775, R.dtype("float16")) + _483: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_28_self_attn_k_proj_weight2, alloc483, alloc485) + R.vm.kill_object(model_decoder_layers_28_self_attn_k_proj_weight2) + gv776: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape668: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc485, gv776, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc485) + model_decoder_layers_28_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1162] + model_decoder_layers_28_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1163] + gv777: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc486: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv777, R.dtype("float16")) + _484: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_28_self_attn_v_proj_weight2, alloc483, model_decoder_layers_28_self_attn_v_proj_bias2, alloc486) + R.vm.kill_object(alloc483) + R.vm.kill_object(model_decoder_layers_28_self_attn_v_proj_weight2) + R.vm.kill_object(model_decoder_layers_28_self_attn_v_proj_bias2) + gv778: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape669: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc486, gv778, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc486) + gv779: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc487: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv779, R.dtype("float16")) + cls.concatenate1(reshape667, reshape668, reshape669, alloc487) + R.vm.kill_object(reshape667) + R.vm.kill_object(reshape668) + R.vm.kill_object(reshape669) + gv780: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape670: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc487, gv780, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc487) + gv781: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc488: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv781, R.dtype("float16")) + _486: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), reshape670, alloc488) + R.vm.kill_object(reshape670) + gv782: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape671: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc488, gv782, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc488) + gv783: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape672: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape671, gv783, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape671) + model_decoder_layers_28_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1166] + model_decoder_layers_28_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1167] + gv784: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc489: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv784, R.dtype("float16")) + _487: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_28_self_attn_out_proj_weight2, reshape672, model_decoder_layers_28_self_attn_out_proj_bias2, alloc489) + R.vm.kill_object(reshape672) + R.vm.kill_object(model_decoder_layers_28_self_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_28_self_attn_out_proj_bias2) + gv785: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc490: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv785, R.dtype("float16")) + cls.add5(alloc482, alloc489, alloc490) + R.vm.kill_object(alloc482) + R.vm.kill_object(alloc489) + model_decoder_layers_28_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1177] + model_decoder_layers_28_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1178] + gv786: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc491: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv786, R.dtype("float16")) + cls.layer_norm2(alloc490, model_decoder_layers_28_encoder_attn_layer_norm_weight2, model_decoder_layers_28_encoder_attn_layer_norm_bias2, alloc491) + R.vm.kill_object(model_decoder_layers_28_encoder_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_28_encoder_attn_layer_norm_bias2) + model_decoder_layers_28_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1173] + model_decoder_layers_28_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1174] + gv787: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc492: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv787, R.dtype("float16")) + _490: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_28_encoder_attn_q_proj_weight2, alloc491, model_decoder_layers_28_encoder_attn_q_proj_bias2, alloc492) + R.vm.kill_object(alloc491) + R.vm.kill_object(model_decoder_layers_28_encoder_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_28_encoder_attn_q_proj_bias2) + gv788: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape673: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc492, gv788, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc492) + gv789: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape674: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape673, gv789, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape673) + gv790: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc493: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv790, R.dtype("float16")) + _491: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), reshape674, alloc493) + R.vm.kill_object(reshape674) + gv791: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape675: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc493, gv791, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc493) + gv792: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape676: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape675, gv792, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape675) + model_decoder_layers_28_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1175] + model_decoder_layers_28_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1176] + gv793: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc494: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv793, R.dtype("float16")) + _492: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_28_encoder_attn_out_proj_weight2, reshape676, model_decoder_layers_28_encoder_attn_out_proj_bias2, alloc494) + R.vm.kill_object(reshape676) + R.vm.kill_object(model_decoder_layers_28_encoder_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_28_encoder_attn_out_proj_bias2) + gv794: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc495: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv794, R.dtype("float16")) + cls.add5(alloc490, alloc494, alloc495) + R.vm.kill_object(alloc490) + R.vm.kill_object(alloc494) + model_decoder_layers_28_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1183] + model_decoder_layers_28_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1184] + gv795: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc496: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv795, R.dtype("float16")) + cls.layer_norm2(alloc495, model_decoder_layers_28_final_layer_norm_weight2, model_decoder_layers_28_final_layer_norm_bias2, alloc496) + R.vm.kill_object(model_decoder_layers_28_final_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_28_final_layer_norm_bias2) + model_decoder_layers_28_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1179] + model_decoder_layers_28_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1180] + gv796: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc497: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv796, R.dtype("float16")) + _495: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_28_fc1_weight2, alloc496, model_decoder_layers_28_fc1_bias2, alloc497) + R.vm.kill_object(alloc496) + R.vm.kill_object(model_decoder_layers_28_fc1_weight2) + R.vm.kill_object(model_decoder_layers_28_fc1_bias2) + model_decoder_layers_28_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1181] + model_decoder_layers_28_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1182] + gv797: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc498: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv797, R.dtype("float16")) + _496: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_28_fc2_weight2, alloc497, model_decoder_layers_28_fc2_bias2, alloc498) + R.vm.kill_object(alloc497) + R.vm.kill_object(model_decoder_layers_28_fc2_weight2) + R.vm.kill_object(model_decoder_layers_28_fc2_bias2) + gv798: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc499: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv798, R.dtype("float16")) + cls.add5(alloc495, alloc498, alloc499) + R.vm.kill_object(alloc495) + R.vm.kill_object(alloc498) + model_decoder_layers_29_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1192] + model_decoder_layers_29_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1193] + gv799: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc500: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv799, R.dtype("float16")) + cls.layer_norm2(alloc499, model_decoder_layers_29_self_attn_layer_norm_weight2, model_decoder_layers_29_self_attn_layer_norm_bias2, alloc500) + R.vm.kill_object(model_decoder_layers_29_self_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_29_self_attn_layer_norm_bias2) + model_decoder_layers_29_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1188] + model_decoder_layers_29_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1189] + gv800: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc501: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv800, R.dtype("float16")) + _499: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_29_self_attn_q_proj_weight2, alloc500, model_decoder_layers_29_self_attn_q_proj_bias2, alloc501) + R.vm.kill_object(model_decoder_layers_29_self_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_29_self_attn_q_proj_bias2) + gv801: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape677: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc501, gv801, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc501) + model_decoder_layers_29_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1185] + gv802: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc502: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv802, R.dtype("float16")) + _500: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_29_self_attn_k_proj_weight2, alloc500, alloc502) + R.vm.kill_object(model_decoder_layers_29_self_attn_k_proj_weight2) + gv803: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape678: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc502, gv803, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc502) + model_decoder_layers_29_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1186] + model_decoder_layers_29_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1187] + gv804: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc503: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv804, R.dtype("float16")) + _501: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_29_self_attn_v_proj_weight2, alloc500, model_decoder_layers_29_self_attn_v_proj_bias2, alloc503) + R.vm.kill_object(alloc500) + R.vm.kill_object(model_decoder_layers_29_self_attn_v_proj_weight2) + R.vm.kill_object(model_decoder_layers_29_self_attn_v_proj_bias2) + gv805: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape679: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc503, gv805, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc503) + gv806: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc504: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv806, R.dtype("float16")) + cls.concatenate1(reshape677, reshape678, reshape679, alloc504) + R.vm.kill_object(reshape677) + R.vm.kill_object(reshape678) + R.vm.kill_object(reshape679) + gv807: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape680: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc504, gv807, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc504) + gv808: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc505: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv808, R.dtype("float16")) + _503: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), reshape680, alloc505) + R.vm.kill_object(reshape680) + gv809: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape681: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc505, gv809, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc505) + gv810: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape682: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape681, gv810, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape681) + model_decoder_layers_29_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1190] + model_decoder_layers_29_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1191] + gv811: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc506: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv811, R.dtype("float16")) + _504: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_29_self_attn_out_proj_weight2, reshape682, model_decoder_layers_29_self_attn_out_proj_bias2, alloc506) + R.vm.kill_object(reshape682) + R.vm.kill_object(model_decoder_layers_29_self_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_29_self_attn_out_proj_bias2) + gv812: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc507: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv812, R.dtype("float16")) + cls.add5(alloc499, alloc506, alloc507) + R.vm.kill_object(alloc499) + R.vm.kill_object(alloc506) + model_decoder_layers_29_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1201] + model_decoder_layers_29_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1202] + gv813: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc508: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv813, R.dtype("float16")) + cls.layer_norm2(alloc507, model_decoder_layers_29_encoder_attn_layer_norm_weight2, model_decoder_layers_29_encoder_attn_layer_norm_bias2, alloc508) + R.vm.kill_object(model_decoder_layers_29_encoder_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_29_encoder_attn_layer_norm_bias2) + model_decoder_layers_29_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1197] + model_decoder_layers_29_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1198] + gv814: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc509: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv814, R.dtype("float16")) + _507: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_29_encoder_attn_q_proj_weight2, alloc508, model_decoder_layers_29_encoder_attn_q_proj_bias2, alloc509) + R.vm.kill_object(alloc508) + R.vm.kill_object(model_decoder_layers_29_encoder_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_29_encoder_attn_q_proj_bias2) + gv815: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape683: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc509, gv815, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc509) + gv816: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape684: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape683, gv816, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape683) + gv817: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc510: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv817, R.dtype("float16")) + _508: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), reshape684, alloc510) + R.vm.kill_object(reshape684) + gv818: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape685: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc510, gv818, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc510) + gv819: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape686: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape685, gv819, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape685) + model_decoder_layers_29_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1199] + model_decoder_layers_29_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1200] + gv820: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc511: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv820, R.dtype("float16")) + _509: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_29_encoder_attn_out_proj_weight2, reshape686, model_decoder_layers_29_encoder_attn_out_proj_bias2, alloc511) + R.vm.kill_object(reshape686) + R.vm.kill_object(model_decoder_layers_29_encoder_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_29_encoder_attn_out_proj_bias2) + gv821: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc512: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv821, R.dtype("float16")) + cls.add5(alloc507, alloc511, alloc512) + R.vm.kill_object(alloc507) + R.vm.kill_object(alloc511) + model_decoder_layers_29_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1207] + model_decoder_layers_29_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1208] + gv822: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc513: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv822, R.dtype("float16")) + cls.layer_norm2(alloc512, model_decoder_layers_29_final_layer_norm_weight2, model_decoder_layers_29_final_layer_norm_bias2, alloc513) + R.vm.kill_object(model_decoder_layers_29_final_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_29_final_layer_norm_bias2) + model_decoder_layers_29_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1203] + model_decoder_layers_29_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1204] + gv823: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc514: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv823, R.dtype("float16")) + _512: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_29_fc1_weight2, alloc513, model_decoder_layers_29_fc1_bias2, alloc514) + R.vm.kill_object(alloc513) + R.vm.kill_object(model_decoder_layers_29_fc1_weight2) + R.vm.kill_object(model_decoder_layers_29_fc1_bias2) + model_decoder_layers_29_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1205] + model_decoder_layers_29_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1206] + gv824: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc515: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv824, R.dtype("float16")) + _513: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_29_fc2_weight2, alloc514, model_decoder_layers_29_fc2_bias2, alloc515) + R.vm.kill_object(alloc514) + R.vm.kill_object(model_decoder_layers_29_fc2_weight2) + R.vm.kill_object(model_decoder_layers_29_fc2_bias2) + gv825: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc516: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv825, R.dtype("float16")) + cls.add5(alloc512, alloc515, alloc516) + R.vm.kill_object(alloc512) + R.vm.kill_object(alloc515) + model_decoder_layers_30_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1216] + model_decoder_layers_30_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1217] + gv826: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc517: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv826, R.dtype("float16")) + cls.layer_norm2(alloc516, model_decoder_layers_30_self_attn_layer_norm_weight2, model_decoder_layers_30_self_attn_layer_norm_bias2, alloc517) + R.vm.kill_object(model_decoder_layers_30_self_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_30_self_attn_layer_norm_bias2) + model_decoder_layers_30_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1212] + model_decoder_layers_30_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1213] + gv827: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc518: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv827, R.dtype("float16")) + _516: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_30_self_attn_q_proj_weight2, alloc517, model_decoder_layers_30_self_attn_q_proj_bias2, alloc518) + R.vm.kill_object(model_decoder_layers_30_self_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_30_self_attn_q_proj_bias2) + gv828: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape687: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc518, gv828, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc518) + model_decoder_layers_30_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1209] + gv829: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc519: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv829, R.dtype("float16")) + _517: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_30_self_attn_k_proj_weight2, alloc517, alloc519) + R.vm.kill_object(model_decoder_layers_30_self_attn_k_proj_weight2) + gv830: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape688: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc519, gv830, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc519) + model_decoder_layers_30_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1210] + model_decoder_layers_30_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1211] + gv831: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc520: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv831, R.dtype("float16")) + _518: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_30_self_attn_v_proj_weight2, alloc517, model_decoder_layers_30_self_attn_v_proj_bias2, alloc520) + R.vm.kill_object(alloc517) + R.vm.kill_object(model_decoder_layers_30_self_attn_v_proj_weight2) + R.vm.kill_object(model_decoder_layers_30_self_attn_v_proj_bias2) + gv832: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape689: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc520, gv832, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc520) + gv833: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc521: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv833, R.dtype("float16")) + cls.concatenate1(reshape687, reshape688, reshape689, alloc521) + R.vm.kill_object(reshape687) + R.vm.kill_object(reshape688) + R.vm.kill_object(reshape689) + gv834: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape690: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc521, gv834, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc521) + gv835: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc522: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv835, R.dtype("float16")) + _520: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), reshape690, alloc522) + R.vm.kill_object(reshape690) + gv836: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape691: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc522, gv836, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc522) + gv837: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape692: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape691, gv837, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape691) + model_decoder_layers_30_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1214] + model_decoder_layers_30_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1215] + gv838: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc523: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv838, R.dtype("float16")) + _521: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_30_self_attn_out_proj_weight2, reshape692, model_decoder_layers_30_self_attn_out_proj_bias2, alloc523) + R.vm.kill_object(reshape692) + R.vm.kill_object(model_decoder_layers_30_self_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_30_self_attn_out_proj_bias2) + gv839: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc524: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv839, R.dtype("float16")) + cls.add5(alloc516, alloc523, alloc524) + R.vm.kill_object(alloc516) + R.vm.kill_object(alloc523) + model_decoder_layers_30_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1225] + model_decoder_layers_30_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1226] + gv840: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc525: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv840, R.dtype("float16")) + cls.layer_norm2(alloc524, model_decoder_layers_30_encoder_attn_layer_norm_weight2, model_decoder_layers_30_encoder_attn_layer_norm_bias2, alloc525) + R.vm.kill_object(model_decoder_layers_30_encoder_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_30_encoder_attn_layer_norm_bias2) + model_decoder_layers_30_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1221] + model_decoder_layers_30_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1222] + gv841: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc526: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv841, R.dtype("float16")) + _524: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_30_encoder_attn_q_proj_weight2, alloc525, model_decoder_layers_30_encoder_attn_q_proj_bias2, alloc526) + R.vm.kill_object(alloc525) + R.vm.kill_object(model_decoder_layers_30_encoder_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_30_encoder_attn_q_proj_bias2) + gv842: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape693: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc526, gv842, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc526) + gv843: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape694: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape693, gv843, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape693) + gv844: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc527: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv844, R.dtype("float16")) + _525: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), reshape694, alloc527) + R.vm.kill_object(reshape694) + gv845: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape695: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc527, gv845, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc527) + gv846: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape696: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape695, gv846, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape695) + model_decoder_layers_30_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1223] + model_decoder_layers_30_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1224] + gv847: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc528: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv847, R.dtype("float16")) + _526: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_30_encoder_attn_out_proj_weight2, reshape696, model_decoder_layers_30_encoder_attn_out_proj_bias2, alloc528) + R.vm.kill_object(reshape696) + R.vm.kill_object(model_decoder_layers_30_encoder_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_30_encoder_attn_out_proj_bias2) + gv848: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc529: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv848, R.dtype("float16")) + cls.add5(alloc524, alloc528, alloc529) + R.vm.kill_object(alloc524) + R.vm.kill_object(alloc528) + model_decoder_layers_30_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1231] + model_decoder_layers_30_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1232] + gv849: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc530: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv849, R.dtype("float16")) + cls.layer_norm2(alloc529, model_decoder_layers_30_final_layer_norm_weight2, model_decoder_layers_30_final_layer_norm_bias2, alloc530) + R.vm.kill_object(model_decoder_layers_30_final_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_30_final_layer_norm_bias2) + model_decoder_layers_30_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1227] + model_decoder_layers_30_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1228] + gv850: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc531: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv850, R.dtype("float16")) + _529: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_30_fc1_weight2, alloc530, model_decoder_layers_30_fc1_bias2, alloc531) + R.vm.kill_object(alloc530) + R.vm.kill_object(model_decoder_layers_30_fc1_weight2) + R.vm.kill_object(model_decoder_layers_30_fc1_bias2) + model_decoder_layers_30_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1229] + model_decoder_layers_30_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1230] + gv851: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc532: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv851, R.dtype("float16")) + _530: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_30_fc2_weight2, alloc531, model_decoder_layers_30_fc2_bias2, alloc532) + R.vm.kill_object(alloc531) + R.vm.kill_object(model_decoder_layers_30_fc2_weight2) + R.vm.kill_object(model_decoder_layers_30_fc2_bias2) + gv852: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc533: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv852, R.dtype("float16")) + cls.add5(alloc529, alloc532, alloc533) + R.vm.kill_object(alloc529) + R.vm.kill_object(alloc532) + model_decoder_layers_31_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1240] + model_decoder_layers_31_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1241] + gv853: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc534: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv853, R.dtype("float16")) + cls.layer_norm2(alloc533, model_decoder_layers_31_self_attn_layer_norm_weight2, model_decoder_layers_31_self_attn_layer_norm_bias2, alloc534) + R.vm.kill_object(model_decoder_layers_31_self_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_31_self_attn_layer_norm_bias2) + model_decoder_layers_31_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1236] + model_decoder_layers_31_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1237] + gv854: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc535: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv854, R.dtype("float16")) + _533: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_31_self_attn_q_proj_weight2, alloc534, model_decoder_layers_31_self_attn_q_proj_bias2, alloc535) + R.vm.kill_object(model_decoder_layers_31_self_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_31_self_attn_q_proj_bias2) + gv855: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape697: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc535, gv855, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc535) + model_decoder_layers_31_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1233] + gv856: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc536: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv856, R.dtype("float16")) + _534: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_31_self_attn_k_proj_weight2, alloc534, alloc536) + R.vm.kill_object(model_decoder_layers_31_self_attn_k_proj_weight2) + gv857: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape698: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc536, gv857, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc536) + model_decoder_layers_31_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1234] + model_decoder_layers_31_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1235] + gv858: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc537: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv858, R.dtype("float16")) + _535: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_31_self_attn_v_proj_weight2, alloc534, model_decoder_layers_31_self_attn_v_proj_bias2, alloc537) + R.vm.kill_object(alloc534) + R.vm.kill_object(model_decoder_layers_31_self_attn_v_proj_weight2) + R.vm.kill_object(model_decoder_layers_31_self_attn_v_proj_bias2) + gv859: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape699: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc537, gv859, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc537) + gv860: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc538: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv860, R.dtype("float16")) + cls.concatenate1(reshape697, reshape698, reshape699, alloc538) + R.vm.kill_object(reshape697) + R.vm.kill_object(reshape698) + R.vm.kill_object(reshape699) + gv861: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape700: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc538, gv861, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc538) + gv862: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc539: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv862, R.dtype("float16")) + _537: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), reshape700, alloc539) + R.vm.kill_object(reshape700) + gv863: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape701: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc539, gv863, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc539) + gv864: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape702: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape701, gv864, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape701) + model_decoder_layers_31_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1238] + model_decoder_layers_31_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1239] + gv865: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc540: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv865, R.dtype("float16")) + _538: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_31_self_attn_out_proj_weight2, reshape702, model_decoder_layers_31_self_attn_out_proj_bias2, alloc540) + R.vm.kill_object(reshape702) + R.vm.kill_object(model_decoder_layers_31_self_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_31_self_attn_out_proj_bias2) + gv866: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc541: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv866, R.dtype("float16")) + cls.add5(alloc533, alloc540, alloc541) + R.vm.kill_object(alloc533) + R.vm.kill_object(alloc540) + model_decoder_layers_31_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1249] + model_decoder_layers_31_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1250] + gv867: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc542: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv867, R.dtype("float16")) + cls.layer_norm2(alloc541, model_decoder_layers_31_encoder_attn_layer_norm_weight2, model_decoder_layers_31_encoder_attn_layer_norm_bias2, alloc542) + R.vm.kill_object(model_decoder_layers_31_encoder_attn_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_31_encoder_attn_layer_norm_bias2) + model_decoder_layers_31_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1245] + model_decoder_layers_31_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1246] + gv868: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc543: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv868, R.dtype("float16")) + _541: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_31_encoder_attn_q_proj_weight2, alloc542, model_decoder_layers_31_encoder_attn_q_proj_bias2, alloc543) + R.vm.kill_object(alloc542) + R.vm.kill_object(model_decoder_layers_31_encoder_attn_q_proj_weight2) + R.vm.kill_object(model_decoder_layers_31_encoder_attn_q_proj_bias2) + gv869: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape703: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc543, gv869, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc543) + gv870: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape704: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape703, gv870, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape703) + gv871: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc544: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv871, R.dtype("float16")) + _542: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), reshape704, alloc544) + R.vm.kill_object(reshape704) + gv872: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape705: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc544, gv872, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc544) + gv873: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape706: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape705, gv873, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape705) + model_decoder_layers_31_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1247] + model_decoder_layers_31_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1248] + gv874: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc545: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv874, R.dtype("float16")) + _543: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_31_encoder_attn_out_proj_weight2, reshape706, model_decoder_layers_31_encoder_attn_out_proj_bias2, alloc545) + R.vm.kill_object(reshape706) + R.vm.kill_object(model_decoder_layers_31_encoder_attn_out_proj_weight2) + R.vm.kill_object(model_decoder_layers_31_encoder_attn_out_proj_bias2) + gv875: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc546: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage6, R.prim_value(0), gv875, R.dtype("float16")) + R.vm.kill_object(storage6) + cls.add5(alloc541, alloc545, alloc546) + R.vm.kill_object(alloc541) + R.vm.kill_object(alloc545) + model_decoder_layers_31_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1255] + model_decoder_layers_31_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1256] + gv876: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc547: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv876, R.dtype("float16")) + cls.layer_norm2(alloc546, model_decoder_layers_31_final_layer_norm_weight2, model_decoder_layers_31_final_layer_norm_bias2, alloc547) + R.vm.kill_object(model_decoder_layers_31_final_layer_norm_weight2) + R.vm.kill_object(model_decoder_layers_31_final_layer_norm_bias2) + model_decoder_layers_31_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1251] + model_decoder_layers_31_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1252] + gv877: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc548: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage4, R.prim_value(0), gv877, R.dtype("float16")) + R.vm.kill_object(storage4) + _546: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_31_fc1_weight2, alloc547, model_decoder_layers_31_fc1_bias2, alloc548) + R.vm.kill_object(alloc547) + R.vm.kill_object(model_decoder_layers_31_fc1_weight2) + R.vm.kill_object(model_decoder_layers_31_fc1_bias2) + model_decoder_layers_31_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1253] + model_decoder_layers_31_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1254] + gv878: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc549: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage5, R.prim_value(0), gv878, R.dtype("float16")) + R.vm.kill_object(storage5) + _547: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_31_fc2_weight2, alloc548, model_decoder_layers_31_fc2_bias2, alloc549) + R.vm.kill_object(alloc548) + R.vm.kill_object(model_decoder_layers_31_fc2_weight2) + R.vm.kill_object(model_decoder_layers_31_fc2_bias2) + gv879: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc550: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage7, R.prim_value(0), gv879, R.dtype("float16")) + R.vm.kill_object(storage7) + cls.add5(alloc546, alloc549, alloc550) + R.vm.kill_object(alloc546) + R.vm.kill_object(alloc549) + model_decoder_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1257] + model_decoder_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1258] + gv880: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc551: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage8, R.prim_value(0), gv880, R.dtype("float16")) + R.vm.kill_object(storage8) + cls.layer_norm2(alloc550, model_decoder_layer_norm_weight2, model_decoder_layer_norm_bias2, alloc551) + R.vm.kill_object(alloc550) + R.vm.kill_object(model_decoder_layer_norm_weight2) + R.vm.kill_object(model_decoder_layer_norm_bias2) + storage9: R.Object = R.vm.alloc_storage(R.shape([20480]), R.prim_value(0), R.dtype("uint8"), R.str("global")) + gv881: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc552: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage9, R.prim_value(0), gv881, R.dtype("float16")) + R.vm.kill_object(storage9) + cls.take2(alloc551, logit_positions, alloc552) + R.vm.kill_object(alloc551) + storage10: R.Object = R.vm.alloc_storage(R.shape([1659712]), R.prim_value(0), R.dtype("uint8"), R.str("global")) + gv882: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(51866), sinfo_args=(R.Shape(ndim=3),)) + alloc553: R.Tensor(dtype="float32", ndim=3) = R.vm.alloc_tensor(storage10, R.prim_value(0), gv882, R.dtype("float32")) + R.vm.kill_object(storage10) + _551: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul5_cublas", model_decoder_embed_tokens_weight2, alloc552, alloc553) + R.vm.kill_object(model_decoder_embed_tokens_weight2) + R.vm.kill_object(alloc552) + R.call_packed("vm.builtin.match_shape", alloc553, shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(51866), R.str("ErrorContext(fn=batch_prefill, loc=return, annotation=R.Tensor((1, batch_size, 51866), dtype=\"float32\")) "), sinfo_args=(R.Tuple,)) + return alloc553 + + @R.function + def create_tir_paged_kv_cache(max_batch_size_: R.Shape(["max_batch_size"]), max_total_seq_len_: R.Shape(["max_total_seq_len"]), prefill_chunk_size_: R.Shape(["prefill_chunk_size"]), page_size_: R.Shape(["page_size"]), support_sliding_window_: R.Shape(["support_sliding_window"])) -> R.Object: + max_batch_size = T.int64() + max_total_seq_len = T.int64() + prefill_chunk_size = T.int64() + page_size = T.int64() + support_sliding_window = T.int64() + R.func_attr({"relax.force_pure": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "seq_len": 15000, "total_seq_len": 1500}}) + cls = Module + shape_heap: R.Tensor(dtype="int64", ndim=1) = R.call_builtin_with_ctx("vm.builtin.alloc_shape_heap", (R.prim_value(5),), sinfo_args=(R.Tensor(dtype="int64", ndim=1),)) + R.call_packed("vm.builtin.check_shape_info", max_batch_size_, R.prim_value(1), R.str("ErrorContext(fn=create_tir_paged_kv_cache, loc=param[0], param=max_batch_size_, annotation=R.Shape([max_batch_size])) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.check_shape_info", max_total_seq_len_, R.prim_value(1), R.str("ErrorContext(fn=create_tir_paged_kv_cache, loc=param[1], param=max_total_seq_len_, annotation=R.Shape([max_total_seq_len])) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.check_shape_info", prefill_chunk_size_, R.prim_value(1), R.str("ErrorContext(fn=create_tir_paged_kv_cache, loc=param[2], param=prefill_chunk_size_, annotation=R.Shape([prefill_chunk_size])) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.check_shape_info", page_size_, R.prim_value(1), R.str("ErrorContext(fn=create_tir_paged_kv_cache, loc=param[3], param=page_size_, annotation=R.Shape([page_size])) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.check_shape_info", support_sliding_window_, R.prim_value(1), R.str("ErrorContext(fn=create_tir_paged_kv_cache, loc=param[4], param=support_sliding_window_, annotation=R.Shape([support_sliding_window])) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.match_shape", max_batch_size_, shape_heap, R.prim_value(1), R.prim_value(1), R.prim_value(0), R.str("ErrorContext(fn=create_tir_paged_kv_cache, loc=param[0], param=max_batch_size_, annotation=R.Shape([max_batch_size])) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.match_shape", max_total_seq_len_, shape_heap, R.prim_value(1), R.prim_value(1), R.prim_value(1), R.str("ErrorContext(fn=create_tir_paged_kv_cache, loc=param[1], param=max_total_seq_len_, annotation=R.Shape([max_total_seq_len])) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.match_shape", prefill_chunk_size_, shape_heap, R.prim_value(1), R.prim_value(1), R.prim_value(2), R.str("ErrorContext(fn=create_tir_paged_kv_cache, loc=param[2], param=prefill_chunk_size_, annotation=R.Shape([prefill_chunk_size])) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.match_shape", page_size_, shape_heap, R.prim_value(1), R.prim_value(1), R.prim_value(3), R.str("ErrorContext(fn=create_tir_paged_kv_cache, loc=param[3], param=page_size_, annotation=R.Shape([page_size])) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.match_shape", support_sliding_window_, shape_heap, R.prim_value(1), R.prim_value(1), R.prim_value(4), R.str("ErrorContext(fn=create_tir_paged_kv_cache, loc=param[4], param=support_sliding_window_, annotation=R.Shape([support_sliding_window])) "), sinfo_args=(R.Tuple,)) + gv2559: R.Shape(ndim=5) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(5), R.prim_value(1), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(1), R.prim_value(2), R.prim_value(1), R.prim_value(3), R.prim_value(1), R.prim_value(4), sinfo_args=(R.Shape(ndim=5),)) + paged_kv_cache: R.Object = R.call_packed("vm.builtin.paged_attention_kv_cache_create_reduced", gv2559, R.prim_value(32), R.prim_value(20), R.prim_value(20), R.prim_value(64), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.const(0, "float16"), cls.tir_kv_cache_transpose_append, cls.batch_prefill_paged_kv, cls.batch_decode_paged_kv, cls.batch_prefill_paged_kv_sliding_window, cls.batch_decode_paged_kv_sliding_window, cls.batch_prefill_ragged_kv, cls.merge_state_inplace, cls.fused_rope, cls.copy_single_page, cls.tir_kv_cache_debug_get_kv, cls.compact_kv_copy, cls.batch_tree_attn, sinfo_args=(R.Object,)) + return paged_kv_cache + + @R.function + def decode(input_ids: R.Tensor((1, 1), dtype="int32"), paged_kv_cache: R.Object, packed_params: R.Tuple(R.Tensor((1280, 128, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1500, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), 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dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"))) -> R.Tensor((1, 1, 51866), dtype="float32"): + R.func_attr({"num_input": 2, "relax.force_pure": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "seq_len": 15000, "total_seq_len": 1500}}) + cls = Module + shape_heap: R.Tensor(dtype="int64", ndim=1) = R.call_builtin_with_ctx("vm.builtin.alloc_shape_heap", (R.prim_value(1),), sinfo_args=(R.Tensor(dtype="int64", ndim=1),)) + R.call_packed("vm.builtin.check_tensor_info", input_ids, R.prim_value(2), R.dtype("int32"), R.str("ErrorContext(fn=decode, loc=param[0], param=input_ids, annotation=R.Tensor((1, 1), dtype=\"int32\")) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.check_tuple_info", packed_params, R.prim_value(1259), R.str("ErrorContext(fn=decode, loc=param[2], param=packed_params, annotation=R.Tuple(R.Tensor((1280, 128, 3), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280, 3), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1500, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), 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dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((5120, 1280), dtype=\"float16\"), R.Tensor((5120,), dtype=\"float16\"), R.Tensor((1280, 5120), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((5120, 1280), dtype=\"float16\"), R.Tensor((5120,), dtype=\"float16\"), R.Tensor((1280, 5120), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((5120, 1280), dtype=\"float16\"), R.Tensor((5120,), dtype=\"float16\"), R.Tensor((1280, 5120), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((5120, 1280), dtype=\"float16\"), R.Tensor((5120,), dtype=\"float16\"), R.Tensor((1280, 5120), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((5120, 1280), dtype=\"float16\"), R.Tensor((5120,), dtype=\"float16\"), R.Tensor((1280, 5120), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((5120, 1280), dtype=\"float16\"), R.Tensor((5120,), dtype=\"float16\"), R.Tensor((1280, 5120), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((5120, 1280), dtype=\"float16\"), R.Tensor((5120,), dtype=\"float16\"), R.Tensor((1280, 5120), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((5120, 1280), dtype=\"float16\"), R.Tensor((5120,), dtype=\"float16\"), R.Tensor((1280, 5120), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"))) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.match_shape", input_ids, shape_heap, R.prim_value(2), R.prim_value(0), R.prim_value(1), R.prim_value(0), R.prim_value(1), R.str("ErrorContext(fn=decode, loc=param[0], param=input_ids, annotation=R.Tensor((1, 1), dtype=\"int32\")) "), sinfo_args=(R.Tuple,)) + model_decoder_embed_tokens_weight5: R.Tensor((51866, 1280), dtype="float16") = packed_params[487] + reshape1353: R.Tensor((1,), dtype="int32") = R.call_packed("vm.builtin.reshape", input_ids, R.shape([1]), sinfo_args=(R.Tensor((1,), dtype="int32"),)) + model_decoder_embed_tokens_weight5_1: R.Tensor((51866, 1280), dtype="float16") = packed_params[487] + storage19: R.Object = R.vm.alloc_storage(R.shape([10240]), R.prim_value(0), R.dtype("uint8"), R.str("global")) + alloc1167: R.Tensor((1, 1280), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1280]), R.dtype("float16")) + cls.take3(model_decoder_embed_tokens_weight5_1, reshape1353, alloc1167) + R.vm.kill_object(reshape1353) + R.vm.kill_object(model_decoder_embed_tokens_weight5_1) + lv264: R.Tensor((1,), dtype="int32") = R.call_packed("vm.builtin.attention_kv_cache_get_query_positions", paged_kv_cache, sinfo_args=(R.Tensor((1,), dtype="int32"),)) + model_decoder_embed_positions_weight5: R.Tensor((448, 1280), dtype="float16") = packed_params[488] + storage20: R.Object = R.vm.alloc_storage(R.shape([7680]), R.prim_value(0), R.dtype("uint8"), R.str("global")) + alloc1168: R.Tensor((1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1280]), R.dtype("float16")) + cls.take4(model_decoder_embed_positions_weight5, lv264, alloc1168) + R.vm.kill_object(lv264) + R.vm.kill_object(model_decoder_embed_positions_weight5) + storage21: R.Object = R.vm.alloc_storage(R.shape([2560]), R.prim_value(0), R.dtype("uint8"), R.str("global")) + alloc1169: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_reshape20_reshape20_add6(alloc1167, alloc1168, alloc1169) + R.vm.kill_object(alloc1167) + R.vm.kill_object(alloc1168) + model_decoder_layers_0_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[496] + model_decoder_layers_0_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[497] + alloc1170: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1169, model_decoder_layers_0_self_attn_layer_norm_weight5, model_decoder_layers_0_self_attn_layer_norm_bias5, alloc1170) + R.vm.kill_object(model_decoder_layers_0_self_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_0_self_attn_layer_norm_bias5) + model_decoder_layers_0_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[492] + model_decoder_layers_0_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[493] + alloc1171: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1170, model_decoder_layers_0_self_attn_q_proj_weight5, model_decoder_layers_0_self_attn_q_proj_bias5, alloc1171) + R.vm.kill_object(model_decoder_layers_0_self_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_0_self_attn_q_proj_bias5) + model_decoder_layers_0_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[489] + storage22: R.Object = R.vm.alloc_storage(R.shape([7680]), R.prim_value(0), R.dtype("uint8"), R.str("global")) + alloc1172: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.NT_matmul(alloc1170, model_decoder_layers_0_self_attn_k_proj_weight5, alloc1172) + R.vm.kill_object(model_decoder_layers_0_self_attn_k_proj_weight5) + model_decoder_layers_0_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[490] + model_decoder_layers_0_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[491] + storage23: R.Object = R.vm.alloc_storage(R.shape([7680]), R.prim_value(0), R.dtype("uint8"), R.str("global")) + alloc1173: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1170, model_decoder_layers_0_self_attn_v_proj_weight5, model_decoder_layers_0_self_attn_v_proj_bias5, alloc1173) + R.vm.kill_object(alloc1170) + R.vm.kill_object(model_decoder_layers_0_self_attn_v_proj_weight5) + R.vm.kill_object(model_decoder_layers_0_self_attn_v_proj_bias5) + alloc1174: R.Tensor((1, 60, 64), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 60, 64]), R.dtype("float16")) + cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22(alloc1171, alloc1172, alloc1173, alloc1174) + R.vm.kill_object(alloc1171) + R.vm.kill_object(alloc1172) + R.vm.kill_object(alloc1173) + alloc1175: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1173: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), alloc1174, alloc1175) + R.vm.kill_object(alloc1174) + lv44: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1175, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1175) + model_decoder_layers_0_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[494] + model_decoder_layers_0_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[495] + alloc1176: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7_add6(lv44, model_decoder_layers_0_self_attn_out_proj_weight5, model_decoder_layers_0_self_attn_out_proj_bias5, alloc1169, alloc1176) + R.vm.kill_object(alloc1169) + R.vm.kill_object(lv44) + R.vm.kill_object(model_decoder_layers_0_self_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_0_self_attn_out_proj_bias5) + model_decoder_layers_0_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[505] + model_decoder_layers_0_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[506] + alloc1177: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1176, model_decoder_layers_0_encoder_attn_layer_norm_weight5, model_decoder_layers_0_encoder_attn_layer_norm_bias5, alloc1177) + R.vm.kill_object(model_decoder_layers_0_encoder_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_0_encoder_attn_layer_norm_bias5) + model_decoder_layers_0_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[501] + model_decoder_layers_0_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[502] + alloc1178: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1177, model_decoder_layers_0_encoder_attn_q_proj_weight5, model_decoder_layers_0_encoder_attn_q_proj_bias5, alloc1178) + R.vm.kill_object(alloc1177) + R.vm.kill_object(model_decoder_layers_0_encoder_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_0_encoder_attn_q_proj_bias5) + lv47: R.Tensor((1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1178, R.shape([1, 20, 64]), sinfo_args=(R.Tensor((1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1178) + alloc1179: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1177: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), lv47, alloc1179) + R.vm.kill_object(lv47) + lv48: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1179, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1179) + model_decoder_layers_0_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[503] + model_decoder_layers_0_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[504] + alloc1180: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7_add6(lv48, model_decoder_layers_0_encoder_attn_out_proj_weight5, model_decoder_layers_0_encoder_attn_out_proj_bias5, alloc1176, alloc1180) + R.vm.kill_object(alloc1176) + R.vm.kill_object(lv48) + R.vm.kill_object(model_decoder_layers_0_encoder_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_0_encoder_attn_out_proj_bias5) + model_decoder_layers_0_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[511] + model_decoder_layers_0_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[512] + alloc1181: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1180, model_decoder_layers_0_final_layer_norm_weight5, model_decoder_layers_0_final_layer_norm_bias5, alloc1181) + R.vm.kill_object(model_decoder_layers_0_final_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_0_final_layer_norm_bias5) + model_decoder_layers_0_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[507] + model_decoder_layers_0_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[508] + alloc1182: R.Tensor((1, 1, 5120), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 5120]), R.dtype("float16")) + cls.fused_NT_matmul1_add8_gelu2(alloc1181, model_decoder_layers_0_fc1_weight5, model_decoder_layers_0_fc1_bias5, alloc1182) + R.vm.kill_object(alloc1181) + R.vm.kill_object(model_decoder_layers_0_fc1_weight5) + R.vm.kill_object(model_decoder_layers_0_fc1_bias5) + model_decoder_layers_0_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[509] + model_decoder_layers_0_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[510] + alloc1183: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul2_add7_add6(alloc1182, model_decoder_layers_0_fc2_weight5, model_decoder_layers_0_fc2_bias5, alloc1180, alloc1183) + R.vm.kill_object(alloc1180) + R.vm.kill_object(alloc1182) + R.vm.kill_object(model_decoder_layers_0_fc2_weight5) + R.vm.kill_object(model_decoder_layers_0_fc2_bias5) + model_decoder_layers_1_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[520] + model_decoder_layers_1_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[521] + alloc1184: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1183, model_decoder_layers_1_self_attn_layer_norm_weight5, model_decoder_layers_1_self_attn_layer_norm_bias5, alloc1184) + R.vm.kill_object(model_decoder_layers_1_self_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_1_self_attn_layer_norm_bias5) + model_decoder_layers_1_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[516] + model_decoder_layers_1_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[517] + alloc1185: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1184, model_decoder_layers_1_self_attn_q_proj_weight5, model_decoder_layers_1_self_attn_q_proj_bias5, alloc1185) + R.vm.kill_object(model_decoder_layers_1_self_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_1_self_attn_q_proj_bias5) + model_decoder_layers_1_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[513] + alloc1186: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.NT_matmul(alloc1184, model_decoder_layers_1_self_attn_k_proj_weight5, alloc1186) + R.vm.kill_object(model_decoder_layers_1_self_attn_k_proj_weight5) + model_decoder_layers_1_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[514] + model_decoder_layers_1_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[515] + alloc1187: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1184, model_decoder_layers_1_self_attn_v_proj_weight5, model_decoder_layers_1_self_attn_v_proj_bias5, alloc1187) + R.vm.kill_object(alloc1184) + R.vm.kill_object(model_decoder_layers_1_self_attn_v_proj_weight5) + R.vm.kill_object(model_decoder_layers_1_self_attn_v_proj_bias5) + alloc1188: R.Tensor((1, 60, 64), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 60, 64]), R.dtype("float16")) + cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22(alloc1185, alloc1186, alloc1187, alloc1188) + R.vm.kill_object(alloc1185) + R.vm.kill_object(alloc1186) + R.vm.kill_object(alloc1187) + alloc1189: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1187: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), alloc1188, alloc1189) + R.vm.kill_object(alloc1188) + lv55: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1189, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1189) + model_decoder_layers_1_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[518] + model_decoder_layers_1_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[519] + alloc1190: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7_add6(lv55, model_decoder_layers_1_self_attn_out_proj_weight5, model_decoder_layers_1_self_attn_out_proj_bias5, alloc1183, alloc1190) + R.vm.kill_object(alloc1183) + R.vm.kill_object(lv55) + R.vm.kill_object(model_decoder_layers_1_self_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_1_self_attn_out_proj_bias5) + model_decoder_layers_1_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[529] + model_decoder_layers_1_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[530] + alloc1191: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1190, model_decoder_layers_1_encoder_attn_layer_norm_weight5, model_decoder_layers_1_encoder_attn_layer_norm_bias5, alloc1191) + R.vm.kill_object(model_decoder_layers_1_encoder_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_1_encoder_attn_layer_norm_bias5) + model_decoder_layers_1_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[525] + model_decoder_layers_1_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[526] + alloc1192: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1191, model_decoder_layers_1_encoder_attn_q_proj_weight5, model_decoder_layers_1_encoder_attn_q_proj_bias5, alloc1192) + R.vm.kill_object(alloc1191) + R.vm.kill_object(model_decoder_layers_1_encoder_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_1_encoder_attn_q_proj_bias5) + lv58: R.Tensor((1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1192, R.shape([1, 20, 64]), sinfo_args=(R.Tensor((1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1192) + alloc1193: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1191: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), lv58, alloc1193) + R.vm.kill_object(lv58) + lv59: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1193, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1193) + model_decoder_layers_1_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[527] + model_decoder_layers_1_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[528] + alloc1194: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7_add6(lv59, model_decoder_layers_1_encoder_attn_out_proj_weight5, model_decoder_layers_1_encoder_attn_out_proj_bias5, alloc1190, alloc1194) + R.vm.kill_object(alloc1190) + R.vm.kill_object(lv59) + R.vm.kill_object(model_decoder_layers_1_encoder_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_1_encoder_attn_out_proj_bias5) + model_decoder_layers_1_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[535] + model_decoder_layers_1_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[536] + alloc1195: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1194, model_decoder_layers_1_final_layer_norm_weight5, model_decoder_layers_1_final_layer_norm_bias5, alloc1195) + R.vm.kill_object(model_decoder_layers_1_final_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_1_final_layer_norm_bias5) + model_decoder_layers_1_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[531] + model_decoder_layers_1_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[532] + alloc1196: R.Tensor((1, 1, 5120), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 5120]), R.dtype("float16")) + cls.fused_NT_matmul1_add8_gelu2(alloc1195, model_decoder_layers_1_fc1_weight5, model_decoder_layers_1_fc1_bias5, alloc1196) + R.vm.kill_object(alloc1195) + R.vm.kill_object(model_decoder_layers_1_fc1_weight5) + R.vm.kill_object(model_decoder_layers_1_fc1_bias5) + model_decoder_layers_1_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[533] + model_decoder_layers_1_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[534] + alloc1197: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul2_add7_add6(alloc1196, model_decoder_layers_1_fc2_weight5, model_decoder_layers_1_fc2_bias5, alloc1194, alloc1197) + R.vm.kill_object(alloc1194) + R.vm.kill_object(alloc1196) + R.vm.kill_object(model_decoder_layers_1_fc2_weight5) + R.vm.kill_object(model_decoder_layers_1_fc2_bias5) + model_decoder_layers_2_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[544] + model_decoder_layers_2_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[545] + alloc1198: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1197, model_decoder_layers_2_self_attn_layer_norm_weight5, model_decoder_layers_2_self_attn_layer_norm_bias5, alloc1198) + R.vm.kill_object(model_decoder_layers_2_self_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_2_self_attn_layer_norm_bias5) + model_decoder_layers_2_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[540] + model_decoder_layers_2_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[541] + alloc1199: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1198, model_decoder_layers_2_self_attn_q_proj_weight5, model_decoder_layers_2_self_attn_q_proj_bias5, alloc1199) + R.vm.kill_object(model_decoder_layers_2_self_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_2_self_attn_q_proj_bias5) + model_decoder_layers_2_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[537] + alloc1200: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.NT_matmul(alloc1198, model_decoder_layers_2_self_attn_k_proj_weight5, alloc1200) + R.vm.kill_object(model_decoder_layers_2_self_attn_k_proj_weight5) + model_decoder_layers_2_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[538] + model_decoder_layers_2_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[539] + alloc1201: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1198, model_decoder_layers_2_self_attn_v_proj_weight5, model_decoder_layers_2_self_attn_v_proj_bias5, alloc1201) + R.vm.kill_object(alloc1198) + R.vm.kill_object(model_decoder_layers_2_self_attn_v_proj_weight5) + R.vm.kill_object(model_decoder_layers_2_self_attn_v_proj_bias5) + alloc1202: R.Tensor((1, 60, 64), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 60, 64]), R.dtype("float16")) + cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22(alloc1199, alloc1200, alloc1201, alloc1202) + R.vm.kill_object(alloc1199) + R.vm.kill_object(alloc1200) + R.vm.kill_object(alloc1201) + alloc1203: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1201: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), alloc1202, alloc1203) + R.vm.kill_object(alloc1202) + lv66: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1203, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1203) + model_decoder_layers_2_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[542] + model_decoder_layers_2_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[543] + alloc1204: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7_add6(lv66, model_decoder_layers_2_self_attn_out_proj_weight5, model_decoder_layers_2_self_attn_out_proj_bias5, alloc1197, alloc1204) + R.vm.kill_object(alloc1197) + R.vm.kill_object(lv66) + R.vm.kill_object(model_decoder_layers_2_self_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_2_self_attn_out_proj_bias5) + model_decoder_layers_2_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[553] + model_decoder_layers_2_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[554] + alloc1205: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1204, model_decoder_layers_2_encoder_attn_layer_norm_weight5, model_decoder_layers_2_encoder_attn_layer_norm_bias5, alloc1205) + R.vm.kill_object(model_decoder_layers_2_encoder_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_2_encoder_attn_layer_norm_bias5) + model_decoder_layers_2_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[549] + model_decoder_layers_2_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[550] + alloc1206: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1205, model_decoder_layers_2_encoder_attn_q_proj_weight5, model_decoder_layers_2_encoder_attn_q_proj_bias5, alloc1206) + R.vm.kill_object(alloc1205) + R.vm.kill_object(model_decoder_layers_2_encoder_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_2_encoder_attn_q_proj_bias5) + lv69: R.Tensor((1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1206, R.shape([1, 20, 64]), sinfo_args=(R.Tensor((1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1206) + alloc1207: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1205: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), lv69, alloc1207) + R.vm.kill_object(lv69) + lv70: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1207, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1207) + model_decoder_layers_2_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[551] + model_decoder_layers_2_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[552] + alloc1208: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7_add6(lv70, model_decoder_layers_2_encoder_attn_out_proj_weight5, model_decoder_layers_2_encoder_attn_out_proj_bias5, alloc1204, alloc1208) + R.vm.kill_object(alloc1204) + R.vm.kill_object(lv70) + R.vm.kill_object(model_decoder_layers_2_encoder_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_2_encoder_attn_out_proj_bias5) + model_decoder_layers_2_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[559] + model_decoder_layers_2_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[560] + alloc1209: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1208, model_decoder_layers_2_final_layer_norm_weight5, model_decoder_layers_2_final_layer_norm_bias5, alloc1209) + R.vm.kill_object(model_decoder_layers_2_final_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_2_final_layer_norm_bias5) + model_decoder_layers_2_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[555] + model_decoder_layers_2_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[556] + alloc1210: R.Tensor((1, 1, 5120), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 5120]), R.dtype("float16")) + cls.fused_NT_matmul1_add8_gelu2(alloc1209, model_decoder_layers_2_fc1_weight5, model_decoder_layers_2_fc1_bias5, alloc1210) + R.vm.kill_object(alloc1209) + R.vm.kill_object(model_decoder_layers_2_fc1_weight5) + R.vm.kill_object(model_decoder_layers_2_fc1_bias5) + model_decoder_layers_2_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[557] + model_decoder_layers_2_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[558] + alloc1211: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul2_add7_add6(alloc1210, model_decoder_layers_2_fc2_weight5, model_decoder_layers_2_fc2_bias5, alloc1208, alloc1211) + R.vm.kill_object(alloc1208) + R.vm.kill_object(alloc1210) + R.vm.kill_object(model_decoder_layers_2_fc2_weight5) + R.vm.kill_object(model_decoder_layers_2_fc2_bias5) + model_decoder_layers_3_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[568] + model_decoder_layers_3_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[569] + alloc1212: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1211, model_decoder_layers_3_self_attn_layer_norm_weight5, model_decoder_layers_3_self_attn_layer_norm_bias5, alloc1212) + R.vm.kill_object(model_decoder_layers_3_self_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_3_self_attn_layer_norm_bias5) + model_decoder_layers_3_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[564] + model_decoder_layers_3_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[565] + alloc1213: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1212, model_decoder_layers_3_self_attn_q_proj_weight5, model_decoder_layers_3_self_attn_q_proj_bias5, alloc1213) + R.vm.kill_object(model_decoder_layers_3_self_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_3_self_attn_q_proj_bias5) + model_decoder_layers_3_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[561] + alloc1214: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.NT_matmul(alloc1212, model_decoder_layers_3_self_attn_k_proj_weight5, alloc1214) + R.vm.kill_object(model_decoder_layers_3_self_attn_k_proj_weight5) + model_decoder_layers_3_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[562] + model_decoder_layers_3_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[563] + alloc1215: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1212, model_decoder_layers_3_self_attn_v_proj_weight5, model_decoder_layers_3_self_attn_v_proj_bias5, alloc1215) + R.vm.kill_object(alloc1212) + R.vm.kill_object(model_decoder_layers_3_self_attn_v_proj_weight5) + R.vm.kill_object(model_decoder_layers_3_self_attn_v_proj_bias5) + alloc1216: R.Tensor((1, 60, 64), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 60, 64]), R.dtype("float16")) + cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22(alloc1213, alloc1214, alloc1215, alloc1216) + R.vm.kill_object(alloc1213) + R.vm.kill_object(alloc1214) + R.vm.kill_object(alloc1215) + alloc1217: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1215: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), alloc1216, alloc1217) + R.vm.kill_object(alloc1216) + lv77: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1217, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1217) + model_decoder_layers_3_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[566] + model_decoder_layers_3_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[567] + alloc1218: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7_add6(lv77, model_decoder_layers_3_self_attn_out_proj_weight5, model_decoder_layers_3_self_attn_out_proj_bias5, alloc1211, alloc1218) + R.vm.kill_object(alloc1211) + R.vm.kill_object(lv77) + R.vm.kill_object(model_decoder_layers_3_self_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_3_self_attn_out_proj_bias5) + model_decoder_layers_3_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[577] + model_decoder_layers_3_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[578] + alloc1219: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1218, model_decoder_layers_3_encoder_attn_layer_norm_weight5, model_decoder_layers_3_encoder_attn_layer_norm_bias5, alloc1219) + R.vm.kill_object(model_decoder_layers_3_encoder_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_3_encoder_attn_layer_norm_bias5) + model_decoder_layers_3_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[573] + model_decoder_layers_3_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[574] + alloc1220: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1219, model_decoder_layers_3_encoder_attn_q_proj_weight5, model_decoder_layers_3_encoder_attn_q_proj_bias5, alloc1220) + R.vm.kill_object(alloc1219) + R.vm.kill_object(model_decoder_layers_3_encoder_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_3_encoder_attn_q_proj_bias5) + lv80: R.Tensor((1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1220, R.shape([1, 20, 64]), sinfo_args=(R.Tensor((1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1220) + alloc1221: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1219: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), lv80, alloc1221) + R.vm.kill_object(lv80) + lv81: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1221, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1221) + model_decoder_layers_3_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[575] + model_decoder_layers_3_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[576] + alloc1222: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7_add6(lv81, model_decoder_layers_3_encoder_attn_out_proj_weight5, model_decoder_layers_3_encoder_attn_out_proj_bias5, alloc1218, alloc1222) + R.vm.kill_object(alloc1218) + R.vm.kill_object(lv81) + R.vm.kill_object(model_decoder_layers_3_encoder_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_3_encoder_attn_out_proj_bias5) + model_decoder_layers_3_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[583] + model_decoder_layers_3_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[584] + alloc1223: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1222, model_decoder_layers_3_final_layer_norm_weight5, model_decoder_layers_3_final_layer_norm_bias5, alloc1223) + R.vm.kill_object(model_decoder_layers_3_final_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_3_final_layer_norm_bias5) + model_decoder_layers_3_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[579] + model_decoder_layers_3_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[580] + alloc1224: R.Tensor((1, 1, 5120), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 5120]), R.dtype("float16")) + cls.fused_NT_matmul1_add8_gelu2(alloc1223, model_decoder_layers_3_fc1_weight5, model_decoder_layers_3_fc1_bias5, alloc1224) + R.vm.kill_object(alloc1223) + R.vm.kill_object(model_decoder_layers_3_fc1_weight5) + R.vm.kill_object(model_decoder_layers_3_fc1_bias5) + model_decoder_layers_3_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[581] + model_decoder_layers_3_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[582] + alloc1225: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul2_add7_add6(alloc1224, model_decoder_layers_3_fc2_weight5, model_decoder_layers_3_fc2_bias5, alloc1222, alloc1225) + R.vm.kill_object(alloc1222) + R.vm.kill_object(alloc1224) + R.vm.kill_object(model_decoder_layers_3_fc2_weight5) + R.vm.kill_object(model_decoder_layers_3_fc2_bias5) + model_decoder_layers_4_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[592] + model_decoder_layers_4_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[593] + alloc1226: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1225, model_decoder_layers_4_self_attn_layer_norm_weight5, model_decoder_layers_4_self_attn_layer_norm_bias5, alloc1226) + R.vm.kill_object(model_decoder_layers_4_self_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_4_self_attn_layer_norm_bias5) + model_decoder_layers_4_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[588] + model_decoder_layers_4_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[589] + alloc1227: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1226, model_decoder_layers_4_self_attn_q_proj_weight5, model_decoder_layers_4_self_attn_q_proj_bias5, alloc1227) + R.vm.kill_object(model_decoder_layers_4_self_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_4_self_attn_q_proj_bias5) + model_decoder_layers_4_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[585] + alloc1228: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.NT_matmul(alloc1226, model_decoder_layers_4_self_attn_k_proj_weight5, alloc1228) + R.vm.kill_object(model_decoder_layers_4_self_attn_k_proj_weight5) + model_decoder_layers_4_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[586] + model_decoder_layers_4_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[587] + alloc1229: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1226, model_decoder_layers_4_self_attn_v_proj_weight5, model_decoder_layers_4_self_attn_v_proj_bias5, alloc1229) + R.vm.kill_object(alloc1226) + R.vm.kill_object(model_decoder_layers_4_self_attn_v_proj_weight5) + R.vm.kill_object(model_decoder_layers_4_self_attn_v_proj_bias5) + alloc1230: R.Tensor((1, 60, 64), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 60, 64]), R.dtype("float16")) + cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22(alloc1227, alloc1228, alloc1229, alloc1230) + R.vm.kill_object(alloc1227) + R.vm.kill_object(alloc1228) + R.vm.kill_object(alloc1229) + alloc1231: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1229: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), alloc1230, alloc1231) + R.vm.kill_object(alloc1230) + lv88: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1231, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1231) + model_decoder_layers_4_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[590] + model_decoder_layers_4_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[591] + alloc1232: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7_add6(lv88, model_decoder_layers_4_self_attn_out_proj_weight5, model_decoder_layers_4_self_attn_out_proj_bias5, alloc1225, alloc1232) + R.vm.kill_object(alloc1225) + R.vm.kill_object(lv88) + R.vm.kill_object(model_decoder_layers_4_self_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_4_self_attn_out_proj_bias5) + model_decoder_layers_4_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[601] + model_decoder_layers_4_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[602] + alloc1233: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1232, model_decoder_layers_4_encoder_attn_layer_norm_weight5, model_decoder_layers_4_encoder_attn_layer_norm_bias5, alloc1233) + R.vm.kill_object(model_decoder_layers_4_encoder_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_4_encoder_attn_layer_norm_bias5) + model_decoder_layers_4_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[597] + model_decoder_layers_4_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[598] + alloc1234: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1233, model_decoder_layers_4_encoder_attn_q_proj_weight5, model_decoder_layers_4_encoder_attn_q_proj_bias5, alloc1234) + R.vm.kill_object(alloc1233) + R.vm.kill_object(model_decoder_layers_4_encoder_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_4_encoder_attn_q_proj_bias5) + lv91: R.Tensor((1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1234, R.shape([1, 20, 64]), sinfo_args=(R.Tensor((1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1234) + alloc1235: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1233: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), lv91, alloc1235) + R.vm.kill_object(lv91) + lv92: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1235, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1235) + model_decoder_layers_4_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[599] + model_decoder_layers_4_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[600] + alloc1236: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7_add6(lv92, model_decoder_layers_4_encoder_attn_out_proj_weight5, model_decoder_layers_4_encoder_attn_out_proj_bias5, alloc1232, alloc1236) + R.vm.kill_object(alloc1232) + R.vm.kill_object(lv92) + R.vm.kill_object(model_decoder_layers_4_encoder_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_4_encoder_attn_out_proj_bias5) + model_decoder_layers_4_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[607] + model_decoder_layers_4_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[608] + alloc1237: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1236, model_decoder_layers_4_final_layer_norm_weight5, model_decoder_layers_4_final_layer_norm_bias5, alloc1237) + R.vm.kill_object(model_decoder_layers_4_final_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_4_final_layer_norm_bias5) + model_decoder_layers_4_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[603] + model_decoder_layers_4_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[604] + alloc1238: R.Tensor((1, 1, 5120), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 5120]), R.dtype("float16")) + cls.fused_NT_matmul1_add8_gelu2(alloc1237, model_decoder_layers_4_fc1_weight5, model_decoder_layers_4_fc1_bias5, alloc1238) + R.vm.kill_object(alloc1237) + R.vm.kill_object(model_decoder_layers_4_fc1_weight5) + R.vm.kill_object(model_decoder_layers_4_fc1_bias5) + model_decoder_layers_4_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[605] + model_decoder_layers_4_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[606] + alloc1239: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul2_add7_add6(alloc1238, model_decoder_layers_4_fc2_weight5, model_decoder_layers_4_fc2_bias5, alloc1236, alloc1239) + R.vm.kill_object(alloc1236) + R.vm.kill_object(alloc1238) + R.vm.kill_object(model_decoder_layers_4_fc2_weight5) + R.vm.kill_object(model_decoder_layers_4_fc2_bias5) + model_decoder_layers_5_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[616] + model_decoder_layers_5_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[617] + alloc1240: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1239, model_decoder_layers_5_self_attn_layer_norm_weight5, model_decoder_layers_5_self_attn_layer_norm_bias5, alloc1240) + R.vm.kill_object(model_decoder_layers_5_self_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_5_self_attn_layer_norm_bias5) + model_decoder_layers_5_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[612] + model_decoder_layers_5_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[613] + alloc1241: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1240, model_decoder_layers_5_self_attn_q_proj_weight5, model_decoder_layers_5_self_attn_q_proj_bias5, alloc1241) + R.vm.kill_object(model_decoder_layers_5_self_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_5_self_attn_q_proj_bias5) + model_decoder_layers_5_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[609] + alloc1242: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.NT_matmul(alloc1240, model_decoder_layers_5_self_attn_k_proj_weight5, alloc1242) + R.vm.kill_object(model_decoder_layers_5_self_attn_k_proj_weight5) + model_decoder_layers_5_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[610] + model_decoder_layers_5_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[611] + alloc1243: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1240, model_decoder_layers_5_self_attn_v_proj_weight5, model_decoder_layers_5_self_attn_v_proj_bias5, alloc1243) + R.vm.kill_object(alloc1240) + R.vm.kill_object(model_decoder_layers_5_self_attn_v_proj_weight5) + R.vm.kill_object(model_decoder_layers_5_self_attn_v_proj_bias5) + alloc1244: R.Tensor((1, 60, 64), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 60, 64]), R.dtype("float16")) + cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22(alloc1241, alloc1242, alloc1243, alloc1244) + R.vm.kill_object(alloc1241) + R.vm.kill_object(alloc1242) + R.vm.kill_object(alloc1243) + alloc1245: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1243: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), alloc1244, alloc1245) + R.vm.kill_object(alloc1244) + lv99: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1245, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1245) + model_decoder_layers_5_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[614] + model_decoder_layers_5_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[615] + alloc1246: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7_add6(lv99, model_decoder_layers_5_self_attn_out_proj_weight5, model_decoder_layers_5_self_attn_out_proj_bias5, alloc1239, alloc1246) + R.vm.kill_object(alloc1239) + R.vm.kill_object(lv99) + R.vm.kill_object(model_decoder_layers_5_self_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_5_self_attn_out_proj_bias5) + model_decoder_layers_5_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[625] + model_decoder_layers_5_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[626] + alloc1247: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1246, model_decoder_layers_5_encoder_attn_layer_norm_weight5, model_decoder_layers_5_encoder_attn_layer_norm_bias5, alloc1247) + R.vm.kill_object(model_decoder_layers_5_encoder_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_5_encoder_attn_layer_norm_bias5) + model_decoder_layers_5_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[621] + model_decoder_layers_5_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[622] + alloc1248: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1247, model_decoder_layers_5_encoder_attn_q_proj_weight5, model_decoder_layers_5_encoder_attn_q_proj_bias5, alloc1248) + R.vm.kill_object(alloc1247) + R.vm.kill_object(model_decoder_layers_5_encoder_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_5_encoder_attn_q_proj_bias5) + lv102: R.Tensor((1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1248, R.shape([1, 20, 64]), sinfo_args=(R.Tensor((1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1248) + alloc1249: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1247: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), lv102, alloc1249) + R.vm.kill_object(lv102) + lv103: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1249, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1249) + model_decoder_layers_5_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[623] + model_decoder_layers_5_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[624] + alloc1250: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7_add6(lv103, model_decoder_layers_5_encoder_attn_out_proj_weight5, model_decoder_layers_5_encoder_attn_out_proj_bias5, alloc1246, alloc1250) + R.vm.kill_object(alloc1246) + R.vm.kill_object(lv103) + R.vm.kill_object(model_decoder_layers_5_encoder_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_5_encoder_attn_out_proj_bias5) + model_decoder_layers_5_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[631] + model_decoder_layers_5_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[632] + alloc1251: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1250, model_decoder_layers_5_final_layer_norm_weight5, model_decoder_layers_5_final_layer_norm_bias5, alloc1251) + R.vm.kill_object(model_decoder_layers_5_final_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_5_final_layer_norm_bias5) + model_decoder_layers_5_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[627] + model_decoder_layers_5_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[628] + alloc1252: R.Tensor((1, 1, 5120), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 5120]), R.dtype("float16")) + cls.fused_NT_matmul1_add8_gelu2(alloc1251, model_decoder_layers_5_fc1_weight5, model_decoder_layers_5_fc1_bias5, alloc1252) + R.vm.kill_object(alloc1251) + R.vm.kill_object(model_decoder_layers_5_fc1_weight5) + R.vm.kill_object(model_decoder_layers_5_fc1_bias5) + model_decoder_layers_5_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[629] + model_decoder_layers_5_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[630] + alloc1253: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul2_add7_add6(alloc1252, model_decoder_layers_5_fc2_weight5, model_decoder_layers_5_fc2_bias5, alloc1250, alloc1253) + R.vm.kill_object(alloc1250) + R.vm.kill_object(alloc1252) + R.vm.kill_object(model_decoder_layers_5_fc2_weight5) + R.vm.kill_object(model_decoder_layers_5_fc2_bias5) + model_decoder_layers_6_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[640] + model_decoder_layers_6_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[641] + alloc1254: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1253, model_decoder_layers_6_self_attn_layer_norm_weight5, model_decoder_layers_6_self_attn_layer_norm_bias5, alloc1254) + R.vm.kill_object(model_decoder_layers_6_self_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_6_self_attn_layer_norm_bias5) + model_decoder_layers_6_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[636] + model_decoder_layers_6_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[637] + alloc1255: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1254, model_decoder_layers_6_self_attn_q_proj_weight5, model_decoder_layers_6_self_attn_q_proj_bias5, alloc1255) + R.vm.kill_object(model_decoder_layers_6_self_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_6_self_attn_q_proj_bias5) + model_decoder_layers_6_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[633] + alloc1256: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.NT_matmul(alloc1254, model_decoder_layers_6_self_attn_k_proj_weight5, alloc1256) + R.vm.kill_object(model_decoder_layers_6_self_attn_k_proj_weight5) + model_decoder_layers_6_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[634] + model_decoder_layers_6_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[635] + alloc1257: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1254, model_decoder_layers_6_self_attn_v_proj_weight5, model_decoder_layers_6_self_attn_v_proj_bias5, alloc1257) + R.vm.kill_object(alloc1254) + R.vm.kill_object(model_decoder_layers_6_self_attn_v_proj_weight5) + R.vm.kill_object(model_decoder_layers_6_self_attn_v_proj_bias5) + alloc1258: R.Tensor((1, 60, 64), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 60, 64]), R.dtype("float16")) + cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22(alloc1255, alloc1256, alloc1257, alloc1258) + R.vm.kill_object(alloc1255) + R.vm.kill_object(alloc1256) + R.vm.kill_object(alloc1257) + alloc1259: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1257: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), alloc1258, alloc1259) + R.vm.kill_object(alloc1258) + lv110: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1259, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1259) + model_decoder_layers_6_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[638] + model_decoder_layers_6_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[639] + alloc1260: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7_add6(lv110, model_decoder_layers_6_self_attn_out_proj_weight5, model_decoder_layers_6_self_attn_out_proj_bias5, alloc1253, alloc1260) + R.vm.kill_object(alloc1253) + R.vm.kill_object(lv110) + R.vm.kill_object(model_decoder_layers_6_self_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_6_self_attn_out_proj_bias5) + model_decoder_layers_6_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[649] + model_decoder_layers_6_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[650] + alloc1261: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1260, model_decoder_layers_6_encoder_attn_layer_norm_weight5, model_decoder_layers_6_encoder_attn_layer_norm_bias5, alloc1261) + R.vm.kill_object(model_decoder_layers_6_encoder_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_6_encoder_attn_layer_norm_bias5) + model_decoder_layers_6_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[645] + model_decoder_layers_6_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[646] + alloc1262: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1261, model_decoder_layers_6_encoder_attn_q_proj_weight5, model_decoder_layers_6_encoder_attn_q_proj_bias5, alloc1262) + R.vm.kill_object(alloc1261) + R.vm.kill_object(model_decoder_layers_6_encoder_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_6_encoder_attn_q_proj_bias5) + lv113: R.Tensor((1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1262, R.shape([1, 20, 64]), sinfo_args=(R.Tensor((1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1262) + alloc1263: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1261: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), lv113, alloc1263) + R.vm.kill_object(lv113) + lv114: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1263, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1263) + model_decoder_layers_6_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[647] + model_decoder_layers_6_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[648] + alloc1264: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7_add6(lv114, model_decoder_layers_6_encoder_attn_out_proj_weight5, model_decoder_layers_6_encoder_attn_out_proj_bias5, alloc1260, alloc1264) + R.vm.kill_object(alloc1260) + R.vm.kill_object(lv114) + R.vm.kill_object(model_decoder_layers_6_encoder_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_6_encoder_attn_out_proj_bias5) + model_decoder_layers_6_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[655] + model_decoder_layers_6_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[656] + alloc1265: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1264, model_decoder_layers_6_final_layer_norm_weight5, model_decoder_layers_6_final_layer_norm_bias5, alloc1265) + R.vm.kill_object(model_decoder_layers_6_final_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_6_final_layer_norm_bias5) + model_decoder_layers_6_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[651] + model_decoder_layers_6_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[652] + alloc1266: R.Tensor((1, 1, 5120), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 5120]), R.dtype("float16")) + cls.fused_NT_matmul1_add8_gelu2(alloc1265, model_decoder_layers_6_fc1_weight5, model_decoder_layers_6_fc1_bias5, alloc1266) + R.vm.kill_object(alloc1265) + R.vm.kill_object(model_decoder_layers_6_fc1_weight5) + R.vm.kill_object(model_decoder_layers_6_fc1_bias5) + model_decoder_layers_6_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[653] + model_decoder_layers_6_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[654] + alloc1267: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul2_add7_add6(alloc1266, model_decoder_layers_6_fc2_weight5, model_decoder_layers_6_fc2_bias5, alloc1264, alloc1267) + R.vm.kill_object(alloc1264) + R.vm.kill_object(alloc1266) + R.vm.kill_object(model_decoder_layers_6_fc2_weight5) + R.vm.kill_object(model_decoder_layers_6_fc2_bias5) + model_decoder_layers_7_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[664] + model_decoder_layers_7_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[665] + alloc1268: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1267, model_decoder_layers_7_self_attn_layer_norm_weight5, model_decoder_layers_7_self_attn_layer_norm_bias5, alloc1268) + R.vm.kill_object(model_decoder_layers_7_self_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_7_self_attn_layer_norm_bias5) + model_decoder_layers_7_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[660] + model_decoder_layers_7_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[661] + alloc1269: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1268, model_decoder_layers_7_self_attn_q_proj_weight5, model_decoder_layers_7_self_attn_q_proj_bias5, alloc1269) + R.vm.kill_object(model_decoder_layers_7_self_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_7_self_attn_q_proj_bias5) + model_decoder_layers_7_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[657] + alloc1270: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.NT_matmul(alloc1268, model_decoder_layers_7_self_attn_k_proj_weight5, alloc1270) + R.vm.kill_object(model_decoder_layers_7_self_attn_k_proj_weight5) + model_decoder_layers_7_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[658] + model_decoder_layers_7_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[659] + alloc1271: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1268, model_decoder_layers_7_self_attn_v_proj_weight5, model_decoder_layers_7_self_attn_v_proj_bias5, alloc1271) + R.vm.kill_object(alloc1268) + R.vm.kill_object(model_decoder_layers_7_self_attn_v_proj_weight5) + R.vm.kill_object(model_decoder_layers_7_self_attn_v_proj_bias5) + alloc1272: R.Tensor((1, 60, 64), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 60, 64]), R.dtype("float16")) + cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22(alloc1269, alloc1270, alloc1271, alloc1272) + R.vm.kill_object(alloc1269) + R.vm.kill_object(alloc1270) + R.vm.kill_object(alloc1271) + alloc1273: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1271: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), alloc1272, alloc1273) + R.vm.kill_object(alloc1272) + lv121: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1273, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1273) + model_decoder_layers_7_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[662] + model_decoder_layers_7_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[663] + alloc1274: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7_add6(lv121, model_decoder_layers_7_self_attn_out_proj_weight5, model_decoder_layers_7_self_attn_out_proj_bias5, alloc1267, alloc1274) + R.vm.kill_object(alloc1267) + R.vm.kill_object(lv121) + R.vm.kill_object(model_decoder_layers_7_self_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_7_self_attn_out_proj_bias5) + model_decoder_layers_7_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[673] + model_decoder_layers_7_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[674] + alloc1275: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1274, model_decoder_layers_7_encoder_attn_layer_norm_weight5, model_decoder_layers_7_encoder_attn_layer_norm_bias5, alloc1275) + R.vm.kill_object(model_decoder_layers_7_encoder_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_7_encoder_attn_layer_norm_bias5) + model_decoder_layers_7_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[669] + model_decoder_layers_7_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[670] + alloc1276: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1275, model_decoder_layers_7_encoder_attn_q_proj_weight5, model_decoder_layers_7_encoder_attn_q_proj_bias5, alloc1276) + R.vm.kill_object(alloc1275) + R.vm.kill_object(model_decoder_layers_7_encoder_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_7_encoder_attn_q_proj_bias5) + lv124: R.Tensor((1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1276, R.shape([1, 20, 64]), sinfo_args=(R.Tensor((1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1276) + alloc1277: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1275: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), lv124, alloc1277) + R.vm.kill_object(lv124) + lv125: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1277, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1277) + model_decoder_layers_7_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[671] + model_decoder_layers_7_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[672] + alloc1278: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7_add6(lv125, model_decoder_layers_7_encoder_attn_out_proj_weight5, model_decoder_layers_7_encoder_attn_out_proj_bias5, alloc1274, alloc1278) + R.vm.kill_object(alloc1274) + R.vm.kill_object(lv125) + R.vm.kill_object(model_decoder_layers_7_encoder_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_7_encoder_attn_out_proj_bias5) + model_decoder_layers_7_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[679] + model_decoder_layers_7_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[680] + alloc1279: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1278, model_decoder_layers_7_final_layer_norm_weight5, model_decoder_layers_7_final_layer_norm_bias5, alloc1279) + R.vm.kill_object(model_decoder_layers_7_final_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_7_final_layer_norm_bias5) + model_decoder_layers_7_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[675] + model_decoder_layers_7_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[676] + alloc1280: R.Tensor((1, 1, 5120), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 5120]), R.dtype("float16")) + cls.fused_NT_matmul1_add8_gelu2(alloc1279, model_decoder_layers_7_fc1_weight5, model_decoder_layers_7_fc1_bias5, alloc1280) + R.vm.kill_object(alloc1279) + R.vm.kill_object(model_decoder_layers_7_fc1_weight5) + R.vm.kill_object(model_decoder_layers_7_fc1_bias5) + model_decoder_layers_7_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[677] + model_decoder_layers_7_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[678] + alloc1281: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul2_add7_add6(alloc1280, model_decoder_layers_7_fc2_weight5, model_decoder_layers_7_fc2_bias5, alloc1278, alloc1281) + R.vm.kill_object(alloc1278) + R.vm.kill_object(alloc1280) + R.vm.kill_object(model_decoder_layers_7_fc2_weight5) + R.vm.kill_object(model_decoder_layers_7_fc2_bias5) + model_decoder_layers_8_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[688] + model_decoder_layers_8_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[689] + alloc1282: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1281, model_decoder_layers_8_self_attn_layer_norm_weight5, model_decoder_layers_8_self_attn_layer_norm_bias5, alloc1282) + R.vm.kill_object(model_decoder_layers_8_self_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_8_self_attn_layer_norm_bias5) + model_decoder_layers_8_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[684] + model_decoder_layers_8_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[685] + alloc1283: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1282, model_decoder_layers_8_self_attn_q_proj_weight5, model_decoder_layers_8_self_attn_q_proj_bias5, alloc1283) + R.vm.kill_object(model_decoder_layers_8_self_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_8_self_attn_q_proj_bias5) + model_decoder_layers_8_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[681] + alloc1284: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.NT_matmul(alloc1282, model_decoder_layers_8_self_attn_k_proj_weight5, alloc1284) + R.vm.kill_object(model_decoder_layers_8_self_attn_k_proj_weight5) + model_decoder_layers_8_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[682] + model_decoder_layers_8_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[683] + alloc1285: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1282, model_decoder_layers_8_self_attn_v_proj_weight5, model_decoder_layers_8_self_attn_v_proj_bias5, alloc1285) + R.vm.kill_object(alloc1282) + R.vm.kill_object(model_decoder_layers_8_self_attn_v_proj_weight5) + R.vm.kill_object(model_decoder_layers_8_self_attn_v_proj_bias5) + alloc1286: R.Tensor((1, 60, 64), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 60, 64]), R.dtype("float16")) + cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22(alloc1283, alloc1284, alloc1285, alloc1286) + R.vm.kill_object(alloc1283) + R.vm.kill_object(alloc1284) + R.vm.kill_object(alloc1285) + alloc1287: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1285: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), alloc1286, alloc1287) + R.vm.kill_object(alloc1286) + lv132: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1287, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1287) + model_decoder_layers_8_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[686] + model_decoder_layers_8_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[687] + alloc1288: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7_add6(lv132, model_decoder_layers_8_self_attn_out_proj_weight5, model_decoder_layers_8_self_attn_out_proj_bias5, alloc1281, alloc1288) + R.vm.kill_object(alloc1281) + R.vm.kill_object(lv132) + R.vm.kill_object(model_decoder_layers_8_self_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_8_self_attn_out_proj_bias5) + model_decoder_layers_8_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[697] + model_decoder_layers_8_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[698] + alloc1289: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1288, model_decoder_layers_8_encoder_attn_layer_norm_weight5, model_decoder_layers_8_encoder_attn_layer_norm_bias5, alloc1289) + R.vm.kill_object(model_decoder_layers_8_encoder_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_8_encoder_attn_layer_norm_bias5) + model_decoder_layers_8_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[693] + model_decoder_layers_8_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[694] + alloc1290: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1289, model_decoder_layers_8_encoder_attn_q_proj_weight5, model_decoder_layers_8_encoder_attn_q_proj_bias5, alloc1290) + R.vm.kill_object(alloc1289) + R.vm.kill_object(model_decoder_layers_8_encoder_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_8_encoder_attn_q_proj_bias5) + lv135: R.Tensor((1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1290, R.shape([1, 20, 64]), sinfo_args=(R.Tensor((1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1290) + alloc1291: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1289: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), lv135, alloc1291) + R.vm.kill_object(lv135) + lv136: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1291, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1291) + model_decoder_layers_8_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[695] + model_decoder_layers_8_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[696] + alloc1292: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7_add6(lv136, model_decoder_layers_8_encoder_attn_out_proj_weight5, model_decoder_layers_8_encoder_attn_out_proj_bias5, alloc1288, alloc1292) + R.vm.kill_object(alloc1288) + R.vm.kill_object(lv136) + R.vm.kill_object(model_decoder_layers_8_encoder_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_8_encoder_attn_out_proj_bias5) + model_decoder_layers_8_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[703] + model_decoder_layers_8_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[704] + alloc1293: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1292, model_decoder_layers_8_final_layer_norm_weight5, model_decoder_layers_8_final_layer_norm_bias5, alloc1293) + R.vm.kill_object(model_decoder_layers_8_final_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_8_final_layer_norm_bias5) + model_decoder_layers_8_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[699] + model_decoder_layers_8_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[700] + alloc1294: R.Tensor((1, 1, 5120), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 5120]), R.dtype("float16")) + cls.fused_NT_matmul1_add8_gelu2(alloc1293, model_decoder_layers_8_fc1_weight5, model_decoder_layers_8_fc1_bias5, alloc1294) + R.vm.kill_object(alloc1293) + R.vm.kill_object(model_decoder_layers_8_fc1_weight5) + R.vm.kill_object(model_decoder_layers_8_fc1_bias5) + model_decoder_layers_8_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[701] + model_decoder_layers_8_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[702] + alloc1295: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul2_add7_add6(alloc1294, model_decoder_layers_8_fc2_weight5, model_decoder_layers_8_fc2_bias5, alloc1292, alloc1295) + R.vm.kill_object(alloc1292) + R.vm.kill_object(alloc1294) + R.vm.kill_object(model_decoder_layers_8_fc2_weight5) + R.vm.kill_object(model_decoder_layers_8_fc2_bias5) + model_decoder_layers_9_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[712] + model_decoder_layers_9_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[713] + alloc1296: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1295, model_decoder_layers_9_self_attn_layer_norm_weight5, model_decoder_layers_9_self_attn_layer_norm_bias5, alloc1296) + R.vm.kill_object(model_decoder_layers_9_self_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_9_self_attn_layer_norm_bias5) + model_decoder_layers_9_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[708] + model_decoder_layers_9_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[709] + alloc1297: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1296, model_decoder_layers_9_self_attn_q_proj_weight5, model_decoder_layers_9_self_attn_q_proj_bias5, alloc1297) + R.vm.kill_object(model_decoder_layers_9_self_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_9_self_attn_q_proj_bias5) + model_decoder_layers_9_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[705] + alloc1298: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.NT_matmul(alloc1296, model_decoder_layers_9_self_attn_k_proj_weight5, alloc1298) + R.vm.kill_object(model_decoder_layers_9_self_attn_k_proj_weight5) + model_decoder_layers_9_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[706] + model_decoder_layers_9_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[707] + alloc1299: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1296, model_decoder_layers_9_self_attn_v_proj_weight5, model_decoder_layers_9_self_attn_v_proj_bias5, alloc1299) + R.vm.kill_object(alloc1296) + R.vm.kill_object(model_decoder_layers_9_self_attn_v_proj_weight5) + R.vm.kill_object(model_decoder_layers_9_self_attn_v_proj_bias5) + alloc1300: R.Tensor((1, 60, 64), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 60, 64]), R.dtype("float16")) + cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22(alloc1297, alloc1298, alloc1299, alloc1300) + R.vm.kill_object(alloc1297) + R.vm.kill_object(alloc1298) + R.vm.kill_object(alloc1299) + alloc1301: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1299: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), alloc1300, alloc1301) + R.vm.kill_object(alloc1300) + lv143: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1301, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1301) + model_decoder_layers_9_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[710] + model_decoder_layers_9_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[711] + alloc1302: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7_add6(lv143, model_decoder_layers_9_self_attn_out_proj_weight5, model_decoder_layers_9_self_attn_out_proj_bias5, alloc1295, alloc1302) + R.vm.kill_object(alloc1295) + R.vm.kill_object(lv143) + R.vm.kill_object(model_decoder_layers_9_self_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_9_self_attn_out_proj_bias5) + model_decoder_layers_9_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[721] + model_decoder_layers_9_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[722] + alloc1303: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1302, model_decoder_layers_9_encoder_attn_layer_norm_weight5, model_decoder_layers_9_encoder_attn_layer_norm_bias5, alloc1303) + R.vm.kill_object(model_decoder_layers_9_encoder_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_9_encoder_attn_layer_norm_bias5) + model_decoder_layers_9_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[717] + model_decoder_layers_9_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[718] + alloc1304: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1303, model_decoder_layers_9_encoder_attn_q_proj_weight5, model_decoder_layers_9_encoder_attn_q_proj_bias5, alloc1304) + R.vm.kill_object(alloc1303) + R.vm.kill_object(model_decoder_layers_9_encoder_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_9_encoder_attn_q_proj_bias5) + lv146: R.Tensor((1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1304, R.shape([1, 20, 64]), sinfo_args=(R.Tensor((1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1304) + alloc1305: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1303: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), lv146, alloc1305) + R.vm.kill_object(lv146) + lv147: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1305, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1305) + model_decoder_layers_9_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[719] + model_decoder_layers_9_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[720] + alloc1306: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7_add6(lv147, model_decoder_layers_9_encoder_attn_out_proj_weight5, model_decoder_layers_9_encoder_attn_out_proj_bias5, alloc1302, alloc1306) + R.vm.kill_object(alloc1302) + R.vm.kill_object(lv147) + R.vm.kill_object(model_decoder_layers_9_encoder_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_9_encoder_attn_out_proj_bias5) + model_decoder_layers_9_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[727] + model_decoder_layers_9_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[728] + alloc1307: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1306, model_decoder_layers_9_final_layer_norm_weight5, model_decoder_layers_9_final_layer_norm_bias5, alloc1307) + R.vm.kill_object(model_decoder_layers_9_final_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_9_final_layer_norm_bias5) + model_decoder_layers_9_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[723] + model_decoder_layers_9_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[724] + alloc1308: R.Tensor((1, 1, 5120), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 5120]), R.dtype("float16")) + cls.fused_NT_matmul1_add8_gelu2(alloc1307, model_decoder_layers_9_fc1_weight5, model_decoder_layers_9_fc1_bias5, alloc1308) + R.vm.kill_object(alloc1307) + R.vm.kill_object(model_decoder_layers_9_fc1_weight5) + R.vm.kill_object(model_decoder_layers_9_fc1_bias5) + model_decoder_layers_9_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[725] + model_decoder_layers_9_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[726] + alloc1309: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul2_add7_add6(alloc1308, model_decoder_layers_9_fc2_weight5, model_decoder_layers_9_fc2_bias5, alloc1306, alloc1309) + R.vm.kill_object(alloc1306) + R.vm.kill_object(alloc1308) + R.vm.kill_object(model_decoder_layers_9_fc2_weight5) + R.vm.kill_object(model_decoder_layers_9_fc2_bias5) + model_decoder_layers_10_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[736] + model_decoder_layers_10_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[737] + alloc1310: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1309, model_decoder_layers_10_self_attn_layer_norm_weight5, model_decoder_layers_10_self_attn_layer_norm_bias5, alloc1310) + R.vm.kill_object(model_decoder_layers_10_self_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_10_self_attn_layer_norm_bias5) + model_decoder_layers_10_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[732] + model_decoder_layers_10_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[733] + alloc1311: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1310, model_decoder_layers_10_self_attn_q_proj_weight5, model_decoder_layers_10_self_attn_q_proj_bias5, alloc1311) + R.vm.kill_object(model_decoder_layers_10_self_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_10_self_attn_q_proj_bias5) + model_decoder_layers_10_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[729] + alloc1312: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.NT_matmul(alloc1310, model_decoder_layers_10_self_attn_k_proj_weight5, alloc1312) + R.vm.kill_object(model_decoder_layers_10_self_attn_k_proj_weight5) + model_decoder_layers_10_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[730] + model_decoder_layers_10_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[731] + alloc1313: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1310, model_decoder_layers_10_self_attn_v_proj_weight5, model_decoder_layers_10_self_attn_v_proj_bias5, alloc1313) + R.vm.kill_object(alloc1310) + R.vm.kill_object(model_decoder_layers_10_self_attn_v_proj_weight5) + R.vm.kill_object(model_decoder_layers_10_self_attn_v_proj_bias5) + alloc1314: R.Tensor((1, 60, 64), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 60, 64]), R.dtype("float16")) + cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22(alloc1311, alloc1312, alloc1313, alloc1314) + R.vm.kill_object(alloc1311) + R.vm.kill_object(alloc1312) + R.vm.kill_object(alloc1313) + alloc1315: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1313: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), alloc1314, alloc1315) + R.vm.kill_object(alloc1314) + lv154: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1315, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1315) + model_decoder_layers_10_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[734] + model_decoder_layers_10_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[735] + alloc1316: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7_add6(lv154, model_decoder_layers_10_self_attn_out_proj_weight5, model_decoder_layers_10_self_attn_out_proj_bias5, alloc1309, alloc1316) + R.vm.kill_object(alloc1309) + R.vm.kill_object(lv154) + R.vm.kill_object(model_decoder_layers_10_self_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_10_self_attn_out_proj_bias5) + model_decoder_layers_10_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[745] + model_decoder_layers_10_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[746] + alloc1317: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1316, model_decoder_layers_10_encoder_attn_layer_norm_weight5, model_decoder_layers_10_encoder_attn_layer_norm_bias5, alloc1317) + R.vm.kill_object(model_decoder_layers_10_encoder_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_10_encoder_attn_layer_norm_bias5) + model_decoder_layers_10_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[741] + model_decoder_layers_10_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[742] + alloc1318: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1317, model_decoder_layers_10_encoder_attn_q_proj_weight5, model_decoder_layers_10_encoder_attn_q_proj_bias5, alloc1318) + R.vm.kill_object(alloc1317) + R.vm.kill_object(model_decoder_layers_10_encoder_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_10_encoder_attn_q_proj_bias5) + lv157: R.Tensor((1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1318, R.shape([1, 20, 64]), sinfo_args=(R.Tensor((1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1318) + alloc1319: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1317: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), lv157, alloc1319) + R.vm.kill_object(lv157) + lv158: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1319, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1319) + model_decoder_layers_10_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[743] + model_decoder_layers_10_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[744] + alloc1320: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7_add6(lv158, model_decoder_layers_10_encoder_attn_out_proj_weight5, model_decoder_layers_10_encoder_attn_out_proj_bias5, alloc1316, alloc1320) + R.vm.kill_object(alloc1316) + R.vm.kill_object(lv158) + R.vm.kill_object(model_decoder_layers_10_encoder_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_10_encoder_attn_out_proj_bias5) + model_decoder_layers_10_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[751] + model_decoder_layers_10_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[752] + alloc1321: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1320, model_decoder_layers_10_final_layer_norm_weight5, model_decoder_layers_10_final_layer_norm_bias5, alloc1321) + R.vm.kill_object(model_decoder_layers_10_final_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_10_final_layer_norm_bias5) + model_decoder_layers_10_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[747] + model_decoder_layers_10_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[748] + alloc1322: R.Tensor((1, 1, 5120), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 5120]), R.dtype("float16")) + cls.fused_NT_matmul1_add8_gelu2(alloc1321, model_decoder_layers_10_fc1_weight5, model_decoder_layers_10_fc1_bias5, alloc1322) + R.vm.kill_object(alloc1321) + R.vm.kill_object(model_decoder_layers_10_fc1_weight5) + R.vm.kill_object(model_decoder_layers_10_fc1_bias5) + model_decoder_layers_10_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[749] + model_decoder_layers_10_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[750] + alloc1323: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul2_add7_add6(alloc1322, model_decoder_layers_10_fc2_weight5, model_decoder_layers_10_fc2_bias5, alloc1320, alloc1323) + R.vm.kill_object(alloc1320) + R.vm.kill_object(alloc1322) + R.vm.kill_object(model_decoder_layers_10_fc2_weight5) + R.vm.kill_object(model_decoder_layers_10_fc2_bias5) + model_decoder_layers_11_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[760] + model_decoder_layers_11_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[761] + alloc1324: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1323, model_decoder_layers_11_self_attn_layer_norm_weight5, model_decoder_layers_11_self_attn_layer_norm_bias5, alloc1324) + R.vm.kill_object(model_decoder_layers_11_self_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_11_self_attn_layer_norm_bias5) + model_decoder_layers_11_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[756] + model_decoder_layers_11_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[757] + alloc1325: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1324, model_decoder_layers_11_self_attn_q_proj_weight5, model_decoder_layers_11_self_attn_q_proj_bias5, alloc1325) + R.vm.kill_object(model_decoder_layers_11_self_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_11_self_attn_q_proj_bias5) + model_decoder_layers_11_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[753] + alloc1326: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.NT_matmul(alloc1324, model_decoder_layers_11_self_attn_k_proj_weight5, alloc1326) + R.vm.kill_object(model_decoder_layers_11_self_attn_k_proj_weight5) + model_decoder_layers_11_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[754] + model_decoder_layers_11_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[755] + alloc1327: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1324, model_decoder_layers_11_self_attn_v_proj_weight5, model_decoder_layers_11_self_attn_v_proj_bias5, alloc1327) + R.vm.kill_object(alloc1324) + R.vm.kill_object(model_decoder_layers_11_self_attn_v_proj_weight5) + R.vm.kill_object(model_decoder_layers_11_self_attn_v_proj_bias5) + alloc1328: R.Tensor((1, 60, 64), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 60, 64]), R.dtype("float16")) + cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22(alloc1325, alloc1326, alloc1327, alloc1328) + R.vm.kill_object(alloc1325) + R.vm.kill_object(alloc1326) + R.vm.kill_object(alloc1327) + alloc1329: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1327: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), alloc1328, alloc1329) + R.vm.kill_object(alloc1328) + lv165: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1329, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1329) + model_decoder_layers_11_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[758] + model_decoder_layers_11_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[759] + alloc1330: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7_add6(lv165, model_decoder_layers_11_self_attn_out_proj_weight5, model_decoder_layers_11_self_attn_out_proj_bias5, alloc1323, alloc1330) + R.vm.kill_object(alloc1323) + R.vm.kill_object(lv165) + R.vm.kill_object(model_decoder_layers_11_self_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_11_self_attn_out_proj_bias5) + model_decoder_layers_11_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[769] + model_decoder_layers_11_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[770] + alloc1331: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1330, model_decoder_layers_11_encoder_attn_layer_norm_weight5, model_decoder_layers_11_encoder_attn_layer_norm_bias5, alloc1331) + R.vm.kill_object(model_decoder_layers_11_encoder_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_11_encoder_attn_layer_norm_bias5) + model_decoder_layers_11_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[765] + model_decoder_layers_11_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[766] + alloc1332: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1331, model_decoder_layers_11_encoder_attn_q_proj_weight5, model_decoder_layers_11_encoder_attn_q_proj_bias5, alloc1332) + R.vm.kill_object(alloc1331) + R.vm.kill_object(model_decoder_layers_11_encoder_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_11_encoder_attn_q_proj_bias5) + lv168: R.Tensor((1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1332, R.shape([1, 20, 64]), sinfo_args=(R.Tensor((1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1332) + alloc1333: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1331: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), lv168, alloc1333) + R.vm.kill_object(lv168) + lv169: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1333, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1333) + model_decoder_layers_11_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[767] + model_decoder_layers_11_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[768] + alloc1334: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7_add6(lv169, model_decoder_layers_11_encoder_attn_out_proj_weight5, model_decoder_layers_11_encoder_attn_out_proj_bias5, alloc1330, alloc1334) + R.vm.kill_object(alloc1330) + R.vm.kill_object(lv169) + R.vm.kill_object(model_decoder_layers_11_encoder_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_11_encoder_attn_out_proj_bias5) + model_decoder_layers_11_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[775] + model_decoder_layers_11_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[776] + alloc1335: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1334, model_decoder_layers_11_final_layer_norm_weight5, model_decoder_layers_11_final_layer_norm_bias5, alloc1335) + R.vm.kill_object(model_decoder_layers_11_final_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_11_final_layer_norm_bias5) + model_decoder_layers_11_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[771] + model_decoder_layers_11_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[772] + alloc1336: R.Tensor((1, 1, 5120), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 5120]), R.dtype("float16")) + cls.fused_NT_matmul1_add8_gelu2(alloc1335, model_decoder_layers_11_fc1_weight5, model_decoder_layers_11_fc1_bias5, alloc1336) + R.vm.kill_object(alloc1335) + R.vm.kill_object(model_decoder_layers_11_fc1_weight5) + R.vm.kill_object(model_decoder_layers_11_fc1_bias5) + model_decoder_layers_11_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[773] + model_decoder_layers_11_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[774] + alloc1337: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul2_add7_add6(alloc1336, model_decoder_layers_11_fc2_weight5, model_decoder_layers_11_fc2_bias5, alloc1334, alloc1337) + R.vm.kill_object(alloc1334) + R.vm.kill_object(alloc1336) + R.vm.kill_object(model_decoder_layers_11_fc2_weight5) + R.vm.kill_object(model_decoder_layers_11_fc2_bias5) + model_decoder_layers_12_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[784] + model_decoder_layers_12_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[785] + alloc1338: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1337, model_decoder_layers_12_self_attn_layer_norm_weight5, model_decoder_layers_12_self_attn_layer_norm_bias5, alloc1338) + R.vm.kill_object(model_decoder_layers_12_self_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_12_self_attn_layer_norm_bias5) + model_decoder_layers_12_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[780] + model_decoder_layers_12_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[781] + alloc1339: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1338, model_decoder_layers_12_self_attn_q_proj_weight5, model_decoder_layers_12_self_attn_q_proj_bias5, alloc1339) + R.vm.kill_object(model_decoder_layers_12_self_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_12_self_attn_q_proj_bias5) + model_decoder_layers_12_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[777] + alloc1340: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.NT_matmul(alloc1338, model_decoder_layers_12_self_attn_k_proj_weight5, alloc1340) + R.vm.kill_object(model_decoder_layers_12_self_attn_k_proj_weight5) + model_decoder_layers_12_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[778] + model_decoder_layers_12_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[779] + alloc1341: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1338, model_decoder_layers_12_self_attn_v_proj_weight5, model_decoder_layers_12_self_attn_v_proj_bias5, alloc1341) + R.vm.kill_object(alloc1338) + R.vm.kill_object(model_decoder_layers_12_self_attn_v_proj_weight5) + R.vm.kill_object(model_decoder_layers_12_self_attn_v_proj_bias5) + alloc1342: R.Tensor((1, 60, 64), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 60, 64]), R.dtype("float16")) + cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22(alloc1339, alloc1340, alloc1341, alloc1342) + R.vm.kill_object(alloc1339) + R.vm.kill_object(alloc1340) + R.vm.kill_object(alloc1341) + alloc1343: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1341: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), alloc1342, alloc1343) + R.vm.kill_object(alloc1342) + lv176: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1343, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1343) + model_decoder_layers_12_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[782] + model_decoder_layers_12_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[783] + alloc1344: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7_add6(lv176, model_decoder_layers_12_self_attn_out_proj_weight5, model_decoder_layers_12_self_attn_out_proj_bias5, alloc1337, alloc1344) + R.vm.kill_object(alloc1337) + R.vm.kill_object(lv176) + R.vm.kill_object(model_decoder_layers_12_self_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_12_self_attn_out_proj_bias5) + model_decoder_layers_12_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[793] + model_decoder_layers_12_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[794] + alloc1345: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1344, model_decoder_layers_12_encoder_attn_layer_norm_weight5, model_decoder_layers_12_encoder_attn_layer_norm_bias5, alloc1345) + R.vm.kill_object(model_decoder_layers_12_encoder_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_12_encoder_attn_layer_norm_bias5) + model_decoder_layers_12_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[789] + model_decoder_layers_12_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[790] + alloc1346: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1345, model_decoder_layers_12_encoder_attn_q_proj_weight5, model_decoder_layers_12_encoder_attn_q_proj_bias5, alloc1346) + R.vm.kill_object(alloc1345) + R.vm.kill_object(model_decoder_layers_12_encoder_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_12_encoder_attn_q_proj_bias5) + lv179: R.Tensor((1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1346, R.shape([1, 20, 64]), sinfo_args=(R.Tensor((1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1346) + alloc1347: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1345: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), lv179, alloc1347) + R.vm.kill_object(lv179) + lv180: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1347, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1347) + model_decoder_layers_12_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[791] + model_decoder_layers_12_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[792] + alloc1348: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7_add6(lv180, model_decoder_layers_12_encoder_attn_out_proj_weight5, model_decoder_layers_12_encoder_attn_out_proj_bias5, alloc1344, alloc1348) + R.vm.kill_object(alloc1344) + R.vm.kill_object(lv180) + R.vm.kill_object(model_decoder_layers_12_encoder_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_12_encoder_attn_out_proj_bias5) + model_decoder_layers_12_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[799] + model_decoder_layers_12_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[800] + alloc1349: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1348, model_decoder_layers_12_final_layer_norm_weight5, model_decoder_layers_12_final_layer_norm_bias5, alloc1349) + R.vm.kill_object(model_decoder_layers_12_final_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_12_final_layer_norm_bias5) + model_decoder_layers_12_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[795] + model_decoder_layers_12_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[796] + alloc1350: R.Tensor((1, 1, 5120), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 5120]), R.dtype("float16")) + cls.fused_NT_matmul1_add8_gelu2(alloc1349, model_decoder_layers_12_fc1_weight5, model_decoder_layers_12_fc1_bias5, alloc1350) + R.vm.kill_object(alloc1349) + R.vm.kill_object(model_decoder_layers_12_fc1_weight5) + R.vm.kill_object(model_decoder_layers_12_fc1_bias5) + model_decoder_layers_12_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[797] + model_decoder_layers_12_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[798] + alloc1351: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul2_add7_add6(alloc1350, model_decoder_layers_12_fc2_weight5, model_decoder_layers_12_fc2_bias5, alloc1348, alloc1351) + R.vm.kill_object(alloc1348) + R.vm.kill_object(alloc1350) + R.vm.kill_object(model_decoder_layers_12_fc2_weight5) + R.vm.kill_object(model_decoder_layers_12_fc2_bias5) + model_decoder_layers_13_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[808] + model_decoder_layers_13_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[809] + alloc1352: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1351, model_decoder_layers_13_self_attn_layer_norm_weight5, model_decoder_layers_13_self_attn_layer_norm_bias5, alloc1352) + R.vm.kill_object(model_decoder_layers_13_self_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_13_self_attn_layer_norm_bias5) + model_decoder_layers_13_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[804] + model_decoder_layers_13_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[805] + alloc1353: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1352, model_decoder_layers_13_self_attn_q_proj_weight5, model_decoder_layers_13_self_attn_q_proj_bias5, alloc1353) + R.vm.kill_object(model_decoder_layers_13_self_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_13_self_attn_q_proj_bias5) + model_decoder_layers_13_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[801] + alloc1354: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.NT_matmul(alloc1352, model_decoder_layers_13_self_attn_k_proj_weight5, alloc1354) + R.vm.kill_object(model_decoder_layers_13_self_attn_k_proj_weight5) + model_decoder_layers_13_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[802] + model_decoder_layers_13_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[803] + alloc1355: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1352, model_decoder_layers_13_self_attn_v_proj_weight5, model_decoder_layers_13_self_attn_v_proj_bias5, alloc1355) + R.vm.kill_object(alloc1352) + R.vm.kill_object(model_decoder_layers_13_self_attn_v_proj_weight5) + R.vm.kill_object(model_decoder_layers_13_self_attn_v_proj_bias5) + alloc1356: R.Tensor((1, 60, 64), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 60, 64]), R.dtype("float16")) + cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22(alloc1353, alloc1354, alloc1355, alloc1356) + R.vm.kill_object(alloc1353) + R.vm.kill_object(alloc1354) + R.vm.kill_object(alloc1355) + alloc1357: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1355: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), alloc1356, alloc1357) + R.vm.kill_object(alloc1356) + lv187: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1357, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1357) + model_decoder_layers_13_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[806] + model_decoder_layers_13_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[807] + alloc1358: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7_add6(lv187, model_decoder_layers_13_self_attn_out_proj_weight5, model_decoder_layers_13_self_attn_out_proj_bias5, alloc1351, alloc1358) + R.vm.kill_object(alloc1351) + R.vm.kill_object(lv187) + R.vm.kill_object(model_decoder_layers_13_self_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_13_self_attn_out_proj_bias5) + model_decoder_layers_13_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[817] + model_decoder_layers_13_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[818] + alloc1359: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1358, model_decoder_layers_13_encoder_attn_layer_norm_weight5, model_decoder_layers_13_encoder_attn_layer_norm_bias5, alloc1359) + R.vm.kill_object(model_decoder_layers_13_encoder_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_13_encoder_attn_layer_norm_bias5) + model_decoder_layers_13_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[813] + model_decoder_layers_13_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[814] + alloc1360: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1359, model_decoder_layers_13_encoder_attn_q_proj_weight5, model_decoder_layers_13_encoder_attn_q_proj_bias5, alloc1360) + R.vm.kill_object(alloc1359) + R.vm.kill_object(model_decoder_layers_13_encoder_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_13_encoder_attn_q_proj_bias5) + lv190: R.Tensor((1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1360, R.shape([1, 20, 64]), sinfo_args=(R.Tensor((1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1360) + alloc1361: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1359: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), lv190, alloc1361) + R.vm.kill_object(lv190) + lv191: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1361, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1361) + model_decoder_layers_13_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[815] + model_decoder_layers_13_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[816] + alloc1362: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7_add6(lv191, model_decoder_layers_13_encoder_attn_out_proj_weight5, model_decoder_layers_13_encoder_attn_out_proj_bias5, alloc1358, alloc1362) + R.vm.kill_object(alloc1358) + R.vm.kill_object(lv191) + R.vm.kill_object(model_decoder_layers_13_encoder_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_13_encoder_attn_out_proj_bias5) + model_decoder_layers_13_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[823] + model_decoder_layers_13_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[824] + alloc1363: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1362, model_decoder_layers_13_final_layer_norm_weight5, model_decoder_layers_13_final_layer_norm_bias5, alloc1363) + R.vm.kill_object(model_decoder_layers_13_final_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_13_final_layer_norm_bias5) + model_decoder_layers_13_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[819] + model_decoder_layers_13_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[820] + alloc1364: R.Tensor((1, 1, 5120), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 5120]), R.dtype("float16")) + cls.fused_NT_matmul1_add8_gelu2(alloc1363, model_decoder_layers_13_fc1_weight5, model_decoder_layers_13_fc1_bias5, alloc1364) + R.vm.kill_object(alloc1363) + R.vm.kill_object(model_decoder_layers_13_fc1_weight5) + R.vm.kill_object(model_decoder_layers_13_fc1_bias5) + model_decoder_layers_13_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[821] + model_decoder_layers_13_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[822] + alloc1365: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul2_add7_add6(alloc1364, model_decoder_layers_13_fc2_weight5, model_decoder_layers_13_fc2_bias5, alloc1362, alloc1365) + R.vm.kill_object(alloc1362) + R.vm.kill_object(alloc1364) + R.vm.kill_object(model_decoder_layers_13_fc2_weight5) + R.vm.kill_object(model_decoder_layers_13_fc2_bias5) + model_decoder_layers_14_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[832] + model_decoder_layers_14_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[833] + alloc1366: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1365, model_decoder_layers_14_self_attn_layer_norm_weight5, model_decoder_layers_14_self_attn_layer_norm_bias5, alloc1366) + R.vm.kill_object(model_decoder_layers_14_self_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_14_self_attn_layer_norm_bias5) + model_decoder_layers_14_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[828] + model_decoder_layers_14_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[829] + alloc1367: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1366, model_decoder_layers_14_self_attn_q_proj_weight5, model_decoder_layers_14_self_attn_q_proj_bias5, alloc1367) + R.vm.kill_object(model_decoder_layers_14_self_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_14_self_attn_q_proj_bias5) + model_decoder_layers_14_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[825] + alloc1368: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.NT_matmul(alloc1366, model_decoder_layers_14_self_attn_k_proj_weight5, alloc1368) + R.vm.kill_object(model_decoder_layers_14_self_attn_k_proj_weight5) + model_decoder_layers_14_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[826] + model_decoder_layers_14_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[827] + alloc1369: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1366, model_decoder_layers_14_self_attn_v_proj_weight5, model_decoder_layers_14_self_attn_v_proj_bias5, alloc1369) + R.vm.kill_object(alloc1366) + R.vm.kill_object(model_decoder_layers_14_self_attn_v_proj_weight5) + R.vm.kill_object(model_decoder_layers_14_self_attn_v_proj_bias5) + alloc1370: R.Tensor((1, 60, 64), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 60, 64]), R.dtype("float16")) + cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22(alloc1367, alloc1368, alloc1369, alloc1370) + R.vm.kill_object(alloc1367) + R.vm.kill_object(alloc1368) + R.vm.kill_object(alloc1369) + alloc1371: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1369: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), alloc1370, alloc1371) + R.vm.kill_object(alloc1370) + lv198: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1371, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1371) + model_decoder_layers_14_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[830] + model_decoder_layers_14_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[831] + alloc1372: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7_add6(lv198, model_decoder_layers_14_self_attn_out_proj_weight5, model_decoder_layers_14_self_attn_out_proj_bias5, alloc1365, alloc1372) + R.vm.kill_object(alloc1365) + R.vm.kill_object(lv198) + R.vm.kill_object(model_decoder_layers_14_self_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_14_self_attn_out_proj_bias5) + model_decoder_layers_14_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[841] + model_decoder_layers_14_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[842] + alloc1373: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1372, model_decoder_layers_14_encoder_attn_layer_norm_weight5, model_decoder_layers_14_encoder_attn_layer_norm_bias5, alloc1373) + R.vm.kill_object(model_decoder_layers_14_encoder_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_14_encoder_attn_layer_norm_bias5) + model_decoder_layers_14_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[837] + model_decoder_layers_14_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[838] + alloc1374: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1373, model_decoder_layers_14_encoder_attn_q_proj_weight5, model_decoder_layers_14_encoder_attn_q_proj_bias5, alloc1374) + R.vm.kill_object(alloc1373) + R.vm.kill_object(model_decoder_layers_14_encoder_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_14_encoder_attn_q_proj_bias5) + lv201: R.Tensor((1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1374, R.shape([1, 20, 64]), sinfo_args=(R.Tensor((1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1374) + alloc1375: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1373: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), lv201, alloc1375) + R.vm.kill_object(lv201) + lv202: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1375, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1375) + model_decoder_layers_14_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[839] + model_decoder_layers_14_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[840] + alloc1376: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7_add6(lv202, model_decoder_layers_14_encoder_attn_out_proj_weight5, model_decoder_layers_14_encoder_attn_out_proj_bias5, alloc1372, alloc1376) + R.vm.kill_object(alloc1372) + R.vm.kill_object(lv202) + R.vm.kill_object(model_decoder_layers_14_encoder_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_14_encoder_attn_out_proj_bias5) + model_decoder_layers_14_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[847] + model_decoder_layers_14_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[848] + alloc1377: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1376, model_decoder_layers_14_final_layer_norm_weight5, model_decoder_layers_14_final_layer_norm_bias5, alloc1377) + R.vm.kill_object(model_decoder_layers_14_final_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_14_final_layer_norm_bias5) + model_decoder_layers_14_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[843] + model_decoder_layers_14_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[844] + alloc1378: R.Tensor((1, 1, 5120), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 5120]), R.dtype("float16")) + cls.fused_NT_matmul1_add8_gelu2(alloc1377, model_decoder_layers_14_fc1_weight5, model_decoder_layers_14_fc1_bias5, alloc1378) + R.vm.kill_object(alloc1377) + R.vm.kill_object(model_decoder_layers_14_fc1_weight5) + R.vm.kill_object(model_decoder_layers_14_fc1_bias5) + model_decoder_layers_14_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[845] + model_decoder_layers_14_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[846] + alloc1379: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul2_add7_add6(alloc1378, model_decoder_layers_14_fc2_weight5, model_decoder_layers_14_fc2_bias5, alloc1376, alloc1379) + R.vm.kill_object(alloc1376) + R.vm.kill_object(alloc1378) + R.vm.kill_object(model_decoder_layers_14_fc2_weight5) + R.vm.kill_object(model_decoder_layers_14_fc2_bias5) + model_decoder_layers_15_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[856] + model_decoder_layers_15_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[857] + alloc1380: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1379, model_decoder_layers_15_self_attn_layer_norm_weight5, model_decoder_layers_15_self_attn_layer_norm_bias5, alloc1380) + R.vm.kill_object(model_decoder_layers_15_self_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_15_self_attn_layer_norm_bias5) + model_decoder_layers_15_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[852] + model_decoder_layers_15_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[853] + alloc1381: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1380, model_decoder_layers_15_self_attn_q_proj_weight5, model_decoder_layers_15_self_attn_q_proj_bias5, alloc1381) + R.vm.kill_object(model_decoder_layers_15_self_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_15_self_attn_q_proj_bias5) + model_decoder_layers_15_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[849] + alloc1382: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.NT_matmul(alloc1380, model_decoder_layers_15_self_attn_k_proj_weight5, alloc1382) + R.vm.kill_object(model_decoder_layers_15_self_attn_k_proj_weight5) + model_decoder_layers_15_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[850] + model_decoder_layers_15_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[851] + alloc1383: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1380, model_decoder_layers_15_self_attn_v_proj_weight5, model_decoder_layers_15_self_attn_v_proj_bias5, alloc1383) + R.vm.kill_object(alloc1380) + R.vm.kill_object(model_decoder_layers_15_self_attn_v_proj_weight5) + R.vm.kill_object(model_decoder_layers_15_self_attn_v_proj_bias5) + alloc1384: R.Tensor((1, 60, 64), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 60, 64]), R.dtype("float16")) + cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22(alloc1381, alloc1382, alloc1383, alloc1384) + R.vm.kill_object(alloc1381) + R.vm.kill_object(alloc1382) + R.vm.kill_object(alloc1383) + alloc1385: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1383: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), alloc1384, alloc1385) + R.vm.kill_object(alloc1384) + lv209: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1385, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1385) + model_decoder_layers_15_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[854] + model_decoder_layers_15_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[855] + alloc1386: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7_add6(lv209, model_decoder_layers_15_self_attn_out_proj_weight5, model_decoder_layers_15_self_attn_out_proj_bias5, alloc1379, alloc1386) + R.vm.kill_object(alloc1379) + R.vm.kill_object(lv209) + R.vm.kill_object(model_decoder_layers_15_self_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_15_self_attn_out_proj_bias5) + model_decoder_layers_15_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[865] + model_decoder_layers_15_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[866] + alloc1387: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1386, model_decoder_layers_15_encoder_attn_layer_norm_weight5, model_decoder_layers_15_encoder_attn_layer_norm_bias5, alloc1387) + R.vm.kill_object(model_decoder_layers_15_encoder_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_15_encoder_attn_layer_norm_bias5) + model_decoder_layers_15_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[861] + model_decoder_layers_15_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[862] + alloc1388: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1387, model_decoder_layers_15_encoder_attn_q_proj_weight5, model_decoder_layers_15_encoder_attn_q_proj_bias5, alloc1388) + R.vm.kill_object(alloc1387) + R.vm.kill_object(model_decoder_layers_15_encoder_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_15_encoder_attn_q_proj_bias5) + lv212: R.Tensor((1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1388, R.shape([1, 20, 64]), sinfo_args=(R.Tensor((1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1388) + alloc1389: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1387: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), lv212, alloc1389) + R.vm.kill_object(lv212) + lv213: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1389, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1389) + model_decoder_layers_15_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[863] + model_decoder_layers_15_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[864] + alloc1390: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7_add6(lv213, model_decoder_layers_15_encoder_attn_out_proj_weight5, model_decoder_layers_15_encoder_attn_out_proj_bias5, alloc1386, alloc1390) + R.vm.kill_object(alloc1386) + R.vm.kill_object(lv213) + R.vm.kill_object(model_decoder_layers_15_encoder_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_15_encoder_attn_out_proj_bias5) + model_decoder_layers_15_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[871] + model_decoder_layers_15_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[872] + alloc1391: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1390, model_decoder_layers_15_final_layer_norm_weight5, model_decoder_layers_15_final_layer_norm_bias5, alloc1391) + R.vm.kill_object(model_decoder_layers_15_final_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_15_final_layer_norm_bias5) + model_decoder_layers_15_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[867] + model_decoder_layers_15_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[868] + alloc1392: R.Tensor((1, 1, 5120), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 5120]), R.dtype("float16")) + cls.fused_NT_matmul1_add8_gelu2(alloc1391, model_decoder_layers_15_fc1_weight5, model_decoder_layers_15_fc1_bias5, alloc1392) + R.vm.kill_object(alloc1391) + R.vm.kill_object(model_decoder_layers_15_fc1_weight5) + R.vm.kill_object(model_decoder_layers_15_fc1_bias5) + model_decoder_layers_15_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[869] + model_decoder_layers_15_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[870] + alloc1393: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul2_add7_add6(alloc1392, model_decoder_layers_15_fc2_weight5, model_decoder_layers_15_fc2_bias5, alloc1390, alloc1393) + R.vm.kill_object(alloc1390) + R.vm.kill_object(alloc1392) + R.vm.kill_object(model_decoder_layers_15_fc2_weight5) + R.vm.kill_object(model_decoder_layers_15_fc2_bias5) + model_decoder_layers_16_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[880] + model_decoder_layers_16_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[881] + alloc1394: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1393, model_decoder_layers_16_self_attn_layer_norm_weight5, model_decoder_layers_16_self_attn_layer_norm_bias5, alloc1394) + R.vm.kill_object(model_decoder_layers_16_self_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_16_self_attn_layer_norm_bias5) + model_decoder_layers_16_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[876] + model_decoder_layers_16_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[877] + alloc1395: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1394, model_decoder_layers_16_self_attn_q_proj_weight5, model_decoder_layers_16_self_attn_q_proj_bias5, alloc1395) + R.vm.kill_object(model_decoder_layers_16_self_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_16_self_attn_q_proj_bias5) + model_decoder_layers_16_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[873] + alloc1396: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.NT_matmul(alloc1394, model_decoder_layers_16_self_attn_k_proj_weight5, alloc1396) + R.vm.kill_object(model_decoder_layers_16_self_attn_k_proj_weight5) + model_decoder_layers_16_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[874] + model_decoder_layers_16_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[875] + alloc1397: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1394, model_decoder_layers_16_self_attn_v_proj_weight5, model_decoder_layers_16_self_attn_v_proj_bias5, alloc1397) + R.vm.kill_object(alloc1394) + R.vm.kill_object(model_decoder_layers_16_self_attn_v_proj_weight5) + R.vm.kill_object(model_decoder_layers_16_self_attn_v_proj_bias5) + alloc1398: R.Tensor((1, 60, 64), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 60, 64]), R.dtype("float16")) + cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22(alloc1395, alloc1396, alloc1397, alloc1398) + R.vm.kill_object(alloc1395) + R.vm.kill_object(alloc1396) + R.vm.kill_object(alloc1397) + alloc1399: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1397: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), alloc1398, alloc1399) + R.vm.kill_object(alloc1398) + lv220: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1399, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1399) + model_decoder_layers_16_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[878] + model_decoder_layers_16_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[879] + alloc1400: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7_add6(lv220, model_decoder_layers_16_self_attn_out_proj_weight5, model_decoder_layers_16_self_attn_out_proj_bias5, alloc1393, alloc1400) + R.vm.kill_object(alloc1393) + R.vm.kill_object(lv220) + R.vm.kill_object(model_decoder_layers_16_self_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_16_self_attn_out_proj_bias5) + model_decoder_layers_16_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[889] + model_decoder_layers_16_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[890] + alloc1401: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1400, model_decoder_layers_16_encoder_attn_layer_norm_weight5, model_decoder_layers_16_encoder_attn_layer_norm_bias5, alloc1401) + R.vm.kill_object(model_decoder_layers_16_encoder_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_16_encoder_attn_layer_norm_bias5) + model_decoder_layers_16_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[885] + model_decoder_layers_16_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[886] + alloc1402: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1401, model_decoder_layers_16_encoder_attn_q_proj_weight5, model_decoder_layers_16_encoder_attn_q_proj_bias5, alloc1402) + R.vm.kill_object(alloc1401) + R.vm.kill_object(model_decoder_layers_16_encoder_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_16_encoder_attn_q_proj_bias5) + lv223: R.Tensor((1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1402, R.shape([1, 20, 64]), sinfo_args=(R.Tensor((1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1402) + alloc1403: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1401: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), lv223, alloc1403) + R.vm.kill_object(lv223) + lv224: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1403, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1403) + model_decoder_layers_16_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[887] + model_decoder_layers_16_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[888] + alloc1404: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7_add6(lv224, model_decoder_layers_16_encoder_attn_out_proj_weight5, model_decoder_layers_16_encoder_attn_out_proj_bias5, alloc1400, alloc1404) + R.vm.kill_object(alloc1400) + R.vm.kill_object(lv224) + R.vm.kill_object(model_decoder_layers_16_encoder_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_16_encoder_attn_out_proj_bias5) + model_decoder_layers_16_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[895] + model_decoder_layers_16_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[896] + alloc1405: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1404, model_decoder_layers_16_final_layer_norm_weight5, model_decoder_layers_16_final_layer_norm_bias5, alloc1405) + R.vm.kill_object(model_decoder_layers_16_final_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_16_final_layer_norm_bias5) + model_decoder_layers_16_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[891] + model_decoder_layers_16_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[892] + alloc1406: R.Tensor((1, 1, 5120), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 5120]), R.dtype("float16")) + cls.fused_NT_matmul1_add8_gelu2(alloc1405, model_decoder_layers_16_fc1_weight5, model_decoder_layers_16_fc1_bias5, alloc1406) + R.vm.kill_object(alloc1405) + R.vm.kill_object(model_decoder_layers_16_fc1_weight5) + R.vm.kill_object(model_decoder_layers_16_fc1_bias5) + model_decoder_layers_16_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[893] + model_decoder_layers_16_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[894] + alloc1407: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul2_add7_add6(alloc1406, model_decoder_layers_16_fc2_weight5, model_decoder_layers_16_fc2_bias5, alloc1404, alloc1407) + R.vm.kill_object(alloc1404) + R.vm.kill_object(alloc1406) + R.vm.kill_object(model_decoder_layers_16_fc2_weight5) + R.vm.kill_object(model_decoder_layers_16_fc2_bias5) + model_decoder_layers_17_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[904] + model_decoder_layers_17_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[905] + alloc1408: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1407, model_decoder_layers_17_self_attn_layer_norm_weight5, model_decoder_layers_17_self_attn_layer_norm_bias5, alloc1408) + R.vm.kill_object(model_decoder_layers_17_self_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_17_self_attn_layer_norm_bias5) + model_decoder_layers_17_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[900] + model_decoder_layers_17_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[901] + alloc1409: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1408, model_decoder_layers_17_self_attn_q_proj_weight5, model_decoder_layers_17_self_attn_q_proj_bias5, alloc1409) + R.vm.kill_object(model_decoder_layers_17_self_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_17_self_attn_q_proj_bias5) + model_decoder_layers_17_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[897] + alloc1410: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.NT_matmul(alloc1408, model_decoder_layers_17_self_attn_k_proj_weight5, alloc1410) + R.vm.kill_object(model_decoder_layers_17_self_attn_k_proj_weight5) + model_decoder_layers_17_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[898] + model_decoder_layers_17_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[899] + alloc1411: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1408, model_decoder_layers_17_self_attn_v_proj_weight5, model_decoder_layers_17_self_attn_v_proj_bias5, alloc1411) + R.vm.kill_object(alloc1408) + R.vm.kill_object(model_decoder_layers_17_self_attn_v_proj_weight5) + R.vm.kill_object(model_decoder_layers_17_self_attn_v_proj_bias5) + alloc1412: R.Tensor((1, 60, 64), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 60, 64]), R.dtype("float16")) + cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22(alloc1409, alloc1410, alloc1411, alloc1412) + R.vm.kill_object(alloc1409) + R.vm.kill_object(alloc1410) + R.vm.kill_object(alloc1411) + alloc1413: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1411: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), alloc1412, alloc1413) + R.vm.kill_object(alloc1412) + lv231: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1413, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1413) + model_decoder_layers_17_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[902] + model_decoder_layers_17_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[903] + alloc1414: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7_add6(lv231, model_decoder_layers_17_self_attn_out_proj_weight5, model_decoder_layers_17_self_attn_out_proj_bias5, alloc1407, alloc1414) + R.vm.kill_object(alloc1407) + R.vm.kill_object(lv231) + R.vm.kill_object(model_decoder_layers_17_self_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_17_self_attn_out_proj_bias5) + model_decoder_layers_17_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[913] + model_decoder_layers_17_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[914] + alloc1415: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1414, model_decoder_layers_17_encoder_attn_layer_norm_weight5, model_decoder_layers_17_encoder_attn_layer_norm_bias5, alloc1415) + R.vm.kill_object(model_decoder_layers_17_encoder_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_17_encoder_attn_layer_norm_bias5) + model_decoder_layers_17_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[909] + model_decoder_layers_17_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[910] + alloc1416: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1415, model_decoder_layers_17_encoder_attn_q_proj_weight5, model_decoder_layers_17_encoder_attn_q_proj_bias5, alloc1416) + R.vm.kill_object(alloc1415) + R.vm.kill_object(model_decoder_layers_17_encoder_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_17_encoder_attn_q_proj_bias5) + lv234: R.Tensor((1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1416, R.shape([1, 20, 64]), sinfo_args=(R.Tensor((1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1416) + alloc1417: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1415: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), lv234, alloc1417) + R.vm.kill_object(lv234) + lv235: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1417, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1417) + model_decoder_layers_17_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[911] + model_decoder_layers_17_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[912] + alloc1418: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7_add6(lv235, model_decoder_layers_17_encoder_attn_out_proj_weight5, model_decoder_layers_17_encoder_attn_out_proj_bias5, alloc1414, alloc1418) + R.vm.kill_object(alloc1414) + R.vm.kill_object(lv235) + R.vm.kill_object(model_decoder_layers_17_encoder_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_17_encoder_attn_out_proj_bias5) + model_decoder_layers_17_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[919] + model_decoder_layers_17_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[920] + alloc1419: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1418, model_decoder_layers_17_final_layer_norm_weight5, model_decoder_layers_17_final_layer_norm_bias5, alloc1419) + R.vm.kill_object(model_decoder_layers_17_final_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_17_final_layer_norm_bias5) + model_decoder_layers_17_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[915] + model_decoder_layers_17_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[916] + alloc1420: R.Tensor((1, 1, 5120), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 5120]), R.dtype("float16")) + cls.fused_NT_matmul1_add8_gelu2(alloc1419, model_decoder_layers_17_fc1_weight5, model_decoder_layers_17_fc1_bias5, alloc1420) + R.vm.kill_object(alloc1419) + R.vm.kill_object(model_decoder_layers_17_fc1_weight5) + R.vm.kill_object(model_decoder_layers_17_fc1_bias5) + model_decoder_layers_17_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[917] + model_decoder_layers_17_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[918] + alloc1421: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul2_add7_add6(alloc1420, model_decoder_layers_17_fc2_weight5, model_decoder_layers_17_fc2_bias5, alloc1418, alloc1421) + R.vm.kill_object(alloc1418) + R.vm.kill_object(alloc1420) + R.vm.kill_object(model_decoder_layers_17_fc2_weight5) + R.vm.kill_object(model_decoder_layers_17_fc2_bias5) + model_decoder_layers_18_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[928] + model_decoder_layers_18_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[929] + alloc1422: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1421, model_decoder_layers_18_self_attn_layer_norm_weight5, model_decoder_layers_18_self_attn_layer_norm_bias5, alloc1422) + R.vm.kill_object(model_decoder_layers_18_self_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_18_self_attn_layer_norm_bias5) + model_decoder_layers_18_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[924] + model_decoder_layers_18_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[925] + alloc1423: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1422, model_decoder_layers_18_self_attn_q_proj_weight5, model_decoder_layers_18_self_attn_q_proj_bias5, alloc1423) + R.vm.kill_object(model_decoder_layers_18_self_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_18_self_attn_q_proj_bias5) + model_decoder_layers_18_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[921] + alloc1424: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.NT_matmul(alloc1422, model_decoder_layers_18_self_attn_k_proj_weight5, alloc1424) + R.vm.kill_object(model_decoder_layers_18_self_attn_k_proj_weight5) + model_decoder_layers_18_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[922] + model_decoder_layers_18_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[923] + alloc1425: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1422, model_decoder_layers_18_self_attn_v_proj_weight5, model_decoder_layers_18_self_attn_v_proj_bias5, alloc1425) + R.vm.kill_object(alloc1422) + R.vm.kill_object(model_decoder_layers_18_self_attn_v_proj_weight5) + R.vm.kill_object(model_decoder_layers_18_self_attn_v_proj_bias5) + alloc1426: R.Tensor((1, 60, 64), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 60, 64]), R.dtype("float16")) + cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22(alloc1423, alloc1424, alloc1425, alloc1426) + R.vm.kill_object(alloc1423) + R.vm.kill_object(alloc1424) + R.vm.kill_object(alloc1425) + alloc1427: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1425: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), alloc1426, alloc1427) + R.vm.kill_object(alloc1426) + lv242: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1427, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1427) + model_decoder_layers_18_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[926] + model_decoder_layers_18_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[927] + alloc1428: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7_add6(lv242, model_decoder_layers_18_self_attn_out_proj_weight5, model_decoder_layers_18_self_attn_out_proj_bias5, alloc1421, alloc1428) + R.vm.kill_object(alloc1421) + R.vm.kill_object(lv242) + R.vm.kill_object(model_decoder_layers_18_self_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_18_self_attn_out_proj_bias5) + model_decoder_layers_18_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[937] + model_decoder_layers_18_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[938] + alloc1429: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1428, model_decoder_layers_18_encoder_attn_layer_norm_weight5, model_decoder_layers_18_encoder_attn_layer_norm_bias5, alloc1429) + R.vm.kill_object(model_decoder_layers_18_encoder_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_18_encoder_attn_layer_norm_bias5) + model_decoder_layers_18_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[933] + model_decoder_layers_18_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[934] + alloc1430: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1429, model_decoder_layers_18_encoder_attn_q_proj_weight5, model_decoder_layers_18_encoder_attn_q_proj_bias5, alloc1430) + R.vm.kill_object(alloc1429) + R.vm.kill_object(model_decoder_layers_18_encoder_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_18_encoder_attn_q_proj_bias5) + lv245: R.Tensor((1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1430, R.shape([1, 20, 64]), sinfo_args=(R.Tensor((1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1430) + alloc1431: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1429: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), lv245, alloc1431) + R.vm.kill_object(lv245) + lv246: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1431, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1431) + model_decoder_layers_18_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[935] + model_decoder_layers_18_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[936] + alloc1432: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7_add6(lv246, model_decoder_layers_18_encoder_attn_out_proj_weight5, model_decoder_layers_18_encoder_attn_out_proj_bias5, alloc1428, alloc1432) + R.vm.kill_object(alloc1428) + R.vm.kill_object(lv246) + R.vm.kill_object(model_decoder_layers_18_encoder_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_18_encoder_attn_out_proj_bias5) + model_decoder_layers_18_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[943] + model_decoder_layers_18_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[944] + alloc1433: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1432, model_decoder_layers_18_final_layer_norm_weight5, model_decoder_layers_18_final_layer_norm_bias5, alloc1433) + R.vm.kill_object(model_decoder_layers_18_final_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_18_final_layer_norm_bias5) + model_decoder_layers_18_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[939] + model_decoder_layers_18_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[940] + alloc1434: R.Tensor((1, 1, 5120), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 5120]), R.dtype("float16")) + cls.fused_NT_matmul1_add8_gelu2(alloc1433, model_decoder_layers_18_fc1_weight5, model_decoder_layers_18_fc1_bias5, alloc1434) + R.vm.kill_object(alloc1433) + R.vm.kill_object(model_decoder_layers_18_fc1_weight5) + R.vm.kill_object(model_decoder_layers_18_fc1_bias5) + model_decoder_layers_18_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[941] + model_decoder_layers_18_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[942] + alloc1435: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul2_add7_add6(alloc1434, model_decoder_layers_18_fc2_weight5, model_decoder_layers_18_fc2_bias5, alloc1432, alloc1435) + R.vm.kill_object(alloc1432) + R.vm.kill_object(alloc1434) + R.vm.kill_object(model_decoder_layers_18_fc2_weight5) + R.vm.kill_object(model_decoder_layers_18_fc2_bias5) + model_decoder_layers_19_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[952] + model_decoder_layers_19_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[953] + alloc1436: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1435, model_decoder_layers_19_self_attn_layer_norm_weight5, model_decoder_layers_19_self_attn_layer_norm_bias5, alloc1436) + R.vm.kill_object(model_decoder_layers_19_self_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_19_self_attn_layer_norm_bias5) + model_decoder_layers_19_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[948] + model_decoder_layers_19_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[949] + alloc1437: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1436, model_decoder_layers_19_self_attn_q_proj_weight5, model_decoder_layers_19_self_attn_q_proj_bias5, alloc1437) + R.vm.kill_object(model_decoder_layers_19_self_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_19_self_attn_q_proj_bias5) + model_decoder_layers_19_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[945] + alloc1438: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.NT_matmul(alloc1436, model_decoder_layers_19_self_attn_k_proj_weight5, alloc1438) + R.vm.kill_object(model_decoder_layers_19_self_attn_k_proj_weight5) + model_decoder_layers_19_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[946] + model_decoder_layers_19_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[947] + alloc1439: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1436, model_decoder_layers_19_self_attn_v_proj_weight5, model_decoder_layers_19_self_attn_v_proj_bias5, alloc1439) + R.vm.kill_object(alloc1436) + R.vm.kill_object(model_decoder_layers_19_self_attn_v_proj_weight5) + R.vm.kill_object(model_decoder_layers_19_self_attn_v_proj_bias5) + alloc1440: R.Tensor((1, 60, 64), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 60, 64]), R.dtype("float16")) + cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22(alloc1437, alloc1438, alloc1439, alloc1440) + R.vm.kill_object(alloc1437) + R.vm.kill_object(alloc1438) + R.vm.kill_object(alloc1439) + alloc1441: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1439: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), alloc1440, alloc1441) + R.vm.kill_object(alloc1440) + lv253: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1441, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1441) + model_decoder_layers_19_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[950] + model_decoder_layers_19_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[951] + alloc1442: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7_add6(lv253, model_decoder_layers_19_self_attn_out_proj_weight5, model_decoder_layers_19_self_attn_out_proj_bias5, alloc1435, alloc1442) + R.vm.kill_object(alloc1435) + R.vm.kill_object(lv253) + R.vm.kill_object(model_decoder_layers_19_self_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_19_self_attn_out_proj_bias5) + model_decoder_layers_19_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[961] + model_decoder_layers_19_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[962] + alloc1443: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1442, model_decoder_layers_19_encoder_attn_layer_norm_weight5, model_decoder_layers_19_encoder_attn_layer_norm_bias5, alloc1443) + R.vm.kill_object(model_decoder_layers_19_encoder_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_19_encoder_attn_layer_norm_bias5) + model_decoder_layers_19_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[957] + model_decoder_layers_19_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[958] + alloc1444: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1443, model_decoder_layers_19_encoder_attn_q_proj_weight5, model_decoder_layers_19_encoder_attn_q_proj_bias5, alloc1444) + R.vm.kill_object(alloc1443) + R.vm.kill_object(model_decoder_layers_19_encoder_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_19_encoder_attn_q_proj_bias5) + lv256: R.Tensor((1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1444, R.shape([1, 20, 64]), sinfo_args=(R.Tensor((1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1444) + alloc1445: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1443: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), lv256, alloc1445) + R.vm.kill_object(lv256) + lv257: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1445, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1445) + model_decoder_layers_19_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[959] + model_decoder_layers_19_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[960] + alloc1446: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7_add6(lv257, model_decoder_layers_19_encoder_attn_out_proj_weight5, model_decoder_layers_19_encoder_attn_out_proj_bias5, alloc1442, alloc1446) + R.vm.kill_object(alloc1442) + R.vm.kill_object(lv257) + R.vm.kill_object(model_decoder_layers_19_encoder_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_19_encoder_attn_out_proj_bias5) + model_decoder_layers_19_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[967] + model_decoder_layers_19_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[968] + alloc1447: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1446, model_decoder_layers_19_final_layer_norm_weight5, model_decoder_layers_19_final_layer_norm_bias5, alloc1447) + R.vm.kill_object(model_decoder_layers_19_final_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_19_final_layer_norm_bias5) + model_decoder_layers_19_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[963] + model_decoder_layers_19_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[964] + alloc1448: R.Tensor((1, 1, 5120), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 5120]), R.dtype("float16")) + cls.fused_NT_matmul1_add8_gelu2(alloc1447, model_decoder_layers_19_fc1_weight5, model_decoder_layers_19_fc1_bias5, alloc1448) + R.vm.kill_object(alloc1447) + R.vm.kill_object(model_decoder_layers_19_fc1_weight5) + R.vm.kill_object(model_decoder_layers_19_fc1_bias5) + model_decoder_layers_19_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[965] + model_decoder_layers_19_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[966] + alloc1449: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul2_add7_add6(alloc1448, model_decoder_layers_19_fc2_weight5, model_decoder_layers_19_fc2_bias5, alloc1446, alloc1449) + R.vm.kill_object(alloc1446) + R.vm.kill_object(alloc1448) + R.vm.kill_object(model_decoder_layers_19_fc2_weight5) + R.vm.kill_object(model_decoder_layers_19_fc2_bias5) + model_decoder_layers_20_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[976] + model_decoder_layers_20_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[977] + alloc1450: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1449, model_decoder_layers_20_self_attn_layer_norm_weight5, model_decoder_layers_20_self_attn_layer_norm_bias5, alloc1450) + R.vm.kill_object(model_decoder_layers_20_self_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_20_self_attn_layer_norm_bias5) + model_decoder_layers_20_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[972] + model_decoder_layers_20_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[973] + alloc1451: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1450, model_decoder_layers_20_self_attn_q_proj_weight5, model_decoder_layers_20_self_attn_q_proj_bias5, alloc1451) + R.vm.kill_object(model_decoder_layers_20_self_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_20_self_attn_q_proj_bias5) + model_decoder_layers_20_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[969] + alloc1452: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.NT_matmul(alloc1450, model_decoder_layers_20_self_attn_k_proj_weight5, alloc1452) + R.vm.kill_object(model_decoder_layers_20_self_attn_k_proj_weight5) + model_decoder_layers_20_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[970] + model_decoder_layers_20_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[971] + alloc1453: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1450, model_decoder_layers_20_self_attn_v_proj_weight5, model_decoder_layers_20_self_attn_v_proj_bias5, alloc1453) + R.vm.kill_object(alloc1450) + R.vm.kill_object(model_decoder_layers_20_self_attn_v_proj_weight5) + R.vm.kill_object(model_decoder_layers_20_self_attn_v_proj_bias5) + alloc1454: R.Tensor((1, 60, 64), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 60, 64]), R.dtype("float16")) + cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22(alloc1451, alloc1452, alloc1453, alloc1454) + R.vm.kill_object(alloc1451) + R.vm.kill_object(alloc1452) + R.vm.kill_object(alloc1453) + alloc1455: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1453: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), alloc1454, alloc1455) + R.vm.kill_object(alloc1454) + lv264_1: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1455, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1455) + model_decoder_layers_20_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[974] + model_decoder_layers_20_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[975] + alloc1456: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7_add6(lv264_1, model_decoder_layers_20_self_attn_out_proj_weight5, model_decoder_layers_20_self_attn_out_proj_bias5, alloc1449, alloc1456) + R.vm.kill_object(alloc1449) + R.vm.kill_object(lv264_1) + R.vm.kill_object(model_decoder_layers_20_self_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_20_self_attn_out_proj_bias5) + model_decoder_layers_20_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[985] + model_decoder_layers_20_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[986] + alloc1457: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1456, model_decoder_layers_20_encoder_attn_layer_norm_weight5, model_decoder_layers_20_encoder_attn_layer_norm_bias5, alloc1457) + R.vm.kill_object(model_decoder_layers_20_encoder_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_20_encoder_attn_layer_norm_bias5) + model_decoder_layers_20_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[981] + model_decoder_layers_20_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[982] + alloc1458: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1457, model_decoder_layers_20_encoder_attn_q_proj_weight5, model_decoder_layers_20_encoder_attn_q_proj_bias5, alloc1458) + R.vm.kill_object(alloc1457) + R.vm.kill_object(model_decoder_layers_20_encoder_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_20_encoder_attn_q_proj_bias5) + lv267: R.Tensor((1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1458, R.shape([1, 20, 64]), sinfo_args=(R.Tensor((1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1458) + alloc1459: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1457: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), lv267, alloc1459) + R.vm.kill_object(lv267) + lv268: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1459, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1459) + model_decoder_layers_20_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[983] + model_decoder_layers_20_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[984] + alloc1460: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7_add6(lv268, model_decoder_layers_20_encoder_attn_out_proj_weight5, model_decoder_layers_20_encoder_attn_out_proj_bias5, alloc1456, alloc1460) + R.vm.kill_object(alloc1456) + R.vm.kill_object(lv268) + R.vm.kill_object(model_decoder_layers_20_encoder_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_20_encoder_attn_out_proj_bias5) + model_decoder_layers_20_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[991] + model_decoder_layers_20_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[992] + alloc1461: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1460, model_decoder_layers_20_final_layer_norm_weight5, model_decoder_layers_20_final_layer_norm_bias5, alloc1461) + R.vm.kill_object(model_decoder_layers_20_final_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_20_final_layer_norm_bias5) + model_decoder_layers_20_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[987] + model_decoder_layers_20_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[988] + alloc1462: R.Tensor((1, 1, 5120), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 5120]), R.dtype("float16")) + cls.fused_NT_matmul1_add8_gelu2(alloc1461, model_decoder_layers_20_fc1_weight5, model_decoder_layers_20_fc1_bias5, alloc1462) + R.vm.kill_object(alloc1461) + R.vm.kill_object(model_decoder_layers_20_fc1_weight5) + R.vm.kill_object(model_decoder_layers_20_fc1_bias5) + model_decoder_layers_20_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[989] + model_decoder_layers_20_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[990] + alloc1463: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul2_add7_add6(alloc1462, model_decoder_layers_20_fc2_weight5, model_decoder_layers_20_fc2_bias5, alloc1460, alloc1463) + R.vm.kill_object(alloc1460) + R.vm.kill_object(alloc1462) + R.vm.kill_object(model_decoder_layers_20_fc2_weight5) + R.vm.kill_object(model_decoder_layers_20_fc2_bias5) + model_decoder_layers_21_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1000] + model_decoder_layers_21_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1001] + alloc1464: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1463, model_decoder_layers_21_self_attn_layer_norm_weight5, model_decoder_layers_21_self_attn_layer_norm_bias5, alloc1464) + R.vm.kill_object(model_decoder_layers_21_self_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_21_self_attn_layer_norm_bias5) + model_decoder_layers_21_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[996] + model_decoder_layers_21_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[997] + alloc1465: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1464, model_decoder_layers_21_self_attn_q_proj_weight5, model_decoder_layers_21_self_attn_q_proj_bias5, alloc1465) + R.vm.kill_object(model_decoder_layers_21_self_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_21_self_attn_q_proj_bias5) + model_decoder_layers_21_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[993] + alloc1466: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.NT_matmul(alloc1464, model_decoder_layers_21_self_attn_k_proj_weight5, alloc1466) + R.vm.kill_object(model_decoder_layers_21_self_attn_k_proj_weight5) + model_decoder_layers_21_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[994] + model_decoder_layers_21_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[995] + alloc1467: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1464, model_decoder_layers_21_self_attn_v_proj_weight5, model_decoder_layers_21_self_attn_v_proj_bias5, alloc1467) + R.vm.kill_object(alloc1464) + R.vm.kill_object(model_decoder_layers_21_self_attn_v_proj_weight5) + R.vm.kill_object(model_decoder_layers_21_self_attn_v_proj_bias5) + alloc1468: R.Tensor((1, 60, 64), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 60, 64]), R.dtype("float16")) + cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22(alloc1465, alloc1466, alloc1467, alloc1468) + R.vm.kill_object(alloc1465) + R.vm.kill_object(alloc1466) + R.vm.kill_object(alloc1467) + alloc1469: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1467: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), alloc1468, alloc1469) + R.vm.kill_object(alloc1468) + lv275: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1469, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1469) + model_decoder_layers_21_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[998] + model_decoder_layers_21_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[999] + alloc1470: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7_add6(lv275, model_decoder_layers_21_self_attn_out_proj_weight5, model_decoder_layers_21_self_attn_out_proj_bias5, alloc1463, alloc1470) + R.vm.kill_object(alloc1463) + R.vm.kill_object(lv275) + R.vm.kill_object(model_decoder_layers_21_self_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_21_self_attn_out_proj_bias5) + model_decoder_layers_21_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1009] + model_decoder_layers_21_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1010] + alloc1471: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1470, model_decoder_layers_21_encoder_attn_layer_norm_weight5, model_decoder_layers_21_encoder_attn_layer_norm_bias5, alloc1471) + R.vm.kill_object(model_decoder_layers_21_encoder_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_21_encoder_attn_layer_norm_bias5) + model_decoder_layers_21_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1005] + model_decoder_layers_21_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1006] + alloc1472: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1471, model_decoder_layers_21_encoder_attn_q_proj_weight5, model_decoder_layers_21_encoder_attn_q_proj_bias5, alloc1472) + R.vm.kill_object(alloc1471) + R.vm.kill_object(model_decoder_layers_21_encoder_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_21_encoder_attn_q_proj_bias5) + lv278: R.Tensor((1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1472, R.shape([1, 20, 64]), sinfo_args=(R.Tensor((1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1472) + alloc1473: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1471: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), lv278, alloc1473) + R.vm.kill_object(lv278) + lv279: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1473, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1473) + model_decoder_layers_21_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1007] + model_decoder_layers_21_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1008] + alloc1474: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7_add6(lv279, model_decoder_layers_21_encoder_attn_out_proj_weight5, model_decoder_layers_21_encoder_attn_out_proj_bias5, alloc1470, alloc1474) + R.vm.kill_object(alloc1470) + R.vm.kill_object(lv279) + R.vm.kill_object(model_decoder_layers_21_encoder_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_21_encoder_attn_out_proj_bias5) + model_decoder_layers_21_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1015] + model_decoder_layers_21_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1016] + alloc1475: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1474, model_decoder_layers_21_final_layer_norm_weight5, model_decoder_layers_21_final_layer_norm_bias5, alloc1475) + R.vm.kill_object(model_decoder_layers_21_final_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_21_final_layer_norm_bias5) + model_decoder_layers_21_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1011] + model_decoder_layers_21_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1012] + alloc1476: R.Tensor((1, 1, 5120), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 5120]), R.dtype("float16")) + cls.fused_NT_matmul1_add8_gelu2(alloc1475, model_decoder_layers_21_fc1_weight5, model_decoder_layers_21_fc1_bias5, alloc1476) + R.vm.kill_object(alloc1475) + R.vm.kill_object(model_decoder_layers_21_fc1_weight5) + R.vm.kill_object(model_decoder_layers_21_fc1_bias5) + model_decoder_layers_21_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1013] + model_decoder_layers_21_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1014] + alloc1477: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul2_add7_add6(alloc1476, model_decoder_layers_21_fc2_weight5, model_decoder_layers_21_fc2_bias5, alloc1474, alloc1477) + R.vm.kill_object(alloc1474) + R.vm.kill_object(alloc1476) + R.vm.kill_object(model_decoder_layers_21_fc2_weight5) + R.vm.kill_object(model_decoder_layers_21_fc2_bias5) + model_decoder_layers_22_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1024] + model_decoder_layers_22_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1025] + alloc1478: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1477, model_decoder_layers_22_self_attn_layer_norm_weight5, model_decoder_layers_22_self_attn_layer_norm_bias5, alloc1478) + R.vm.kill_object(model_decoder_layers_22_self_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_22_self_attn_layer_norm_bias5) + model_decoder_layers_22_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1020] + model_decoder_layers_22_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1021] + alloc1479: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1478, model_decoder_layers_22_self_attn_q_proj_weight5, model_decoder_layers_22_self_attn_q_proj_bias5, alloc1479) + R.vm.kill_object(model_decoder_layers_22_self_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_22_self_attn_q_proj_bias5) + model_decoder_layers_22_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1017] + alloc1480: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.NT_matmul(alloc1478, model_decoder_layers_22_self_attn_k_proj_weight5, alloc1480) + R.vm.kill_object(model_decoder_layers_22_self_attn_k_proj_weight5) + model_decoder_layers_22_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1018] + model_decoder_layers_22_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1019] + alloc1481: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1478, model_decoder_layers_22_self_attn_v_proj_weight5, model_decoder_layers_22_self_attn_v_proj_bias5, alloc1481) + R.vm.kill_object(alloc1478) + R.vm.kill_object(model_decoder_layers_22_self_attn_v_proj_weight5) + R.vm.kill_object(model_decoder_layers_22_self_attn_v_proj_bias5) + alloc1482: R.Tensor((1, 60, 64), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 60, 64]), R.dtype("float16")) + cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22(alloc1479, alloc1480, alloc1481, alloc1482) + R.vm.kill_object(alloc1479) + R.vm.kill_object(alloc1480) + R.vm.kill_object(alloc1481) + alloc1483: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1481: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), alloc1482, alloc1483) + R.vm.kill_object(alloc1482) + lv286: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1483, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1483) + model_decoder_layers_22_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1022] + model_decoder_layers_22_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1023] + alloc1484: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7_add6(lv286, model_decoder_layers_22_self_attn_out_proj_weight5, model_decoder_layers_22_self_attn_out_proj_bias5, alloc1477, alloc1484) + R.vm.kill_object(alloc1477) + R.vm.kill_object(lv286) + R.vm.kill_object(model_decoder_layers_22_self_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_22_self_attn_out_proj_bias5) + model_decoder_layers_22_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1033] + model_decoder_layers_22_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1034] + alloc1485: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1484, model_decoder_layers_22_encoder_attn_layer_norm_weight5, model_decoder_layers_22_encoder_attn_layer_norm_bias5, alloc1485) + R.vm.kill_object(model_decoder_layers_22_encoder_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_22_encoder_attn_layer_norm_bias5) + model_decoder_layers_22_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1029] + model_decoder_layers_22_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1030] + alloc1486: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1485, model_decoder_layers_22_encoder_attn_q_proj_weight5, model_decoder_layers_22_encoder_attn_q_proj_bias5, alloc1486) + R.vm.kill_object(alloc1485) + R.vm.kill_object(model_decoder_layers_22_encoder_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_22_encoder_attn_q_proj_bias5) + lv289: R.Tensor((1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1486, R.shape([1, 20, 64]), sinfo_args=(R.Tensor((1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1486) + alloc1487: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1485: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), lv289, alloc1487) + R.vm.kill_object(lv289) + lv290: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1487, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1487) + model_decoder_layers_22_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1031] + model_decoder_layers_22_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1032] + alloc1488: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7_add6(lv290, model_decoder_layers_22_encoder_attn_out_proj_weight5, model_decoder_layers_22_encoder_attn_out_proj_bias5, alloc1484, alloc1488) + R.vm.kill_object(alloc1484) + R.vm.kill_object(lv290) + R.vm.kill_object(model_decoder_layers_22_encoder_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_22_encoder_attn_out_proj_bias5) + model_decoder_layers_22_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1039] + model_decoder_layers_22_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1040] + alloc1489: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1488, model_decoder_layers_22_final_layer_norm_weight5, model_decoder_layers_22_final_layer_norm_bias5, alloc1489) + R.vm.kill_object(model_decoder_layers_22_final_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_22_final_layer_norm_bias5) + model_decoder_layers_22_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1035] + model_decoder_layers_22_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1036] + alloc1490: R.Tensor((1, 1, 5120), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 5120]), R.dtype("float16")) + cls.fused_NT_matmul1_add8_gelu2(alloc1489, model_decoder_layers_22_fc1_weight5, model_decoder_layers_22_fc1_bias5, alloc1490) + R.vm.kill_object(alloc1489) + R.vm.kill_object(model_decoder_layers_22_fc1_weight5) + R.vm.kill_object(model_decoder_layers_22_fc1_bias5) + model_decoder_layers_22_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1037] + model_decoder_layers_22_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1038] + alloc1491: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul2_add7_add6(alloc1490, model_decoder_layers_22_fc2_weight5, model_decoder_layers_22_fc2_bias5, alloc1488, alloc1491) + R.vm.kill_object(alloc1488) + R.vm.kill_object(alloc1490) + R.vm.kill_object(model_decoder_layers_22_fc2_weight5) + R.vm.kill_object(model_decoder_layers_22_fc2_bias5) + model_decoder_layers_23_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1048] + model_decoder_layers_23_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1049] + alloc1492: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1491, model_decoder_layers_23_self_attn_layer_norm_weight5, model_decoder_layers_23_self_attn_layer_norm_bias5, alloc1492) + R.vm.kill_object(model_decoder_layers_23_self_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_23_self_attn_layer_norm_bias5) + model_decoder_layers_23_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1044] + model_decoder_layers_23_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1045] + alloc1493: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1492, model_decoder_layers_23_self_attn_q_proj_weight5, model_decoder_layers_23_self_attn_q_proj_bias5, alloc1493) + R.vm.kill_object(model_decoder_layers_23_self_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_23_self_attn_q_proj_bias5) + model_decoder_layers_23_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1041] + alloc1494: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.NT_matmul(alloc1492, model_decoder_layers_23_self_attn_k_proj_weight5, alloc1494) + R.vm.kill_object(model_decoder_layers_23_self_attn_k_proj_weight5) + model_decoder_layers_23_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1042] + model_decoder_layers_23_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1043] + alloc1495: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1492, model_decoder_layers_23_self_attn_v_proj_weight5, model_decoder_layers_23_self_attn_v_proj_bias5, alloc1495) + R.vm.kill_object(alloc1492) + R.vm.kill_object(model_decoder_layers_23_self_attn_v_proj_weight5) + R.vm.kill_object(model_decoder_layers_23_self_attn_v_proj_bias5) + alloc1496: R.Tensor((1, 60, 64), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 60, 64]), R.dtype("float16")) + cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22(alloc1493, alloc1494, alloc1495, alloc1496) + R.vm.kill_object(alloc1493) + R.vm.kill_object(alloc1494) + R.vm.kill_object(alloc1495) + alloc1497: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1495: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), alloc1496, alloc1497) + R.vm.kill_object(alloc1496) + lv297: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1497, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1497) + model_decoder_layers_23_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1046] + model_decoder_layers_23_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1047] + alloc1498: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7_add6(lv297, model_decoder_layers_23_self_attn_out_proj_weight5, model_decoder_layers_23_self_attn_out_proj_bias5, alloc1491, alloc1498) + R.vm.kill_object(alloc1491) + R.vm.kill_object(lv297) + R.vm.kill_object(model_decoder_layers_23_self_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_23_self_attn_out_proj_bias5) + model_decoder_layers_23_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1057] + model_decoder_layers_23_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1058] + alloc1499: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1498, model_decoder_layers_23_encoder_attn_layer_norm_weight5, model_decoder_layers_23_encoder_attn_layer_norm_bias5, alloc1499) + R.vm.kill_object(model_decoder_layers_23_encoder_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_23_encoder_attn_layer_norm_bias5) + model_decoder_layers_23_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1053] + model_decoder_layers_23_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1054] + alloc1500: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1499, model_decoder_layers_23_encoder_attn_q_proj_weight5, model_decoder_layers_23_encoder_attn_q_proj_bias5, alloc1500) + R.vm.kill_object(alloc1499) + R.vm.kill_object(model_decoder_layers_23_encoder_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_23_encoder_attn_q_proj_bias5) + lv300: R.Tensor((1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1500, R.shape([1, 20, 64]), sinfo_args=(R.Tensor((1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1500) + alloc1501: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1499: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), lv300, alloc1501) + R.vm.kill_object(lv300) + lv301: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1501, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1501) + model_decoder_layers_23_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1055] + model_decoder_layers_23_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1056] + alloc1502: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7_add6(lv301, model_decoder_layers_23_encoder_attn_out_proj_weight5, model_decoder_layers_23_encoder_attn_out_proj_bias5, alloc1498, alloc1502) + R.vm.kill_object(alloc1498) + R.vm.kill_object(lv301) + R.vm.kill_object(model_decoder_layers_23_encoder_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_23_encoder_attn_out_proj_bias5) + model_decoder_layers_23_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1063] + model_decoder_layers_23_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1064] + alloc1503: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1502, model_decoder_layers_23_final_layer_norm_weight5, model_decoder_layers_23_final_layer_norm_bias5, alloc1503) + R.vm.kill_object(model_decoder_layers_23_final_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_23_final_layer_norm_bias5) + model_decoder_layers_23_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1059] + model_decoder_layers_23_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1060] + alloc1504: R.Tensor((1, 1, 5120), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 5120]), R.dtype("float16")) + cls.fused_NT_matmul1_add8_gelu2(alloc1503, model_decoder_layers_23_fc1_weight5, model_decoder_layers_23_fc1_bias5, alloc1504) + R.vm.kill_object(alloc1503) + R.vm.kill_object(model_decoder_layers_23_fc1_weight5) + R.vm.kill_object(model_decoder_layers_23_fc1_bias5) + model_decoder_layers_23_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1061] + model_decoder_layers_23_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1062] + alloc1505: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul2_add7_add6(alloc1504, model_decoder_layers_23_fc2_weight5, model_decoder_layers_23_fc2_bias5, alloc1502, alloc1505) + R.vm.kill_object(alloc1502) + R.vm.kill_object(alloc1504) + R.vm.kill_object(model_decoder_layers_23_fc2_weight5) + R.vm.kill_object(model_decoder_layers_23_fc2_bias5) + model_decoder_layers_24_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1072] + model_decoder_layers_24_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1073] + alloc1506: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1505, model_decoder_layers_24_self_attn_layer_norm_weight5, model_decoder_layers_24_self_attn_layer_norm_bias5, alloc1506) + R.vm.kill_object(model_decoder_layers_24_self_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_24_self_attn_layer_norm_bias5) + model_decoder_layers_24_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1068] + model_decoder_layers_24_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1069] + alloc1507: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1506, model_decoder_layers_24_self_attn_q_proj_weight5, model_decoder_layers_24_self_attn_q_proj_bias5, alloc1507) + R.vm.kill_object(model_decoder_layers_24_self_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_24_self_attn_q_proj_bias5) + model_decoder_layers_24_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1065] + alloc1508: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.NT_matmul(alloc1506, model_decoder_layers_24_self_attn_k_proj_weight5, alloc1508) + R.vm.kill_object(model_decoder_layers_24_self_attn_k_proj_weight5) + model_decoder_layers_24_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1066] + model_decoder_layers_24_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1067] + alloc1509: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1506, model_decoder_layers_24_self_attn_v_proj_weight5, model_decoder_layers_24_self_attn_v_proj_bias5, alloc1509) + R.vm.kill_object(alloc1506) + R.vm.kill_object(model_decoder_layers_24_self_attn_v_proj_weight5) + R.vm.kill_object(model_decoder_layers_24_self_attn_v_proj_bias5) + alloc1510: R.Tensor((1, 60, 64), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 60, 64]), R.dtype("float16")) + cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22(alloc1507, alloc1508, alloc1509, alloc1510) + R.vm.kill_object(alloc1507) + R.vm.kill_object(alloc1508) + R.vm.kill_object(alloc1509) + alloc1511: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1509: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), alloc1510, alloc1511) + R.vm.kill_object(alloc1510) + lv308: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1511, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1511) + model_decoder_layers_24_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1070] + model_decoder_layers_24_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1071] + alloc1512: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7_add6(lv308, model_decoder_layers_24_self_attn_out_proj_weight5, model_decoder_layers_24_self_attn_out_proj_bias5, alloc1505, alloc1512) + R.vm.kill_object(alloc1505) + R.vm.kill_object(lv308) + R.vm.kill_object(model_decoder_layers_24_self_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_24_self_attn_out_proj_bias5) + model_decoder_layers_24_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1081] + model_decoder_layers_24_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1082] + alloc1513: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1512, model_decoder_layers_24_encoder_attn_layer_norm_weight5, model_decoder_layers_24_encoder_attn_layer_norm_bias5, alloc1513) + R.vm.kill_object(model_decoder_layers_24_encoder_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_24_encoder_attn_layer_norm_bias5) + model_decoder_layers_24_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1077] + model_decoder_layers_24_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1078] + alloc1514: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1513, model_decoder_layers_24_encoder_attn_q_proj_weight5, model_decoder_layers_24_encoder_attn_q_proj_bias5, alloc1514) + R.vm.kill_object(alloc1513) + R.vm.kill_object(model_decoder_layers_24_encoder_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_24_encoder_attn_q_proj_bias5) + lv311: R.Tensor((1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1514, R.shape([1, 20, 64]), sinfo_args=(R.Tensor((1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1514) + alloc1515: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1513: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), lv311, alloc1515) + R.vm.kill_object(lv311) + lv312: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1515, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1515) + model_decoder_layers_24_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1079] + model_decoder_layers_24_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1080] + alloc1516: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7_add6(lv312, model_decoder_layers_24_encoder_attn_out_proj_weight5, model_decoder_layers_24_encoder_attn_out_proj_bias5, alloc1512, alloc1516) + R.vm.kill_object(alloc1512) + R.vm.kill_object(lv312) + R.vm.kill_object(model_decoder_layers_24_encoder_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_24_encoder_attn_out_proj_bias5) + model_decoder_layers_24_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1087] + model_decoder_layers_24_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1088] + alloc1517: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1516, model_decoder_layers_24_final_layer_norm_weight5, model_decoder_layers_24_final_layer_norm_bias5, alloc1517) + R.vm.kill_object(model_decoder_layers_24_final_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_24_final_layer_norm_bias5) + model_decoder_layers_24_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1083] + model_decoder_layers_24_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1084] + alloc1518: R.Tensor((1, 1, 5120), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 5120]), R.dtype("float16")) + cls.fused_NT_matmul1_add8_gelu2(alloc1517, model_decoder_layers_24_fc1_weight5, model_decoder_layers_24_fc1_bias5, alloc1518) + R.vm.kill_object(alloc1517) + R.vm.kill_object(model_decoder_layers_24_fc1_weight5) + R.vm.kill_object(model_decoder_layers_24_fc1_bias5) + model_decoder_layers_24_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1085] + model_decoder_layers_24_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1086] + alloc1519: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul2_add7_add6(alloc1518, model_decoder_layers_24_fc2_weight5, model_decoder_layers_24_fc2_bias5, alloc1516, alloc1519) + R.vm.kill_object(alloc1516) + R.vm.kill_object(alloc1518) + R.vm.kill_object(model_decoder_layers_24_fc2_weight5) + R.vm.kill_object(model_decoder_layers_24_fc2_bias5) + model_decoder_layers_25_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1096] + model_decoder_layers_25_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1097] + alloc1520: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1519, model_decoder_layers_25_self_attn_layer_norm_weight5, model_decoder_layers_25_self_attn_layer_norm_bias5, alloc1520) + R.vm.kill_object(model_decoder_layers_25_self_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_25_self_attn_layer_norm_bias5) + model_decoder_layers_25_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1092] + model_decoder_layers_25_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1093] + alloc1521: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1520, model_decoder_layers_25_self_attn_q_proj_weight5, model_decoder_layers_25_self_attn_q_proj_bias5, alloc1521) + R.vm.kill_object(model_decoder_layers_25_self_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_25_self_attn_q_proj_bias5) + model_decoder_layers_25_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1089] + alloc1522: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.NT_matmul(alloc1520, model_decoder_layers_25_self_attn_k_proj_weight5, alloc1522) + R.vm.kill_object(model_decoder_layers_25_self_attn_k_proj_weight5) + model_decoder_layers_25_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1090] + model_decoder_layers_25_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1091] + alloc1523: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1520, model_decoder_layers_25_self_attn_v_proj_weight5, model_decoder_layers_25_self_attn_v_proj_bias5, alloc1523) + R.vm.kill_object(alloc1520) + R.vm.kill_object(model_decoder_layers_25_self_attn_v_proj_weight5) + R.vm.kill_object(model_decoder_layers_25_self_attn_v_proj_bias5) + alloc1524: R.Tensor((1, 60, 64), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 60, 64]), R.dtype("float16")) + cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22(alloc1521, alloc1522, alloc1523, alloc1524) + R.vm.kill_object(alloc1521) + R.vm.kill_object(alloc1522) + R.vm.kill_object(alloc1523) + alloc1525: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1523: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), alloc1524, alloc1525) + R.vm.kill_object(alloc1524) + lv319: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1525, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1525) + model_decoder_layers_25_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1094] + model_decoder_layers_25_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1095] + alloc1526: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7_add6(lv319, model_decoder_layers_25_self_attn_out_proj_weight5, model_decoder_layers_25_self_attn_out_proj_bias5, alloc1519, alloc1526) + R.vm.kill_object(alloc1519) + R.vm.kill_object(lv319) + R.vm.kill_object(model_decoder_layers_25_self_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_25_self_attn_out_proj_bias5) + model_decoder_layers_25_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1105] + model_decoder_layers_25_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1106] + alloc1527: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1526, model_decoder_layers_25_encoder_attn_layer_norm_weight5, model_decoder_layers_25_encoder_attn_layer_norm_bias5, alloc1527) + R.vm.kill_object(model_decoder_layers_25_encoder_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_25_encoder_attn_layer_norm_bias5) + model_decoder_layers_25_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1101] + model_decoder_layers_25_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1102] + alloc1528: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1527, model_decoder_layers_25_encoder_attn_q_proj_weight5, model_decoder_layers_25_encoder_attn_q_proj_bias5, alloc1528) + R.vm.kill_object(alloc1527) + R.vm.kill_object(model_decoder_layers_25_encoder_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_25_encoder_attn_q_proj_bias5) + lv322: R.Tensor((1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1528, R.shape([1, 20, 64]), sinfo_args=(R.Tensor((1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1528) + alloc1529: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1527: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), lv322, alloc1529) + R.vm.kill_object(lv322) + lv323: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1529, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1529) + model_decoder_layers_25_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1103] + model_decoder_layers_25_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1104] + alloc1530: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7_add6(lv323, model_decoder_layers_25_encoder_attn_out_proj_weight5, model_decoder_layers_25_encoder_attn_out_proj_bias5, alloc1526, alloc1530) + R.vm.kill_object(alloc1526) + R.vm.kill_object(lv323) + R.vm.kill_object(model_decoder_layers_25_encoder_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_25_encoder_attn_out_proj_bias5) + model_decoder_layers_25_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1111] + model_decoder_layers_25_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1112] + alloc1531: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1530, model_decoder_layers_25_final_layer_norm_weight5, model_decoder_layers_25_final_layer_norm_bias5, alloc1531) + R.vm.kill_object(model_decoder_layers_25_final_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_25_final_layer_norm_bias5) + model_decoder_layers_25_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1107] + model_decoder_layers_25_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1108] + alloc1532: R.Tensor((1, 1, 5120), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 5120]), R.dtype("float16")) + cls.fused_NT_matmul1_add8_gelu2(alloc1531, model_decoder_layers_25_fc1_weight5, model_decoder_layers_25_fc1_bias5, alloc1532) + R.vm.kill_object(alloc1531) + R.vm.kill_object(model_decoder_layers_25_fc1_weight5) + R.vm.kill_object(model_decoder_layers_25_fc1_bias5) + model_decoder_layers_25_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1109] + model_decoder_layers_25_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1110] + alloc1533: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul2_add7_add6(alloc1532, model_decoder_layers_25_fc2_weight5, model_decoder_layers_25_fc2_bias5, alloc1530, alloc1533) + R.vm.kill_object(alloc1530) + R.vm.kill_object(alloc1532) + R.vm.kill_object(model_decoder_layers_25_fc2_weight5) + R.vm.kill_object(model_decoder_layers_25_fc2_bias5) + model_decoder_layers_26_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1120] + model_decoder_layers_26_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1121] + alloc1534: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1533, model_decoder_layers_26_self_attn_layer_norm_weight5, model_decoder_layers_26_self_attn_layer_norm_bias5, alloc1534) + R.vm.kill_object(model_decoder_layers_26_self_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_26_self_attn_layer_norm_bias5) + model_decoder_layers_26_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1116] + model_decoder_layers_26_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1117] + alloc1535: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1534, model_decoder_layers_26_self_attn_q_proj_weight5, model_decoder_layers_26_self_attn_q_proj_bias5, alloc1535) + R.vm.kill_object(model_decoder_layers_26_self_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_26_self_attn_q_proj_bias5) + model_decoder_layers_26_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1113] + alloc1536: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.NT_matmul(alloc1534, model_decoder_layers_26_self_attn_k_proj_weight5, alloc1536) + R.vm.kill_object(model_decoder_layers_26_self_attn_k_proj_weight5) + model_decoder_layers_26_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1114] + model_decoder_layers_26_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1115] + alloc1537: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1534, model_decoder_layers_26_self_attn_v_proj_weight5, model_decoder_layers_26_self_attn_v_proj_bias5, alloc1537) + R.vm.kill_object(alloc1534) + R.vm.kill_object(model_decoder_layers_26_self_attn_v_proj_weight5) + R.vm.kill_object(model_decoder_layers_26_self_attn_v_proj_bias5) + alloc1538: R.Tensor((1, 60, 64), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 60, 64]), R.dtype("float16")) + cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22(alloc1535, alloc1536, alloc1537, alloc1538) + R.vm.kill_object(alloc1535) + R.vm.kill_object(alloc1536) + R.vm.kill_object(alloc1537) + alloc1539: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1537: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), alloc1538, alloc1539) + R.vm.kill_object(alloc1538) + lv330: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1539, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1539) + model_decoder_layers_26_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1118] + model_decoder_layers_26_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1119] + alloc1540: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7_add6(lv330, model_decoder_layers_26_self_attn_out_proj_weight5, model_decoder_layers_26_self_attn_out_proj_bias5, alloc1533, alloc1540) + R.vm.kill_object(alloc1533) + R.vm.kill_object(lv330) + R.vm.kill_object(model_decoder_layers_26_self_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_26_self_attn_out_proj_bias5) + model_decoder_layers_26_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1129] + model_decoder_layers_26_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1130] + alloc1541: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1540, model_decoder_layers_26_encoder_attn_layer_norm_weight5, model_decoder_layers_26_encoder_attn_layer_norm_bias5, alloc1541) + R.vm.kill_object(model_decoder_layers_26_encoder_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_26_encoder_attn_layer_norm_bias5) + model_decoder_layers_26_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1125] + model_decoder_layers_26_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1126] + alloc1542: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1541, model_decoder_layers_26_encoder_attn_q_proj_weight5, model_decoder_layers_26_encoder_attn_q_proj_bias5, alloc1542) + R.vm.kill_object(alloc1541) + R.vm.kill_object(model_decoder_layers_26_encoder_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_26_encoder_attn_q_proj_bias5) + lv333: R.Tensor((1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1542, R.shape([1, 20, 64]), sinfo_args=(R.Tensor((1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1542) + alloc1543: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1541: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), lv333, alloc1543) + R.vm.kill_object(lv333) + lv334: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1543, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1543) + model_decoder_layers_26_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1127] + model_decoder_layers_26_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1128] + alloc1544: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7_add6(lv334, model_decoder_layers_26_encoder_attn_out_proj_weight5, model_decoder_layers_26_encoder_attn_out_proj_bias5, alloc1540, alloc1544) + R.vm.kill_object(alloc1540) + R.vm.kill_object(lv334) + R.vm.kill_object(model_decoder_layers_26_encoder_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_26_encoder_attn_out_proj_bias5) + model_decoder_layers_26_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1135] + model_decoder_layers_26_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1136] + alloc1545: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1544, model_decoder_layers_26_final_layer_norm_weight5, model_decoder_layers_26_final_layer_norm_bias5, alloc1545) + R.vm.kill_object(model_decoder_layers_26_final_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_26_final_layer_norm_bias5) + model_decoder_layers_26_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1131] + model_decoder_layers_26_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1132] + alloc1546: R.Tensor((1, 1, 5120), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 5120]), R.dtype("float16")) + cls.fused_NT_matmul1_add8_gelu2(alloc1545, model_decoder_layers_26_fc1_weight5, model_decoder_layers_26_fc1_bias5, alloc1546) + R.vm.kill_object(alloc1545) + R.vm.kill_object(model_decoder_layers_26_fc1_weight5) + R.vm.kill_object(model_decoder_layers_26_fc1_bias5) + model_decoder_layers_26_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1133] + model_decoder_layers_26_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1134] + alloc1547: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul2_add7_add6(alloc1546, model_decoder_layers_26_fc2_weight5, model_decoder_layers_26_fc2_bias5, alloc1544, alloc1547) + R.vm.kill_object(alloc1544) + R.vm.kill_object(alloc1546) + R.vm.kill_object(model_decoder_layers_26_fc2_weight5) + R.vm.kill_object(model_decoder_layers_26_fc2_bias5) + model_decoder_layers_27_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1144] + model_decoder_layers_27_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1145] + alloc1548: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1547, model_decoder_layers_27_self_attn_layer_norm_weight5, model_decoder_layers_27_self_attn_layer_norm_bias5, alloc1548) + R.vm.kill_object(model_decoder_layers_27_self_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_27_self_attn_layer_norm_bias5) + model_decoder_layers_27_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1140] + model_decoder_layers_27_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1141] + alloc1549: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1548, model_decoder_layers_27_self_attn_q_proj_weight5, model_decoder_layers_27_self_attn_q_proj_bias5, alloc1549) + R.vm.kill_object(model_decoder_layers_27_self_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_27_self_attn_q_proj_bias5) + model_decoder_layers_27_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1137] + alloc1550: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.NT_matmul(alloc1548, model_decoder_layers_27_self_attn_k_proj_weight5, alloc1550) + R.vm.kill_object(model_decoder_layers_27_self_attn_k_proj_weight5) + model_decoder_layers_27_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1138] + model_decoder_layers_27_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1139] + alloc1551: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1548, model_decoder_layers_27_self_attn_v_proj_weight5, model_decoder_layers_27_self_attn_v_proj_bias5, alloc1551) + R.vm.kill_object(alloc1548) + R.vm.kill_object(model_decoder_layers_27_self_attn_v_proj_weight5) + R.vm.kill_object(model_decoder_layers_27_self_attn_v_proj_bias5) + alloc1552: R.Tensor((1, 60, 64), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 60, 64]), R.dtype("float16")) + cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22(alloc1549, alloc1550, alloc1551, alloc1552) + R.vm.kill_object(alloc1549) + R.vm.kill_object(alloc1550) + R.vm.kill_object(alloc1551) + alloc1553: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1551: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), alloc1552, alloc1553) + R.vm.kill_object(alloc1552) + lv341: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1553, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1553) + model_decoder_layers_27_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1142] + model_decoder_layers_27_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1143] + alloc1554: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7_add6(lv341, model_decoder_layers_27_self_attn_out_proj_weight5, model_decoder_layers_27_self_attn_out_proj_bias5, alloc1547, alloc1554) + R.vm.kill_object(alloc1547) + R.vm.kill_object(lv341) + R.vm.kill_object(model_decoder_layers_27_self_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_27_self_attn_out_proj_bias5) + model_decoder_layers_27_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1153] + model_decoder_layers_27_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1154] + alloc1555: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1554, model_decoder_layers_27_encoder_attn_layer_norm_weight5, model_decoder_layers_27_encoder_attn_layer_norm_bias5, alloc1555) + R.vm.kill_object(model_decoder_layers_27_encoder_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_27_encoder_attn_layer_norm_bias5) + model_decoder_layers_27_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1149] + model_decoder_layers_27_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1150] + alloc1556: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1555, model_decoder_layers_27_encoder_attn_q_proj_weight5, model_decoder_layers_27_encoder_attn_q_proj_bias5, alloc1556) + R.vm.kill_object(alloc1555) + R.vm.kill_object(model_decoder_layers_27_encoder_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_27_encoder_attn_q_proj_bias5) + lv344: R.Tensor((1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1556, R.shape([1, 20, 64]), sinfo_args=(R.Tensor((1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1556) + alloc1557: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1555: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), lv344, alloc1557) + R.vm.kill_object(lv344) + lv345: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1557, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1557) + model_decoder_layers_27_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1151] + model_decoder_layers_27_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1152] + alloc1558: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7_add6(lv345, model_decoder_layers_27_encoder_attn_out_proj_weight5, model_decoder_layers_27_encoder_attn_out_proj_bias5, alloc1554, alloc1558) + R.vm.kill_object(alloc1554) + R.vm.kill_object(lv345) + R.vm.kill_object(model_decoder_layers_27_encoder_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_27_encoder_attn_out_proj_bias5) + model_decoder_layers_27_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1159] + model_decoder_layers_27_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1160] + alloc1559: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1558, model_decoder_layers_27_final_layer_norm_weight5, model_decoder_layers_27_final_layer_norm_bias5, alloc1559) + R.vm.kill_object(model_decoder_layers_27_final_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_27_final_layer_norm_bias5) + model_decoder_layers_27_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1155] + model_decoder_layers_27_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1156] + alloc1560: R.Tensor((1, 1, 5120), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 5120]), R.dtype("float16")) + cls.fused_NT_matmul1_add8_gelu2(alloc1559, model_decoder_layers_27_fc1_weight5, model_decoder_layers_27_fc1_bias5, alloc1560) + R.vm.kill_object(alloc1559) + R.vm.kill_object(model_decoder_layers_27_fc1_weight5) + R.vm.kill_object(model_decoder_layers_27_fc1_bias5) + model_decoder_layers_27_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1157] + model_decoder_layers_27_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1158] + alloc1561: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul2_add7_add6(alloc1560, model_decoder_layers_27_fc2_weight5, model_decoder_layers_27_fc2_bias5, alloc1558, alloc1561) + R.vm.kill_object(alloc1558) + R.vm.kill_object(alloc1560) + R.vm.kill_object(model_decoder_layers_27_fc2_weight5) + R.vm.kill_object(model_decoder_layers_27_fc2_bias5) + model_decoder_layers_28_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1168] + model_decoder_layers_28_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1169] + alloc1562: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1561, model_decoder_layers_28_self_attn_layer_norm_weight5, model_decoder_layers_28_self_attn_layer_norm_bias5, alloc1562) + R.vm.kill_object(model_decoder_layers_28_self_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_28_self_attn_layer_norm_bias5) + model_decoder_layers_28_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1164] + model_decoder_layers_28_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1165] + alloc1563: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1562, model_decoder_layers_28_self_attn_q_proj_weight5, model_decoder_layers_28_self_attn_q_proj_bias5, alloc1563) + R.vm.kill_object(model_decoder_layers_28_self_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_28_self_attn_q_proj_bias5) + model_decoder_layers_28_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1161] + alloc1564: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.NT_matmul(alloc1562, model_decoder_layers_28_self_attn_k_proj_weight5, alloc1564) + R.vm.kill_object(model_decoder_layers_28_self_attn_k_proj_weight5) + model_decoder_layers_28_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1162] + model_decoder_layers_28_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1163] + alloc1565: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1562, model_decoder_layers_28_self_attn_v_proj_weight5, model_decoder_layers_28_self_attn_v_proj_bias5, alloc1565) + R.vm.kill_object(alloc1562) + R.vm.kill_object(model_decoder_layers_28_self_attn_v_proj_weight5) + R.vm.kill_object(model_decoder_layers_28_self_attn_v_proj_bias5) + alloc1566: R.Tensor((1, 60, 64), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 60, 64]), R.dtype("float16")) + cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22(alloc1563, alloc1564, alloc1565, alloc1566) + R.vm.kill_object(alloc1563) + R.vm.kill_object(alloc1564) + R.vm.kill_object(alloc1565) + alloc1567: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1565: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), alloc1566, alloc1567) + R.vm.kill_object(alloc1566) + lv352: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1567, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1567) + model_decoder_layers_28_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1166] + model_decoder_layers_28_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1167] + alloc1568: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7_add6(lv352, model_decoder_layers_28_self_attn_out_proj_weight5, model_decoder_layers_28_self_attn_out_proj_bias5, alloc1561, alloc1568) + R.vm.kill_object(alloc1561) + R.vm.kill_object(lv352) + R.vm.kill_object(model_decoder_layers_28_self_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_28_self_attn_out_proj_bias5) + model_decoder_layers_28_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1177] + model_decoder_layers_28_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1178] + alloc1569: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1568, model_decoder_layers_28_encoder_attn_layer_norm_weight5, model_decoder_layers_28_encoder_attn_layer_norm_bias5, alloc1569) + R.vm.kill_object(model_decoder_layers_28_encoder_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_28_encoder_attn_layer_norm_bias5) + model_decoder_layers_28_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1173] + model_decoder_layers_28_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1174] + alloc1570: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1569, model_decoder_layers_28_encoder_attn_q_proj_weight5, model_decoder_layers_28_encoder_attn_q_proj_bias5, alloc1570) + R.vm.kill_object(alloc1569) + R.vm.kill_object(model_decoder_layers_28_encoder_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_28_encoder_attn_q_proj_bias5) + lv355: R.Tensor((1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1570, R.shape([1, 20, 64]), sinfo_args=(R.Tensor((1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1570) + alloc1571: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1569: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), lv355, alloc1571) + R.vm.kill_object(lv355) + lv356: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1571, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1571) + model_decoder_layers_28_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1175] + model_decoder_layers_28_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1176] + alloc1572: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7_add6(lv356, model_decoder_layers_28_encoder_attn_out_proj_weight5, model_decoder_layers_28_encoder_attn_out_proj_bias5, alloc1568, alloc1572) + R.vm.kill_object(alloc1568) + R.vm.kill_object(lv356) + R.vm.kill_object(model_decoder_layers_28_encoder_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_28_encoder_attn_out_proj_bias5) + model_decoder_layers_28_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1183] + model_decoder_layers_28_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1184] + alloc1573: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1572, model_decoder_layers_28_final_layer_norm_weight5, model_decoder_layers_28_final_layer_norm_bias5, alloc1573) + R.vm.kill_object(model_decoder_layers_28_final_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_28_final_layer_norm_bias5) + model_decoder_layers_28_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1179] + model_decoder_layers_28_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1180] + alloc1574: R.Tensor((1, 1, 5120), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 5120]), R.dtype("float16")) + cls.fused_NT_matmul1_add8_gelu2(alloc1573, model_decoder_layers_28_fc1_weight5, model_decoder_layers_28_fc1_bias5, alloc1574) + R.vm.kill_object(alloc1573) + R.vm.kill_object(model_decoder_layers_28_fc1_weight5) + R.vm.kill_object(model_decoder_layers_28_fc1_bias5) + model_decoder_layers_28_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1181] + model_decoder_layers_28_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1182] + alloc1575: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul2_add7_add6(alloc1574, model_decoder_layers_28_fc2_weight5, model_decoder_layers_28_fc2_bias5, alloc1572, alloc1575) + R.vm.kill_object(alloc1572) + R.vm.kill_object(alloc1574) + R.vm.kill_object(model_decoder_layers_28_fc2_weight5) + R.vm.kill_object(model_decoder_layers_28_fc2_bias5) + model_decoder_layers_29_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1192] + model_decoder_layers_29_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1193] + alloc1576: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1575, model_decoder_layers_29_self_attn_layer_norm_weight5, model_decoder_layers_29_self_attn_layer_norm_bias5, alloc1576) + R.vm.kill_object(model_decoder_layers_29_self_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_29_self_attn_layer_norm_bias5) + model_decoder_layers_29_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1188] + model_decoder_layers_29_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1189] + alloc1577: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1576, model_decoder_layers_29_self_attn_q_proj_weight5, model_decoder_layers_29_self_attn_q_proj_bias5, alloc1577) + R.vm.kill_object(model_decoder_layers_29_self_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_29_self_attn_q_proj_bias5) + model_decoder_layers_29_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1185] + alloc1578: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.NT_matmul(alloc1576, model_decoder_layers_29_self_attn_k_proj_weight5, alloc1578) + R.vm.kill_object(model_decoder_layers_29_self_attn_k_proj_weight5) + model_decoder_layers_29_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1186] + model_decoder_layers_29_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1187] + alloc1579: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1576, model_decoder_layers_29_self_attn_v_proj_weight5, model_decoder_layers_29_self_attn_v_proj_bias5, alloc1579) + R.vm.kill_object(alloc1576) + R.vm.kill_object(model_decoder_layers_29_self_attn_v_proj_weight5) + R.vm.kill_object(model_decoder_layers_29_self_attn_v_proj_bias5) + alloc1580: R.Tensor((1, 60, 64), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 60, 64]), R.dtype("float16")) + cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22(alloc1577, alloc1578, alloc1579, alloc1580) + R.vm.kill_object(alloc1577) + R.vm.kill_object(alloc1578) + R.vm.kill_object(alloc1579) + alloc1581: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1579: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), alloc1580, alloc1581) + R.vm.kill_object(alloc1580) + lv363: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1581, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1581) + model_decoder_layers_29_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1190] + model_decoder_layers_29_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1191] + alloc1582: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7_add6(lv363, model_decoder_layers_29_self_attn_out_proj_weight5, model_decoder_layers_29_self_attn_out_proj_bias5, alloc1575, alloc1582) + R.vm.kill_object(alloc1575) + R.vm.kill_object(lv363) + R.vm.kill_object(model_decoder_layers_29_self_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_29_self_attn_out_proj_bias5) + model_decoder_layers_29_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1201] + model_decoder_layers_29_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1202] + alloc1583: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1582, model_decoder_layers_29_encoder_attn_layer_norm_weight5, model_decoder_layers_29_encoder_attn_layer_norm_bias5, alloc1583) + R.vm.kill_object(model_decoder_layers_29_encoder_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_29_encoder_attn_layer_norm_bias5) + model_decoder_layers_29_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1197] + model_decoder_layers_29_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1198] + alloc1584: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1583, model_decoder_layers_29_encoder_attn_q_proj_weight5, model_decoder_layers_29_encoder_attn_q_proj_bias5, alloc1584) + R.vm.kill_object(alloc1583) + R.vm.kill_object(model_decoder_layers_29_encoder_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_29_encoder_attn_q_proj_bias5) + lv366: R.Tensor((1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1584, R.shape([1, 20, 64]), sinfo_args=(R.Tensor((1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1584) + alloc1585: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1583: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), lv366, alloc1585) + R.vm.kill_object(lv366) + lv367: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1585, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1585) + model_decoder_layers_29_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1199] + model_decoder_layers_29_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1200] + alloc1586: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7_add6(lv367, model_decoder_layers_29_encoder_attn_out_proj_weight5, model_decoder_layers_29_encoder_attn_out_proj_bias5, alloc1582, alloc1586) + R.vm.kill_object(alloc1582) + R.vm.kill_object(lv367) + R.vm.kill_object(model_decoder_layers_29_encoder_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_29_encoder_attn_out_proj_bias5) + model_decoder_layers_29_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1207] + model_decoder_layers_29_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1208] + alloc1587: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1586, model_decoder_layers_29_final_layer_norm_weight5, model_decoder_layers_29_final_layer_norm_bias5, alloc1587) + R.vm.kill_object(model_decoder_layers_29_final_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_29_final_layer_norm_bias5) + model_decoder_layers_29_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1203] + model_decoder_layers_29_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1204] + alloc1588: R.Tensor((1, 1, 5120), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 5120]), R.dtype("float16")) + cls.fused_NT_matmul1_add8_gelu2(alloc1587, model_decoder_layers_29_fc1_weight5, model_decoder_layers_29_fc1_bias5, alloc1588) + R.vm.kill_object(alloc1587) + R.vm.kill_object(model_decoder_layers_29_fc1_weight5) + R.vm.kill_object(model_decoder_layers_29_fc1_bias5) + model_decoder_layers_29_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1205] + model_decoder_layers_29_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1206] + alloc1589: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul2_add7_add6(alloc1588, model_decoder_layers_29_fc2_weight5, model_decoder_layers_29_fc2_bias5, alloc1586, alloc1589) + R.vm.kill_object(alloc1586) + R.vm.kill_object(alloc1588) + R.vm.kill_object(model_decoder_layers_29_fc2_weight5) + R.vm.kill_object(model_decoder_layers_29_fc2_bias5) + model_decoder_layers_30_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1216] + model_decoder_layers_30_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1217] + alloc1590: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1589, model_decoder_layers_30_self_attn_layer_norm_weight5, model_decoder_layers_30_self_attn_layer_norm_bias5, alloc1590) + R.vm.kill_object(model_decoder_layers_30_self_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_30_self_attn_layer_norm_bias5) + model_decoder_layers_30_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1212] + model_decoder_layers_30_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1213] + alloc1591: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1590, model_decoder_layers_30_self_attn_q_proj_weight5, model_decoder_layers_30_self_attn_q_proj_bias5, alloc1591) + R.vm.kill_object(model_decoder_layers_30_self_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_30_self_attn_q_proj_bias5) + model_decoder_layers_30_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1209] + alloc1592: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.NT_matmul(alloc1590, model_decoder_layers_30_self_attn_k_proj_weight5, alloc1592) + R.vm.kill_object(model_decoder_layers_30_self_attn_k_proj_weight5) + model_decoder_layers_30_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1210] + model_decoder_layers_30_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1211] + alloc1593: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1590, model_decoder_layers_30_self_attn_v_proj_weight5, model_decoder_layers_30_self_attn_v_proj_bias5, alloc1593) + R.vm.kill_object(alloc1590) + R.vm.kill_object(model_decoder_layers_30_self_attn_v_proj_weight5) + R.vm.kill_object(model_decoder_layers_30_self_attn_v_proj_bias5) + alloc1594: R.Tensor((1, 60, 64), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 60, 64]), R.dtype("float16")) + cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22(alloc1591, alloc1592, alloc1593, alloc1594) + R.vm.kill_object(alloc1591) + R.vm.kill_object(alloc1592) + R.vm.kill_object(alloc1593) + alloc1595: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1593: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), alloc1594, alloc1595) + R.vm.kill_object(alloc1594) + lv374: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1595, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1595) + model_decoder_layers_30_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1214] + model_decoder_layers_30_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1215] + alloc1596: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7_add6(lv374, model_decoder_layers_30_self_attn_out_proj_weight5, model_decoder_layers_30_self_attn_out_proj_bias5, alloc1589, alloc1596) + R.vm.kill_object(alloc1589) + R.vm.kill_object(lv374) + R.vm.kill_object(model_decoder_layers_30_self_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_30_self_attn_out_proj_bias5) + model_decoder_layers_30_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1225] + model_decoder_layers_30_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1226] + alloc1597: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1596, model_decoder_layers_30_encoder_attn_layer_norm_weight5, model_decoder_layers_30_encoder_attn_layer_norm_bias5, alloc1597) + R.vm.kill_object(model_decoder_layers_30_encoder_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_30_encoder_attn_layer_norm_bias5) + model_decoder_layers_30_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1221] + model_decoder_layers_30_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1222] + alloc1598: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1597, model_decoder_layers_30_encoder_attn_q_proj_weight5, model_decoder_layers_30_encoder_attn_q_proj_bias5, alloc1598) + R.vm.kill_object(alloc1597) + R.vm.kill_object(model_decoder_layers_30_encoder_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_30_encoder_attn_q_proj_bias5) + lv377: R.Tensor((1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1598, R.shape([1, 20, 64]), sinfo_args=(R.Tensor((1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1598) + alloc1599: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1597: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), lv377, alloc1599) + R.vm.kill_object(lv377) + lv378: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1599, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1599) + model_decoder_layers_30_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1223] + model_decoder_layers_30_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1224] + alloc1600: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7_add6(lv378, model_decoder_layers_30_encoder_attn_out_proj_weight5, model_decoder_layers_30_encoder_attn_out_proj_bias5, alloc1596, alloc1600) + R.vm.kill_object(alloc1596) + R.vm.kill_object(lv378) + R.vm.kill_object(model_decoder_layers_30_encoder_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_30_encoder_attn_out_proj_bias5) + model_decoder_layers_30_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1231] + model_decoder_layers_30_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1232] + alloc1601: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1600, model_decoder_layers_30_final_layer_norm_weight5, model_decoder_layers_30_final_layer_norm_bias5, alloc1601) + R.vm.kill_object(model_decoder_layers_30_final_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_30_final_layer_norm_bias5) + model_decoder_layers_30_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1227] + model_decoder_layers_30_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1228] + alloc1602: R.Tensor((1, 1, 5120), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 5120]), R.dtype("float16")) + cls.fused_NT_matmul1_add8_gelu2(alloc1601, model_decoder_layers_30_fc1_weight5, model_decoder_layers_30_fc1_bias5, alloc1602) + R.vm.kill_object(alloc1601) + R.vm.kill_object(model_decoder_layers_30_fc1_weight5) + R.vm.kill_object(model_decoder_layers_30_fc1_bias5) + model_decoder_layers_30_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1229] + model_decoder_layers_30_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1230] + alloc1603: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul2_add7_add6(alloc1602, model_decoder_layers_30_fc2_weight5, model_decoder_layers_30_fc2_bias5, alloc1600, alloc1603) + R.vm.kill_object(alloc1600) + R.vm.kill_object(alloc1602) + R.vm.kill_object(model_decoder_layers_30_fc2_weight5) + R.vm.kill_object(model_decoder_layers_30_fc2_bias5) + model_decoder_layers_31_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1240] + model_decoder_layers_31_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1241] + alloc1604: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1603, model_decoder_layers_31_self_attn_layer_norm_weight5, model_decoder_layers_31_self_attn_layer_norm_bias5, alloc1604) + R.vm.kill_object(model_decoder_layers_31_self_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_31_self_attn_layer_norm_bias5) + model_decoder_layers_31_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1236] + model_decoder_layers_31_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1237] + alloc1605: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1604, model_decoder_layers_31_self_attn_q_proj_weight5, model_decoder_layers_31_self_attn_q_proj_bias5, alloc1605) + R.vm.kill_object(model_decoder_layers_31_self_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_31_self_attn_q_proj_bias5) + model_decoder_layers_31_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1233] + alloc1606: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.NT_matmul(alloc1604, model_decoder_layers_31_self_attn_k_proj_weight5, alloc1606) + R.vm.kill_object(model_decoder_layers_31_self_attn_k_proj_weight5) + model_decoder_layers_31_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1234] + model_decoder_layers_31_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1235] + alloc1607: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1604, model_decoder_layers_31_self_attn_v_proj_weight5, model_decoder_layers_31_self_attn_v_proj_bias5, alloc1607) + R.vm.kill_object(alloc1604) + R.vm.kill_object(model_decoder_layers_31_self_attn_v_proj_weight5) + R.vm.kill_object(model_decoder_layers_31_self_attn_v_proj_bias5) + alloc1608: R.Tensor((1, 60, 64), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 60, 64]), R.dtype("float16")) + cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22(alloc1605, alloc1606, alloc1607, alloc1608) + R.vm.kill_object(alloc1605) + R.vm.kill_object(alloc1606) + R.vm.kill_object(alloc1607) + alloc1609: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1607: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), alloc1608, alloc1609) + R.vm.kill_object(alloc1608) + lv385: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1609, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1609) + model_decoder_layers_31_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1238] + model_decoder_layers_31_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1239] + alloc1610: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage22, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + R.vm.kill_object(storage22) + cls.fused_NT_matmul_add7_add6(lv385, model_decoder_layers_31_self_attn_out_proj_weight5, model_decoder_layers_31_self_attn_out_proj_bias5, alloc1603, alloc1610) + R.vm.kill_object(alloc1603) + R.vm.kill_object(lv385) + R.vm.kill_object(model_decoder_layers_31_self_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_31_self_attn_out_proj_bias5) + model_decoder_layers_31_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1249] + model_decoder_layers_31_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1250] + alloc1611: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1610, model_decoder_layers_31_encoder_attn_layer_norm_weight5, model_decoder_layers_31_encoder_attn_layer_norm_bias5, alloc1611) + R.vm.kill_object(model_decoder_layers_31_encoder_attn_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_31_encoder_attn_layer_norm_bias5) + model_decoder_layers_31_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1245] + model_decoder_layers_31_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1246] + alloc1612: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.fused_NT_matmul_add7(alloc1611, model_decoder_layers_31_encoder_attn_q_proj_weight5, model_decoder_layers_31_encoder_attn_q_proj_bias5, alloc1612) + R.vm.kill_object(alloc1611) + R.vm.kill_object(model_decoder_layers_31_encoder_attn_q_proj_weight5) + R.vm.kill_object(model_decoder_layers_31_encoder_attn_q_proj_bias5) + lv388: R.Tensor((1, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1612, R.shape([1, 20, 64]), sinfo_args=(R.Tensor((1, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1612) + alloc1613: R.Tensor((1, 20, 64), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 20, 64]), R.dtype("float16")) + _1611: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), lv388, alloc1613) + R.vm.kill_object(lv388) + lv389: R.Tensor((1, 1, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1613, R.shape([1, 1, 1280]), sinfo_args=(R.Tensor((1, 1, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1613) + model_decoder_layers_31_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1247] + model_decoder_layers_31_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1248] + alloc1614: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage20, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + R.vm.kill_object(storage20) + cls.fused_NT_matmul_add7_add6(lv389, model_decoder_layers_31_encoder_attn_out_proj_weight5, model_decoder_layers_31_encoder_attn_out_proj_bias5, alloc1610, alloc1614) + R.vm.kill_object(alloc1610) + R.vm.kill_object(lv389) + R.vm.kill_object(model_decoder_layers_31_encoder_attn_out_proj_weight5) + R.vm.kill_object(model_decoder_layers_31_encoder_attn_out_proj_bias5) + model_decoder_layers_31_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1255] + model_decoder_layers_31_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1256] + alloc1615: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + cls.layer_norm3(alloc1614, model_decoder_layers_31_final_layer_norm_weight5, model_decoder_layers_31_final_layer_norm_bias5, alloc1615) + R.vm.kill_object(model_decoder_layers_31_final_layer_norm_weight5) + R.vm.kill_object(model_decoder_layers_31_final_layer_norm_bias5) + model_decoder_layers_31_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1251] + model_decoder_layers_31_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1252] + alloc1616: R.Tensor((1, 1, 5120), dtype="float16") = R.vm.alloc_tensor(storage19, R.prim_value(0), R.shape([1, 1, 5120]), R.dtype("float16")) + R.vm.kill_object(storage19) + cls.fused_NT_matmul1_add8_gelu2(alloc1615, model_decoder_layers_31_fc1_weight5, model_decoder_layers_31_fc1_bias5, alloc1616) + R.vm.kill_object(alloc1615) + R.vm.kill_object(model_decoder_layers_31_fc1_weight5) + R.vm.kill_object(model_decoder_layers_31_fc1_bias5) + model_decoder_layers_31_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1253] + model_decoder_layers_31_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1254] + alloc1617: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage21, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + R.vm.kill_object(storage21) + cls.fused_NT_matmul2_add7_add6(alloc1616, model_decoder_layers_31_fc2_weight5, model_decoder_layers_31_fc2_bias5, alloc1614, alloc1617) + R.vm.kill_object(alloc1614) + R.vm.kill_object(alloc1616) + R.vm.kill_object(model_decoder_layers_31_fc2_weight5) + R.vm.kill_object(model_decoder_layers_31_fc2_bias5) + model_decoder_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1257] + model_decoder_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1258] + alloc1618: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage23, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + R.vm.kill_object(storage23) + cls.layer_norm3(alloc1617, model_decoder_layer_norm_weight5, model_decoder_layer_norm_bias5, alloc1618) + R.vm.kill_object(alloc1617) + R.vm.kill_object(model_decoder_layer_norm_weight5) + R.vm.kill_object(model_decoder_layer_norm_bias5) + storage: R.Object = R.vm.alloc_storage(R.shape([207464]), R.prim_value(0), R.dtype("uint8"), R.str("global")) + alloc1619: R.Tensor((1, 1, 51866), dtype="float32") = R.vm.alloc_tensor(storage, R.prim_value(0), R.shape([1, 1, 51866]), R.dtype("float32")) + R.vm.kill_object(storage) + cls.NT_matmul3(alloc1618, model_decoder_embed_tokens_weight5, alloc1619) + R.vm.kill_object(model_decoder_embed_tokens_weight5) + R.vm.kill_object(alloc1618) + return alloc1619 + + @R.function + def multinomial_from_uniform(probs: R.Tensor(("batch_size", "vocab_size"), dtype="float32"), uniform_samples: R.Tensor(("num_samples",), dtype="float32"), sample_indices: R.Tensor(("num_samples",), dtype="int32")) -> R.Tensor(("num_samples",), dtype="int32"): + num_samples = T.int64() + batch_size = T.int64() + vocab_size = T.int64() + R.func_attr({"relax.force_pure": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "num_positions": 48, "num_samples": 8}}) + cls = Module + shape_heap: R.Tensor(dtype="int64", ndim=1) = R.call_builtin_with_ctx("vm.builtin.alloc_shape_heap", (R.prim_value(3),), sinfo_args=(R.Tensor(dtype="int64", ndim=1),)) + R.call_packed("vm.builtin.check_tensor_info", probs, R.prim_value(2), R.dtype("float32"), R.str("ErrorContext(fn=multinomial_from_uniform, loc=param[0], param=probs, annotation=R.Tensor((batch_size, vocab_size), dtype=\"float32\")) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.check_tensor_info", uniform_samples, R.prim_value(1), R.dtype("float32"), R.str("ErrorContext(fn=multinomial_from_uniform, loc=param[1], param=uniform_samples, annotation=R.Tensor((num_samples,), dtype=\"float32\")) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.check_tensor_info", sample_indices, R.prim_value(1), R.dtype("int32"), R.str("ErrorContext(fn=multinomial_from_uniform, loc=param[2], param=sample_indices, annotation=R.Tensor((num_samples,), dtype=\"int32\")) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.match_shape", probs, shape_heap, R.prim_value(2), R.prim_value(1), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.str("ErrorContext(fn=multinomial_from_uniform, loc=param[0], param=probs, annotation=R.Tensor((batch_size, vocab_size), dtype=\"float32\")) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.match_shape", uniform_samples, shape_heap, R.prim_value(1), R.prim_value(1), R.prim_value(2), R.str("ErrorContext(fn=multinomial_from_uniform, loc=param[1], param=uniform_samples, annotation=R.Tensor((num_samples,), dtype=\"float32\")) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.match_shape", sample_indices, shape_heap, R.prim_value(1), R.prim_value(3), R.prim_value(2), R.str("ErrorContext(fn=multinomial_from_uniform, loc=param[2], param=sample_indices, annotation=R.Tensor((num_samples,), dtype=\"int32\")) "), sinfo_args=(R.Tuple,)) + gv6: R.Shape(ndim=2) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(2), R.prim_value(1), R.prim_value(2), R.prim_value(0), R.prim_value(1), sinfo_args=(R.Shape(ndim=2),)) + uniform_samples_1: R.Tensor((num_samples, 1), dtype="float32") = R.call_packed("vm.builtin.reshape", uniform_samples, gv6, sinfo_args=(R.Tensor((num_samples, 1), dtype="float32"),)) + gv7: R.Shape(ndim=2) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(2), R.prim_value(1), R.prim_value(2), R.prim_value(0), R.prim_value(1), sinfo_args=(R.Shape(ndim=2),)) + sample_indices_1: R.Tensor((num_samples, 1), dtype="int32") = R.call_packed("vm.builtin.reshape", sample_indices, gv7, sinfo_args=(R.Tensor((num_samples, 1), dtype="int32"),)) + storage3: R.Object = R.vm.alloc_storage(R.shape([32]), R.prim_value(0), R.dtype("uint8"), R.str("global")) + gv8: R.Shape(ndim=2) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(2), R.prim_value(1), R.prim_value(2), R.prim_value(0), R.prim_value(1), sinfo_args=(R.Shape(ndim=2),)) + alloc3: R.Tensor(dtype="int32", ndim=2) = R.vm.alloc_tensor(storage3, R.prim_value(0), gv8, R.dtype("int32")) + R.vm.kill_object(storage3) + cls.parallel_sampling_from_prob(probs, uniform_samples_1, sample_indices_1, alloc3) + R.vm.kill_object(uniform_samples_1) + R.vm.kill_object(sample_indices_1) + gv9: R.Shape(ndim=1) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(1), R.prim_value(1), R.prim_value(2), sinfo_args=(R.Shape(ndim=1),)) + gv: R.Tensor((num_samples,), dtype="int32") = R.call_packed("vm.builtin.reshape", alloc3, gv9, sinfo_args=(R.Tensor((num_samples,), dtype="int32"),)) + R.vm.kill_object(alloc3) + return gv + + @R.function + def prefill(input_ids: R.Tensor((1, "seq_len"), dtype="int32"), paged_kv_cache: R.Object, packed_params: R.Tuple(R.Tensor((1280, 128, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1500, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), 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R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"))) -> R.Tensor((1, 1, 51866), dtype="float32"): + seq_len = T.int64() + R.func_attr({"num_input": 2, "relax.force_pure": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "seq_len": 15000, "total_seq_len": 1500}}) + cls = Module + shape_heap: R.Tensor(dtype="int64", ndim=1) = R.call_builtin_with_ctx("vm.builtin.alloc_shape_heap", (R.prim_value(2),), sinfo_args=(R.Tensor(dtype="int64", ndim=1),)) + R.call_packed("vm.builtin.check_tensor_info", input_ids, R.prim_value(2), R.dtype("int32"), R.str("ErrorContext(fn=prefill, loc=param[0], param=input_ids, annotation=R.Tensor((1, seq_len), dtype=\"int32\")) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.check_tuple_info", packed_params, R.prim_value(1259), R.str("ErrorContext(fn=prefill, loc=param[2], param=packed_params, annotation=R.Tuple(R.Tensor((1280, 128, 3), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280, 3), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1500, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((5120, 1280), dtype=\"float16\"), R.Tensor((5120,), dtype=\"float16\"), R.Tensor((1280, 5120), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((5120, 1280), dtype=\"float16\"), R.Tensor((5120,), dtype=\"float16\"), R.Tensor((1280, 5120), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((5120, 1280), dtype=\"float16\"), R.Tensor((5120,), 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5120), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((5120, 1280), dtype=\"float16\"), R.Tensor((5120,), dtype=\"float16\"), R.Tensor((1280, 5120), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((5120, 1280), dtype=\"float16\"), R.Tensor((5120,), dtype=\"float16\"), R.Tensor((1280, 5120), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((5120, 1280), dtype=\"float16\"), R.Tensor((5120,), dtype=\"float16\"), R.Tensor((1280, 5120), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((5120, 1280), dtype=\"float16\"), R.Tensor((5120,), dtype=\"float16\"), R.Tensor((1280, 5120), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((5120, 1280), dtype=\"float16\"), R.Tensor((5120,), dtype=\"float16\"), R.Tensor((1280, 5120), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((5120, 1280), dtype=\"float16\"), R.Tensor((5120,), dtype=\"float16\"), R.Tensor((1280, 5120), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((5120, 1280), dtype=\"float16\"), R.Tensor((5120,), dtype=\"float16\"), R.Tensor((1280, 5120), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((5120, 1280), dtype=\"float16\"), R.Tensor((5120,), dtype=\"float16\"), R.Tensor((1280, 5120), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((5120, 1280), dtype=\"float16\"), R.Tensor((5120,), dtype=\"float16\"), R.Tensor((1280, 5120), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((5120, 1280), dtype=\"float16\"), R.Tensor((5120,), dtype=\"float16\"), R.Tensor((1280, 5120), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280, 1280), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((5120, 1280), dtype=\"float16\"), R.Tensor((5120,), dtype=\"float16\"), R.Tensor((1280, 5120), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"), R.Tensor((1280,), dtype=\"float16\"))) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.match_shape", input_ids, shape_heap, R.prim_value(2), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.str("ErrorContext(fn=prefill, loc=param[0], param=input_ids, annotation=R.Tensor((1, seq_len), dtype=\"int32\")) "), sinfo_args=(R.Tuple,)) + model_decoder_embed_tokens_weight4: R.Tensor((51866, 1280), dtype="float16") = packed_params[487] + gv2580: R.Shape(ndim=1) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(1), R.prim_value(1), R.prim_value(0), sinfo_args=(R.Shape(ndim=1),)) + reshape1030: R.Tensor((seq_len,), dtype="int32") = R.call_packed("vm.builtin.reshape", input_ids, gv2580, sinfo_args=(R.Tensor((seq_len,), dtype="int32"),)) + model_decoder_embed_tokens_weight4_1: R.Tensor((51866, 1280), dtype="float16") = packed_params[487] + storage37: R.Object = R.vm.alloc_storage(R.shape([153600000]), R.prim_value(0), R.dtype("uint8"), R.str("global")) + gv2581: R.Shape(ndim=2) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(2), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=2),)) + alloc1982: R.Tensor(dtype="float16", ndim=2) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv2581, R.dtype("float16")) + cls.take(model_decoder_embed_tokens_weight4_1, reshape1030, alloc1982) + R.vm.kill_object(reshape1030) + R.vm.kill_object(model_decoder_embed_tokens_weight4_1) + gv2582: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1031: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1982, gv2582, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1982) + lv198: R.Tensor((seq_len,), dtype="int32") = R.call_packed("vm.builtin.attention_kv_cache_get_query_positions", paged_kv_cache, sinfo_args=(R.Tensor((seq_len,), dtype="int32"),)) + model_decoder_embed_positions_weight4: R.Tensor((448, 1280), dtype="float16") = packed_params[488] + storage38: R.Object = R.vm.alloc_storage(R.shape([115200000]), R.prim_value(0), R.dtype("uint8"), R.str("global")) + gv2583: R.Shape(ndim=2) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(2), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=2),)) + alloc1983: R.Tensor(dtype="float16", ndim=2) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv2583, R.dtype("float16")) + cls.take1(model_decoder_embed_positions_weight4, lv198, alloc1983) + R.vm.kill_object(lv198) + R.vm.kill_object(model_decoder_embed_positions_weight4) + gv2584: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1032: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1983, gv2584, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(alloc1983) + storage39: R.Object = R.vm.alloc_storage(R.shape([115200000]), R.prim_value(0), R.dtype("uint8"), R.str("global")) + gv2585: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1984: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv2585, R.dtype("float16")) + cls.add5(reshape1031, reshape1032, alloc1984) + R.vm.kill_object(reshape1031) + R.vm.kill_object(reshape1032) + model_decoder_layers_0_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[496] + model_decoder_layers_0_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[497] + gv2586: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1985: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv2586, R.dtype("float16")) + cls.layer_norm2(alloc1984, model_decoder_layers_0_self_attn_layer_norm_weight4, model_decoder_layers_0_self_attn_layer_norm_bias4, alloc1985) + R.vm.kill_object(model_decoder_layers_0_self_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_0_self_attn_layer_norm_bias4) + model_decoder_layers_0_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[492] + model_decoder_layers_0_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[493] + gv2587: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1986: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv2587, R.dtype("float16")) + _1985: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_0_self_attn_q_proj_weight4, alloc1985, model_decoder_layers_0_self_attn_q_proj_bias4, alloc1986) + R.vm.kill_object(model_decoder_layers_0_self_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_0_self_attn_q_proj_bias4) + gv2588: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1033: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1986, gv2588, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1986) + model_decoder_layers_0_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[489] + storage40: R.Object = R.vm.alloc_storage(R.shape([115200000]), R.prim_value(0), R.dtype("uint8"), R.str("global")) + gv2589: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1987: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv2589, R.dtype("float16")) + _1986: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_0_self_attn_k_proj_weight4, alloc1985, alloc1987) + R.vm.kill_object(model_decoder_layers_0_self_attn_k_proj_weight4) + gv2590: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1034: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1987, gv2590, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1987) + model_decoder_layers_0_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[490] + model_decoder_layers_0_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[491] + storage41: R.Object = R.vm.alloc_storage(R.shape([115200000]), R.prim_value(0), R.dtype("uint8"), R.str("global")) + gv2591: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1988: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2591, R.dtype("float16")) + _1987: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_0_self_attn_v_proj_weight4, alloc1985, model_decoder_layers_0_self_attn_v_proj_bias4, alloc1988) + R.vm.kill_object(alloc1985) + R.vm.kill_object(model_decoder_layers_0_self_attn_v_proj_weight4) + R.vm.kill_object(model_decoder_layers_0_self_attn_v_proj_bias4) + gv2592: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1035: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1988, gv2592, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1988) + gv2593: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc1989: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv2593, R.dtype("float16")) + cls.concatenate1(reshape1033, reshape1034, reshape1035, alloc1989) + R.vm.kill_object(reshape1033) + R.vm.kill_object(reshape1034) + R.vm.kill_object(reshape1035) + gv2594: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1036: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1989, gv2594, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc1989) + gv2595: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc1990: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv2595, R.dtype("float16")) + _1989: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), reshape1036, alloc1990) + R.vm.kill_object(reshape1036) + gv2596: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1037: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1990, gv2596, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1990) + gv2597: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1038: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1037, gv2597, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1037) + model_decoder_layers_0_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[494] + model_decoder_layers_0_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[495] + gv2598: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1991: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv2598, R.dtype("float16")) + _1990: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_0_self_attn_out_proj_weight4, reshape1038, model_decoder_layers_0_self_attn_out_proj_bias4, alloc1991) + R.vm.kill_object(reshape1038) + R.vm.kill_object(model_decoder_layers_0_self_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_0_self_attn_out_proj_bias4) + gv2599: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1992: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2599, R.dtype("float16")) + cls.add5(alloc1984, alloc1991, alloc1992) + R.vm.kill_object(alloc1984) + R.vm.kill_object(alloc1991) + model_decoder_layers_0_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[505] + model_decoder_layers_0_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[506] + gv2600: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1993: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv2600, R.dtype("float16")) + cls.layer_norm2(alloc1992, model_decoder_layers_0_encoder_attn_layer_norm_weight4, model_decoder_layers_0_encoder_attn_layer_norm_bias4, alloc1993) + R.vm.kill_object(model_decoder_layers_0_encoder_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_0_encoder_attn_layer_norm_bias4) + model_decoder_layers_0_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[501] + model_decoder_layers_0_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[502] + gv2601: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1994: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv2601, R.dtype("float16")) + _1993: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_0_encoder_attn_q_proj_weight4, alloc1993, model_decoder_layers_0_encoder_attn_q_proj_bias4, alloc1994) + R.vm.kill_object(alloc1993) + R.vm.kill_object(model_decoder_layers_0_encoder_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_0_encoder_attn_q_proj_bias4) + gv2602: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1039: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1994, gv2602, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1994) + gv2603: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1040: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1039, gv2603, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape1039) + gv2604: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc1995: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv2604, R.dtype("float16")) + _1994: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), reshape1040, alloc1995) + R.vm.kill_object(reshape1040) + gv2605: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1041: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc1995, gv2605, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc1995) + gv2606: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1042: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1041, gv2606, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1041) + model_decoder_layers_0_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[503] + model_decoder_layers_0_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[504] + gv2607: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1996: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv2607, R.dtype("float16")) + _1995: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_0_encoder_attn_out_proj_weight4, reshape1042, model_decoder_layers_0_encoder_attn_out_proj_bias4, alloc1996) + R.vm.kill_object(reshape1042) + R.vm.kill_object(model_decoder_layers_0_encoder_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_0_encoder_attn_out_proj_bias4) + gv2608: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1997: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv2608, R.dtype("float16")) + cls.add5(alloc1992, alloc1996, alloc1997) + R.vm.kill_object(alloc1992) + R.vm.kill_object(alloc1996) + model_decoder_layers_0_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[511] + model_decoder_layers_0_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[512] + gv2609: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc1998: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv2609, R.dtype("float16")) + cls.layer_norm2(alloc1997, model_decoder_layers_0_final_layer_norm_weight4, model_decoder_layers_0_final_layer_norm_bias4, alloc1998) + R.vm.kill_object(model_decoder_layers_0_final_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_0_final_layer_norm_bias4) + model_decoder_layers_0_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[507] + model_decoder_layers_0_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[508] + gv2610: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc1999: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv2610, R.dtype("float16")) + _1998: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_0_fc1_weight4, alloc1998, model_decoder_layers_0_fc1_bias4, alloc1999) + R.vm.kill_object(alloc1998) + R.vm.kill_object(model_decoder_layers_0_fc1_weight4) + R.vm.kill_object(model_decoder_layers_0_fc1_bias4) + model_decoder_layers_0_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[509] + model_decoder_layers_0_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[510] + gv2611: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2000: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2611, R.dtype("float16")) + _1999: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_0_fc2_weight4, alloc1999, model_decoder_layers_0_fc2_bias4, alloc2000) + R.vm.kill_object(alloc1999) + R.vm.kill_object(model_decoder_layers_0_fc2_weight4) + R.vm.kill_object(model_decoder_layers_0_fc2_bias4) + gv2612: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2001: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv2612, R.dtype("float16")) + cls.add5(alloc1997, alloc2000, alloc2001) + R.vm.kill_object(alloc1997) + R.vm.kill_object(alloc2000) + model_decoder_layers_1_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[520] + model_decoder_layers_1_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[521] + gv2613: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2002: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv2613, R.dtype("float16")) + cls.layer_norm2(alloc2001, model_decoder_layers_1_self_attn_layer_norm_weight4, model_decoder_layers_1_self_attn_layer_norm_bias4, alloc2002) + R.vm.kill_object(model_decoder_layers_1_self_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_1_self_attn_layer_norm_bias4) + model_decoder_layers_1_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[516] + model_decoder_layers_1_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[517] + gv2614: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2003: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv2614, R.dtype("float16")) + _2002: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_1_self_attn_q_proj_weight4, alloc2002, model_decoder_layers_1_self_attn_q_proj_bias4, alloc2003) + R.vm.kill_object(model_decoder_layers_1_self_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_1_self_attn_q_proj_bias4) + gv2615: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1043: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2003, gv2615, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2003) + model_decoder_layers_1_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[513] + gv2616: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2004: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2616, R.dtype("float16")) + _2003: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_1_self_attn_k_proj_weight4, alloc2002, alloc2004) + R.vm.kill_object(model_decoder_layers_1_self_attn_k_proj_weight4) + gv2617: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1044: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2004, gv2617, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2004) + model_decoder_layers_1_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[514] + model_decoder_layers_1_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[515] + gv2618: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2005: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv2618, R.dtype("float16")) + _2004: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_1_self_attn_v_proj_weight4, alloc2002, model_decoder_layers_1_self_attn_v_proj_bias4, alloc2005) + R.vm.kill_object(alloc2002) + R.vm.kill_object(model_decoder_layers_1_self_attn_v_proj_weight4) + R.vm.kill_object(model_decoder_layers_1_self_attn_v_proj_bias4) + gv2619: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1045: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2005, gv2619, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2005) + gv2620: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc2006: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv2620, R.dtype("float16")) + cls.concatenate1(reshape1043, reshape1044, reshape1045, alloc2006) + R.vm.kill_object(reshape1043) + R.vm.kill_object(reshape1044) + R.vm.kill_object(reshape1045) + gv2621: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1046: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2006, gv2621, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc2006) + gv2622: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc2007: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv2622, R.dtype("float16")) + _2006: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), reshape1046, alloc2007) + R.vm.kill_object(reshape1046) + gv2623: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1047: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2007, gv2623, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2007) + gv2624: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1048: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1047, gv2624, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1047) + model_decoder_layers_1_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[518] + model_decoder_layers_1_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[519] + gv2625: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2008: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2625, R.dtype("float16")) + _2007: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_1_self_attn_out_proj_weight4, reshape1048, model_decoder_layers_1_self_attn_out_proj_bias4, alloc2008) + R.vm.kill_object(reshape1048) + R.vm.kill_object(model_decoder_layers_1_self_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_1_self_attn_out_proj_bias4) + gv2626: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2009: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv2626, R.dtype("float16")) + cls.add5(alloc2001, alloc2008, alloc2009) + R.vm.kill_object(alloc2001) + R.vm.kill_object(alloc2008) + model_decoder_layers_1_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[529] + model_decoder_layers_1_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[530] + gv2627: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2010: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv2627, R.dtype("float16")) + cls.layer_norm2(alloc2009, model_decoder_layers_1_encoder_attn_layer_norm_weight4, model_decoder_layers_1_encoder_attn_layer_norm_bias4, alloc2010) + R.vm.kill_object(model_decoder_layers_1_encoder_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_1_encoder_attn_layer_norm_bias4) + model_decoder_layers_1_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[525] + model_decoder_layers_1_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[526] + gv2628: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2011: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2628, R.dtype("float16")) + _2010: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_1_encoder_attn_q_proj_weight4, alloc2010, model_decoder_layers_1_encoder_attn_q_proj_bias4, alloc2011) + R.vm.kill_object(alloc2010) + R.vm.kill_object(model_decoder_layers_1_encoder_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_1_encoder_attn_q_proj_bias4) + gv2629: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1049: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2011, gv2629, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2011) + gv2630: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1050: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1049, gv2630, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape1049) + gv2631: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc2012: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv2631, R.dtype("float16")) + _2011: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), reshape1050, alloc2012) + R.vm.kill_object(reshape1050) + gv2632: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1051: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2012, gv2632, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2012) + gv2633: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1052: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1051, gv2633, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1051) + model_decoder_layers_1_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[527] + model_decoder_layers_1_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[528] + gv2634: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2013: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2634, R.dtype("float16")) + _2012: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_1_encoder_attn_out_proj_weight4, reshape1052, model_decoder_layers_1_encoder_attn_out_proj_bias4, alloc2013) + R.vm.kill_object(reshape1052) + R.vm.kill_object(model_decoder_layers_1_encoder_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_1_encoder_attn_out_proj_bias4) + gv2635: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2014: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv2635, R.dtype("float16")) + cls.add5(alloc2009, alloc2013, alloc2014) + R.vm.kill_object(alloc2009) + R.vm.kill_object(alloc2013) + model_decoder_layers_1_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[535] + model_decoder_layers_1_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[536] + gv2636: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2015: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv2636, R.dtype("float16")) + cls.layer_norm2(alloc2014, model_decoder_layers_1_final_layer_norm_weight4, model_decoder_layers_1_final_layer_norm_bias4, alloc2015) + R.vm.kill_object(model_decoder_layers_1_final_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_1_final_layer_norm_bias4) + model_decoder_layers_1_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[531] + model_decoder_layers_1_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[532] + gv2637: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc2016: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv2637, R.dtype("float16")) + _2015: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_1_fc1_weight4, alloc2015, model_decoder_layers_1_fc1_bias4, alloc2016) + R.vm.kill_object(alloc2015) + R.vm.kill_object(model_decoder_layers_1_fc1_weight4) + R.vm.kill_object(model_decoder_layers_1_fc1_bias4) + model_decoder_layers_1_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[533] + model_decoder_layers_1_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[534] + gv2638: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2017: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2638, R.dtype("float16")) + _2016: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_1_fc2_weight4, alloc2016, model_decoder_layers_1_fc2_bias4, alloc2017) + R.vm.kill_object(alloc2016) + R.vm.kill_object(model_decoder_layers_1_fc2_weight4) + R.vm.kill_object(model_decoder_layers_1_fc2_bias4) + gv2639: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2018: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv2639, R.dtype("float16")) + cls.add5(alloc2014, alloc2017, alloc2018) + R.vm.kill_object(alloc2014) + R.vm.kill_object(alloc2017) + model_decoder_layers_2_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[544] + model_decoder_layers_2_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[545] + gv2640: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2019: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv2640, R.dtype("float16")) + cls.layer_norm2(alloc2018, model_decoder_layers_2_self_attn_layer_norm_weight4, model_decoder_layers_2_self_attn_layer_norm_bias4, alloc2019) + R.vm.kill_object(model_decoder_layers_2_self_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_2_self_attn_layer_norm_bias4) + model_decoder_layers_2_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[540] + model_decoder_layers_2_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[541] + gv2641: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2020: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2641, R.dtype("float16")) + _2019: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_2_self_attn_q_proj_weight4, alloc2019, model_decoder_layers_2_self_attn_q_proj_bias4, alloc2020) + R.vm.kill_object(model_decoder_layers_2_self_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_2_self_attn_q_proj_bias4) + gv2642: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1053: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2020, gv2642, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2020) + model_decoder_layers_2_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[537] + gv2643: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2021: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv2643, R.dtype("float16")) + _2020: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_2_self_attn_k_proj_weight4, alloc2019, alloc2021) + R.vm.kill_object(model_decoder_layers_2_self_attn_k_proj_weight4) + gv2644: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1054: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2021, gv2644, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2021) + model_decoder_layers_2_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[538] + model_decoder_layers_2_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[539] + gv2645: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2022: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv2645, R.dtype("float16")) + _2021: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_2_self_attn_v_proj_weight4, alloc2019, model_decoder_layers_2_self_attn_v_proj_bias4, alloc2022) + R.vm.kill_object(alloc2019) + R.vm.kill_object(model_decoder_layers_2_self_attn_v_proj_weight4) + R.vm.kill_object(model_decoder_layers_2_self_attn_v_proj_bias4) + gv2646: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1055: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2022, gv2646, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2022) + gv2647: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc2023: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv2647, R.dtype("float16")) + cls.concatenate1(reshape1053, reshape1054, reshape1055, alloc2023) + R.vm.kill_object(reshape1053) + R.vm.kill_object(reshape1054) + R.vm.kill_object(reshape1055) + gv2648: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1056: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2023, gv2648, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc2023) + gv2649: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc2024: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2649, R.dtype("float16")) + _2023: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), reshape1056, alloc2024) + R.vm.kill_object(reshape1056) + gv2650: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1057: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2024, gv2650, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2024) + gv2651: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1058: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1057, gv2651, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1057) + model_decoder_layers_2_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[542] + model_decoder_layers_2_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[543] + gv2652: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2025: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv2652, R.dtype("float16")) + _2024: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_2_self_attn_out_proj_weight4, reshape1058, model_decoder_layers_2_self_attn_out_proj_bias4, alloc2025) + R.vm.kill_object(reshape1058) + R.vm.kill_object(model_decoder_layers_2_self_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_2_self_attn_out_proj_bias4) + gv2653: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2026: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2653, R.dtype("float16")) + cls.add5(alloc2018, alloc2025, alloc2026) + R.vm.kill_object(alloc2018) + R.vm.kill_object(alloc2025) + model_decoder_layers_2_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[553] + model_decoder_layers_2_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[554] + gv2654: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2027: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv2654, R.dtype("float16")) + cls.layer_norm2(alloc2026, model_decoder_layers_2_encoder_attn_layer_norm_weight4, model_decoder_layers_2_encoder_attn_layer_norm_bias4, alloc2027) + R.vm.kill_object(model_decoder_layers_2_encoder_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_2_encoder_attn_layer_norm_bias4) + model_decoder_layers_2_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[549] + model_decoder_layers_2_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[550] + gv2655: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2028: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv2655, R.dtype("float16")) + _2027: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_2_encoder_attn_q_proj_weight4, alloc2027, model_decoder_layers_2_encoder_attn_q_proj_bias4, alloc2028) + R.vm.kill_object(alloc2027) + R.vm.kill_object(model_decoder_layers_2_encoder_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_2_encoder_attn_q_proj_bias4) + gv2656: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1059: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2028, gv2656, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2028) + gv2657: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1060: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1059, gv2657, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape1059) + gv2658: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc2029: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv2658, R.dtype("float16")) + _2028: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), reshape1060, alloc2029) + R.vm.kill_object(reshape1060) + gv2659: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1061: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2029, gv2659, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2029) + gv2660: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1062: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1061, gv2660, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1061) + model_decoder_layers_2_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[551] + model_decoder_layers_2_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[552] + gv2661: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2030: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv2661, R.dtype("float16")) + _2029: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_2_encoder_attn_out_proj_weight4, reshape1062, model_decoder_layers_2_encoder_attn_out_proj_bias4, alloc2030) + R.vm.kill_object(reshape1062) + R.vm.kill_object(model_decoder_layers_2_encoder_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_2_encoder_attn_out_proj_bias4) + gv2662: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2031: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv2662, R.dtype("float16")) + cls.add5(alloc2026, alloc2030, alloc2031) + R.vm.kill_object(alloc2026) + R.vm.kill_object(alloc2030) + model_decoder_layers_2_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[559] + model_decoder_layers_2_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[560] + gv2663: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2032: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2663, R.dtype("float16")) + cls.layer_norm2(alloc2031, model_decoder_layers_2_final_layer_norm_weight4, model_decoder_layers_2_final_layer_norm_bias4, alloc2032) + R.vm.kill_object(model_decoder_layers_2_final_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_2_final_layer_norm_bias4) + model_decoder_layers_2_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[555] + model_decoder_layers_2_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[556] + gv2664: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc2033: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv2664, R.dtype("float16")) + _2032: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_2_fc1_weight4, alloc2032, model_decoder_layers_2_fc1_bias4, alloc2033) + R.vm.kill_object(alloc2032) + R.vm.kill_object(model_decoder_layers_2_fc1_weight4) + R.vm.kill_object(model_decoder_layers_2_fc1_bias4) + model_decoder_layers_2_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[557] + model_decoder_layers_2_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[558] + gv2665: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2034: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2665, R.dtype("float16")) + _2033: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_2_fc2_weight4, alloc2033, model_decoder_layers_2_fc2_bias4, alloc2034) + R.vm.kill_object(alloc2033) + R.vm.kill_object(model_decoder_layers_2_fc2_weight4) + R.vm.kill_object(model_decoder_layers_2_fc2_bias4) + gv2666: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2035: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv2666, R.dtype("float16")) + cls.add5(alloc2031, alloc2034, alloc2035) + R.vm.kill_object(alloc2031) + R.vm.kill_object(alloc2034) + model_decoder_layers_3_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[568] + model_decoder_layers_3_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[569] + gv2667: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2036: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv2667, R.dtype("float16")) + cls.layer_norm2(alloc2035, model_decoder_layers_3_self_attn_layer_norm_weight4, model_decoder_layers_3_self_attn_layer_norm_bias4, alloc2036) + R.vm.kill_object(model_decoder_layers_3_self_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_3_self_attn_layer_norm_bias4) + model_decoder_layers_3_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[564] + model_decoder_layers_3_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[565] + gv2668: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2037: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2668, R.dtype("float16")) + _2036: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_3_self_attn_q_proj_weight4, alloc2036, model_decoder_layers_3_self_attn_q_proj_bias4, alloc2037) + R.vm.kill_object(model_decoder_layers_3_self_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_3_self_attn_q_proj_bias4) + gv2669: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1063: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2037, gv2669, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2037) + model_decoder_layers_3_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[561] + gv2670: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2038: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv2670, R.dtype("float16")) + _2037: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_3_self_attn_k_proj_weight4, alloc2036, alloc2038) + R.vm.kill_object(model_decoder_layers_3_self_attn_k_proj_weight4) + gv2671: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1064: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2038, gv2671, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2038) + model_decoder_layers_3_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[562] + model_decoder_layers_3_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[563] + gv2672: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2039: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv2672, R.dtype("float16")) + _2038: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_3_self_attn_v_proj_weight4, alloc2036, model_decoder_layers_3_self_attn_v_proj_bias4, alloc2039) + R.vm.kill_object(alloc2036) + R.vm.kill_object(model_decoder_layers_3_self_attn_v_proj_weight4) + R.vm.kill_object(model_decoder_layers_3_self_attn_v_proj_bias4) + gv2673: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1065: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2039, gv2673, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2039) + gv2674: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc2040: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv2674, R.dtype("float16")) + cls.concatenate1(reshape1063, reshape1064, reshape1065, alloc2040) + R.vm.kill_object(reshape1063) + R.vm.kill_object(reshape1064) + R.vm.kill_object(reshape1065) + gv2675: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1066: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2040, gv2675, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc2040) + gv2676: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc2041: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2676, R.dtype("float16")) + _2040: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), reshape1066, alloc2041) + R.vm.kill_object(reshape1066) + gv2677: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1067: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2041, gv2677, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2041) + gv2678: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1068: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1067, gv2678, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1067) + model_decoder_layers_3_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[566] + model_decoder_layers_3_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[567] + gv2679: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2042: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv2679, R.dtype("float16")) + _2041: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_3_self_attn_out_proj_weight4, reshape1068, model_decoder_layers_3_self_attn_out_proj_bias4, alloc2042) + R.vm.kill_object(reshape1068) + R.vm.kill_object(model_decoder_layers_3_self_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_3_self_attn_out_proj_bias4) + gv2680: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2043: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2680, R.dtype("float16")) + cls.add5(alloc2035, alloc2042, alloc2043) + R.vm.kill_object(alloc2035) + R.vm.kill_object(alloc2042) + model_decoder_layers_3_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[577] + model_decoder_layers_3_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[578] + gv2681: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2044: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv2681, R.dtype("float16")) + cls.layer_norm2(alloc2043, model_decoder_layers_3_encoder_attn_layer_norm_weight4, model_decoder_layers_3_encoder_attn_layer_norm_bias4, alloc2044) + R.vm.kill_object(model_decoder_layers_3_encoder_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_3_encoder_attn_layer_norm_bias4) + model_decoder_layers_3_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[573] + model_decoder_layers_3_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[574] + gv2682: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2045: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv2682, R.dtype("float16")) + _2044: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_3_encoder_attn_q_proj_weight4, alloc2044, model_decoder_layers_3_encoder_attn_q_proj_bias4, alloc2045) + R.vm.kill_object(alloc2044) + R.vm.kill_object(model_decoder_layers_3_encoder_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_3_encoder_attn_q_proj_bias4) + gv2683: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1069: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2045, gv2683, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2045) + gv2684: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1070: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1069, gv2684, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape1069) + gv2685: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc2046: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv2685, R.dtype("float16")) + _2045: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), reshape1070, alloc2046) + R.vm.kill_object(reshape1070) + gv2686: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1071: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2046, gv2686, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2046) + gv2687: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1072: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1071, gv2687, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1071) + model_decoder_layers_3_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[575] + model_decoder_layers_3_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[576] + gv2688: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2047: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv2688, R.dtype("float16")) + _2046: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_3_encoder_attn_out_proj_weight4, reshape1072, model_decoder_layers_3_encoder_attn_out_proj_bias4, alloc2047) + R.vm.kill_object(reshape1072) + R.vm.kill_object(model_decoder_layers_3_encoder_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_3_encoder_attn_out_proj_bias4) + gv2689: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2048: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv2689, R.dtype("float16")) + cls.add5(alloc2043, alloc2047, alloc2048) + R.vm.kill_object(alloc2043) + R.vm.kill_object(alloc2047) + model_decoder_layers_3_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[583] + model_decoder_layers_3_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[584] + gv2690: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2049: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2690, R.dtype("float16")) + cls.layer_norm2(alloc2048, model_decoder_layers_3_final_layer_norm_weight4, model_decoder_layers_3_final_layer_norm_bias4, alloc2049) + R.vm.kill_object(model_decoder_layers_3_final_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_3_final_layer_norm_bias4) + model_decoder_layers_3_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[579] + model_decoder_layers_3_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[580] + gv2691: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc2050: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv2691, R.dtype("float16")) + _2049: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_3_fc1_weight4, alloc2049, model_decoder_layers_3_fc1_bias4, alloc2050) + R.vm.kill_object(alloc2049) + R.vm.kill_object(model_decoder_layers_3_fc1_weight4) + R.vm.kill_object(model_decoder_layers_3_fc1_bias4) + model_decoder_layers_3_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[581] + model_decoder_layers_3_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[582] + gv2692: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2051: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2692, R.dtype("float16")) + _2050: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_3_fc2_weight4, alloc2050, model_decoder_layers_3_fc2_bias4, alloc2051) + R.vm.kill_object(alloc2050) + R.vm.kill_object(model_decoder_layers_3_fc2_weight4) + R.vm.kill_object(model_decoder_layers_3_fc2_bias4) + gv2693: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2052: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv2693, R.dtype("float16")) + cls.add5(alloc2048, alloc2051, alloc2052) + R.vm.kill_object(alloc2048) + R.vm.kill_object(alloc2051) + model_decoder_layers_4_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[592] + model_decoder_layers_4_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[593] + gv2694: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2053: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2694, R.dtype("float16")) + cls.layer_norm2(alloc2052, model_decoder_layers_4_self_attn_layer_norm_weight4, model_decoder_layers_4_self_attn_layer_norm_bias4, alloc2053) + R.vm.kill_object(model_decoder_layers_4_self_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_4_self_attn_layer_norm_bias4) + model_decoder_layers_4_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[588] + model_decoder_layers_4_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[589] + gv2695: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2054: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv2695, R.dtype("float16")) + _2053: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_4_self_attn_q_proj_weight4, alloc2053, model_decoder_layers_4_self_attn_q_proj_bias4, alloc2054) + R.vm.kill_object(model_decoder_layers_4_self_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_4_self_attn_q_proj_bias4) + gv2696: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1073: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2054, gv2696, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2054) + model_decoder_layers_4_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[585] + gv2697: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2055: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv2697, R.dtype("float16")) + _2054: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_4_self_attn_k_proj_weight4, alloc2053, alloc2055) + R.vm.kill_object(model_decoder_layers_4_self_attn_k_proj_weight4) + gv2698: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1074: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2055, gv2698, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2055) + model_decoder_layers_4_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[586] + model_decoder_layers_4_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[587] + gv2699: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2056: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv2699, R.dtype("float16")) + _2055: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_4_self_attn_v_proj_weight4, alloc2053, model_decoder_layers_4_self_attn_v_proj_bias4, alloc2056) + R.vm.kill_object(alloc2053) + R.vm.kill_object(model_decoder_layers_4_self_attn_v_proj_weight4) + R.vm.kill_object(model_decoder_layers_4_self_attn_v_proj_bias4) + gv2700: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1075: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2056, gv2700, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2056) + gv2701: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc2057: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2701, R.dtype("float16")) + cls.concatenate1(reshape1073, reshape1074, reshape1075, alloc2057) + R.vm.kill_object(reshape1073) + R.vm.kill_object(reshape1074) + R.vm.kill_object(reshape1075) + gv2702: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1076: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2057, gv2702, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc2057) + gv2703: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc2058: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv2703, R.dtype("float16")) + _2057: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), reshape1076, alloc2058) + R.vm.kill_object(reshape1076) + gv2704: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1077: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2058, gv2704, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2058) + gv2705: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1078: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1077, gv2705, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1077) + model_decoder_layers_4_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[590] + model_decoder_layers_4_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[591] + gv2706: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2059: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv2706, R.dtype("float16")) + _2058: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_4_self_attn_out_proj_weight4, reshape1078, model_decoder_layers_4_self_attn_out_proj_bias4, alloc2059) + R.vm.kill_object(reshape1078) + R.vm.kill_object(model_decoder_layers_4_self_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_4_self_attn_out_proj_bias4) + gv2707: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2060: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2707, R.dtype("float16")) + cls.add5(alloc2052, alloc2059, alloc2060) + R.vm.kill_object(alloc2052) + R.vm.kill_object(alloc2059) + model_decoder_layers_4_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[601] + model_decoder_layers_4_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[602] + gv2708: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2061: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv2708, R.dtype("float16")) + cls.layer_norm2(alloc2060, model_decoder_layers_4_encoder_attn_layer_norm_weight4, model_decoder_layers_4_encoder_attn_layer_norm_bias4, alloc2061) + R.vm.kill_object(model_decoder_layers_4_encoder_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_4_encoder_attn_layer_norm_bias4) + model_decoder_layers_4_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[597] + model_decoder_layers_4_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[598] + gv2709: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2062: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv2709, R.dtype("float16")) + _2061: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_4_encoder_attn_q_proj_weight4, alloc2061, model_decoder_layers_4_encoder_attn_q_proj_bias4, alloc2062) + R.vm.kill_object(alloc2061) + R.vm.kill_object(model_decoder_layers_4_encoder_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_4_encoder_attn_q_proj_bias4) + gv2710: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1079: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2062, gv2710, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2062) + gv2711: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1080: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1079, gv2711, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape1079) + gv2712: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc2063: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv2712, R.dtype("float16")) + _2062: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), reshape1080, alloc2063) + R.vm.kill_object(reshape1080) + gv2713: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1081: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2063, gv2713, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2063) + gv2714: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1082: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1081, gv2714, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1081) + model_decoder_layers_4_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[599] + model_decoder_layers_4_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[600] + gv2715: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2064: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv2715, R.dtype("float16")) + _2063: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_4_encoder_attn_out_proj_weight4, reshape1082, model_decoder_layers_4_encoder_attn_out_proj_bias4, alloc2064) + R.vm.kill_object(reshape1082) + R.vm.kill_object(model_decoder_layers_4_encoder_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_4_encoder_attn_out_proj_bias4) + gv2716: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2065: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv2716, R.dtype("float16")) + cls.add5(alloc2060, alloc2064, alloc2065) + R.vm.kill_object(alloc2060) + R.vm.kill_object(alloc2064) + model_decoder_layers_4_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[607] + model_decoder_layers_4_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[608] + gv2717: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2066: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv2717, R.dtype("float16")) + cls.layer_norm2(alloc2065, model_decoder_layers_4_final_layer_norm_weight4, model_decoder_layers_4_final_layer_norm_bias4, alloc2066) + R.vm.kill_object(model_decoder_layers_4_final_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_4_final_layer_norm_bias4) + model_decoder_layers_4_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[603] + model_decoder_layers_4_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[604] + gv2718: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc2067: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv2718, R.dtype("float16")) + _2066: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_4_fc1_weight4, alloc2066, model_decoder_layers_4_fc1_bias4, alloc2067) + R.vm.kill_object(alloc2066) + R.vm.kill_object(model_decoder_layers_4_fc1_weight4) + R.vm.kill_object(model_decoder_layers_4_fc1_bias4) + model_decoder_layers_4_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[605] + model_decoder_layers_4_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[606] + gv2719: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2068: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2719, R.dtype("float16")) + _2067: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_4_fc2_weight4, alloc2067, model_decoder_layers_4_fc2_bias4, alloc2068) + R.vm.kill_object(alloc2067) + R.vm.kill_object(model_decoder_layers_4_fc2_weight4) + R.vm.kill_object(model_decoder_layers_4_fc2_bias4) + gv2720: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2069: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv2720, R.dtype("float16")) + cls.add5(alloc2065, alloc2068, alloc2069) + R.vm.kill_object(alloc2065) + R.vm.kill_object(alloc2068) + model_decoder_layers_5_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[616] + model_decoder_layers_5_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[617] + gv2721: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2070: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv2721, R.dtype("float16")) + cls.layer_norm2(alloc2069, model_decoder_layers_5_self_attn_layer_norm_weight4, model_decoder_layers_5_self_attn_layer_norm_bias4, alloc2070) + R.vm.kill_object(model_decoder_layers_5_self_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_5_self_attn_layer_norm_bias4) + model_decoder_layers_5_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[612] + model_decoder_layers_5_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[613] + gv2722: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2071: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv2722, R.dtype("float16")) + _2070: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_5_self_attn_q_proj_weight4, alloc2070, model_decoder_layers_5_self_attn_q_proj_bias4, alloc2071) + R.vm.kill_object(model_decoder_layers_5_self_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_5_self_attn_q_proj_bias4) + gv2723: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1083: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2071, gv2723, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2071) + model_decoder_layers_5_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[609] + gv2724: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2072: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2724, R.dtype("float16")) + _2071: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_5_self_attn_k_proj_weight4, alloc2070, alloc2072) + R.vm.kill_object(model_decoder_layers_5_self_attn_k_proj_weight4) + gv2725: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1084: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2072, gv2725, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2072) + model_decoder_layers_5_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[610] + model_decoder_layers_5_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[611] + gv2726: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2073: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv2726, R.dtype("float16")) + _2072: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_5_self_attn_v_proj_weight4, alloc2070, model_decoder_layers_5_self_attn_v_proj_bias4, alloc2073) + R.vm.kill_object(alloc2070) + R.vm.kill_object(model_decoder_layers_5_self_attn_v_proj_weight4) + R.vm.kill_object(model_decoder_layers_5_self_attn_v_proj_bias4) + gv2727: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1085: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2073, gv2727, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2073) + gv2728: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc2074: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv2728, R.dtype("float16")) + cls.concatenate1(reshape1083, reshape1084, reshape1085, alloc2074) + R.vm.kill_object(reshape1083) + R.vm.kill_object(reshape1084) + R.vm.kill_object(reshape1085) + gv2729: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1086: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2074, gv2729, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc2074) + gv2730: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc2075: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv2730, R.dtype("float16")) + _2074: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), reshape1086, alloc2075) + R.vm.kill_object(reshape1086) + gv2731: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1087: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2075, gv2731, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2075) + gv2732: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1088: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1087, gv2732, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1087) + model_decoder_layers_5_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[614] + model_decoder_layers_5_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[615] + gv2733: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2076: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2733, R.dtype("float16")) + _2075: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_5_self_attn_out_proj_weight4, reshape1088, model_decoder_layers_5_self_attn_out_proj_bias4, alloc2076) + R.vm.kill_object(reshape1088) + R.vm.kill_object(model_decoder_layers_5_self_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_5_self_attn_out_proj_bias4) + gv2734: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2077: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv2734, R.dtype("float16")) + cls.add5(alloc2069, alloc2076, alloc2077) + R.vm.kill_object(alloc2069) + R.vm.kill_object(alloc2076) + model_decoder_layers_5_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[625] + model_decoder_layers_5_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[626] + gv2735: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2078: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv2735, R.dtype("float16")) + cls.layer_norm2(alloc2077, model_decoder_layers_5_encoder_attn_layer_norm_weight4, model_decoder_layers_5_encoder_attn_layer_norm_bias4, alloc2078) + R.vm.kill_object(model_decoder_layers_5_encoder_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_5_encoder_attn_layer_norm_bias4) + model_decoder_layers_5_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[621] + model_decoder_layers_5_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[622] + gv2736: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2079: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv2736, R.dtype("float16")) + _2078: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_5_encoder_attn_q_proj_weight4, alloc2078, model_decoder_layers_5_encoder_attn_q_proj_bias4, alloc2079) + R.vm.kill_object(alloc2078) + R.vm.kill_object(model_decoder_layers_5_encoder_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_5_encoder_attn_q_proj_bias4) + gv2737: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1089: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2079, gv2737, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2079) + gv2738: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1090: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1089, gv2738, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape1089) + gv2739: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc2080: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2739, R.dtype("float16")) + _2079: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), reshape1090, alloc2080) + R.vm.kill_object(reshape1090) + gv2740: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1091: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2080, gv2740, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2080) + gv2741: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1092: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1091, gv2741, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1091) + model_decoder_layers_5_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[623] + model_decoder_layers_5_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[624] + gv2742: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2081: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv2742, R.dtype("float16")) + _2080: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_5_encoder_attn_out_proj_weight4, reshape1092, model_decoder_layers_5_encoder_attn_out_proj_bias4, alloc2081) + R.vm.kill_object(reshape1092) + R.vm.kill_object(model_decoder_layers_5_encoder_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_5_encoder_attn_out_proj_bias4) + gv2743: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2082: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv2743, R.dtype("float16")) + cls.add5(alloc2077, alloc2081, alloc2082) + R.vm.kill_object(alloc2077) + R.vm.kill_object(alloc2081) + model_decoder_layers_5_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[631] + model_decoder_layers_5_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[632] + gv2744: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2083: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2744, R.dtype("float16")) + cls.layer_norm2(alloc2082, model_decoder_layers_5_final_layer_norm_weight4, model_decoder_layers_5_final_layer_norm_bias4, alloc2083) + R.vm.kill_object(model_decoder_layers_5_final_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_5_final_layer_norm_bias4) + model_decoder_layers_5_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[627] + model_decoder_layers_5_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[628] + gv2745: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc2084: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv2745, R.dtype("float16")) + _2083: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_5_fc1_weight4, alloc2083, model_decoder_layers_5_fc1_bias4, alloc2084) + R.vm.kill_object(alloc2083) + R.vm.kill_object(model_decoder_layers_5_fc1_weight4) + R.vm.kill_object(model_decoder_layers_5_fc1_bias4) + model_decoder_layers_5_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[629] + model_decoder_layers_5_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[630] + gv2746: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2085: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv2746, R.dtype("float16")) + _2084: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_5_fc2_weight4, alloc2084, model_decoder_layers_5_fc2_bias4, alloc2085) + R.vm.kill_object(alloc2084) + R.vm.kill_object(model_decoder_layers_5_fc2_weight4) + R.vm.kill_object(model_decoder_layers_5_fc2_bias4) + gv2747: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2086: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv2747, R.dtype("float16")) + cls.add5(alloc2082, alloc2085, alloc2086) + R.vm.kill_object(alloc2082) + R.vm.kill_object(alloc2085) + model_decoder_layers_6_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[640] + model_decoder_layers_6_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[641] + gv2748: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2087: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2748, R.dtype("float16")) + cls.layer_norm2(alloc2086, model_decoder_layers_6_self_attn_layer_norm_weight4, model_decoder_layers_6_self_attn_layer_norm_bias4, alloc2087) + R.vm.kill_object(model_decoder_layers_6_self_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_6_self_attn_layer_norm_bias4) + model_decoder_layers_6_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[636] + model_decoder_layers_6_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[637] + gv2749: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2088: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv2749, R.dtype("float16")) + _2087: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_6_self_attn_q_proj_weight4, alloc2087, model_decoder_layers_6_self_attn_q_proj_bias4, alloc2088) + R.vm.kill_object(model_decoder_layers_6_self_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_6_self_attn_q_proj_bias4) + gv2750: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1093: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2088, gv2750, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2088) + model_decoder_layers_6_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[633] + gv2751: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2089: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv2751, R.dtype("float16")) + _2088: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_6_self_attn_k_proj_weight4, alloc2087, alloc2089) + R.vm.kill_object(model_decoder_layers_6_self_attn_k_proj_weight4) + gv2752: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1094: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2089, gv2752, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2089) + model_decoder_layers_6_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[634] + model_decoder_layers_6_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[635] + gv2753: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2090: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv2753, R.dtype("float16")) + _2089: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_6_self_attn_v_proj_weight4, alloc2087, model_decoder_layers_6_self_attn_v_proj_bias4, alloc2090) + R.vm.kill_object(alloc2087) + R.vm.kill_object(model_decoder_layers_6_self_attn_v_proj_weight4) + R.vm.kill_object(model_decoder_layers_6_self_attn_v_proj_bias4) + gv2754: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1095: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2090, gv2754, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2090) + gv2755: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc2091: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2755, R.dtype("float16")) + cls.concatenate1(reshape1093, reshape1094, reshape1095, alloc2091) + R.vm.kill_object(reshape1093) + R.vm.kill_object(reshape1094) + R.vm.kill_object(reshape1095) + gv2756: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1096: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2091, gv2756, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc2091) + gv2757: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc2092: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv2757, R.dtype("float16")) + _2091: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), reshape1096, alloc2092) + R.vm.kill_object(reshape1096) + gv2758: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1097: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2092, gv2758, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2092) + gv2759: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1098: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1097, gv2759, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1097) + model_decoder_layers_6_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[638] + model_decoder_layers_6_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[639] + gv2760: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2093: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv2760, R.dtype("float16")) + _2092: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_6_self_attn_out_proj_weight4, reshape1098, model_decoder_layers_6_self_attn_out_proj_bias4, alloc2093) + R.vm.kill_object(reshape1098) + R.vm.kill_object(model_decoder_layers_6_self_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_6_self_attn_out_proj_bias4) + gv2761: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2094: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2761, R.dtype("float16")) + cls.add5(alloc2086, alloc2093, alloc2094) + R.vm.kill_object(alloc2086) + R.vm.kill_object(alloc2093) + model_decoder_layers_6_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[649] + model_decoder_layers_6_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[650] + gv2762: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2095: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv2762, R.dtype("float16")) + cls.layer_norm2(alloc2094, model_decoder_layers_6_encoder_attn_layer_norm_weight4, model_decoder_layers_6_encoder_attn_layer_norm_bias4, alloc2095) + R.vm.kill_object(model_decoder_layers_6_encoder_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_6_encoder_attn_layer_norm_bias4) + model_decoder_layers_6_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[645] + model_decoder_layers_6_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[646] + gv2763: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2096: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv2763, R.dtype("float16")) + _2095: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_6_encoder_attn_q_proj_weight4, alloc2095, model_decoder_layers_6_encoder_attn_q_proj_bias4, alloc2096) + R.vm.kill_object(alloc2095) + R.vm.kill_object(model_decoder_layers_6_encoder_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_6_encoder_attn_q_proj_bias4) + gv2764: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1099: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2096, gv2764, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2096) + gv2765: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1100: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1099, gv2765, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape1099) + gv2766: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc2097: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv2766, R.dtype("float16")) + _2096: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), reshape1100, alloc2097) + R.vm.kill_object(reshape1100) + gv2767: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1101: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2097, gv2767, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2097) + gv2768: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1102: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1101, gv2768, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1101) + model_decoder_layers_6_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[647] + model_decoder_layers_6_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[648] + gv2769: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2098: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv2769, R.dtype("float16")) + _2097: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_6_encoder_attn_out_proj_weight4, reshape1102, model_decoder_layers_6_encoder_attn_out_proj_bias4, alloc2098) + R.vm.kill_object(reshape1102) + R.vm.kill_object(model_decoder_layers_6_encoder_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_6_encoder_attn_out_proj_bias4) + gv2770: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2099: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv2770, R.dtype("float16")) + cls.add5(alloc2094, alloc2098, alloc2099) + R.vm.kill_object(alloc2094) + R.vm.kill_object(alloc2098) + model_decoder_layers_6_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[655] + model_decoder_layers_6_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[656] + gv2771: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2100: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv2771, R.dtype("float16")) + cls.layer_norm2(alloc2099, model_decoder_layers_6_final_layer_norm_weight4, model_decoder_layers_6_final_layer_norm_bias4, alloc2100) + R.vm.kill_object(model_decoder_layers_6_final_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_6_final_layer_norm_bias4) + model_decoder_layers_6_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[651] + model_decoder_layers_6_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[652] + gv2772: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc2101: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv2772, R.dtype("float16")) + _2100: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_6_fc1_weight4, alloc2100, model_decoder_layers_6_fc1_bias4, alloc2101) + R.vm.kill_object(alloc2100) + R.vm.kill_object(model_decoder_layers_6_fc1_weight4) + R.vm.kill_object(model_decoder_layers_6_fc1_bias4) + model_decoder_layers_6_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[653] + model_decoder_layers_6_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[654] + gv2773: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2102: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2773, R.dtype("float16")) + _2101: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_6_fc2_weight4, alloc2101, model_decoder_layers_6_fc2_bias4, alloc2102) + R.vm.kill_object(alloc2101) + R.vm.kill_object(model_decoder_layers_6_fc2_weight4) + R.vm.kill_object(model_decoder_layers_6_fc2_bias4) + gv2774: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2103: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv2774, R.dtype("float16")) + cls.add5(alloc2099, alloc2102, alloc2103) + R.vm.kill_object(alloc2099) + R.vm.kill_object(alloc2102) + model_decoder_layers_7_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[664] + model_decoder_layers_7_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[665] + gv2775: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2104: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv2775, R.dtype("float16")) + cls.layer_norm2(alloc2103, model_decoder_layers_7_self_attn_layer_norm_weight4, model_decoder_layers_7_self_attn_layer_norm_bias4, alloc2104) + R.vm.kill_object(model_decoder_layers_7_self_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_7_self_attn_layer_norm_bias4) + model_decoder_layers_7_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[660] + model_decoder_layers_7_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[661] + gv2776: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2105: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv2776, R.dtype("float16")) + _2104: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_7_self_attn_q_proj_weight4, alloc2104, model_decoder_layers_7_self_attn_q_proj_bias4, alloc2105) + R.vm.kill_object(model_decoder_layers_7_self_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_7_self_attn_q_proj_bias4) + gv2777: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1103: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2105, gv2777, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2105) + model_decoder_layers_7_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[657] + gv2778: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2106: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2778, R.dtype("float16")) + _2105: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_7_self_attn_k_proj_weight4, alloc2104, alloc2106) + R.vm.kill_object(model_decoder_layers_7_self_attn_k_proj_weight4) + gv2779: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1104: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2106, gv2779, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2106) + model_decoder_layers_7_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[658] + model_decoder_layers_7_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[659] + gv2780: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2107: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv2780, R.dtype("float16")) + _2106: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_7_self_attn_v_proj_weight4, alloc2104, model_decoder_layers_7_self_attn_v_proj_bias4, alloc2107) + R.vm.kill_object(alloc2104) + R.vm.kill_object(model_decoder_layers_7_self_attn_v_proj_weight4) + R.vm.kill_object(model_decoder_layers_7_self_attn_v_proj_bias4) + gv2781: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1105: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2107, gv2781, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2107) + gv2782: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc2108: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv2782, R.dtype("float16")) + cls.concatenate1(reshape1103, reshape1104, reshape1105, alloc2108) + R.vm.kill_object(reshape1103) + R.vm.kill_object(reshape1104) + R.vm.kill_object(reshape1105) + gv2783: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1106: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2108, gv2783, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc2108) + gv2784: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc2109: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv2784, R.dtype("float16")) + _2108: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), reshape1106, alloc2109) + R.vm.kill_object(reshape1106) + gv2785: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1107: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2109, gv2785, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2109) + gv2786: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1108: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1107, gv2786, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1107) + model_decoder_layers_7_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[662] + model_decoder_layers_7_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[663] + gv2787: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2110: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2787, R.dtype("float16")) + _2109: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_7_self_attn_out_proj_weight4, reshape1108, model_decoder_layers_7_self_attn_out_proj_bias4, alloc2110) + R.vm.kill_object(reshape1108) + R.vm.kill_object(model_decoder_layers_7_self_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_7_self_attn_out_proj_bias4) + gv2788: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2111: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv2788, R.dtype("float16")) + cls.add5(alloc2103, alloc2110, alloc2111) + R.vm.kill_object(alloc2103) + R.vm.kill_object(alloc2110) + model_decoder_layers_7_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[673] + model_decoder_layers_7_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[674] + gv2789: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2112: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv2789, R.dtype("float16")) + cls.layer_norm2(alloc2111, model_decoder_layers_7_encoder_attn_layer_norm_weight4, model_decoder_layers_7_encoder_attn_layer_norm_bias4, alloc2112) + R.vm.kill_object(model_decoder_layers_7_encoder_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_7_encoder_attn_layer_norm_bias4) + model_decoder_layers_7_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[669] + model_decoder_layers_7_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[670] + gv2790: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2113: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv2790, R.dtype("float16")) + _2112: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_7_encoder_attn_q_proj_weight4, alloc2112, model_decoder_layers_7_encoder_attn_q_proj_bias4, alloc2113) + R.vm.kill_object(alloc2112) + R.vm.kill_object(model_decoder_layers_7_encoder_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_7_encoder_attn_q_proj_bias4) + gv2791: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1109: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2113, gv2791, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2113) + gv2792: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1110: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1109, gv2792, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape1109) + gv2793: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc2114: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2793, R.dtype("float16")) + _2113: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), reshape1110, alloc2114) + R.vm.kill_object(reshape1110) + gv2794: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1111: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2114, gv2794, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2114) + gv2795: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1112: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1111, gv2795, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1111) + model_decoder_layers_7_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[671] + model_decoder_layers_7_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[672] + gv2796: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2115: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv2796, R.dtype("float16")) + _2114: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_7_encoder_attn_out_proj_weight4, reshape1112, model_decoder_layers_7_encoder_attn_out_proj_bias4, alloc2115) + R.vm.kill_object(reshape1112) + R.vm.kill_object(model_decoder_layers_7_encoder_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_7_encoder_attn_out_proj_bias4) + gv2797: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2116: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv2797, R.dtype("float16")) + cls.add5(alloc2111, alloc2115, alloc2116) + R.vm.kill_object(alloc2111) + R.vm.kill_object(alloc2115) + model_decoder_layers_7_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[679] + model_decoder_layers_7_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[680] + gv2798: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2117: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2798, R.dtype("float16")) + cls.layer_norm2(alloc2116, model_decoder_layers_7_final_layer_norm_weight4, model_decoder_layers_7_final_layer_norm_bias4, alloc2117) + R.vm.kill_object(model_decoder_layers_7_final_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_7_final_layer_norm_bias4) + model_decoder_layers_7_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[675] + model_decoder_layers_7_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[676] + gv2799: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc2118: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv2799, R.dtype("float16")) + _2117: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_7_fc1_weight4, alloc2117, model_decoder_layers_7_fc1_bias4, alloc2118) + R.vm.kill_object(alloc2117) + R.vm.kill_object(model_decoder_layers_7_fc1_weight4) + R.vm.kill_object(model_decoder_layers_7_fc1_bias4) + model_decoder_layers_7_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[677] + model_decoder_layers_7_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[678] + gv2800: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2119: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv2800, R.dtype("float16")) + _2118: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_7_fc2_weight4, alloc2118, model_decoder_layers_7_fc2_bias4, alloc2119) + R.vm.kill_object(alloc2118) + R.vm.kill_object(model_decoder_layers_7_fc2_weight4) + R.vm.kill_object(model_decoder_layers_7_fc2_bias4) + gv2801: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2120: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv2801, R.dtype("float16")) + cls.add5(alloc2116, alloc2119, alloc2120) + R.vm.kill_object(alloc2116) + R.vm.kill_object(alloc2119) + model_decoder_layers_8_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[688] + model_decoder_layers_8_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[689] + gv2802: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2121: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2802, R.dtype("float16")) + cls.layer_norm2(alloc2120, model_decoder_layers_8_self_attn_layer_norm_weight4, model_decoder_layers_8_self_attn_layer_norm_bias4, alloc2121) + R.vm.kill_object(model_decoder_layers_8_self_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_8_self_attn_layer_norm_bias4) + model_decoder_layers_8_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[684] + model_decoder_layers_8_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[685] + gv2803: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2122: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv2803, R.dtype("float16")) + _2121: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_8_self_attn_q_proj_weight4, alloc2121, model_decoder_layers_8_self_attn_q_proj_bias4, alloc2122) + R.vm.kill_object(model_decoder_layers_8_self_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_8_self_attn_q_proj_bias4) + gv2804: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1113: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2122, gv2804, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2122) + model_decoder_layers_8_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[681] + gv2805: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2123: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv2805, R.dtype("float16")) + _2122: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_8_self_attn_k_proj_weight4, alloc2121, alloc2123) + R.vm.kill_object(model_decoder_layers_8_self_attn_k_proj_weight4) + gv2806: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1114: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2123, gv2806, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2123) + model_decoder_layers_8_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[682] + model_decoder_layers_8_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[683] + gv2807: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2124: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv2807, R.dtype("float16")) + _2123: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_8_self_attn_v_proj_weight4, alloc2121, model_decoder_layers_8_self_attn_v_proj_bias4, alloc2124) + R.vm.kill_object(alloc2121) + R.vm.kill_object(model_decoder_layers_8_self_attn_v_proj_weight4) + R.vm.kill_object(model_decoder_layers_8_self_attn_v_proj_bias4) + gv2808: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1115: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2124, gv2808, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2124) + gv2809: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc2125: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2809, R.dtype("float16")) + cls.concatenate1(reshape1113, reshape1114, reshape1115, alloc2125) + R.vm.kill_object(reshape1113) + R.vm.kill_object(reshape1114) + R.vm.kill_object(reshape1115) + gv2810: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1116: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2125, gv2810, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc2125) + gv2811: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc2126: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv2811, R.dtype("float16")) + _2125: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), reshape1116, alloc2126) + R.vm.kill_object(reshape1116) + gv2812: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1117: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2126, gv2812, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2126) + gv2813: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1118: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1117, gv2813, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1117) + model_decoder_layers_8_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[686] + model_decoder_layers_8_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[687] + gv2814: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2127: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv2814, R.dtype("float16")) + _2126: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_8_self_attn_out_proj_weight4, reshape1118, model_decoder_layers_8_self_attn_out_proj_bias4, alloc2127) + R.vm.kill_object(reshape1118) + R.vm.kill_object(model_decoder_layers_8_self_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_8_self_attn_out_proj_bias4) + gv2815: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2128: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2815, R.dtype("float16")) + cls.add5(alloc2120, alloc2127, alloc2128) + R.vm.kill_object(alloc2120) + R.vm.kill_object(alloc2127) + model_decoder_layers_8_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[697] + model_decoder_layers_8_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[698] + gv2816: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2129: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv2816, R.dtype("float16")) + cls.layer_norm2(alloc2128, model_decoder_layers_8_encoder_attn_layer_norm_weight4, model_decoder_layers_8_encoder_attn_layer_norm_bias4, alloc2129) + R.vm.kill_object(model_decoder_layers_8_encoder_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_8_encoder_attn_layer_norm_bias4) + model_decoder_layers_8_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[693] + model_decoder_layers_8_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[694] + gv2817: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2130: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv2817, R.dtype("float16")) + _2129: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_8_encoder_attn_q_proj_weight4, alloc2129, model_decoder_layers_8_encoder_attn_q_proj_bias4, alloc2130) + R.vm.kill_object(alloc2129) + R.vm.kill_object(model_decoder_layers_8_encoder_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_8_encoder_attn_q_proj_bias4) + gv2818: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1119: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2130, gv2818, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2130) + gv2819: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1120: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1119, gv2819, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape1119) + gv2820: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc2131: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv2820, R.dtype("float16")) + _2130: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), reshape1120, alloc2131) + R.vm.kill_object(reshape1120) + gv2821: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1121: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2131, gv2821, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2131) + gv2822: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1122: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1121, gv2822, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1121) + model_decoder_layers_8_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[695] + model_decoder_layers_8_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[696] + gv2823: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2132: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv2823, R.dtype("float16")) + _2131: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_8_encoder_attn_out_proj_weight4, reshape1122, model_decoder_layers_8_encoder_attn_out_proj_bias4, alloc2132) + R.vm.kill_object(reshape1122) + R.vm.kill_object(model_decoder_layers_8_encoder_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_8_encoder_attn_out_proj_bias4) + gv2824: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2133: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv2824, R.dtype("float16")) + cls.add5(alloc2128, alloc2132, alloc2133) + R.vm.kill_object(alloc2128) + R.vm.kill_object(alloc2132) + model_decoder_layers_8_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[703] + model_decoder_layers_8_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[704] + gv2825: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2134: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv2825, R.dtype("float16")) + cls.layer_norm2(alloc2133, model_decoder_layers_8_final_layer_norm_weight4, model_decoder_layers_8_final_layer_norm_bias4, alloc2134) + R.vm.kill_object(model_decoder_layers_8_final_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_8_final_layer_norm_bias4) + model_decoder_layers_8_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[699] + model_decoder_layers_8_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[700] + gv2826: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc2135: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv2826, R.dtype("float16")) + _2134: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_8_fc1_weight4, alloc2134, model_decoder_layers_8_fc1_bias4, alloc2135) + R.vm.kill_object(alloc2134) + R.vm.kill_object(model_decoder_layers_8_fc1_weight4) + R.vm.kill_object(model_decoder_layers_8_fc1_bias4) + model_decoder_layers_8_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[701] + model_decoder_layers_8_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[702] + gv2827: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2136: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2827, R.dtype("float16")) + _2135: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_8_fc2_weight4, alloc2135, model_decoder_layers_8_fc2_bias4, alloc2136) + R.vm.kill_object(alloc2135) + R.vm.kill_object(model_decoder_layers_8_fc2_weight4) + R.vm.kill_object(model_decoder_layers_8_fc2_bias4) + gv2828: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2137: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv2828, R.dtype("float16")) + cls.add5(alloc2133, alloc2136, alloc2137) + R.vm.kill_object(alloc2133) + R.vm.kill_object(alloc2136) + model_decoder_layers_9_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[712] + model_decoder_layers_9_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[713] + gv2829: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2138: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv2829, R.dtype("float16")) + cls.layer_norm2(alloc2137, model_decoder_layers_9_self_attn_layer_norm_weight4, model_decoder_layers_9_self_attn_layer_norm_bias4, alloc2138) + R.vm.kill_object(model_decoder_layers_9_self_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_9_self_attn_layer_norm_bias4) + model_decoder_layers_9_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[708] + model_decoder_layers_9_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[709] + gv2830: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2139: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv2830, R.dtype("float16")) + _2138: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_9_self_attn_q_proj_weight4, alloc2138, model_decoder_layers_9_self_attn_q_proj_bias4, alloc2139) + R.vm.kill_object(model_decoder_layers_9_self_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_9_self_attn_q_proj_bias4) + gv2831: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1123: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2139, gv2831, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2139) + model_decoder_layers_9_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[705] + gv2832: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2140: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2832, R.dtype("float16")) + _2139: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_9_self_attn_k_proj_weight4, alloc2138, alloc2140) + R.vm.kill_object(model_decoder_layers_9_self_attn_k_proj_weight4) + gv2833: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1124: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2140, gv2833, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2140) + model_decoder_layers_9_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[706] + model_decoder_layers_9_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[707] + gv2834: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2141: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv2834, R.dtype("float16")) + _2140: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_9_self_attn_v_proj_weight4, alloc2138, model_decoder_layers_9_self_attn_v_proj_bias4, alloc2141) + R.vm.kill_object(alloc2138) + R.vm.kill_object(model_decoder_layers_9_self_attn_v_proj_weight4) + R.vm.kill_object(model_decoder_layers_9_self_attn_v_proj_bias4) + gv2835: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1125: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2141, gv2835, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2141) + gv2836: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc2142: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv2836, R.dtype("float16")) + cls.concatenate1(reshape1123, reshape1124, reshape1125, alloc2142) + R.vm.kill_object(reshape1123) + R.vm.kill_object(reshape1124) + R.vm.kill_object(reshape1125) + gv2837: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1126: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2142, gv2837, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc2142) + gv2838: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc2143: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv2838, R.dtype("float16")) + _2142: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), reshape1126, alloc2143) + R.vm.kill_object(reshape1126) + gv2839: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1127: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2143, gv2839, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2143) + gv2840: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1128: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1127, gv2840, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1127) + model_decoder_layers_9_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[710] + model_decoder_layers_9_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[711] + gv2841: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2144: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2841, R.dtype("float16")) + _2143: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_9_self_attn_out_proj_weight4, reshape1128, model_decoder_layers_9_self_attn_out_proj_bias4, alloc2144) + R.vm.kill_object(reshape1128) + R.vm.kill_object(model_decoder_layers_9_self_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_9_self_attn_out_proj_bias4) + gv2842: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2145: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv2842, R.dtype("float16")) + cls.add5(alloc2137, alloc2144, alloc2145) + R.vm.kill_object(alloc2137) + R.vm.kill_object(alloc2144) + model_decoder_layers_9_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[721] + model_decoder_layers_9_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[722] + gv2843: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2146: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv2843, R.dtype("float16")) + cls.layer_norm2(alloc2145, model_decoder_layers_9_encoder_attn_layer_norm_weight4, model_decoder_layers_9_encoder_attn_layer_norm_bias4, alloc2146) + R.vm.kill_object(model_decoder_layers_9_encoder_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_9_encoder_attn_layer_norm_bias4) + model_decoder_layers_9_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[717] + model_decoder_layers_9_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[718] + gv2844: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2147: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv2844, R.dtype("float16")) + _2146: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_9_encoder_attn_q_proj_weight4, alloc2146, model_decoder_layers_9_encoder_attn_q_proj_bias4, alloc2147) + R.vm.kill_object(alloc2146) + R.vm.kill_object(model_decoder_layers_9_encoder_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_9_encoder_attn_q_proj_bias4) + gv2845: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1129: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2147, gv2845, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2147) + gv2846: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1130: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1129, gv2846, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape1129) + gv2847: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc2148: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2847, R.dtype("float16")) + _2147: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), reshape1130, alloc2148) + R.vm.kill_object(reshape1130) + gv2848: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1131: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2148, gv2848, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2148) + gv2849: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1132: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1131, gv2849, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1131) + model_decoder_layers_9_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[719] + model_decoder_layers_9_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[720] + gv2850: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2149: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv2850, R.dtype("float16")) + _2148: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_9_encoder_attn_out_proj_weight4, reshape1132, model_decoder_layers_9_encoder_attn_out_proj_bias4, alloc2149) + R.vm.kill_object(reshape1132) + R.vm.kill_object(model_decoder_layers_9_encoder_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_9_encoder_attn_out_proj_bias4) + gv2851: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2150: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv2851, R.dtype("float16")) + cls.add5(alloc2145, alloc2149, alloc2150) + R.vm.kill_object(alloc2145) + R.vm.kill_object(alloc2149) + model_decoder_layers_9_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[727] + model_decoder_layers_9_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[728] + gv2852: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2151: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2852, R.dtype("float16")) + cls.layer_norm2(alloc2150, model_decoder_layers_9_final_layer_norm_weight4, model_decoder_layers_9_final_layer_norm_bias4, alloc2151) + R.vm.kill_object(model_decoder_layers_9_final_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_9_final_layer_norm_bias4) + model_decoder_layers_9_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[723] + model_decoder_layers_9_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[724] + gv2853: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc2152: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv2853, R.dtype("float16")) + _2151: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_9_fc1_weight4, alloc2151, model_decoder_layers_9_fc1_bias4, alloc2152) + R.vm.kill_object(alloc2151) + R.vm.kill_object(model_decoder_layers_9_fc1_weight4) + R.vm.kill_object(model_decoder_layers_9_fc1_bias4) + model_decoder_layers_9_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[725] + model_decoder_layers_9_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[726] + gv2854: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2153: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv2854, R.dtype("float16")) + _2152: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_9_fc2_weight4, alloc2152, model_decoder_layers_9_fc2_bias4, alloc2153) + R.vm.kill_object(alloc2152) + R.vm.kill_object(model_decoder_layers_9_fc2_weight4) + R.vm.kill_object(model_decoder_layers_9_fc2_bias4) + gv2855: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2154: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv2855, R.dtype("float16")) + cls.add5(alloc2150, alloc2153, alloc2154) + R.vm.kill_object(alloc2150) + R.vm.kill_object(alloc2153) + model_decoder_layers_10_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[736] + model_decoder_layers_10_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[737] + gv2856: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2155: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2856, R.dtype("float16")) + cls.layer_norm2(alloc2154, model_decoder_layers_10_self_attn_layer_norm_weight4, model_decoder_layers_10_self_attn_layer_norm_bias4, alloc2155) + R.vm.kill_object(model_decoder_layers_10_self_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_10_self_attn_layer_norm_bias4) + model_decoder_layers_10_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[732] + model_decoder_layers_10_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[733] + gv2857: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2156: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv2857, R.dtype("float16")) + _2155: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_10_self_attn_q_proj_weight4, alloc2155, model_decoder_layers_10_self_attn_q_proj_bias4, alloc2156) + R.vm.kill_object(model_decoder_layers_10_self_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_10_self_attn_q_proj_bias4) + gv2858: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1133: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2156, gv2858, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2156) + model_decoder_layers_10_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[729] + gv2859: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2157: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv2859, R.dtype("float16")) + _2156: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_10_self_attn_k_proj_weight4, alloc2155, alloc2157) + R.vm.kill_object(model_decoder_layers_10_self_attn_k_proj_weight4) + gv2860: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1134: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2157, gv2860, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2157) + model_decoder_layers_10_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[730] + model_decoder_layers_10_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[731] + gv2861: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2158: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv2861, R.dtype("float16")) + _2157: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_10_self_attn_v_proj_weight4, alloc2155, model_decoder_layers_10_self_attn_v_proj_bias4, alloc2158) + R.vm.kill_object(alloc2155) + R.vm.kill_object(model_decoder_layers_10_self_attn_v_proj_weight4) + R.vm.kill_object(model_decoder_layers_10_self_attn_v_proj_bias4) + gv2862: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1135: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2158, gv2862, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2158) + gv2863: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc2159: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2863, R.dtype("float16")) + cls.concatenate1(reshape1133, reshape1134, reshape1135, alloc2159) + R.vm.kill_object(reshape1133) + R.vm.kill_object(reshape1134) + R.vm.kill_object(reshape1135) + gv2864: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1136: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2159, gv2864, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc2159) + gv2865: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc2160: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv2865, R.dtype("float16")) + _2159: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), reshape1136, alloc2160) + R.vm.kill_object(reshape1136) + gv2866: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1137: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2160, gv2866, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2160) + gv2867: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1138: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1137, gv2867, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1137) + model_decoder_layers_10_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[734] + model_decoder_layers_10_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[735] + gv2868: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2161: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv2868, R.dtype("float16")) + _2160: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_10_self_attn_out_proj_weight4, reshape1138, model_decoder_layers_10_self_attn_out_proj_bias4, alloc2161) + R.vm.kill_object(reshape1138) + R.vm.kill_object(model_decoder_layers_10_self_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_10_self_attn_out_proj_bias4) + gv2869: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2162: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2869, R.dtype("float16")) + cls.add5(alloc2154, alloc2161, alloc2162) + R.vm.kill_object(alloc2154) + R.vm.kill_object(alloc2161) + model_decoder_layers_10_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[745] + model_decoder_layers_10_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[746] + gv2870: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2163: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv2870, R.dtype("float16")) + cls.layer_norm2(alloc2162, model_decoder_layers_10_encoder_attn_layer_norm_weight4, model_decoder_layers_10_encoder_attn_layer_norm_bias4, alloc2163) + R.vm.kill_object(model_decoder_layers_10_encoder_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_10_encoder_attn_layer_norm_bias4) + model_decoder_layers_10_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[741] + model_decoder_layers_10_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[742] + gv2871: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2164: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv2871, R.dtype("float16")) + _2163: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_10_encoder_attn_q_proj_weight4, alloc2163, model_decoder_layers_10_encoder_attn_q_proj_bias4, alloc2164) + R.vm.kill_object(alloc2163) + R.vm.kill_object(model_decoder_layers_10_encoder_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_10_encoder_attn_q_proj_bias4) + gv2872: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1139: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2164, gv2872, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2164) + gv2873: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1140: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1139, gv2873, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape1139) + gv2874: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc2165: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv2874, R.dtype("float16")) + _2164: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), reshape1140, alloc2165) + R.vm.kill_object(reshape1140) + gv2875: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1141: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2165, gv2875, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2165) + gv2876: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1142: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1141, gv2876, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1141) + model_decoder_layers_10_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[743] + model_decoder_layers_10_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[744] + gv2877: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2166: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv2877, R.dtype("float16")) + _2165: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_10_encoder_attn_out_proj_weight4, reshape1142, model_decoder_layers_10_encoder_attn_out_proj_bias4, alloc2166) + R.vm.kill_object(reshape1142) + R.vm.kill_object(model_decoder_layers_10_encoder_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_10_encoder_attn_out_proj_bias4) + gv2878: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2167: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv2878, R.dtype("float16")) + cls.add5(alloc2162, alloc2166, alloc2167) + R.vm.kill_object(alloc2162) + R.vm.kill_object(alloc2166) + model_decoder_layers_10_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[751] + model_decoder_layers_10_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[752] + gv2879: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2168: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv2879, R.dtype("float16")) + cls.layer_norm2(alloc2167, model_decoder_layers_10_final_layer_norm_weight4, model_decoder_layers_10_final_layer_norm_bias4, alloc2168) + R.vm.kill_object(model_decoder_layers_10_final_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_10_final_layer_norm_bias4) + model_decoder_layers_10_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[747] + model_decoder_layers_10_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[748] + gv2880: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc2169: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv2880, R.dtype("float16")) + _2168: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_10_fc1_weight4, alloc2168, model_decoder_layers_10_fc1_bias4, alloc2169) + R.vm.kill_object(alloc2168) + R.vm.kill_object(model_decoder_layers_10_fc1_weight4) + R.vm.kill_object(model_decoder_layers_10_fc1_bias4) + model_decoder_layers_10_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[749] + model_decoder_layers_10_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[750] + gv2881: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2170: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2881, R.dtype("float16")) + _2169: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_10_fc2_weight4, alloc2169, model_decoder_layers_10_fc2_bias4, alloc2170) + R.vm.kill_object(alloc2169) + R.vm.kill_object(model_decoder_layers_10_fc2_weight4) + R.vm.kill_object(model_decoder_layers_10_fc2_bias4) + gv2882: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2171: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv2882, R.dtype("float16")) + cls.add5(alloc2167, alloc2170, alloc2171) + R.vm.kill_object(alloc2167) + R.vm.kill_object(alloc2170) + model_decoder_layers_11_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[760] + model_decoder_layers_11_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[761] + gv2883: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2172: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv2883, R.dtype("float16")) + cls.layer_norm2(alloc2171, model_decoder_layers_11_self_attn_layer_norm_weight4, model_decoder_layers_11_self_attn_layer_norm_bias4, alloc2172) + R.vm.kill_object(model_decoder_layers_11_self_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_11_self_attn_layer_norm_bias4) + model_decoder_layers_11_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[756] + model_decoder_layers_11_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[757] + gv2884: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2173: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv2884, R.dtype("float16")) + _2172: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_11_self_attn_q_proj_weight4, alloc2172, model_decoder_layers_11_self_attn_q_proj_bias4, alloc2173) + R.vm.kill_object(model_decoder_layers_11_self_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_11_self_attn_q_proj_bias4) + gv2885: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1143: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2173, gv2885, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2173) + model_decoder_layers_11_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[753] + gv2886: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2174: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2886, R.dtype("float16")) + _2173: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_11_self_attn_k_proj_weight4, alloc2172, alloc2174) + R.vm.kill_object(model_decoder_layers_11_self_attn_k_proj_weight4) + gv2887: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1144: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2174, gv2887, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2174) + model_decoder_layers_11_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[754] + model_decoder_layers_11_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[755] + gv2888: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2175: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv2888, R.dtype("float16")) + _2174: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_11_self_attn_v_proj_weight4, alloc2172, model_decoder_layers_11_self_attn_v_proj_bias4, alloc2175) + R.vm.kill_object(alloc2172) + R.vm.kill_object(model_decoder_layers_11_self_attn_v_proj_weight4) + R.vm.kill_object(model_decoder_layers_11_self_attn_v_proj_bias4) + gv2889: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1145: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2175, gv2889, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2175) + gv2890: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc2176: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv2890, R.dtype("float16")) + cls.concatenate1(reshape1143, reshape1144, reshape1145, alloc2176) + R.vm.kill_object(reshape1143) + R.vm.kill_object(reshape1144) + R.vm.kill_object(reshape1145) + gv2891: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1146: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2176, gv2891, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc2176) + gv2892: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc2177: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv2892, R.dtype("float16")) + _2176: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), reshape1146, alloc2177) + R.vm.kill_object(reshape1146) + gv2893: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1147: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2177, gv2893, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2177) + gv2894: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1148: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1147, gv2894, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1147) + model_decoder_layers_11_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[758] + model_decoder_layers_11_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[759] + gv2895: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2178: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2895, R.dtype("float16")) + _2177: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_11_self_attn_out_proj_weight4, reshape1148, model_decoder_layers_11_self_attn_out_proj_bias4, alloc2178) + R.vm.kill_object(reshape1148) + R.vm.kill_object(model_decoder_layers_11_self_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_11_self_attn_out_proj_bias4) + gv2896: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2179: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv2896, R.dtype("float16")) + cls.add5(alloc2171, alloc2178, alloc2179) + R.vm.kill_object(alloc2171) + R.vm.kill_object(alloc2178) + model_decoder_layers_11_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[769] + model_decoder_layers_11_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[770] + gv2897: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2180: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv2897, R.dtype("float16")) + cls.layer_norm2(alloc2179, model_decoder_layers_11_encoder_attn_layer_norm_weight4, model_decoder_layers_11_encoder_attn_layer_norm_bias4, alloc2180) + R.vm.kill_object(model_decoder_layers_11_encoder_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_11_encoder_attn_layer_norm_bias4) + model_decoder_layers_11_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[765] + model_decoder_layers_11_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[766] + gv2898: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2181: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv2898, R.dtype("float16")) + _2180: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_11_encoder_attn_q_proj_weight4, alloc2180, model_decoder_layers_11_encoder_attn_q_proj_bias4, alloc2181) + R.vm.kill_object(alloc2180) + R.vm.kill_object(model_decoder_layers_11_encoder_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_11_encoder_attn_q_proj_bias4) + gv2899: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1149: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2181, gv2899, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2181) + gv2900: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1150: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1149, gv2900, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape1149) + gv2901: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc2182: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2901, R.dtype("float16")) + _2181: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), reshape1150, alloc2182) + R.vm.kill_object(reshape1150) + gv2902: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1151: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2182, gv2902, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2182) + gv2903: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1152: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1151, gv2903, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1151) + model_decoder_layers_11_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[767] + model_decoder_layers_11_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[768] + gv2904: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2183: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv2904, R.dtype("float16")) + _2182: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_11_encoder_attn_out_proj_weight4, reshape1152, model_decoder_layers_11_encoder_attn_out_proj_bias4, alloc2183) + R.vm.kill_object(reshape1152) + R.vm.kill_object(model_decoder_layers_11_encoder_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_11_encoder_attn_out_proj_bias4) + gv2905: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2184: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv2905, R.dtype("float16")) + cls.add5(alloc2179, alloc2183, alloc2184) + R.vm.kill_object(alloc2179) + R.vm.kill_object(alloc2183) + model_decoder_layers_11_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[775] + model_decoder_layers_11_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[776] + gv2906: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2185: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2906, R.dtype("float16")) + cls.layer_norm2(alloc2184, model_decoder_layers_11_final_layer_norm_weight4, model_decoder_layers_11_final_layer_norm_bias4, alloc2185) + R.vm.kill_object(model_decoder_layers_11_final_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_11_final_layer_norm_bias4) + model_decoder_layers_11_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[771] + model_decoder_layers_11_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[772] + gv2907: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc2186: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv2907, R.dtype("float16")) + _2185: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_11_fc1_weight4, alloc2185, model_decoder_layers_11_fc1_bias4, alloc2186) + R.vm.kill_object(alloc2185) + R.vm.kill_object(model_decoder_layers_11_fc1_weight4) + R.vm.kill_object(model_decoder_layers_11_fc1_bias4) + model_decoder_layers_11_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[773] + model_decoder_layers_11_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[774] + gv2908: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2187: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv2908, R.dtype("float16")) + _2186: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_11_fc2_weight4, alloc2186, model_decoder_layers_11_fc2_bias4, alloc2187) + R.vm.kill_object(alloc2186) + R.vm.kill_object(model_decoder_layers_11_fc2_weight4) + R.vm.kill_object(model_decoder_layers_11_fc2_bias4) + gv2909: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2188: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv2909, R.dtype("float16")) + cls.add5(alloc2184, alloc2187, alloc2188) + R.vm.kill_object(alloc2184) + R.vm.kill_object(alloc2187) + model_decoder_layers_12_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[784] + model_decoder_layers_12_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[785] + gv2910: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2189: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2910, R.dtype("float16")) + cls.layer_norm2(alloc2188, model_decoder_layers_12_self_attn_layer_norm_weight4, model_decoder_layers_12_self_attn_layer_norm_bias4, alloc2189) + R.vm.kill_object(model_decoder_layers_12_self_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_12_self_attn_layer_norm_bias4) + model_decoder_layers_12_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[780] + model_decoder_layers_12_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[781] + gv2911: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2190: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv2911, R.dtype("float16")) + _2189: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_12_self_attn_q_proj_weight4, alloc2189, model_decoder_layers_12_self_attn_q_proj_bias4, alloc2190) + R.vm.kill_object(model_decoder_layers_12_self_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_12_self_attn_q_proj_bias4) + gv2912: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1153: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2190, gv2912, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2190) + model_decoder_layers_12_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[777] + gv2913: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2191: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv2913, R.dtype("float16")) + _2190: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_12_self_attn_k_proj_weight4, alloc2189, alloc2191) + R.vm.kill_object(model_decoder_layers_12_self_attn_k_proj_weight4) + gv2914: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1154: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2191, gv2914, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2191) + model_decoder_layers_12_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[778] + model_decoder_layers_12_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[779] + gv2915: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2192: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv2915, R.dtype("float16")) + _2191: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_12_self_attn_v_proj_weight4, alloc2189, model_decoder_layers_12_self_attn_v_proj_bias4, alloc2192) + R.vm.kill_object(alloc2189) + R.vm.kill_object(model_decoder_layers_12_self_attn_v_proj_weight4) + R.vm.kill_object(model_decoder_layers_12_self_attn_v_proj_bias4) + gv2916: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1155: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2192, gv2916, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2192) + gv2917: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc2193: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2917, R.dtype("float16")) + cls.concatenate1(reshape1153, reshape1154, reshape1155, alloc2193) + R.vm.kill_object(reshape1153) + R.vm.kill_object(reshape1154) + R.vm.kill_object(reshape1155) + gv2918: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1156: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2193, gv2918, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc2193) + gv2919: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc2194: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv2919, R.dtype("float16")) + _2193: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), reshape1156, alloc2194) + R.vm.kill_object(reshape1156) + gv2920: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1157: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2194, gv2920, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2194) + gv2921: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1158: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1157, gv2921, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1157) + model_decoder_layers_12_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[782] + model_decoder_layers_12_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[783] + gv2922: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2195: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv2922, R.dtype("float16")) + _2194: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_12_self_attn_out_proj_weight4, reshape1158, model_decoder_layers_12_self_attn_out_proj_bias4, alloc2195) + R.vm.kill_object(reshape1158) + R.vm.kill_object(model_decoder_layers_12_self_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_12_self_attn_out_proj_bias4) + gv2923: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2196: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2923, R.dtype("float16")) + cls.add5(alloc2188, alloc2195, alloc2196) + R.vm.kill_object(alloc2188) + R.vm.kill_object(alloc2195) + model_decoder_layers_12_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[793] + model_decoder_layers_12_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[794] + gv2924: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2197: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv2924, R.dtype("float16")) + cls.layer_norm2(alloc2196, model_decoder_layers_12_encoder_attn_layer_norm_weight4, model_decoder_layers_12_encoder_attn_layer_norm_bias4, alloc2197) + R.vm.kill_object(model_decoder_layers_12_encoder_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_12_encoder_attn_layer_norm_bias4) + model_decoder_layers_12_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[789] + model_decoder_layers_12_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[790] + gv2925: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2198: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv2925, R.dtype("float16")) + _2197: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_12_encoder_attn_q_proj_weight4, alloc2197, model_decoder_layers_12_encoder_attn_q_proj_bias4, alloc2198) + R.vm.kill_object(alloc2197) + R.vm.kill_object(model_decoder_layers_12_encoder_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_12_encoder_attn_q_proj_bias4) + gv2926: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1159: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2198, gv2926, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2198) + gv2927: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1160: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1159, gv2927, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape1159) + gv2928: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc2199: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv2928, R.dtype("float16")) + _2198: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), reshape1160, alloc2199) + R.vm.kill_object(reshape1160) + gv2929: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1161: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2199, gv2929, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2199) + gv2930: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1162: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1161, gv2930, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1161) + model_decoder_layers_12_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[791] + model_decoder_layers_12_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[792] + gv2931: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2200: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv2931, R.dtype("float16")) + _2199: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_12_encoder_attn_out_proj_weight4, reshape1162, model_decoder_layers_12_encoder_attn_out_proj_bias4, alloc2200) + R.vm.kill_object(reshape1162) + R.vm.kill_object(model_decoder_layers_12_encoder_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_12_encoder_attn_out_proj_bias4) + gv2932: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2201: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv2932, R.dtype("float16")) + cls.add5(alloc2196, alloc2200, alloc2201) + R.vm.kill_object(alloc2196) + R.vm.kill_object(alloc2200) + model_decoder_layers_12_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[799] + model_decoder_layers_12_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[800] + gv2933: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2202: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv2933, R.dtype("float16")) + cls.layer_norm2(alloc2201, model_decoder_layers_12_final_layer_norm_weight4, model_decoder_layers_12_final_layer_norm_bias4, alloc2202) + R.vm.kill_object(model_decoder_layers_12_final_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_12_final_layer_norm_bias4) + model_decoder_layers_12_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[795] + model_decoder_layers_12_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[796] + gv2934: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc2203: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv2934, R.dtype("float16")) + _2202: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_12_fc1_weight4, alloc2202, model_decoder_layers_12_fc1_bias4, alloc2203) + R.vm.kill_object(alloc2202) + R.vm.kill_object(model_decoder_layers_12_fc1_weight4) + R.vm.kill_object(model_decoder_layers_12_fc1_bias4) + model_decoder_layers_12_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[797] + model_decoder_layers_12_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[798] + gv2935: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2204: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2935, R.dtype("float16")) + _2203: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_12_fc2_weight4, alloc2203, model_decoder_layers_12_fc2_bias4, alloc2204) + R.vm.kill_object(alloc2203) + R.vm.kill_object(model_decoder_layers_12_fc2_weight4) + R.vm.kill_object(model_decoder_layers_12_fc2_bias4) + gv2936: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2205: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv2936, R.dtype("float16")) + cls.add5(alloc2201, alloc2204, alloc2205) + R.vm.kill_object(alloc2201) + R.vm.kill_object(alloc2204) + model_decoder_layers_13_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[808] + model_decoder_layers_13_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[809] + gv2937: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2206: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv2937, R.dtype("float16")) + cls.layer_norm2(alloc2205, model_decoder_layers_13_self_attn_layer_norm_weight4, model_decoder_layers_13_self_attn_layer_norm_bias4, alloc2206) + R.vm.kill_object(model_decoder_layers_13_self_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_13_self_attn_layer_norm_bias4) + model_decoder_layers_13_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[804] + model_decoder_layers_13_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[805] + gv2938: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2207: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv2938, R.dtype("float16")) + _2206: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_13_self_attn_q_proj_weight4, alloc2206, model_decoder_layers_13_self_attn_q_proj_bias4, alloc2207) + R.vm.kill_object(model_decoder_layers_13_self_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_13_self_attn_q_proj_bias4) + gv2939: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1163: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2207, gv2939, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2207) + model_decoder_layers_13_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[801] + gv2940: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2208: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2940, R.dtype("float16")) + _2207: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_13_self_attn_k_proj_weight4, alloc2206, alloc2208) + R.vm.kill_object(model_decoder_layers_13_self_attn_k_proj_weight4) + gv2941: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1164: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2208, gv2941, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2208) + model_decoder_layers_13_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[802] + model_decoder_layers_13_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[803] + gv2942: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2209: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv2942, R.dtype("float16")) + _2208: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_13_self_attn_v_proj_weight4, alloc2206, model_decoder_layers_13_self_attn_v_proj_bias4, alloc2209) + R.vm.kill_object(alloc2206) + R.vm.kill_object(model_decoder_layers_13_self_attn_v_proj_weight4) + R.vm.kill_object(model_decoder_layers_13_self_attn_v_proj_bias4) + gv2943: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1165: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2209, gv2943, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2209) + gv2944: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc2210: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv2944, R.dtype("float16")) + cls.concatenate1(reshape1163, reshape1164, reshape1165, alloc2210) + R.vm.kill_object(reshape1163) + R.vm.kill_object(reshape1164) + R.vm.kill_object(reshape1165) + gv2945: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1166: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2210, gv2945, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc2210) + gv2946: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc2211: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv2946, R.dtype("float16")) + _2210: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), reshape1166, alloc2211) + R.vm.kill_object(reshape1166) + gv2947: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1167: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2211, gv2947, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2211) + gv2948: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1168: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1167, gv2948, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1167) + model_decoder_layers_13_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[806] + model_decoder_layers_13_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[807] + gv2949: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2212: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2949, R.dtype("float16")) + _2211: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_13_self_attn_out_proj_weight4, reshape1168, model_decoder_layers_13_self_attn_out_proj_bias4, alloc2212) + R.vm.kill_object(reshape1168) + R.vm.kill_object(model_decoder_layers_13_self_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_13_self_attn_out_proj_bias4) + gv2950: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2213: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv2950, R.dtype("float16")) + cls.add5(alloc2205, alloc2212, alloc2213) + R.vm.kill_object(alloc2205) + R.vm.kill_object(alloc2212) + model_decoder_layers_13_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[817] + model_decoder_layers_13_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[818] + gv2951: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2214: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv2951, R.dtype("float16")) + cls.layer_norm2(alloc2213, model_decoder_layers_13_encoder_attn_layer_norm_weight4, model_decoder_layers_13_encoder_attn_layer_norm_bias4, alloc2214) + R.vm.kill_object(model_decoder_layers_13_encoder_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_13_encoder_attn_layer_norm_bias4) + model_decoder_layers_13_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[813] + model_decoder_layers_13_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[814] + gv2952: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2215: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv2952, R.dtype("float16")) + _2214: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_13_encoder_attn_q_proj_weight4, alloc2214, model_decoder_layers_13_encoder_attn_q_proj_bias4, alloc2215) + R.vm.kill_object(alloc2214) + R.vm.kill_object(model_decoder_layers_13_encoder_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_13_encoder_attn_q_proj_bias4) + gv2953: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1169: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2215, gv2953, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2215) + gv2954: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1170: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1169, gv2954, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape1169) + gv2955: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc2216: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2955, R.dtype("float16")) + _2215: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), reshape1170, alloc2216) + R.vm.kill_object(reshape1170) + gv2956: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1171: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2216, gv2956, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2216) + gv2957: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1172: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1171, gv2957, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1171) + model_decoder_layers_13_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[815] + model_decoder_layers_13_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[816] + gv2958: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2217: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv2958, R.dtype("float16")) + _2216: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_13_encoder_attn_out_proj_weight4, reshape1172, model_decoder_layers_13_encoder_attn_out_proj_bias4, alloc2217) + R.vm.kill_object(reshape1172) + R.vm.kill_object(model_decoder_layers_13_encoder_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_13_encoder_attn_out_proj_bias4) + gv2959: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2218: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv2959, R.dtype("float16")) + cls.add5(alloc2213, alloc2217, alloc2218) + R.vm.kill_object(alloc2213) + R.vm.kill_object(alloc2217) + model_decoder_layers_13_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[823] + model_decoder_layers_13_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[824] + gv2960: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2219: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2960, R.dtype("float16")) + cls.layer_norm2(alloc2218, model_decoder_layers_13_final_layer_norm_weight4, model_decoder_layers_13_final_layer_norm_bias4, alloc2219) + R.vm.kill_object(model_decoder_layers_13_final_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_13_final_layer_norm_bias4) + model_decoder_layers_13_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[819] + model_decoder_layers_13_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[820] + gv2961: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc2220: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv2961, R.dtype("float16")) + _2219: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_13_fc1_weight4, alloc2219, model_decoder_layers_13_fc1_bias4, alloc2220) + R.vm.kill_object(alloc2219) + R.vm.kill_object(model_decoder_layers_13_fc1_weight4) + R.vm.kill_object(model_decoder_layers_13_fc1_bias4) + model_decoder_layers_13_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[821] + model_decoder_layers_13_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[822] + gv2962: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2221: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv2962, R.dtype("float16")) + _2220: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_13_fc2_weight4, alloc2220, model_decoder_layers_13_fc2_bias4, alloc2221) + R.vm.kill_object(alloc2220) + R.vm.kill_object(model_decoder_layers_13_fc2_weight4) + R.vm.kill_object(model_decoder_layers_13_fc2_bias4) + gv2963: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2222: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv2963, R.dtype("float16")) + cls.add5(alloc2218, alloc2221, alloc2222) + R.vm.kill_object(alloc2218) + R.vm.kill_object(alloc2221) + model_decoder_layers_14_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[832] + model_decoder_layers_14_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[833] + gv2964: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2223: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2964, R.dtype("float16")) + cls.layer_norm2(alloc2222, model_decoder_layers_14_self_attn_layer_norm_weight4, model_decoder_layers_14_self_attn_layer_norm_bias4, alloc2223) + R.vm.kill_object(model_decoder_layers_14_self_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_14_self_attn_layer_norm_bias4) + model_decoder_layers_14_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[828] + model_decoder_layers_14_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[829] + gv2965: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2224: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv2965, R.dtype("float16")) + _2223: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_14_self_attn_q_proj_weight4, alloc2223, model_decoder_layers_14_self_attn_q_proj_bias4, alloc2224) + R.vm.kill_object(model_decoder_layers_14_self_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_14_self_attn_q_proj_bias4) + gv2966: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1173: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2224, gv2966, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2224) + model_decoder_layers_14_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[825] + gv2967: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2225: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv2967, R.dtype("float16")) + _2224: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_14_self_attn_k_proj_weight4, alloc2223, alloc2225) + R.vm.kill_object(model_decoder_layers_14_self_attn_k_proj_weight4) + gv2968: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1174: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2225, gv2968, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2225) + model_decoder_layers_14_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[826] + model_decoder_layers_14_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[827] + gv2969: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2226: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv2969, R.dtype("float16")) + _2225: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_14_self_attn_v_proj_weight4, alloc2223, model_decoder_layers_14_self_attn_v_proj_bias4, alloc2226) + R.vm.kill_object(alloc2223) + R.vm.kill_object(model_decoder_layers_14_self_attn_v_proj_weight4) + R.vm.kill_object(model_decoder_layers_14_self_attn_v_proj_bias4) + gv2970: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1175: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2226, gv2970, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2226) + gv2971: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc2227: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2971, R.dtype("float16")) + cls.concatenate1(reshape1173, reshape1174, reshape1175, alloc2227) + R.vm.kill_object(reshape1173) + R.vm.kill_object(reshape1174) + R.vm.kill_object(reshape1175) + gv2972: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1176: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2227, gv2972, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc2227) + gv2973: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc2228: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv2973, R.dtype("float16")) + _2227: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), reshape1176, alloc2228) + R.vm.kill_object(reshape1176) + gv2974: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1177: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2228, gv2974, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2228) + gv2975: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1178: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1177, gv2975, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1177) + model_decoder_layers_14_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[830] + model_decoder_layers_14_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[831] + gv2976: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2229: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv2976, R.dtype("float16")) + _2228: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_14_self_attn_out_proj_weight4, reshape1178, model_decoder_layers_14_self_attn_out_proj_bias4, alloc2229) + R.vm.kill_object(reshape1178) + R.vm.kill_object(model_decoder_layers_14_self_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_14_self_attn_out_proj_bias4) + gv2977: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2230: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2977, R.dtype("float16")) + cls.add5(alloc2222, alloc2229, alloc2230) + R.vm.kill_object(alloc2222) + R.vm.kill_object(alloc2229) + model_decoder_layers_14_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[841] + model_decoder_layers_14_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[842] + gv2978: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2231: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv2978, R.dtype("float16")) + cls.layer_norm2(alloc2230, model_decoder_layers_14_encoder_attn_layer_norm_weight4, model_decoder_layers_14_encoder_attn_layer_norm_bias4, alloc2231) + R.vm.kill_object(model_decoder_layers_14_encoder_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_14_encoder_attn_layer_norm_bias4) + model_decoder_layers_14_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[837] + model_decoder_layers_14_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[838] + gv2979: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2232: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv2979, R.dtype("float16")) + _2231: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_14_encoder_attn_q_proj_weight4, alloc2231, model_decoder_layers_14_encoder_attn_q_proj_bias4, alloc2232) + R.vm.kill_object(alloc2231) + R.vm.kill_object(model_decoder_layers_14_encoder_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_14_encoder_attn_q_proj_bias4) + gv2980: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1179: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2232, gv2980, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2232) + gv2981: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1180: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1179, gv2981, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape1179) + gv2982: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc2233: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv2982, R.dtype("float16")) + _2232: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), reshape1180, alloc2233) + R.vm.kill_object(reshape1180) + gv2983: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1181: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2233, gv2983, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2233) + gv2984: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1182: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1181, gv2984, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1181) + model_decoder_layers_14_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[839] + model_decoder_layers_14_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[840] + gv2985: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2234: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv2985, R.dtype("float16")) + _2233: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_14_encoder_attn_out_proj_weight4, reshape1182, model_decoder_layers_14_encoder_attn_out_proj_bias4, alloc2234) + R.vm.kill_object(reshape1182) + R.vm.kill_object(model_decoder_layers_14_encoder_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_14_encoder_attn_out_proj_bias4) + gv2986: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2235: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv2986, R.dtype("float16")) + cls.add5(alloc2230, alloc2234, alloc2235) + R.vm.kill_object(alloc2230) + R.vm.kill_object(alloc2234) + model_decoder_layers_14_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[847] + model_decoder_layers_14_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[848] + gv2987: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2236: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv2987, R.dtype("float16")) + cls.layer_norm2(alloc2235, model_decoder_layers_14_final_layer_norm_weight4, model_decoder_layers_14_final_layer_norm_bias4, alloc2236) + R.vm.kill_object(model_decoder_layers_14_final_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_14_final_layer_norm_bias4) + model_decoder_layers_14_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[843] + model_decoder_layers_14_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[844] + gv2988: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc2237: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv2988, R.dtype("float16")) + _2236: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_14_fc1_weight4, alloc2236, model_decoder_layers_14_fc1_bias4, alloc2237) + R.vm.kill_object(alloc2236) + R.vm.kill_object(model_decoder_layers_14_fc1_weight4) + R.vm.kill_object(model_decoder_layers_14_fc1_bias4) + model_decoder_layers_14_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[845] + model_decoder_layers_14_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[846] + gv2989: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2238: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2989, R.dtype("float16")) + _2237: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_14_fc2_weight4, alloc2237, model_decoder_layers_14_fc2_bias4, alloc2238) + R.vm.kill_object(alloc2237) + R.vm.kill_object(model_decoder_layers_14_fc2_weight4) + R.vm.kill_object(model_decoder_layers_14_fc2_bias4) + gv2990: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2239: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv2990, R.dtype("float16")) + cls.add5(alloc2235, alloc2238, alloc2239) + R.vm.kill_object(alloc2235) + R.vm.kill_object(alloc2238) + model_decoder_layers_15_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[856] + model_decoder_layers_15_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[857] + gv2991: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2240: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv2991, R.dtype("float16")) + cls.layer_norm2(alloc2239, model_decoder_layers_15_self_attn_layer_norm_weight4, model_decoder_layers_15_self_attn_layer_norm_bias4, alloc2240) + R.vm.kill_object(model_decoder_layers_15_self_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_15_self_attn_layer_norm_bias4) + model_decoder_layers_15_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[852] + model_decoder_layers_15_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[853] + gv2992: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2241: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv2992, R.dtype("float16")) + _2240: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_15_self_attn_q_proj_weight4, alloc2240, model_decoder_layers_15_self_attn_q_proj_bias4, alloc2241) + R.vm.kill_object(model_decoder_layers_15_self_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_15_self_attn_q_proj_bias4) + gv2993: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1183: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2241, gv2993, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2241) + model_decoder_layers_15_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[849] + gv2994: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2242: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv2994, R.dtype("float16")) + _2241: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_15_self_attn_k_proj_weight4, alloc2240, alloc2242) + R.vm.kill_object(model_decoder_layers_15_self_attn_k_proj_weight4) + gv2995: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1184: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2242, gv2995, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2242) + model_decoder_layers_15_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[850] + model_decoder_layers_15_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[851] + gv2996: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2243: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv2996, R.dtype("float16")) + _2242: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_15_self_attn_v_proj_weight4, alloc2240, model_decoder_layers_15_self_attn_v_proj_bias4, alloc2243) + R.vm.kill_object(alloc2240) + R.vm.kill_object(model_decoder_layers_15_self_attn_v_proj_weight4) + R.vm.kill_object(model_decoder_layers_15_self_attn_v_proj_bias4) + gv2997: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1185: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2243, gv2997, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2243) + gv2998: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc2244: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv2998, R.dtype("float16")) + cls.concatenate1(reshape1183, reshape1184, reshape1185, alloc2244) + R.vm.kill_object(reshape1183) + R.vm.kill_object(reshape1184) + R.vm.kill_object(reshape1185) + gv2999: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1186: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2244, gv2999, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc2244) + gv3000: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc2245: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv3000, R.dtype("float16")) + _2244: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), reshape1186, alloc2245) + R.vm.kill_object(reshape1186) + gv3001: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1187: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2245, gv3001, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2245) + gv3002: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1188: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1187, gv3002, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1187) + model_decoder_layers_15_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[854] + model_decoder_layers_15_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[855] + gv3003: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2246: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3003, R.dtype("float16")) + _2245: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_15_self_attn_out_proj_weight4, reshape1188, model_decoder_layers_15_self_attn_out_proj_bias4, alloc2246) + R.vm.kill_object(reshape1188) + R.vm.kill_object(model_decoder_layers_15_self_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_15_self_attn_out_proj_bias4) + gv3004: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2247: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3004, R.dtype("float16")) + cls.add5(alloc2239, alloc2246, alloc2247) + R.vm.kill_object(alloc2239) + R.vm.kill_object(alloc2246) + model_decoder_layers_15_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[865] + model_decoder_layers_15_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[866] + gv3005: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2248: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv3005, R.dtype("float16")) + cls.layer_norm2(alloc2247, model_decoder_layers_15_encoder_attn_layer_norm_weight4, model_decoder_layers_15_encoder_attn_layer_norm_bias4, alloc2248) + R.vm.kill_object(model_decoder_layers_15_encoder_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_15_encoder_attn_layer_norm_bias4) + model_decoder_layers_15_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[861] + model_decoder_layers_15_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[862] + gv3006: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2249: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv3006, R.dtype("float16")) + _2248: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_15_encoder_attn_q_proj_weight4, alloc2248, model_decoder_layers_15_encoder_attn_q_proj_bias4, alloc2249) + R.vm.kill_object(alloc2248) + R.vm.kill_object(model_decoder_layers_15_encoder_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_15_encoder_attn_q_proj_bias4) + gv3007: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1189: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2249, gv3007, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2249) + gv3008: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1190: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1189, gv3008, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape1189) + gv3009: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc2250: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3009, R.dtype("float16")) + _2249: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), reshape1190, alloc2250) + R.vm.kill_object(reshape1190) + gv3010: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1191: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2250, gv3010, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2250) + gv3011: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1192: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1191, gv3011, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1191) + model_decoder_layers_15_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[863] + model_decoder_layers_15_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[864] + gv3012: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2251: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv3012, R.dtype("float16")) + _2250: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_15_encoder_attn_out_proj_weight4, reshape1192, model_decoder_layers_15_encoder_attn_out_proj_bias4, alloc2251) + R.vm.kill_object(reshape1192) + R.vm.kill_object(model_decoder_layers_15_encoder_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_15_encoder_attn_out_proj_bias4) + gv3013: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2252: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv3013, R.dtype("float16")) + cls.add5(alloc2247, alloc2251, alloc2252) + R.vm.kill_object(alloc2247) + R.vm.kill_object(alloc2251) + model_decoder_layers_15_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[871] + model_decoder_layers_15_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[872] + gv3014: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2253: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3014, R.dtype("float16")) + cls.layer_norm2(alloc2252, model_decoder_layers_15_final_layer_norm_weight4, model_decoder_layers_15_final_layer_norm_bias4, alloc2253) + R.vm.kill_object(model_decoder_layers_15_final_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_15_final_layer_norm_bias4) + model_decoder_layers_15_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[867] + model_decoder_layers_15_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[868] + gv3015: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc2254: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv3015, R.dtype("float16")) + _2253: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_15_fc1_weight4, alloc2253, model_decoder_layers_15_fc1_bias4, alloc2254) + R.vm.kill_object(alloc2253) + R.vm.kill_object(model_decoder_layers_15_fc1_weight4) + R.vm.kill_object(model_decoder_layers_15_fc1_bias4) + model_decoder_layers_15_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[869] + model_decoder_layers_15_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[870] + gv3016: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2255: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3016, R.dtype("float16")) + _2254: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_15_fc2_weight4, alloc2254, model_decoder_layers_15_fc2_bias4, alloc2255) + R.vm.kill_object(alloc2254) + R.vm.kill_object(model_decoder_layers_15_fc2_weight4) + R.vm.kill_object(model_decoder_layers_15_fc2_bias4) + gv3017: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2256: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv3017, R.dtype("float16")) + cls.add5(alloc2252, alloc2255, alloc2256) + R.vm.kill_object(alloc2252) + R.vm.kill_object(alloc2255) + model_decoder_layers_16_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[880] + model_decoder_layers_16_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[881] + gv3018: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2257: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3018, R.dtype("float16")) + cls.layer_norm2(alloc2256, model_decoder_layers_16_self_attn_layer_norm_weight4, model_decoder_layers_16_self_attn_layer_norm_bias4, alloc2257) + R.vm.kill_object(model_decoder_layers_16_self_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_16_self_attn_layer_norm_bias4) + model_decoder_layers_16_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[876] + model_decoder_layers_16_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[877] + gv3019: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2258: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv3019, R.dtype("float16")) + _2257: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_16_self_attn_q_proj_weight4, alloc2257, model_decoder_layers_16_self_attn_q_proj_bias4, alloc2258) + R.vm.kill_object(model_decoder_layers_16_self_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_16_self_attn_q_proj_bias4) + gv3020: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1193: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2258, gv3020, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2258) + model_decoder_layers_16_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[873] + gv3021: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2259: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3021, R.dtype("float16")) + _2258: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_16_self_attn_k_proj_weight4, alloc2257, alloc2259) + R.vm.kill_object(model_decoder_layers_16_self_attn_k_proj_weight4) + gv3022: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1194: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2259, gv3022, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2259) + model_decoder_layers_16_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[874] + model_decoder_layers_16_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[875] + gv3023: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2260: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv3023, R.dtype("float16")) + _2259: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_16_self_attn_v_proj_weight4, alloc2257, model_decoder_layers_16_self_attn_v_proj_bias4, alloc2260) + R.vm.kill_object(alloc2257) + R.vm.kill_object(model_decoder_layers_16_self_attn_v_proj_weight4) + R.vm.kill_object(model_decoder_layers_16_self_attn_v_proj_bias4) + gv3024: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1195: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2260, gv3024, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2260) + gv3025: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc2261: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3025, R.dtype("float16")) + cls.concatenate1(reshape1193, reshape1194, reshape1195, alloc2261) + R.vm.kill_object(reshape1193) + R.vm.kill_object(reshape1194) + R.vm.kill_object(reshape1195) + gv3026: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1196: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2261, gv3026, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc2261) + gv3027: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc2262: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv3027, R.dtype("float16")) + _2261: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), reshape1196, alloc2262) + R.vm.kill_object(reshape1196) + gv3028: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1197: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2262, gv3028, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2262) + gv3029: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1198: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1197, gv3029, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1197) + model_decoder_layers_16_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[878] + model_decoder_layers_16_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[879] + gv3030: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2263: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3030, R.dtype("float16")) + _2262: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_16_self_attn_out_proj_weight4, reshape1198, model_decoder_layers_16_self_attn_out_proj_bias4, alloc2263) + R.vm.kill_object(reshape1198) + R.vm.kill_object(model_decoder_layers_16_self_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_16_self_attn_out_proj_bias4) + gv3031: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2264: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3031, R.dtype("float16")) + cls.add5(alloc2256, alloc2263, alloc2264) + R.vm.kill_object(alloc2256) + R.vm.kill_object(alloc2263) + model_decoder_layers_16_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[889] + model_decoder_layers_16_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[890] + gv3032: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2265: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv3032, R.dtype("float16")) + cls.layer_norm2(alloc2264, model_decoder_layers_16_encoder_attn_layer_norm_weight4, model_decoder_layers_16_encoder_attn_layer_norm_bias4, alloc2265) + R.vm.kill_object(model_decoder_layers_16_encoder_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_16_encoder_attn_layer_norm_bias4) + model_decoder_layers_16_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[885] + model_decoder_layers_16_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[886] + gv3033: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2266: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv3033, R.dtype("float16")) + _2265: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_16_encoder_attn_q_proj_weight4, alloc2265, model_decoder_layers_16_encoder_attn_q_proj_bias4, alloc2266) + R.vm.kill_object(alloc2265) + R.vm.kill_object(model_decoder_layers_16_encoder_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_16_encoder_attn_q_proj_bias4) + gv3034: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1199: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2266, gv3034, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2266) + gv3035: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1200: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1199, gv3035, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape1199) + gv3036: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc2267: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3036, R.dtype("float16")) + _2266: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), reshape1200, alloc2267) + R.vm.kill_object(reshape1200) + gv3037: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1201: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2267, gv3037, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2267) + gv3038: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1202: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1201, gv3038, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1201) + model_decoder_layers_16_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[887] + model_decoder_layers_16_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[888] + gv3039: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2268: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv3039, R.dtype("float16")) + _2267: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_16_encoder_attn_out_proj_weight4, reshape1202, model_decoder_layers_16_encoder_attn_out_proj_bias4, alloc2268) + R.vm.kill_object(reshape1202) + R.vm.kill_object(model_decoder_layers_16_encoder_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_16_encoder_attn_out_proj_bias4) + gv3040: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2269: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv3040, R.dtype("float16")) + cls.add5(alloc2264, alloc2268, alloc2269) + R.vm.kill_object(alloc2264) + R.vm.kill_object(alloc2268) + model_decoder_layers_16_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[895] + model_decoder_layers_16_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[896] + gv3041: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2270: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3041, R.dtype("float16")) + cls.layer_norm2(alloc2269, model_decoder_layers_16_final_layer_norm_weight4, model_decoder_layers_16_final_layer_norm_bias4, alloc2270) + R.vm.kill_object(model_decoder_layers_16_final_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_16_final_layer_norm_bias4) + model_decoder_layers_16_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[891] + model_decoder_layers_16_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[892] + gv3042: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc2271: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv3042, R.dtype("float16")) + _2270: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_16_fc1_weight4, alloc2270, model_decoder_layers_16_fc1_bias4, alloc2271) + R.vm.kill_object(alloc2270) + R.vm.kill_object(model_decoder_layers_16_fc1_weight4) + R.vm.kill_object(model_decoder_layers_16_fc1_bias4) + model_decoder_layers_16_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[893] + model_decoder_layers_16_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[894] + gv3043: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2272: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3043, R.dtype("float16")) + _2271: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_16_fc2_weight4, alloc2271, model_decoder_layers_16_fc2_bias4, alloc2272) + R.vm.kill_object(alloc2271) + R.vm.kill_object(model_decoder_layers_16_fc2_weight4) + R.vm.kill_object(model_decoder_layers_16_fc2_bias4) + gv3044: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2273: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv3044, R.dtype("float16")) + cls.add5(alloc2269, alloc2272, alloc2273) + R.vm.kill_object(alloc2269) + R.vm.kill_object(alloc2272) + model_decoder_layers_17_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[904] + model_decoder_layers_17_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[905] + gv3045: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2274: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3045, R.dtype("float16")) + cls.layer_norm2(alloc2273, model_decoder_layers_17_self_attn_layer_norm_weight4, model_decoder_layers_17_self_attn_layer_norm_bias4, alloc2274) + R.vm.kill_object(model_decoder_layers_17_self_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_17_self_attn_layer_norm_bias4) + model_decoder_layers_17_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[900] + model_decoder_layers_17_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[901] + gv3046: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2275: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv3046, R.dtype("float16")) + _2274: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_17_self_attn_q_proj_weight4, alloc2274, model_decoder_layers_17_self_attn_q_proj_bias4, alloc2275) + R.vm.kill_object(model_decoder_layers_17_self_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_17_self_attn_q_proj_bias4) + gv3047: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1203: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2275, gv3047, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2275) + model_decoder_layers_17_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[897] + gv3048: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2276: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3048, R.dtype("float16")) + _2275: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_17_self_attn_k_proj_weight4, alloc2274, alloc2276) + R.vm.kill_object(model_decoder_layers_17_self_attn_k_proj_weight4) + gv3049: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1204: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2276, gv3049, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2276) + model_decoder_layers_17_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[898] + model_decoder_layers_17_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[899] + gv3050: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2277: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv3050, R.dtype("float16")) + _2276: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_17_self_attn_v_proj_weight4, alloc2274, model_decoder_layers_17_self_attn_v_proj_bias4, alloc2277) + R.vm.kill_object(alloc2274) + R.vm.kill_object(model_decoder_layers_17_self_attn_v_proj_weight4) + R.vm.kill_object(model_decoder_layers_17_self_attn_v_proj_bias4) + gv3051: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1205: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2277, gv3051, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2277) + gv3052: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc2278: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3052, R.dtype("float16")) + cls.concatenate1(reshape1203, reshape1204, reshape1205, alloc2278) + R.vm.kill_object(reshape1203) + R.vm.kill_object(reshape1204) + R.vm.kill_object(reshape1205) + gv3053: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1206: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2278, gv3053, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc2278) + gv3054: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc2279: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv3054, R.dtype("float16")) + _2278: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), reshape1206, alloc2279) + R.vm.kill_object(reshape1206) + gv3055: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1207: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2279, gv3055, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2279) + gv3056: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1208: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1207, gv3056, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1207) + model_decoder_layers_17_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[902] + model_decoder_layers_17_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[903] + gv3057: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2280: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3057, R.dtype("float16")) + _2279: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_17_self_attn_out_proj_weight4, reshape1208, model_decoder_layers_17_self_attn_out_proj_bias4, alloc2280) + R.vm.kill_object(reshape1208) + R.vm.kill_object(model_decoder_layers_17_self_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_17_self_attn_out_proj_bias4) + gv3058: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2281: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3058, R.dtype("float16")) + cls.add5(alloc2273, alloc2280, alloc2281) + R.vm.kill_object(alloc2273) + R.vm.kill_object(alloc2280) + model_decoder_layers_17_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[913] + model_decoder_layers_17_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[914] + gv3059: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2282: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv3059, R.dtype("float16")) + cls.layer_norm2(alloc2281, model_decoder_layers_17_encoder_attn_layer_norm_weight4, model_decoder_layers_17_encoder_attn_layer_norm_bias4, alloc2282) + R.vm.kill_object(model_decoder_layers_17_encoder_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_17_encoder_attn_layer_norm_bias4) + model_decoder_layers_17_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[909] + model_decoder_layers_17_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[910] + gv3060: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2283: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv3060, R.dtype("float16")) + _2282: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_17_encoder_attn_q_proj_weight4, alloc2282, model_decoder_layers_17_encoder_attn_q_proj_bias4, alloc2283) + R.vm.kill_object(alloc2282) + R.vm.kill_object(model_decoder_layers_17_encoder_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_17_encoder_attn_q_proj_bias4) + gv3061: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1209: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2283, gv3061, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2283) + gv3062: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1210: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1209, gv3062, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape1209) + gv3063: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc2284: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3063, R.dtype("float16")) + _2283: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), reshape1210, alloc2284) + R.vm.kill_object(reshape1210) + gv3064: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1211: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2284, gv3064, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2284) + gv3065: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1212: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1211, gv3065, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1211) + model_decoder_layers_17_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[911] + model_decoder_layers_17_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[912] + gv3066: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2285: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv3066, R.dtype("float16")) + _2284: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_17_encoder_attn_out_proj_weight4, reshape1212, model_decoder_layers_17_encoder_attn_out_proj_bias4, alloc2285) + R.vm.kill_object(reshape1212) + R.vm.kill_object(model_decoder_layers_17_encoder_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_17_encoder_attn_out_proj_bias4) + gv3067: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2286: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv3067, R.dtype("float16")) + cls.add5(alloc2281, alloc2285, alloc2286) + R.vm.kill_object(alloc2281) + R.vm.kill_object(alloc2285) + model_decoder_layers_17_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[919] + model_decoder_layers_17_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[920] + gv3068: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2287: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3068, R.dtype("float16")) + cls.layer_norm2(alloc2286, model_decoder_layers_17_final_layer_norm_weight4, model_decoder_layers_17_final_layer_norm_bias4, alloc2287) + R.vm.kill_object(model_decoder_layers_17_final_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_17_final_layer_norm_bias4) + model_decoder_layers_17_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[915] + model_decoder_layers_17_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[916] + gv3069: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc2288: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv3069, R.dtype("float16")) + _2287: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_17_fc1_weight4, alloc2287, model_decoder_layers_17_fc1_bias4, alloc2288) + R.vm.kill_object(alloc2287) + R.vm.kill_object(model_decoder_layers_17_fc1_weight4) + R.vm.kill_object(model_decoder_layers_17_fc1_bias4) + model_decoder_layers_17_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[917] + model_decoder_layers_17_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[918] + gv3070: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2289: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3070, R.dtype("float16")) + _2288: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_17_fc2_weight4, alloc2288, model_decoder_layers_17_fc2_bias4, alloc2289) + R.vm.kill_object(alloc2288) + R.vm.kill_object(model_decoder_layers_17_fc2_weight4) + R.vm.kill_object(model_decoder_layers_17_fc2_bias4) + gv3071: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2290: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv3071, R.dtype("float16")) + cls.add5(alloc2286, alloc2289, alloc2290) + R.vm.kill_object(alloc2286) + R.vm.kill_object(alloc2289) + model_decoder_layers_18_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[928] + model_decoder_layers_18_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[929] + gv3072: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2291: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3072, R.dtype("float16")) + cls.layer_norm2(alloc2290, model_decoder_layers_18_self_attn_layer_norm_weight4, model_decoder_layers_18_self_attn_layer_norm_bias4, alloc2291) + R.vm.kill_object(model_decoder_layers_18_self_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_18_self_attn_layer_norm_bias4) + model_decoder_layers_18_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[924] + model_decoder_layers_18_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[925] + gv3073: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2292: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv3073, R.dtype("float16")) + _2291: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_18_self_attn_q_proj_weight4, alloc2291, model_decoder_layers_18_self_attn_q_proj_bias4, alloc2292) + R.vm.kill_object(model_decoder_layers_18_self_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_18_self_attn_q_proj_bias4) + gv3074: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1213: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2292, gv3074, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2292) + model_decoder_layers_18_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[921] + gv3075: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2293: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3075, R.dtype("float16")) + _2292: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_18_self_attn_k_proj_weight4, alloc2291, alloc2293) + R.vm.kill_object(model_decoder_layers_18_self_attn_k_proj_weight4) + gv3076: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1214: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2293, gv3076, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2293) + model_decoder_layers_18_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[922] + model_decoder_layers_18_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[923] + gv3077: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2294: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv3077, R.dtype("float16")) + _2293: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_18_self_attn_v_proj_weight4, alloc2291, model_decoder_layers_18_self_attn_v_proj_bias4, alloc2294) + R.vm.kill_object(alloc2291) + R.vm.kill_object(model_decoder_layers_18_self_attn_v_proj_weight4) + R.vm.kill_object(model_decoder_layers_18_self_attn_v_proj_bias4) + gv3078: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1215: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2294, gv3078, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2294) + gv3079: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc2295: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3079, R.dtype("float16")) + cls.concatenate1(reshape1213, reshape1214, reshape1215, alloc2295) + R.vm.kill_object(reshape1213) + R.vm.kill_object(reshape1214) + R.vm.kill_object(reshape1215) + gv3080: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1216: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2295, gv3080, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc2295) + gv3081: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc2296: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv3081, R.dtype("float16")) + _2295: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), reshape1216, alloc2296) + R.vm.kill_object(reshape1216) + gv3082: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1217: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2296, gv3082, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2296) + gv3083: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1218: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1217, gv3083, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1217) + model_decoder_layers_18_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[926] + model_decoder_layers_18_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[927] + gv3084: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2297: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3084, R.dtype("float16")) + _2296: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_18_self_attn_out_proj_weight4, reshape1218, model_decoder_layers_18_self_attn_out_proj_bias4, alloc2297) + R.vm.kill_object(reshape1218) + R.vm.kill_object(model_decoder_layers_18_self_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_18_self_attn_out_proj_bias4) + gv3085: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2298: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3085, R.dtype("float16")) + cls.add5(alloc2290, alloc2297, alloc2298) + R.vm.kill_object(alloc2290) + R.vm.kill_object(alloc2297) + model_decoder_layers_18_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[937] + model_decoder_layers_18_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[938] + gv3086: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2299: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv3086, R.dtype("float16")) + cls.layer_norm2(alloc2298, model_decoder_layers_18_encoder_attn_layer_norm_weight4, model_decoder_layers_18_encoder_attn_layer_norm_bias4, alloc2299) + R.vm.kill_object(model_decoder_layers_18_encoder_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_18_encoder_attn_layer_norm_bias4) + model_decoder_layers_18_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[933] + model_decoder_layers_18_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[934] + gv3087: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2300: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv3087, R.dtype("float16")) + _2299: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_18_encoder_attn_q_proj_weight4, alloc2299, model_decoder_layers_18_encoder_attn_q_proj_bias4, alloc2300) + R.vm.kill_object(alloc2299) + R.vm.kill_object(model_decoder_layers_18_encoder_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_18_encoder_attn_q_proj_bias4) + gv3088: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1219: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2300, gv3088, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2300) + gv3089: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1220: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1219, gv3089, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape1219) + gv3090: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc2301: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3090, R.dtype("float16")) + _2300: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), reshape1220, alloc2301) + R.vm.kill_object(reshape1220) + gv3091: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1221: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2301, gv3091, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2301) + gv3092: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1222: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1221, gv3092, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1221) + model_decoder_layers_18_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[935] + model_decoder_layers_18_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[936] + gv3093: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2302: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv3093, R.dtype("float16")) + _2301: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_18_encoder_attn_out_proj_weight4, reshape1222, model_decoder_layers_18_encoder_attn_out_proj_bias4, alloc2302) + R.vm.kill_object(reshape1222) + R.vm.kill_object(model_decoder_layers_18_encoder_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_18_encoder_attn_out_proj_bias4) + gv3094: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2303: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv3094, R.dtype("float16")) + cls.add5(alloc2298, alloc2302, alloc2303) + R.vm.kill_object(alloc2298) + R.vm.kill_object(alloc2302) + model_decoder_layers_18_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[943] + model_decoder_layers_18_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[944] + gv3095: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2304: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3095, R.dtype("float16")) + cls.layer_norm2(alloc2303, model_decoder_layers_18_final_layer_norm_weight4, model_decoder_layers_18_final_layer_norm_bias4, alloc2304) + R.vm.kill_object(model_decoder_layers_18_final_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_18_final_layer_norm_bias4) + model_decoder_layers_18_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[939] + model_decoder_layers_18_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[940] + gv3096: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc2305: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv3096, R.dtype("float16")) + _2304: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_18_fc1_weight4, alloc2304, model_decoder_layers_18_fc1_bias4, alloc2305) + R.vm.kill_object(alloc2304) + R.vm.kill_object(model_decoder_layers_18_fc1_weight4) + R.vm.kill_object(model_decoder_layers_18_fc1_bias4) + model_decoder_layers_18_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[941] + model_decoder_layers_18_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[942] + gv3097: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2306: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3097, R.dtype("float16")) + _2305: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_18_fc2_weight4, alloc2305, model_decoder_layers_18_fc2_bias4, alloc2306) + R.vm.kill_object(alloc2305) + R.vm.kill_object(model_decoder_layers_18_fc2_weight4) + R.vm.kill_object(model_decoder_layers_18_fc2_bias4) + gv3098: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2307: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv3098, R.dtype("float16")) + cls.add5(alloc2303, alloc2306, alloc2307) + R.vm.kill_object(alloc2303) + R.vm.kill_object(alloc2306) + model_decoder_layers_19_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[952] + model_decoder_layers_19_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[953] + gv3099: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2308: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3099, R.dtype("float16")) + cls.layer_norm2(alloc2307, model_decoder_layers_19_self_attn_layer_norm_weight4, model_decoder_layers_19_self_attn_layer_norm_bias4, alloc2308) + R.vm.kill_object(model_decoder_layers_19_self_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_19_self_attn_layer_norm_bias4) + model_decoder_layers_19_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[948] + model_decoder_layers_19_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[949] + gv3100: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2309: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv3100, R.dtype("float16")) + _2308: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_19_self_attn_q_proj_weight4, alloc2308, model_decoder_layers_19_self_attn_q_proj_bias4, alloc2309) + R.vm.kill_object(model_decoder_layers_19_self_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_19_self_attn_q_proj_bias4) + gv3101: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1223: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2309, gv3101, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2309) + model_decoder_layers_19_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[945] + gv3102: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2310: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3102, R.dtype("float16")) + _2309: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_19_self_attn_k_proj_weight4, alloc2308, alloc2310) + R.vm.kill_object(model_decoder_layers_19_self_attn_k_proj_weight4) + gv3103: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1224: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2310, gv3103, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2310) + model_decoder_layers_19_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[946] + model_decoder_layers_19_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[947] + gv3104: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2311: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv3104, R.dtype("float16")) + _2310: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_19_self_attn_v_proj_weight4, alloc2308, model_decoder_layers_19_self_attn_v_proj_bias4, alloc2311) + R.vm.kill_object(alloc2308) + R.vm.kill_object(model_decoder_layers_19_self_attn_v_proj_weight4) + R.vm.kill_object(model_decoder_layers_19_self_attn_v_proj_bias4) + gv3105: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1225: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2311, gv3105, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2311) + gv3106: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc2312: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3106, R.dtype("float16")) + cls.concatenate1(reshape1223, reshape1224, reshape1225, alloc2312) + R.vm.kill_object(reshape1223) + R.vm.kill_object(reshape1224) + R.vm.kill_object(reshape1225) + gv3107: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1226: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2312, gv3107, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc2312) + gv3108: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc2313: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv3108, R.dtype("float16")) + _2312: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), reshape1226, alloc2313) + R.vm.kill_object(reshape1226) + gv3109: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1227: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2313, gv3109, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2313) + gv3110: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1228: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1227, gv3110, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1227) + model_decoder_layers_19_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[950] + model_decoder_layers_19_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[951] + gv3111: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2314: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3111, R.dtype("float16")) + _2313: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_19_self_attn_out_proj_weight4, reshape1228, model_decoder_layers_19_self_attn_out_proj_bias4, alloc2314) + R.vm.kill_object(reshape1228) + R.vm.kill_object(model_decoder_layers_19_self_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_19_self_attn_out_proj_bias4) + gv3112: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2315: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3112, R.dtype("float16")) + cls.add5(alloc2307, alloc2314, alloc2315) + R.vm.kill_object(alloc2307) + R.vm.kill_object(alloc2314) + model_decoder_layers_19_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[961] + model_decoder_layers_19_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[962] + gv3113: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2316: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv3113, R.dtype("float16")) + cls.layer_norm2(alloc2315, model_decoder_layers_19_encoder_attn_layer_norm_weight4, model_decoder_layers_19_encoder_attn_layer_norm_bias4, alloc2316) + R.vm.kill_object(model_decoder_layers_19_encoder_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_19_encoder_attn_layer_norm_bias4) + model_decoder_layers_19_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[957] + model_decoder_layers_19_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[958] + gv3114: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2317: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv3114, R.dtype("float16")) + _2316: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_19_encoder_attn_q_proj_weight4, alloc2316, model_decoder_layers_19_encoder_attn_q_proj_bias4, alloc2317) + R.vm.kill_object(alloc2316) + R.vm.kill_object(model_decoder_layers_19_encoder_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_19_encoder_attn_q_proj_bias4) + gv3115: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1229: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2317, gv3115, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2317) + gv3116: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1230: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1229, gv3116, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape1229) + gv3117: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc2318: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3117, R.dtype("float16")) + _2317: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), reshape1230, alloc2318) + R.vm.kill_object(reshape1230) + gv3118: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1231: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2318, gv3118, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2318) + gv3119: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1232: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1231, gv3119, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1231) + model_decoder_layers_19_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[959] + model_decoder_layers_19_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[960] + gv3120: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2319: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv3120, R.dtype("float16")) + _2318: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_19_encoder_attn_out_proj_weight4, reshape1232, model_decoder_layers_19_encoder_attn_out_proj_bias4, alloc2319) + R.vm.kill_object(reshape1232) + R.vm.kill_object(model_decoder_layers_19_encoder_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_19_encoder_attn_out_proj_bias4) + gv3121: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2320: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv3121, R.dtype("float16")) + cls.add5(alloc2315, alloc2319, alloc2320) + R.vm.kill_object(alloc2315) + R.vm.kill_object(alloc2319) + model_decoder_layers_19_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[967] + model_decoder_layers_19_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[968] + gv3122: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2321: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3122, R.dtype("float16")) + cls.layer_norm2(alloc2320, model_decoder_layers_19_final_layer_norm_weight4, model_decoder_layers_19_final_layer_norm_bias4, alloc2321) + R.vm.kill_object(model_decoder_layers_19_final_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_19_final_layer_norm_bias4) + model_decoder_layers_19_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[963] + model_decoder_layers_19_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[964] + gv3123: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc2322: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv3123, R.dtype("float16")) + _2321: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_19_fc1_weight4, alloc2321, model_decoder_layers_19_fc1_bias4, alloc2322) + R.vm.kill_object(alloc2321) + R.vm.kill_object(model_decoder_layers_19_fc1_weight4) + R.vm.kill_object(model_decoder_layers_19_fc1_bias4) + model_decoder_layers_19_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[965] + model_decoder_layers_19_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[966] + gv3124: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2323: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3124, R.dtype("float16")) + _2322: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_19_fc2_weight4, alloc2322, model_decoder_layers_19_fc2_bias4, alloc2323) + R.vm.kill_object(alloc2322) + R.vm.kill_object(model_decoder_layers_19_fc2_weight4) + R.vm.kill_object(model_decoder_layers_19_fc2_bias4) + gv3125: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2324: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv3125, R.dtype("float16")) + cls.add5(alloc2320, alloc2323, alloc2324) + R.vm.kill_object(alloc2320) + R.vm.kill_object(alloc2323) + model_decoder_layers_20_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[976] + model_decoder_layers_20_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[977] + gv3126: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2325: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3126, R.dtype("float16")) + cls.layer_norm2(alloc2324, model_decoder_layers_20_self_attn_layer_norm_weight4, model_decoder_layers_20_self_attn_layer_norm_bias4, alloc2325) + R.vm.kill_object(model_decoder_layers_20_self_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_20_self_attn_layer_norm_bias4) + model_decoder_layers_20_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[972] + model_decoder_layers_20_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[973] + gv3127: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2326: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv3127, R.dtype("float16")) + _2325: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_20_self_attn_q_proj_weight4, alloc2325, model_decoder_layers_20_self_attn_q_proj_bias4, alloc2326) + R.vm.kill_object(model_decoder_layers_20_self_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_20_self_attn_q_proj_bias4) + gv3128: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1233: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2326, gv3128, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2326) + model_decoder_layers_20_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[969] + gv3129: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2327: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3129, R.dtype("float16")) + _2326: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_20_self_attn_k_proj_weight4, alloc2325, alloc2327) + R.vm.kill_object(model_decoder_layers_20_self_attn_k_proj_weight4) + gv3130: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1234: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2327, gv3130, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2327) + model_decoder_layers_20_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[970] + model_decoder_layers_20_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[971] + gv3131: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2328: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv3131, R.dtype("float16")) + _2327: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_20_self_attn_v_proj_weight4, alloc2325, model_decoder_layers_20_self_attn_v_proj_bias4, alloc2328) + R.vm.kill_object(alloc2325) + R.vm.kill_object(model_decoder_layers_20_self_attn_v_proj_weight4) + R.vm.kill_object(model_decoder_layers_20_self_attn_v_proj_bias4) + gv3132: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1235: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2328, gv3132, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2328) + gv3133: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc2329: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3133, R.dtype("float16")) + cls.concatenate1(reshape1233, reshape1234, reshape1235, alloc2329) + R.vm.kill_object(reshape1233) + R.vm.kill_object(reshape1234) + R.vm.kill_object(reshape1235) + gv3134: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1236: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2329, gv3134, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc2329) + gv3135: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc2330: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv3135, R.dtype("float16")) + _2329: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), reshape1236, alloc2330) + R.vm.kill_object(reshape1236) + gv3136: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1237: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2330, gv3136, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2330) + gv3137: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1238: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1237, gv3137, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1237) + model_decoder_layers_20_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[974] + model_decoder_layers_20_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[975] + gv3138: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2331: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3138, R.dtype("float16")) + _2330: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_20_self_attn_out_proj_weight4, reshape1238, model_decoder_layers_20_self_attn_out_proj_bias4, alloc2331) + R.vm.kill_object(reshape1238) + R.vm.kill_object(model_decoder_layers_20_self_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_20_self_attn_out_proj_bias4) + gv3139: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2332: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3139, R.dtype("float16")) + cls.add5(alloc2324, alloc2331, alloc2332) + R.vm.kill_object(alloc2324) + R.vm.kill_object(alloc2331) + model_decoder_layers_20_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[985] + model_decoder_layers_20_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[986] + gv3140: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2333: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv3140, R.dtype("float16")) + cls.layer_norm2(alloc2332, model_decoder_layers_20_encoder_attn_layer_norm_weight4, model_decoder_layers_20_encoder_attn_layer_norm_bias4, alloc2333) + R.vm.kill_object(model_decoder_layers_20_encoder_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_20_encoder_attn_layer_norm_bias4) + model_decoder_layers_20_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[981] + model_decoder_layers_20_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[982] + gv3141: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2334: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv3141, R.dtype("float16")) + _2333: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_20_encoder_attn_q_proj_weight4, alloc2333, model_decoder_layers_20_encoder_attn_q_proj_bias4, alloc2334) + R.vm.kill_object(alloc2333) + R.vm.kill_object(model_decoder_layers_20_encoder_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_20_encoder_attn_q_proj_bias4) + gv3142: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1239: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2334, gv3142, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2334) + gv3143: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1240: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1239, gv3143, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape1239) + gv3144: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc2335: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3144, R.dtype("float16")) + _2334: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), reshape1240, alloc2335) + R.vm.kill_object(reshape1240) + gv3145: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1241: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2335, gv3145, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2335) + gv3146: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1242: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1241, gv3146, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1241) + model_decoder_layers_20_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[983] + model_decoder_layers_20_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[984] + gv3147: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2336: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv3147, R.dtype("float16")) + _2335: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_20_encoder_attn_out_proj_weight4, reshape1242, model_decoder_layers_20_encoder_attn_out_proj_bias4, alloc2336) + R.vm.kill_object(reshape1242) + R.vm.kill_object(model_decoder_layers_20_encoder_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_20_encoder_attn_out_proj_bias4) + gv3148: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2337: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv3148, R.dtype("float16")) + cls.add5(alloc2332, alloc2336, alloc2337) + R.vm.kill_object(alloc2332) + R.vm.kill_object(alloc2336) + model_decoder_layers_20_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[991] + model_decoder_layers_20_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[992] + gv3149: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2338: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3149, R.dtype("float16")) + cls.layer_norm2(alloc2337, model_decoder_layers_20_final_layer_norm_weight4, model_decoder_layers_20_final_layer_norm_bias4, alloc2338) + R.vm.kill_object(model_decoder_layers_20_final_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_20_final_layer_norm_bias4) + model_decoder_layers_20_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[987] + model_decoder_layers_20_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[988] + gv3150: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc2339: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv3150, R.dtype("float16")) + _2338: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_20_fc1_weight4, alloc2338, model_decoder_layers_20_fc1_bias4, alloc2339) + R.vm.kill_object(alloc2338) + R.vm.kill_object(model_decoder_layers_20_fc1_weight4) + R.vm.kill_object(model_decoder_layers_20_fc1_bias4) + model_decoder_layers_20_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[989] + model_decoder_layers_20_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[990] + gv3151: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2340: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3151, R.dtype("float16")) + _2339: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_20_fc2_weight4, alloc2339, model_decoder_layers_20_fc2_bias4, alloc2340) + R.vm.kill_object(alloc2339) + R.vm.kill_object(model_decoder_layers_20_fc2_weight4) + R.vm.kill_object(model_decoder_layers_20_fc2_bias4) + gv3152: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2341: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv3152, R.dtype("float16")) + cls.add5(alloc2337, alloc2340, alloc2341) + R.vm.kill_object(alloc2337) + R.vm.kill_object(alloc2340) + model_decoder_layers_21_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1000] + model_decoder_layers_21_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1001] + gv3153: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2342: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3153, R.dtype("float16")) + cls.layer_norm2(alloc2341, model_decoder_layers_21_self_attn_layer_norm_weight4, model_decoder_layers_21_self_attn_layer_norm_bias4, alloc2342) + R.vm.kill_object(model_decoder_layers_21_self_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_21_self_attn_layer_norm_bias4) + model_decoder_layers_21_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[996] + model_decoder_layers_21_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[997] + gv3154: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2343: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv3154, R.dtype("float16")) + _2342: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_21_self_attn_q_proj_weight4, alloc2342, model_decoder_layers_21_self_attn_q_proj_bias4, alloc2343) + R.vm.kill_object(model_decoder_layers_21_self_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_21_self_attn_q_proj_bias4) + gv3155: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1243: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2343, gv3155, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2343) + model_decoder_layers_21_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[993] + gv3156: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2344: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3156, R.dtype("float16")) + _2343: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_21_self_attn_k_proj_weight4, alloc2342, alloc2344) + R.vm.kill_object(model_decoder_layers_21_self_attn_k_proj_weight4) + gv3157: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1244: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2344, gv3157, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2344) + model_decoder_layers_21_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[994] + model_decoder_layers_21_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[995] + gv3158: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2345: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv3158, R.dtype("float16")) + _2344: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_21_self_attn_v_proj_weight4, alloc2342, model_decoder_layers_21_self_attn_v_proj_bias4, alloc2345) + R.vm.kill_object(alloc2342) + R.vm.kill_object(model_decoder_layers_21_self_attn_v_proj_weight4) + R.vm.kill_object(model_decoder_layers_21_self_attn_v_proj_bias4) + gv3159: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1245: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2345, gv3159, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2345) + gv3160: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc2346: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3160, R.dtype("float16")) + cls.concatenate1(reshape1243, reshape1244, reshape1245, alloc2346) + R.vm.kill_object(reshape1243) + R.vm.kill_object(reshape1244) + R.vm.kill_object(reshape1245) + gv3161: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1246: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2346, gv3161, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc2346) + gv3162: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc2347: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv3162, R.dtype("float16")) + _2346: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), reshape1246, alloc2347) + R.vm.kill_object(reshape1246) + gv3163: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1247: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2347, gv3163, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2347) + gv3164: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1248: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1247, gv3164, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1247) + model_decoder_layers_21_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[998] + model_decoder_layers_21_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[999] + gv3165: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2348: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3165, R.dtype("float16")) + _2347: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_21_self_attn_out_proj_weight4, reshape1248, model_decoder_layers_21_self_attn_out_proj_bias4, alloc2348) + R.vm.kill_object(reshape1248) + R.vm.kill_object(model_decoder_layers_21_self_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_21_self_attn_out_proj_bias4) + gv3166: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2349: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3166, R.dtype("float16")) + cls.add5(alloc2341, alloc2348, alloc2349) + R.vm.kill_object(alloc2341) + R.vm.kill_object(alloc2348) + model_decoder_layers_21_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1009] + model_decoder_layers_21_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1010] + gv3167: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2350: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv3167, R.dtype("float16")) + cls.layer_norm2(alloc2349, model_decoder_layers_21_encoder_attn_layer_norm_weight4, model_decoder_layers_21_encoder_attn_layer_norm_bias4, alloc2350) + R.vm.kill_object(model_decoder_layers_21_encoder_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_21_encoder_attn_layer_norm_bias4) + model_decoder_layers_21_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1005] + model_decoder_layers_21_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1006] + gv3168: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2351: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv3168, R.dtype("float16")) + _2350: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_21_encoder_attn_q_proj_weight4, alloc2350, model_decoder_layers_21_encoder_attn_q_proj_bias4, alloc2351) + R.vm.kill_object(alloc2350) + R.vm.kill_object(model_decoder_layers_21_encoder_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_21_encoder_attn_q_proj_bias4) + gv3169: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1249: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2351, gv3169, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2351) + gv3170: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1250: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1249, gv3170, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape1249) + gv3171: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc2352: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3171, R.dtype("float16")) + _2351: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), reshape1250, alloc2352) + R.vm.kill_object(reshape1250) + gv3172: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1251: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2352, gv3172, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2352) + gv3173: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1252: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1251, gv3173, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1251) + model_decoder_layers_21_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1007] + model_decoder_layers_21_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1008] + gv3174: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2353: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv3174, R.dtype("float16")) + _2352: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_21_encoder_attn_out_proj_weight4, reshape1252, model_decoder_layers_21_encoder_attn_out_proj_bias4, alloc2353) + R.vm.kill_object(reshape1252) + R.vm.kill_object(model_decoder_layers_21_encoder_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_21_encoder_attn_out_proj_bias4) + gv3175: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2354: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv3175, R.dtype("float16")) + cls.add5(alloc2349, alloc2353, alloc2354) + R.vm.kill_object(alloc2349) + R.vm.kill_object(alloc2353) + model_decoder_layers_21_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1015] + model_decoder_layers_21_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1016] + gv3176: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2355: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3176, R.dtype("float16")) + cls.layer_norm2(alloc2354, model_decoder_layers_21_final_layer_norm_weight4, model_decoder_layers_21_final_layer_norm_bias4, alloc2355) + R.vm.kill_object(model_decoder_layers_21_final_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_21_final_layer_norm_bias4) + model_decoder_layers_21_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1011] + model_decoder_layers_21_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1012] + gv3177: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc2356: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv3177, R.dtype("float16")) + _2355: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_21_fc1_weight4, alloc2355, model_decoder_layers_21_fc1_bias4, alloc2356) + R.vm.kill_object(alloc2355) + R.vm.kill_object(model_decoder_layers_21_fc1_weight4) + R.vm.kill_object(model_decoder_layers_21_fc1_bias4) + model_decoder_layers_21_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1013] + model_decoder_layers_21_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1014] + gv3178: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2357: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3178, R.dtype("float16")) + _2356: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_21_fc2_weight4, alloc2356, model_decoder_layers_21_fc2_bias4, alloc2357) + R.vm.kill_object(alloc2356) + R.vm.kill_object(model_decoder_layers_21_fc2_weight4) + R.vm.kill_object(model_decoder_layers_21_fc2_bias4) + gv3179: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2358: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv3179, R.dtype("float16")) + cls.add5(alloc2354, alloc2357, alloc2358) + R.vm.kill_object(alloc2354) + R.vm.kill_object(alloc2357) + model_decoder_layers_22_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1024] + model_decoder_layers_22_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1025] + gv3180: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2359: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3180, R.dtype("float16")) + cls.layer_norm2(alloc2358, model_decoder_layers_22_self_attn_layer_norm_weight4, model_decoder_layers_22_self_attn_layer_norm_bias4, alloc2359) + R.vm.kill_object(model_decoder_layers_22_self_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_22_self_attn_layer_norm_bias4) + model_decoder_layers_22_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1020] + model_decoder_layers_22_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1021] + gv3181: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2360: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv3181, R.dtype("float16")) + _2359: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_22_self_attn_q_proj_weight4, alloc2359, model_decoder_layers_22_self_attn_q_proj_bias4, alloc2360) + R.vm.kill_object(model_decoder_layers_22_self_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_22_self_attn_q_proj_bias4) + gv3182: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1253: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2360, gv3182, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2360) + model_decoder_layers_22_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1017] + gv3183: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2361: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3183, R.dtype("float16")) + _2360: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_22_self_attn_k_proj_weight4, alloc2359, alloc2361) + R.vm.kill_object(model_decoder_layers_22_self_attn_k_proj_weight4) + gv3184: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1254: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2361, gv3184, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2361) + model_decoder_layers_22_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1018] + model_decoder_layers_22_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1019] + gv3185: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2362: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv3185, R.dtype("float16")) + _2361: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_22_self_attn_v_proj_weight4, alloc2359, model_decoder_layers_22_self_attn_v_proj_bias4, alloc2362) + R.vm.kill_object(alloc2359) + R.vm.kill_object(model_decoder_layers_22_self_attn_v_proj_weight4) + R.vm.kill_object(model_decoder_layers_22_self_attn_v_proj_bias4) + gv3186: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1255: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2362, gv3186, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2362) + gv3187: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc2363: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3187, R.dtype("float16")) + cls.concatenate1(reshape1253, reshape1254, reshape1255, alloc2363) + R.vm.kill_object(reshape1253) + R.vm.kill_object(reshape1254) + R.vm.kill_object(reshape1255) + gv3188: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1256: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2363, gv3188, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc2363) + gv3189: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc2364: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv3189, R.dtype("float16")) + _2363: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), reshape1256, alloc2364) + R.vm.kill_object(reshape1256) + gv3190: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1257: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2364, gv3190, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2364) + gv3191: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1258: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1257, gv3191, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1257) + model_decoder_layers_22_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1022] + model_decoder_layers_22_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1023] + gv3192: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2365: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3192, R.dtype("float16")) + _2364: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_22_self_attn_out_proj_weight4, reshape1258, model_decoder_layers_22_self_attn_out_proj_bias4, alloc2365) + R.vm.kill_object(reshape1258) + R.vm.kill_object(model_decoder_layers_22_self_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_22_self_attn_out_proj_bias4) + gv3193: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2366: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3193, R.dtype("float16")) + cls.add5(alloc2358, alloc2365, alloc2366) + R.vm.kill_object(alloc2358) + R.vm.kill_object(alloc2365) + model_decoder_layers_22_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1033] + model_decoder_layers_22_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1034] + gv3194: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2367: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv3194, R.dtype("float16")) + cls.layer_norm2(alloc2366, model_decoder_layers_22_encoder_attn_layer_norm_weight4, model_decoder_layers_22_encoder_attn_layer_norm_bias4, alloc2367) + R.vm.kill_object(model_decoder_layers_22_encoder_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_22_encoder_attn_layer_norm_bias4) + model_decoder_layers_22_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1029] + model_decoder_layers_22_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1030] + gv3195: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2368: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv3195, R.dtype("float16")) + _2367: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_22_encoder_attn_q_proj_weight4, alloc2367, model_decoder_layers_22_encoder_attn_q_proj_bias4, alloc2368) + R.vm.kill_object(alloc2367) + R.vm.kill_object(model_decoder_layers_22_encoder_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_22_encoder_attn_q_proj_bias4) + gv3196: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1259: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2368, gv3196, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2368) + gv3197: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1260: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1259, gv3197, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape1259) + gv3198: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc2369: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3198, R.dtype("float16")) + _2368: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), reshape1260, alloc2369) + R.vm.kill_object(reshape1260) + gv3199: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1261: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2369, gv3199, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2369) + gv3200: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1262: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1261, gv3200, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1261) + model_decoder_layers_22_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1031] + model_decoder_layers_22_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1032] + gv3201: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2370: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv3201, R.dtype("float16")) + _2369: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_22_encoder_attn_out_proj_weight4, reshape1262, model_decoder_layers_22_encoder_attn_out_proj_bias4, alloc2370) + R.vm.kill_object(reshape1262) + R.vm.kill_object(model_decoder_layers_22_encoder_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_22_encoder_attn_out_proj_bias4) + gv3202: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2371: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv3202, R.dtype("float16")) + cls.add5(alloc2366, alloc2370, alloc2371) + R.vm.kill_object(alloc2366) + R.vm.kill_object(alloc2370) + model_decoder_layers_22_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1039] + model_decoder_layers_22_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1040] + gv3203: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2372: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3203, R.dtype("float16")) + cls.layer_norm2(alloc2371, model_decoder_layers_22_final_layer_norm_weight4, model_decoder_layers_22_final_layer_norm_bias4, alloc2372) + R.vm.kill_object(model_decoder_layers_22_final_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_22_final_layer_norm_bias4) + model_decoder_layers_22_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1035] + model_decoder_layers_22_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1036] + gv3204: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc2373: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv3204, R.dtype("float16")) + _2372: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_22_fc1_weight4, alloc2372, model_decoder_layers_22_fc1_bias4, alloc2373) + R.vm.kill_object(alloc2372) + R.vm.kill_object(model_decoder_layers_22_fc1_weight4) + R.vm.kill_object(model_decoder_layers_22_fc1_bias4) + model_decoder_layers_22_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1037] + model_decoder_layers_22_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1038] + gv3205: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2374: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3205, R.dtype("float16")) + _2373: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_22_fc2_weight4, alloc2373, model_decoder_layers_22_fc2_bias4, alloc2374) + R.vm.kill_object(alloc2373) + R.vm.kill_object(model_decoder_layers_22_fc2_weight4) + R.vm.kill_object(model_decoder_layers_22_fc2_bias4) + gv3206: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2375: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv3206, R.dtype("float16")) + cls.add5(alloc2371, alloc2374, alloc2375) + R.vm.kill_object(alloc2371) + R.vm.kill_object(alloc2374) + model_decoder_layers_23_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1048] + model_decoder_layers_23_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1049] + gv3207: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2376: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3207, R.dtype("float16")) + cls.layer_norm2(alloc2375, model_decoder_layers_23_self_attn_layer_norm_weight4, model_decoder_layers_23_self_attn_layer_norm_bias4, alloc2376) + R.vm.kill_object(model_decoder_layers_23_self_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_23_self_attn_layer_norm_bias4) + model_decoder_layers_23_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1044] + model_decoder_layers_23_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1045] + gv3208: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2377: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv3208, R.dtype("float16")) + _2376: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_23_self_attn_q_proj_weight4, alloc2376, model_decoder_layers_23_self_attn_q_proj_bias4, alloc2377) + R.vm.kill_object(model_decoder_layers_23_self_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_23_self_attn_q_proj_bias4) + gv3209: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1263: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2377, gv3209, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2377) + model_decoder_layers_23_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1041] + gv3210: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2378: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3210, R.dtype("float16")) + _2377: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_23_self_attn_k_proj_weight4, alloc2376, alloc2378) + R.vm.kill_object(model_decoder_layers_23_self_attn_k_proj_weight4) + gv3211: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1264: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2378, gv3211, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2378) + model_decoder_layers_23_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1042] + model_decoder_layers_23_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1043] + gv3212: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2379: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv3212, R.dtype("float16")) + _2378: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_23_self_attn_v_proj_weight4, alloc2376, model_decoder_layers_23_self_attn_v_proj_bias4, alloc2379) + R.vm.kill_object(alloc2376) + R.vm.kill_object(model_decoder_layers_23_self_attn_v_proj_weight4) + R.vm.kill_object(model_decoder_layers_23_self_attn_v_proj_bias4) + gv3213: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1265: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2379, gv3213, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2379) + gv3214: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc2380: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3214, R.dtype("float16")) + cls.concatenate1(reshape1263, reshape1264, reshape1265, alloc2380) + R.vm.kill_object(reshape1263) + R.vm.kill_object(reshape1264) + R.vm.kill_object(reshape1265) + gv3215: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1266: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2380, gv3215, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc2380) + gv3216: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc2381: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv3216, R.dtype("float16")) + _2380: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), reshape1266, alloc2381) + R.vm.kill_object(reshape1266) + gv3217: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1267: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2381, gv3217, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2381) + gv3218: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1268: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1267, gv3218, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1267) + model_decoder_layers_23_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1046] + model_decoder_layers_23_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1047] + gv3219: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2382: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3219, R.dtype("float16")) + _2381: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_23_self_attn_out_proj_weight4, reshape1268, model_decoder_layers_23_self_attn_out_proj_bias4, alloc2382) + R.vm.kill_object(reshape1268) + R.vm.kill_object(model_decoder_layers_23_self_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_23_self_attn_out_proj_bias4) + gv3220: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2383: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3220, R.dtype("float16")) + cls.add5(alloc2375, alloc2382, alloc2383) + R.vm.kill_object(alloc2375) + R.vm.kill_object(alloc2382) + model_decoder_layers_23_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1057] + model_decoder_layers_23_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1058] + gv3221: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2384: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv3221, R.dtype("float16")) + cls.layer_norm2(alloc2383, model_decoder_layers_23_encoder_attn_layer_norm_weight4, model_decoder_layers_23_encoder_attn_layer_norm_bias4, alloc2384) + R.vm.kill_object(model_decoder_layers_23_encoder_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_23_encoder_attn_layer_norm_bias4) + model_decoder_layers_23_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1053] + model_decoder_layers_23_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1054] + gv3222: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2385: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv3222, R.dtype("float16")) + _2384: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_23_encoder_attn_q_proj_weight4, alloc2384, model_decoder_layers_23_encoder_attn_q_proj_bias4, alloc2385) + R.vm.kill_object(alloc2384) + R.vm.kill_object(model_decoder_layers_23_encoder_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_23_encoder_attn_q_proj_bias4) + gv3223: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1269: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2385, gv3223, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2385) + gv3224: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1270: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1269, gv3224, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape1269) + gv3225: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc2386: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3225, R.dtype("float16")) + _2385: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), reshape1270, alloc2386) + R.vm.kill_object(reshape1270) + gv3226: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1271: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2386, gv3226, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2386) + gv3227: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1272: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1271, gv3227, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1271) + model_decoder_layers_23_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1055] + model_decoder_layers_23_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1056] + gv3228: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2387: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv3228, R.dtype("float16")) + _2386: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_23_encoder_attn_out_proj_weight4, reshape1272, model_decoder_layers_23_encoder_attn_out_proj_bias4, alloc2387) + R.vm.kill_object(reshape1272) + R.vm.kill_object(model_decoder_layers_23_encoder_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_23_encoder_attn_out_proj_bias4) + gv3229: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2388: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv3229, R.dtype("float16")) + cls.add5(alloc2383, alloc2387, alloc2388) + R.vm.kill_object(alloc2383) + R.vm.kill_object(alloc2387) + model_decoder_layers_23_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1063] + model_decoder_layers_23_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1064] + gv3230: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2389: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3230, R.dtype("float16")) + cls.layer_norm2(alloc2388, model_decoder_layers_23_final_layer_norm_weight4, model_decoder_layers_23_final_layer_norm_bias4, alloc2389) + R.vm.kill_object(model_decoder_layers_23_final_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_23_final_layer_norm_bias4) + model_decoder_layers_23_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1059] + model_decoder_layers_23_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1060] + gv3231: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc2390: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv3231, R.dtype("float16")) + _2389: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_23_fc1_weight4, alloc2389, model_decoder_layers_23_fc1_bias4, alloc2390) + R.vm.kill_object(alloc2389) + R.vm.kill_object(model_decoder_layers_23_fc1_weight4) + R.vm.kill_object(model_decoder_layers_23_fc1_bias4) + model_decoder_layers_23_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1061] + model_decoder_layers_23_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1062] + gv3232: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2391: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3232, R.dtype("float16")) + _2390: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_23_fc2_weight4, alloc2390, model_decoder_layers_23_fc2_bias4, alloc2391) + R.vm.kill_object(alloc2390) + R.vm.kill_object(model_decoder_layers_23_fc2_weight4) + R.vm.kill_object(model_decoder_layers_23_fc2_bias4) + gv3233: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2392: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv3233, R.dtype("float16")) + cls.add5(alloc2388, alloc2391, alloc2392) + R.vm.kill_object(alloc2388) + R.vm.kill_object(alloc2391) + model_decoder_layers_24_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1072] + model_decoder_layers_24_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1073] + gv3234: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2393: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3234, R.dtype("float16")) + cls.layer_norm2(alloc2392, model_decoder_layers_24_self_attn_layer_norm_weight4, model_decoder_layers_24_self_attn_layer_norm_bias4, alloc2393) + R.vm.kill_object(model_decoder_layers_24_self_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_24_self_attn_layer_norm_bias4) + model_decoder_layers_24_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1068] + model_decoder_layers_24_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1069] + gv3235: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2394: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv3235, R.dtype("float16")) + _2393: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_24_self_attn_q_proj_weight4, alloc2393, model_decoder_layers_24_self_attn_q_proj_bias4, alloc2394) + R.vm.kill_object(model_decoder_layers_24_self_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_24_self_attn_q_proj_bias4) + gv3236: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1273: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2394, gv3236, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2394) + model_decoder_layers_24_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1065] + gv3237: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2395: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3237, R.dtype("float16")) + _2394: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_24_self_attn_k_proj_weight4, alloc2393, alloc2395) + R.vm.kill_object(model_decoder_layers_24_self_attn_k_proj_weight4) + gv3238: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1274: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2395, gv3238, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2395) + model_decoder_layers_24_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1066] + model_decoder_layers_24_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1067] + gv3239: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2396: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv3239, R.dtype("float16")) + _2395: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_24_self_attn_v_proj_weight4, alloc2393, model_decoder_layers_24_self_attn_v_proj_bias4, alloc2396) + R.vm.kill_object(alloc2393) + R.vm.kill_object(model_decoder_layers_24_self_attn_v_proj_weight4) + R.vm.kill_object(model_decoder_layers_24_self_attn_v_proj_bias4) + gv3240: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1275: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2396, gv3240, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2396) + gv3241: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc2397: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3241, R.dtype("float16")) + cls.concatenate1(reshape1273, reshape1274, reshape1275, alloc2397) + R.vm.kill_object(reshape1273) + R.vm.kill_object(reshape1274) + R.vm.kill_object(reshape1275) + gv3242: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1276: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2397, gv3242, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc2397) + gv3243: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc2398: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv3243, R.dtype("float16")) + _2397: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), reshape1276, alloc2398) + R.vm.kill_object(reshape1276) + gv3244: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1277: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2398, gv3244, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2398) + gv3245: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1278: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1277, gv3245, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1277) + model_decoder_layers_24_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1070] + model_decoder_layers_24_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1071] + gv3246: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2399: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3246, R.dtype("float16")) + _2398: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_24_self_attn_out_proj_weight4, reshape1278, model_decoder_layers_24_self_attn_out_proj_bias4, alloc2399) + R.vm.kill_object(reshape1278) + R.vm.kill_object(model_decoder_layers_24_self_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_24_self_attn_out_proj_bias4) + gv3247: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2400: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3247, R.dtype("float16")) + cls.add5(alloc2392, alloc2399, alloc2400) + R.vm.kill_object(alloc2392) + R.vm.kill_object(alloc2399) + model_decoder_layers_24_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1081] + model_decoder_layers_24_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1082] + gv3248: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2401: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv3248, R.dtype("float16")) + cls.layer_norm2(alloc2400, model_decoder_layers_24_encoder_attn_layer_norm_weight4, model_decoder_layers_24_encoder_attn_layer_norm_bias4, alloc2401) + R.vm.kill_object(model_decoder_layers_24_encoder_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_24_encoder_attn_layer_norm_bias4) + model_decoder_layers_24_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1077] + model_decoder_layers_24_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1078] + gv3249: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2402: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv3249, R.dtype("float16")) + _2401: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_24_encoder_attn_q_proj_weight4, alloc2401, model_decoder_layers_24_encoder_attn_q_proj_bias4, alloc2402) + R.vm.kill_object(alloc2401) + R.vm.kill_object(model_decoder_layers_24_encoder_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_24_encoder_attn_q_proj_bias4) + gv3250: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1279: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2402, gv3250, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2402) + gv3251: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1280: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1279, gv3251, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape1279) + gv3252: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc2403: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3252, R.dtype("float16")) + _2402: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), reshape1280, alloc2403) + R.vm.kill_object(reshape1280) + gv3253: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1281: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2403, gv3253, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2403) + gv3254: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1282: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1281, gv3254, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1281) + model_decoder_layers_24_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1079] + model_decoder_layers_24_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1080] + gv3255: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2404: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv3255, R.dtype("float16")) + _2403: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_24_encoder_attn_out_proj_weight4, reshape1282, model_decoder_layers_24_encoder_attn_out_proj_bias4, alloc2404) + R.vm.kill_object(reshape1282) + R.vm.kill_object(model_decoder_layers_24_encoder_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_24_encoder_attn_out_proj_bias4) + gv3256: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2405: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv3256, R.dtype("float16")) + cls.add5(alloc2400, alloc2404, alloc2405) + R.vm.kill_object(alloc2400) + R.vm.kill_object(alloc2404) + model_decoder_layers_24_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1087] + model_decoder_layers_24_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1088] + gv3257: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2406: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3257, R.dtype("float16")) + cls.layer_norm2(alloc2405, model_decoder_layers_24_final_layer_norm_weight4, model_decoder_layers_24_final_layer_norm_bias4, alloc2406) + R.vm.kill_object(model_decoder_layers_24_final_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_24_final_layer_norm_bias4) + model_decoder_layers_24_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1083] + model_decoder_layers_24_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1084] + gv3258: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc2407: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv3258, R.dtype("float16")) + _2406: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_24_fc1_weight4, alloc2406, model_decoder_layers_24_fc1_bias4, alloc2407) + R.vm.kill_object(alloc2406) + R.vm.kill_object(model_decoder_layers_24_fc1_weight4) + R.vm.kill_object(model_decoder_layers_24_fc1_bias4) + model_decoder_layers_24_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1085] + model_decoder_layers_24_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1086] + gv3259: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2408: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3259, R.dtype("float16")) + _2407: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_24_fc2_weight4, alloc2407, model_decoder_layers_24_fc2_bias4, alloc2408) + R.vm.kill_object(alloc2407) + R.vm.kill_object(model_decoder_layers_24_fc2_weight4) + R.vm.kill_object(model_decoder_layers_24_fc2_bias4) + gv3260: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2409: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv3260, R.dtype("float16")) + cls.add5(alloc2405, alloc2408, alloc2409) + R.vm.kill_object(alloc2405) + R.vm.kill_object(alloc2408) + model_decoder_layers_25_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1096] + model_decoder_layers_25_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1097] + gv3261: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2410: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3261, R.dtype("float16")) + cls.layer_norm2(alloc2409, model_decoder_layers_25_self_attn_layer_norm_weight4, model_decoder_layers_25_self_attn_layer_norm_bias4, alloc2410) + R.vm.kill_object(model_decoder_layers_25_self_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_25_self_attn_layer_norm_bias4) + model_decoder_layers_25_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1092] + model_decoder_layers_25_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1093] + gv3262: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2411: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv3262, R.dtype("float16")) + _2410: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_25_self_attn_q_proj_weight4, alloc2410, model_decoder_layers_25_self_attn_q_proj_bias4, alloc2411) + R.vm.kill_object(model_decoder_layers_25_self_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_25_self_attn_q_proj_bias4) + gv3263: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1283: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2411, gv3263, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2411) + model_decoder_layers_25_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1089] + gv3264: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2412: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3264, R.dtype("float16")) + _2411: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_25_self_attn_k_proj_weight4, alloc2410, alloc2412) + R.vm.kill_object(model_decoder_layers_25_self_attn_k_proj_weight4) + gv3265: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1284: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2412, gv3265, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2412) + model_decoder_layers_25_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1090] + model_decoder_layers_25_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1091] + gv3266: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2413: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv3266, R.dtype("float16")) + _2412: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_25_self_attn_v_proj_weight4, alloc2410, model_decoder_layers_25_self_attn_v_proj_bias4, alloc2413) + R.vm.kill_object(alloc2410) + R.vm.kill_object(model_decoder_layers_25_self_attn_v_proj_weight4) + R.vm.kill_object(model_decoder_layers_25_self_attn_v_proj_bias4) + gv3267: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1285: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2413, gv3267, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2413) + gv3268: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc2414: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3268, R.dtype("float16")) + cls.concatenate1(reshape1283, reshape1284, reshape1285, alloc2414) + R.vm.kill_object(reshape1283) + R.vm.kill_object(reshape1284) + R.vm.kill_object(reshape1285) + gv3269: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1286: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2414, gv3269, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc2414) + gv3270: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc2415: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv3270, R.dtype("float16")) + _2414: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), reshape1286, alloc2415) + R.vm.kill_object(reshape1286) + gv3271: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1287: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2415, gv3271, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2415) + gv3272: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1288: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1287, gv3272, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1287) + model_decoder_layers_25_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1094] + model_decoder_layers_25_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1095] + gv3273: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2416: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3273, R.dtype("float16")) + _2415: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_25_self_attn_out_proj_weight4, reshape1288, model_decoder_layers_25_self_attn_out_proj_bias4, alloc2416) + R.vm.kill_object(reshape1288) + R.vm.kill_object(model_decoder_layers_25_self_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_25_self_attn_out_proj_bias4) + gv3274: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2417: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3274, R.dtype("float16")) + cls.add5(alloc2409, alloc2416, alloc2417) + R.vm.kill_object(alloc2409) + R.vm.kill_object(alloc2416) + model_decoder_layers_25_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1105] + model_decoder_layers_25_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1106] + gv3275: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2418: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv3275, R.dtype("float16")) + cls.layer_norm2(alloc2417, model_decoder_layers_25_encoder_attn_layer_norm_weight4, model_decoder_layers_25_encoder_attn_layer_norm_bias4, alloc2418) + R.vm.kill_object(model_decoder_layers_25_encoder_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_25_encoder_attn_layer_norm_bias4) + model_decoder_layers_25_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1101] + model_decoder_layers_25_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1102] + gv3276: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2419: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv3276, R.dtype("float16")) + _2418: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_25_encoder_attn_q_proj_weight4, alloc2418, model_decoder_layers_25_encoder_attn_q_proj_bias4, alloc2419) + R.vm.kill_object(alloc2418) + R.vm.kill_object(model_decoder_layers_25_encoder_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_25_encoder_attn_q_proj_bias4) + gv3277: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1289: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2419, gv3277, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2419) + gv3278: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1290: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1289, gv3278, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape1289) + gv3279: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc2420: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3279, R.dtype("float16")) + _2419: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), reshape1290, alloc2420) + R.vm.kill_object(reshape1290) + gv3280: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1291: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2420, gv3280, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2420) + gv3281: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1292: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1291, gv3281, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1291) + model_decoder_layers_25_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1103] + model_decoder_layers_25_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1104] + gv3282: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2421: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv3282, R.dtype("float16")) + _2420: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_25_encoder_attn_out_proj_weight4, reshape1292, model_decoder_layers_25_encoder_attn_out_proj_bias4, alloc2421) + R.vm.kill_object(reshape1292) + R.vm.kill_object(model_decoder_layers_25_encoder_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_25_encoder_attn_out_proj_bias4) + gv3283: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2422: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv3283, R.dtype("float16")) + cls.add5(alloc2417, alloc2421, alloc2422) + R.vm.kill_object(alloc2417) + R.vm.kill_object(alloc2421) + model_decoder_layers_25_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1111] + model_decoder_layers_25_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1112] + gv3284: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2423: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3284, R.dtype("float16")) + cls.layer_norm2(alloc2422, model_decoder_layers_25_final_layer_norm_weight4, model_decoder_layers_25_final_layer_norm_bias4, alloc2423) + R.vm.kill_object(model_decoder_layers_25_final_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_25_final_layer_norm_bias4) + model_decoder_layers_25_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1107] + model_decoder_layers_25_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1108] + gv3285: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc2424: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv3285, R.dtype("float16")) + _2423: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_25_fc1_weight4, alloc2423, model_decoder_layers_25_fc1_bias4, alloc2424) + R.vm.kill_object(alloc2423) + R.vm.kill_object(model_decoder_layers_25_fc1_weight4) + R.vm.kill_object(model_decoder_layers_25_fc1_bias4) + model_decoder_layers_25_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1109] + model_decoder_layers_25_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1110] + gv3286: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2425: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3286, R.dtype("float16")) + _2424: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_25_fc2_weight4, alloc2424, model_decoder_layers_25_fc2_bias4, alloc2425) + R.vm.kill_object(alloc2424) + R.vm.kill_object(model_decoder_layers_25_fc2_weight4) + R.vm.kill_object(model_decoder_layers_25_fc2_bias4) + gv3287: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2426: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv3287, R.dtype("float16")) + cls.add5(alloc2422, alloc2425, alloc2426) + R.vm.kill_object(alloc2422) + R.vm.kill_object(alloc2425) + model_decoder_layers_26_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1120] + model_decoder_layers_26_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1121] + gv3288: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2427: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3288, R.dtype("float16")) + cls.layer_norm2(alloc2426, model_decoder_layers_26_self_attn_layer_norm_weight4, model_decoder_layers_26_self_attn_layer_norm_bias4, alloc2427) + R.vm.kill_object(model_decoder_layers_26_self_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_26_self_attn_layer_norm_bias4) + model_decoder_layers_26_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1116] + model_decoder_layers_26_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1117] + gv3289: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2428: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv3289, R.dtype("float16")) + _2427: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_26_self_attn_q_proj_weight4, alloc2427, model_decoder_layers_26_self_attn_q_proj_bias4, alloc2428) + R.vm.kill_object(model_decoder_layers_26_self_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_26_self_attn_q_proj_bias4) + gv3290: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1293: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2428, gv3290, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2428) + model_decoder_layers_26_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1113] + gv3291: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2429: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3291, R.dtype("float16")) + _2428: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_26_self_attn_k_proj_weight4, alloc2427, alloc2429) + R.vm.kill_object(model_decoder_layers_26_self_attn_k_proj_weight4) + gv3292: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1294: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2429, gv3292, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2429) + model_decoder_layers_26_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1114] + model_decoder_layers_26_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1115] + gv3293: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2430: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv3293, R.dtype("float16")) + _2429: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_26_self_attn_v_proj_weight4, alloc2427, model_decoder_layers_26_self_attn_v_proj_bias4, alloc2430) + R.vm.kill_object(alloc2427) + R.vm.kill_object(model_decoder_layers_26_self_attn_v_proj_weight4) + R.vm.kill_object(model_decoder_layers_26_self_attn_v_proj_bias4) + gv3294: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1295: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2430, gv3294, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2430) + gv3295: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc2431: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3295, R.dtype("float16")) + cls.concatenate1(reshape1293, reshape1294, reshape1295, alloc2431) + R.vm.kill_object(reshape1293) + R.vm.kill_object(reshape1294) + R.vm.kill_object(reshape1295) + gv3296: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1296: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2431, gv3296, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc2431) + gv3297: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc2432: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv3297, R.dtype("float16")) + _2431: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), reshape1296, alloc2432) + R.vm.kill_object(reshape1296) + gv3298: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1297: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2432, gv3298, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2432) + gv3299: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1298: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1297, gv3299, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1297) + model_decoder_layers_26_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1118] + model_decoder_layers_26_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1119] + gv3300: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2433: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3300, R.dtype("float16")) + _2432: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_26_self_attn_out_proj_weight4, reshape1298, model_decoder_layers_26_self_attn_out_proj_bias4, alloc2433) + R.vm.kill_object(reshape1298) + R.vm.kill_object(model_decoder_layers_26_self_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_26_self_attn_out_proj_bias4) + gv3301: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2434: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3301, R.dtype("float16")) + cls.add5(alloc2426, alloc2433, alloc2434) + R.vm.kill_object(alloc2426) + R.vm.kill_object(alloc2433) + model_decoder_layers_26_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1129] + model_decoder_layers_26_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1130] + gv3302: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2435: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv3302, R.dtype("float16")) + cls.layer_norm2(alloc2434, model_decoder_layers_26_encoder_attn_layer_norm_weight4, model_decoder_layers_26_encoder_attn_layer_norm_bias4, alloc2435) + R.vm.kill_object(model_decoder_layers_26_encoder_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_26_encoder_attn_layer_norm_bias4) + model_decoder_layers_26_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1125] + model_decoder_layers_26_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1126] + gv3303: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2436: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv3303, R.dtype("float16")) + _2435: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_26_encoder_attn_q_proj_weight4, alloc2435, model_decoder_layers_26_encoder_attn_q_proj_bias4, alloc2436) + R.vm.kill_object(alloc2435) + R.vm.kill_object(model_decoder_layers_26_encoder_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_26_encoder_attn_q_proj_bias4) + gv3304: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1299: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2436, gv3304, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2436) + gv3305: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1300: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1299, gv3305, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape1299) + gv3306: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc2437: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3306, R.dtype("float16")) + _2436: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), reshape1300, alloc2437) + R.vm.kill_object(reshape1300) + gv3307: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1301: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2437, gv3307, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2437) + gv3308: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1302: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1301, gv3308, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1301) + model_decoder_layers_26_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1127] + model_decoder_layers_26_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1128] + gv3309: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2438: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv3309, R.dtype("float16")) + _2437: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_26_encoder_attn_out_proj_weight4, reshape1302, model_decoder_layers_26_encoder_attn_out_proj_bias4, alloc2438) + R.vm.kill_object(reshape1302) + R.vm.kill_object(model_decoder_layers_26_encoder_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_26_encoder_attn_out_proj_bias4) + gv3310: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2439: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv3310, R.dtype("float16")) + cls.add5(alloc2434, alloc2438, alloc2439) + R.vm.kill_object(alloc2434) + R.vm.kill_object(alloc2438) + model_decoder_layers_26_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1135] + model_decoder_layers_26_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1136] + gv3311: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2440: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3311, R.dtype("float16")) + cls.layer_norm2(alloc2439, model_decoder_layers_26_final_layer_norm_weight4, model_decoder_layers_26_final_layer_norm_bias4, alloc2440) + R.vm.kill_object(model_decoder_layers_26_final_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_26_final_layer_norm_bias4) + model_decoder_layers_26_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1131] + model_decoder_layers_26_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1132] + gv3312: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc2441: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv3312, R.dtype("float16")) + _2440: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_26_fc1_weight4, alloc2440, model_decoder_layers_26_fc1_bias4, alloc2441) + R.vm.kill_object(alloc2440) + R.vm.kill_object(model_decoder_layers_26_fc1_weight4) + R.vm.kill_object(model_decoder_layers_26_fc1_bias4) + model_decoder_layers_26_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1133] + model_decoder_layers_26_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1134] + gv3313: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2442: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3313, R.dtype("float16")) + _2441: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_26_fc2_weight4, alloc2441, model_decoder_layers_26_fc2_bias4, alloc2442) + R.vm.kill_object(alloc2441) + R.vm.kill_object(model_decoder_layers_26_fc2_weight4) + R.vm.kill_object(model_decoder_layers_26_fc2_bias4) + gv3314: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2443: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv3314, R.dtype("float16")) + cls.add5(alloc2439, alloc2442, alloc2443) + R.vm.kill_object(alloc2439) + R.vm.kill_object(alloc2442) + model_decoder_layers_27_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1144] + model_decoder_layers_27_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1145] + gv3315: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2444: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3315, R.dtype("float16")) + cls.layer_norm2(alloc2443, model_decoder_layers_27_self_attn_layer_norm_weight4, model_decoder_layers_27_self_attn_layer_norm_bias4, alloc2444) + R.vm.kill_object(model_decoder_layers_27_self_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_27_self_attn_layer_norm_bias4) + model_decoder_layers_27_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1140] + model_decoder_layers_27_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1141] + gv3316: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2445: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv3316, R.dtype("float16")) + _2444: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_27_self_attn_q_proj_weight4, alloc2444, model_decoder_layers_27_self_attn_q_proj_bias4, alloc2445) + R.vm.kill_object(model_decoder_layers_27_self_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_27_self_attn_q_proj_bias4) + gv3317: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1303: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2445, gv3317, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2445) + model_decoder_layers_27_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1137] + gv3318: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2446: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3318, R.dtype("float16")) + _2445: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_27_self_attn_k_proj_weight4, alloc2444, alloc2446) + R.vm.kill_object(model_decoder_layers_27_self_attn_k_proj_weight4) + gv3319: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1304: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2446, gv3319, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2446) + model_decoder_layers_27_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1138] + model_decoder_layers_27_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1139] + gv3320: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2447: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv3320, R.dtype("float16")) + _2446: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_27_self_attn_v_proj_weight4, alloc2444, model_decoder_layers_27_self_attn_v_proj_bias4, alloc2447) + R.vm.kill_object(alloc2444) + R.vm.kill_object(model_decoder_layers_27_self_attn_v_proj_weight4) + R.vm.kill_object(model_decoder_layers_27_self_attn_v_proj_bias4) + gv3321: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1305: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2447, gv3321, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2447) + gv3322: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc2448: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3322, R.dtype("float16")) + cls.concatenate1(reshape1303, reshape1304, reshape1305, alloc2448) + R.vm.kill_object(reshape1303) + R.vm.kill_object(reshape1304) + R.vm.kill_object(reshape1305) + gv3323: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1306: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2448, gv3323, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc2448) + gv3324: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc2449: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv3324, R.dtype("float16")) + _2448: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), reshape1306, alloc2449) + R.vm.kill_object(reshape1306) + gv3325: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1307: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2449, gv3325, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2449) + gv3326: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1308: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1307, gv3326, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1307) + model_decoder_layers_27_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1142] + model_decoder_layers_27_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1143] + gv3327: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2450: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3327, R.dtype("float16")) + _2449: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_27_self_attn_out_proj_weight4, reshape1308, model_decoder_layers_27_self_attn_out_proj_bias4, alloc2450) + R.vm.kill_object(reshape1308) + R.vm.kill_object(model_decoder_layers_27_self_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_27_self_attn_out_proj_bias4) + gv3328: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2451: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3328, R.dtype("float16")) + cls.add5(alloc2443, alloc2450, alloc2451) + R.vm.kill_object(alloc2443) + R.vm.kill_object(alloc2450) + model_decoder_layers_27_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1153] + model_decoder_layers_27_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1154] + gv3329: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2452: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv3329, R.dtype("float16")) + cls.layer_norm2(alloc2451, model_decoder_layers_27_encoder_attn_layer_norm_weight4, model_decoder_layers_27_encoder_attn_layer_norm_bias4, alloc2452) + R.vm.kill_object(model_decoder_layers_27_encoder_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_27_encoder_attn_layer_norm_bias4) + model_decoder_layers_27_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1149] + model_decoder_layers_27_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1150] + gv3330: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2453: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv3330, R.dtype("float16")) + _2452: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_27_encoder_attn_q_proj_weight4, alloc2452, model_decoder_layers_27_encoder_attn_q_proj_bias4, alloc2453) + R.vm.kill_object(alloc2452) + R.vm.kill_object(model_decoder_layers_27_encoder_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_27_encoder_attn_q_proj_bias4) + gv3331: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1309: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2453, gv3331, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2453) + gv3332: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1310: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1309, gv3332, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape1309) + gv3333: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc2454: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3333, R.dtype("float16")) + _2453: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), reshape1310, alloc2454) + R.vm.kill_object(reshape1310) + gv3334: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1311: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2454, gv3334, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2454) + gv3335: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1312: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1311, gv3335, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1311) + model_decoder_layers_27_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1151] + model_decoder_layers_27_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1152] + gv3336: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2455: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv3336, R.dtype("float16")) + _2454: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_27_encoder_attn_out_proj_weight4, reshape1312, model_decoder_layers_27_encoder_attn_out_proj_bias4, alloc2455) + R.vm.kill_object(reshape1312) + R.vm.kill_object(model_decoder_layers_27_encoder_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_27_encoder_attn_out_proj_bias4) + gv3337: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2456: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv3337, R.dtype("float16")) + cls.add5(alloc2451, alloc2455, alloc2456) + R.vm.kill_object(alloc2451) + R.vm.kill_object(alloc2455) + model_decoder_layers_27_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1159] + model_decoder_layers_27_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1160] + gv3338: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2457: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3338, R.dtype("float16")) + cls.layer_norm2(alloc2456, model_decoder_layers_27_final_layer_norm_weight4, model_decoder_layers_27_final_layer_norm_bias4, alloc2457) + R.vm.kill_object(model_decoder_layers_27_final_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_27_final_layer_norm_bias4) + model_decoder_layers_27_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1155] + model_decoder_layers_27_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1156] + gv3339: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc2458: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv3339, R.dtype("float16")) + _2457: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_27_fc1_weight4, alloc2457, model_decoder_layers_27_fc1_bias4, alloc2458) + R.vm.kill_object(alloc2457) + R.vm.kill_object(model_decoder_layers_27_fc1_weight4) + R.vm.kill_object(model_decoder_layers_27_fc1_bias4) + model_decoder_layers_27_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1157] + model_decoder_layers_27_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1158] + gv3340: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2459: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3340, R.dtype("float16")) + _2458: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_27_fc2_weight4, alloc2458, model_decoder_layers_27_fc2_bias4, alloc2459) + R.vm.kill_object(alloc2458) + R.vm.kill_object(model_decoder_layers_27_fc2_weight4) + R.vm.kill_object(model_decoder_layers_27_fc2_bias4) + gv3341: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2460: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv3341, R.dtype("float16")) + cls.add5(alloc2456, alloc2459, alloc2460) + R.vm.kill_object(alloc2456) + R.vm.kill_object(alloc2459) + model_decoder_layers_28_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1168] + model_decoder_layers_28_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1169] + gv3342: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2461: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3342, R.dtype("float16")) + cls.layer_norm2(alloc2460, model_decoder_layers_28_self_attn_layer_norm_weight4, model_decoder_layers_28_self_attn_layer_norm_bias4, alloc2461) + R.vm.kill_object(model_decoder_layers_28_self_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_28_self_attn_layer_norm_bias4) + model_decoder_layers_28_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1164] + model_decoder_layers_28_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1165] + gv3343: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2462: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv3343, R.dtype("float16")) + _2461: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_28_self_attn_q_proj_weight4, alloc2461, model_decoder_layers_28_self_attn_q_proj_bias4, alloc2462) + R.vm.kill_object(model_decoder_layers_28_self_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_28_self_attn_q_proj_bias4) + gv3344: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1313: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2462, gv3344, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2462) + model_decoder_layers_28_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1161] + gv3345: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2463: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3345, R.dtype("float16")) + _2462: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_28_self_attn_k_proj_weight4, alloc2461, alloc2463) + R.vm.kill_object(model_decoder_layers_28_self_attn_k_proj_weight4) + gv3346: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1314: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2463, gv3346, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2463) + model_decoder_layers_28_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1162] + model_decoder_layers_28_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1163] + gv3347: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2464: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv3347, R.dtype("float16")) + _2463: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_28_self_attn_v_proj_weight4, alloc2461, model_decoder_layers_28_self_attn_v_proj_bias4, alloc2464) + R.vm.kill_object(alloc2461) + R.vm.kill_object(model_decoder_layers_28_self_attn_v_proj_weight4) + R.vm.kill_object(model_decoder_layers_28_self_attn_v_proj_bias4) + gv3348: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1315: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2464, gv3348, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2464) + gv3349: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc2465: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3349, R.dtype("float16")) + cls.concatenate1(reshape1313, reshape1314, reshape1315, alloc2465) + R.vm.kill_object(reshape1313) + R.vm.kill_object(reshape1314) + R.vm.kill_object(reshape1315) + gv3350: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1316: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2465, gv3350, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc2465) + gv3351: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc2466: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv3351, R.dtype("float16")) + _2465: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), reshape1316, alloc2466) + R.vm.kill_object(reshape1316) + gv3352: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1317: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2466, gv3352, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2466) + gv3353: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1318: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1317, gv3353, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1317) + model_decoder_layers_28_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1166] + model_decoder_layers_28_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1167] + gv3354: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2467: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3354, R.dtype("float16")) + _2466: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_28_self_attn_out_proj_weight4, reshape1318, model_decoder_layers_28_self_attn_out_proj_bias4, alloc2467) + R.vm.kill_object(reshape1318) + R.vm.kill_object(model_decoder_layers_28_self_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_28_self_attn_out_proj_bias4) + gv3355: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2468: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3355, R.dtype("float16")) + cls.add5(alloc2460, alloc2467, alloc2468) + R.vm.kill_object(alloc2460) + R.vm.kill_object(alloc2467) + model_decoder_layers_28_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1177] + model_decoder_layers_28_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1178] + gv3356: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2469: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv3356, R.dtype("float16")) + cls.layer_norm2(alloc2468, model_decoder_layers_28_encoder_attn_layer_norm_weight4, model_decoder_layers_28_encoder_attn_layer_norm_bias4, alloc2469) + R.vm.kill_object(model_decoder_layers_28_encoder_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_28_encoder_attn_layer_norm_bias4) + model_decoder_layers_28_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1173] + model_decoder_layers_28_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1174] + gv3357: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2470: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv3357, R.dtype("float16")) + _2469: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_28_encoder_attn_q_proj_weight4, alloc2469, model_decoder_layers_28_encoder_attn_q_proj_bias4, alloc2470) + R.vm.kill_object(alloc2469) + R.vm.kill_object(model_decoder_layers_28_encoder_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_28_encoder_attn_q_proj_bias4) + gv3358: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1319: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2470, gv3358, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2470) + gv3359: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1320: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1319, gv3359, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape1319) + gv3360: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc2471: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3360, R.dtype("float16")) + _2470: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), reshape1320, alloc2471) + R.vm.kill_object(reshape1320) + gv3361: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1321: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2471, gv3361, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2471) + gv3362: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1322: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1321, gv3362, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1321) + model_decoder_layers_28_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1175] + model_decoder_layers_28_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1176] + gv3363: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2472: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv3363, R.dtype("float16")) + _2471: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_28_encoder_attn_out_proj_weight4, reshape1322, model_decoder_layers_28_encoder_attn_out_proj_bias4, alloc2472) + R.vm.kill_object(reshape1322) + R.vm.kill_object(model_decoder_layers_28_encoder_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_28_encoder_attn_out_proj_bias4) + gv3364: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2473: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv3364, R.dtype("float16")) + cls.add5(alloc2468, alloc2472, alloc2473) + R.vm.kill_object(alloc2468) + R.vm.kill_object(alloc2472) + model_decoder_layers_28_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1183] + model_decoder_layers_28_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1184] + gv3365: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2474: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3365, R.dtype("float16")) + cls.layer_norm2(alloc2473, model_decoder_layers_28_final_layer_norm_weight4, model_decoder_layers_28_final_layer_norm_bias4, alloc2474) + R.vm.kill_object(model_decoder_layers_28_final_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_28_final_layer_norm_bias4) + model_decoder_layers_28_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1179] + model_decoder_layers_28_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1180] + gv3366: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc2475: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv3366, R.dtype("float16")) + _2474: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_28_fc1_weight4, alloc2474, model_decoder_layers_28_fc1_bias4, alloc2475) + R.vm.kill_object(alloc2474) + R.vm.kill_object(model_decoder_layers_28_fc1_weight4) + R.vm.kill_object(model_decoder_layers_28_fc1_bias4) + model_decoder_layers_28_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1181] + model_decoder_layers_28_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1182] + gv3367: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2476: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3367, R.dtype("float16")) + _2475: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_28_fc2_weight4, alloc2475, model_decoder_layers_28_fc2_bias4, alloc2476) + R.vm.kill_object(alloc2475) + R.vm.kill_object(model_decoder_layers_28_fc2_weight4) + R.vm.kill_object(model_decoder_layers_28_fc2_bias4) + gv3368: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2477: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv3368, R.dtype("float16")) + cls.add5(alloc2473, alloc2476, alloc2477) + R.vm.kill_object(alloc2473) + R.vm.kill_object(alloc2476) + model_decoder_layers_29_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1192] + model_decoder_layers_29_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1193] + gv3369: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2478: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3369, R.dtype("float16")) + cls.layer_norm2(alloc2477, model_decoder_layers_29_self_attn_layer_norm_weight4, model_decoder_layers_29_self_attn_layer_norm_bias4, alloc2478) + R.vm.kill_object(model_decoder_layers_29_self_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_29_self_attn_layer_norm_bias4) + model_decoder_layers_29_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1188] + model_decoder_layers_29_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1189] + gv3370: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2479: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv3370, R.dtype("float16")) + _2478: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_29_self_attn_q_proj_weight4, alloc2478, model_decoder_layers_29_self_attn_q_proj_bias4, alloc2479) + R.vm.kill_object(model_decoder_layers_29_self_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_29_self_attn_q_proj_bias4) + gv3371: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1323: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2479, gv3371, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2479) + model_decoder_layers_29_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1185] + gv3372: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2480: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3372, R.dtype("float16")) + _2479: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_29_self_attn_k_proj_weight4, alloc2478, alloc2480) + R.vm.kill_object(model_decoder_layers_29_self_attn_k_proj_weight4) + gv3373: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1324: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2480, gv3373, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2480) + model_decoder_layers_29_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1186] + model_decoder_layers_29_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1187] + gv3374: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2481: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv3374, R.dtype("float16")) + _2480: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_29_self_attn_v_proj_weight4, alloc2478, model_decoder_layers_29_self_attn_v_proj_bias4, alloc2481) + R.vm.kill_object(alloc2478) + R.vm.kill_object(model_decoder_layers_29_self_attn_v_proj_weight4) + R.vm.kill_object(model_decoder_layers_29_self_attn_v_proj_bias4) + gv3375: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1325: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2481, gv3375, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2481) + gv3376: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc2482: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3376, R.dtype("float16")) + cls.concatenate1(reshape1323, reshape1324, reshape1325, alloc2482) + R.vm.kill_object(reshape1323) + R.vm.kill_object(reshape1324) + R.vm.kill_object(reshape1325) + gv3377: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1326: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2482, gv3377, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc2482) + gv3378: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc2483: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv3378, R.dtype("float16")) + _2482: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), reshape1326, alloc2483) + R.vm.kill_object(reshape1326) + gv3379: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1327: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2483, gv3379, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2483) + gv3380: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1328: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1327, gv3380, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1327) + model_decoder_layers_29_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1190] + model_decoder_layers_29_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1191] + gv3381: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2484: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3381, R.dtype("float16")) + _2483: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_29_self_attn_out_proj_weight4, reshape1328, model_decoder_layers_29_self_attn_out_proj_bias4, alloc2484) + R.vm.kill_object(reshape1328) + R.vm.kill_object(model_decoder_layers_29_self_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_29_self_attn_out_proj_bias4) + gv3382: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2485: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3382, R.dtype("float16")) + cls.add5(alloc2477, alloc2484, alloc2485) + R.vm.kill_object(alloc2477) + R.vm.kill_object(alloc2484) + model_decoder_layers_29_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1201] + model_decoder_layers_29_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1202] + gv3383: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2486: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv3383, R.dtype("float16")) + cls.layer_norm2(alloc2485, model_decoder_layers_29_encoder_attn_layer_norm_weight4, model_decoder_layers_29_encoder_attn_layer_norm_bias4, alloc2486) + R.vm.kill_object(model_decoder_layers_29_encoder_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_29_encoder_attn_layer_norm_bias4) + model_decoder_layers_29_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1197] + model_decoder_layers_29_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1198] + gv3384: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2487: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv3384, R.dtype("float16")) + _2486: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_29_encoder_attn_q_proj_weight4, alloc2486, model_decoder_layers_29_encoder_attn_q_proj_bias4, alloc2487) + R.vm.kill_object(alloc2486) + R.vm.kill_object(model_decoder_layers_29_encoder_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_29_encoder_attn_q_proj_bias4) + gv3385: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1329: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2487, gv3385, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2487) + gv3386: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1330: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1329, gv3386, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape1329) + gv3387: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc2488: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3387, R.dtype("float16")) + _2487: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), reshape1330, alloc2488) + R.vm.kill_object(reshape1330) + gv3388: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1331: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2488, gv3388, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2488) + gv3389: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1332: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1331, gv3389, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1331) + model_decoder_layers_29_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1199] + model_decoder_layers_29_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1200] + gv3390: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2489: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv3390, R.dtype("float16")) + _2488: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_29_encoder_attn_out_proj_weight4, reshape1332, model_decoder_layers_29_encoder_attn_out_proj_bias4, alloc2489) + R.vm.kill_object(reshape1332) + R.vm.kill_object(model_decoder_layers_29_encoder_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_29_encoder_attn_out_proj_bias4) + gv3391: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2490: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv3391, R.dtype("float16")) + cls.add5(alloc2485, alloc2489, alloc2490) + R.vm.kill_object(alloc2485) + R.vm.kill_object(alloc2489) + model_decoder_layers_29_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1207] + model_decoder_layers_29_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1208] + gv3392: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2491: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3392, R.dtype("float16")) + cls.layer_norm2(alloc2490, model_decoder_layers_29_final_layer_norm_weight4, model_decoder_layers_29_final_layer_norm_bias4, alloc2491) + R.vm.kill_object(model_decoder_layers_29_final_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_29_final_layer_norm_bias4) + model_decoder_layers_29_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1203] + model_decoder_layers_29_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1204] + gv3393: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc2492: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv3393, R.dtype("float16")) + _2491: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_29_fc1_weight4, alloc2491, model_decoder_layers_29_fc1_bias4, alloc2492) + R.vm.kill_object(alloc2491) + R.vm.kill_object(model_decoder_layers_29_fc1_weight4) + R.vm.kill_object(model_decoder_layers_29_fc1_bias4) + model_decoder_layers_29_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1205] + model_decoder_layers_29_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1206] + gv3394: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2493: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3394, R.dtype("float16")) + _2492: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_29_fc2_weight4, alloc2492, model_decoder_layers_29_fc2_bias4, alloc2493) + R.vm.kill_object(alloc2492) + R.vm.kill_object(model_decoder_layers_29_fc2_weight4) + R.vm.kill_object(model_decoder_layers_29_fc2_bias4) + gv3395: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2494: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv3395, R.dtype("float16")) + cls.add5(alloc2490, alloc2493, alloc2494) + R.vm.kill_object(alloc2490) + R.vm.kill_object(alloc2493) + model_decoder_layers_30_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1216] + model_decoder_layers_30_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1217] + gv3396: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2495: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3396, R.dtype("float16")) + cls.layer_norm2(alloc2494, model_decoder_layers_30_self_attn_layer_norm_weight4, model_decoder_layers_30_self_attn_layer_norm_bias4, alloc2495) + R.vm.kill_object(model_decoder_layers_30_self_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_30_self_attn_layer_norm_bias4) + model_decoder_layers_30_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1212] + model_decoder_layers_30_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1213] + gv3397: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2496: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv3397, R.dtype("float16")) + _2495: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_30_self_attn_q_proj_weight4, alloc2495, model_decoder_layers_30_self_attn_q_proj_bias4, alloc2496) + R.vm.kill_object(model_decoder_layers_30_self_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_30_self_attn_q_proj_bias4) + gv3398: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1333: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2496, gv3398, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2496) + model_decoder_layers_30_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1209] + gv3399: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2497: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3399, R.dtype("float16")) + _2496: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_30_self_attn_k_proj_weight4, alloc2495, alloc2497) + R.vm.kill_object(model_decoder_layers_30_self_attn_k_proj_weight4) + gv3400: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1334: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2497, gv3400, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2497) + model_decoder_layers_30_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1210] + model_decoder_layers_30_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1211] + gv3401: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2498: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv3401, R.dtype("float16")) + _2497: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_30_self_attn_v_proj_weight4, alloc2495, model_decoder_layers_30_self_attn_v_proj_bias4, alloc2498) + R.vm.kill_object(alloc2495) + R.vm.kill_object(model_decoder_layers_30_self_attn_v_proj_weight4) + R.vm.kill_object(model_decoder_layers_30_self_attn_v_proj_bias4) + gv3402: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1335: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2498, gv3402, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2498) + gv3403: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc2499: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3403, R.dtype("float16")) + cls.concatenate1(reshape1333, reshape1334, reshape1335, alloc2499) + R.vm.kill_object(reshape1333) + R.vm.kill_object(reshape1334) + R.vm.kill_object(reshape1335) + gv3404: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1336: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2499, gv3404, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc2499) + gv3405: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc2500: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv3405, R.dtype("float16")) + _2499: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), reshape1336, alloc2500) + R.vm.kill_object(reshape1336) + gv3406: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1337: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2500, gv3406, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2500) + gv3407: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1338: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1337, gv3407, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1337) + model_decoder_layers_30_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1214] + model_decoder_layers_30_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1215] + gv3408: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2501: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3408, R.dtype("float16")) + _2500: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_30_self_attn_out_proj_weight4, reshape1338, model_decoder_layers_30_self_attn_out_proj_bias4, alloc2501) + R.vm.kill_object(reshape1338) + R.vm.kill_object(model_decoder_layers_30_self_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_30_self_attn_out_proj_bias4) + gv3409: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2502: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3409, R.dtype("float16")) + cls.add5(alloc2494, alloc2501, alloc2502) + R.vm.kill_object(alloc2494) + R.vm.kill_object(alloc2501) + model_decoder_layers_30_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1225] + model_decoder_layers_30_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1226] + gv3410: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2503: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv3410, R.dtype("float16")) + cls.layer_norm2(alloc2502, model_decoder_layers_30_encoder_attn_layer_norm_weight4, model_decoder_layers_30_encoder_attn_layer_norm_bias4, alloc2503) + R.vm.kill_object(model_decoder_layers_30_encoder_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_30_encoder_attn_layer_norm_bias4) + model_decoder_layers_30_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1221] + model_decoder_layers_30_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1222] + gv3411: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2504: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv3411, R.dtype("float16")) + _2503: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_30_encoder_attn_q_proj_weight4, alloc2503, model_decoder_layers_30_encoder_attn_q_proj_bias4, alloc2504) + R.vm.kill_object(alloc2503) + R.vm.kill_object(model_decoder_layers_30_encoder_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_30_encoder_attn_q_proj_bias4) + gv3412: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1339: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2504, gv3412, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2504) + gv3413: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1340: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1339, gv3413, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape1339) + gv3414: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc2505: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3414, R.dtype("float16")) + _2504: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), reshape1340, alloc2505) + R.vm.kill_object(reshape1340) + gv3415: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1341: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2505, gv3415, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2505) + gv3416: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1342: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1341, gv3416, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1341) + model_decoder_layers_30_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1223] + model_decoder_layers_30_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1224] + gv3417: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2506: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv3417, R.dtype("float16")) + _2505: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_30_encoder_attn_out_proj_weight4, reshape1342, model_decoder_layers_30_encoder_attn_out_proj_bias4, alloc2506) + R.vm.kill_object(reshape1342) + R.vm.kill_object(model_decoder_layers_30_encoder_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_30_encoder_attn_out_proj_bias4) + gv3418: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2507: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv3418, R.dtype("float16")) + cls.add5(alloc2502, alloc2506, alloc2507) + R.vm.kill_object(alloc2502) + R.vm.kill_object(alloc2506) + model_decoder_layers_30_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1231] + model_decoder_layers_30_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1232] + gv3419: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2508: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3419, R.dtype("float16")) + cls.layer_norm2(alloc2507, model_decoder_layers_30_final_layer_norm_weight4, model_decoder_layers_30_final_layer_norm_bias4, alloc2508) + R.vm.kill_object(model_decoder_layers_30_final_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_30_final_layer_norm_bias4) + model_decoder_layers_30_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1227] + model_decoder_layers_30_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1228] + gv3420: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc2509: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv3420, R.dtype("float16")) + _2508: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_30_fc1_weight4, alloc2508, model_decoder_layers_30_fc1_bias4, alloc2509) + R.vm.kill_object(alloc2508) + R.vm.kill_object(model_decoder_layers_30_fc1_weight4) + R.vm.kill_object(model_decoder_layers_30_fc1_bias4) + model_decoder_layers_30_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1229] + model_decoder_layers_30_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1230] + gv3421: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2510: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3421, R.dtype("float16")) + _2509: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_30_fc2_weight4, alloc2509, model_decoder_layers_30_fc2_bias4, alloc2510) + R.vm.kill_object(alloc2509) + R.vm.kill_object(model_decoder_layers_30_fc2_weight4) + R.vm.kill_object(model_decoder_layers_30_fc2_bias4) + gv3422: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2511: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv3422, R.dtype("float16")) + cls.add5(alloc2507, alloc2510, alloc2511) + R.vm.kill_object(alloc2507) + R.vm.kill_object(alloc2510) + model_decoder_layers_31_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1240] + model_decoder_layers_31_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1241] + gv3423: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2512: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3423, R.dtype("float16")) + cls.layer_norm2(alloc2511, model_decoder_layers_31_self_attn_layer_norm_weight4, model_decoder_layers_31_self_attn_layer_norm_bias4, alloc2512) + R.vm.kill_object(model_decoder_layers_31_self_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_31_self_attn_layer_norm_bias4) + model_decoder_layers_31_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1236] + model_decoder_layers_31_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1237] + gv3424: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2513: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv3424, R.dtype("float16")) + _2512: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_31_self_attn_q_proj_weight4, alloc2512, model_decoder_layers_31_self_attn_q_proj_bias4, alloc2513) + R.vm.kill_object(model_decoder_layers_31_self_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_31_self_attn_q_proj_bias4) + gv3425: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1343: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2513, gv3425, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2513) + model_decoder_layers_31_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1233] + gv3426: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2514: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3426, R.dtype("float16")) + _2513: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul1_cublas", model_decoder_layers_31_self_attn_k_proj_weight4, alloc2512, alloc2514) + R.vm.kill_object(model_decoder_layers_31_self_attn_k_proj_weight4) + gv3427: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1344: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2514, gv3427, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2514) + model_decoder_layers_31_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1234] + model_decoder_layers_31_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1235] + gv3428: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2515: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv3428, R.dtype("float16")) + _2514: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_31_self_attn_v_proj_weight4, alloc2512, model_decoder_layers_31_self_attn_v_proj_bias4, alloc2515) + R.vm.kill_object(alloc2512) + R.vm.kill_object(model_decoder_layers_31_self_attn_v_proj_weight4) + R.vm.kill_object(model_decoder_layers_31_self_attn_v_proj_bias4) + gv3429: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1345: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2515, gv3429, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2515) + gv3430: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + alloc2516: R.Tensor(dtype="float16", ndim=4) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3430, R.dtype("float16")) + cls.concatenate1(reshape1343, reshape1344, reshape1345, alloc2516) + R.vm.kill_object(reshape1343) + R.vm.kill_object(reshape1344) + R.vm.kill_object(reshape1345) + gv3431: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(60), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1346: R.Tensor((seq_len, 60, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2516, gv3431, sinfo_args=(R.Tensor((seq_len, 60, 64), dtype="float16"),)) + R.vm.kill_object(alloc2516) + gv3432: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc2517: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv3432, R.dtype("float16")) + _2516: R.Object = R.call_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), reshape1346, alloc2517) + R.vm.kill_object(reshape1346) + gv3433: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1347: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2517, gv3433, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2517) + gv3434: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1348: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1347, gv3434, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1347) + model_decoder_layers_31_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1238] + model_decoder_layers_31_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1239] + gv3435: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2518: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3435, R.dtype("float16")) + _2517: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_31_self_attn_out_proj_weight4, reshape1348, model_decoder_layers_31_self_attn_out_proj_bias4, alloc2518) + R.vm.kill_object(reshape1348) + R.vm.kill_object(model_decoder_layers_31_self_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_31_self_attn_out_proj_bias4) + gv3436: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2519: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3436, R.dtype("float16")) + cls.add5(alloc2511, alloc2518, alloc2519) + R.vm.kill_object(alloc2511) + R.vm.kill_object(alloc2518) + model_decoder_layers_31_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1249] + model_decoder_layers_31_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1250] + gv3437: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2520: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv3437, R.dtype("float16")) + cls.layer_norm2(alloc2519, model_decoder_layers_31_encoder_attn_layer_norm_weight4, model_decoder_layers_31_encoder_attn_layer_norm_bias4, alloc2520) + R.vm.kill_object(model_decoder_layers_31_encoder_attn_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_31_encoder_attn_layer_norm_bias4) + model_decoder_layers_31_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1245] + model_decoder_layers_31_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1246] + gv3438: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2521: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv3438, R.dtype("float16")) + _2520: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_31_encoder_attn_q_proj_weight4, alloc2520, model_decoder_layers_31_encoder_attn_q_proj_bias4, alloc2521) + R.vm.kill_object(alloc2520) + R.vm.kill_object(model_decoder_layers_31_encoder_attn_q_proj_weight4) + R.vm.kill_object(model_decoder_layers_31_encoder_attn_q_proj_bias4) + gv3439: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1349: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2521, gv3439, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2521) + gv3440: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + reshape1350: R.Tensor((seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1349, gv3440, sinfo_args=(R.Tensor((seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(reshape1349) + gv3441: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=3),)) + alloc2522: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3441, R.dtype("float16")) + _2521: R.Object = R.call_packed("vm.builtin.attention_kv_cache_cross_attention", paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), reshape1350, alloc2522) + R.vm.kill_object(reshape1350) + gv3442: R.Shape(ndim=4) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(4), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(20), R.prim_value(0), R.prim_value(64), sinfo_args=(R.Shape(ndim=4),)) + reshape1351: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.call_packed("vm.builtin.reshape", alloc2522, gv3442, sinfo_args=(R.Tensor((1, seq_len, 20, 64), dtype="float16"),)) + R.vm.kill_object(alloc2522) + gv3443: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + reshape1352: R.Tensor((1, seq_len, 1280), dtype="float16") = R.call_packed("vm.builtin.reshape", reshape1351, gv3443, sinfo_args=(R.Tensor((1, seq_len, 1280), dtype="float16"),)) + R.vm.kill_object(reshape1351) + model_decoder_layers_31_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1247] + model_decoder_layers_31_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1248] + gv3444: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2523: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv3444, R.dtype("float16")) + _2522: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", model_decoder_layers_31_encoder_attn_out_proj_weight4, reshape1352, model_decoder_layers_31_encoder_attn_out_proj_bias4, alloc2523) + R.vm.kill_object(reshape1352) + R.vm.kill_object(model_decoder_layers_31_encoder_attn_out_proj_weight4) + R.vm.kill_object(model_decoder_layers_31_encoder_attn_out_proj_bias4) + gv3445: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2524: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage39, R.prim_value(0), gv3445, R.dtype("float16")) + R.vm.kill_object(storage39) + cls.add5(alloc2519, alloc2523, alloc2524) + R.vm.kill_object(alloc2519) + R.vm.kill_object(alloc2523) + model_decoder_layers_31_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1255] + model_decoder_layers_31_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1256] + gv3446: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2525: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3446, R.dtype("float16")) + cls.layer_norm2(alloc2524, model_decoder_layers_31_final_layer_norm_weight4, model_decoder_layers_31_final_layer_norm_bias4, alloc2525) + R.vm.kill_object(model_decoder_layers_31_final_layer_norm_weight4) + R.vm.kill_object(model_decoder_layers_31_final_layer_norm_bias4) + model_decoder_layers_31_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1251] + model_decoder_layers_31_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1252] + gv3447: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(5120), sinfo_args=(R.Shape(ndim=3),)) + alloc2526: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage37, R.prim_value(0), gv3447, R.dtype("float16")) + R.vm.kill_object(storage37) + _2525: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", model_decoder_layers_31_fc1_weight4, alloc2525, model_decoder_layers_31_fc1_bias4, alloc2526) + R.vm.kill_object(alloc2525) + R.vm.kill_object(model_decoder_layers_31_fc1_weight4) + R.vm.kill_object(model_decoder_layers_31_fc1_bias4) + model_decoder_layers_31_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1253] + model_decoder_layers_31_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1254] + gv3448: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2527: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage38, R.prim_value(0), gv3448, R.dtype("float16")) + R.vm.kill_object(storage38) + _2526: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", model_decoder_layers_31_fc2_weight4, alloc2526, model_decoder_layers_31_fc2_bias4, alloc2527) + R.vm.kill_object(alloc2526) + R.vm.kill_object(model_decoder_layers_31_fc2_weight4) + R.vm.kill_object(model_decoder_layers_31_fc2_bias4) + gv3449: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2528: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage40, R.prim_value(0), gv3449, R.dtype("float16")) + R.vm.kill_object(storage40) + cls.add5(alloc2524, alloc2527, alloc2528) + R.vm.kill_object(alloc2524) + R.vm.kill_object(alloc2527) + model_decoder_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1257] + model_decoder_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1258] + gv3450: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1280), sinfo_args=(R.Shape(ndim=3),)) + alloc2529: R.Tensor(dtype="float16", ndim=3) = R.vm.alloc_tensor(storage41, R.prim_value(0), gv3450, R.dtype("float16")) + R.vm.kill_object(storage41) + cls.layer_norm2(alloc2528, model_decoder_layer_norm_weight4, model_decoder_layer_norm_bias4, alloc2529) + R.vm.kill_object(alloc2528) + R.vm.kill_object(model_decoder_layer_norm_weight4) + R.vm.kill_object(model_decoder_layer_norm_bias4) + storage42: R.Object = R.vm.alloc_storage(R.shape([2560]), R.prim_value(0), R.dtype("uint8"), R.str("global")) + alloc2530: R.Tensor((1, 1, 1280), dtype="float16") = R.vm.alloc_tensor(storage42, R.prim_value(0), R.shape([1, 1, 1280]), R.dtype("float16")) + R.vm.kill_object(storage42) + cls.index(alloc2529, alloc2530) + R.vm.kill_object(alloc2529) + storage: R.Object = R.vm.alloc_storage(R.shape([207464]), R.prim_value(0), R.dtype("uint8"), R.str("global")) + alloc2531: R.Tensor((1, 1, 51866), dtype="float32") = R.vm.alloc_tensor(storage, R.prim_value(0), R.shape([1, 1, 51866]), R.dtype("float32")) + R.vm.kill_object(storage) + _2530: R.Object = R.call_packed("fused_relax_permute_dims_relax_matmul2_cublas", model_decoder_embed_tokens_weight4, alloc2530, alloc2531) + R.vm.kill_object(model_decoder_embed_tokens_weight4) + R.vm.kill_object(alloc2530) + return alloc2531 + + @R.function + def renormalize_by_top_p(probs: R.Tensor(("batch_size", "vocab_size"), dtype="float32"), top_p: R.Tensor(("batch_size",), dtype="float32"), init_pivots: R.Tensor(("batch_size", 3), dtype="float32")) -> R.Tensor(("batch_size", "vocab_size"), dtype="float32"): + batch_size = T.int64() + vocab_size = T.int64() + R.func_attr({"relax.force_pure": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "num_positions": 48, "num_samples": 8}}) + cls = Module + shape_heap: R.Tensor(dtype="int64", ndim=1) = R.call_builtin_with_ctx("vm.builtin.alloc_shape_heap", (R.prim_value(3),), sinfo_args=(R.Tensor(dtype="int64", ndim=1),)) + R.call_packed("vm.builtin.check_tensor_info", probs, R.prim_value(2), R.dtype("float32"), R.str("ErrorContext(fn=renormalize_by_top_p, loc=param[0], param=probs, annotation=R.Tensor((batch_size, vocab_size), dtype=\"float32\")) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.check_tensor_info", top_p, R.prim_value(1), R.dtype("float32"), R.str("ErrorContext(fn=renormalize_by_top_p, loc=param[1], param=top_p, annotation=R.Tensor((batch_size,), dtype=\"float32\")) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.check_tensor_info", init_pivots, R.prim_value(2), R.dtype("float32"), R.str("ErrorContext(fn=renormalize_by_top_p, loc=param[2], param=init_pivots, annotation=R.Tensor((batch_size, 3), dtype=\"float32\")) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.match_shape", probs, shape_heap, R.prim_value(2), R.prim_value(1), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.str("ErrorContext(fn=renormalize_by_top_p, loc=param[0], param=probs, annotation=R.Tensor((batch_size, vocab_size), dtype=\"float32\")) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.match_shape", top_p, shape_heap, R.prim_value(1), R.prim_value(3), R.prim_value(0), R.str("ErrorContext(fn=renormalize_by_top_p, loc=param[1], param=top_p, annotation=R.Tensor((batch_size,), dtype=\"float32\")) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.match_shape", init_pivots, shape_heap, R.prim_value(2), R.prim_value(3), R.prim_value(0), R.prim_value(0), R.prim_value(3), R.str("ErrorContext(fn=renormalize_by_top_p, loc=param[2], param=init_pivots, annotation=R.Tensor((batch_size, 3), dtype=\"float32\")) "), sinfo_args=(R.Tuple,)) + cls.shape_func4(shape_heap) + storage43: R.Object = R.vm.alloc_storage(R.shape([32]), R.prim_value(0), R.dtype("uint8"), R.str("global")) + gv3451: R.Shape(ndim=1) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(1), R.prim_value(1), R.prim_value(0), sinfo_args=(R.Shape(ndim=1),)) + alloc2532: R.Tensor(dtype="float32", ndim=1) = R.vm.alloc_tensor(storage43, R.prim_value(0), gv3451, R.dtype("float32")) + R.vm.kill_object(storage43) + storage44: R.Object = R.vm.alloc_storage(R.shape([32]), R.prim_value(0), R.dtype("uint8"), R.str("global")) + gv3452: R.Shape(ndim=1) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(1), R.prim_value(1), R.prim_value(0), sinfo_args=(R.Shape(ndim=1),)) + alloc2533: R.Tensor(dtype="float32", ndim=1) = R.vm.alloc_tensor(storage44, R.prim_value(0), gv3452, R.dtype("float32")) + R.vm.kill_object(storage44) + cls.top_p_pivot_cutoff(probs, top_p, init_pivots, alloc2532, alloc2533) + lv6: R.Tuple(R.Tensor(dtype="float32", ndim=1), R.Tensor(dtype="float32", ndim=1)) = alloc2532, alloc2533 + gv3453: R.Shape(ndim=1) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(1), R.prim_value(1), R.prim_value(2), sinfo_args=(R.Shape(ndim=1),)) + storage45: R.Object = R.vm.alloc_storage(gv3453, R.prim_value(0), R.dtype("uint8"), R.str("global")) + gv3454: R.Shape(ndim=2) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(2), R.prim_value(1), R.prim_value(0), R.prim_value(1), R.prim_value(1), sinfo_args=(R.Shape(ndim=2),)) + alloc2534: R.Tensor(dtype="float32", ndim=2) = R.vm.alloc_tensor(storage45, R.prim_value(0), gv3454, R.dtype("float32")) + R.vm.kill_object(storage45) + cls.top_p_renorm_after_cutoff(probs, alloc2532, alloc2533, alloc2534) + R.vm.kill_object(alloc2532) + R.vm.kill_object(alloc2533) + R.call_packed("vm.builtin.match_shape", alloc2534, shape_heap, R.prim_value(2), R.prim_value(3), R.prim_value(0), R.prim_value(3), R.prim_value(1), R.str("ErrorContext(fn=renormalize_by_top_p, loc=return, annotation=R.Tensor((batch_size, vocab_size), dtype=\"float32\")) "), sinfo_args=(R.Tuple,)) + return alloc2534 + + @R.function + def sample_with_top_p(sorted_probs: R.Tensor(("batch_size", "vocab_size"), dtype="float32"), sorted_indices: R.Tensor(("batch_size", "vocab_size"), dtype="int32"), uniform_samples: R.Tensor(("num_samples",), dtype="float32"), sample_indices: R.Tensor(("num_samples",), dtype="int32"), top_p: R.Tensor(("batch_size",), dtype="float32")) -> R.Tensor(("num_samples",), dtype="int32"): + num_samples = T.int64() + batch_size = T.int64() + vocab_size = T.int64() + R.func_attr({"relax.force_pure": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "num_positions": 48, "num_samples": 8}}) + cls = Module + shape_heap: R.Tensor(dtype="int64", ndim=1) = R.call_builtin_with_ctx("vm.builtin.alloc_shape_heap", (R.prim_value(6),), sinfo_args=(R.Tensor(dtype="int64", ndim=1),)) + R.call_packed("vm.builtin.check_tensor_info", sorted_probs, R.prim_value(2), R.dtype("float32"), R.str("ErrorContext(fn=sample_with_top_p, loc=param[0], param=sorted_probs, annotation=R.Tensor((batch_size, vocab_size), dtype=\"float32\")) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.check_tensor_info", sorted_indices, R.prim_value(2), R.dtype("int32"), R.str("ErrorContext(fn=sample_with_top_p, loc=param[1], param=sorted_indices, annotation=R.Tensor((batch_size, vocab_size), dtype=\"int32\")) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.check_tensor_info", uniform_samples, R.prim_value(1), R.dtype("float32"), R.str("ErrorContext(fn=sample_with_top_p, loc=param[2], param=uniform_samples, annotation=R.Tensor((num_samples,), dtype=\"float32\")) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.check_tensor_info", sample_indices, R.prim_value(1), R.dtype("int32"), R.str("ErrorContext(fn=sample_with_top_p, loc=param[3], param=sample_indices, annotation=R.Tensor((num_samples,), dtype=\"int32\")) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.check_tensor_info", top_p, R.prim_value(1), R.dtype("float32"), R.str("ErrorContext(fn=sample_with_top_p, loc=param[4], param=top_p, annotation=R.Tensor((batch_size,), dtype=\"float32\")) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.match_shape", sorted_probs, shape_heap, R.prim_value(2), R.prim_value(1), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.str("ErrorContext(fn=sample_with_top_p, loc=param[0], param=sorted_probs, annotation=R.Tensor((batch_size, vocab_size), dtype=\"float32\")) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.match_shape", sorted_indices, shape_heap, R.prim_value(2), R.prim_value(3), R.prim_value(0), R.prim_value(3), R.prim_value(1), R.str("ErrorContext(fn=sample_with_top_p, loc=param[1], param=sorted_indices, annotation=R.Tensor((batch_size, vocab_size), dtype=\"int32\")) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.match_shape", uniform_samples, shape_heap, R.prim_value(1), R.prim_value(1), R.prim_value(2), R.str("ErrorContext(fn=sample_with_top_p, loc=param[2], param=uniform_samples, annotation=R.Tensor((num_samples,), dtype=\"float32\")) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.match_shape", sample_indices, shape_heap, R.prim_value(1), R.prim_value(3), R.prim_value(2), R.str("ErrorContext(fn=sample_with_top_p, loc=param[3], param=sample_indices, annotation=R.Tensor((num_samples,), dtype=\"int32\")) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.match_shape", top_p, shape_heap, R.prim_value(1), R.prim_value(3), R.prim_value(0), R.str("ErrorContext(fn=sample_with_top_p, loc=param[4], param=top_p, annotation=R.Tensor((batch_size,), dtype=\"float32\")) "), sinfo_args=(R.Tuple,)) + cls.shape_func3(shape_heap) + gv2568: R.Shape(ndim=2) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(2), R.prim_value(1), R.prim_value(2), R.prim_value(0), R.prim_value(1), sinfo_args=(R.Shape(ndim=2),)) + uniform_samples1: R.Tensor((num_samples, 1), dtype="float32") = R.call_packed("vm.builtin.reshape", uniform_samples, gv2568, sinfo_args=(R.Tensor((num_samples, 1), dtype="float32"),)) + gv2569: R.Shape(ndim=2) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(2), R.prim_value(1), R.prim_value(2), R.prim_value(0), R.prim_value(1), sinfo_args=(R.Shape(ndim=2),)) + sample_indices1: R.Tensor((num_samples, 1), dtype="int32") = R.call_packed("vm.builtin.reshape", sample_indices, gv2569, sinfo_args=(R.Tensor((num_samples, 1), dtype="int32"),)) + gv2570: R.Shape(ndim=2) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(2), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), sinfo_args=(R.Shape(ndim=2),)) + sample_indices2: R.Tensor((batch_size, 1), dtype="float32") = R.call_packed("vm.builtin.reshape", top_p, gv2570, sinfo_args=(R.Tensor((batch_size, 1), dtype="float32"),)) + storage33: R.Object = R.vm.alloc_storage(R.shape([32]), R.prim_value(0), R.dtype("uint8"), R.str("global")) + gv2571: R.Shape(ndim=2) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(2), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), sinfo_args=(R.Shape(ndim=2),)) + alloc1978: R.Tensor(dtype="int32", ndim=2) = R.vm.alloc_tensor(storage33, R.prim_value(0), gv2571, R.dtype("int32")) + gv2572: R.Shape(ndim=1) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(1), R.prim_value(1), R.prim_value(1), sinfo_args=(R.Shape(ndim=1),)) + R.call_packed("vm.builtin.call_tir_dyn", cls.full, alloc1978, gv2572, sinfo_args=(R.Tuple,)) + gv2573: R.Shape(ndim=1) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(1), R.prim_value(1), R.prim_value(3), sinfo_args=(R.Shape(ndim=1),)) + storage34: R.Object = R.vm.alloc_storage(gv2573, R.prim_value(0), R.dtype("uint8"), R.str("global")) + gv2574: R.Shape(ndim=1) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(1), R.prim_value(1), R.prim_value(4), sinfo_args=(R.Shape(ndim=1),)) + lv1: R.Tensor(dtype="uint8", ndim=1) = R.vm.alloc_tensor(storage34, R.prim_value(0), gv2574, R.dtype("uint8")) + R.vm.kill_object(storage34) + gv2575: R.Shape(ndim=1) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(1), R.prim_value(1), R.prim_value(5), sinfo_args=(R.Shape(ndim=1),)) + storage35: R.Object = R.vm.alloc_storage(gv2575, R.prim_value(0), R.dtype("uint8"), R.str("global")) + gv2576: R.Shape(ndim=2) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(2), R.prim_value(1), R.prim_value(0), R.prim_value(1), R.prim_value(1), sinfo_args=(R.Shape(ndim=2),)) + alloc1979: R.Tensor(dtype="float32", ndim=2) = R.vm.alloc_tensor(storage35, R.prim_value(0), gv2576, R.dtype("float32")) + R.vm.kill_object(storage35) + cls.cumsum(sorted_probs, lv1, alloc1979) + R.vm.kill_object(lv1) + storage36: R.Object = R.vm.alloc_storage(R.shape([32]), R.prim_value(0), R.dtype("uint8"), R.str("global")) + gv2577: R.Shape(ndim=2) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(2), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), sinfo_args=(R.Shape(ndim=2),)) + alloc1980: R.Tensor(dtype="float32", ndim=2) = R.vm.alloc_tensor(storage36, R.prim_value(0), gv2577, R.dtype("float32")) + R.vm.kill_object(storage36) + cls.get_renorm_prob(alloc1979, sample_indices2, alloc1978, alloc1980) + R.vm.kill_object(sample_indices2) + R.vm.kill_object(alloc1978) + gv2578: R.Shape(ndim=2) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(2), R.prim_value(1), R.prim_value(2), R.prim_value(0), R.prim_value(1), sinfo_args=(R.Shape(ndim=2),)) + alloc1981: R.Tensor(dtype="int32", ndim=2) = R.vm.alloc_tensor(storage33, R.prim_value(0), gv2578, R.dtype("int32")) + R.vm.kill_object(storage33) + cls.get_index_from_sorted(alloc1979, sorted_indices, alloc1980, uniform_samples1, sample_indices1, alloc1981) + R.vm.kill_object(uniform_samples1) + R.vm.kill_object(sample_indices1) + R.vm.kill_object(alloc1979) + R.vm.kill_object(alloc1980) + gv2579: R.Shape(ndim=1) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(1), R.prim_value(1), R.prim_value(2), sinfo_args=(R.Shape(ndim=1),)) + gv2: R.Tensor((num_samples,), dtype="int32") = R.call_packed("vm.builtin.reshape", alloc1981, gv2579, sinfo_args=(R.Tensor((num_samples,), dtype="int32"),)) + R.vm.kill_object(alloc1981) + return gv2 + + @R.function + def sampler_take_probs(unsorted_probs: R.Tensor(("batch_size", "vocab_size"), dtype="float32"), sorted_indices: R.Tensor(("batch_size", "vocab_size"), dtype="int32"), sample_indices: R.Tensor(("num_samples",), dtype="int32"), sampling_result: R.Tensor(("num_samples",), dtype="int32"), lobprob_offsets: R.Tensor(("num_positions",), dtype="int32")) -> R.Tuple(R.Tensor(("num_samples",), dtype="float32"), R.Tensor(("num_positions",), dtype="float32"), R.Tensor(("num_positions",), dtype="int32")): + num_samples = T.int64() + num_positions = T.int64() + batch_size = T.int64() + vocab_size = T.int64() + R.func_attr({"relax.force_pure": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "num_positions": 48, "num_samples": 8}}) + cls = Module + shape_heap: R.Tensor(dtype="int64", ndim=1) = R.call_builtin_with_ctx("vm.builtin.alloc_shape_heap", (R.prim_value(4),), sinfo_args=(R.Tensor(dtype="int64", ndim=1),)) + R.call_packed("vm.builtin.check_tensor_info", unsorted_probs, R.prim_value(2), R.dtype("float32"), R.str("ErrorContext(fn=sampler_take_probs, loc=param[0], param=unsorted_probs, annotation=R.Tensor((batch_size, vocab_size), dtype=\"float32\")) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.check_tensor_info", sorted_indices, R.prim_value(2), R.dtype("int32"), R.str("ErrorContext(fn=sampler_take_probs, loc=param[1], param=sorted_indices, annotation=R.Tensor((batch_size, vocab_size), dtype=\"int32\")) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.check_tensor_info", sample_indices, R.prim_value(1), R.dtype("int32"), R.str("ErrorContext(fn=sampler_take_probs, loc=param[2], param=sample_indices, annotation=R.Tensor((num_samples,), dtype=\"int32\")) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.check_tensor_info", sampling_result, R.prim_value(1), R.dtype("int32"), R.str("ErrorContext(fn=sampler_take_probs, loc=param[3], param=sampling_result, annotation=R.Tensor((num_samples,), dtype=\"int32\")) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.check_tensor_info", lobprob_offsets, R.prim_value(1), R.dtype("int32"), R.str("ErrorContext(fn=sampler_take_probs, loc=param[4], param=lobprob_offsets, annotation=R.Tensor((num_positions,), dtype=\"int32\")) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.match_shape", unsorted_probs, shape_heap, R.prim_value(2), R.prim_value(1), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.str("ErrorContext(fn=sampler_take_probs, loc=param[0], param=unsorted_probs, annotation=R.Tensor((batch_size, vocab_size), dtype=\"float32\")) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.match_shape", sorted_indices, shape_heap, R.prim_value(2), R.prim_value(3), R.prim_value(0), R.prim_value(3), R.prim_value(1), R.str("ErrorContext(fn=sampler_take_probs, loc=param[1], param=sorted_indices, annotation=R.Tensor((batch_size, vocab_size), dtype=\"int32\")) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.match_shape", sample_indices, shape_heap, R.prim_value(1), R.prim_value(1), R.prim_value(2), R.str("ErrorContext(fn=sampler_take_probs, loc=param[2], param=sample_indices, annotation=R.Tensor((num_samples,), dtype=\"int32\")) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.match_shape", sampling_result, shape_heap, R.prim_value(1), R.prim_value(3), R.prim_value(2), R.str("ErrorContext(fn=sampler_take_probs, loc=param[3], param=sampling_result, annotation=R.Tensor((num_samples,), dtype=\"int32\")) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.match_shape", lobprob_offsets, shape_heap, R.prim_value(1), R.prim_value(1), R.prim_value(3), R.str("ErrorContext(fn=sampler_take_probs, loc=param[4], param=lobprob_offsets, annotation=R.Tensor((num_positions,), dtype=\"int32\")) "), sinfo_args=(R.Tuple,)) + storage: R.Object = R.vm.alloc_storage(R.shape([32]), R.prim_value(0), R.dtype("uint8"), R.str("global")) + gv: R.Shape(ndim=1) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(1), R.prim_value(1), R.prim_value(2), sinfo_args=(R.Shape(ndim=1),)) + alloc: R.Tensor(dtype="float32", ndim=1) = R.vm.alloc_tensor(storage, R.prim_value(0), gv, R.dtype("float32")) + R.vm.kill_object(storage) + storage1: R.Object = R.vm.alloc_storage(R.shape([192]), R.prim_value(0), R.dtype("uint8"), R.str("global")) + gv1: R.Shape(ndim=1) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(1), R.prim_value(1), R.prim_value(3), sinfo_args=(R.Shape(ndim=1),)) + alloc1: R.Tensor(dtype="float32", ndim=1) = R.vm.alloc_tensor(storage1, R.prim_value(0), gv1, R.dtype("float32")) + R.vm.kill_object(storage1) + storage2: R.Object = R.vm.alloc_storage(R.shape([192]), R.prim_value(0), R.dtype("uint8"), R.str("global")) + gv2: R.Shape(ndim=1) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(1), R.prim_value(1), R.prim_value(3), sinfo_args=(R.Shape(ndim=1),)) + alloc2: R.Tensor(dtype="int32", ndim=1) = R.vm.alloc_tensor(storage2, R.prim_value(0), gv2, R.dtype("int32")) + R.vm.kill_object(storage2) + cls.sampler_take_probs_tir(unsorted_probs, sorted_indices, sample_indices, sampling_result, lobprob_offsets, alloc, alloc1, alloc2) + gv3: R.Tuple(R.Tensor(dtype="float32", ndim=1), R.Tensor(dtype="float32", ndim=1), R.Tensor(dtype="int32", ndim=1)) = alloc, alloc1, alloc2 + R.vm.kill_object(alloc) + R.vm.kill_object(alloc1) + R.vm.kill_object(alloc2) + gv3_1: R.Tensor(dtype="float32", ndim=1) = gv3[0] + R.call_packed("vm.builtin.match_shape", gv3_1, shape_heap, R.prim_value(1), R.prim_value(3), R.prim_value(2), R.str("ErrorContext(fn=sampler_take_probs, loc=return, annotation=R.Tuple(R.Tensor((num_samples,), dtype=\"float32\"), R.Tensor((num_positions,), dtype=\"float32\"), R.Tensor((num_positions,), dtype=\"int32\"))) "), sinfo_args=(R.Tuple,)) + gv4: R.Tensor(dtype="float32", ndim=1) = gv3[1] + R.call_packed("vm.builtin.match_shape", gv4, shape_heap, R.prim_value(1), R.prim_value(3), R.prim_value(3), R.str("ErrorContext(fn=sampler_take_probs, loc=return, annotation=R.Tuple(R.Tensor((num_samples,), dtype=\"float32\"), R.Tensor((num_positions,), dtype=\"float32\"), R.Tensor((num_positions,), dtype=\"int32\"))) "), sinfo_args=(R.Tuple,)) + gv5: R.Tensor(dtype="int32", ndim=1) = gv3[2] + R.call_packed("vm.builtin.match_shape", gv5, shape_heap, R.prim_value(1), R.prim_value(3), R.prim_value(3), R.str("ErrorContext(fn=sampler_take_probs, loc=return, annotation=R.Tuple(R.Tensor((num_samples,), dtype=\"float32\"), R.Tensor((num_positions,), dtype=\"float32\"), R.Tensor((num_positions,), dtype=\"int32\"))) "), sinfo_args=(R.Tuple,)) + return gv3 + + @R.function + def sampler_verify_draft_tokens(draft_probs: R.Tensor(("num_nodes", "vocab_size"), dtype="float32"), draft_tokens: R.Tensor(("num_nodes",), dtype="int32"), model_probs: R.Tensor(("num_nodes", "vocab_size"), dtype="float32"), token_tree_first_child: R.Tensor(("num_nodes",), dtype="int32"), token_tree_next_sibling: R.Tensor(("num_nodes",), dtype="int32"), uniform_samples: R.Tensor(("num_nodes",), dtype="float32"), token_tree_parent_ptr: R.Tensor(("nbatch",), dtype="int32")) -> R.Tuple(R.Tensor(("num_nodes", "vocab_size"), dtype="float32"), R.Tensor(("nbatch",), dtype="int32")): + num_nodes = T.int64() + vocab_size = T.int64() + nbatch = T.int64() + R.func_attr({"relax.force_pure": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "num_positions": 48, "num_samples": 8}}) + cls = Module + shape_heap: R.Tensor(dtype="int64", ndim=1) = R.call_builtin_with_ctx("vm.builtin.alloc_shape_heap", (R.prim_value(3),), sinfo_args=(R.Tensor(dtype="int64", ndim=1),)) + R.call_packed("vm.builtin.check_tensor_info", draft_probs, R.prim_value(2), R.dtype("float32"), R.str("ErrorContext(fn=sampler_verify_draft_tokens, loc=param[0], param=draft_probs, annotation=R.Tensor((num_nodes, vocab_size), dtype=\"float32\")) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.check_tensor_info", draft_tokens, R.prim_value(1), R.dtype("int32"), R.str("ErrorContext(fn=sampler_verify_draft_tokens, loc=param[1], param=draft_tokens, annotation=R.Tensor((num_nodes,), dtype=\"int32\")) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.check_tensor_info", model_probs, R.prim_value(2), R.dtype("float32"), R.str("ErrorContext(fn=sampler_verify_draft_tokens, loc=param[2], param=model_probs, annotation=R.Tensor((num_nodes, vocab_size), dtype=\"float32\")) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.check_tensor_info", token_tree_first_child, R.prim_value(1), R.dtype("int32"), R.str("ErrorContext(fn=sampler_verify_draft_tokens, loc=param[3], param=token_tree_first_child, annotation=R.Tensor((num_nodes,), dtype=\"int32\")) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.check_tensor_info", token_tree_next_sibling, R.prim_value(1), R.dtype("int32"), R.str("ErrorContext(fn=sampler_verify_draft_tokens, loc=param[4], param=token_tree_next_sibling, annotation=R.Tensor((num_nodes,), dtype=\"int32\")) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.check_tensor_info", uniform_samples, R.prim_value(1), R.dtype("float32"), R.str("ErrorContext(fn=sampler_verify_draft_tokens, loc=param[5], param=uniform_samples, annotation=R.Tensor((num_nodes,), dtype=\"float32\")) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.check_tensor_info", token_tree_parent_ptr, R.prim_value(1), R.dtype("int32"), R.str("ErrorContext(fn=sampler_verify_draft_tokens, loc=param[6], param=token_tree_parent_ptr, annotation=R.Tensor((nbatch,), dtype=\"int32\")) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.match_shape", draft_probs, shape_heap, R.prim_value(2), R.prim_value(1), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.str("ErrorContext(fn=sampler_verify_draft_tokens, loc=param[0], param=draft_probs, annotation=R.Tensor((num_nodes, vocab_size), dtype=\"float32\")) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.match_shape", draft_tokens, shape_heap, R.prim_value(1), R.prim_value(3), R.prim_value(0), R.str("ErrorContext(fn=sampler_verify_draft_tokens, loc=param[1], param=draft_tokens, annotation=R.Tensor((num_nodes,), dtype=\"int32\")) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.match_shape", model_probs, shape_heap, R.prim_value(2), R.prim_value(3), R.prim_value(0), R.prim_value(3), R.prim_value(1), R.str("ErrorContext(fn=sampler_verify_draft_tokens, loc=param[2], param=model_probs, annotation=R.Tensor((num_nodes, vocab_size), dtype=\"float32\")) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.match_shape", token_tree_first_child, shape_heap, R.prim_value(1), R.prim_value(3), R.prim_value(0), R.str("ErrorContext(fn=sampler_verify_draft_tokens, loc=param[3], param=token_tree_first_child, annotation=R.Tensor((num_nodes,), dtype=\"int32\")) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.match_shape", token_tree_next_sibling, shape_heap, R.prim_value(1), R.prim_value(3), R.prim_value(0), R.str("ErrorContext(fn=sampler_verify_draft_tokens, loc=param[4], param=token_tree_next_sibling, annotation=R.Tensor((num_nodes,), dtype=\"int32\")) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.match_shape", uniform_samples, shape_heap, R.prim_value(1), R.prim_value(3), R.prim_value(0), R.str("ErrorContext(fn=sampler_verify_draft_tokens, loc=param[5], param=uniform_samples, annotation=R.Tensor((num_nodes,), dtype=\"float32\")) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.match_shape", token_tree_parent_ptr, shape_heap, R.prim_value(1), R.prim_value(1), R.prim_value(2), R.str("ErrorContext(fn=sampler_verify_draft_tokens, loc=param[6], param=token_tree_parent_ptr, annotation=R.Tensor((nbatch,), dtype=\"int32\")) "), sinfo_args=(R.Tuple,)) + cls.batch_verify_on_gpu_single_kernel(draft_probs, draft_tokens, model_probs, token_tree_first_child, token_tree_next_sibling, uniform_samples, token_tree_parent_ptr) + gv4: R.Tuple(R.Tensor((num_nodes, vocab_size), dtype="float32"), R.Tensor((nbatch,), dtype="int32")) = model_probs, token_tree_parent_ptr + return gv4 + + @R.function + def softmax_with_temperature(logits: R.Tensor(("batch_size", 1, "vocab_size"), dtype="float32"), temperature: R.Tensor(("batch_size",), dtype="float32")) -> R.Tensor(("batch_size", 1, "vocab_size"), dtype="float32"): + batch_size = T.int64() + vocab_size = T.int64() + R.func_attr({"relax.force_pure": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "seq_len": 15000, "total_seq_len": 1500}}) + cls = Module + shape_heap: R.Tensor(dtype="int64", ndim=1) = R.call_builtin_with_ctx("vm.builtin.alloc_shape_heap", (R.prim_value(5),), sinfo_args=(R.Tensor(dtype="int64", ndim=1),)) + R.call_packed("vm.builtin.check_tensor_info", logits, R.prim_value(3), R.dtype("float32"), R.str("ErrorContext(fn=softmax_with_temperature, loc=param[0], param=logits, annotation=R.Tensor((batch_size, 1, vocab_size), dtype=\"float32\")) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.check_tensor_info", temperature, R.prim_value(1), R.dtype("float32"), R.str("ErrorContext(fn=softmax_with_temperature, loc=param[1], param=temperature, annotation=R.Tensor((batch_size,), dtype=\"float32\")) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.match_shape", logits, shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(1), R.str("ErrorContext(fn=softmax_with_temperature, loc=param[0], param=logits, annotation=R.Tensor((batch_size, 1, vocab_size), dtype=\"float32\")) "), sinfo_args=(R.Tuple,)) + R.call_packed("vm.builtin.match_shape", temperature, shape_heap, R.prim_value(1), R.prim_value(3), R.prim_value(0), R.str("ErrorContext(fn=softmax_with_temperature, loc=param[1], param=temperature, annotation=R.Tensor((batch_size,), dtype=\"float32\")) "), sinfo_args=(R.Tuple,)) + cls.shape_func5(shape_heap) + gv3455: R.Shape(ndim=2) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(2), R.prim_value(1), R.prim_value(0), R.prim_value(1), R.prim_value(1), sinfo_args=(R.Shape(ndim=2),)) + lv: R.Tensor((batch_size, vocab_size), dtype="float32") = R.call_packed("vm.builtin.reshape", logits, gv3455, sinfo_args=(R.Tensor((batch_size, vocab_size), dtype="float32"),)) + gv3456: R.Shape(ndim=1) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(1), R.prim_value(1), R.prim_value(2), sinfo_args=(R.Shape(ndim=1),)) + storage46: R.Object = R.vm.alloc_storage(gv3456, R.prim_value(0), R.dtype("uint8"), R.str("global")) + gv3457: R.Shape(ndim=2) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(2), R.prim_value(1), R.prim_value(0), R.prim_value(1), R.prim_value(3), sinfo_args=(R.Shape(ndim=2),)) + alloc2535: R.Tensor(dtype="float32", ndim=2) = R.vm.alloc_tensor(storage46, R.prim_value(0), gv3457, R.dtype("float32")) + R.vm.kill_object(storage46) + gv3458: R.Shape(ndim=1) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(1), R.prim_value(1), R.prim_value(2), sinfo_args=(R.Shape(ndim=1),)) + storage47: R.Object = R.vm.alloc_storage(gv3458, R.prim_value(0), R.dtype("uint8"), R.str("global")) + gv3459: R.Shape(ndim=2) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(2), R.prim_value(1), R.prim_value(0), R.prim_value(1), R.prim_value(3), sinfo_args=(R.Shape(ndim=2),)) + alloc2536: R.Tensor(dtype="float32", ndim=2) = R.vm.alloc_tensor(storage47, R.prim_value(0), gv3459, R.dtype("float32")) + R.vm.kill_object(storage47) + cls.chunk_lse(lv, temperature, alloc2535, alloc2536) + lv1: R.Tuple(R.Tensor(dtype="float32", ndim=2), R.Tensor(dtype="float32", ndim=2)) = alloc2535, alloc2536 + gv3460: R.Shape(ndim=1) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(1), R.prim_value(1), R.prim_value(4), sinfo_args=(R.Shape(ndim=1),)) + storage48: R.Object = R.vm.alloc_storage(gv3460, R.prim_value(0), R.dtype("uint8"), R.str("global")) + gv3461: R.Shape(ndim=2) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(2), R.prim_value(1), R.prim_value(0), R.prim_value(1), R.prim_value(1), sinfo_args=(R.Shape(ndim=2),)) + alloc2537: R.Tensor(dtype="float32", ndim=2) = R.vm.alloc_tensor(storage48, R.prim_value(0), gv3461, R.dtype("float32")) + R.vm.kill_object(storage48) + cls.softmax_with_chunked_sum(lv, temperature, alloc2535, alloc2536, alloc2537) + R.vm.kill_object(lv) + R.vm.kill_object(alloc2535) + R.vm.kill_object(alloc2536) + gv3462: R.Shape(ndim=3) = R.call_packed("vm.builtin.make_shape", shape_heap, R.prim_value(3), R.prim_value(1), R.prim_value(0), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.prim_value(1), sinfo_args=(R.Shape(ndim=3),)) + gv: R.Tensor((batch_size, 1, vocab_size), dtype="float32") = R.call_packed("vm.builtin.reshape", alloc2537, gv3462, sinfo_args=(R.Tensor((batch_size, 1, vocab_size), dtype="float32"),)) + R.vm.kill_object(alloc2537) + return gv + +# Metadata omitted. Use show_meta=True in script() method to show it. \ No newline at end of file diff --git a/debug/debug-phase0.py b/debug/debug-phase0.py new file mode 100644 index 0000000000000000000000000000000000000000..f3d93e61da2b0354ffd275eeec0cee797af7f0ef --- /dev/null +++ b/debug/debug-phase0.py @@ -0,0 +1,16603 @@ +# from tvm.script import ir as I +# from tvm.script import tir as T +# from tvm.script import relax as R + +@I.ir_module +class Module: + @T.prim_func + def apply_bitmask_inplace(var_logits: T.handle, var_seq_ids: T.handle, var_bitmask: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": T.bool(True), "tir.noalias": T.bool(True)}) + batch_size, vocab_size = T.int32(is_size_var=True), T.int32(is_size_var=True) + logits = T.match_buffer(var_logits, (batch_size, vocab_size)) + num_seq = T.int32(is_size_var=True) + seq_ids = T.match_buffer(var_seq_ids, (num_seq,), "int32") + bitmask = T.match_buffer(var_bitmask, (batch_size, (vocab_size + 31) // 32), "int32") + # with T.block("root"): + for fused_s_v_0 in T.thread_binding((num_seq * vocab_size + 1023) // 1024, thread="blockIdx.x"): + for fused_s_v_1 in T.thread_binding(1024, thread="threadIdx.x"): + with T.block("block"): + vs = T.axis.spatial(num_seq, (fused_s_v_0 * 1024 + fused_s_v_1) // vocab_size) + vv = T.axis.spatial(vocab_size, (fused_s_v_0 * 1024 + fused_s_v_1) % vocab_size) + T.where(fused_s_v_0 * 1024 + fused_s_v_1 < num_seq * vocab_size) + T.reads(bitmask[seq_ids[vs], vv // 32], seq_ids[vs], logits[seq_ids[vs], vv]) + T.writes(logits[seq_ids[vs], vv]) + logits[seq_ids[vs], vv] = T.if_then_else(T.bitwise_and(T.shift_right(bitmask[seq_ids[vs], vv // 32], vv % 32), 1) == 1, logits[seq_ids[vs], vv], T.float32(-3.4028234663852886e+38)) + + @T.prim_func + def apply_logit_bias_inplace(var_logits: T.handle, var_pos2seq_id: T.handle, var_token_ids: T.handle, var_logit_bias: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": T.bool(True), "tir.noalias": T.bool(True)}) + batch_size, vocab_size = T.int32(is_size_var=True), T.int32(is_size_var=True) + logits = T.match_buffer(var_logits, (batch_size, vocab_size)) + num_token = T.int32(is_size_var=True) + pos2seq_id = T.match_buffer(var_pos2seq_id, (num_token,), "int32") + token_ids = T.match_buffer(var_token_ids, (num_token,), "int32") + logit_bias = T.match_buffer(var_logit_bias, (num_token,)) + # with T.block("root"): + for p0 in T.thread_binding((num_token + 1023) // 1024, thread="blockIdx.x"): + for p1 in T.thread_binding(1024, thread="threadIdx.x"): + with T.block("block"): + vp = T.axis.spatial(num_token, p0 * 1024 + p1) + T.where(p0 * 1024 + p1 < num_token) + T.reads(logits[pos2seq_id[vp], token_ids[vp]], pos2seq_id[vp], token_ids[vp], logit_bias[vp]) + T.writes(logits[pos2seq_id[vp], token_ids[vp]]) + logits[pos2seq_id[vp], token_ids[vp]] = logits[pos2seq_id[vp], token_ids[vp]] + logit_bias[vp] + + @T.prim_func + def apply_penalty_inplace(var_logits: T.handle, var_seq_ids: T.handle, var_pos2seq_id: T.handle, var_token_ids: T.handle, var_token_cnt: T.handle, var_penalties: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": T.bool(True), "tir.noalias": T.bool(True)}) + batch_size, vocab_size = T.int32(is_size_var=True), T.int32(is_size_var=True) + logits = T.match_buffer(var_logits, (batch_size, vocab_size)) + num_seq = T.int32(is_size_var=True) + seq_ids = T.match_buffer(var_seq_ids, (num_seq,), "int32") + num_token = T.int32(is_size_var=True) + pos2seq_id = T.match_buffer(var_pos2seq_id, (num_token,), "int32") + token_ids = T.match_buffer(var_token_ids, (num_token,), "int32") + token_cnt = T.match_buffer(var_token_cnt, (num_token,), "int32") + penalties = T.match_buffer(var_penalties, (num_seq, 3)) + # with T.block("root"): + for p0 in T.thread_binding((num_token + 1023) // 1024, thread="blockIdx.x"): + for p1 in T.thread_binding(1024, thread="threadIdx.x"): + with T.block("block"): + vp = T.axis.spatial(num_token, p0 * 1024 + p1) + T.where(p0 * 1024 + p1 < num_token) + T.reads(logits[seq_ids[pos2seq_id[vp]], token_ids[vp]], seq_ids[pos2seq_id[vp]], pos2seq_id[vp], token_ids[vp], penalties[pos2seq_id[vp], 0:3], token_cnt[vp]) + T.writes(logits[seq_ids[pos2seq_id[vp]], token_ids[vp]]) + logits[seq_ids[pos2seq_id[vp]], token_ids[vp]] = logits[seq_ids[pos2seq_id[vp]], token_ids[vp]] - (penalties[pos2seq_id[vp], 0] + T.Cast("float32", token_cnt[vp]) * penalties[pos2seq_id[vp], 1]) + logits[seq_ids[pos2seq_id[vp]], token_ids[vp]] = T.if_then_else(logits[seq_ids[pos2seq_id[vp]], token_ids[vp]] > T.float32(0), logits[seq_ids[pos2seq_id[vp]], token_ids[vp]] * penalties[pos2seq_id[vp], 2], logits[seq_ids[pos2seq_id[vp]], token_ids[vp]] / penalties[pos2seq_id[vp], 2]) + + @T.prim_func + def batch_decode_paged_kv(_0: T.int32, Q_handle: T.handle, pages_handle: T.handle, page_table_indptr_handle: T.handle, page_table_values_handle: T.handle, var_length_info: T.handle, k_rope_pos_offset_handle: T.handle, q_rope_position_handle: T.handle, output_handle: T.handle, lse_handle: T.handle, rotary_mode: T.int32, rope_scale: T.float32, rope_theta: T.float32, attn_score_scaling_factor: T.float32): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) + B = T.int32(is_size_var=True) + Q = T.match_buffer(Q_handle, (B, 20, 64), "float16") + max_num_pages = T.int32(is_size_var=True) + pages = T.match_buffer(pages_handle, (max_num_pages, 2, 20, 16, 64), "float16") + page_table_indptr = T.match_buffer(page_table_indptr_handle, (B + 1,), "int32", offset_factor=1) + nnz_pages = T.int32(is_size_var=True) + page_table_values = T.match_buffer(page_table_values_handle, (nnz_pages,), "int32", offset_factor=1) + length_info = T.match_buffer(var_length_info, (B,), "int32", offset_factor=1) + k_rope_pos_offset = T.match_buffer(k_rope_pos_offset_handle, (B,), "int32", offset_factor=1) + q_rope_position = T.match_buffer(q_rope_position_handle, (B,), "int32", offset_factor=1) + output = T.match_buffer(output_handle, (B, 20, 64), "float16") + lse = T.match_buffer(lse_handle, (B, 20)) + # with T.block("root"): + sm_scale: T.float32 = T.float32(0.18033688011112042) + for bx in T.thread_binding(B, thread="blockIdx.x"): + for fused_by_bz in T.thread_binding(20, thread="blockIdx.y"): + for ty in T.thread_binding(1, thread="threadIdx.y"): + for tx in T.thread_binding(16, thread="threadIdx.x"): + for tz in T.thread_binding(32, thread="threadIdx.z"): + with T.block("attn"): + T.reads(page_table_indptr[bx:bx + 2], length_info[bx], q_rope_position[bx], Q[bx, fused_by_bz // 20 + ty + fused_by_bz % 20, tx * 4 - 32:tx * 4 - 32 + 68]) + T.writes(output[bx, fused_by_bz % 20 + fused_by_bz // 20 + ty, tx * 4:tx * 4 + 4], lse[bx, fused_by_bz % 20 + fused_by_bz // 20 + ty]) + Q_local = T.alloc_buffer((4,), "float16", scope="local") + kv_chunk_len = T.alloc_buffer((1,), "int32", scope="local") + K_smem = T.alloc_buffer((64, 64), "float16", scope="shared") + V_smem = T.alloc_buffer((64, 64), "float16", scope="shared") + O_allreduce = T.alloc_buffer((32, 1, 64), scope="shared") + md_allreduce = T.alloc_buffer((32, 1, 2), scope="shared") + S_reduce_local = T.alloc_buffer((1,), scope="local") + t0 = T.alloc_buffer((1,), scope="local") + S_local = T.alloc_buffer((2,), scope="local") + QK_local = T.alloc_buffer((4,), scope="local") + V_local = T.alloc_buffer((4,), "float16", scope="local") + m_prev = T.alloc_buffer((1,), scope="local") + d_prev = T.alloc_buffer((1,), scope="local") + other_m = T.alloc_buffer((1,), scope="local") + other_d = T.alloc_buffer((1,), scope="local") + exp_mprev = T.alloc_buffer((1,), scope="local") + exp_otherm = T.alloc_buffer((1,), scope="local") + other_o = T.alloc_buffer((4,), scope="local") + st_m = T.alloc_buffer((1,), scope="local") + st_d = T.alloc_buffer((1,), scope="local") + O_local = T.alloc_buffer((4,), scope="local") + by: T.int32 = fused_by_bz % 20 + bz: T.int32 = fused_by_bz // 20 + batch_idx: T.int32 = bx + cur_page_indptr_begin: T.int32 = page_table_indptr[batch_idx] + cur_page_indptr_end: T.int32 = page_table_indptr[batch_idx + 1] + kv_chunk_len[0] = T.if_then_else(cur_page_indptr_begin != cur_page_indptr_end, (cur_page_indptr_end - cur_page_indptr_begin - 1) * 16 + length_info[batch_idx], 0) + st_m[0] = T.float32(-50000) + st_d[0] = T.float32(1) + for vec in T.vectorized(4): + O_local[vec] = T.float32(0) + for vec in T.vectorized(4): + Q_local[vec] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", q_rope_position[batch_idx]) * rope_scale / T.pow(rope_theta, T.Cast("float32", (tx * 4 + vec) * 2 % 64) / T.float32(64))) * T.Cast("float32", Q[bx, by + bz + ty, tx * 4 + vec]) + T.sin(T.Cast("float32", q_rope_position[batch_idx]) * rope_scale / T.pow(rope_theta, T.Cast("float32", (tx * 4 + vec) * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(tx * 4 + vec < 32, Q[bx, by + bz + ty, tx * 4 + vec + 32] * T.float16(-1), Q[bx, by + bz + ty, tx * 4 + vec - 32]))), Q[bx, by + bz + ty, tx * 4 + vec]) + for iterator in range((kv_chunk_len[0] + 63) // 64): + tile_start_s: T.int32 = (tz + ty) * 2 + tile_start_g: T.int32 = (iterator * 32 + tz + ty) * 2 + for j in range(2): + with T.block("KV_load"): + T.reads() + T.writes() + row_g: T.int32 = tile_start_g + j + if row_g < kv_chunk_len[0]: + seq_offset: T.int32 = row_g + page_no: T.int32 = page_table_values[cur_page_indptr_begin + seq_offset // 16] + page_offset: T.int32 = seq_offset % 16 + for vec in T.vectorized(4): + K_smem[tile_start_s + j, tx * 4 + vec] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", k_rope_pos_offset[batch_idx] + row_g) * rope_scale / T.pow(rope_theta, T.Cast("float32", (tx * 4 + vec) * 2 % 64) / T.float32(64))) * T.Cast("float32", pages[page_no, 0, by, page_offset, tx * 4 + vec]) + T.sin(T.Cast("float32", k_rope_pos_offset[batch_idx] + row_g) * rope_scale / T.pow(rope_theta, T.Cast("float32", (tx * 4 + vec) * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(tx * 4 + vec < 32, pages[page_no, 0, by, page_offset, tx * 4 + vec + 32] * T.float16(-1), pages[page_no, 0, by, page_offset, tx * 4 + vec - 32]))), pages[page_no, 0, by, page_offset, tx * 4 + vec]) + V_smem[tile_start_s + j, tx * 4 + vec] = pages[page_no, 1, by, page_offset, tx * 4 + vec] + else: + for vec in T.vectorized(4): + K_smem[tile_start_s + j, tx * 4 + vec] = T.float16(0) + V_smem[tile_start_s + j, tx * 4 + vec] = T.float16(0) + T.tvm_storage_sync("shared") + m_prev[0] = st_m[0] + for j in range(2): + for vec in T.vectorized(4): + QK_local[vec] = T.Cast("float32", Q_local[vec]) * T.Cast("float32", K_smem[tz * 2 + j, tx * 4 + vec]) * attn_score_scaling_factor * sm_scale + S_reduce_local[0] = T.float32(0) + for vec in T.unroll(4): + S_reduce_local[0] = S_reduce_local[0] + QK_local[vec] + with T.block("block_cross_thread"): + T.reads(S_reduce_local[0]) + T.writes(t0[0]) + T.attr(T.comm_reducer(lambda x0, y0: x0 + y0, [T.float32(0)]), "reduce_scope", T.reinterpret("handle", T.uint64(0))) + T.tvm_thread_allreduce(T.uint32(1), S_reduce_local[0], T.bool(True), t0[0], tx) + S_local[j] = T.float32(-50000) + if (iterator * 32 + tz) * 2 + j < kv_chunk_len[0]: + S_local[j] = t0[0] + st_m[0] = T.max(st_m[0], S_local[j]) + o_scale: T.float32 = T.exp2(m_prev[0] - st_m[0]) + st_d[0] = st_d[0] * o_scale + for j in range(2): + S_local[j] = T.exp2(S_local[j] - st_m[0]) + st_d[0] = st_d[0] + S_local[j] + for j in T.vectorized(4): + O_local[j] = O_local[j] * o_scale + for j in range(2): + for vec in T.vectorized(4): + V_local[vec] = V_smem[tz * 2 + j, tx * 4 + vec] + for vec in T.vectorized(4): + O_local[vec] = O_local[vec] + T.Cast("float32", V_local[vec]) * S_local[j] + for vec in T.vectorized(4): + O_allreduce[tz, ty, tx * 4 + vec] = O_local[vec] + md_allreduce[tz, ty, 0] = st_m[0] + md_allreduce[tz, ty, 1] = st_d[0] + T.tvm_storage_sync("shared") + st_m[0] = T.float32(-50000) + st_d[0] = T.float32(1) + for vec in T.vectorized(4): + O_local[vec] = T.float32(0) + for j in range(32): + m_prev[0] = st_m[0] + d_prev[0] = st_d[0] + other_m[0] = md_allreduce[j, ty, 0] + other_d[0] = md_allreduce[j, ty, 1] + for vec in T.vectorized(4): + other_o[vec] = O_allreduce[j, ty, tx * 4 + vec] + st_m[0] = T.max(st_m[0], other_m[0]) + st_d[0] = d_prev[0] * T.exp2(m_prev[0] - st_m[0]) + other_d[0] * T.exp2(other_m[0] - st_m[0]) + exp_mprev[0] = T.exp2(m_prev[0] - st_m[0]) + exp_otherm[0] = T.exp2(other_m[0] - st_m[0]) + for vec in T.vectorized(4): + O_local[vec] = O_local[vec] * exp_mprev[0] + other_o[vec] * exp_otherm[0] + for vec in T.vectorized(4): + O_local[vec] = O_local[vec] / st_d[0] + for vec in T.vectorized(4): + output[batch_idx, by + bz + ty, tx * 4 + vec] = T.Cast("float16", O_local[vec]) + lse[batch_idx, by + bz + ty] = st_m[0] + T.log2(st_d[0]) + + @T.prim_func + def batch_decode_paged_kv_sliding_window(_0: T.int32, Q_handle: T.handle, pages_handle: T.handle, page_table_indptr_handle: T.handle, page_table_values_handle: T.handle, var_length_info: T.handle, k_rope_pos_offset_handle: T.handle, q_rope_position_handle: T.handle, output_handle: T.handle, lse_handle: T.handle, rotary_mode: T.int32, rope_scale: T.float32, rope_theta: T.float32, attn_score_scaling_factor: T.float32): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) + B = T.int32(is_size_var=True) + Q = T.match_buffer(Q_handle, (B, 20, 64), "float16") + max_num_pages = T.int32(is_size_var=True) + pages = T.match_buffer(pages_handle, (max_num_pages, 2, 20, 16, 64), "float16") + page_table_indptr = T.match_buffer(page_table_indptr_handle, (B + 1,), "int32", offset_factor=1) + nnz_pages = T.int32(is_size_var=True) + page_table_values = T.match_buffer(page_table_values_handle, (nnz_pages,), "int32", offset_factor=1) + length_info = T.match_buffer(var_length_info, (3, B), "int32", offset_factor=1) + k_rope_pos_offset = T.match_buffer(k_rope_pos_offset_handle, (B,), "int32", offset_factor=1) + q_rope_position = T.match_buffer(q_rope_position_handle, (B,), "int32", offset_factor=1) + output = T.match_buffer(output_handle, (B, 20, 64), "float16") + lse = T.match_buffer(lse_handle, (B, 20)) + # with T.block("root"): + sm_scale: T.float32 = T.float32(0.18033688011112042) + for bx in T.thread_binding(B, thread="blockIdx.x"): + for fused_by_bz in T.thread_binding(20, thread="blockIdx.y"): + for ty in T.thread_binding(1, thread="threadIdx.y"): + for tx in T.thread_binding(16, thread="threadIdx.x"): + for tz in T.thread_binding(32, thread="threadIdx.z"): + with T.block("attn"): + T.reads(page_table_indptr[bx:bx + 2], length_info[0:3, bx], q_rope_position[bx], Q[bx, fused_by_bz // 20 + ty + fused_by_bz % 20, tx * 4 - 32:tx * 4 - 32 + 68]) + T.writes(output[bx, fused_by_bz % 20 + fused_by_bz // 20 + ty, tx * 4:tx * 4 + 4], lse[bx, fused_by_bz % 20 + fused_by_bz // 20 + ty]) + Q_local = T.alloc_buffer((4,), "float16", scope="local") + kv_chunk_len = T.alloc_buffer((1,), "int32", scope="local") + K_smem = T.alloc_buffer((64, 64), "float16", scope="shared") + V_smem = T.alloc_buffer((64, 64), "float16", scope="shared") + O_allreduce = T.alloc_buffer((32, 1, 64), scope="shared") + md_allreduce = T.alloc_buffer((32, 1, 2), scope="shared") + S_reduce_local = T.alloc_buffer((1,), scope="local") + t0 = T.alloc_buffer((1,), scope="local") + S_local = T.alloc_buffer((2,), scope="local") + QK_local = T.alloc_buffer((4,), scope="local") + V_local = T.alloc_buffer((4,), "float16", scope="local") + m_prev = T.alloc_buffer((1,), scope="local") + d_prev = T.alloc_buffer((1,), scope="local") + other_m = T.alloc_buffer((1,), scope="local") + other_d = T.alloc_buffer((1,), scope="local") + exp_mprev = T.alloc_buffer((1,), scope="local") + exp_otherm = T.alloc_buffer((1,), scope="local") + other_o = T.alloc_buffer((4,), scope="local") + st_m = T.alloc_buffer((1,), scope="local") + st_d = T.alloc_buffer((1,), scope="local") + O_local = T.alloc_buffer((4,), scope="local") + by: T.int32 = fused_by_bz % 20 + bz: T.int32 = fused_by_bz // 20 + batch_idx: T.int32 = bx + cur_page_indptr_begin: T.int32 = page_table_indptr[batch_idx] + cur_page_indptr_end: T.int32 = page_table_indptr[batch_idx + 1] + kv_chunk_len[0] = T.if_then_else(cur_page_indptr_begin != cur_page_indptr_end, (cur_page_indptr_end - cur_page_indptr_begin - 1) * 16 + length_info[0, batch_idx] - length_info[1, batch_idx] + length_info[2, batch_idx], 0) + st_m[0] = T.float32(-50000) + st_d[0] = T.float32(1) + for vec in T.vectorized(4): + O_local[vec] = T.float32(0) + for vec in T.vectorized(4): + Q_local[vec] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", q_rope_position[batch_idx]) * rope_scale / T.pow(rope_theta, T.Cast("float32", (tx * 4 + vec) * 2 % 64) / T.float32(64))) * T.Cast("float32", Q[bx, by + bz + ty, tx * 4 + vec]) + T.sin(T.Cast("float32", q_rope_position[batch_idx]) * rope_scale / T.pow(rope_theta, T.Cast("float32", (tx * 4 + vec) * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(tx * 4 + vec < 32, Q[bx, by + bz + ty, tx * 4 + vec + 32] * T.float16(-1), Q[bx, by + bz + ty, tx * 4 + vec - 32]))), Q[bx, by + bz + ty, tx * 4 + vec]) + for iterator in range((kv_chunk_len[0] + 63) // 64): + tile_start_s: T.int32 = (tz + ty) * 2 + tile_start_g: T.int32 = (iterator * 32 + tz + ty) * 2 + for j in range(2): + with T.block("KV_load"): + T.reads() + T.writes() + row_g: T.int32 = tile_start_g + j + if row_g < kv_chunk_len[0]: + seq_offset: T.int32 = T.if_then_else(row_g < length_info[2, batch_idx], row_g, row_g - length_info[2, batch_idx] + length_info[1, batch_idx]) + page_no: T.int32 = page_table_values[cur_page_indptr_begin + seq_offset // 16] + page_offset: T.int32 = seq_offset % 16 + for vec in T.vectorized(4): + K_smem[tile_start_s + j, tx * 4 + vec] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", k_rope_pos_offset[batch_idx] + row_g) * rope_scale / T.pow(rope_theta, T.Cast("float32", (tx * 4 + vec) * 2 % 64) / T.float32(64))) * T.Cast("float32", pages[page_no, 0, by, page_offset, tx * 4 + vec]) + T.sin(T.Cast("float32", k_rope_pos_offset[batch_idx] + row_g) * rope_scale / T.pow(rope_theta, T.Cast("float32", (tx * 4 + vec) * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(tx * 4 + vec < 32, pages[page_no, 0, by, page_offset, tx * 4 + vec + 32] * T.float16(-1), pages[page_no, 0, by, page_offset, tx * 4 + vec - 32]))), pages[page_no, 0, by, page_offset, tx * 4 + vec]) + V_smem[tile_start_s + j, tx * 4 + vec] = pages[page_no, 1, by, page_offset, tx * 4 + vec] + else: + for vec in T.vectorized(4): + K_smem[tile_start_s + j, tx * 4 + vec] = T.float16(0) + V_smem[tile_start_s + j, tx * 4 + vec] = T.float16(0) + T.tvm_storage_sync("shared") + m_prev[0] = st_m[0] + for j in range(2): + for vec in T.vectorized(4): + QK_local[vec] = T.Cast("float32", Q_local[vec]) * T.Cast("float32", K_smem[tz * 2 + j, tx * 4 + vec]) * attn_score_scaling_factor * sm_scale + S_reduce_local[0] = T.float32(0) + for vec in T.unroll(4): + S_reduce_local[0] = S_reduce_local[0] + QK_local[vec] + with T.block("block_cross_thread"): + T.reads(S_reduce_local[0]) + T.writes(t0[0]) + T.attr(T.comm_reducer(lambda x0, y0: x0 + y0, [T.float32(0)]), "reduce_scope", T.reinterpret("handle", T.uint64(0))) + T.tvm_thread_allreduce(T.uint32(1), S_reduce_local[0], T.bool(True), t0[0], tx) + S_local[j] = T.float32(-50000) + if (iterator * 32 + tz) * 2 + j < kv_chunk_len[0]: + S_local[j] = t0[0] + st_m[0] = T.max(st_m[0], S_local[j]) + o_scale: T.float32 = T.exp2(m_prev[0] - st_m[0]) + st_d[0] = st_d[0] * o_scale + for j in range(2): + S_local[j] = T.exp2(S_local[j] - st_m[0]) + st_d[0] = st_d[0] + S_local[j] + for j in T.vectorized(4): + O_local[j] = O_local[j] * o_scale + for j in range(2): + for vec in T.vectorized(4): + V_local[vec] = V_smem[tz * 2 + j, tx * 4 + vec] + for vec in T.vectorized(4): + O_local[vec] = O_local[vec] + T.Cast("float32", V_local[vec]) * S_local[j] + for vec in T.vectorized(4): + O_allreduce[tz, ty, tx * 4 + vec] = O_local[vec] + md_allreduce[tz, ty, 0] = st_m[0] + md_allreduce[tz, ty, 1] = st_d[0] + T.tvm_storage_sync("shared") + st_m[0] = T.float32(-50000) + st_d[0] = T.float32(1) + for vec in T.vectorized(4): + O_local[vec] = T.float32(0) + for j in range(32): + m_prev[0] = st_m[0] + d_prev[0] = st_d[0] + other_m[0] = md_allreduce[j, ty, 0] + other_d[0] = md_allreduce[j, ty, 1] + for vec in T.vectorized(4): + other_o[vec] = O_allreduce[j, ty, tx * 4 + vec] + st_m[0] = T.max(st_m[0], other_m[0]) + st_d[0] = d_prev[0] * T.exp2(m_prev[0] - st_m[0]) + other_d[0] * T.exp2(other_m[0] - st_m[0]) + exp_mprev[0] = T.exp2(m_prev[0] - st_m[0]) + exp_otherm[0] = T.exp2(other_m[0] - st_m[0]) + for vec in T.vectorized(4): + O_local[vec] = O_local[vec] * exp_mprev[0] + other_o[vec] * exp_otherm[0] + for vec in T.vectorized(4): + O_local[vec] = O_local[vec] / st_d[0] + for vec in T.vectorized(4): + output[batch_idx, by + bz + ty, tx * 4 + vec] = T.Cast("float16", O_local[vec]) + lse[batch_idx, by + bz + ty] = st_m[0] + T.log2(st_d[0]) + + @T.prim_func + def batch_prefill_paged_kv(_0: T.int32, var_q: T.handle, var_q_indptr: T.handle, var_pages: T.handle, var_page_indptr: T.handle, var_page_values: T.handle, var_length_info: T.handle, var_k_rope_pos_offset: T.handle, var_q_rope_position: T.handle, var_output: T.handle, var_lse: T.handle, causal: T.int32, rotary_mode: T.int32, rope_scale: T.float32, rope_theta: T.float32, attn_score_scaling_factor: T.float32): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) + total_len = T.int32(is_size_var=True) + q = T.match_buffer(var_q, (total_len, 20, 64), "float16") + batch_size = T.int32(is_size_var=True) + q_indptr = T.match_buffer(var_q_indptr, (batch_size + 1,), "int32", offset_factor=1) + max_num_pages = T.int32(is_size_var=True) + pages = T.match_buffer(var_pages, (max_num_pages, 2, 20, 16, 64), "float16") + page_indptr = T.match_buffer(var_page_indptr, (batch_size + 1,), "int32", offset_factor=1) + nnz_pages = T.int32(is_size_var=True) + page_values = T.match_buffer(var_page_values, (nnz_pages,), "int32", offset_factor=1) + length_info = T.match_buffer(var_length_info, (batch_size,), "int32", offset_factor=1) + k_rope_pos_offset = T.match_buffer(var_k_rope_pos_offset, (batch_size,), "int32", offset_factor=1) + q_rope_position = T.match_buffer(var_q_rope_position, (total_len,), "int32", offset_factor=1) + output = T.match_buffer(var_output, (total_len, 20, 64), "float16") + lse = T.match_buffer(var_lse, (total_len, 20)) + # with T.block("root"): + for lbx in T.thread_binding(16, thread="blockIdx.x"): + for lby in T.thread_binding(20, thread="blockIdx.y"): + for lty in T.thread_binding(4, thread="threadIdx.y"): + for ltx in T.thread_binding(32, thread="threadIdx.x"): + with T.block("attn"): + bx, by, ty, tx = T.axis.remap("SSSS", [lbx, lby, lty, ltx]) + T.reads() + T.writes() + tile_id = T.alloc_buffer((1,), "int32", scope="local") + batch_idx = T.alloc_buffer((1,), "int32", scope="local") + batch_tiles = T.alloc_buffer((1,), "int32", scope="local") + batch_rows = T.alloc_buffer((1,), "int32", scope="local") + iterator = T.alloc_buffer((1,), "int32", scope="local") + kv_chunk_len = T.alloc_buffer((1,), "int32", scope="local") + Q_smem = T.alloc_buffer((32, 64), "float16", scope="shared") + K_smem = T.alloc_buffer((16, 64), "float16", scope="shared") + V_smem = T.alloc_buffer((16, 64), "float16", scope="shared") + S_smem = T.alloc_buffer((32, 16), scope="shared") + S_local = T.alloc_buffer((32, 16), scope="local") + O_local = T.alloc_buffer((32, 64), scope="local") + m_smem = T.alloc_buffer((32,), scope="shared") + m_prev_smem = T.alloc_buffer((32,), scope="shared") + d_smem = T.alloc_buffer((32,), scope="shared") + m_new = T.alloc_buffer((1,), scope="local") + m_prev = T.alloc_buffer((1,), scope="local") + d_new = T.alloc_buffer((1,), scope="local") + tile_id[0] = bx + batch_idx[0] = 0 + batch_rows[0] = q_indptr[1] - q_indptr[0] + batch_tiles[0] = (batch_rows[0] + 32 - 1) // 32 + while T.tvm_thread_invariant(batch_idx[0] < batch_size): + while tile_id[0] >= batch_tiles[0] and batch_idx[0] < batch_size: + tile_id[0] = tile_id[0] - batch_tiles[0] + batch_idx[0] = batch_idx[0] + 1 + if batch_idx[0] < batch_size: + b_idx: T.int32 = batch_idx[0] + batch_rows[0] = q_indptr[b_idx + 1] - q_indptr[b_idx] + batch_tiles[0] = (batch_rows[0] + 32 - 1) // 32 + if T.tvm_thread_invariant(batch_idx[0] < batch_size): + b_idx: T.int32 = batch_idx[0] + LH_start: T.int32 = tile_id[0] * 32 + q_indptr_val: T.int32 = q_indptr[b_idx] + cur_page_indptr_begin: T.int32 = page_indptr[b_idx] + cur_page_indptr_end: T.int32 = page_indptr[b_idx + 1] + kv_chunk_len[0] = T.if_then_else(cur_page_indptr_begin != cur_page_indptr_end, (cur_page_indptr_end - cur_page_indptr_begin - 1) * 16 + length_info[b_idx], 0) + T.tvm_storage_sync("shared") + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + m_smem[row] = T.float32(-50000) + d_smem[row] = T.float32(1) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(4, 4): + with T.block("O_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + T.reads() + T.writes(O_local[i, j]) + O_local[i, j] = T.float32(0) + T.tvm_storage_sync("shared") + for li_lj_fused_0 in range(4): + for li_lj_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for li_lj_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for li_lj_fused_3 in T.vectorized(4): + with T.block("Q_load"): + i = T.axis.spatial(32, (li_lj_fused_0 * 512 + li_lj_fused_1 * 128 + li_lj_fused_2 * 4 + li_lj_fused_3) // 64) + j = T.axis.spatial(64, (li_lj_fused_0 * 512 + li_lj_fused_1 * 128 + li_lj_fused_2 * 4 + li_lj_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = q_indptr_val + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + Q_smem[i, j] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", q[cur_L, cur_H_qo, j]) + T.sin(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(j < 32, q[cur_L, cur_H_qo, j + 32] * T.float16(-1), q[cur_L, cur_H_qo, j - 32]))), q[cur_L, cur_H_qo, j]) + else: + Q_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + for iterator_1 in range((kv_chunk_len[0] + 15) // 16): + L_kv_start: T.int32 = iterator_1 * 16 + for lz_ly_fused_0 in range(2): + for lz_ly_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for lz_ly_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for lz_ly_fused_3 in T.vectorized(4): + with T.block("K_load"): + i = T.axis.spatial(16, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) // 64) + j = T.axis.spatial(64, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = L_kv_start + i + if cur_L < kv_chunk_len[0]: + seq_offset: T.int32 = cur_L + page_no: T.int32 = page_values[cur_page_indptr_begin + seq_offset // 16] + page_offset: T.int32 = seq_offset % 16 + K_smem[i, j] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", k_rope_pos_offset[b_idx] + cur_L) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", pages[page_no, 0, by, page_offset, j]) + T.sin(T.Cast("float32", k_rope_pos_offset[b_idx] + cur_L) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(j < 32, pages[page_no, 0, by, page_offset, j + 32] * T.float16(-1), pages[page_no, 0, by, page_offset, j - 32]))), pages[page_no, 0, by, page_offset, j]) + else: + K_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + for lz_ly_fused_0 in range(2): + for lz_ly_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for lz_ly_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for lz_ly_fused_3 in T.vectorized(4): + with T.block("V_load"): + i = T.axis.spatial(16, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) // 64) + j = T.axis.spatial(64, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = L_kv_start + i + if cur_L < kv_chunk_len[0]: + seq_offset: T.int32 = cur_L + page_no: T.int32 = page_values[cur_page_indptr_begin + seq_offset // 16] + page_offset: T.int32 = seq_offset % 16 + V_smem[i, j] = pages[page_no, 1, by, page_offset, j] + else: + V_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + with T.block(""): + T.reads(Q_smem[0:32, 0:64], K_smem[0:16, 0:64]) + T.writes(S_local[0:32, 0:16]) + for li_0_lj_0_fused_0_init in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1_init in T.thread_binding(32, thread="threadIdx.x"): + for li_1_init, lj_1_init in T.grid(2, 2): + with T.block("S_gemm_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) // 8 * 2 + li_1_init) + j = T.axis.spatial(16, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) % 8 * 2 + lj_1_init) + T.reads() + T.writes(S_local[i, j]) + S_local[i, j] = T.float32(0) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for lk_0, li_1, lj_1, lk_1 in T.grid(8, 2, 2, 8): + with T.block("S_gemm_update"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 8 * 2 + li_1) + j = T.axis.spatial(16, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 8 * 2 + lj_1) + k = T.axis.reduce(64, lk_0 * 8 + lk_1) + T.reads(S_local[i, j], Q_smem[i, k], K_smem[j, k]) + T.writes(S_local[i, j]) + S_local[i, j] = S_local[i, j] + T.Cast("float32", Q_smem[i, k]) * T.Cast("float32", K_smem[j, k]) * attn_score_scaling_factor * T.float32(0.18033688011112042) + T.tvm_storage_sync("shared") + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(2, 2): + with T.block("S_store"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 8 * 2 + li_1) + j = T.axis.spatial(16, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 8 * 2 + lj_1) + T.reads(S_local[i, j]) + T.writes(S_smem[i, j]) + S_smem[i, j] = S_local[i, j] + T.tvm_storage_sync("shared") + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + with T.block("update1"): + T.reads(m_smem[row], kv_chunk_len[0], q_indptr[b_idx:b_idx + 2], m_new[i], S_smem[row, 0:16], d_smem[row], m_prev[i]) + T.writes(m_prev[i], m_new[i], d_new[i]) + m_prev[i] = m_smem[row] + m_new[i] = m_smem[row] + row_: T.int32 = LH_start + row + for j in range(16): + if T.if_then_else(causal > 0, L_kv_start + j < kv_chunk_len[0] - (q_indptr[b_idx + 1] - q_indptr[b_idx]) + row_ + 1, L_kv_start + j < kv_chunk_len[0]): + m_new[i] = T.max(m_new[i], S_smem[row, j]) + d_new[i] = d_smem[row] * T.exp2(m_prev[i] - m_new[i]) + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + with T.block("update"): + T.reads(kv_chunk_len[0], q_indptr[b_idx:b_idx + 2], S_smem[row, 0:16], m_new[i]) + T.writes(S_smem[row, 0:16]) + for j in range(16): + if row < 32: + row_: T.int32 = LH_start + row + if T.if_then_else(causal > 0, L_kv_start + j < kv_chunk_len[0] - (q_indptr[b_idx + 1] - q_indptr[b_idx]) + row_ + 1, L_kv_start + j < kv_chunk_len[0]): + S_smem[row, j] = T.exp2(S_smem[row, j] - m_new[i]) + else: + S_smem[row, j] = T.exp2(T.float32(-50000) - m_new[i]) + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + with T.block("update"): + T.reads(d_new[i], S_smem[row, 0:16], m_new[i], m_prev[i]) + T.writes(d_new[i], m_smem[row], d_smem[row], m_prev_smem[row]) + for j in range(16): + d_new[i] = d_new[i] + S_smem[row, j] + m_smem[row] = m_new[i] + d_smem[row] = d_new[i] + m_prev_smem[row] = m_prev[i] + T.tvm_storage_sync("shared") + with T.block(""): + T.reads(m_prev_smem[0:32], m_smem[0:32], S_smem[0:32, 0:16], V_smem[0:16, 0:64]) + T.writes(O_local[0:32, 0:64]) + for li_0_lj_0_fused_0_init in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1_init in T.thread_binding(32, thread="threadIdx.x"): + for li_1_init, lj_1_init in T.grid(4, 4): + with T.block("O_gemm_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) // 16 * 4 + li_1_init) + j = T.axis.spatial(64, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) % 16 * 4 + lj_1_init) + T.reads() + T.writes(O_local[i, j]) + O_local[i, j] = O_local[i, j] * T.exp2(m_prev_smem[i] - m_smem[i]) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for lk_0, lk_1, li_1, lj_1 in T.grid(2, 8, 4, 4): + with T.block("O_gemm_update"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + k = T.axis.reduce(16, lk_0 * 8 + lk_1) + T.reads(O_local[i, j], m_prev_smem[i], m_smem[i], S_smem[i, k], V_smem[k, j]) + T.writes(O_local[i, j]) + O_local[i, j] = O_local[i, j] + S_smem[i, k] * T.Cast("float32", V_smem[k, j]) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(4, 4): + with T.block("O_store"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + T.reads(q_indptr[b_idx:b_idx + 2], O_local[i, j], d_smem[i]) + T.writes(output[q_indptr[b_idx] + (LH_start + i), by, j]) + cur_L: T.int32 = q_indptr[b_idx] + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + output[cur_L, cur_H_qo, j] = T.Cast("float16", O_local[i, j] / d_smem[i]) + for li_0 in range(1): + for li_1 in T.thread_binding(4, thread="threadIdx.y"): + for li_2 in T.thread_binding(32, thread="threadIdx.x"): + with T.block("lse_store"): + i = T.axis.spatial(32, li_0 * 128 + li_1 * 32 + li_2) + T.where((li_0 * 4 + li_1) * 32 + li_2 < 32) + T.reads(q_indptr[b_idx:b_idx + 2], m_smem[i], d_smem[i]) + T.writes(lse[q_indptr[b_idx] + (LH_start + i), by]) + cur_L: T.int32 = q_indptr[b_idx] + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + lse[cur_L, cur_H_qo] = m_smem[i] + T.log2(d_smem[i]) + tile_id[0] = tile_id[0] + 16 + + @T.prim_func + def batch_prefill_paged_kv_sliding_window(_0: T.int32, var_q: T.handle, var_q_indptr: T.handle, var_pages: T.handle, var_page_indptr: T.handle, var_page_values: T.handle, var_length_info: T.handle, var_k_rope_pos_offset: T.handle, var_q_rope_position: T.handle, var_output: T.handle, var_lse: T.handle, causal: T.int32, rotary_mode: T.int32, rope_scale: T.float32, rope_theta: T.float32, attn_score_scaling_factor: T.float32): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) + total_len = T.int32(is_size_var=True) + q = T.match_buffer(var_q, (total_len, 20, 64), "float16") + batch_size = T.int32(is_size_var=True) + q_indptr = T.match_buffer(var_q_indptr, (batch_size + 1,), "int32", offset_factor=1) + max_num_pages = T.int32(is_size_var=True) + pages = T.match_buffer(var_pages, (max_num_pages, 2, 20, 16, 64), "float16") + page_indptr = T.match_buffer(var_page_indptr, (batch_size + 1,), "int32", offset_factor=1) + nnz_pages = T.int32(is_size_var=True) + page_values = T.match_buffer(var_page_values, (nnz_pages,), "int32", offset_factor=1) + length_info = T.match_buffer(var_length_info, (3, batch_size), "int32", offset_factor=1) + k_rope_pos_offset = T.match_buffer(var_k_rope_pos_offset, (batch_size,), "int32", offset_factor=1) + q_rope_position = T.match_buffer(var_q_rope_position, (total_len,), "int32", offset_factor=1) + output = T.match_buffer(var_output, (total_len, 20, 64), "float16") + lse = T.match_buffer(var_lse, (total_len, 20)) + # with T.block("root"): + for lbx in T.thread_binding(16, thread="blockIdx.x"): + for lby in T.thread_binding(20, thread="blockIdx.y"): + for lty in T.thread_binding(4, thread="threadIdx.y"): + for ltx in T.thread_binding(32, thread="threadIdx.x"): + with T.block("attn"): + bx, by, ty, tx = T.axis.remap("SSSS", [lbx, lby, lty, ltx]) + T.reads() + T.writes() + tile_id = T.alloc_buffer((1,), "int32", scope="local") + batch_idx = T.alloc_buffer((1,), "int32", scope="local") + batch_tiles = T.alloc_buffer((1,), "int32", scope="local") + batch_rows = T.alloc_buffer((1,), "int32", scope="local") + iterator = T.alloc_buffer((1,), "int32", scope="local") + kv_chunk_len = T.alloc_buffer((1,), "int32", scope="local") + Q_smem = T.alloc_buffer((32, 64), "float16", scope="shared") + K_smem = T.alloc_buffer((16, 64), "float16", scope="shared") + V_smem = T.alloc_buffer((16, 64), "float16", scope="shared") + S_smem = T.alloc_buffer((32, 16), scope="shared") + S_local = T.alloc_buffer((32, 16), scope="local") + O_local = T.alloc_buffer((32, 64), scope="local") + m_smem = T.alloc_buffer((32,), scope="shared") + m_prev_smem = T.alloc_buffer((32,), scope="shared") + d_smem = T.alloc_buffer((32,), scope="shared") + m_new = T.alloc_buffer((1,), scope="local") + m_prev = T.alloc_buffer((1,), scope="local") + d_new = T.alloc_buffer((1,), scope="local") + tile_id[0] = bx + batch_idx[0] = 0 + batch_rows[0] = q_indptr[1] - q_indptr[0] + batch_tiles[0] = (batch_rows[0] + 32 - 1) // 32 + while T.tvm_thread_invariant(batch_idx[0] < batch_size): + while tile_id[0] >= batch_tiles[0] and batch_idx[0] < batch_size: + tile_id[0] = tile_id[0] - batch_tiles[0] + batch_idx[0] = batch_idx[0] + 1 + if batch_idx[0] < batch_size: + b_idx: T.int32 = batch_idx[0] + batch_rows[0] = q_indptr[b_idx + 1] - q_indptr[b_idx] + batch_tiles[0] = (batch_rows[0] + 32 - 1) // 32 + if T.tvm_thread_invariant(batch_idx[0] < batch_size): + b_idx: T.int32 = batch_idx[0] + LH_start: T.int32 = tile_id[0] * 32 + q_indptr_val: T.int32 = q_indptr[b_idx] + cur_page_indptr_begin: T.int32 = page_indptr[b_idx] + cur_page_indptr_end: T.int32 = page_indptr[b_idx + 1] + kv_chunk_len[0] = T.if_then_else(cur_page_indptr_begin != cur_page_indptr_end, (cur_page_indptr_end - cur_page_indptr_begin - 1) * 16 + length_info[0, b_idx] - length_info[1, b_idx] + length_info[2, b_idx], 0) + T.tvm_storage_sync("shared") + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + m_smem[row] = T.float32(-50000) + d_smem[row] = T.float32(1) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(4, 4): + with T.block("O_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + T.reads() + T.writes(O_local[i, j]) + O_local[i, j] = T.float32(0) + T.tvm_storage_sync("shared") + for li_lj_fused_0 in range(4): + for li_lj_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for li_lj_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for li_lj_fused_3 in T.vectorized(4): + with T.block("Q_load"): + i = T.axis.spatial(32, (li_lj_fused_0 * 512 + li_lj_fused_1 * 128 + li_lj_fused_2 * 4 + li_lj_fused_3) // 64) + j = T.axis.spatial(64, (li_lj_fused_0 * 512 + li_lj_fused_1 * 128 + li_lj_fused_2 * 4 + li_lj_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = q_indptr_val + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + Q_smem[i, j] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", q[cur_L, cur_H_qo, j]) + T.sin(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(j < 32, q[cur_L, cur_H_qo, j + 32] * T.float16(-1), q[cur_L, cur_H_qo, j - 32]))), q[cur_L, cur_H_qo, j]) + else: + Q_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + for iterator_1 in range((kv_chunk_len[0] + 15) // 16): + L_kv_start: T.int32 = iterator_1 * 16 + for lz_ly_fused_0 in range(2): + for lz_ly_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for lz_ly_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for lz_ly_fused_3 in T.vectorized(4): + with T.block("K_load"): + i = T.axis.spatial(16, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) // 64) + j = T.axis.spatial(64, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = L_kv_start + i + if cur_L < kv_chunk_len[0]: + seq_offset: T.int32 = T.if_then_else(cur_L < length_info[2, b_idx], cur_L, cur_L - length_info[2, b_idx] + length_info[1, b_idx]) + page_no: T.int32 = page_values[cur_page_indptr_begin + seq_offset // 16] + page_offset: T.int32 = seq_offset % 16 + K_smem[i, j] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", k_rope_pos_offset[b_idx] + cur_L) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", pages[page_no, 0, by, page_offset, j]) + T.sin(T.Cast("float32", k_rope_pos_offset[b_idx] + cur_L) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(j < 32, pages[page_no, 0, by, page_offset, j + 32] * T.float16(-1), pages[page_no, 0, by, page_offset, j - 32]))), pages[page_no, 0, by, page_offset, j]) + else: + K_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + for lz_ly_fused_0 in range(2): + for lz_ly_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for lz_ly_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for lz_ly_fused_3 in T.vectorized(4): + with T.block("V_load"): + i = T.axis.spatial(16, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) // 64) + j = T.axis.spatial(64, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = L_kv_start + i + if cur_L < kv_chunk_len[0]: + seq_offset: T.int32 = T.if_then_else(cur_L < length_info[2, b_idx], cur_L, cur_L - length_info[2, b_idx] + length_info[1, b_idx]) + page_no: T.int32 = page_values[cur_page_indptr_begin + seq_offset // 16] + page_offset: T.int32 = seq_offset % 16 + V_smem[i, j] = pages[page_no, 1, by, page_offset, j] + else: + V_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + with T.block(""): + T.reads(Q_smem[0:32, 0:64], K_smem[0:16, 0:64]) + T.writes(S_local[0:32, 0:16]) + for li_0_lj_0_fused_0_init in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1_init in T.thread_binding(32, thread="threadIdx.x"): + for li_1_init, lj_1_init in T.grid(2, 2): + with T.block("S_gemm_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) // 8 * 2 + li_1_init) + j = T.axis.spatial(16, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) % 8 * 2 + lj_1_init) + T.reads() + T.writes(S_local[i, j]) + S_local[i, j] = T.float32(0) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for lk_0, li_1, lj_1, lk_1 in T.grid(8, 2, 2, 8): + with T.block("S_gemm_update"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 8 * 2 + li_1) + j = T.axis.spatial(16, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 8 * 2 + lj_1) + k = T.axis.reduce(64, lk_0 * 8 + lk_1) + T.reads(S_local[i, j], Q_smem[i, k], K_smem[j, k]) + T.writes(S_local[i, j]) + S_local[i, j] = S_local[i, j] + T.Cast("float32", Q_smem[i, k]) * T.Cast("float32", K_smem[j, k]) * attn_score_scaling_factor * T.float32(0.18033688011112042) + T.tvm_storage_sync("shared") + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(2, 2): + with T.block("S_store"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 8 * 2 + li_1) + j = T.axis.spatial(16, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 8 * 2 + lj_1) + T.reads(S_local[i, j]) + T.writes(S_smem[i, j]) + S_smem[i, j] = S_local[i, j] + T.tvm_storage_sync("shared") + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + with T.block("update1"): + T.reads(m_smem[row], kv_chunk_len[0], q_indptr[b_idx:b_idx + 2], m_new[i], S_smem[row, 0:16], d_smem[row], m_prev[i]) + T.writes(m_prev[i], m_new[i], d_new[i]) + m_prev[i] = m_smem[row] + m_new[i] = m_smem[row] + row_: T.int32 = LH_start + row + for j in range(16): + if T.if_then_else(causal > 0, L_kv_start + j < kv_chunk_len[0] - (q_indptr[b_idx + 1] - q_indptr[b_idx]) + row_ + 1, L_kv_start + j < kv_chunk_len[0]): + m_new[i] = T.max(m_new[i], S_smem[row, j]) + d_new[i] = d_smem[row] * T.exp2(m_prev[i] - m_new[i]) + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + with T.block("update"): + T.reads(kv_chunk_len[0], q_indptr[b_idx:b_idx + 2], S_smem[row, 0:16], m_new[i]) + T.writes(S_smem[row, 0:16]) + for j in range(16): + if row < 32: + row_: T.int32 = LH_start + row + if T.if_then_else(causal > 0, L_kv_start + j < kv_chunk_len[0] - (q_indptr[b_idx + 1] - q_indptr[b_idx]) + row_ + 1, L_kv_start + j < kv_chunk_len[0]): + S_smem[row, j] = T.exp2(S_smem[row, j] - m_new[i]) + else: + S_smem[row, j] = T.exp2(T.float32(-50000) - m_new[i]) + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + with T.block("update"): + T.reads(d_new[i], S_smem[row, 0:16], m_new[i], m_prev[i]) + T.writes(d_new[i], m_smem[row], d_smem[row], m_prev_smem[row]) + for j in range(16): + d_new[i] = d_new[i] + S_smem[row, j] + m_smem[row] = m_new[i] + d_smem[row] = d_new[i] + m_prev_smem[row] = m_prev[i] + T.tvm_storage_sync("shared") + with T.block(""): + T.reads(m_prev_smem[0:32], m_smem[0:32], S_smem[0:32, 0:16], V_smem[0:16, 0:64]) + T.writes(O_local[0:32, 0:64]) + for li_0_lj_0_fused_0_init in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1_init in T.thread_binding(32, thread="threadIdx.x"): + for li_1_init, lj_1_init in T.grid(4, 4): + with T.block("O_gemm_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) // 16 * 4 + li_1_init) + j = T.axis.spatial(64, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) % 16 * 4 + lj_1_init) + T.reads() + T.writes(O_local[i, j]) + O_local[i, j] = O_local[i, j] * T.exp2(m_prev_smem[i] - m_smem[i]) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for lk_0, lk_1, li_1, lj_1 in T.grid(2, 8, 4, 4): + with T.block("O_gemm_update"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + k = T.axis.reduce(16, lk_0 * 8 + lk_1) + T.reads(O_local[i, j], m_prev_smem[i], m_smem[i], S_smem[i, k], V_smem[k, j]) + T.writes(O_local[i, j]) + O_local[i, j] = O_local[i, j] + S_smem[i, k] * T.Cast("float32", V_smem[k, j]) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(4, 4): + with T.block("O_store"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + T.reads(q_indptr[b_idx:b_idx + 2], O_local[i, j], d_smem[i]) + T.writes(output[q_indptr[b_idx] + (LH_start + i), by, j]) + cur_L: T.int32 = q_indptr[b_idx] + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + output[cur_L, cur_H_qo, j] = T.Cast("float16", O_local[i, j] / d_smem[i]) + for li_0 in range(1): + for li_1 in T.thread_binding(4, thread="threadIdx.y"): + for li_2 in T.thread_binding(32, thread="threadIdx.x"): + with T.block("lse_store"): + i = T.axis.spatial(32, li_0 * 128 + li_1 * 32 + li_2) + T.where((li_0 * 4 + li_1) * 32 + li_2 < 32) + T.reads(q_indptr[b_idx:b_idx + 2], m_smem[i], d_smem[i]) + T.writes(lse[q_indptr[b_idx] + (LH_start + i), by]) + cur_L: T.int32 = q_indptr[b_idx] + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + lse[cur_L, cur_H_qo] = m_smem[i] + T.log2(d_smem[i]) + tile_id[0] = tile_id[0] + 16 + + @T.prim_func + def batch_prefill_ragged_kv(var_q: T.handle, var_q_indptr: T.handle, var_k: T.handle, var_v: T.handle, var_kv_indptr: T.handle, var_q_rope_position: T.handle, var_k_rope_pos_offset: T.handle, var_output: T.handle, var_lse: T.handle, causal: T.int32, rotary_mode: T.int32, rope_scale: T.float32, rope_theta: T.float32, attn_score_scaling_factor: T.float32): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) + qo_len = T.int32(is_size_var=True) + q = T.match_buffer(var_q, (qo_len, 20, 64), "float16") + batch_size = T.int32(is_size_var=True) + q_indptr = T.match_buffer(var_q_indptr, (batch_size + 1,), "int32", offset_factor=1) + kv_len = T.int32(is_size_var=True) + k = T.match_buffer(var_k, (kv_len, 20, 64), "float16") + v = T.match_buffer(var_v, (kv_len, 20, 64), "float16") + kv_indptr = T.match_buffer(var_kv_indptr, (batch_size + 1,), "int32", offset_factor=1) + q_rope_position = T.match_buffer(var_q_rope_position, (qo_len,), "int32", offset_factor=1) + k_rope_pos_offset = T.match_buffer(var_k_rope_pos_offset, (batch_size,), "int32", offset_factor=1) + output = T.match_buffer(var_output, (qo_len, 20, 64), "float16") + lse = T.match_buffer(var_lse, (qo_len, 20)) + # with T.block("root"): + for lbx in T.thread_binding(16, thread="blockIdx.x"): + for lby in T.thread_binding(20, thread="blockIdx.y"): + for lty in T.thread_binding(4, thread="threadIdx.y"): + for ltx in T.thread_binding(32, thread="threadIdx.x"): + with T.block("attn"): + bx, by, ty, tx = T.axis.remap("SSSS", [lbx, lby, lty, ltx]) + T.reads() + T.writes() + tile_id = T.alloc_buffer((1,), "int32", scope="local") + batch_idx = T.alloc_buffer((1,), "int32", scope="local") + batch_tiles = T.alloc_buffer((1,), "int32", scope="local") + batch_rows = T.alloc_buffer((1,), "int32", scope="local") + iterator = T.alloc_buffer((1,), "int32", scope="local") + kv_chunk_len = T.alloc_buffer((1,), "int32", scope="local") + Q_smem = T.alloc_buffer((32, 64), "float16", scope="shared") + K_smem = T.alloc_buffer((16, 64), "float16", scope="shared") + V_smem = T.alloc_buffer((16, 64), "float16", scope="shared") + S_smem = T.alloc_buffer((32, 16), scope="shared") + S_local = T.alloc_buffer((32, 16), scope="local") + O_local = T.alloc_buffer((32, 64), scope="local") + m_smem = T.alloc_buffer((32,), scope="shared") + m_prev_smem = T.alloc_buffer((32,), scope="shared") + d_smem = T.alloc_buffer((32,), scope="shared") + m_new = T.alloc_buffer((1,), scope="local") + m_prev = T.alloc_buffer((1,), scope="local") + d_new = T.alloc_buffer((1,), scope="local") + tile_id[0] = bx + batch_idx[0] = 0 + batch_rows[0] = q_indptr[1] - q_indptr[0] + batch_tiles[0] = (batch_rows[0] + 32 - 1) // 32 + while T.tvm_thread_invariant(batch_idx[0] < batch_size): + while tile_id[0] >= batch_tiles[0] and batch_idx[0] < batch_size: + tile_id[0] = tile_id[0] - batch_tiles[0] + batch_idx[0] = batch_idx[0] + 1 + if batch_idx[0] < batch_size: + b_idx: T.int32 = batch_idx[0] + batch_rows[0] = q_indptr[b_idx + 1] - q_indptr[b_idx] + batch_tiles[0] = (batch_rows[0] + 32 - 1) // 32 + if T.tvm_thread_invariant(batch_idx[0] < batch_size): + b_idx: T.int32 = batch_idx[0] + q_indptr_val: T.int32 = q_indptr[b_idx] + LH_start: T.int32 = tile_id[0] * 32 + kv_chunk_len[0] = kv_indptr[b_idx + 1] - kv_indptr[b_idx] + T.tvm_storage_sync("shared") + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + m_smem[row] = T.float32(-50000) + d_smem[row] = T.float32(1) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(4, 4): + with T.block("O_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + T.reads() + T.writes(O_local[i, j]) + O_local[i, j] = T.float32(0) + T.tvm_storage_sync("shared") + for li_lj_fused_0 in range(4): + for li_lj_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for li_lj_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for li_lj_fused_3 in T.vectorized(4): + with T.block("Q_load"): + i = T.axis.spatial(32, (li_lj_fused_0 * 512 + li_lj_fused_1 * 128 + li_lj_fused_2 * 4 + li_lj_fused_3) // 64) + j = T.axis.spatial(64, (li_lj_fused_0 * 512 + li_lj_fused_1 * 128 + li_lj_fused_2 * 4 + li_lj_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = q_indptr_val + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + Q_smem[i, j] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", q[cur_L, cur_H_qo, j]) + T.sin(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(j < 32, q[cur_L, cur_H_qo, j + 32] * T.float16(-1), q[cur_L, cur_H_qo, j - 32]))), q[cur_L, cur_H_qo, j]) + else: + Q_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + for iterator_1 in range((kv_chunk_len[0] + 15) // 16): + L_kv_start: T.int32 = iterator_1 * 16 + L_kv_base: T.int32 = kv_indptr[b_idx] + for lz_ly_fused_0 in range(2): + for lz_ly_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for lz_ly_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for lz_ly_fused_3 in T.vectorized(4): + with T.block("K_load"): + i = T.axis.spatial(16, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) // 64) + j = T.axis.spatial(64, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = L_kv_start + i + if cur_L < kv_chunk_len[0]: + K_smem[i, j] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", k_rope_pos_offset[b_idx] + cur_L) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", k[L_kv_base + cur_L, by, j]) + T.sin(T.Cast("float32", k_rope_pos_offset[b_idx] + cur_L) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(j < 32, k[L_kv_base + cur_L, by, j + 32] * T.float16(-1), k[L_kv_base + cur_L, by, j - 32]))), k[L_kv_base + cur_L, by, j]) + else: + K_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + for lz_ly_fused_0 in range(2): + for lz_ly_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for lz_ly_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for lz_ly_fused_3 in T.vectorized(4): + with T.block("V_load"): + i = T.axis.spatial(16, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) // 64) + j = T.axis.spatial(64, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = L_kv_start + i + if cur_L < kv_chunk_len[0]: + V_smem[i, j] = v[L_kv_base + cur_L, by, j] + else: + V_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + with T.block(""): + T.reads(Q_smem[0:32, 0:64], K_smem[0:16, 0:64]) + T.writes(S_local[0:32, 0:16]) + for li_0_lj_0_fused_0_init in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1_init in T.thread_binding(32, thread="threadIdx.x"): + for li_1_init, lj_1_init in T.grid(2, 2): + with T.block("S_gemm_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) // 8 * 2 + li_1_init) + j = T.axis.spatial(16, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) % 8 * 2 + lj_1_init) + T.reads() + T.writes(S_local[i, j]) + S_local[i, j] = T.float32(0) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for lk_0, li_1, lj_1, lk_1 in T.grid(8, 2, 2, 8): + with T.block("S_gemm_update"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 8 * 2 + li_1) + j = T.axis.spatial(16, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 8 * 2 + lj_1) + k_1 = T.axis.reduce(64, lk_0 * 8 + lk_1) + T.reads(S_local[i, j], Q_smem[i, k_1], K_smem[j, k_1]) + T.writes(S_local[i, j]) + S_local[i, j] = S_local[i, j] + T.Cast("float32", Q_smem[i, k_1]) * T.Cast("float32", K_smem[j, k_1]) * attn_score_scaling_factor * T.float32(0.18033688011112042) + T.tvm_storage_sync("shared") + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(2, 2): + with T.block("S_store"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 8 * 2 + li_1) + j = T.axis.spatial(16, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 8 * 2 + lj_1) + T.reads(S_local[i, j]) + T.writes(S_smem[i, j]) + S_smem[i, j] = S_local[i, j] + T.tvm_storage_sync("shared") + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + with T.block("update1"): + T.reads(m_smem[row], kv_chunk_len[0], q_indptr[b_idx:b_idx + 2], m_new[i], S_smem[row, 0:16], d_smem[row], m_prev[i]) + T.writes(m_prev[i], m_new[i], d_new[i]) + m_prev[i] = m_smem[row] + m_new[i] = m_smem[row] + row_: T.int32 = LH_start + row + for j in range(16): + if T.if_then_else(causal > 0, L_kv_start + j < kv_chunk_len[0] - (q_indptr[b_idx + 1] - q_indptr[b_idx]) + row_ + 1, L_kv_start + j < kv_chunk_len[0]): + m_new[i] = T.max(m_new[i], S_smem[row, j]) + d_new[i] = d_smem[row] * T.exp2(m_prev[i] - m_new[i]) + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + with T.block("update"): + T.reads(kv_chunk_len[0], q_indptr[b_idx:b_idx + 2], S_smem[row, 0:16], m_new[i]) + T.writes(S_smem[row, 0:16]) + for j in range(16): + if row < 32: + row_: T.int32 = LH_start + row + if T.if_then_else(causal > 0, L_kv_start + j < kv_chunk_len[0] - (q_indptr[b_idx + 1] - q_indptr[b_idx]) + row_ + 1, L_kv_start + j < kv_chunk_len[0]): + S_smem[row, j] = T.exp2(S_smem[row, j] - m_new[i]) + else: + S_smem[row, j] = T.exp2(T.float32(-50000) - m_new[i]) + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + with T.block("update"): + T.reads(d_new[i], S_smem[row, 0:16], m_new[i], m_prev[i]) + T.writes(d_new[i], m_smem[row], d_smem[row], m_prev_smem[row]) + for j in range(16): + d_new[i] = d_new[i] + S_smem[row, j] + m_smem[row] = m_new[i] + d_smem[row] = d_new[i] + m_prev_smem[row] = m_prev[i] + T.tvm_storage_sync("shared") + with T.block(""): + T.reads(m_prev_smem[0:32], m_smem[0:32], S_smem[0:32, 0:16], V_smem[0:16, 0:64]) + T.writes(O_local[0:32, 0:64]) + for li_0_lj_0_fused_0_init in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1_init in T.thread_binding(32, thread="threadIdx.x"): + for li_1_init, lj_1_init in T.grid(4, 4): + with T.block("O_gemm_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) // 16 * 4 + li_1_init) + j = T.axis.spatial(64, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) % 16 * 4 + lj_1_init) + T.reads() + T.writes(O_local[i, j]) + O_local[i, j] = O_local[i, j] * T.exp2(m_prev_smem[i] - m_smem[i]) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for lk_0, lk_1, li_1, lj_1 in T.grid(2, 8, 4, 4): + with T.block("O_gemm_update"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + k_1 = T.axis.reduce(16, lk_0 * 8 + lk_1) + T.reads(O_local[i, j], m_prev_smem[i], m_smem[i], S_smem[i, k_1], V_smem[k_1, j]) + T.writes(O_local[i, j]) + O_local[i, j] = O_local[i, j] + S_smem[i, k_1] * T.Cast("float32", V_smem[k_1, j]) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(4, 4): + with T.block("O_store"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + T.reads(q_indptr[b_idx:b_idx + 2], O_local[i, j], d_smem[i]) + T.writes(output[q_indptr[b_idx] + (LH_start + i), by, j]) + cur_L: T.int32 = q_indptr[b_idx] + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + output[cur_L, cur_H_qo, j] = T.Cast("float16", O_local[i, j] / d_smem[i]) + for li_0 in range(1): + for li_1 in T.thread_binding(4, thread="threadIdx.y"): + for li_2 in T.thread_binding(32, thread="threadIdx.x"): + with T.block("lse_store"): + i = T.axis.spatial(32, li_0 * 128 + li_1 * 32 + li_2) + T.where((li_0 * 4 + li_1) * 32 + li_2 < 32) + T.reads(q_indptr[b_idx:b_idx + 2], m_smem[i], d_smem[i]) + T.writes(lse[q_indptr[b_idx] + (LH_start + i), by]) + cur_L: T.int32 = q_indptr[b_idx] + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + lse[cur_L, cur_H_qo] = m_smem[i] + T.log2(d_smem[i]) + tile_id[0] = tile_id[0] + 16 + + @T.prim_func + def batch_tree_attn(var_q: T.handle, var_q_indptr: T.handle, var_k: T.handle, var_v: T.handle, var_kv_indptr: T.handle, var_q_rope_position: T.handle, var_mn_indptr: T.handle, var_mask: T.handle, var_output: T.handle, var_lse: T.handle, rotary_mode: T.int32, rope_scale: T.float32, rope_theta: T.float32, attn_score_scaling_factor: T.float32, batch_size: T.int32): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) + qo_len = T.int32(is_size_var=True) + q = T.match_buffer(var_q, (qo_len, 20, 64), "float16") + q_indptr = T.match_buffer(var_q_indptr, (batch_size + 1,), "int32", offset_factor=1) + kv_len = T.int32(is_size_var=True) + k = T.match_buffer(var_k, (kv_len, 20, 64), "float16") + v = T.match_buffer(var_v, (kv_len, 20, 64), "float16") + kv_indptr = T.match_buffer(var_kv_indptr, (batch_size + 1,), "int32", offset_factor=1) + q_rope_position = T.match_buffer(var_q_rope_position, (qo_len,), "int32", offset_factor=1) + mn_indptr = T.match_buffer(var_mn_indptr, (batch_size + 1,), "int32", offset_factor=1) + tree_size = T.int32(is_size_var=True) + mask = T.match_buffer(var_mask, (tree_size,), "int32", offset_factor=1) + output = T.match_buffer(var_output, (qo_len, 20, 64), "float16") + lse = T.match_buffer(var_lse, (qo_len, 20)) + # with T.block("root"): + for lbx in T.thread_binding(16, thread="blockIdx.x"): + for lby in T.thread_binding(20, thread="blockIdx.y"): + for lty in T.thread_binding(4, thread="threadIdx.y"): + for ltx in T.thread_binding(32, thread="threadIdx.x"): + with T.block("attn"): + bx, by, ty, tx = T.axis.remap("SSSS", [lbx, lby, lty, ltx]) + T.reads() + T.writes() + tile_id = T.alloc_buffer((1,), "int32", scope="local") + batch_idx = T.alloc_buffer((1,), "int32", scope="local") + batch_tiles = T.alloc_buffer((1,), "int32", scope="local") + batch_rows = T.alloc_buffer((1,), "int32", scope="local") + iterator = T.alloc_buffer((1,), "int32", scope="local") + kv_chunk_len = T.alloc_buffer((1,), "int32", scope="local") + Q_smem = T.alloc_buffer((32, 64), "float16", scope="shared") + K_smem = T.alloc_buffer((16, 64), "float16", scope="shared") + V_smem = T.alloc_buffer((16, 64), "float16", scope="shared") + S_smem = T.alloc_buffer((32, 16), scope="shared") + S_local = T.alloc_buffer((32, 16), scope="local") + O_local = T.alloc_buffer((32, 64), scope="local") + m_smem = T.alloc_buffer((32,), scope="shared") + m_prev_smem = T.alloc_buffer((32,), scope="shared") + d_smem = T.alloc_buffer((32,), scope="shared") + m_new = T.alloc_buffer((1,), scope="local") + m_prev = T.alloc_buffer((1,), scope="local") + d_new = T.alloc_buffer((1,), scope="local") + tile_id[0] = bx + batch_idx[0] = 0 + batch_rows[0] = q_indptr[1] - q_indptr[0] + batch_tiles[0] = (batch_rows[0] + 32 - 1) // 32 + while T.tvm_thread_invariant(batch_idx[0] < batch_size): + while tile_id[0] >= batch_tiles[0] and batch_idx[0] < batch_size: + tile_id[0] = tile_id[0] - batch_tiles[0] + batch_idx[0] = batch_idx[0] + 1 + if batch_idx[0] < batch_size: + b_idx: T.int32 = batch_idx[0] + batch_rows[0] = q_indptr[b_idx + 1] - q_indptr[b_idx] + batch_tiles[0] = (batch_rows[0] + 32 - 1) // 32 + if T.tvm_thread_invariant(batch_idx[0] < batch_size): + b_idx: T.int32 = batch_idx[0] + LH_start: T.int32 = tile_id[0] * 32 + q_indptr_val: T.int32 = q_indptr[b_idx] + kv_chunk_len[0] = kv_indptr[b_idx + 1] - kv_indptr[b_idx] + T.tvm_storage_sync("shared") + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + m_smem[row] = T.float32(-50000) + d_smem[row] = T.float32(1) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(4, 4): + with T.block("O_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + T.reads() + T.writes(O_local[i, j]) + O_local[i, j] = T.float32(0) + T.tvm_storage_sync("shared") + for li_lj_fused_0 in range(4): + for li_lj_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for li_lj_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for li_lj_fused_3 in T.vectorized(4): + with T.block("Q_load"): + i = T.axis.spatial(32, (li_lj_fused_0 * 512 + li_lj_fused_1 * 128 + li_lj_fused_2 * 4 + li_lj_fused_3) // 64) + j = T.axis.spatial(64, (li_lj_fused_0 * 512 + li_lj_fused_1 * 128 + li_lj_fused_2 * 4 + li_lj_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = q_indptr_val + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + Q_smem[i, j] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64)))) * q[cur_L, cur_H_qo, j] + T.Cast("float16", T.sin(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64)))) * T.if_then_else(j < 32, q[cur_L, cur_H_qo, j + 32] * T.float16(-1), q[cur_L, cur_H_qo, j - 32]), q[cur_L, cur_H_qo, j]) + else: + Q_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + for iterator_1 in range((kv_chunk_len[0] + 15) // 16): + L_kv_start: T.int32 = iterator_1 * 16 + L_kv_base: T.int32 = kv_indptr[b_idx] + for lz_ly_fused_0 in range(2): + for lz_ly_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for lz_ly_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for lz_ly_fused_3 in T.vectorized(4): + with T.block("KV_load"): + i = T.axis.spatial(16, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) // 64) + j = T.axis.spatial(64, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = L_kv_base + L_kv_start + i + if L_kv_start + i < kv_chunk_len[0]: + K_smem[i, j] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64)))) * k[cur_L, by, j] + T.Cast("float16", T.sin(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64)))) * T.if_then_else(j < 32, k[cur_L, by, j + 32] * T.float16(-1), k[cur_L, by, j - 32]), k[cur_L, by, j]) + V_smem[i, j] = v[cur_L, by, j] + else: + K_smem[i, j] = T.float16(0) + V_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + with T.block(""): + T.reads(Q_smem[0:32, 0:64], K_smem[0:16, 0:64]) + T.writes(S_local[0:32, 0:16]) + for li_0_lj_0_fused_0_init in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1_init in T.thread_binding(32, thread="threadIdx.x"): + for li_1_init, lj_1_init in T.grid(2, 2): + with T.block("S_gemm_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) // 8 * 2 + li_1_init) + j = T.axis.spatial(16, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) % 8 * 2 + lj_1_init) + T.reads() + T.writes(S_local[i, j]) + S_local[i, j] = T.float32(0) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for lk_0, li_1, lj_1, lk_1 in T.grid(8, 2, 2, 8): + with T.block("S_gemm_update"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 8 * 2 + li_1) + j = T.axis.spatial(16, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 8 * 2 + lj_1) + k_1 = T.axis.reduce(64, lk_0 * 8 + lk_1) + T.reads(S_local[i, j], Q_smem[i, k_1], K_smem[j, k_1]) + T.writes(S_local[i, j]) + S_local[i, j] = S_local[i, j] + T.Cast("float32", Q_smem[i, k_1]) * T.Cast("float32", K_smem[j, k_1]) * attn_score_scaling_factor * T.float32(0.18033688011112042) + T.tvm_storage_sync("shared") + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(2, 2): + with T.block("S_store"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 8 * 2 + li_1) + j = T.axis.spatial(16, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 8 * 2 + lj_1) + T.reads(S_local[i, j]) + T.writes(S_smem[i, j]) + S_smem[i, j] = S_local[i, j] + T.tvm_storage_sync("shared") + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + with T.block("update1"): + T.reads(m_smem[row], kv_chunk_len[0], mask[mn_indptr[b_idx] + (LH_start + row) * (q_indptr[b_idx + 1] - q_indptr[b_idx]) + L_kv_start:mn_indptr[b_idx] + (LH_start + row) * (q_indptr[b_idx + 1] - q_indptr[b_idx]) + L_kv_start + 16], mn_indptr[b_idx], q_indptr[b_idx:b_idx + 2], m_new[i], S_smem[row, 0:16], d_smem[row], m_prev[i]) + T.writes(m_prev[i], m_new[i], d_new[i]) + m_prev[i] = m_smem[row] + m_new[i] = m_smem[row] + row_: T.int32 = LH_start + row + for j in range(16): + if L_kv_start + j < kv_chunk_len[0] and mask[mn_indptr[b_idx] + row_ * (q_indptr[b_idx + 1] - q_indptr[b_idx]) + (L_kv_start + j)] == 1: + m_new[i] = T.max(m_new[i], S_smem[row, j]) + d_new[i] = d_smem[row] * T.exp2(m_prev[i] - m_new[i]) + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + with T.block("update"): + T.reads(kv_chunk_len[0], mask[mn_indptr[b_idx] + (LH_start + row) * (q_indptr[b_idx + 1] - q_indptr[b_idx]) + L_kv_start:mn_indptr[b_idx] + (LH_start + row) * (q_indptr[b_idx + 1] - q_indptr[b_idx]) + L_kv_start + 16], mn_indptr[b_idx], q_indptr[b_idx:b_idx + 2], S_smem[row, 0:16], m_new[i]) + T.writes(S_smem[row, 0:16]) + for j in range(16): + if row < 32: + row_: T.int32 = LH_start + row + if L_kv_start + j < kv_chunk_len[0] and mask[mn_indptr[b_idx] + row_ * (q_indptr[b_idx + 1] - q_indptr[b_idx]) + (L_kv_start + j)] == 1: + S_smem[row, j] = T.exp2(S_smem[row, j] - m_new[i]) + else: + S_smem[row, j] = T.exp2(T.float32(-50000) - m_new[i]) + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + with T.block("update"): + T.reads(d_new[i], S_smem[row, 0:16], m_new[i], m_prev[i]) + T.writes(d_new[i], m_smem[row], d_smem[row], m_prev_smem[row]) + for j in range(16): + d_new[i] = d_new[i] + S_smem[row, j] + m_smem[row] = m_new[i] + d_smem[row] = d_new[i] + m_prev_smem[row] = m_prev[i] + T.tvm_storage_sync("shared") + with T.block(""): + T.reads(m_prev_smem[0:32], m_smem[0:32], S_smem[0:32, 0:16], V_smem[0:16, 0:64]) + T.writes(O_local[0:32, 0:64]) + for li_0_lj_0_fused_0_init in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1_init in T.thread_binding(32, thread="threadIdx.x"): + for li_1_init, lj_1_init in T.grid(4, 4): + with T.block("O_gemm_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) // 16 * 4 + li_1_init) + j = T.axis.spatial(64, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) % 16 * 4 + lj_1_init) + T.reads() + T.writes(O_local[i, j]) + O_local[i, j] = O_local[i, j] * T.exp2(m_prev_smem[i] - m_smem[i]) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for lk_0, lk_1, li_1, lj_1 in T.grid(2, 8, 4, 4): + with T.block("O_gemm_update"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + k_1 = T.axis.reduce(16, lk_0 * 8 + lk_1) + T.reads(O_local[i, j], m_prev_smem[i], m_smem[i], S_smem[i, k_1], V_smem[k_1, j]) + T.writes(O_local[i, j]) + O_local[i, j] = O_local[i, j] + S_smem[i, k_1] * T.Cast("float32", V_smem[k_1, j]) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(4, 4): + with T.block("O_store"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + T.reads(q_indptr[b_idx:b_idx + 2], O_local[i, j], d_smem[i]) + T.writes(output[q_indptr[b_idx] + (LH_start + i), by, j]) + cur_L: T.int32 = q_indptr[b_idx] + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + output[cur_L, cur_H_qo, j] = T.Cast("float16", O_local[i, j] / d_smem[i]) + for li_0 in range(1): + for li_1 in T.thread_binding(4, thread="threadIdx.y"): + for li_2 in T.thread_binding(32, thread="threadIdx.x"): + with T.block("lse_store"): + i = T.axis.spatial(32, li_0 * 128 + li_1 * 32 + li_2) + T.where((li_0 * 4 + li_1) * 32 + li_2 < 32) + T.reads(q_indptr[b_idx:b_idx + 2], m_smem[i], d_smem[i]) + T.writes(lse[q_indptr[b_idx] + (LH_start + i), by]) + cur_L: T.int32 = q_indptr[b_idx] + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + lse[cur_L, cur_H_qo] = m_smem[i] + T.log2(d_smem[i]) + tile_id[0] = tile_id[0] + 16 + + @T.prim_func(private=True) + def batch_verify_on_gpu_single_kernel(var_draft_probs: T.handle, var_draft_tokens: T.handle, var_model_probs: T.handle, var_token_tree_first_child: T.handle, var_token_tree_next_sibling: T.handle, var_uniform_samples: T.handle, var_token_tree_parent_ptr: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + num_nodes, vocab_size = T.int32(is_size_var=True), T.int64() + draft_probs = T.match_buffer(var_draft_probs, (num_nodes, vocab_size)) + draft_tokens = T.match_buffer(var_draft_tokens, (num_nodes,), "int32") + model_probs = T.match_buffer(var_model_probs, (num_nodes, vocab_size)) + token_tree_first_child = T.match_buffer(var_token_tree_first_child, (num_nodes,), "int32") + token_tree_next_sibling = T.match_buffer(var_token_tree_next_sibling, (num_nodes,), "int32") + uniform_samples = T.match_buffer(var_uniform_samples, (num_nodes,)) + nbatch = T.int32(is_size_var=True) + token_tree_parent_ptr = T.match_buffer(var_token_tree_parent_ptr, (nbatch,), "int32") + # with T.block("root"): + child_ptr = T.alloc_buffer((1,), "int32", scope="local") + parent_ptr = T.alloc_buffer((1,), "int32", scope="local") + child_token = T.alloc_buffer((1,), "int32", scope="local") + done = T.alloc_buffer((1,), "bool", scope="local") + psum = T.alloc_buffer((1,), scope="local") + t0 = T.alloc_buffer((1,), scope="local") + model_prob_local = T.alloc_buffer((1,), scope="local") + draft_prob_local = T.alloc_buffer((1,), scope="local") + p_child = T.alloc_buffer((1,), scope="local") + q_child = T.alloc_buffer((1,), scope="local") + uniform_sample = T.alloc_buffer((1,), scope="local") + pred_shared = T.alloc_buffer((1,), "bool", scope="shared") + pred_local = T.alloc_buffer((1,), "bool", scope="local") + for _bx in T.thread_binding(nbatch, thread="blockIdx.x"): + for _tx in T.thread_binding(1024, thread="threadIdx.x"): + with T.block("CTA"): + b, tx = T.axis.remap("SS", [_bx, _tx]) + T.reads(token_tree_parent_ptr[b], token_tree_first_child[T.min(parent_ptr[0], child_ptr[0]):T.min(parent_ptr[0], child_ptr[0]) + (T.max(parent_ptr[0], child_ptr[0]) + 1 - T.min(parent_ptr[0], child_ptr[0]))], parent_ptr[0], done[0], child_ptr[0], draft_tokens[child_ptr[0]], model_probs[parent_ptr[0], T.min(T.Cast("int64", child_token[0]), T.Cast("int64", tx)):T.min(T.Cast("int64", child_token[0]), T.Cast("int64", tx)) + (T.max(T.Cast("int64", child_token[0]), (vocab_size + T.int64(1023)) // T.int64(1024) * T.int64(1024) + T.Cast("int64", tx) - T.int64(1024)) + T.int64(1) - T.min(T.Cast("int64", child_token[0]), T.Cast("int64", tx)))], child_token[0], draft_probs[child_ptr[0], T.min(T.Cast("int64", child_token[0]), T.Cast("int64", tx)):T.min(T.Cast("int64", child_token[0]), T.Cast("int64", tx)) + (T.max(T.Cast("int64", child_token[0]), (vocab_size + T.int64(1023)) // T.int64(1024) * T.int64(1024) + T.Cast("int64", tx) - T.int64(1024)) + T.int64(1) - T.min(T.Cast("int64", child_token[0]), T.Cast("int64", tx)))], uniform_samples[child_ptr[0]], p_child[0], uniform_sample[0], q_child[0], pred_shared[0], pred_local[0], model_prob_local[0], draft_prob_local[0], psum[0], t0[0], token_tree_next_sibling[child_ptr[0]]) + T.writes(parent_ptr[0], child_ptr[0], done[0], child_token[0], p_child[0], q_child[0], uniform_sample[0], pred_shared[0], pred_local[0], psum[0], model_prob_local[0], draft_prob_local[0], t0[0], model_probs[parent_ptr[0], T.Cast("int64", tx):T.Cast("int64", tx) + ((vocab_size + T.int64(1023)) // T.int64(1024) * T.int64(1024) - T.int64(1023))], token_tree_parent_ptr[b]) + parent_ptr[0] = token_tree_parent_ptr[b] + child_ptr[0] = token_tree_first_child[parent_ptr[0]] + done[0] = T.bool(False) + while not done[0]: + T.tvm_storage_sync("shared") + if child_ptr[0] == -1: + done[0] = T.bool(True) + T.tvm_storage_sync("shared") + else: + if tx == 0: + child_token[0] = draft_tokens[child_ptr[0]] + p_child[0] = model_probs[parent_ptr[0], child_token[0]] + q_child[0] = draft_probs[child_ptr[0], child_token[0]] + uniform_sample[0] = uniform_samples[child_ptr[0]] + pred_shared[0] = p_child[0] >= uniform_sample[0] * q_child[0] + T.tvm_storage_sync("shared") + pred_local[0] = pred_shared[0] + if pred_local[0]: + parent_ptr[0] = child_ptr[0] + child_ptr[0] = token_tree_first_child[child_ptr[0]] + else: + psum[0] = T.float32(0) + for i in range((vocab_size + T.int64(1023)) // T.int64(1024)): + if i * T.int64(1024) + T.Cast("int64", tx) < vocab_size: + model_prob_local[0] = model_probs[parent_ptr[0], i * T.int64(1024) + T.Cast("int64", tx)] + draft_prob_local[0] = draft_probs[child_ptr[0], i * T.int64(1024) + T.Cast("int64", tx)] + model_prob_local[0] = T.max(model_prob_local[0] - draft_prob_local[0], T.float32(0)) + psum[0] = psum[0] + model_prob_local[0] + with T.block("block_cross_thread"): + T.reads(psum[0]) + T.writes(t0[0]) + T.attr(T.comm_reducer(lambda x0, y0: x0 + y0, [T.float32(0)]), "reduce_scope", T.reinterpret("handle", T.uint64(0))) + T.tvm_thread_allreduce(T.uint32(1), psum[0], T.bool(True), t0[0], tx) + if t0[0] < T.float32(9.9999999999999995e-08): + parent_ptr[0] = child_ptr[0] + child_ptr[0] = token_tree_first_child[child_ptr[0]] + else: + for i in range((vocab_size + T.int64(1023)) // T.int64(1024)): + if i * T.int64(1024) + T.Cast("int64", tx) < vocab_size: + model_prob_local[0] = model_probs[parent_ptr[0], i * T.int64(1024) + T.Cast("int64", tx)] + draft_prob_local[0] = draft_probs[child_ptr[0], i * T.int64(1024) + T.Cast("int64", tx)] + model_prob_local[0] = T.max(model_prob_local[0] - draft_prob_local[0], T.float32(0)) + model_probs[parent_ptr[0], i * T.int64(1024) + T.Cast("int64", tx)] = model_prob_local[0] / t0[0] + child_ptr[0] = token_tree_next_sibling[child_ptr[0]] + if tx == 0: + token_tree_parent_ptr[b] = parent_ptr[0] + + @T.prim_func + def chunk_lse(var_A: T.handle, var_temperature: T.handle, var_chunked_sum: T.handle, var_chunked_max: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.noalias": T.bool(True)}) + batch_size, vocab_size = T.int64(is_size_var=True), T.int64(is_size_var=True) + A = T.match_buffer(var_A, (batch_size, vocab_size)) + temperature = T.match_buffer(var_temperature, (batch_size,)) + num_chunks = T.int64(is_size_var=True) + chunked_sum = T.match_buffer(var_chunked_sum, (batch_size, num_chunks)) + chunked_max = T.match_buffer(var_chunked_max, (batch_size, num_chunks)) + # with T.block("root"): + A_pad = T.alloc_buffer((batch_size, num_chunks, T.int64(4096))) + temp_max = T.alloc_buffer((batch_size, num_chunks)) + temp_sum = T.alloc_buffer((batch_size, num_chunks)) + for l0, l1, l2 in T.grid(batch_size, num_chunks, T.int64(4096)): + with T.block("pad"): + v0, v1, v2 = T.axis.remap("SSS", [l0, l1, l2]) + T.reads(temperature[v0], A[v0, v1 * T.int64(4096) + v2]) + T.writes(A_pad[v0, v1, v2]) + A_pad[v0, v1, v2] = T.if_then_else(v1 * T.int64(4096) + v2 < vocab_size, T.if_then_else(temperature[v0] > T.float32(1.0000000000000001e-05), A[v0, v1 * T.int64(4096) + v2] / temperature[v0], A[v0, v1 * T.int64(4096) + v2]), T.float32(-3.4028234663852886e+38)) + for l0, l1, l2 in T.grid(batch_size, num_chunks, T.int64(4096)): + with T.block("max"): + v0, v1, v2 = T.axis.remap("SSR", [l0, l1, l2]) + T.reads(A_pad[v0, v1, v2]) + T.writes(temp_max[v0, v1]) + with T.init(): + temp_max[v0, v1] = T.float32(-3.4028234663852886e+38) + temp_max[v0, v1] = T.max(temp_max[v0, v1], A_pad[v0, v1, v2]) + for l0, l1, l2 in T.grid(batch_size, num_chunks, T.int64(4096)): + with T.block("sum_exp"): + v0, v1, v2 = T.axis.remap("SSR", [l0, l1, l2]) + T.reads(temperature[v0], A_pad[v0, v1, v2], temp_max[v0, v1]) + T.writes(temp_sum[v0, v1]) + with T.init(): + temp_sum[v0, v1] = T.float32(0) + temp_sum[v0, v1] = temp_sum[v0, v1] + T.if_then_else(v1 * T.int64(4096) + v2 < vocab_size, T.Select(temperature[v0] > T.float32(1.0000000000000001e-05), T.exp(A_pad[v0, v1, v2] - temp_max[v0, v1]), T.Cast("float32", A_pad[v0, v1, v2] == temp_max[v0, v1])), T.float32(0)) + for l0, l1, l2 in T.grid(batch_size, num_chunks, T.int64(1)): + with T.block("log"): + v0, v1, v2 = T.axis.remap("SSS", [l0, l1, l2]) + T.reads(temperature[v0], temp_sum[v0, v1], temp_max[v0, v1]) + T.writes(chunked_sum[v0, v1], chunked_max[v0, v1]) + chunked_sum[v0, v1] = T.Select(temperature[v0] > T.float32(1.0000000000000001e-05), T.log(temp_sum[v0, v1]), temp_sum[v0, v1]) + chunked_max[v0, v1] = temp_max[v0, v1] + + @T.prim_func + def compact_kv_copy(var_pages: T.handle, var_copy_length_indptr: T.handle, var_copy_src_dst_pos: T.handle, batch_size: T.int32): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) + num_pages = T.int32() + pages = T.match_buffer(var_pages, (num_pages, 2, 20, 16, 64), "float16") + copy_length_indptr = T.match_buffer(var_copy_length_indptr, (batch_size + 1,), "int32", offset_factor=1) + total_copy_length = T.int32() + copy_src_dst_pos = T.match_buffer(var_copy_src_dst_pos, (2, total_copy_length), "int32", offset_factor=1) + with T.block("root"): + T.reads() + T.writes() + for bhd_o in T.thread_binding((batch_size * 1280 + 1023) // 1024, thread="blockIdx.x"): + for bhd_i in T.thread_binding(1024, thread="threadIdx.x"): + b: T.int32 = (bhd_o * 1024 + bhd_i) // 1280 + h: T.int32 = (bhd_o * 1024 + bhd_i) // 64 % 20 + d: T.int32 = (bhd_o * 1024 + bhd_i) % 64 + if bhd_o * 1024 + bhd_i < batch_size * 20 * 64: + for i in range(copy_length_indptr[b + 1] - copy_length_indptr[b]): + src_pos: T.int32 = copy_src_dst_pos[0, copy_length_indptr[b] + i] + dst_pos: T.int32 = copy_src_dst_pos[1, copy_length_indptr[b] + i] + pages[dst_pos // 16, 0, h, dst_pos % 16, d] = pages[src_pos // 16, 0, h, src_pos % 16, d] + pages[dst_pos // 16, 1, h, dst_pos % 16, d] = pages[src_pos // 16, 1, h, src_pos % 16, d] + + @T.prim_func + def copy_single_page(var_pages: T.handle, src_page_id: T.int64, tgt_page_id: T.int64, copy_length: T.int64): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) + num_pages, page_size = T.int32(), T.int64() + pages = T.match_buffer(var_pages, (num_pages, 2, 20, page_size, 64), "float16") + # with T.block("root"): + for b in T.thread_binding((copy_length * T.int64(1280) + T.int64(1023)) // T.int64(1024), thread="blockIdx.x"): + for t in T.thread_binding(1024, thread="threadIdx.x"): + with T.block("copy"): + vh = T.axis.spatial(20, T.Cast("int32", (b * T.int64(1024) + T.Cast("int64", t)) // (copy_length * T.int64(64)))) + vp = T.axis.spatial(copy_length, (b * T.int64(1024) + T.Cast("int64", t)) % (copy_length * T.int64(64)) // T.int64(64)) + vd = T.axis.spatial(64, T.Cast("int32", (b * T.int64(1024) + T.Cast("int64", t)) % T.int64(64))) + T.reads(pages[src_page_id, 0:2, vh, vp, vd]) + T.writes(pages[tgt_page_id, 0:2, vh, vp, vd]) + pages[tgt_page_id, 0, vh, vp, vd] = pages[src_page_id, 0, vh, vp, vd] + pages[tgt_page_id, 1, vh, vp, vd] = pages[src_page_id, 1, vh, vp, vd] + + @T.prim_func + def full(var_result: T.handle, value: T.int32): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32})}) + batch_size = T.int32(is_size_var=True) + result = T.match_buffer(var_result, (batch_size, 1), "int32") + # with T.block("root"): + for i in range(batch_size): + with T.block("block"): + vi = T.axis.spatial(batch_size, i) + T.reads() + T.writes(result[vi, 0]) + result[vi, 0] = value + + @T.prim_func + def fused_rope(var_qkv: T.handle, var_position_map: T.handle, var_q: T.handle, var_k: T.handle, var_v: T.handle, apply_rope: T.int32): + T.func_attr({"op_pattern": 8, "target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.noalias": T.bool(True)}) + seq_len = T.int64() + qkv = T.match_buffer(var_qkv, (seq_len, 60, 64), "float16") + position_map = T.match_buffer(var_position_map, (seq_len,), "int32", offset_factor=1) + q = T.match_buffer(var_q, (seq_len, 20, 64), "float16") + k = T.match_buffer(var_k, (seq_len, 20, 64), "float16") + v = T.match_buffer(var_v, (seq_len, 20, 64), "float16") + # with T.block("root"): + for iters_0, iters_1, iters_2 in T.grid(seq_len, 60, 64): + with T.block("llama_fused_rope"): + s, h, d = T.axis.remap("SSS", [iters_0, iters_1, iters_2]) + T.reads(position_map[s], qkv[s, h, d - 32:d - 32 + 65]) + T.writes(q[s, h, d], k[s, h - 20, d], v[s, h - 40, d]) + if h < 20: + q[s, h, d] = T.if_then_else(apply_rope > 0 and d < 64, T.Cast("float16", T.cos(T.Cast("float32", position_map[s]) / T.pow(T.float32(1), T.Cast("float32", d * 2 % 64) / T.float32(64))) * T.Cast("float32", qkv[s, h, d]) + T.sin(T.Cast("float32", position_map[s]) / T.pow(T.float32(1), T.Cast("float32", d * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(d < 32, qkv[s, h, d + 32] * T.float16(-1), qkv[s, h, d - 32]))), qkv[s, h, d]) + else: + if h < 40: + k[s, h - 20, d] = T.if_then_else(apply_rope > 0 and d < 64, T.Cast("float16", T.cos(T.Cast("float32", position_map[s]) / T.pow(T.float32(1), T.Cast("float32", d * 2 % 64) / T.float32(64))) * T.Cast("float32", qkv[s, h, d]) + T.sin(T.Cast("float32", position_map[s]) / T.pow(T.float32(1), T.Cast("float32", d * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(d < 32, qkv[s, h, d + 32] * T.float16(-1), qkv[s, h, d - 32]))), qkv[s, h, d]) + else: + v[s, h - 40, d] = qkv[s, h, d] + + @T.prim_func + def gather_probs(var_src: T.handle, var_indices: T.handle, var_dst: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.noalias": T.bool(True)}) + m, n = T.int32(is_size_var=True), T.int32(is_size_var=True) + src = T.match_buffer(var_src, (m, n)) + batch_size = T.int32(is_size_var=True) + indices = T.match_buffer(var_indices, (batch_size,), "int32") + dst = T.match_buffer(var_dst, (batch_size, n)) + # with T.block("root"): + for b, j in T.grid(batch_size, n): + with T.block("gather_2d"): + vb, vj = T.axis.remap("SS", [b, j]) + T.reads(src[indices[vb], vj], indices[vb]) + T.writes(dst[vb, vj]) + dst[vb, vj] = src[indices[vb], vj] + + @T.prim_func(private=True) + def get_index_from_sorted(A: T.handle, B: T.handle, C: T.handle, D: T.handle, E: T.handle, F: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32})}) + batch, vocab_size = T.int64(), T.int64() + cumsum_sorted = T.match_buffer(A, (batch, vocab_size)) + indices = T.match_buffer(B, (batch, vocab_size), "int32") + renorm_prob = T.match_buffer(C, (batch, 1)) + out_batch = T.int64() + usample = T.match_buffer(D, (out_batch, 1)) + sample_indices = T.match_buffer(E, (out_batch, 1), "int32") + output_index = T.match_buffer(F, (out_batch, 1), "int32") + # with T.block("root"): + for ax0, ax1 in T.grid(out_batch, vocab_size): + with T.block("T_get_index_from_sorted"): + v_ax0, v_ax1 = T.axis.remap("SS", [ax0, ax1]) + T.reads(usample[v_ax0, T.int64(0)], cumsum_sorted[sample_indices[v_ax0, T.int64(0)], v_ax1 - T.int64(1):v_ax1 - T.int64(1) + T.int64(2)], sample_indices[v_ax0, T.int64(0)], renorm_prob[sample_indices[v_ax0, T.int64(0)], 0], indices[sample_indices[v_ax0, T.int64(0)], T.min(T.int64(0), v_ax1):T.min(T.int64(0), v_ax1) + (T.max(T.int64(0), v_ax1) + T.int64(1) - T.min(T.int64(0), v_ax1))]) + T.writes(output_index[v_ax0, 0]) + if usample[v_ax0, T.int64(0)] < cumsum_sorted[sample_indices[v_ax0, T.int64(0)], v_ax1] / renorm_prob[sample_indices[v_ax0, T.int64(0)], 0] or v_ax1 + T.int64(1) == vocab_size: + if v_ax1 == T.int64(0): + output_index[v_ax0, 0] = indices[sample_indices[v_ax0, T.int64(0)], 0] + else: + if usample[v_ax0, T.int64(0)] >= cumsum_sorted[sample_indices[v_ax0, T.int64(0)], v_ax1 - T.int64(1)] / renorm_prob[sample_indices[v_ax0, T.int64(0)], 0]: + output_index[v_ax0, 0] = indices[sample_indices[v_ax0, T.int64(0)], v_ax1] + + @T.prim_func(private=True) + def get_renorm_prob(A: T.handle, B: T.handle, C: T.handle, D: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32})}) + batch, vocab_size = T.int64(), T.int64() + cumsum_sorted = T.match_buffer(A, (batch, vocab_size)) + top_p = T.match_buffer(B, (batch, 1)) + top_k = T.match_buffer(C, (batch, 1), "int32") + renorm_prob = T.match_buffer(D, (batch, 1)) + # with T.block("root"): + for ax0, ax1 in T.grid(batch, vocab_size): + with T.block("T_get_renorm_prob"): + v_ax0, v_ax1 = T.axis.remap("SS", [ax0, ax1]) + T.reads(cumsum_sorted[v_ax0, T.min(T.min(T.int64(0), v_ax1), v_ax1 + T.int64(1)):T.min(T.min(T.int64(0), v_ax1), v_ax1 + T.int64(1)) + (T.max(T.max(T.int64(0), v_ax1), v_ax1 + T.int64(1)) + T.int64(1) - T.min(T.min(T.int64(0), v_ax1), v_ax1 + T.int64(1)))], top_p[v_ax0, 0], top_k[v_ax0, 0]) + T.writes(renorm_prob[v_ax0, 0]) + if not (cumsum_sorted[v_ax0, 0] < top_p[v_ax0, 0] and top_k[v_ax0, 0] > 1): + renorm_prob[v_ax0, 0] = cumsum_sorted[v_ax0, 0] + else: + if cumsum_sorted[v_ax0, v_ax1] < top_p[v_ax0, 0] and v_ax1 + T.int64(1) < T.Cast("int64", top_k[v_ax0, 0]): + if v_ax1 + T.int64(1) == vocab_size: + renorm_prob[v_ax0, 0] = cumsum_sorted[v_ax0, v_ax1] + else: + if not (cumsum_sorted[v_ax0, v_ax1 + T.int64(1)] < top_p[v_ax0, 0] and v_ax1 + T.int64(1) + T.int64(1) < T.Cast("int64", top_k[v_ax0, 0])): + renorm_prob[v_ax0, 0] = cumsum_sorted[v_ax0, v_ax1 + T.int64(1)] + + @T.prim_func(private=True) + def index(var_layer_norm355: T.handle, index: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16")): + T.func_attr({"target": T.target({"arch": "sm_89", "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.noalias": T.bool(True)}) + seq_len = T.int64() + layer_norm355 = T.match_buffer(var_layer_norm355, (T.int64(1), seq_len, T.int64(1280)), "float16") + # with T.block("root"): + for i, _, k in T.grid(T.int64(1), T.int64(1), T.int64(1280)): + with T.block("index"): + v_i, v__, v_k = T.axis.remap("SSS", [i, _, k]) + T.reads(layer_norm355[v_i, seq_len - T.int64(1), v_k]) + T.writes(index[v_i, v__, v_k]) + index[v_i, v__, v_k] = layer_norm355[v_i, seq_len - T.int64(1), v_k] + + @T.prim_func + def merge_state_inplace(v: T.handle, s: T.handle, v_other: T.handle, s_other: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) + N, H, D = T.int32(is_size_var=True), T.int32(is_size_var=True), T.int32(is_size_var=True) + V = T.match_buffer(v, (N, H, D), "float16") + S = T.match_buffer(s, (N, H)) + V_other = T.match_buffer(v_other, (N, H, D), "float16") + S_other = T.match_buffer(s_other, (N, H)) + # with T.block("root"): + for bx in T.thread_binding(N, thread="blockIdx.x"): + for by in T.thread_binding(1, thread="blockIdx.y"): + for ty in T.thread_binding(20, thread="threadIdx.y"): + for tx in T.thread_binding(16, thread="threadIdx.x"): + with T.block("merge"): + T.reads(S[bx, ty + by * 20], S_other[bx, ty + by * 20], V[bx, ty + by * 20, tx * 4:tx * 4 + 4], V_other[bx, ty + by * 20, tx * 4:tx * 4 + 4]) + T.writes(V[bx, ty + by * 20, tx * 4:tx * 4 + 4], S[bx, ty + by * 20]) + s_val = T.alloc_buffer((1,), scope="local") + s_other_val = T.alloc_buffer((1,), scope="local") + s_max = T.alloc_buffer((1,), scope="local") + scale = T.alloc_buffer((1,), scope="local") + other_scale = T.alloc_buffer((1,), scope="local") + v_vec = T.alloc_buffer((4,), "float16", scope="local") + v_other_vec = T.alloc_buffer((4,), "float16", scope="local") + s_val[0] = S[bx, ty + by * 20] + s_other_val[0] = S_other[bx, ty + by * 20] + s_max[0] = T.max(s_val[0], s_other_val[0]) + s_val[0] = T.exp2(s_val[0] - s_max[0]) + s_other_val[0] = T.exp2(s_other_val[0] - s_max[0]) + scale[0] = s_val[0] / (s_val[0] + s_other_val[0]) + other_scale[0] = s_other_val[0] / (s_val[0] + s_other_val[0]) + for vec in T.vectorized(4): + v_vec[vec] = V[bx, ty + by * 20, tx * 4 + vec] + for vec in T.vectorized(4): + v_other_vec[vec] = V_other[bx, ty + by * 20, tx * 4 + vec] + for vec in range(4): + v_vec[vec] = T.Cast("float16", T.Cast("float32", v_vec[vec]) * scale[0] + T.Cast("float32", v_other_vec[vec]) * other_scale[0]) + for vec in T.vectorized(4): + V[bx, ty + by * 20, tx * 4 + vec] = v_vec[vec] + S[bx, ty + by * 20] = T.log2(s_val[0] + s_other_val[0]) + s_max[0] + + @T.prim_func + def sampler_take_probs_tir(var_unsorted_probs: T.handle, var_sorted_indices: T.handle, var_sample_indices: T.handle, var_sampling_results: T.handle, var_top_prob_offsets: T.handle, var_sampled_values: T.handle, var_top_prob_probs: T.handle, var_top_prob_indices: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32})}) + batch_size, vocab_size = T.int32(is_size_var=True), T.int32(is_size_var=True) + unsorted_probs = T.match_buffer(var_unsorted_probs, (batch_size, vocab_size)) + sorted_indices = T.match_buffer(var_sorted_indices, (batch_size, vocab_size), "int32") + num_samples = T.int32(is_size_var=True) + sample_indices = T.match_buffer(var_sample_indices, (num_samples,), "int32") + sampling_results = T.match_buffer(var_sampling_results, (num_samples,), "int32") + num_positions = T.int32(is_size_var=True) + top_prob_offsets = T.match_buffer(var_top_prob_offsets, (num_positions,), "int32") + sampled_values = T.match_buffer(var_sampled_values, (num_samples,)) + top_prob_probs = T.match_buffer(var_top_prob_probs, (num_positions,)) + top_prob_indices = T.match_buffer(var_top_prob_indices, (num_positions,), "int32") + # with T.block("root"): + for i in range(num_positions + num_samples): + with T.block("block"): + vi = T.axis.spatial(num_positions + num_samples, i) + T.reads(top_prob_offsets[vi], sorted_indices[top_prob_offsets[vi] // vocab_size, top_prob_offsets[vi] % vocab_size], unsorted_probs[T.min(top_prob_offsets[vi] // vocab_size, sample_indices[vi - num_positions]):T.min(top_prob_offsets[vi] // vocab_size, sample_indices[vi - num_positions]) + (T.max(top_prob_offsets[vi] // vocab_size, sample_indices[vi - num_positions]) + 1 - T.min(top_prob_offsets[vi] // vocab_size, sample_indices[vi - num_positions])), T.min(sorted_indices[top_prob_offsets[vi] // vocab_size, top_prob_offsets[vi] % vocab_size], sampling_results[vi - num_positions]):T.min(sorted_indices[top_prob_offsets[vi] // vocab_size, top_prob_offsets[vi] % vocab_size], sampling_results[vi - num_positions]) + (T.max(sorted_indices[top_prob_offsets[vi] // vocab_size, top_prob_offsets[vi] % vocab_size], sampling_results[vi - num_positions]) + 1 - T.min(sorted_indices[top_prob_offsets[vi] // vocab_size, top_prob_offsets[vi] % vocab_size], sampling_results[vi - num_positions]))], sample_indices[vi - num_positions], sampling_results[vi - num_positions]) + T.writes(top_prob_indices[vi], top_prob_probs[vi], sampled_values[vi - num_positions]) + if vi < num_positions: + row: T.int32 = top_prob_offsets[vi] // vocab_size + col: T.int32 = top_prob_offsets[vi] % vocab_size + top_prob_indices[vi] = sorted_indices[row, col] + top_prob_probs[vi] = unsorted_probs[row, sorted_indices[row, col]] + else: + vj: T.int32 = vi - num_positions + sampled_values[vj] = unsorted_probs[sample_indices[vj], sampling_results[vj]] + + @T.prim_func + def scatter_probs(var_src: T.handle, var_indices: T.handle, var_dst: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.noalias": T.bool(True)}) + batch_size, n = T.int32(is_size_var=True), T.int32(is_size_var=True) + src = T.match_buffer(var_src, (batch_size, n)) + indices = T.match_buffer(var_indices, (batch_size,), "int32") + m = T.int32(is_size_var=True) + dst = T.match_buffer(var_dst, (m, n)) + # with T.block("root"): + for b, j in T.grid(batch_size, n): + with T.block("scatter_2d"): + vb, vj = T.axis.remap("SS", [b, j]) + T.reads(src[vb, vj], indices[vb]) + T.writes(dst[indices[vb], vj]) + dst[indices[vb], vj] = src[vb, vj] + + @T.prim_func + def softmax_with_chunked_sum(var_A: T.handle, var_temperature: T.handle, var_chunked_sum: T.handle, var_chunked_max: T.handle, var_softmax: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + batch_size, vocab_size = T.int64(is_size_var=True), T.int64(is_size_var=True) + A = T.match_buffer(var_A, (batch_size, vocab_size)) + temperature = T.match_buffer(var_temperature, (batch_size,)) + num_chunks = T.int64(is_size_var=True) + chunked_sum = T.match_buffer(var_chunked_sum, (batch_size, num_chunks)) + chunked_max = T.match_buffer(var_chunked_max, (batch_size, num_chunks)) + softmax = T.match_buffer(var_softmax, (batch_size, vocab_size)) + # with T.block("root"): + temp_max_shared = T.alloc_buffer((batch_size,), scope="shared") + temp_sum_shared = T.alloc_buffer((batch_size,), scope="shared") + for l0_l1_fused in T.thread_binding(batch_size * num_chunks, thread="blockIdx.x"): + for ax0_1 in T.thread_binding(T.int64(32), thread="threadIdx.x"): + for ax0_0 in T.serial((num_chunks + T.int64(31)) // T.int64(32), annotations={"pragma_auto_unroll_max_step": 64, "pragma_unroll_explicit": 1}): + with T.block("max"): + v0 = T.axis.spatial(batch_size, l0_l1_fused % (num_chunks * batch_size) // num_chunks) + v1 = T.axis.reduce(num_chunks, ax0_0 * T.int64(32) + ax0_1) + T.where(ax0_0 * T.int64(32) + ax0_1 < num_chunks) + T.reads(chunked_max[v0, v1]) + T.writes(temp_max_shared[v0]) + with T.init(): + temp_max_shared[v0] = T.float32(-3.4028234663852886e+38) + temp_max_shared[v0] = T.max(temp_max_shared[v0], chunked_max[v0, v1]) + for ax0_1 in T.thread_binding(T.int64(32), thread="threadIdx.x"): + for ax0_0 in T.serial((num_chunks + T.int64(31)) // T.int64(32), annotations={"pragma_auto_unroll_max_step": 64, "pragma_unroll_explicit": 1}): + with T.block("sum_exp"): + v0 = T.axis.spatial(batch_size, l0_l1_fused % (num_chunks * batch_size) // num_chunks) + v1 = T.axis.reduce(num_chunks, ax0_0 * T.int64(32) + ax0_1) + T.where(ax0_0 * T.int64(32) + ax0_1 < num_chunks) + T.reads(temperature[v0], chunked_sum[v0, v1], chunked_max[v0, v1], temp_max_shared[v0]) + T.writes(temp_sum_shared[v0]) + with T.init(): + temp_sum_shared[v0] = T.float32(0) + temp_sum_shared[v0] = temp_sum_shared[v0] + T.Select(temperature[v0] > T.float32(1.0000000000000001e-05), T.exp(chunked_sum[v0, v1] + chunked_max[v0, v1] - temp_max_shared[v0]), T.Cast("float32", chunked_max[v0, v1] == temp_max_shared[v0]) * chunked_sum[v0, v1]) + for l2_0 in T.serial(T.int64(4), annotations={"pragma_auto_unroll_max_step": 64, "pragma_unroll_explicit": 1}): + for l2_1 in T.thread_binding(T.int64(32), thread="threadIdx.y"): + for l2_2 in T.thread_binding(T.int64(32), thread="threadIdx.x"): + with T.block("log_pad"): + v0 = T.axis.spatial(batch_size, l0_l1_fused % (num_chunks * batch_size) // num_chunks) + v1 = T.axis.spatial(num_chunks, l0_l1_fused % num_chunks) + v2 = T.axis.spatial(T.int64(4096), l2_0 * T.int64(1024) + l2_1 * T.int64(32) + l2_2) + T.reads(temperature[v0], A[v0, v1 * T.int64(4096) + v2], temp_sum_shared[v0], temp_max_shared[v0]) + T.writes(softmax[v0, v1 * T.int64(4096) + v2]) + if v1 * T.int64(4096) + v2 < vocab_size: + softmax[v0, v1 * T.int64(4096) + v2] = T.if_then_else(temperature[v0] > T.float32(1.0000000000000001e-05), T.exp(A[v0, v1 * T.int64(4096) + v2] / temperature[v0] - (T.log(temp_sum_shared[v0]) + temp_max_shared[v0])), T.Cast("float32", A[v0, v1 * T.int64(4096) + v2] == temp_max_shared[v0]) / temp_sum_shared[v0]) + + @T.prim_func(private=True) + def take_sorted_probs(var_probs: T.handle, var_lv1: T.handle, var_take_sorted_probs: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.noalias": T.bool(True)}) + batch_size, vocab_size = T.int64(), T.int64() + probs = T.match_buffer(var_probs, (batch_size, vocab_size)) + lv1 = T.match_buffer(var_lv1, (batch_size, vocab_size), "int32") + batch_size_1, vocab_size_1 = T.int64(), T.int64() + take_sorted_probs = T.match_buffer(var_take_sorted_probs, (batch_size_1, vocab_size_1)) + # with T.block("root"): + for i, j in T.grid(batch_size_1, vocab_size_1): + with T.block("take_sorted_probs"): + v_i, v_j = T.axis.remap("SS", [i, j]) + T.reads(probs[v_i, lv1[v_i, v_j]], lv1[v_i, v_j]) + T.writes(take_sorted_probs[v_i, v_j]) + take_sorted_probs[v_i, v_j] = probs[v_i, lv1[v_i, v_j]] + + @T.prim_func + def tir_kv_cache_debug_get_kv(var_pages: T.handle, var_position_map: T.handle, var_k_data: T.handle, var_v_data: T.handle, layer_id: T.int64): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.noalias": T.bool(True)}) + num_pages, page_size = T.int64(), T.int64(is_size_var=True) + pages = T.match_buffer(var_pages, (num_pages, 2, 20, page_size, 64), "float16") + seqlen = T.int64(is_size_var=True) + position_map = T.match_buffer(var_position_map, (seqlen,), "int32", offset_factor=1) + k_data = T.match_buffer(var_k_data, (32, seqlen, 20, 64), "float16") + v_data = T.match_buffer(var_v_data, (32, seqlen, 20, 64), "float16") + # with T.block("root"): + for p, h, d in T.grid(seqlen, 20, 64): + with T.block("copy0"): + vp, vh, vd = T.axis.remap("SSS", [p, h, d]) + T.reads(position_map[vp], pages[T.Cast("int64", position_map[vp]) // page_size, 0:2, vh, T.Cast("int64", position_map[vp]) % page_size, vd]) + T.writes(k_data[layer_id, vp, vh, vd], v_data[layer_id, vp, vh, vd]) + position: T.int32 = position_map[vp] + k_data[layer_id, vp, vh, vd] = pages[T.Cast("int64", position) // page_size, 0, vh, T.Cast("int64", position) % page_size, vd] + v_data[layer_id, vp, vh, vd] = pages[T.Cast("int64", position) // page_size, 1, vh, T.Cast("int64", position) % page_size, vd] + + @T.prim_func + def tir_kv_cache_transpose_append(var_pages: T.handle, var_k_data: T.handle, var_v_data: T.handle, var_position_map: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.noalias": T.bool(True)}) + num_pages = T.int64() + pages = T.match_buffer(var_pages, (num_pages, 2, 20, 16, 64), "float16") + ntoken = T.int64(is_size_var=True) + k_data = T.match_buffer(var_k_data, (ntoken, 20, 64), "float16") + v_data = T.match_buffer(var_v_data, (ntoken, 20, 64), "float16") + position_map = T.match_buffer(var_position_map, (ntoken,), "int32", offset_factor=1) + # with T.block("root"): + for global_pos, h, f in T.grid(ntoken, 20, 64): + if position_map[global_pos] != -1: + with T.block("k_transpose_append"): + vgpos, vh, vf = T.axis.remap("SSS", [global_pos, h, f]) + T.reads(position_map[vgpos], k_data[vgpos, vh, vf]) + T.writes(pages[position_map[vgpos] // 16, 0, vh, position_map[vgpos] % 16, vf]) + position: T.int32 = position_map[vgpos] + pages[position // 16, 0, vh, position % 16, vf] = k_data[vgpos, vh, vf] + with T.block("v_transpose_append"): + vgpos, vh, vf = T.axis.remap("SSS", [global_pos, h, f]) + T.reads(position_map[vgpos], v_data[vgpos, vh, vf]) + T.writes(pages[position_map[vgpos] // 16, 1, vh, position_map[vgpos] % 16, vf]) + position: T.int32 = position_map[vgpos] + pages[position // 16, 1, vh, position % 16, vf] = v_data[vgpos, vh, vf] + + @T.prim_func(private=True) + def top_p_pivot_cutoff(var_prob: T.handle, var_top_p_arr: T.handle, var_init_pivots: T.handle, var_final_pivot: T.handle, var_final_lsum: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + B, N = T.int32(), T.int32() + prob = T.match_buffer(var_prob, (B, N)) + top_p_arr = T.match_buffer(var_top_p_arr, (B,)) + init_pivots = T.match_buffer(var_init_pivots, (B, 3)) + final_pivot = T.match_buffer(var_final_pivot, (B,)) + final_lsum = T.match_buffer(var_final_lsum, (B,)) + # with T.block("root"): + pivot = T.alloc_buffer((3,), scope="local") + top_p = T.alloc_buffer((1,), scope="local") + L = T.alloc_buffer((1,), scope="shared") + R_1 = T.alloc_buffer((1,), scope="shared") + L_local = T.alloc_buffer((1,), scope="local") + R_local = T.alloc_buffer((1,), scope="local") + q = T.alloc_buffer((1,), scope="local") + lsum = T.alloc_buffer((3,), scope="local") + lmin_broadcast = T.alloc_buffer((1,), scope="shared") + lmin_broadcast_local = T.alloc_buffer((1,), scope="local") + lmin = T.alloc_buffer((3,), scope="local") + cmin = T.alloc_buffer((3,), "int32", scope="local") + total_sum = T.alloc_buffer((1,), scope="local") + it = T.alloc_buffer((1,), "int32", scope="local") + es_local = T.alloc_buffer((1,), "bool", scope="local") + es = T.alloc_buffer((1,), "bool", scope="shared") + find_pivot_local = T.alloc_buffer((1,), "bool", scope="local") + find_pivot = T.alloc_buffer((1,), "bool", scope="shared") + total_sum_reduce = T.alloc_buffer((1,), scope="local") + lsum_reduce = T.alloc_buffer((1,), scope="local") + lmin_reduce = T.alloc_buffer((1,), scope="local") + cmin_reduce = T.alloc_buffer((1,), "int32", scope="local") + for _bx in T.thread_binding(B, thread="blockIdx.x"): + for _tx in T.thread_binding(1024, thread="threadIdx.x"): + with T.block("CTA"): + b, tx = T.axis.remap("SS", [_bx, _tx]) + T.reads(top_p_arr[b], top_p[0], L[0], R_1[0], init_pivots[b, 0:3], L_local[0], R_local[0], find_pivot_local[0], it[0], es_local[0], prob[b, it[0] * 1024 + tx], total_sum[0], q[0], pivot[T.min(0, it[0]):T.min(0, it[0]) + (T.max(2, it[0]) + 1 - T.min(0, it[0]))], lsum[T.min(0, it[0]):T.min(0, it[0]) + (T.max(2, it[0]) + 1 - T.min(0, it[0]))], lmin[T.min(0, it[0]):T.min(0, it[0]) + (T.max(2, it[0]) + 1 - T.min(0, it[0]))], cmin[T.min(0, it[0]):T.min(0, it[0]) + (T.max(2, it[0]) + 1 - T.min(0, it[0]))], total_sum_reduce[0], es[0], lmin_reduce[0], lmin_broadcast[0], lmin_broadcast_local[0], lsum_reduce[0], cmin_reduce[0], find_pivot[0]) + T.writes(top_p[0], L[0], R_1[0], find_pivot[0], L_local[0], R_local[0], pivot[0:3], find_pivot_local[0], final_lsum[b], final_pivot[b], lsum[0:3], lmin[0:3], cmin[0:3], total_sum[0], it[0], es_local[0], q[0], total_sum_reduce[0], es[0], lsum_reduce[0], lmin_reduce[0], lmin_broadcast[0], lmin_broadcast_local[0], cmin_reduce[0]) + top_p[0] = top_p_arr[b] + if tx == 0: + L[0] = T.float32(1) - top_p[0] + R_1[0] = T.float32(9.9999999999999995e-08) + find_pivot[0] = T.bool(False) + T.tvm_storage_sync("shared") + L_local[0] = L[0] + R_local[0] = R_1[0] + for i in T.unroll(3): + pivot[i] = init_pivots[b, i] + find_pivot_local[0] = T.bool(False) + if L_local[0] - R_local[0] <= T.float32(9.9999999999999995e-08): + if tx == 0: + final_lsum[b] = T.float32(1) + final_pivot[b] = T.float32(0) + find_pivot_local[0] = T.bool(True) + while T.tvm_thread_invariant(L_local[0] - R_local[0] > T.float32(9.9999999999999995e-08) and not find_pivot_local[0]): + T.tvm_storage_sync("shared") + for pidx in T.unroll(3): + lsum[pidx] = T.float32(0) + lmin[pidx] = T.float32(3.4028234663852886e+38) + cmin[pidx] = 0 + total_sum[0] = T.float32(0) + it[0] = 0 + es_local[0] = T.bool(False) + while it[0] < (N + 1024 - 1) // 1024 and not es_local[0]: + q[0] = T.if_then_else(it[0] * 1024 + tx < N, prob[b, it[0] * 1024 + tx], T.float32(0)) + total_sum[0] = total_sum[0] + q[0] + for pidx in T.unroll(3): + if q[0] >= pivot[pidx]: + lsum[pidx] = lsum[pidx] + q[0] + if lmin[pidx] > q[0]: + lmin[pidx] = q[0] + cmin[pidx] = 1 + else: + if lmin[pidx] == q[0]: + cmin[pidx] = cmin[pidx] + 1 + it[0] = it[0] + 1 + if it[0] % 32 == 0: + with T.block("block_cross_thread"): + T.reads(total_sum[0]) + T.writes(total_sum_reduce[0]) + T.attr(T.comm_reducer(lambda x0, y0: x0 + y0, [T.float32(0)]), "reduce_scope", T.reinterpret("handle", T.uint64(0))) + T.tvm_thread_allreduce(T.uint32(1), total_sum[0], T.bool(True), total_sum_reduce[0], tx) + if tx == 0: + es[0] = T.float32(1) - total_sum_reduce[0] < pivot[2] + T.tvm_storage_sync("shared") + es_local[0] = es[0] + T.tvm_storage_sync("shared") + for pidx in range(3): + with T.block("block_cross_thread"): + T.reads(lsum[pidx]) + T.writes(lsum_reduce[0]) + T.attr(T.comm_reducer(lambda x0, y0: x0 + y0, [T.float32(0)]), "reduce_scope", T.reinterpret("handle", T.uint64(0))) + T.tvm_thread_allreduce(T.uint32(1), lsum[pidx], T.bool(True), lsum_reduce[0], tx) + with T.block("block_cross_thread"): + T.reads(lmin[pidx]) + T.writes(lmin_reduce[0]) + T.attr(T.comm_reducer(lambda x0, y0: T.min(x0, y0), [T.float32(0)]), "reduce_scope", T.reinterpret("handle", T.uint64(0))) + T.tvm_thread_allreduce(T.uint32(1), lmin[pidx], T.bool(True), lmin_reduce[0], tx) + if tx == 0: + lmin_broadcast[0] = lmin_reduce[0] + T.tvm_storage_sync("shared") + lmin_broadcast_local[0] = lmin_broadcast[0] + if lmin[pidx] > lmin_broadcast_local[0]: + cmin[pidx] = 0 + if tx == 0: + lsum[pidx] = lsum_reduce[0] + lmin[pidx] = lmin_reduce[0] + with T.block("block_cross_thread"): + T.reads(cmin[pidx]) + T.writes(cmin_reduce[0]) + T.attr(T.comm_reducer(lambda x0, y0: x0 + y0, [0]), "reduce_scope", T.reinterpret("handle", T.uint64(0))) + T.tvm_thread_allreduce(T.uint32(1), cmin[pidx], T.bool(True), cmin_reduce[0], tx) + if tx == 0: + cmin[pidx] = cmin_reduce[0] + T.tvm_storage_sync("shared") + if tx == 0: + it[0] = 0 + while it[0] < 3 and not find_pivot_local[0]: + if lsum[it[0]] >= top_p[0] and top_p[0] > lsum[it[0]] - T.Cast("float32", cmin[it[0]]) * lmin[it[0]]: + find_pivot[0] = T.bool(True) + find_pivot_local[0] = T.bool(True) + final_pivot[b] = pivot[it[0]] + final_lsum[b] = lsum[it[0]] + else: + if lsum[it[0]] - lmin[it[0]] * T.Cast("float32", cmin[it[0]]) >= top_p[0]: + R_1[0] = pivot[it[0]] + final_lsum[b] = lsum[it[0]] + else: + if lsum[it[0]] < top_p[0]: + L[0] = pivot[it[0]] + it[0] = it[0] + 1 + T.tvm_storage_sync("shared") + L_local[0] = L[0] + R_local[0] = R_1[0] + find_pivot_local[0] = find_pivot[0] + for pidx in T.unroll(3): + pivot[pidx] = L[0] - T.Cast("float32", pidx + 1) * (L_local[0] - R_local[0]) / T.float32(4) + if tx == 0: + if not find_pivot_local[0]: + final_pivot[b] = R_local[0] + if R_local[0] == T.float32(9.9999999999999995e-08): + final_lsum[b] = lsum[2] + + @T.prim_func(private=True) + def top_p_renorm_after_cutoff(var_prob: T.handle, var_final_pivot: T.handle, var_final_lsum: T.handle, var_renorm_prob: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + B, N = T.int32(), T.int32() + prob = T.match_buffer(var_prob, (B, N)) + final_pivot = T.match_buffer(var_final_pivot, (B,)) + final_lsum = T.match_buffer(var_final_lsum, (B,)) + renorm_prob = T.match_buffer(var_renorm_prob, (B, N)) + # with T.block("root"): + pivot = T.alloc_buffer((1,), scope="local") + lsum = T.alloc_buffer((1,), scope="local") + for _by in T.thread_binding(B, thread="blockIdx.y"): + for _bx in T.thread_binding((B + 511) // B, thread="blockIdx.x"): + for _tx in T.thread_binding(1024, thread="threadIdx.x"): + with T.block("CTA"): + by, bx, tx = T.axis.remap("SSS", [_by, _bx, _tx]) + T.reads(final_pivot[by], final_lsum[by], prob[by, T.Select(0 <= (B + 511) // B, 0, (((B + 511) // B * 1024 + N - 1) // ((B + 511) // B * 1024) - 1) * ((B + 511) // B)) * 1024 + bx * 1024 + tx:T.Select(0 <= (B + 511) // B, 0, (((B + 511) // B * 1024 + N - 1) // ((B + 511) // B * 1024) - 1) * ((B + 511) // B)) * 1024 + bx * 1024 + tx + (T.Select(0 <= (B + 511) // B, (N - 1) // ((B + 511) // B * 1024) * ((B + 511) // B), 0 - (((B + 511) // B * 1024 + N - 1) // ((B + 511) // B * 1024) - 1) * ((B + 511) // B)) * 1024 + 1)], pivot[0], lsum[0]) + T.writes(pivot[0], lsum[0], renorm_prob[by, T.Select(0 <= (B + 511) // B, 0, (((B + 511) // B * 1024 + N - 1) // ((B + 511) // B * 1024) - 1) * ((B + 511) // B)) * 1024 + bx * 1024 + tx:T.Select(0 <= (B + 511) // B, 0, (((B + 511) // B * 1024 + N - 1) // ((B + 511) // B * 1024) - 1) * ((B + 511) // B)) * 1024 + bx * 1024 + tx + (T.Select(0 <= (B + 511) // B, (N - 1) // ((B + 511) // B * 1024) * ((B + 511) // B), 0 - (((B + 511) // B * 1024 + N - 1) // ((B + 511) // B * 1024) - 1) * ((B + 511) // B)) * 1024 + 1)]) + pivot[0] = final_pivot[by] + lsum[0] = final_lsum[by] + for i in range(((B + 511) // B * 1024 + N - 1) // ((B + 511) // B * 1024)): + if i * ((512 + B - 1) // B) * 1024 + bx * 1024 + tx < N: + renorm_prob[by, i * ((512 + B - 1) // B) * 1024 + bx * 1024 + tx] = T.if_then_else(prob[by, i * ((512 + B - 1) // B) * 1024 + bx * 1024 + tx] >= pivot[0], prob[by, i * ((512 + B - 1) // B) * 1024 + bx * 1024 + tx] / lsum[0], T.float32(0)) + + @R.function + def argsort_probs(probs: R.Tensor(("batch_size", "vocab_size"), dtype="float32")) -> R.Tuple(R.Tensor(("batch_size", "vocab_size"), dtype="float32"), R.Tensor(("batch_size", "vocab_size"), dtype="int32")): + batch_size = T.int64() + vocab_size = T.int64() + R.func_attr({"relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "num_positions": 48, "num_samples": 8}}) + cls = Module + with R.dataflow(): + lv1: R.Tensor((batch_size, vocab_size), dtype="int32") = R.argsort(probs, axis=-1, descending=True, dtype="int32") + lv2 = R.call_tir(cls.take_sorted_probs, (probs, lv1), out_sinfo=R.Tensor((batch_size, vocab_size), dtype="float32")) + gv1: R.Tuple(R.Tensor((batch_size, vocab_size), dtype="float32"), R.Tensor((batch_size, vocab_size), dtype="int32")) = lv2, lv1 + R.output(gv1) + return gv1 + + @R.function + def batch_compute_cross_attn_kv(encoder_hidden_states: R.Tensor(("batch_size", 1500, 1280), dtype="float16"), paged_kv_cache: R.Object, packed_params: R.Tuple(R.Tensor((1280, 128, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1500, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), 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R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"))) -> R.Object: + batch_size = T.int64() + R.func_attr({"num_input": 2, "relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "seq_len": 15000, "total_seq_len": 1500}}) + with R.dataflow(): + model_encoder_conv1_weight1: R.Tensor((1280, 128, 3), dtype="float16") = packed_params[0] + model_encoder_conv1_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1] + model_encoder_conv2_weight1: R.Tensor((1280, 1280, 3), dtype="float16") = packed_params[2] + model_encoder_conv2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[3] + model_encoder_embed_positions_weight1: R.Tensor((1500, 1280), dtype="float16") = packed_params[4] + model_encoder_layers_0_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[5] + model_encoder_layers_0_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[6] + model_encoder_layers_0_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[7] + model_encoder_layers_0_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[8] + model_encoder_layers_0_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[9] + model_encoder_layers_0_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[10] + model_encoder_layers_0_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[11] + model_encoder_layers_0_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[12] + model_encoder_layers_0_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[13] + model_encoder_layers_0_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[14] + model_encoder_layers_0_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[15] + model_encoder_layers_0_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[16] + model_encoder_layers_0_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[17] + model_encoder_layers_0_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[18] + model_encoder_layers_0_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[19] + model_encoder_layers_1_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[20] + model_encoder_layers_1_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[21] + model_encoder_layers_1_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[22] + model_encoder_layers_1_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[23] + model_encoder_layers_1_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[24] + model_encoder_layers_1_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[25] + model_encoder_layers_1_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[26] + model_encoder_layers_1_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[27] + model_encoder_layers_1_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[28] + model_encoder_layers_1_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[29] + model_encoder_layers_1_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[30] + model_encoder_layers_1_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[31] + model_encoder_layers_1_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[32] + model_encoder_layers_1_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[33] + model_encoder_layers_1_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[34] + model_encoder_layers_2_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[35] + model_encoder_layers_2_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[36] + model_encoder_layers_2_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[37] + model_encoder_layers_2_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[38] + model_encoder_layers_2_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[39] + model_encoder_layers_2_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[40] + model_encoder_layers_2_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[41] + model_encoder_layers_2_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[42] + model_encoder_layers_2_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[43] + model_encoder_layers_2_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[44] + model_encoder_layers_2_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[45] + model_encoder_layers_2_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[46] + model_encoder_layers_2_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[47] + model_encoder_layers_2_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[48] + model_encoder_layers_2_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[49] + model_encoder_layers_3_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[50] + model_encoder_layers_3_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[51] + model_encoder_layers_3_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[52] + model_encoder_layers_3_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[53] + model_encoder_layers_3_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[54] + model_encoder_layers_3_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[55] + model_encoder_layers_3_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[56] + model_encoder_layers_3_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[57] + model_encoder_layers_3_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[58] + model_encoder_layers_3_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[59] + model_encoder_layers_3_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[60] + model_encoder_layers_3_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[61] + model_encoder_layers_3_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[62] + model_encoder_layers_3_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[63] + model_encoder_layers_3_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[64] + model_encoder_layers_4_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[65] + model_encoder_layers_4_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[66] + model_encoder_layers_4_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[67] + model_encoder_layers_4_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[68] + model_encoder_layers_4_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[69] + model_encoder_layers_4_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[70] + model_encoder_layers_4_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[71] + model_encoder_layers_4_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[72] + model_encoder_layers_4_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[73] + model_encoder_layers_4_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[74] + model_encoder_layers_4_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[75] + model_encoder_layers_4_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[76] + model_encoder_layers_4_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[77] + model_encoder_layers_4_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[78] + model_encoder_layers_4_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[79] + model_encoder_layers_5_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[80] + model_encoder_layers_5_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[81] + model_encoder_layers_5_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[82] + model_encoder_layers_5_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[83] + model_encoder_layers_5_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[84] + model_encoder_layers_5_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[85] + model_encoder_layers_5_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[86] + model_encoder_layers_5_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[87] + model_encoder_layers_5_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[88] + model_encoder_layers_5_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[89] + model_encoder_layers_5_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[90] + model_encoder_layers_5_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[91] + model_encoder_layers_5_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[92] + model_encoder_layers_5_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[93] + model_encoder_layers_5_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[94] + model_encoder_layers_6_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[95] + model_encoder_layers_6_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[96] + model_encoder_layers_6_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[97] + model_encoder_layers_6_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[98] + model_encoder_layers_6_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[99] + model_encoder_layers_6_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[100] + model_encoder_layers_6_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[101] + model_encoder_layers_6_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[102] + model_encoder_layers_6_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[103] + model_encoder_layers_6_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[104] + model_encoder_layers_6_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[105] + model_encoder_layers_6_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[106] + model_encoder_layers_6_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[107] + model_encoder_layers_6_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[108] + model_encoder_layers_6_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[109] + model_encoder_layers_7_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[110] + model_encoder_layers_7_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[111] + model_encoder_layers_7_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[112] + model_encoder_layers_7_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[113] + model_encoder_layers_7_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[114] + model_encoder_layers_7_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[115] + model_encoder_layers_7_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[116] + model_encoder_layers_7_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[117] + model_encoder_layers_7_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[118] + model_encoder_layers_7_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[119] + model_encoder_layers_7_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[120] + model_encoder_layers_7_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[121] + model_encoder_layers_7_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[122] + model_encoder_layers_7_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[123] + model_encoder_layers_7_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[124] + model_encoder_layers_8_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[125] + model_encoder_layers_8_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[126] + model_encoder_layers_8_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[127] + model_encoder_layers_8_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[128] + model_encoder_layers_8_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[129] + model_encoder_layers_8_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[130] + model_encoder_layers_8_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[131] + model_encoder_layers_8_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[132] + model_encoder_layers_8_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[133] + model_encoder_layers_8_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[134] + model_encoder_layers_8_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[135] + model_encoder_layers_8_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[136] + model_encoder_layers_8_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[137] + model_encoder_layers_8_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[138] + model_encoder_layers_8_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[139] + model_encoder_layers_9_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[140] + model_encoder_layers_9_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[141] + model_encoder_layers_9_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[142] + model_encoder_layers_9_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[143] + model_encoder_layers_9_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[144] + model_encoder_layers_9_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[145] + model_encoder_layers_9_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[146] + model_encoder_layers_9_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[147] + model_encoder_layers_9_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[148] + model_encoder_layers_9_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[149] + model_encoder_layers_9_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[150] + model_encoder_layers_9_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[151] + model_encoder_layers_9_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[152] + model_encoder_layers_9_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[153] + model_encoder_layers_9_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[154] + model_encoder_layers_10_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[155] + model_encoder_layers_10_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[156] + model_encoder_layers_10_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[157] + model_encoder_layers_10_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[158] + model_encoder_layers_10_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[159] + model_encoder_layers_10_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[160] + model_encoder_layers_10_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[161] + model_encoder_layers_10_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[162] + model_encoder_layers_10_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[163] + model_encoder_layers_10_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[164] + model_encoder_layers_10_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[165] + model_encoder_layers_10_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[166] + model_encoder_layers_10_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[167] + model_encoder_layers_10_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[168] + model_encoder_layers_10_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[169] + model_encoder_layers_11_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[170] + model_encoder_layers_11_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[171] + model_encoder_layers_11_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[172] + model_encoder_layers_11_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[173] + model_encoder_layers_11_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[174] + model_encoder_layers_11_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[175] + model_encoder_layers_11_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[176] + model_encoder_layers_11_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[177] + model_encoder_layers_11_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[178] + model_encoder_layers_11_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[179] + model_encoder_layers_11_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[180] + model_encoder_layers_11_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[181] + model_encoder_layers_11_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[182] + model_encoder_layers_11_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[183] + model_encoder_layers_11_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[184] + model_encoder_layers_12_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[185] + model_encoder_layers_12_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[186] + model_encoder_layers_12_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[187] + model_encoder_layers_12_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[188] + model_encoder_layers_12_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[189] + model_encoder_layers_12_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[190] + model_encoder_layers_12_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[191] + model_encoder_layers_12_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[192] + model_encoder_layers_12_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[193] + model_encoder_layers_12_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[194] + model_encoder_layers_12_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[195] + model_encoder_layers_12_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[196] + model_encoder_layers_12_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[197] + model_encoder_layers_12_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[198] + model_encoder_layers_12_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[199] + model_encoder_layers_13_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[200] + model_encoder_layers_13_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[201] + model_encoder_layers_13_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[202] + model_encoder_layers_13_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[203] + model_encoder_layers_13_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[204] + model_encoder_layers_13_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[205] + model_encoder_layers_13_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[206] + model_encoder_layers_13_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[207] + model_encoder_layers_13_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[208] + model_encoder_layers_13_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[209] + model_encoder_layers_13_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[210] + model_encoder_layers_13_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[211] + model_encoder_layers_13_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[212] + model_encoder_layers_13_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[213] + model_encoder_layers_13_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[214] + model_encoder_layers_14_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[215] + model_encoder_layers_14_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[216] + model_encoder_layers_14_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[217] + model_encoder_layers_14_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[218] + model_encoder_layers_14_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[219] + model_encoder_layers_14_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[220] + model_encoder_layers_14_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[221] + model_encoder_layers_14_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[222] + model_encoder_layers_14_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[223] + model_encoder_layers_14_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[224] + model_encoder_layers_14_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[225] + model_encoder_layers_14_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[226] + model_encoder_layers_14_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[227] + model_encoder_layers_14_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[228] + model_encoder_layers_14_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[229] + model_encoder_layers_15_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[230] + model_encoder_layers_15_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[231] + model_encoder_layers_15_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[232] + model_encoder_layers_15_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[233] + model_encoder_layers_15_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[234] + model_encoder_layers_15_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[235] + model_encoder_layers_15_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[236] + model_encoder_layers_15_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[237] + model_encoder_layers_15_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[238] + model_encoder_layers_15_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[239] + model_encoder_layers_15_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[240] + model_encoder_layers_15_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[241] + model_encoder_layers_15_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[242] + model_encoder_layers_15_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[243] + model_encoder_layers_15_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[244] + model_encoder_layers_16_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[245] + model_encoder_layers_16_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[246] + model_encoder_layers_16_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[247] + model_encoder_layers_16_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[248] + model_encoder_layers_16_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[249] + model_encoder_layers_16_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[250] + model_encoder_layers_16_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[251] + model_encoder_layers_16_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[252] + model_encoder_layers_16_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[253] + model_encoder_layers_16_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[254] + model_encoder_layers_16_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[255] + model_encoder_layers_16_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[256] + model_encoder_layers_16_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[257] + model_encoder_layers_16_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[258] + model_encoder_layers_16_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[259] + model_encoder_layers_17_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[260] + model_encoder_layers_17_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[261] + model_encoder_layers_17_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[262] + model_encoder_layers_17_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[263] + model_encoder_layers_17_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[264] + model_encoder_layers_17_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[265] + model_encoder_layers_17_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[266] + model_encoder_layers_17_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[267] + model_encoder_layers_17_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[268] + model_encoder_layers_17_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[269] + model_encoder_layers_17_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[270] + model_encoder_layers_17_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[271] + model_encoder_layers_17_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[272] + model_encoder_layers_17_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[273] + model_encoder_layers_17_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[274] + model_encoder_layers_18_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[275] + model_encoder_layers_18_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[276] + model_encoder_layers_18_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[277] + model_encoder_layers_18_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[278] + model_encoder_layers_18_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[279] + model_encoder_layers_18_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[280] + model_encoder_layers_18_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[281] + model_encoder_layers_18_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[282] + model_encoder_layers_18_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[283] + model_encoder_layers_18_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[284] + model_encoder_layers_18_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[285] + model_encoder_layers_18_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[286] + model_encoder_layers_18_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[287] + model_encoder_layers_18_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[288] + model_encoder_layers_18_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[289] + model_encoder_layers_19_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[290] + model_encoder_layers_19_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[291] + model_encoder_layers_19_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[292] + model_encoder_layers_19_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[293] + model_encoder_layers_19_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[294] + model_encoder_layers_19_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[295] + model_encoder_layers_19_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[296] + model_encoder_layers_19_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[297] + model_encoder_layers_19_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[298] + model_encoder_layers_19_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[299] + model_encoder_layers_19_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[300] + model_encoder_layers_19_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[301] + model_encoder_layers_19_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[302] + model_encoder_layers_19_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[303] + model_encoder_layers_19_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[304] + model_encoder_layers_20_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[305] + model_encoder_layers_20_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[306] + model_encoder_layers_20_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[307] + model_encoder_layers_20_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[308] + model_encoder_layers_20_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[309] + model_encoder_layers_20_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[310] + model_encoder_layers_20_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[311] + model_encoder_layers_20_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[312] + model_encoder_layers_20_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[313] + model_encoder_layers_20_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[314] + model_encoder_layers_20_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[315] + model_encoder_layers_20_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[316] + model_encoder_layers_20_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[317] + model_encoder_layers_20_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[318] + model_encoder_layers_20_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[319] + model_encoder_layers_21_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[320] + model_encoder_layers_21_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[321] + model_encoder_layers_21_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[322] + model_encoder_layers_21_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[323] + model_encoder_layers_21_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[324] + model_encoder_layers_21_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[325] + model_encoder_layers_21_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[326] + model_encoder_layers_21_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[327] + model_encoder_layers_21_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[328] + model_encoder_layers_21_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[329] + model_encoder_layers_21_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[330] + model_encoder_layers_21_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[331] + model_encoder_layers_21_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[332] + model_encoder_layers_21_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[333] + model_encoder_layers_21_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[334] + model_encoder_layers_22_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[335] + model_encoder_layers_22_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[336] + model_encoder_layers_22_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[337] + model_encoder_layers_22_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[338] + model_encoder_layers_22_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[339] + model_encoder_layers_22_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[340] + model_encoder_layers_22_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[341] + model_encoder_layers_22_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[342] + model_encoder_layers_22_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[343] + model_encoder_layers_22_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[344] + model_encoder_layers_22_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[345] + model_encoder_layers_22_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[346] + model_encoder_layers_22_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[347] + model_encoder_layers_22_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[348] + model_encoder_layers_22_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[349] + model_encoder_layers_23_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[350] + model_encoder_layers_23_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[351] + model_encoder_layers_23_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[352] + model_encoder_layers_23_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[353] + model_encoder_layers_23_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[354] + model_encoder_layers_23_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[355] + model_encoder_layers_23_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[356] + model_encoder_layers_23_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[357] + model_encoder_layers_23_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[358] + model_encoder_layers_23_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[359] + model_encoder_layers_23_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[360] + model_encoder_layers_23_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[361] + model_encoder_layers_23_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[362] + model_encoder_layers_23_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[363] + model_encoder_layers_23_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[364] + model_encoder_layers_24_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[365] + model_encoder_layers_24_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[366] + model_encoder_layers_24_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[367] + model_encoder_layers_24_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[368] + model_encoder_layers_24_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[369] + model_encoder_layers_24_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[370] + model_encoder_layers_24_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[371] + model_encoder_layers_24_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[372] + model_encoder_layers_24_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[373] + model_encoder_layers_24_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[374] + model_encoder_layers_24_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[375] + model_encoder_layers_24_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[376] + model_encoder_layers_24_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[377] + model_encoder_layers_24_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[378] + model_encoder_layers_24_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[379] + model_encoder_layers_25_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[380] + model_encoder_layers_25_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[381] + model_encoder_layers_25_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[382] + model_encoder_layers_25_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[383] + model_encoder_layers_25_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[384] + model_encoder_layers_25_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[385] + model_encoder_layers_25_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[386] + model_encoder_layers_25_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[387] + model_encoder_layers_25_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[388] + model_encoder_layers_25_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[389] + model_encoder_layers_25_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[390] + model_encoder_layers_25_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[391] + model_encoder_layers_25_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[392] + model_encoder_layers_25_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[393] + model_encoder_layers_25_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[394] + model_encoder_layers_26_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[395] + model_encoder_layers_26_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[396] + model_encoder_layers_26_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[397] + model_encoder_layers_26_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[398] + model_encoder_layers_26_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[399] + model_encoder_layers_26_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[400] + model_encoder_layers_26_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[401] + model_encoder_layers_26_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[402] + model_encoder_layers_26_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[403] + model_encoder_layers_26_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[404] + model_encoder_layers_26_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[405] + model_encoder_layers_26_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[406] + model_encoder_layers_26_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[407] + model_encoder_layers_26_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[408] + model_encoder_layers_26_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[409] + model_encoder_layers_27_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[410] + model_encoder_layers_27_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[411] + model_encoder_layers_27_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[412] + model_encoder_layers_27_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[413] + model_encoder_layers_27_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[414] + model_encoder_layers_27_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[415] + model_encoder_layers_27_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[416] + model_encoder_layers_27_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[417] + model_encoder_layers_27_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[418] + model_encoder_layers_27_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[419] + model_encoder_layers_27_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[420] + model_encoder_layers_27_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[421] + model_encoder_layers_27_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[422] + model_encoder_layers_27_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[423] + model_encoder_layers_27_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[424] + model_encoder_layers_28_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[425] + model_encoder_layers_28_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[426] + model_encoder_layers_28_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[427] + model_encoder_layers_28_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[428] + model_encoder_layers_28_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[429] + model_encoder_layers_28_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[430] + model_encoder_layers_28_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[431] + model_encoder_layers_28_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[432] + model_encoder_layers_28_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[433] + model_encoder_layers_28_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[434] + model_encoder_layers_28_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[435] + model_encoder_layers_28_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[436] + model_encoder_layers_28_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[437] + model_encoder_layers_28_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[438] + model_encoder_layers_28_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[439] + model_encoder_layers_29_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[440] + model_encoder_layers_29_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[441] + model_encoder_layers_29_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[442] + model_encoder_layers_29_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[443] + model_encoder_layers_29_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[444] + model_encoder_layers_29_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[445] + model_encoder_layers_29_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[446] + model_encoder_layers_29_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[447] + model_encoder_layers_29_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[448] + model_encoder_layers_29_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[449] + model_encoder_layers_29_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[450] + model_encoder_layers_29_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[451] + model_encoder_layers_29_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[452] + model_encoder_layers_29_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[453] + model_encoder_layers_29_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[454] + model_encoder_layers_30_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[455] + model_encoder_layers_30_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[456] + model_encoder_layers_30_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[457] + model_encoder_layers_30_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[458] + model_encoder_layers_30_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[459] + model_encoder_layers_30_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[460] + model_encoder_layers_30_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[461] + model_encoder_layers_30_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[462] + model_encoder_layers_30_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[463] + model_encoder_layers_30_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[464] + model_encoder_layers_30_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[465] + model_encoder_layers_30_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[466] + model_encoder_layers_30_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[467] + model_encoder_layers_30_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[468] + model_encoder_layers_30_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[469] + model_encoder_layers_31_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[470] + model_encoder_layers_31_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[471] + model_encoder_layers_31_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[472] + model_encoder_layers_31_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[473] + model_encoder_layers_31_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[474] + model_encoder_layers_31_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[475] + model_encoder_layers_31_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[476] + model_encoder_layers_31_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[477] + model_encoder_layers_31_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[478] + model_encoder_layers_31_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[479] + model_encoder_layers_31_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[480] + model_encoder_layers_31_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[481] + model_encoder_layers_31_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[482] + model_encoder_layers_31_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[483] + model_encoder_layers_31_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[484] + model_encoder_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[485] + model_encoder_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[486] + model_decoder_embed_tokens_weight1: R.Tensor((51866, 1280), dtype="float16") = packed_params[487] + model_decoder_embed_positions_weight1: R.Tensor((448, 1280), dtype="float16") = packed_params[488] + model_decoder_layers_0_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[489] + model_decoder_layers_0_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[490] + model_decoder_layers_0_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[491] + model_decoder_layers_0_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[492] + model_decoder_layers_0_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[493] + model_decoder_layers_0_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[494] + model_decoder_layers_0_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[495] + model_decoder_layers_0_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[496] + model_decoder_layers_0_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[497] + model_decoder_layers_0_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[498] + model_decoder_layers_0_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[499] + model_decoder_layers_0_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[500] + model_decoder_layers_0_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[501] + model_decoder_layers_0_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[502] + model_decoder_layers_0_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[503] + model_decoder_layers_0_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[504] + model_decoder_layers_0_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[505] + model_decoder_layers_0_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[506] + model_decoder_layers_0_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[507] + model_decoder_layers_0_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[508] + model_decoder_layers_0_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[509] + model_decoder_layers_0_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[510] + model_decoder_layers_0_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[511] + model_decoder_layers_0_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[512] + model_decoder_layers_1_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[513] + model_decoder_layers_1_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[514] + model_decoder_layers_1_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[515] + model_decoder_layers_1_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[516] + model_decoder_layers_1_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[517] + model_decoder_layers_1_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[518] + model_decoder_layers_1_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[519] + model_decoder_layers_1_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[520] + model_decoder_layers_1_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[521] + model_decoder_layers_1_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[522] + model_decoder_layers_1_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[523] + model_decoder_layers_1_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[524] + model_decoder_layers_1_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[525] + model_decoder_layers_1_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[526] + model_decoder_layers_1_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[527] + model_decoder_layers_1_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[528] + model_decoder_layers_1_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[529] + model_decoder_layers_1_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[530] + model_decoder_layers_1_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[531] + model_decoder_layers_1_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[532] + model_decoder_layers_1_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[533] + model_decoder_layers_1_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[534] + model_decoder_layers_1_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[535] + model_decoder_layers_1_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[536] + model_decoder_layers_2_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[537] + model_decoder_layers_2_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[538] + model_decoder_layers_2_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[539] + model_decoder_layers_2_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[540] + model_decoder_layers_2_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[541] + model_decoder_layers_2_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[542] + model_decoder_layers_2_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[543] + model_decoder_layers_2_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[544] + model_decoder_layers_2_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[545] + model_decoder_layers_2_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[546] + model_decoder_layers_2_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[547] + model_decoder_layers_2_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[548] + model_decoder_layers_2_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[549] + model_decoder_layers_2_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[550] + model_decoder_layers_2_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[551] + model_decoder_layers_2_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[552] + model_decoder_layers_2_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[553] + model_decoder_layers_2_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[554] + model_decoder_layers_2_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[555] + model_decoder_layers_2_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[556] + model_decoder_layers_2_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[557] + model_decoder_layers_2_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[558] + model_decoder_layers_2_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[559] + model_decoder_layers_2_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[560] + model_decoder_layers_3_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[561] + model_decoder_layers_3_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[562] + model_decoder_layers_3_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[563] + model_decoder_layers_3_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[564] + model_decoder_layers_3_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[565] + model_decoder_layers_3_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[566] + model_decoder_layers_3_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[567] + model_decoder_layers_3_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[568] + model_decoder_layers_3_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[569] + model_decoder_layers_3_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[570] + model_decoder_layers_3_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[571] + model_decoder_layers_3_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[572] + model_decoder_layers_3_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[573] + model_decoder_layers_3_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[574] + model_decoder_layers_3_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[575] + model_decoder_layers_3_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[576] + model_decoder_layers_3_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[577] + model_decoder_layers_3_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[578] + model_decoder_layers_3_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[579] + model_decoder_layers_3_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[580] + model_decoder_layers_3_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[581] + model_decoder_layers_3_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[582] + model_decoder_layers_3_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[583] + model_decoder_layers_3_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[584] + model_decoder_layers_4_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[585] + model_decoder_layers_4_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[586] + model_decoder_layers_4_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[587] + model_decoder_layers_4_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[588] + model_decoder_layers_4_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[589] + model_decoder_layers_4_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[590] + model_decoder_layers_4_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[591] + model_decoder_layers_4_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[592] + model_decoder_layers_4_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[593] + model_decoder_layers_4_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[594] + model_decoder_layers_4_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[595] + model_decoder_layers_4_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[596] + model_decoder_layers_4_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[597] + model_decoder_layers_4_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[598] + model_decoder_layers_4_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[599] + model_decoder_layers_4_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[600] + model_decoder_layers_4_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[601] + model_decoder_layers_4_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[602] + model_decoder_layers_4_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[603] + model_decoder_layers_4_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[604] + model_decoder_layers_4_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[605] + model_decoder_layers_4_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[606] + model_decoder_layers_4_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[607] + model_decoder_layers_4_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[608] + model_decoder_layers_5_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[609] + model_decoder_layers_5_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[610] + model_decoder_layers_5_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[611] + model_decoder_layers_5_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[612] + model_decoder_layers_5_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[613] + model_decoder_layers_5_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[614] + model_decoder_layers_5_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[615] + model_decoder_layers_5_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[616] + model_decoder_layers_5_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[617] + model_decoder_layers_5_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[618] + model_decoder_layers_5_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[619] + model_decoder_layers_5_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[620] + model_decoder_layers_5_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[621] + model_decoder_layers_5_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[622] + model_decoder_layers_5_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[623] + model_decoder_layers_5_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[624] + model_decoder_layers_5_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[625] + model_decoder_layers_5_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[626] + model_decoder_layers_5_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[627] + model_decoder_layers_5_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[628] + model_decoder_layers_5_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[629] + model_decoder_layers_5_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[630] + model_decoder_layers_5_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[631] + model_decoder_layers_5_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[632] + model_decoder_layers_6_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[633] + model_decoder_layers_6_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[634] + model_decoder_layers_6_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[635] + model_decoder_layers_6_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[636] + model_decoder_layers_6_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[637] + model_decoder_layers_6_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[638] + model_decoder_layers_6_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[639] + model_decoder_layers_6_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[640] + model_decoder_layers_6_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[641] + model_decoder_layers_6_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[642] + model_decoder_layers_6_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[643] + model_decoder_layers_6_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[644] + model_decoder_layers_6_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[645] + model_decoder_layers_6_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[646] + model_decoder_layers_6_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[647] + model_decoder_layers_6_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[648] + model_decoder_layers_6_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[649] + model_decoder_layers_6_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[650] + model_decoder_layers_6_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[651] + model_decoder_layers_6_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[652] + model_decoder_layers_6_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[653] + model_decoder_layers_6_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[654] + model_decoder_layers_6_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[655] + model_decoder_layers_6_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[656] + model_decoder_layers_7_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[657] + model_decoder_layers_7_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[658] + model_decoder_layers_7_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[659] + model_decoder_layers_7_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[660] + model_decoder_layers_7_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[661] + model_decoder_layers_7_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[662] + model_decoder_layers_7_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[663] + model_decoder_layers_7_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[664] + model_decoder_layers_7_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[665] + model_decoder_layers_7_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[666] + model_decoder_layers_7_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[667] + model_decoder_layers_7_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[668] + model_decoder_layers_7_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[669] + model_decoder_layers_7_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[670] + model_decoder_layers_7_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[671] + model_decoder_layers_7_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[672] + model_decoder_layers_7_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[673] + model_decoder_layers_7_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[674] + model_decoder_layers_7_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[675] + model_decoder_layers_7_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[676] + model_decoder_layers_7_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[677] + model_decoder_layers_7_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[678] + model_decoder_layers_7_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[679] + model_decoder_layers_7_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[680] + model_decoder_layers_8_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[681] + model_decoder_layers_8_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[682] + model_decoder_layers_8_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[683] + model_decoder_layers_8_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[684] + model_decoder_layers_8_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[685] + model_decoder_layers_8_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[686] + model_decoder_layers_8_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[687] + model_decoder_layers_8_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[688] + model_decoder_layers_8_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[689] + model_decoder_layers_8_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[690] + model_decoder_layers_8_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[691] + model_decoder_layers_8_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[692] + model_decoder_layers_8_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[693] + model_decoder_layers_8_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[694] + model_decoder_layers_8_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[695] + model_decoder_layers_8_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[696] + model_decoder_layers_8_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[697] + model_decoder_layers_8_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[698] + model_decoder_layers_8_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[699] + model_decoder_layers_8_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[700] + model_decoder_layers_8_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[701] + model_decoder_layers_8_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[702] + model_decoder_layers_8_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[703] + model_decoder_layers_8_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[704] + model_decoder_layers_9_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[705] + model_decoder_layers_9_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[706] + model_decoder_layers_9_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[707] + model_decoder_layers_9_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[708] + model_decoder_layers_9_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[709] + model_decoder_layers_9_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[710] + model_decoder_layers_9_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[711] + model_decoder_layers_9_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[712] + model_decoder_layers_9_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[713] + model_decoder_layers_9_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[714] + model_decoder_layers_9_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[715] + model_decoder_layers_9_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[716] + model_decoder_layers_9_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[717] + model_decoder_layers_9_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[718] + model_decoder_layers_9_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[719] + model_decoder_layers_9_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[720] + model_decoder_layers_9_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[721] + model_decoder_layers_9_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[722] + model_decoder_layers_9_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[723] + model_decoder_layers_9_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[724] + model_decoder_layers_9_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[725] + model_decoder_layers_9_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[726] + model_decoder_layers_9_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[727] + model_decoder_layers_9_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[728] + model_decoder_layers_10_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[729] + model_decoder_layers_10_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[730] + model_decoder_layers_10_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[731] + model_decoder_layers_10_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[732] + model_decoder_layers_10_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[733] + model_decoder_layers_10_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[734] + model_decoder_layers_10_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[735] + model_decoder_layers_10_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[736] + model_decoder_layers_10_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[737] + model_decoder_layers_10_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[738] + model_decoder_layers_10_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[739] + model_decoder_layers_10_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[740] + model_decoder_layers_10_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[741] + model_decoder_layers_10_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[742] + model_decoder_layers_10_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[743] + model_decoder_layers_10_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[744] + model_decoder_layers_10_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[745] + model_decoder_layers_10_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[746] + model_decoder_layers_10_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[747] + model_decoder_layers_10_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[748] + model_decoder_layers_10_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[749] + model_decoder_layers_10_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[750] + model_decoder_layers_10_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[751] + model_decoder_layers_10_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[752] + model_decoder_layers_11_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[753] + model_decoder_layers_11_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[754] + model_decoder_layers_11_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[755] + model_decoder_layers_11_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[756] + model_decoder_layers_11_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[757] + model_decoder_layers_11_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[758] + model_decoder_layers_11_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[759] + model_decoder_layers_11_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[760] + model_decoder_layers_11_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[761] + model_decoder_layers_11_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[762] + model_decoder_layers_11_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[763] + model_decoder_layers_11_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[764] + model_decoder_layers_11_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[765] + model_decoder_layers_11_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[766] + model_decoder_layers_11_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[767] + model_decoder_layers_11_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[768] + model_decoder_layers_11_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[769] + model_decoder_layers_11_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[770] + model_decoder_layers_11_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[771] + model_decoder_layers_11_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[772] + model_decoder_layers_11_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[773] + model_decoder_layers_11_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[774] + model_decoder_layers_11_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[775] + model_decoder_layers_11_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[776] + model_decoder_layers_12_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[777] + model_decoder_layers_12_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[778] + model_decoder_layers_12_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[779] + model_decoder_layers_12_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[780] + model_decoder_layers_12_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[781] + model_decoder_layers_12_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[782] + model_decoder_layers_12_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[783] + model_decoder_layers_12_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[784] + model_decoder_layers_12_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[785] + model_decoder_layers_12_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[786] + model_decoder_layers_12_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[787] + model_decoder_layers_12_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[788] + model_decoder_layers_12_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[789] + model_decoder_layers_12_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[790] + model_decoder_layers_12_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[791] + model_decoder_layers_12_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[792] + model_decoder_layers_12_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[793] + model_decoder_layers_12_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[794] + model_decoder_layers_12_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[795] + model_decoder_layers_12_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[796] + model_decoder_layers_12_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[797] + model_decoder_layers_12_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[798] + model_decoder_layers_12_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[799] + model_decoder_layers_12_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[800] + model_decoder_layers_13_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[801] + model_decoder_layers_13_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[802] + model_decoder_layers_13_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[803] + model_decoder_layers_13_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[804] + model_decoder_layers_13_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[805] + model_decoder_layers_13_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[806] + model_decoder_layers_13_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[807] + model_decoder_layers_13_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[808] + model_decoder_layers_13_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[809] + model_decoder_layers_13_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[810] + model_decoder_layers_13_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[811] + model_decoder_layers_13_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[812] + model_decoder_layers_13_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[813] + model_decoder_layers_13_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[814] + model_decoder_layers_13_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[815] + model_decoder_layers_13_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[816] + model_decoder_layers_13_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[817] + model_decoder_layers_13_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[818] + model_decoder_layers_13_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[819] + model_decoder_layers_13_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[820] + model_decoder_layers_13_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[821] + model_decoder_layers_13_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[822] + model_decoder_layers_13_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[823] + model_decoder_layers_13_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[824] + model_decoder_layers_14_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[825] + model_decoder_layers_14_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[826] + model_decoder_layers_14_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[827] + model_decoder_layers_14_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[828] + model_decoder_layers_14_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[829] + model_decoder_layers_14_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[830] + model_decoder_layers_14_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[831] + model_decoder_layers_14_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[832] + model_decoder_layers_14_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[833] + model_decoder_layers_14_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[834] + model_decoder_layers_14_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[835] + model_decoder_layers_14_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[836] + model_decoder_layers_14_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[837] + model_decoder_layers_14_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[838] + model_decoder_layers_14_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[839] + model_decoder_layers_14_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[840] + model_decoder_layers_14_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[841] + model_decoder_layers_14_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[842] + model_decoder_layers_14_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[843] + model_decoder_layers_14_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[844] + model_decoder_layers_14_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[845] + model_decoder_layers_14_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[846] + model_decoder_layers_14_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[847] + model_decoder_layers_14_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[848] + model_decoder_layers_15_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[849] + model_decoder_layers_15_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[850] + model_decoder_layers_15_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[851] + model_decoder_layers_15_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[852] + model_decoder_layers_15_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[853] + model_decoder_layers_15_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[854] + model_decoder_layers_15_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[855] + model_decoder_layers_15_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[856] + model_decoder_layers_15_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[857] + model_decoder_layers_15_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[858] + model_decoder_layers_15_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[859] + model_decoder_layers_15_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[860] + model_decoder_layers_15_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[861] + model_decoder_layers_15_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[862] + model_decoder_layers_15_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[863] + model_decoder_layers_15_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[864] + model_decoder_layers_15_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[865] + model_decoder_layers_15_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[866] + model_decoder_layers_15_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[867] + model_decoder_layers_15_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[868] + model_decoder_layers_15_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[869] + model_decoder_layers_15_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[870] + model_decoder_layers_15_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[871] + model_decoder_layers_15_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[872] + model_decoder_layers_16_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[873] + model_decoder_layers_16_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[874] + model_decoder_layers_16_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[875] + model_decoder_layers_16_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[876] + model_decoder_layers_16_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[877] + model_decoder_layers_16_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[878] + model_decoder_layers_16_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[879] + model_decoder_layers_16_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[880] + model_decoder_layers_16_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[881] + model_decoder_layers_16_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[882] + model_decoder_layers_16_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[883] + model_decoder_layers_16_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[884] + model_decoder_layers_16_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[885] + model_decoder_layers_16_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[886] + model_decoder_layers_16_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[887] + model_decoder_layers_16_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[888] + model_decoder_layers_16_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[889] + model_decoder_layers_16_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[890] + model_decoder_layers_16_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[891] + model_decoder_layers_16_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[892] + model_decoder_layers_16_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[893] + model_decoder_layers_16_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[894] + model_decoder_layers_16_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[895] + model_decoder_layers_16_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[896] + model_decoder_layers_17_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[897] + model_decoder_layers_17_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[898] + model_decoder_layers_17_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[899] + model_decoder_layers_17_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[900] + model_decoder_layers_17_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[901] + model_decoder_layers_17_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[902] + model_decoder_layers_17_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[903] + model_decoder_layers_17_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[904] + model_decoder_layers_17_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[905] + model_decoder_layers_17_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[906] + model_decoder_layers_17_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[907] + model_decoder_layers_17_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[908] + model_decoder_layers_17_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[909] + model_decoder_layers_17_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[910] + model_decoder_layers_17_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[911] + model_decoder_layers_17_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[912] + model_decoder_layers_17_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[913] + model_decoder_layers_17_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[914] + model_decoder_layers_17_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[915] + model_decoder_layers_17_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[916] + model_decoder_layers_17_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[917] + model_decoder_layers_17_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[918] + model_decoder_layers_17_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[919] + model_decoder_layers_17_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[920] + model_decoder_layers_18_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[921] + model_decoder_layers_18_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[922] + model_decoder_layers_18_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[923] + model_decoder_layers_18_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[924] + model_decoder_layers_18_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[925] + model_decoder_layers_18_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[926] + model_decoder_layers_18_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[927] + model_decoder_layers_18_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[928] + model_decoder_layers_18_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[929] + model_decoder_layers_18_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[930] + model_decoder_layers_18_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[931] + model_decoder_layers_18_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[932] + model_decoder_layers_18_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[933] + model_decoder_layers_18_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[934] + model_decoder_layers_18_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[935] + model_decoder_layers_18_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[936] + model_decoder_layers_18_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[937] + model_decoder_layers_18_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[938] + model_decoder_layers_18_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[939] + model_decoder_layers_18_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[940] + model_decoder_layers_18_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[941] + model_decoder_layers_18_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[942] + model_decoder_layers_18_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[943] + model_decoder_layers_18_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[944] + model_decoder_layers_19_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[945] + model_decoder_layers_19_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[946] + model_decoder_layers_19_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[947] + model_decoder_layers_19_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[948] + model_decoder_layers_19_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[949] + model_decoder_layers_19_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[950] + model_decoder_layers_19_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[951] + model_decoder_layers_19_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[952] + model_decoder_layers_19_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[953] + model_decoder_layers_19_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[954] + model_decoder_layers_19_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[955] + model_decoder_layers_19_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[956] + model_decoder_layers_19_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[957] + model_decoder_layers_19_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[958] + model_decoder_layers_19_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[959] + model_decoder_layers_19_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[960] + model_decoder_layers_19_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[961] + model_decoder_layers_19_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[962] + model_decoder_layers_19_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[963] + model_decoder_layers_19_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[964] + model_decoder_layers_19_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[965] + model_decoder_layers_19_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[966] + model_decoder_layers_19_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[967] + model_decoder_layers_19_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[968] + model_decoder_layers_20_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[969] + model_decoder_layers_20_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[970] + model_decoder_layers_20_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[971] + model_decoder_layers_20_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[972] + model_decoder_layers_20_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[973] + model_decoder_layers_20_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[974] + model_decoder_layers_20_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[975] + model_decoder_layers_20_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[976] + model_decoder_layers_20_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[977] + model_decoder_layers_20_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[978] + model_decoder_layers_20_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[979] + model_decoder_layers_20_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[980] + model_decoder_layers_20_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[981] + model_decoder_layers_20_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[982] + model_decoder_layers_20_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[983] + model_decoder_layers_20_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[984] + model_decoder_layers_20_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[985] + model_decoder_layers_20_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[986] + model_decoder_layers_20_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[987] + model_decoder_layers_20_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[988] + model_decoder_layers_20_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[989] + model_decoder_layers_20_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[990] + model_decoder_layers_20_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[991] + model_decoder_layers_20_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[992] + model_decoder_layers_21_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[993] + model_decoder_layers_21_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[994] + model_decoder_layers_21_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[995] + model_decoder_layers_21_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[996] + model_decoder_layers_21_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[997] + model_decoder_layers_21_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[998] + model_decoder_layers_21_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[999] + model_decoder_layers_21_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1000] + model_decoder_layers_21_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1001] + model_decoder_layers_21_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1002] + model_decoder_layers_21_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1003] + model_decoder_layers_21_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1004] + model_decoder_layers_21_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1005] + model_decoder_layers_21_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1006] + model_decoder_layers_21_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1007] + model_decoder_layers_21_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1008] + model_decoder_layers_21_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1009] + model_decoder_layers_21_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1010] + model_decoder_layers_21_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[1011] + model_decoder_layers_21_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[1012] + model_decoder_layers_21_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[1013] + model_decoder_layers_21_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1014] + model_decoder_layers_21_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1015] + model_decoder_layers_21_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1016] + model_decoder_layers_22_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1017] + model_decoder_layers_22_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1018] + model_decoder_layers_22_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1019] + model_decoder_layers_22_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1020] + model_decoder_layers_22_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1021] + model_decoder_layers_22_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1022] + model_decoder_layers_22_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1023] + model_decoder_layers_22_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1024] + model_decoder_layers_22_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1025] + model_decoder_layers_22_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1026] + model_decoder_layers_22_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1027] + model_decoder_layers_22_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1028] + model_decoder_layers_22_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1029] + model_decoder_layers_22_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1030] + model_decoder_layers_22_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1031] + model_decoder_layers_22_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1032] + model_decoder_layers_22_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1033] + model_decoder_layers_22_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1034] + model_decoder_layers_22_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[1035] + model_decoder_layers_22_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[1036] + model_decoder_layers_22_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[1037] + model_decoder_layers_22_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1038] + model_decoder_layers_22_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1039] + model_decoder_layers_22_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1040] + model_decoder_layers_23_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1041] + model_decoder_layers_23_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1042] + model_decoder_layers_23_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1043] + model_decoder_layers_23_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1044] + model_decoder_layers_23_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1045] + model_decoder_layers_23_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1046] + model_decoder_layers_23_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1047] + model_decoder_layers_23_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1048] + model_decoder_layers_23_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1049] + model_decoder_layers_23_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1050] + model_decoder_layers_23_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1051] + model_decoder_layers_23_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1052] + model_decoder_layers_23_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1053] + model_decoder_layers_23_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1054] + model_decoder_layers_23_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1055] + model_decoder_layers_23_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1056] + model_decoder_layers_23_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1057] + model_decoder_layers_23_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1058] + model_decoder_layers_23_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[1059] + model_decoder_layers_23_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[1060] + model_decoder_layers_23_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[1061] + model_decoder_layers_23_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1062] + model_decoder_layers_23_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1063] + model_decoder_layers_23_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1064] + model_decoder_layers_24_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1065] + model_decoder_layers_24_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1066] + model_decoder_layers_24_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1067] + model_decoder_layers_24_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1068] + model_decoder_layers_24_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1069] + model_decoder_layers_24_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1070] + model_decoder_layers_24_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1071] + model_decoder_layers_24_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1072] + model_decoder_layers_24_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1073] + model_decoder_layers_24_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1074] + model_decoder_layers_24_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1075] + model_decoder_layers_24_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1076] + model_decoder_layers_24_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1077] + model_decoder_layers_24_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1078] + model_decoder_layers_24_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1079] + model_decoder_layers_24_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1080] + model_decoder_layers_24_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1081] + model_decoder_layers_24_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1082] + model_decoder_layers_24_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[1083] + model_decoder_layers_24_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[1084] + model_decoder_layers_24_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[1085] + model_decoder_layers_24_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1086] + model_decoder_layers_24_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1087] + model_decoder_layers_24_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1088] + model_decoder_layers_25_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1089] + model_decoder_layers_25_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1090] + model_decoder_layers_25_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1091] + model_decoder_layers_25_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1092] + model_decoder_layers_25_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1093] + model_decoder_layers_25_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1094] + model_decoder_layers_25_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1095] + model_decoder_layers_25_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1096] + model_decoder_layers_25_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1097] + model_decoder_layers_25_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1098] + model_decoder_layers_25_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1099] + model_decoder_layers_25_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1100] + model_decoder_layers_25_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1101] + model_decoder_layers_25_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1102] + model_decoder_layers_25_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1103] + model_decoder_layers_25_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1104] + model_decoder_layers_25_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1105] + model_decoder_layers_25_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1106] + model_decoder_layers_25_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[1107] + model_decoder_layers_25_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[1108] + model_decoder_layers_25_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[1109] + model_decoder_layers_25_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1110] + model_decoder_layers_25_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1111] + model_decoder_layers_25_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1112] + model_decoder_layers_26_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1113] + model_decoder_layers_26_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1114] + model_decoder_layers_26_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1115] + model_decoder_layers_26_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1116] + model_decoder_layers_26_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1117] + model_decoder_layers_26_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1118] + model_decoder_layers_26_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1119] + model_decoder_layers_26_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1120] + model_decoder_layers_26_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1121] + model_decoder_layers_26_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1122] + model_decoder_layers_26_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1123] + model_decoder_layers_26_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1124] + model_decoder_layers_26_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1125] + model_decoder_layers_26_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1126] + model_decoder_layers_26_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1127] + model_decoder_layers_26_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1128] + model_decoder_layers_26_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1129] + model_decoder_layers_26_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1130] + model_decoder_layers_26_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[1131] + model_decoder_layers_26_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[1132] + model_decoder_layers_26_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[1133] + model_decoder_layers_26_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1134] + model_decoder_layers_26_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1135] + model_decoder_layers_26_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1136] + model_decoder_layers_27_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1137] + model_decoder_layers_27_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1138] + model_decoder_layers_27_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1139] + model_decoder_layers_27_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1140] + model_decoder_layers_27_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1141] + model_decoder_layers_27_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1142] + model_decoder_layers_27_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1143] + model_decoder_layers_27_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1144] + model_decoder_layers_27_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1145] + model_decoder_layers_27_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1146] + model_decoder_layers_27_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1147] + model_decoder_layers_27_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1148] + model_decoder_layers_27_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1149] + model_decoder_layers_27_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1150] + model_decoder_layers_27_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1151] + model_decoder_layers_27_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1152] + model_decoder_layers_27_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1153] + model_decoder_layers_27_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1154] + model_decoder_layers_27_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[1155] + model_decoder_layers_27_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[1156] + model_decoder_layers_27_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[1157] + model_decoder_layers_27_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1158] + model_decoder_layers_27_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1159] + model_decoder_layers_27_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1160] + model_decoder_layers_28_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1161] + model_decoder_layers_28_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1162] + model_decoder_layers_28_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1163] + model_decoder_layers_28_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1164] + model_decoder_layers_28_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1165] + model_decoder_layers_28_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1166] + model_decoder_layers_28_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1167] + model_decoder_layers_28_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1168] + model_decoder_layers_28_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1169] + model_decoder_layers_28_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1170] + model_decoder_layers_28_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1171] + model_decoder_layers_28_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1172] + model_decoder_layers_28_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1173] + model_decoder_layers_28_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1174] + model_decoder_layers_28_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1175] + model_decoder_layers_28_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1176] + model_decoder_layers_28_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1177] + model_decoder_layers_28_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1178] + model_decoder_layers_28_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[1179] + model_decoder_layers_28_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[1180] + model_decoder_layers_28_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[1181] + model_decoder_layers_28_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1182] + model_decoder_layers_28_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1183] + model_decoder_layers_28_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1184] + model_decoder_layers_29_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1185] + model_decoder_layers_29_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1186] + model_decoder_layers_29_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1187] + model_decoder_layers_29_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1188] + model_decoder_layers_29_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1189] + model_decoder_layers_29_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1190] + model_decoder_layers_29_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1191] + model_decoder_layers_29_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1192] + model_decoder_layers_29_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1193] + model_decoder_layers_29_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1194] + model_decoder_layers_29_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1195] + model_decoder_layers_29_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1196] + model_decoder_layers_29_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1197] + model_decoder_layers_29_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1198] + model_decoder_layers_29_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1199] + model_decoder_layers_29_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1200] + model_decoder_layers_29_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1201] + model_decoder_layers_29_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1202] + model_decoder_layers_29_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[1203] + model_decoder_layers_29_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[1204] + model_decoder_layers_29_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[1205] + model_decoder_layers_29_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1206] + model_decoder_layers_29_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1207] + model_decoder_layers_29_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1208] + model_decoder_layers_30_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1209] + model_decoder_layers_30_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1210] + model_decoder_layers_30_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1211] + model_decoder_layers_30_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1212] + model_decoder_layers_30_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1213] + model_decoder_layers_30_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1214] + model_decoder_layers_30_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1215] + model_decoder_layers_30_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1216] + model_decoder_layers_30_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1217] + model_decoder_layers_30_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1218] + model_decoder_layers_30_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1219] + model_decoder_layers_30_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1220] + model_decoder_layers_30_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1221] + model_decoder_layers_30_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1222] + model_decoder_layers_30_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1223] + model_decoder_layers_30_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1224] + model_decoder_layers_30_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1225] + model_decoder_layers_30_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1226] + model_decoder_layers_30_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[1227] + model_decoder_layers_30_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[1228] + model_decoder_layers_30_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[1229] + model_decoder_layers_30_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1230] + model_decoder_layers_30_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1231] + model_decoder_layers_30_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1232] + model_decoder_layers_31_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1233] + model_decoder_layers_31_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1234] + model_decoder_layers_31_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1235] + model_decoder_layers_31_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1236] + model_decoder_layers_31_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1237] + model_decoder_layers_31_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1238] + model_decoder_layers_31_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1239] + model_decoder_layers_31_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1240] + model_decoder_layers_31_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1241] + model_decoder_layers_31_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1242] + model_decoder_layers_31_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1243] + model_decoder_layers_31_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1244] + model_decoder_layers_31_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1245] + model_decoder_layers_31_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1246] + model_decoder_layers_31_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1247] + model_decoder_layers_31_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1248] + model_decoder_layers_31_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1249] + model_decoder_layers_31_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1250] + model_decoder_layers_31_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[1251] + model_decoder_layers_31_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[1252] + model_decoder_layers_31_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[1253] + model_decoder_layers_31_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1254] + model_decoder_layers_31_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1255] + model_decoder_layers_31_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1256] + model_decoder_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1257] + model_decoder_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1258] + permute_dims193: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_encoder_attn_k_proj_weight1, axes=None) + matmul192: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims193, out_dtype="void") + reshape256: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul192, R.shape([batch_size, 1500, 20, 64])) + permute_dims194: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_encoder_attn_v_proj_weight1, axes=None) + matmul193: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims194, out_dtype="void") + add225: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul193, model_decoder_layers_0_encoder_attn_v_proj_bias1) + reshape257: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add225, R.shape([batch_size, 1500, 20, 64])) + reshape258: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape256, R.shape([batch_size * 1500, 20, 64])) + reshape259: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape257, R.shape([batch_size * 1500, 20, 64])) + lv36: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", paged_kv_cache, R.prim_value(0), reshape258, reshape259, sinfo_args=(R.Object,)) + permute_dims195: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_1_encoder_attn_k_proj_weight1, axes=None) + matmul194: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims195, out_dtype="void") + reshape260: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul194, R.shape([batch_size, 1500, 20, 64])) + permute_dims196: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_1_encoder_attn_v_proj_weight1, axes=None) + matmul195: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims196, out_dtype="void") + add226: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul195, model_decoder_layers_1_encoder_attn_v_proj_bias1) + reshape261: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add226, R.shape([batch_size, 1500, 20, 64])) + reshape262: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape260, R.shape([batch_size * 1500, 20, 64])) + reshape263: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape261, R.shape([batch_size * 1500, 20, 64])) + lv37: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv36, R.prim_value(1), reshape262, reshape263, sinfo_args=(R.Object,)) + permute_dims197: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_2_encoder_attn_k_proj_weight1, axes=None) + matmul196: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims197, out_dtype="void") + reshape264: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul196, R.shape([batch_size, 1500, 20, 64])) + permute_dims198: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_2_encoder_attn_v_proj_weight1, axes=None) + matmul197: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims198, out_dtype="void") + add227: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul197, model_decoder_layers_2_encoder_attn_v_proj_bias1) + reshape265: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add227, R.shape([batch_size, 1500, 20, 64])) + reshape266: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape264, R.shape([batch_size * 1500, 20, 64])) + reshape267: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape265, R.shape([batch_size * 1500, 20, 64])) + lv38: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv37, R.prim_value(2), reshape266, reshape267, sinfo_args=(R.Object,)) + permute_dims199: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_3_encoder_attn_k_proj_weight1, axes=None) + matmul198: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims199, out_dtype="void") + reshape268: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul198, R.shape([batch_size, 1500, 20, 64])) + permute_dims200: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_3_encoder_attn_v_proj_weight1, axes=None) + matmul199: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims200, out_dtype="void") + add228: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul199, model_decoder_layers_3_encoder_attn_v_proj_bias1) + reshape269: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add228, R.shape([batch_size, 1500, 20, 64])) + reshape270: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape268, R.shape([batch_size * 1500, 20, 64])) + reshape271: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape269, R.shape([batch_size * 1500, 20, 64])) + lv39: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv38, R.prim_value(3), reshape270, reshape271, sinfo_args=(R.Object,)) + permute_dims201: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_4_encoder_attn_k_proj_weight1, axes=None) + matmul200: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims201, out_dtype="void") + reshape272: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul200, R.shape([batch_size, 1500, 20, 64])) + permute_dims202: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_4_encoder_attn_v_proj_weight1, axes=None) + matmul201: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims202, out_dtype="void") + add229: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul201, model_decoder_layers_4_encoder_attn_v_proj_bias1) + reshape273: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add229, R.shape([batch_size, 1500, 20, 64])) + reshape274: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape272, R.shape([batch_size * 1500, 20, 64])) + reshape275: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape273, R.shape([batch_size * 1500, 20, 64])) + lv40: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv39, R.prim_value(4), reshape274, reshape275, sinfo_args=(R.Object,)) + permute_dims203: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_5_encoder_attn_k_proj_weight1, axes=None) + matmul202: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims203, out_dtype="void") + reshape276: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul202, R.shape([batch_size, 1500, 20, 64])) + permute_dims204: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_5_encoder_attn_v_proj_weight1, axes=None) + matmul203: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims204, out_dtype="void") + add230: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul203, model_decoder_layers_5_encoder_attn_v_proj_bias1) + reshape277: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add230, R.shape([batch_size, 1500, 20, 64])) + reshape278: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape276, R.shape([batch_size * 1500, 20, 64])) + reshape279: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape277, R.shape([batch_size * 1500, 20, 64])) + lv41: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv40, R.prim_value(5), reshape278, reshape279, sinfo_args=(R.Object,)) + permute_dims205: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_6_encoder_attn_k_proj_weight1, axes=None) + matmul204: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims205, out_dtype="void") + reshape280: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul204, R.shape([batch_size, 1500, 20, 64])) + permute_dims206: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_6_encoder_attn_v_proj_weight1, axes=None) + matmul205: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims206, out_dtype="void") + add231: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul205, model_decoder_layers_6_encoder_attn_v_proj_bias1) + reshape281: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add231, R.shape([batch_size, 1500, 20, 64])) + reshape282: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape280, R.shape([batch_size * 1500, 20, 64])) + reshape283: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape281, R.shape([batch_size * 1500, 20, 64])) + lv42: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv41, R.prim_value(6), reshape282, reshape283, sinfo_args=(R.Object,)) + permute_dims207: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_7_encoder_attn_k_proj_weight1, axes=None) + matmul206: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims207, out_dtype="void") + reshape284: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul206, R.shape([batch_size, 1500, 20, 64])) + permute_dims208: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_7_encoder_attn_v_proj_weight1, axes=None) + matmul207: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims208, out_dtype="void") + add232: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul207, model_decoder_layers_7_encoder_attn_v_proj_bias1) + reshape285: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add232, R.shape([batch_size, 1500, 20, 64])) + reshape286: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape284, R.shape([batch_size * 1500, 20, 64])) + reshape287: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape285, R.shape([batch_size * 1500, 20, 64])) + lv43: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv42, R.prim_value(7), reshape286, reshape287, sinfo_args=(R.Object,)) + permute_dims209: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_8_encoder_attn_k_proj_weight1, axes=None) + matmul208: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims209, out_dtype="void") + reshape288: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul208, R.shape([batch_size, 1500, 20, 64])) + permute_dims210: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_8_encoder_attn_v_proj_weight1, axes=None) + matmul209: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims210, out_dtype="void") + add233: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul209, model_decoder_layers_8_encoder_attn_v_proj_bias1) + reshape289: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add233, R.shape([batch_size, 1500, 20, 64])) + reshape290: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape288, R.shape([batch_size * 1500, 20, 64])) + reshape291: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape289, R.shape([batch_size * 1500, 20, 64])) + lv44: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv43, R.prim_value(8), reshape290, reshape291, sinfo_args=(R.Object,)) + permute_dims211: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_9_encoder_attn_k_proj_weight1, axes=None) + matmul210: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims211, out_dtype="void") + reshape292: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul210, R.shape([batch_size, 1500, 20, 64])) + permute_dims212: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_9_encoder_attn_v_proj_weight1, axes=None) + matmul211: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims212, out_dtype="void") + add234: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul211, model_decoder_layers_9_encoder_attn_v_proj_bias1) + reshape293: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add234, R.shape([batch_size, 1500, 20, 64])) + reshape294: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape292, R.shape([batch_size * 1500, 20, 64])) + reshape295: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape293, R.shape([batch_size * 1500, 20, 64])) + lv45: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv44, R.prim_value(9), reshape294, reshape295, sinfo_args=(R.Object,)) + permute_dims213: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_10_encoder_attn_k_proj_weight1, axes=None) + matmul212: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims213, out_dtype="void") + reshape296: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul212, R.shape([batch_size, 1500, 20, 64])) + permute_dims214: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_10_encoder_attn_v_proj_weight1, axes=None) + matmul213: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims214, out_dtype="void") + add235: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul213, model_decoder_layers_10_encoder_attn_v_proj_bias1) + reshape297: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add235, R.shape([batch_size, 1500, 20, 64])) + reshape298: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape296, R.shape([batch_size * 1500, 20, 64])) + reshape299: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape297, R.shape([batch_size * 1500, 20, 64])) + lv46: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv45, R.prim_value(10), reshape298, reshape299, sinfo_args=(R.Object,)) + permute_dims215: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_11_encoder_attn_k_proj_weight1, axes=None) + matmul214: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims215, out_dtype="void") + reshape300: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul214, R.shape([batch_size, 1500, 20, 64])) + permute_dims216: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_11_encoder_attn_v_proj_weight1, axes=None) + matmul215: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims216, out_dtype="void") + add236: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul215, model_decoder_layers_11_encoder_attn_v_proj_bias1) + reshape301: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add236, R.shape([batch_size, 1500, 20, 64])) + reshape302: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape300, R.shape([batch_size * 1500, 20, 64])) + reshape303: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape301, R.shape([batch_size * 1500, 20, 64])) + lv47: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv46, R.prim_value(11), reshape302, reshape303, sinfo_args=(R.Object,)) + permute_dims217: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_12_encoder_attn_k_proj_weight1, axes=None) + matmul216: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims217, out_dtype="void") + reshape304: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul216, R.shape([batch_size, 1500, 20, 64])) + permute_dims218: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_12_encoder_attn_v_proj_weight1, axes=None) + matmul217: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims218, out_dtype="void") + add237: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul217, model_decoder_layers_12_encoder_attn_v_proj_bias1) + reshape305: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add237, R.shape([batch_size, 1500, 20, 64])) + reshape306: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape304, R.shape([batch_size * 1500, 20, 64])) + reshape307: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape305, R.shape([batch_size * 1500, 20, 64])) + lv48: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv47, R.prim_value(12), reshape306, reshape307, sinfo_args=(R.Object,)) + permute_dims219: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_13_encoder_attn_k_proj_weight1, axes=None) + matmul218: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims219, out_dtype="void") + reshape308: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul218, R.shape([batch_size, 1500, 20, 64])) + permute_dims220: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_13_encoder_attn_v_proj_weight1, axes=None) + matmul219: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims220, out_dtype="void") + add238: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul219, model_decoder_layers_13_encoder_attn_v_proj_bias1) + reshape309: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add238, R.shape([batch_size, 1500, 20, 64])) + reshape310: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape308, R.shape([batch_size * 1500, 20, 64])) + reshape311: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape309, R.shape([batch_size * 1500, 20, 64])) + lv49: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv48, R.prim_value(13), reshape310, reshape311, sinfo_args=(R.Object,)) + permute_dims221: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_14_encoder_attn_k_proj_weight1, axes=None) + matmul220: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims221, out_dtype="void") + reshape312: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul220, R.shape([batch_size, 1500, 20, 64])) + permute_dims222: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_14_encoder_attn_v_proj_weight1, axes=None) + matmul221: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims222, out_dtype="void") + add239: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul221, model_decoder_layers_14_encoder_attn_v_proj_bias1) + reshape313: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add239, R.shape([batch_size, 1500, 20, 64])) + reshape314: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape312, R.shape([batch_size * 1500, 20, 64])) + reshape315: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape313, R.shape([batch_size * 1500, 20, 64])) + lv50: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv49, R.prim_value(14), reshape314, reshape315, sinfo_args=(R.Object,)) + permute_dims223: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_15_encoder_attn_k_proj_weight1, axes=None) + matmul222: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims223, out_dtype="void") + reshape316: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul222, R.shape([batch_size, 1500, 20, 64])) + permute_dims224: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_15_encoder_attn_v_proj_weight1, axes=None) + matmul223: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims224, out_dtype="void") + add240: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul223, model_decoder_layers_15_encoder_attn_v_proj_bias1) + reshape317: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add240, R.shape([batch_size, 1500, 20, 64])) + reshape318: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape316, R.shape([batch_size * 1500, 20, 64])) + reshape319: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape317, R.shape([batch_size * 1500, 20, 64])) + lv51: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv50, R.prim_value(15), reshape318, reshape319, sinfo_args=(R.Object,)) + permute_dims225: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_16_encoder_attn_k_proj_weight1, axes=None) + matmul224: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims225, out_dtype="void") + reshape320: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul224, R.shape([batch_size, 1500, 20, 64])) + permute_dims226: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_16_encoder_attn_v_proj_weight1, axes=None) + matmul225: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims226, out_dtype="void") + add241: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul225, model_decoder_layers_16_encoder_attn_v_proj_bias1) + reshape321: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add241, R.shape([batch_size, 1500, 20, 64])) + reshape322: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape320, R.shape([batch_size * 1500, 20, 64])) + reshape323: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape321, R.shape([batch_size * 1500, 20, 64])) + lv52: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv51, R.prim_value(16), reshape322, reshape323, sinfo_args=(R.Object,)) + permute_dims227: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_17_encoder_attn_k_proj_weight1, axes=None) + matmul226: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims227, out_dtype="void") + reshape324: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul226, R.shape([batch_size, 1500, 20, 64])) + permute_dims228: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_17_encoder_attn_v_proj_weight1, axes=None) + matmul227: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims228, out_dtype="void") + add242: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul227, model_decoder_layers_17_encoder_attn_v_proj_bias1) + reshape325: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add242, R.shape([batch_size, 1500, 20, 64])) + reshape326: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape324, R.shape([batch_size * 1500, 20, 64])) + reshape327: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape325, R.shape([batch_size * 1500, 20, 64])) + lv53: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv52, R.prim_value(17), reshape326, reshape327, sinfo_args=(R.Object,)) + permute_dims229: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_18_encoder_attn_k_proj_weight1, axes=None) + matmul228: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims229, out_dtype="void") + reshape328: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul228, R.shape([batch_size, 1500, 20, 64])) + permute_dims230: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_18_encoder_attn_v_proj_weight1, axes=None) + matmul229: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims230, out_dtype="void") + add243: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul229, model_decoder_layers_18_encoder_attn_v_proj_bias1) + reshape329: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add243, R.shape([batch_size, 1500, 20, 64])) + reshape330: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape328, R.shape([batch_size * 1500, 20, 64])) + reshape331: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape329, R.shape([batch_size * 1500, 20, 64])) + lv54: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv53, R.prim_value(18), reshape330, reshape331, sinfo_args=(R.Object,)) + permute_dims231: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_19_encoder_attn_k_proj_weight1, axes=None) + matmul230: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims231, out_dtype="void") + reshape332: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul230, R.shape([batch_size, 1500, 20, 64])) + permute_dims232: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_19_encoder_attn_v_proj_weight1, axes=None) + matmul231: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims232, out_dtype="void") + add244: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul231, model_decoder_layers_19_encoder_attn_v_proj_bias1) + reshape333: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add244, R.shape([batch_size, 1500, 20, 64])) + reshape334: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape332, R.shape([batch_size * 1500, 20, 64])) + reshape335: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape333, R.shape([batch_size * 1500, 20, 64])) + lv55: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv54, R.prim_value(19), reshape334, reshape335, sinfo_args=(R.Object,)) + permute_dims233: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_20_encoder_attn_k_proj_weight1, axes=None) + matmul232: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims233, out_dtype="void") + reshape336: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul232, R.shape([batch_size, 1500, 20, 64])) + permute_dims234: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_20_encoder_attn_v_proj_weight1, axes=None) + matmul233: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims234, out_dtype="void") + add245: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul233, model_decoder_layers_20_encoder_attn_v_proj_bias1) + reshape337: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add245, R.shape([batch_size, 1500, 20, 64])) + reshape338: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape336, R.shape([batch_size * 1500, 20, 64])) + reshape339: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape337, R.shape([batch_size * 1500, 20, 64])) + lv56: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv55, R.prim_value(20), reshape338, reshape339, sinfo_args=(R.Object,)) + permute_dims235: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_21_encoder_attn_k_proj_weight1, axes=None) + matmul234: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims235, out_dtype="void") + reshape340: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul234, R.shape([batch_size, 1500, 20, 64])) + permute_dims236: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_21_encoder_attn_v_proj_weight1, axes=None) + matmul235: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims236, out_dtype="void") + add246: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul235, model_decoder_layers_21_encoder_attn_v_proj_bias1) + reshape341: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add246, R.shape([batch_size, 1500, 20, 64])) + reshape342: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape340, R.shape([batch_size * 1500, 20, 64])) + reshape343: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape341, R.shape([batch_size * 1500, 20, 64])) + lv57: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv56, R.prim_value(21), reshape342, reshape343, sinfo_args=(R.Object,)) + permute_dims237: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_22_encoder_attn_k_proj_weight1, axes=None) + matmul236: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims237, out_dtype="void") + reshape344: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul236, R.shape([batch_size, 1500, 20, 64])) + permute_dims238: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_22_encoder_attn_v_proj_weight1, axes=None) + matmul237: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims238, out_dtype="void") + add247: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul237, model_decoder_layers_22_encoder_attn_v_proj_bias1) + reshape345: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add247, R.shape([batch_size, 1500, 20, 64])) + reshape346: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape344, R.shape([batch_size * 1500, 20, 64])) + reshape347: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape345, R.shape([batch_size * 1500, 20, 64])) + lv58: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv57, R.prim_value(22), reshape346, reshape347, sinfo_args=(R.Object,)) + permute_dims239: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_23_encoder_attn_k_proj_weight1, axes=None) + matmul238: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims239, out_dtype="void") + reshape348: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul238, R.shape([batch_size, 1500, 20, 64])) + permute_dims240: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_23_encoder_attn_v_proj_weight1, axes=None) + matmul239: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims240, out_dtype="void") + add248: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul239, model_decoder_layers_23_encoder_attn_v_proj_bias1) + reshape349: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add248, R.shape([batch_size, 1500, 20, 64])) + reshape350: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape348, R.shape([batch_size * 1500, 20, 64])) + reshape351: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape349, R.shape([batch_size * 1500, 20, 64])) + lv59: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv58, R.prim_value(23), reshape350, reshape351, sinfo_args=(R.Object,)) + permute_dims241: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_24_encoder_attn_k_proj_weight1, axes=None) + matmul240: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims241, out_dtype="void") + reshape352: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul240, R.shape([batch_size, 1500, 20, 64])) + permute_dims242: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_24_encoder_attn_v_proj_weight1, axes=None) + matmul241: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims242, out_dtype="void") + add249: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul241, model_decoder_layers_24_encoder_attn_v_proj_bias1) + reshape353: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add249, R.shape([batch_size, 1500, 20, 64])) + reshape354: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape352, R.shape([batch_size * 1500, 20, 64])) + reshape355: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape353, R.shape([batch_size * 1500, 20, 64])) + lv60: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv59, R.prim_value(24), reshape354, reshape355, sinfo_args=(R.Object,)) + permute_dims243: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_25_encoder_attn_k_proj_weight1, axes=None) + matmul242: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims243, out_dtype="void") + reshape356: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul242, R.shape([batch_size, 1500, 20, 64])) + permute_dims244: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_25_encoder_attn_v_proj_weight1, axes=None) + matmul243: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims244, out_dtype="void") + add250: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul243, model_decoder_layers_25_encoder_attn_v_proj_bias1) + reshape357: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add250, R.shape([batch_size, 1500, 20, 64])) + reshape358: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape356, R.shape([batch_size * 1500, 20, 64])) + reshape359: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape357, R.shape([batch_size * 1500, 20, 64])) + lv61: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv60, R.prim_value(25), reshape358, reshape359, sinfo_args=(R.Object,)) + permute_dims245: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_26_encoder_attn_k_proj_weight1, axes=None) + matmul244: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims245, out_dtype="void") + reshape360: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul244, R.shape([batch_size, 1500, 20, 64])) + permute_dims246: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_26_encoder_attn_v_proj_weight1, axes=None) + matmul245: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims246, out_dtype="void") + add251: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul245, model_decoder_layers_26_encoder_attn_v_proj_bias1) + reshape361: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add251, R.shape([batch_size, 1500, 20, 64])) + reshape362: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape360, R.shape([batch_size * 1500, 20, 64])) + reshape363: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape361, R.shape([batch_size * 1500, 20, 64])) + lv62: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv61, R.prim_value(26), reshape362, reshape363, sinfo_args=(R.Object,)) + permute_dims247: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_27_encoder_attn_k_proj_weight1, axes=None) + matmul246: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims247, out_dtype="void") + reshape364: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul246, R.shape([batch_size, 1500, 20, 64])) + permute_dims248: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_27_encoder_attn_v_proj_weight1, axes=None) + matmul247: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims248, out_dtype="void") + add252: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul247, model_decoder_layers_27_encoder_attn_v_proj_bias1) + reshape365: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add252, R.shape([batch_size, 1500, 20, 64])) + reshape366: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape364, R.shape([batch_size * 1500, 20, 64])) + reshape367: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape365, R.shape([batch_size * 1500, 20, 64])) + lv63: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv62, R.prim_value(27), reshape366, reshape367, sinfo_args=(R.Object,)) + permute_dims249: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_28_encoder_attn_k_proj_weight1, axes=None) + matmul248: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims249, out_dtype="void") + reshape368: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul248, R.shape([batch_size, 1500, 20, 64])) + permute_dims250: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_28_encoder_attn_v_proj_weight1, axes=None) + matmul249: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims250, out_dtype="void") + add253: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul249, model_decoder_layers_28_encoder_attn_v_proj_bias1) + reshape369: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add253, R.shape([batch_size, 1500, 20, 64])) + reshape370: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape368, R.shape([batch_size * 1500, 20, 64])) + reshape371: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape369, R.shape([batch_size * 1500, 20, 64])) + lv64: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv63, R.prim_value(28), reshape370, reshape371, sinfo_args=(R.Object,)) + permute_dims251: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_29_encoder_attn_k_proj_weight1, axes=None) + matmul250: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims251, out_dtype="void") + reshape372: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul250, R.shape([batch_size, 1500, 20, 64])) + permute_dims252: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_29_encoder_attn_v_proj_weight1, axes=None) + matmul251: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims252, out_dtype="void") + add254: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul251, model_decoder_layers_29_encoder_attn_v_proj_bias1) + reshape373: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add254, R.shape([batch_size, 1500, 20, 64])) + reshape374: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape372, R.shape([batch_size * 1500, 20, 64])) + reshape375: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape373, R.shape([batch_size * 1500, 20, 64])) + lv65: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv64, R.prim_value(29), reshape374, reshape375, sinfo_args=(R.Object,)) + permute_dims253: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_30_encoder_attn_k_proj_weight1, axes=None) + matmul252: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims253, out_dtype="void") + reshape376: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul252, R.shape([batch_size, 1500, 20, 64])) + permute_dims254: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_30_encoder_attn_v_proj_weight1, axes=None) + matmul253: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims254, out_dtype="void") + add255: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul253, model_decoder_layers_30_encoder_attn_v_proj_bias1) + reshape377: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add255, R.shape([batch_size, 1500, 20, 64])) + reshape378: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape376, R.shape([batch_size * 1500, 20, 64])) + reshape379: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape377, R.shape([batch_size * 1500, 20, 64])) + lv66: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv65, R.prim_value(30), reshape378, reshape379, sinfo_args=(R.Object,)) + permute_dims255: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_31_encoder_attn_k_proj_weight1, axes=None) + matmul254: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims255, out_dtype="void") + reshape380: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul254, R.shape([batch_size, 1500, 20, 64])) + permute_dims256: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_31_encoder_attn_v_proj_weight1, axes=None) + matmul255: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims256, out_dtype="void") + add256: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul255, model_decoder_layers_31_encoder_attn_v_proj_bias1) + reshape381: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add256, R.shape([batch_size, 1500, 20, 64])) + reshape382: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape380, R.shape([batch_size * 1500, 20, 64])) + reshape383: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape381, R.shape([batch_size * 1500, 20, 64])) + lv67: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv66, R.prim_value(31), reshape382, reshape383, sinfo_args=(R.Object,)) + gv1: R.Object = lv67 + R.output(gv1) + return gv1 + + @R.function + def batch_decode(input_ids: R.Tensor(("batch_size", 1), dtype="int32"), paged_kv_cache: R.Object, packed_params: R.Tuple(R.Tensor((1280, 128, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1500, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), 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dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), 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R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"))) -> R.Tensor(("batch_size", 1, 51866), dtype="float32"): + batch_size = T.int64() + R.func_attr({"num_input": 2, "relax.memory_plan_dynamic_func_output": 1, "relax.rewrite_cuda_graph.capture_symbolic_vars": ["batch_size"], "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "seq_len": 15000, "total_seq_len": 1500}}) + with R.dataflow(): + model_encoder_conv1_weight3: R.Tensor((1280, 128, 3), dtype="float16") = packed_params[0] + model_encoder_conv1_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1] + model_encoder_conv2_weight3: R.Tensor((1280, 1280, 3), dtype="float16") = packed_params[2] + model_encoder_conv2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[3] + model_encoder_embed_positions_weight3: R.Tensor((1500, 1280), dtype="float16") = packed_params[4] + model_encoder_layers_0_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[5] + model_encoder_layers_0_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[6] + model_encoder_layers_0_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[7] + model_encoder_layers_0_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[8] + model_encoder_layers_0_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[9] + model_encoder_layers_0_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[10] + model_encoder_layers_0_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[11] + model_encoder_layers_0_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[12] + model_encoder_layers_0_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[13] + model_encoder_layers_0_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[14] + model_encoder_layers_0_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[15] + model_encoder_layers_0_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[16] + model_encoder_layers_0_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[17] + model_encoder_layers_0_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[18] + model_encoder_layers_0_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[19] + model_encoder_layers_1_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[20] + model_encoder_layers_1_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[21] + model_encoder_layers_1_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[22] + model_encoder_layers_1_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[23] + model_encoder_layers_1_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[24] + model_encoder_layers_1_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[25] + model_encoder_layers_1_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[26] + model_encoder_layers_1_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[27] + model_encoder_layers_1_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[28] + model_encoder_layers_1_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[29] + model_encoder_layers_1_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[30] + model_encoder_layers_1_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[31] + model_encoder_layers_1_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[32] + model_encoder_layers_1_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[33] + model_encoder_layers_1_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[34] + model_encoder_layers_2_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[35] + model_encoder_layers_2_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[36] + model_encoder_layers_2_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[37] + model_encoder_layers_2_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[38] + model_encoder_layers_2_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[39] + model_encoder_layers_2_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[40] + model_encoder_layers_2_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[41] + model_encoder_layers_2_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[42] + model_encoder_layers_2_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[43] + model_encoder_layers_2_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[44] + model_encoder_layers_2_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[45] + model_encoder_layers_2_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[46] + model_encoder_layers_2_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[47] + model_encoder_layers_2_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[48] + model_encoder_layers_2_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[49] + model_encoder_layers_3_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[50] + model_encoder_layers_3_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[51] + model_encoder_layers_3_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[52] + model_encoder_layers_3_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[53] + model_encoder_layers_3_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[54] + model_encoder_layers_3_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[55] + model_encoder_layers_3_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[56] + model_encoder_layers_3_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[57] + model_encoder_layers_3_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[58] + model_encoder_layers_3_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[59] + model_encoder_layers_3_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[60] + model_encoder_layers_3_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[61] + model_encoder_layers_3_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[62] + model_encoder_layers_3_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[63] + model_encoder_layers_3_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[64] + model_encoder_layers_4_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[65] + model_encoder_layers_4_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[66] + model_encoder_layers_4_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[67] + model_encoder_layers_4_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[68] + model_encoder_layers_4_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[69] + model_encoder_layers_4_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[70] + model_encoder_layers_4_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[71] + model_encoder_layers_4_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[72] + model_encoder_layers_4_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[73] + model_encoder_layers_4_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[74] + model_encoder_layers_4_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[75] + model_encoder_layers_4_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[76] + model_encoder_layers_4_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[77] + model_encoder_layers_4_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[78] + model_encoder_layers_4_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[79] + model_encoder_layers_5_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[80] + model_encoder_layers_5_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[81] + model_encoder_layers_5_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[82] + model_encoder_layers_5_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[83] + model_encoder_layers_5_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[84] + model_encoder_layers_5_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[85] + model_encoder_layers_5_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[86] + model_encoder_layers_5_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[87] + model_encoder_layers_5_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[88] + model_encoder_layers_5_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[89] + model_encoder_layers_5_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[90] + model_encoder_layers_5_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[91] + model_encoder_layers_5_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[92] + model_encoder_layers_5_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[93] + model_encoder_layers_5_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[94] + model_encoder_layers_6_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[95] + model_encoder_layers_6_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[96] + model_encoder_layers_6_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[97] + model_encoder_layers_6_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[98] + model_encoder_layers_6_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[99] + model_encoder_layers_6_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[100] + model_encoder_layers_6_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[101] + model_encoder_layers_6_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[102] + model_encoder_layers_6_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[103] + model_encoder_layers_6_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[104] + model_encoder_layers_6_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[105] + model_encoder_layers_6_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[106] + model_encoder_layers_6_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[107] + model_encoder_layers_6_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[108] + model_encoder_layers_6_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[109] + model_encoder_layers_7_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[110] + model_encoder_layers_7_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[111] + model_encoder_layers_7_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[112] + model_encoder_layers_7_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[113] + model_encoder_layers_7_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[114] + model_encoder_layers_7_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[115] + model_encoder_layers_7_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[116] + model_encoder_layers_7_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[117] + model_encoder_layers_7_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[118] + model_encoder_layers_7_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[119] + model_encoder_layers_7_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[120] + model_encoder_layers_7_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[121] + model_encoder_layers_7_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[122] + model_encoder_layers_7_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[123] + model_encoder_layers_7_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[124] + model_encoder_layers_8_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[125] + model_encoder_layers_8_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[126] + model_encoder_layers_8_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[127] + model_encoder_layers_8_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[128] + model_encoder_layers_8_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[129] + model_encoder_layers_8_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[130] + model_encoder_layers_8_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[131] + model_encoder_layers_8_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[132] + model_encoder_layers_8_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[133] + model_encoder_layers_8_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[134] + model_encoder_layers_8_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[135] + model_encoder_layers_8_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[136] + model_encoder_layers_8_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[137] + model_encoder_layers_8_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[138] + model_encoder_layers_8_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[139] + model_encoder_layers_9_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[140] + model_encoder_layers_9_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[141] + model_encoder_layers_9_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[142] + model_encoder_layers_9_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[143] + model_encoder_layers_9_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[144] + model_encoder_layers_9_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[145] + model_encoder_layers_9_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[146] + model_encoder_layers_9_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[147] + model_encoder_layers_9_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[148] + model_encoder_layers_9_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[149] + model_encoder_layers_9_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[150] + model_encoder_layers_9_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[151] + model_encoder_layers_9_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[152] + model_encoder_layers_9_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[153] + model_encoder_layers_9_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[154] + model_encoder_layers_10_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[155] + model_encoder_layers_10_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[156] + model_encoder_layers_10_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[157] + model_encoder_layers_10_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[158] + model_encoder_layers_10_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[159] + model_encoder_layers_10_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[160] + model_encoder_layers_10_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[161] + model_encoder_layers_10_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[162] + model_encoder_layers_10_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[163] + model_encoder_layers_10_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[164] + model_encoder_layers_10_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[165] + model_encoder_layers_10_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[166] + model_encoder_layers_10_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[167] + model_encoder_layers_10_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[168] + model_encoder_layers_10_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[169] + model_encoder_layers_11_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[170] + model_encoder_layers_11_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[171] + model_encoder_layers_11_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[172] + model_encoder_layers_11_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[173] + model_encoder_layers_11_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[174] + model_encoder_layers_11_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[175] + model_encoder_layers_11_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[176] + model_encoder_layers_11_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[177] + model_encoder_layers_11_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[178] + model_encoder_layers_11_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[179] + model_encoder_layers_11_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[180] + model_encoder_layers_11_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[181] + model_encoder_layers_11_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[182] + model_encoder_layers_11_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[183] + model_encoder_layers_11_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[184] + model_encoder_layers_12_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[185] + model_encoder_layers_12_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[186] + model_encoder_layers_12_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[187] + model_encoder_layers_12_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[188] + model_encoder_layers_12_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[189] + model_encoder_layers_12_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[190] + model_encoder_layers_12_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[191] + model_encoder_layers_12_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[192] + model_encoder_layers_12_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[193] + model_encoder_layers_12_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[194] + model_encoder_layers_12_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[195] + model_encoder_layers_12_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[196] + model_encoder_layers_12_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[197] + model_encoder_layers_12_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[198] + model_encoder_layers_12_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[199] + model_encoder_layers_13_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[200] + model_encoder_layers_13_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[201] + model_encoder_layers_13_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[202] + model_encoder_layers_13_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[203] + model_encoder_layers_13_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[204] + model_encoder_layers_13_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[205] + model_encoder_layers_13_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[206] + model_encoder_layers_13_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[207] + model_encoder_layers_13_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[208] + model_encoder_layers_13_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[209] + model_encoder_layers_13_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[210] + model_encoder_layers_13_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[211] + model_encoder_layers_13_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[212] + model_encoder_layers_13_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[213] + model_encoder_layers_13_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[214] + model_encoder_layers_14_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[215] + model_encoder_layers_14_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[216] + model_encoder_layers_14_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[217] + model_encoder_layers_14_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[218] + model_encoder_layers_14_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[219] + model_encoder_layers_14_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[220] + model_encoder_layers_14_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[221] + model_encoder_layers_14_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[222] + model_encoder_layers_14_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[223] + model_encoder_layers_14_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[224] + model_encoder_layers_14_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[225] + model_encoder_layers_14_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[226] + model_encoder_layers_14_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[227] + model_encoder_layers_14_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[228] + model_encoder_layers_14_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[229] + model_encoder_layers_15_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[230] + model_encoder_layers_15_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[231] + model_encoder_layers_15_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[232] + model_encoder_layers_15_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[233] + model_encoder_layers_15_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[234] + model_encoder_layers_15_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[235] + model_encoder_layers_15_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[236] + model_encoder_layers_15_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[237] + model_encoder_layers_15_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[238] + model_encoder_layers_15_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[239] + model_encoder_layers_15_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[240] + model_encoder_layers_15_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[241] + model_encoder_layers_15_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[242] + model_encoder_layers_15_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[243] + model_encoder_layers_15_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[244] + model_encoder_layers_16_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[245] + model_encoder_layers_16_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[246] + model_encoder_layers_16_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[247] + model_encoder_layers_16_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[248] + model_encoder_layers_16_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[249] + model_encoder_layers_16_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[250] + model_encoder_layers_16_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[251] + model_encoder_layers_16_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[252] + model_encoder_layers_16_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[253] + model_encoder_layers_16_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[254] + model_encoder_layers_16_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[255] + model_encoder_layers_16_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[256] + model_encoder_layers_16_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[257] + model_encoder_layers_16_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[258] + model_encoder_layers_16_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[259] + model_encoder_layers_17_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[260] + model_encoder_layers_17_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[261] + model_encoder_layers_17_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[262] + model_encoder_layers_17_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[263] + model_encoder_layers_17_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[264] + model_encoder_layers_17_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[265] + model_encoder_layers_17_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[266] + model_encoder_layers_17_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[267] + model_encoder_layers_17_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[268] + model_encoder_layers_17_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[269] + model_encoder_layers_17_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[270] + model_encoder_layers_17_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[271] + model_encoder_layers_17_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[272] + model_encoder_layers_17_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[273] + model_encoder_layers_17_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[274] + model_encoder_layers_18_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[275] + model_encoder_layers_18_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[276] + model_encoder_layers_18_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[277] + model_encoder_layers_18_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[278] + model_encoder_layers_18_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[279] + model_encoder_layers_18_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[280] + model_encoder_layers_18_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[281] + model_encoder_layers_18_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[282] + model_encoder_layers_18_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[283] + model_encoder_layers_18_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[284] + model_encoder_layers_18_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[285] + model_encoder_layers_18_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[286] + model_encoder_layers_18_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[287] + model_encoder_layers_18_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[288] + model_encoder_layers_18_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[289] + model_encoder_layers_19_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[290] + model_encoder_layers_19_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[291] + model_encoder_layers_19_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[292] + model_encoder_layers_19_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[293] + model_encoder_layers_19_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[294] + model_encoder_layers_19_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[295] + model_encoder_layers_19_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[296] + model_encoder_layers_19_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[297] + model_encoder_layers_19_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[298] + model_encoder_layers_19_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[299] + model_encoder_layers_19_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[300] + model_encoder_layers_19_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[301] + model_encoder_layers_19_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[302] + model_encoder_layers_19_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[303] + model_encoder_layers_19_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[304] + model_encoder_layers_20_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[305] + model_encoder_layers_20_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[306] + model_encoder_layers_20_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[307] + model_encoder_layers_20_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[308] + model_encoder_layers_20_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[309] + model_encoder_layers_20_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[310] + model_encoder_layers_20_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[311] + model_encoder_layers_20_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[312] + model_encoder_layers_20_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[313] + model_encoder_layers_20_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[314] + model_encoder_layers_20_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[315] + model_encoder_layers_20_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[316] + model_encoder_layers_20_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[317] + model_encoder_layers_20_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[318] + model_encoder_layers_20_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[319] + model_encoder_layers_21_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[320] + model_encoder_layers_21_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[321] + model_encoder_layers_21_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[322] + model_encoder_layers_21_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[323] + model_encoder_layers_21_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[324] + model_encoder_layers_21_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[325] + model_encoder_layers_21_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[326] + model_encoder_layers_21_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[327] + model_encoder_layers_21_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[328] + model_encoder_layers_21_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[329] + model_encoder_layers_21_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[330] + model_encoder_layers_21_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[331] + model_encoder_layers_21_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[332] + model_encoder_layers_21_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[333] + model_encoder_layers_21_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[334] + model_encoder_layers_22_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[335] + model_encoder_layers_22_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[336] + model_encoder_layers_22_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[337] + model_encoder_layers_22_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[338] + model_encoder_layers_22_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[339] + model_encoder_layers_22_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[340] + model_encoder_layers_22_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[341] + model_encoder_layers_22_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[342] + model_encoder_layers_22_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[343] + model_encoder_layers_22_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[344] + model_encoder_layers_22_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[345] + model_encoder_layers_22_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[346] + model_encoder_layers_22_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[347] + model_encoder_layers_22_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[348] + model_encoder_layers_22_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[349] + model_encoder_layers_23_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[350] + model_encoder_layers_23_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[351] + model_encoder_layers_23_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[352] + model_encoder_layers_23_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[353] + model_encoder_layers_23_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[354] + model_encoder_layers_23_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[355] + model_encoder_layers_23_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[356] + model_encoder_layers_23_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[357] + model_encoder_layers_23_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[358] + model_encoder_layers_23_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[359] + model_encoder_layers_23_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[360] + model_encoder_layers_23_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[361] + model_encoder_layers_23_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[362] + model_encoder_layers_23_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[363] + model_encoder_layers_23_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[364] + model_encoder_layers_24_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[365] + model_encoder_layers_24_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[366] + model_encoder_layers_24_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[367] + model_encoder_layers_24_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[368] + model_encoder_layers_24_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[369] + model_encoder_layers_24_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[370] + model_encoder_layers_24_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[371] + model_encoder_layers_24_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[372] + model_encoder_layers_24_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[373] + model_encoder_layers_24_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[374] + model_encoder_layers_24_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[375] + model_encoder_layers_24_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[376] + model_encoder_layers_24_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[377] + model_encoder_layers_24_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[378] + model_encoder_layers_24_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[379] + model_encoder_layers_25_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[380] + model_encoder_layers_25_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[381] + model_encoder_layers_25_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[382] + model_encoder_layers_25_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[383] + model_encoder_layers_25_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[384] + model_encoder_layers_25_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[385] + model_encoder_layers_25_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[386] + model_encoder_layers_25_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[387] + model_encoder_layers_25_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[388] + model_encoder_layers_25_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[389] + model_encoder_layers_25_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[390] + model_encoder_layers_25_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[391] + model_encoder_layers_25_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[392] + model_encoder_layers_25_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[393] + model_encoder_layers_25_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[394] + model_encoder_layers_26_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[395] + model_encoder_layers_26_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[396] + model_encoder_layers_26_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[397] + model_encoder_layers_26_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[398] + model_encoder_layers_26_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[399] + model_encoder_layers_26_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[400] + model_encoder_layers_26_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[401] + model_encoder_layers_26_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[402] + model_encoder_layers_26_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[403] + model_encoder_layers_26_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[404] + model_encoder_layers_26_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[405] + model_encoder_layers_26_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[406] + model_encoder_layers_26_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[407] + model_encoder_layers_26_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[408] + model_encoder_layers_26_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[409] + model_encoder_layers_27_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[410] + model_encoder_layers_27_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[411] + model_encoder_layers_27_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[412] + model_encoder_layers_27_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[413] + model_encoder_layers_27_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[414] + model_encoder_layers_27_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[415] + model_encoder_layers_27_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[416] + model_encoder_layers_27_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[417] + model_encoder_layers_27_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[418] + model_encoder_layers_27_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[419] + model_encoder_layers_27_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[420] + model_encoder_layers_27_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[421] + model_encoder_layers_27_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[422] + model_encoder_layers_27_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[423] + model_encoder_layers_27_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[424] + model_encoder_layers_28_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[425] + model_encoder_layers_28_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[426] + model_encoder_layers_28_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[427] + model_encoder_layers_28_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[428] + model_encoder_layers_28_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[429] + model_encoder_layers_28_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[430] + model_encoder_layers_28_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[431] + model_encoder_layers_28_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[432] + model_encoder_layers_28_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[433] + model_encoder_layers_28_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[434] + model_encoder_layers_28_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[435] + model_encoder_layers_28_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[436] + model_encoder_layers_28_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[437] + model_encoder_layers_28_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[438] + model_encoder_layers_28_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[439] + model_encoder_layers_29_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[440] + model_encoder_layers_29_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[441] + model_encoder_layers_29_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[442] + model_encoder_layers_29_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[443] + model_encoder_layers_29_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[444] + model_encoder_layers_29_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[445] + model_encoder_layers_29_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[446] + model_encoder_layers_29_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[447] + model_encoder_layers_29_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[448] + model_encoder_layers_29_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[449] + model_encoder_layers_29_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[450] + model_encoder_layers_29_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[451] + model_encoder_layers_29_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[452] + model_encoder_layers_29_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[453] + model_encoder_layers_29_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[454] + model_encoder_layers_30_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[455] + model_encoder_layers_30_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[456] + model_encoder_layers_30_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[457] + model_encoder_layers_30_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[458] + model_encoder_layers_30_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[459] + model_encoder_layers_30_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[460] + model_encoder_layers_30_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[461] + model_encoder_layers_30_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[462] + model_encoder_layers_30_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[463] + model_encoder_layers_30_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[464] + model_encoder_layers_30_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[465] + model_encoder_layers_30_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[466] + model_encoder_layers_30_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[467] + model_encoder_layers_30_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[468] + model_encoder_layers_30_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[469] + model_encoder_layers_31_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[470] + model_encoder_layers_31_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[471] + model_encoder_layers_31_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[472] + model_encoder_layers_31_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[473] + model_encoder_layers_31_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[474] + model_encoder_layers_31_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[475] + model_encoder_layers_31_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[476] + model_encoder_layers_31_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[477] + model_encoder_layers_31_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[478] + model_encoder_layers_31_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[479] + model_encoder_layers_31_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[480] + model_encoder_layers_31_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[481] + model_encoder_layers_31_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[482] + model_encoder_layers_31_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[483] + model_encoder_layers_31_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[484] + model_encoder_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[485] + model_encoder_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[486] + model_decoder_embed_tokens_weight3: R.Tensor((51866, 1280), dtype="float16") = packed_params[487] + model_decoder_embed_positions_weight3: R.Tensor((448, 1280), dtype="float16") = packed_params[488] + model_decoder_layers_0_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[489] + model_decoder_layers_0_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[490] + model_decoder_layers_0_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[491] + model_decoder_layers_0_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[492] + model_decoder_layers_0_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[493] + model_decoder_layers_0_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[494] + model_decoder_layers_0_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[495] + model_decoder_layers_0_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[496] + model_decoder_layers_0_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[497] + model_decoder_layers_0_encoder_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[498] + model_decoder_layers_0_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[499] + model_decoder_layers_0_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[500] + model_decoder_layers_0_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[501] + model_decoder_layers_0_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[502] + model_decoder_layers_0_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[503] + model_decoder_layers_0_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[504] + model_decoder_layers_0_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[505] + model_decoder_layers_0_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[506] + model_decoder_layers_0_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[507] + model_decoder_layers_0_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[508] + model_decoder_layers_0_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[509] + model_decoder_layers_0_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[510] + model_decoder_layers_0_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[511] + model_decoder_layers_0_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[512] + model_decoder_layers_1_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[513] + model_decoder_layers_1_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[514] + model_decoder_layers_1_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[515] + model_decoder_layers_1_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[516] + model_decoder_layers_1_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[517] + model_decoder_layers_1_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[518] + model_decoder_layers_1_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[519] + model_decoder_layers_1_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[520] + model_decoder_layers_1_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[521] + model_decoder_layers_1_encoder_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[522] + model_decoder_layers_1_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[523] + model_decoder_layers_1_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[524] + model_decoder_layers_1_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[525] + model_decoder_layers_1_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[526] + model_decoder_layers_1_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[527] + model_decoder_layers_1_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[528] + model_decoder_layers_1_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[529] + model_decoder_layers_1_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[530] + model_decoder_layers_1_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[531] + model_decoder_layers_1_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[532] + model_decoder_layers_1_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[533] + model_decoder_layers_1_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[534] + model_decoder_layers_1_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[535] + model_decoder_layers_1_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[536] + model_decoder_layers_2_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[537] + model_decoder_layers_2_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[538] + model_decoder_layers_2_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[539] + model_decoder_layers_2_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[540] + model_decoder_layers_2_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[541] + model_decoder_layers_2_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[542] + model_decoder_layers_2_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[543] + model_decoder_layers_2_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[544] + model_decoder_layers_2_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[545] + model_decoder_layers_2_encoder_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[546] + model_decoder_layers_2_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[547] + model_decoder_layers_2_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[548] + model_decoder_layers_2_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[549] + model_decoder_layers_2_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[550] + model_decoder_layers_2_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[551] + model_decoder_layers_2_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[552] + model_decoder_layers_2_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[553] + model_decoder_layers_2_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[554] + model_decoder_layers_2_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[555] + model_decoder_layers_2_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[556] + model_decoder_layers_2_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[557] + model_decoder_layers_2_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[558] + model_decoder_layers_2_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[559] + model_decoder_layers_2_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[560] + model_decoder_layers_3_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[561] + model_decoder_layers_3_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[562] + model_decoder_layers_3_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[563] + model_decoder_layers_3_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[564] + model_decoder_layers_3_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[565] + model_decoder_layers_3_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[566] + model_decoder_layers_3_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[567] + model_decoder_layers_3_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[568] + model_decoder_layers_3_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[569] + model_decoder_layers_3_encoder_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[570] + model_decoder_layers_3_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[571] + model_decoder_layers_3_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[572] + model_decoder_layers_3_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[573] + model_decoder_layers_3_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[574] + model_decoder_layers_3_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[575] + model_decoder_layers_3_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[576] + model_decoder_layers_3_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[577] + model_decoder_layers_3_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[578] + model_decoder_layers_3_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[579] + model_decoder_layers_3_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[580] + model_decoder_layers_3_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[581] + model_decoder_layers_3_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[582] + model_decoder_layers_3_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[583] + model_decoder_layers_3_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[584] + model_decoder_layers_4_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[585] + model_decoder_layers_4_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[586] + model_decoder_layers_4_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[587] + model_decoder_layers_4_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[588] + model_decoder_layers_4_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[589] + model_decoder_layers_4_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[590] + model_decoder_layers_4_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[591] + model_decoder_layers_4_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[592] + model_decoder_layers_4_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[593] + model_decoder_layers_4_encoder_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[594] + model_decoder_layers_4_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[595] + model_decoder_layers_4_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[596] + model_decoder_layers_4_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[597] + model_decoder_layers_4_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[598] + model_decoder_layers_4_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[599] + model_decoder_layers_4_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[600] + model_decoder_layers_4_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[601] + model_decoder_layers_4_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[602] + model_decoder_layers_4_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[603] + model_decoder_layers_4_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[604] + model_decoder_layers_4_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[605] + model_decoder_layers_4_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[606] + model_decoder_layers_4_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[607] + model_decoder_layers_4_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[608] + model_decoder_layers_5_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[609] + model_decoder_layers_5_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[610] + model_decoder_layers_5_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[611] + model_decoder_layers_5_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[612] + model_decoder_layers_5_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[613] + model_decoder_layers_5_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[614] + model_decoder_layers_5_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[615] + model_decoder_layers_5_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[616] + model_decoder_layers_5_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[617] + model_decoder_layers_5_encoder_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[618] + model_decoder_layers_5_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[619] + model_decoder_layers_5_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[620] + model_decoder_layers_5_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[621] + model_decoder_layers_5_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[622] + model_decoder_layers_5_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[623] + model_decoder_layers_5_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[624] + model_decoder_layers_5_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[625] + model_decoder_layers_5_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[626] + model_decoder_layers_5_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[627] + model_decoder_layers_5_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[628] + model_decoder_layers_5_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[629] + model_decoder_layers_5_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[630] + model_decoder_layers_5_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[631] + model_decoder_layers_5_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[632] + model_decoder_layers_6_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[633] + model_decoder_layers_6_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[634] + model_decoder_layers_6_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[635] + model_decoder_layers_6_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[636] + model_decoder_layers_6_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[637] + model_decoder_layers_6_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[638] + model_decoder_layers_6_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[639] + model_decoder_layers_6_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[640] + model_decoder_layers_6_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[641] + model_decoder_layers_6_encoder_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[642] + model_decoder_layers_6_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[643] + model_decoder_layers_6_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[644] + model_decoder_layers_6_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[645] + model_decoder_layers_6_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[646] + model_decoder_layers_6_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[647] + model_decoder_layers_6_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[648] + model_decoder_layers_6_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[649] + model_decoder_layers_6_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[650] + model_decoder_layers_6_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[651] + model_decoder_layers_6_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[652] + model_decoder_layers_6_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[653] + model_decoder_layers_6_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[654] + model_decoder_layers_6_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[655] + model_decoder_layers_6_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[656] + model_decoder_layers_7_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[657] + model_decoder_layers_7_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[658] + model_decoder_layers_7_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[659] + model_decoder_layers_7_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[660] + model_decoder_layers_7_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[661] + model_decoder_layers_7_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[662] + model_decoder_layers_7_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[663] + model_decoder_layers_7_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[664] + model_decoder_layers_7_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[665] + model_decoder_layers_7_encoder_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[666] + model_decoder_layers_7_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[667] + model_decoder_layers_7_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[668] + model_decoder_layers_7_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[669] + model_decoder_layers_7_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[670] + model_decoder_layers_7_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[671] + model_decoder_layers_7_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[672] + model_decoder_layers_7_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[673] + model_decoder_layers_7_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[674] + model_decoder_layers_7_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[675] + model_decoder_layers_7_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[676] + model_decoder_layers_7_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[677] + model_decoder_layers_7_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[678] + model_decoder_layers_7_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[679] + model_decoder_layers_7_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[680] + model_decoder_layers_8_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[681] + model_decoder_layers_8_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[682] + model_decoder_layers_8_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[683] + model_decoder_layers_8_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[684] + model_decoder_layers_8_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[685] + model_decoder_layers_8_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[686] + model_decoder_layers_8_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[687] + model_decoder_layers_8_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[688] + model_decoder_layers_8_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[689] + model_decoder_layers_8_encoder_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[690] + model_decoder_layers_8_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[691] + model_decoder_layers_8_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[692] + model_decoder_layers_8_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[693] + model_decoder_layers_8_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[694] + model_decoder_layers_8_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[695] + model_decoder_layers_8_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[696] + model_decoder_layers_8_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[697] + model_decoder_layers_8_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[698] + model_decoder_layers_8_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[699] + model_decoder_layers_8_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[700] + model_decoder_layers_8_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[701] + model_decoder_layers_8_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[702] + model_decoder_layers_8_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[703] + model_decoder_layers_8_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[704] + model_decoder_layers_9_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[705] + model_decoder_layers_9_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[706] + model_decoder_layers_9_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[707] + model_decoder_layers_9_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[708] + model_decoder_layers_9_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[709] + model_decoder_layers_9_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[710] + model_decoder_layers_9_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[711] + model_decoder_layers_9_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[712] + model_decoder_layers_9_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[713] + model_decoder_layers_9_encoder_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[714] + model_decoder_layers_9_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[715] + model_decoder_layers_9_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[716] + model_decoder_layers_9_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[717] + model_decoder_layers_9_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[718] + model_decoder_layers_9_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[719] + model_decoder_layers_9_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[720] + model_decoder_layers_9_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[721] + model_decoder_layers_9_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[722] + model_decoder_layers_9_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[723] + model_decoder_layers_9_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[724] + model_decoder_layers_9_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[725] + model_decoder_layers_9_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[726] + model_decoder_layers_9_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[727] + model_decoder_layers_9_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[728] + model_decoder_layers_10_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[729] + model_decoder_layers_10_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[730] + model_decoder_layers_10_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[731] + model_decoder_layers_10_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[732] + model_decoder_layers_10_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[733] + model_decoder_layers_10_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[734] + model_decoder_layers_10_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[735] + model_decoder_layers_10_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[736] + model_decoder_layers_10_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[737] + model_decoder_layers_10_encoder_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[738] + model_decoder_layers_10_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[739] + model_decoder_layers_10_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[740] + model_decoder_layers_10_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[741] + model_decoder_layers_10_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[742] + model_decoder_layers_10_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[743] + model_decoder_layers_10_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[744] + model_decoder_layers_10_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[745] + model_decoder_layers_10_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[746] + model_decoder_layers_10_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[747] + model_decoder_layers_10_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[748] + model_decoder_layers_10_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[749] + model_decoder_layers_10_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[750] + model_decoder_layers_10_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[751] + model_decoder_layers_10_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[752] + model_decoder_layers_11_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[753] + model_decoder_layers_11_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[754] + model_decoder_layers_11_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[755] + model_decoder_layers_11_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[756] + model_decoder_layers_11_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[757] + model_decoder_layers_11_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[758] + model_decoder_layers_11_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[759] + model_decoder_layers_11_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[760] + model_decoder_layers_11_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[761] + model_decoder_layers_11_encoder_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[762] + model_decoder_layers_11_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[763] + model_decoder_layers_11_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[764] + model_decoder_layers_11_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[765] + model_decoder_layers_11_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[766] + model_decoder_layers_11_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[767] + model_decoder_layers_11_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[768] + model_decoder_layers_11_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[769] + model_decoder_layers_11_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[770] + model_decoder_layers_11_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[771] + model_decoder_layers_11_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[772] + model_decoder_layers_11_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[773] + model_decoder_layers_11_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[774] + model_decoder_layers_11_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[775] + model_decoder_layers_11_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[776] + model_decoder_layers_12_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[777] + model_decoder_layers_12_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[778] + model_decoder_layers_12_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[779] + model_decoder_layers_12_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[780] + model_decoder_layers_12_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[781] + model_decoder_layers_12_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[782] + model_decoder_layers_12_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[783] + model_decoder_layers_12_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[784] + model_decoder_layers_12_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[785] + model_decoder_layers_12_encoder_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[786] + model_decoder_layers_12_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[787] + model_decoder_layers_12_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[788] + model_decoder_layers_12_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[789] + model_decoder_layers_12_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[790] + model_decoder_layers_12_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[791] + model_decoder_layers_12_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[792] + model_decoder_layers_12_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[793] + model_decoder_layers_12_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[794] + model_decoder_layers_12_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[795] + model_decoder_layers_12_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[796] + model_decoder_layers_12_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[797] + model_decoder_layers_12_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[798] + model_decoder_layers_12_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[799] + model_decoder_layers_12_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[800] + model_decoder_layers_13_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[801] + model_decoder_layers_13_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[802] + model_decoder_layers_13_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[803] + model_decoder_layers_13_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[804] + model_decoder_layers_13_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[805] + model_decoder_layers_13_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[806] + model_decoder_layers_13_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[807] + model_decoder_layers_13_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[808] + model_decoder_layers_13_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[809] + model_decoder_layers_13_encoder_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[810] + model_decoder_layers_13_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[811] + model_decoder_layers_13_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[812] + model_decoder_layers_13_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[813] + model_decoder_layers_13_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[814] + model_decoder_layers_13_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[815] + model_decoder_layers_13_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[816] + model_decoder_layers_13_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[817] + model_decoder_layers_13_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[818] + model_decoder_layers_13_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[819] + model_decoder_layers_13_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[820] + model_decoder_layers_13_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[821] + model_decoder_layers_13_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[822] + model_decoder_layers_13_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[823] + model_decoder_layers_13_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[824] + model_decoder_layers_14_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[825] + model_decoder_layers_14_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[826] + model_decoder_layers_14_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[827] + model_decoder_layers_14_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[828] + model_decoder_layers_14_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[829] + model_decoder_layers_14_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[830] + model_decoder_layers_14_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[831] + model_decoder_layers_14_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[832] + model_decoder_layers_14_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[833] + model_decoder_layers_14_encoder_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[834] + model_decoder_layers_14_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[835] + model_decoder_layers_14_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[836] + model_decoder_layers_14_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[837] + model_decoder_layers_14_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[838] + model_decoder_layers_14_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[839] + model_decoder_layers_14_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[840] + model_decoder_layers_14_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[841] + model_decoder_layers_14_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[842] + model_decoder_layers_14_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[843] + model_decoder_layers_14_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[844] + model_decoder_layers_14_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[845] + model_decoder_layers_14_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[846] + model_decoder_layers_14_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[847] + model_decoder_layers_14_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[848] + model_decoder_layers_15_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[849] + model_decoder_layers_15_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[850] + model_decoder_layers_15_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[851] + model_decoder_layers_15_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[852] + model_decoder_layers_15_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[853] + model_decoder_layers_15_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[854] + model_decoder_layers_15_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[855] + model_decoder_layers_15_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[856] + model_decoder_layers_15_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[857] + model_decoder_layers_15_encoder_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[858] + model_decoder_layers_15_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[859] + model_decoder_layers_15_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[860] + model_decoder_layers_15_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[861] + model_decoder_layers_15_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[862] + model_decoder_layers_15_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[863] + model_decoder_layers_15_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[864] + model_decoder_layers_15_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[865] + model_decoder_layers_15_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[866] + model_decoder_layers_15_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[867] + model_decoder_layers_15_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[868] + model_decoder_layers_15_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[869] + model_decoder_layers_15_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[870] + model_decoder_layers_15_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[871] + model_decoder_layers_15_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[872] + model_decoder_layers_16_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[873] + model_decoder_layers_16_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[874] + model_decoder_layers_16_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[875] + model_decoder_layers_16_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[876] + model_decoder_layers_16_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[877] + model_decoder_layers_16_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[878] + model_decoder_layers_16_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[879] + model_decoder_layers_16_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[880] + model_decoder_layers_16_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[881] + model_decoder_layers_16_encoder_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[882] + model_decoder_layers_16_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[883] + model_decoder_layers_16_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[884] + model_decoder_layers_16_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[885] + model_decoder_layers_16_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[886] + model_decoder_layers_16_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[887] + model_decoder_layers_16_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[888] + model_decoder_layers_16_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[889] + model_decoder_layers_16_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[890] + model_decoder_layers_16_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[891] + model_decoder_layers_16_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[892] + model_decoder_layers_16_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[893] + model_decoder_layers_16_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[894] + model_decoder_layers_16_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[895] + model_decoder_layers_16_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[896] + model_decoder_layers_17_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[897] + model_decoder_layers_17_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[898] + model_decoder_layers_17_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[899] + model_decoder_layers_17_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[900] + model_decoder_layers_17_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[901] + model_decoder_layers_17_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[902] + model_decoder_layers_17_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[903] + model_decoder_layers_17_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[904] + model_decoder_layers_17_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[905] + model_decoder_layers_17_encoder_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[906] + model_decoder_layers_17_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[907] + model_decoder_layers_17_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[908] + model_decoder_layers_17_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[909] + model_decoder_layers_17_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[910] + model_decoder_layers_17_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[911] + model_decoder_layers_17_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[912] + model_decoder_layers_17_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[913] + model_decoder_layers_17_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[914] + model_decoder_layers_17_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[915] + model_decoder_layers_17_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[916] + model_decoder_layers_17_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[917] + model_decoder_layers_17_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[918] + model_decoder_layers_17_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[919] + model_decoder_layers_17_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[920] + model_decoder_layers_18_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[921] + model_decoder_layers_18_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[922] + model_decoder_layers_18_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[923] + model_decoder_layers_18_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[924] + model_decoder_layers_18_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[925] + model_decoder_layers_18_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[926] + model_decoder_layers_18_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[927] + model_decoder_layers_18_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[928] + model_decoder_layers_18_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[929] + model_decoder_layers_18_encoder_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[930] + model_decoder_layers_18_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[931] + model_decoder_layers_18_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[932] + model_decoder_layers_18_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[933] + model_decoder_layers_18_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[934] + model_decoder_layers_18_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[935] + model_decoder_layers_18_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[936] + model_decoder_layers_18_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[937] + model_decoder_layers_18_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[938] + model_decoder_layers_18_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[939] + model_decoder_layers_18_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[940] + model_decoder_layers_18_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[941] + model_decoder_layers_18_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[942] + model_decoder_layers_18_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[943] + model_decoder_layers_18_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[944] + model_decoder_layers_19_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[945] + model_decoder_layers_19_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[946] + model_decoder_layers_19_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[947] + model_decoder_layers_19_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[948] + model_decoder_layers_19_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[949] + model_decoder_layers_19_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[950] + model_decoder_layers_19_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[951] + model_decoder_layers_19_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[952] + model_decoder_layers_19_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[953] + model_decoder_layers_19_encoder_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[954] + model_decoder_layers_19_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[955] + model_decoder_layers_19_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[956] + model_decoder_layers_19_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[957] + model_decoder_layers_19_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[958] + model_decoder_layers_19_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[959] + model_decoder_layers_19_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[960] + model_decoder_layers_19_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[961] + model_decoder_layers_19_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[962] + model_decoder_layers_19_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[963] + model_decoder_layers_19_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[964] + model_decoder_layers_19_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[965] + model_decoder_layers_19_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[966] + model_decoder_layers_19_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[967] + model_decoder_layers_19_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[968] + model_decoder_layers_20_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[969] + model_decoder_layers_20_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[970] + model_decoder_layers_20_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[971] + model_decoder_layers_20_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[972] + model_decoder_layers_20_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[973] + model_decoder_layers_20_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[974] + model_decoder_layers_20_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[975] + model_decoder_layers_20_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[976] + model_decoder_layers_20_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[977] + model_decoder_layers_20_encoder_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[978] + model_decoder_layers_20_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[979] + model_decoder_layers_20_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[980] + model_decoder_layers_20_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[981] + model_decoder_layers_20_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[982] + model_decoder_layers_20_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[983] + model_decoder_layers_20_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[984] + model_decoder_layers_20_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[985] + model_decoder_layers_20_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[986] + model_decoder_layers_20_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[987] + model_decoder_layers_20_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[988] + model_decoder_layers_20_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[989] + model_decoder_layers_20_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[990] + model_decoder_layers_20_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[991] + model_decoder_layers_20_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[992] + model_decoder_layers_21_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[993] + model_decoder_layers_21_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[994] + model_decoder_layers_21_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[995] + model_decoder_layers_21_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[996] + model_decoder_layers_21_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[997] + model_decoder_layers_21_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[998] + model_decoder_layers_21_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[999] + model_decoder_layers_21_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1000] + model_decoder_layers_21_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1001] + model_decoder_layers_21_encoder_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1002] + model_decoder_layers_21_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1003] + model_decoder_layers_21_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1004] + model_decoder_layers_21_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1005] + model_decoder_layers_21_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1006] + model_decoder_layers_21_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1007] + model_decoder_layers_21_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1008] + model_decoder_layers_21_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1009] + model_decoder_layers_21_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1010] + model_decoder_layers_21_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1011] + model_decoder_layers_21_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1012] + model_decoder_layers_21_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1013] + model_decoder_layers_21_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1014] + model_decoder_layers_21_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1015] + model_decoder_layers_21_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1016] + model_decoder_layers_22_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1017] + model_decoder_layers_22_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1018] + model_decoder_layers_22_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1019] + model_decoder_layers_22_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1020] + model_decoder_layers_22_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1021] + model_decoder_layers_22_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1022] + model_decoder_layers_22_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1023] + model_decoder_layers_22_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1024] + model_decoder_layers_22_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1025] + model_decoder_layers_22_encoder_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1026] + model_decoder_layers_22_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1027] + model_decoder_layers_22_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1028] + model_decoder_layers_22_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1029] + model_decoder_layers_22_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1030] + model_decoder_layers_22_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1031] + model_decoder_layers_22_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1032] + model_decoder_layers_22_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1033] + model_decoder_layers_22_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1034] + model_decoder_layers_22_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1035] + model_decoder_layers_22_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1036] + model_decoder_layers_22_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1037] + model_decoder_layers_22_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1038] + model_decoder_layers_22_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1039] + model_decoder_layers_22_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1040] + model_decoder_layers_23_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1041] + model_decoder_layers_23_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1042] + model_decoder_layers_23_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1043] + model_decoder_layers_23_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1044] + model_decoder_layers_23_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1045] + model_decoder_layers_23_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1046] + model_decoder_layers_23_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1047] + model_decoder_layers_23_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1048] + model_decoder_layers_23_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1049] + model_decoder_layers_23_encoder_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1050] + model_decoder_layers_23_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1051] + model_decoder_layers_23_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1052] + model_decoder_layers_23_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1053] + model_decoder_layers_23_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1054] + model_decoder_layers_23_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1055] + model_decoder_layers_23_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1056] + model_decoder_layers_23_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1057] + model_decoder_layers_23_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1058] + model_decoder_layers_23_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1059] + model_decoder_layers_23_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1060] + model_decoder_layers_23_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1061] + model_decoder_layers_23_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1062] + model_decoder_layers_23_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1063] + model_decoder_layers_23_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1064] + model_decoder_layers_24_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1065] + model_decoder_layers_24_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1066] + model_decoder_layers_24_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1067] + model_decoder_layers_24_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1068] + model_decoder_layers_24_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1069] + model_decoder_layers_24_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1070] + model_decoder_layers_24_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1071] + model_decoder_layers_24_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1072] + model_decoder_layers_24_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1073] + model_decoder_layers_24_encoder_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1074] + model_decoder_layers_24_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1075] + model_decoder_layers_24_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1076] + model_decoder_layers_24_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1077] + model_decoder_layers_24_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1078] + model_decoder_layers_24_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1079] + model_decoder_layers_24_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1080] + model_decoder_layers_24_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1081] + model_decoder_layers_24_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1082] + model_decoder_layers_24_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1083] + model_decoder_layers_24_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1084] + model_decoder_layers_24_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1085] + model_decoder_layers_24_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1086] + model_decoder_layers_24_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1087] + model_decoder_layers_24_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1088] + model_decoder_layers_25_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1089] + model_decoder_layers_25_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1090] + model_decoder_layers_25_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1091] + model_decoder_layers_25_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1092] + model_decoder_layers_25_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1093] + model_decoder_layers_25_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1094] + model_decoder_layers_25_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1095] + model_decoder_layers_25_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1096] + model_decoder_layers_25_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1097] + model_decoder_layers_25_encoder_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1098] + model_decoder_layers_25_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1099] + model_decoder_layers_25_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1100] + model_decoder_layers_25_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1101] + model_decoder_layers_25_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1102] + model_decoder_layers_25_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1103] + model_decoder_layers_25_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1104] + model_decoder_layers_25_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1105] + model_decoder_layers_25_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1106] + model_decoder_layers_25_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1107] + model_decoder_layers_25_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1108] + model_decoder_layers_25_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1109] + model_decoder_layers_25_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1110] + model_decoder_layers_25_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1111] + model_decoder_layers_25_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1112] + model_decoder_layers_26_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1113] + model_decoder_layers_26_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1114] + model_decoder_layers_26_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1115] + model_decoder_layers_26_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1116] + model_decoder_layers_26_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1117] + model_decoder_layers_26_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1118] + model_decoder_layers_26_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1119] + model_decoder_layers_26_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1120] + model_decoder_layers_26_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1121] + model_decoder_layers_26_encoder_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1122] + model_decoder_layers_26_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1123] + model_decoder_layers_26_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1124] + model_decoder_layers_26_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1125] + model_decoder_layers_26_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1126] + model_decoder_layers_26_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1127] + model_decoder_layers_26_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1128] + model_decoder_layers_26_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1129] + model_decoder_layers_26_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1130] + model_decoder_layers_26_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1131] + model_decoder_layers_26_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1132] + model_decoder_layers_26_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1133] + model_decoder_layers_26_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1134] + model_decoder_layers_26_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1135] + model_decoder_layers_26_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1136] + model_decoder_layers_27_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1137] + model_decoder_layers_27_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1138] + model_decoder_layers_27_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1139] + model_decoder_layers_27_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1140] + model_decoder_layers_27_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1141] + model_decoder_layers_27_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1142] + model_decoder_layers_27_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1143] + model_decoder_layers_27_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1144] + model_decoder_layers_27_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1145] + model_decoder_layers_27_encoder_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1146] + model_decoder_layers_27_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1147] + model_decoder_layers_27_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1148] + model_decoder_layers_27_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1149] + model_decoder_layers_27_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1150] + model_decoder_layers_27_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1151] + model_decoder_layers_27_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1152] + model_decoder_layers_27_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1153] + model_decoder_layers_27_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1154] + model_decoder_layers_27_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1155] + model_decoder_layers_27_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1156] + model_decoder_layers_27_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1157] + model_decoder_layers_27_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1158] + model_decoder_layers_27_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1159] + model_decoder_layers_27_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1160] + model_decoder_layers_28_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1161] + model_decoder_layers_28_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1162] + model_decoder_layers_28_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1163] + model_decoder_layers_28_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1164] + model_decoder_layers_28_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1165] + model_decoder_layers_28_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1166] + model_decoder_layers_28_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1167] + model_decoder_layers_28_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1168] + model_decoder_layers_28_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1169] + model_decoder_layers_28_encoder_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1170] + model_decoder_layers_28_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1171] + model_decoder_layers_28_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1172] + model_decoder_layers_28_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1173] + model_decoder_layers_28_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1174] + model_decoder_layers_28_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1175] + model_decoder_layers_28_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1176] + model_decoder_layers_28_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1177] + model_decoder_layers_28_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1178] + model_decoder_layers_28_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1179] + model_decoder_layers_28_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1180] + model_decoder_layers_28_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1181] + model_decoder_layers_28_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1182] + model_decoder_layers_28_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1183] + model_decoder_layers_28_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1184] + model_decoder_layers_29_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1185] + model_decoder_layers_29_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1186] + model_decoder_layers_29_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1187] + model_decoder_layers_29_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1188] + model_decoder_layers_29_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1189] + model_decoder_layers_29_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1190] + model_decoder_layers_29_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1191] + model_decoder_layers_29_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1192] + model_decoder_layers_29_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1193] + model_decoder_layers_29_encoder_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1194] + model_decoder_layers_29_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1195] + model_decoder_layers_29_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1196] + model_decoder_layers_29_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1197] + model_decoder_layers_29_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1198] + model_decoder_layers_29_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1199] + model_decoder_layers_29_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1200] + model_decoder_layers_29_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1201] + model_decoder_layers_29_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1202] + model_decoder_layers_29_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1203] + model_decoder_layers_29_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1204] + model_decoder_layers_29_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1205] + model_decoder_layers_29_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1206] + model_decoder_layers_29_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1207] + model_decoder_layers_29_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1208] + model_decoder_layers_30_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1209] + model_decoder_layers_30_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1210] + model_decoder_layers_30_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1211] + model_decoder_layers_30_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1212] + model_decoder_layers_30_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1213] + model_decoder_layers_30_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1214] + model_decoder_layers_30_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1215] + model_decoder_layers_30_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1216] + model_decoder_layers_30_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1217] + model_decoder_layers_30_encoder_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1218] + model_decoder_layers_30_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1219] + model_decoder_layers_30_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1220] + model_decoder_layers_30_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1221] + model_decoder_layers_30_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1222] + model_decoder_layers_30_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1223] + model_decoder_layers_30_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1224] + model_decoder_layers_30_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1225] + model_decoder_layers_30_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1226] + model_decoder_layers_30_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1227] + model_decoder_layers_30_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1228] + model_decoder_layers_30_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1229] + model_decoder_layers_30_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1230] + model_decoder_layers_30_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1231] + model_decoder_layers_30_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1232] + model_decoder_layers_31_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1233] + model_decoder_layers_31_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1234] + model_decoder_layers_31_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1235] + model_decoder_layers_31_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1236] + model_decoder_layers_31_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1237] + model_decoder_layers_31_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1238] + model_decoder_layers_31_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1239] + model_decoder_layers_31_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1240] + model_decoder_layers_31_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1241] + model_decoder_layers_31_encoder_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1242] + model_decoder_layers_31_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1243] + model_decoder_layers_31_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1244] + model_decoder_layers_31_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1245] + model_decoder_layers_31_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1246] + model_decoder_layers_31_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1247] + model_decoder_layers_31_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1248] + model_decoder_layers_31_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1249] + model_decoder_layers_31_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1250] + model_decoder_layers_31_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1251] + model_decoder_layers_31_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1252] + model_decoder_layers_31_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1253] + model_decoder_layers_31_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1254] + model_decoder_layers_31_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1255] + model_decoder_layers_31_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1256] + model_decoder_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1257] + model_decoder_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1258] + reshape707: R.Tensor((batch_size,), dtype="int32") = R.reshape(input_ids, R.shape([batch_size])) + take3: R.Tensor((batch_size, 1280), dtype="float16") = R.take(model_decoder_embed_tokens_weight3, reshape707, axis=0) + reshape708: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(take3, R.shape([batch_size, 1, 1280])) + lv133: R.Tensor((batch_size,), dtype="int32") = R.call_pure_packed("vm.builtin.attention_kv_cache_get_query_positions", paged_kv_cache, sinfo_args=(R.Tensor((batch_size,), dtype="int32"),)) + take4: R.Tensor((batch_size, 1280), dtype="float16") = R.take(model_decoder_embed_positions_weight3, lv133, axis=0) + reshape709: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(take4, R.shape([batch_size, 1, 1280])) + add578: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(reshape708, reshape709) + layer_norm162: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add578, model_decoder_layers_0_self_attn_layer_norm_weight3, model_decoder_layers_0_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims514: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_self_attn_q_proj_weight3, axes=None) + matmul513: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm162, permute_dims514, out_dtype="void") + add579: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul513, model_decoder_layers_0_self_attn_q_proj_bias3) + reshape710: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add579, R.shape([batch_size, 1, 20, 64])) + permute_dims515: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_self_attn_k_proj_weight3, axes=None) + matmul514: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm162, permute_dims515, out_dtype="void") + reshape711: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul514, R.shape([batch_size, 1, 20, 64])) + permute_dims516: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_self_attn_v_proj_weight3, axes=None) + matmul515: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm162, permute_dims516, out_dtype="void") + add580: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul515, model_decoder_layers_0_self_attn_v_proj_bias3) + reshape712: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add580, R.shape([batch_size, 1, 20, 64])) + concat32: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape710, reshape711, reshape712), axis=2) + reshape713: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat32, R.shape([batch_size, 60, 64])) + lv134 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), reshape713), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape714: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv134, R.shape([batch_size, 1, 20, 64])) + reshape715: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape714, R.shape([batch_size, 1, 1280])) + permute_dims517: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_self_attn_out_proj_weight3, axes=None) + matmul516: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape715, permute_dims517, out_dtype="void") + add581: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul516, model_decoder_layers_0_self_attn_out_proj_bias3) + add582: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add578, add581) + layer_norm163: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add582, model_decoder_layers_0_encoder_attn_layer_norm_weight3, model_decoder_layers_0_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims518: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_encoder_attn_q_proj_weight3, axes=None) + matmul517: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm163, permute_dims518, out_dtype="void") + add583: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul517, model_decoder_layers_0_encoder_attn_q_proj_bias3) + reshape716: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add583, R.shape([batch_size, 1, 20, 64])) + reshape717: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape716, R.shape([batch_size, 20, 64])) + lv135 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), reshape717), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape718: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv135, R.shape([batch_size, 1, 20, 64])) + reshape719: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape718, R.shape([batch_size, 1, 1280])) + permute_dims519: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_encoder_attn_out_proj_weight3, axes=None) + matmul518: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape719, permute_dims519, out_dtype="void") + add584: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul518, model_decoder_layers_0_encoder_attn_out_proj_bias3) + add585: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add582, add584) + layer_norm164: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add585, model_decoder_layers_0_final_layer_norm_weight3, model_decoder_layers_0_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims520: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_0_fc1_weight3, axes=None) + matmul519: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm164, permute_dims520, out_dtype="void") + add586: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul519, model_decoder_layers_0_fc1_bias3) + gelu66: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add586) + permute_dims521: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_fc2_weight3, axes=None) + matmul520: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu66, permute_dims521, out_dtype="void") + add587: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul520, model_decoder_layers_0_fc2_bias3) + add588: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add585, add587) + layer_norm165: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add588, model_decoder_layers_1_self_attn_layer_norm_weight3, model_decoder_layers_1_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims522: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_1_self_attn_q_proj_weight3, axes=None) + matmul521: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm165, permute_dims522, out_dtype="void") + add589: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul521, model_decoder_layers_1_self_attn_q_proj_bias3) + reshape720: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add589, R.shape([batch_size, 1, 20, 64])) + permute_dims523: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_1_self_attn_k_proj_weight3, axes=None) + matmul522: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm165, permute_dims523, out_dtype="void") + reshape721: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul522, R.shape([batch_size, 1, 20, 64])) + permute_dims524: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_1_self_attn_v_proj_weight3, axes=None) + matmul523: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm165, permute_dims524, out_dtype="void") + add590: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul523, model_decoder_layers_1_self_attn_v_proj_bias3) + reshape722: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add590, R.shape([batch_size, 1, 20, 64])) + concat33: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape720, reshape721, reshape722), axis=2) + reshape723: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat33, R.shape([batch_size, 60, 64])) + lv136 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), reshape723), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape724: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv136, R.shape([batch_size, 1, 20, 64])) + reshape725: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape724, R.shape([batch_size, 1, 1280])) + permute_dims525: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_1_self_attn_out_proj_weight3, axes=None) + matmul524: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape725, permute_dims525, out_dtype="void") + add591: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul524, model_decoder_layers_1_self_attn_out_proj_bias3) + add592: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add588, add591) + layer_norm166: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add592, model_decoder_layers_1_encoder_attn_layer_norm_weight3, model_decoder_layers_1_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims526: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_1_encoder_attn_q_proj_weight3, axes=None) + matmul525: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm166, permute_dims526, out_dtype="void") + add593: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul525, model_decoder_layers_1_encoder_attn_q_proj_bias3) + reshape726: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add593, R.shape([batch_size, 1, 20, 64])) + reshape727: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape726, R.shape([batch_size, 20, 64])) + lv137 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), reshape727), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape728: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv137, R.shape([batch_size, 1, 20, 64])) + reshape729: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape728, R.shape([batch_size, 1, 1280])) + permute_dims527: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_1_encoder_attn_out_proj_weight3, axes=None) + matmul526: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape729, permute_dims527, out_dtype="void") + add594: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul526, model_decoder_layers_1_encoder_attn_out_proj_bias3) + add595: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add592, add594) + layer_norm167: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add595, model_decoder_layers_1_final_layer_norm_weight3, model_decoder_layers_1_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims528: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_1_fc1_weight3, axes=None) + matmul527: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm167, permute_dims528, out_dtype="void") + add596: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul527, model_decoder_layers_1_fc1_bias3) + gelu67: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add596) + permute_dims529: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_1_fc2_weight3, axes=None) + matmul528: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu67, permute_dims529, out_dtype="void") + add597: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul528, model_decoder_layers_1_fc2_bias3) + add598: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add595, add597) + layer_norm168: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add598, model_decoder_layers_2_self_attn_layer_norm_weight3, model_decoder_layers_2_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims530: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_2_self_attn_q_proj_weight3, axes=None) + matmul529: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm168, permute_dims530, out_dtype="void") + add599: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul529, model_decoder_layers_2_self_attn_q_proj_bias3) + reshape730: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add599, R.shape([batch_size, 1, 20, 64])) + permute_dims531: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_2_self_attn_k_proj_weight3, axes=None) + matmul530: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm168, permute_dims531, out_dtype="void") + reshape731: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul530, R.shape([batch_size, 1, 20, 64])) + permute_dims532: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_2_self_attn_v_proj_weight3, axes=None) + matmul531: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm168, permute_dims532, out_dtype="void") + add600: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul531, model_decoder_layers_2_self_attn_v_proj_bias3) + reshape732: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add600, R.shape([batch_size, 1, 20, 64])) + concat34: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape730, reshape731, reshape732), axis=2) + reshape733: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat34, R.shape([batch_size, 60, 64])) + lv138 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), reshape733), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape734: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv138, R.shape([batch_size, 1, 20, 64])) + reshape735: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape734, R.shape([batch_size, 1, 1280])) + permute_dims533: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_2_self_attn_out_proj_weight3, axes=None) + matmul532: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape735, permute_dims533, out_dtype="void") + add601: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul532, model_decoder_layers_2_self_attn_out_proj_bias3) + add602: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add598, add601) + layer_norm169: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add602, model_decoder_layers_2_encoder_attn_layer_norm_weight3, model_decoder_layers_2_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims534: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_2_encoder_attn_q_proj_weight3, axes=None) + matmul533: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm169, permute_dims534, out_dtype="void") + add603: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul533, model_decoder_layers_2_encoder_attn_q_proj_bias3) + reshape736: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add603, R.shape([batch_size, 1, 20, 64])) + reshape737: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape736, R.shape([batch_size, 20, 64])) + lv139 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), reshape737), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape738: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv139, R.shape([batch_size, 1, 20, 64])) + reshape739: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape738, R.shape([batch_size, 1, 1280])) + permute_dims535: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_2_encoder_attn_out_proj_weight3, axes=None) + matmul534: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape739, permute_dims535, out_dtype="void") + add604: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul534, model_decoder_layers_2_encoder_attn_out_proj_bias3) + add605: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add602, add604) + layer_norm170: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add605, model_decoder_layers_2_final_layer_norm_weight3, model_decoder_layers_2_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims536: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_2_fc1_weight3, axes=None) + matmul535: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm170, permute_dims536, out_dtype="void") + add606: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul535, model_decoder_layers_2_fc1_bias3) + gelu68: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add606) + permute_dims537: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_2_fc2_weight3, axes=None) + matmul536: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu68, permute_dims537, out_dtype="void") + add607: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul536, model_decoder_layers_2_fc2_bias3) + add608: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add605, add607) + layer_norm171: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add608, model_decoder_layers_3_self_attn_layer_norm_weight3, model_decoder_layers_3_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims538: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_3_self_attn_q_proj_weight3, axes=None) + matmul537: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm171, permute_dims538, out_dtype="void") + add609: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul537, model_decoder_layers_3_self_attn_q_proj_bias3) + reshape740: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add609, R.shape([batch_size, 1, 20, 64])) + permute_dims539: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_3_self_attn_k_proj_weight3, axes=None) + matmul538: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm171, permute_dims539, out_dtype="void") + reshape741: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul538, R.shape([batch_size, 1, 20, 64])) + permute_dims540: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_3_self_attn_v_proj_weight3, axes=None) + matmul539: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm171, permute_dims540, out_dtype="void") + add610: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul539, model_decoder_layers_3_self_attn_v_proj_bias3) + reshape742: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add610, R.shape([batch_size, 1, 20, 64])) + concat35: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape740, reshape741, reshape742), axis=2) + reshape743: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat35, R.shape([batch_size, 60, 64])) + lv140 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), reshape743), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape744: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv140, R.shape([batch_size, 1, 20, 64])) + reshape745: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape744, R.shape([batch_size, 1, 1280])) + permute_dims541: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_3_self_attn_out_proj_weight3, axes=None) + matmul540: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape745, permute_dims541, out_dtype="void") + add611: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul540, model_decoder_layers_3_self_attn_out_proj_bias3) + add612: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add608, add611) + layer_norm172: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add612, model_decoder_layers_3_encoder_attn_layer_norm_weight3, model_decoder_layers_3_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims542: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_3_encoder_attn_q_proj_weight3, axes=None) + matmul541: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm172, permute_dims542, out_dtype="void") + add613: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul541, model_decoder_layers_3_encoder_attn_q_proj_bias3) + reshape746: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add613, R.shape([batch_size, 1, 20, 64])) + reshape747: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape746, R.shape([batch_size, 20, 64])) + lv141 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), reshape747), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape748: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv141, R.shape([batch_size, 1, 20, 64])) + reshape749: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape748, R.shape([batch_size, 1, 1280])) + permute_dims543: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_3_encoder_attn_out_proj_weight3, axes=None) + matmul542: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape749, permute_dims543, out_dtype="void") + add614: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul542, model_decoder_layers_3_encoder_attn_out_proj_bias3) + add615: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add612, add614) + layer_norm173: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add615, model_decoder_layers_3_final_layer_norm_weight3, model_decoder_layers_3_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims544: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_3_fc1_weight3, axes=None) + matmul543: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm173, permute_dims544, out_dtype="void") + add616: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul543, model_decoder_layers_3_fc1_bias3) + gelu69: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add616) + permute_dims545: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_3_fc2_weight3, axes=None) + matmul544: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu69, permute_dims545, out_dtype="void") + add617: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul544, model_decoder_layers_3_fc2_bias3) + add618: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add615, add617) + layer_norm174: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add618, model_decoder_layers_4_self_attn_layer_norm_weight3, model_decoder_layers_4_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims546: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_4_self_attn_q_proj_weight3, axes=None) + matmul545: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm174, permute_dims546, out_dtype="void") + add619: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul545, model_decoder_layers_4_self_attn_q_proj_bias3) + reshape750: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add619, R.shape([batch_size, 1, 20, 64])) + permute_dims547: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_4_self_attn_k_proj_weight3, axes=None) + matmul546: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm174, permute_dims547, out_dtype="void") + reshape751: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul546, R.shape([batch_size, 1, 20, 64])) + permute_dims548: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_4_self_attn_v_proj_weight3, axes=None) + matmul547: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm174, permute_dims548, out_dtype="void") + add620: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul547, model_decoder_layers_4_self_attn_v_proj_bias3) + reshape752: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add620, R.shape([batch_size, 1, 20, 64])) + concat36: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape750, reshape751, reshape752), axis=2) + reshape753: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat36, R.shape([batch_size, 60, 64])) + lv142 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), reshape753), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape754: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv142, R.shape([batch_size, 1, 20, 64])) + reshape755: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape754, R.shape([batch_size, 1, 1280])) + permute_dims549: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_4_self_attn_out_proj_weight3, axes=None) + matmul548: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape755, permute_dims549, out_dtype="void") + add621: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul548, model_decoder_layers_4_self_attn_out_proj_bias3) + add622: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add618, add621) + layer_norm175: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add622, model_decoder_layers_4_encoder_attn_layer_norm_weight3, model_decoder_layers_4_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims550: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_4_encoder_attn_q_proj_weight3, axes=None) + matmul549: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm175, permute_dims550, out_dtype="void") + add623: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul549, model_decoder_layers_4_encoder_attn_q_proj_bias3) + reshape756: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add623, R.shape([batch_size, 1, 20, 64])) + reshape757: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape756, R.shape([batch_size, 20, 64])) + lv143 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), reshape757), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape758: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv143, R.shape([batch_size, 1, 20, 64])) + reshape759: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape758, R.shape([batch_size, 1, 1280])) + permute_dims551: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_4_encoder_attn_out_proj_weight3, axes=None) + matmul550: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape759, permute_dims551, out_dtype="void") + add624: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul550, model_decoder_layers_4_encoder_attn_out_proj_bias3) + add625: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add622, add624) + layer_norm176: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add625, model_decoder_layers_4_final_layer_norm_weight3, model_decoder_layers_4_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims552: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_4_fc1_weight3, axes=None) + matmul551: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm176, permute_dims552, out_dtype="void") + add626: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul551, model_decoder_layers_4_fc1_bias3) + gelu70: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add626) + permute_dims553: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_4_fc2_weight3, axes=None) + matmul552: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu70, permute_dims553, out_dtype="void") + add627: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul552, model_decoder_layers_4_fc2_bias3) + add628: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add625, add627) + layer_norm177: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add628, model_decoder_layers_5_self_attn_layer_norm_weight3, model_decoder_layers_5_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims554: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_5_self_attn_q_proj_weight3, axes=None) + matmul553: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm177, permute_dims554, out_dtype="void") + add629: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul553, model_decoder_layers_5_self_attn_q_proj_bias3) + reshape760: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add629, R.shape([batch_size, 1, 20, 64])) + permute_dims555: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_5_self_attn_k_proj_weight3, axes=None) + matmul554: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm177, permute_dims555, out_dtype="void") + reshape761: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul554, R.shape([batch_size, 1, 20, 64])) + permute_dims556: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_5_self_attn_v_proj_weight3, axes=None) + matmul555: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm177, permute_dims556, out_dtype="void") + add630: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul555, model_decoder_layers_5_self_attn_v_proj_bias3) + reshape762: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add630, R.shape([batch_size, 1, 20, 64])) + concat37: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape760, reshape761, reshape762), axis=2) + reshape763: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat37, R.shape([batch_size, 60, 64])) + lv144 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), reshape763), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape764: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv144, R.shape([batch_size, 1, 20, 64])) + reshape765: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape764, R.shape([batch_size, 1, 1280])) + permute_dims557: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_5_self_attn_out_proj_weight3, axes=None) + matmul556: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape765, permute_dims557, out_dtype="void") + add631: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul556, model_decoder_layers_5_self_attn_out_proj_bias3) + add632: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add628, add631) + layer_norm178: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add632, model_decoder_layers_5_encoder_attn_layer_norm_weight3, model_decoder_layers_5_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims558: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_5_encoder_attn_q_proj_weight3, axes=None) + matmul557: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm178, permute_dims558, out_dtype="void") + add633: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul557, model_decoder_layers_5_encoder_attn_q_proj_bias3) + reshape766: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add633, R.shape([batch_size, 1, 20, 64])) + reshape767: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape766, R.shape([batch_size, 20, 64])) + lv145 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), reshape767), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape768: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv145, R.shape([batch_size, 1, 20, 64])) + reshape769: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape768, R.shape([batch_size, 1, 1280])) + permute_dims559: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_5_encoder_attn_out_proj_weight3, axes=None) + matmul558: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape769, permute_dims559, out_dtype="void") + add634: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul558, model_decoder_layers_5_encoder_attn_out_proj_bias3) + add635: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add632, add634) + layer_norm179: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add635, model_decoder_layers_5_final_layer_norm_weight3, model_decoder_layers_5_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims560: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_5_fc1_weight3, axes=None) + matmul559: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm179, permute_dims560, out_dtype="void") + add636: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul559, model_decoder_layers_5_fc1_bias3) + gelu71: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add636) + permute_dims561: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_5_fc2_weight3, axes=None) + matmul560: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu71, permute_dims561, out_dtype="void") + add637: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul560, model_decoder_layers_5_fc2_bias3) + add638: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add635, add637) + layer_norm180: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add638, model_decoder_layers_6_self_attn_layer_norm_weight3, model_decoder_layers_6_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims562: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_6_self_attn_q_proj_weight3, axes=None) + matmul561: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm180, permute_dims562, out_dtype="void") + add639: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul561, model_decoder_layers_6_self_attn_q_proj_bias3) + reshape770: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add639, R.shape([batch_size, 1, 20, 64])) + permute_dims563: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_6_self_attn_k_proj_weight3, axes=None) + matmul562: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm180, permute_dims563, out_dtype="void") + reshape771: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul562, R.shape([batch_size, 1, 20, 64])) + permute_dims564: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_6_self_attn_v_proj_weight3, axes=None) + matmul563: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm180, permute_dims564, out_dtype="void") + add640: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul563, model_decoder_layers_6_self_attn_v_proj_bias3) + reshape772: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add640, R.shape([batch_size, 1, 20, 64])) + concat38: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape770, reshape771, reshape772), axis=2) + reshape773: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat38, R.shape([batch_size, 60, 64])) + lv146 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), reshape773), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape774: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv146, R.shape([batch_size, 1, 20, 64])) + reshape775: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape774, R.shape([batch_size, 1, 1280])) + permute_dims565: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_6_self_attn_out_proj_weight3, axes=None) + matmul564: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape775, permute_dims565, out_dtype="void") + add641: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul564, model_decoder_layers_6_self_attn_out_proj_bias3) + add642: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add638, add641) + layer_norm181: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add642, model_decoder_layers_6_encoder_attn_layer_norm_weight3, model_decoder_layers_6_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims566: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_6_encoder_attn_q_proj_weight3, axes=None) + matmul565: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm181, permute_dims566, out_dtype="void") + add643: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul565, model_decoder_layers_6_encoder_attn_q_proj_bias3) + reshape776: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add643, R.shape([batch_size, 1, 20, 64])) + reshape777: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape776, R.shape([batch_size, 20, 64])) + lv147 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), reshape777), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape778: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv147, R.shape([batch_size, 1, 20, 64])) + reshape779: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape778, R.shape([batch_size, 1, 1280])) + permute_dims567: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_6_encoder_attn_out_proj_weight3, axes=None) + matmul566: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape779, permute_dims567, out_dtype="void") + add644: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul566, model_decoder_layers_6_encoder_attn_out_proj_bias3) + add645: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add642, add644) + layer_norm182: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add645, model_decoder_layers_6_final_layer_norm_weight3, model_decoder_layers_6_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims568: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_6_fc1_weight3, axes=None) + matmul567: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm182, permute_dims568, out_dtype="void") + add646: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul567, model_decoder_layers_6_fc1_bias3) + gelu72: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add646) + permute_dims569: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_6_fc2_weight3, axes=None) + matmul568: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu72, permute_dims569, out_dtype="void") + add647: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul568, model_decoder_layers_6_fc2_bias3) + add648: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add645, add647) + layer_norm183: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add648, model_decoder_layers_7_self_attn_layer_norm_weight3, model_decoder_layers_7_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims570: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_7_self_attn_q_proj_weight3, axes=None) + matmul569: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm183, permute_dims570, out_dtype="void") + add649: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul569, model_decoder_layers_7_self_attn_q_proj_bias3) + reshape780: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add649, R.shape([batch_size, 1, 20, 64])) + permute_dims571: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_7_self_attn_k_proj_weight3, axes=None) + matmul570: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm183, permute_dims571, out_dtype="void") + reshape781: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul570, R.shape([batch_size, 1, 20, 64])) + permute_dims572: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_7_self_attn_v_proj_weight3, axes=None) + matmul571: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm183, permute_dims572, out_dtype="void") + add650: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul571, model_decoder_layers_7_self_attn_v_proj_bias3) + reshape782: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add650, R.shape([batch_size, 1, 20, 64])) + concat39: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape780, reshape781, reshape782), axis=2) + reshape783: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat39, R.shape([batch_size, 60, 64])) + lv148 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), reshape783), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape784: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv148, R.shape([batch_size, 1, 20, 64])) + reshape785: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape784, R.shape([batch_size, 1, 1280])) + permute_dims573: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_7_self_attn_out_proj_weight3, axes=None) + matmul572: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape785, permute_dims573, out_dtype="void") + add651: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul572, model_decoder_layers_7_self_attn_out_proj_bias3) + add652: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add648, add651) + layer_norm184: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add652, model_decoder_layers_7_encoder_attn_layer_norm_weight3, model_decoder_layers_7_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims574: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_7_encoder_attn_q_proj_weight3, axes=None) + matmul573: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm184, permute_dims574, out_dtype="void") + add653: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul573, model_decoder_layers_7_encoder_attn_q_proj_bias3) + reshape786: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add653, R.shape([batch_size, 1, 20, 64])) + reshape787: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape786, R.shape([batch_size, 20, 64])) + lv149 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), reshape787), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape788: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv149, R.shape([batch_size, 1, 20, 64])) + reshape789: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape788, R.shape([batch_size, 1, 1280])) + permute_dims575: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_7_encoder_attn_out_proj_weight3, axes=None) + matmul574: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape789, permute_dims575, out_dtype="void") + add654: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul574, model_decoder_layers_7_encoder_attn_out_proj_bias3) + add655: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add652, add654) + layer_norm185: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add655, model_decoder_layers_7_final_layer_norm_weight3, model_decoder_layers_7_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims576: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_7_fc1_weight3, axes=None) + matmul575: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm185, permute_dims576, out_dtype="void") + add656: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul575, model_decoder_layers_7_fc1_bias3) + gelu73: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add656) + permute_dims577: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_7_fc2_weight3, axes=None) + matmul576: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu73, permute_dims577, out_dtype="void") + add657: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul576, model_decoder_layers_7_fc2_bias3) + add658: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add655, add657) + layer_norm186: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add658, model_decoder_layers_8_self_attn_layer_norm_weight3, model_decoder_layers_8_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims578: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_8_self_attn_q_proj_weight3, axes=None) + matmul577: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm186, permute_dims578, out_dtype="void") + add659: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul577, model_decoder_layers_8_self_attn_q_proj_bias3) + reshape790: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add659, R.shape([batch_size, 1, 20, 64])) + permute_dims579: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_8_self_attn_k_proj_weight3, axes=None) + matmul578: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm186, permute_dims579, out_dtype="void") + reshape791: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul578, R.shape([batch_size, 1, 20, 64])) + permute_dims580: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_8_self_attn_v_proj_weight3, axes=None) + matmul579: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm186, permute_dims580, out_dtype="void") + add660: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul579, model_decoder_layers_8_self_attn_v_proj_bias3) + reshape792: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add660, R.shape([batch_size, 1, 20, 64])) + concat40: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape790, reshape791, reshape792), axis=2) + reshape793: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat40, R.shape([batch_size, 60, 64])) + lv150 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), reshape793), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape794: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv150, R.shape([batch_size, 1, 20, 64])) + reshape795: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape794, R.shape([batch_size, 1, 1280])) + permute_dims581: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_8_self_attn_out_proj_weight3, axes=None) + matmul580: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape795, permute_dims581, out_dtype="void") + add661: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul580, model_decoder_layers_8_self_attn_out_proj_bias3) + add662: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add658, add661) + layer_norm187: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add662, model_decoder_layers_8_encoder_attn_layer_norm_weight3, model_decoder_layers_8_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims582: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_8_encoder_attn_q_proj_weight3, axes=None) + matmul581: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm187, permute_dims582, out_dtype="void") + add663: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul581, model_decoder_layers_8_encoder_attn_q_proj_bias3) + reshape796: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add663, R.shape([batch_size, 1, 20, 64])) + reshape797: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape796, R.shape([batch_size, 20, 64])) + lv151 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), reshape797), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape798: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv151, R.shape([batch_size, 1, 20, 64])) + reshape799: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape798, R.shape([batch_size, 1, 1280])) + permute_dims583: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_8_encoder_attn_out_proj_weight3, axes=None) + matmul582: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape799, permute_dims583, out_dtype="void") + add664: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul582, model_decoder_layers_8_encoder_attn_out_proj_bias3) + add665: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add662, add664) + layer_norm188: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add665, model_decoder_layers_8_final_layer_norm_weight3, model_decoder_layers_8_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims584: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_8_fc1_weight3, axes=None) + matmul583: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm188, permute_dims584, out_dtype="void") + add666: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul583, model_decoder_layers_8_fc1_bias3) + gelu74: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add666) + permute_dims585: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_8_fc2_weight3, axes=None) + matmul584: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu74, permute_dims585, out_dtype="void") + add667: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul584, model_decoder_layers_8_fc2_bias3) + add668: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add665, add667) + layer_norm189: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add668, model_decoder_layers_9_self_attn_layer_norm_weight3, model_decoder_layers_9_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims586: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_9_self_attn_q_proj_weight3, axes=None) + matmul585: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm189, permute_dims586, out_dtype="void") + add669: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul585, model_decoder_layers_9_self_attn_q_proj_bias3) + reshape800: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add669, R.shape([batch_size, 1, 20, 64])) + permute_dims587: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_9_self_attn_k_proj_weight3, axes=None) + matmul586: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm189, permute_dims587, out_dtype="void") + reshape801: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul586, R.shape([batch_size, 1, 20, 64])) + permute_dims588: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_9_self_attn_v_proj_weight3, axes=None) + matmul587: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm189, permute_dims588, out_dtype="void") + add670: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul587, model_decoder_layers_9_self_attn_v_proj_bias3) + reshape802: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add670, R.shape([batch_size, 1, 20, 64])) + concat41: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape800, reshape801, reshape802), axis=2) + reshape803: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat41, R.shape([batch_size, 60, 64])) + lv152 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), reshape803), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape804: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv152, R.shape([batch_size, 1, 20, 64])) + reshape805: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape804, R.shape([batch_size, 1, 1280])) + permute_dims589: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_9_self_attn_out_proj_weight3, axes=None) + matmul588: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape805, permute_dims589, out_dtype="void") + add671: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul588, model_decoder_layers_9_self_attn_out_proj_bias3) + add672: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add668, add671) + layer_norm190: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add672, model_decoder_layers_9_encoder_attn_layer_norm_weight3, model_decoder_layers_9_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims590: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_9_encoder_attn_q_proj_weight3, axes=None) + matmul589: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm190, permute_dims590, out_dtype="void") + add673: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul589, model_decoder_layers_9_encoder_attn_q_proj_bias3) + reshape806: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add673, R.shape([batch_size, 1, 20, 64])) + reshape807: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape806, R.shape([batch_size, 20, 64])) + lv153 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), reshape807), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape808: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv153, R.shape([batch_size, 1, 20, 64])) + reshape809: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape808, R.shape([batch_size, 1, 1280])) + permute_dims591: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_9_encoder_attn_out_proj_weight3, axes=None) + matmul590: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape809, permute_dims591, out_dtype="void") + add674: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul590, model_decoder_layers_9_encoder_attn_out_proj_bias3) + add675: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add672, add674) + layer_norm191: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add675, model_decoder_layers_9_final_layer_norm_weight3, model_decoder_layers_9_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims592: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_9_fc1_weight3, axes=None) + matmul591: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm191, permute_dims592, out_dtype="void") + add676: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul591, model_decoder_layers_9_fc1_bias3) + gelu75: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add676) + permute_dims593: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_9_fc2_weight3, axes=None) + matmul592: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu75, permute_dims593, out_dtype="void") + add677: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul592, model_decoder_layers_9_fc2_bias3) + add678: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add675, add677) + layer_norm192: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add678, model_decoder_layers_10_self_attn_layer_norm_weight3, model_decoder_layers_10_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims594: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_10_self_attn_q_proj_weight3, axes=None) + matmul593: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm192, permute_dims594, out_dtype="void") + add679: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul593, model_decoder_layers_10_self_attn_q_proj_bias3) + reshape810: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add679, R.shape([batch_size, 1, 20, 64])) + permute_dims595: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_10_self_attn_k_proj_weight3, axes=None) + matmul594: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm192, permute_dims595, out_dtype="void") + reshape811: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul594, R.shape([batch_size, 1, 20, 64])) + permute_dims596: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_10_self_attn_v_proj_weight3, axes=None) + matmul595: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm192, permute_dims596, out_dtype="void") + add680: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul595, model_decoder_layers_10_self_attn_v_proj_bias3) + reshape812: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add680, R.shape([batch_size, 1, 20, 64])) + concat42: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape810, reshape811, reshape812), axis=2) + reshape813: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat42, R.shape([batch_size, 60, 64])) + lv154 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), reshape813), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape814: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv154, R.shape([batch_size, 1, 20, 64])) + reshape815: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape814, R.shape([batch_size, 1, 1280])) + permute_dims597: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_10_self_attn_out_proj_weight3, axes=None) + matmul596: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape815, permute_dims597, out_dtype="void") + add681: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul596, model_decoder_layers_10_self_attn_out_proj_bias3) + add682: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add678, add681) + layer_norm193: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add682, model_decoder_layers_10_encoder_attn_layer_norm_weight3, model_decoder_layers_10_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims598: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_10_encoder_attn_q_proj_weight3, axes=None) + matmul597: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm193, permute_dims598, out_dtype="void") + add683: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul597, model_decoder_layers_10_encoder_attn_q_proj_bias3) + reshape816: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add683, R.shape([batch_size, 1, 20, 64])) + reshape817: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape816, R.shape([batch_size, 20, 64])) + lv155 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), reshape817), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape818: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv155, R.shape([batch_size, 1, 20, 64])) + reshape819: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape818, R.shape([batch_size, 1, 1280])) + permute_dims599: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_10_encoder_attn_out_proj_weight3, axes=None) + matmul598: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape819, permute_dims599, out_dtype="void") + add684: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul598, model_decoder_layers_10_encoder_attn_out_proj_bias3) + add685: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add682, add684) + layer_norm194: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add685, model_decoder_layers_10_final_layer_norm_weight3, model_decoder_layers_10_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims600: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_10_fc1_weight3, axes=None) + matmul599: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm194, permute_dims600, out_dtype="void") + add686: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul599, model_decoder_layers_10_fc1_bias3) + gelu76: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add686) + permute_dims601: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_10_fc2_weight3, axes=None) + matmul600: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu76, permute_dims601, out_dtype="void") + add687: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul600, model_decoder_layers_10_fc2_bias3) + add688: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add685, add687) + layer_norm195: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add688, model_decoder_layers_11_self_attn_layer_norm_weight3, model_decoder_layers_11_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims602: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_11_self_attn_q_proj_weight3, axes=None) + matmul601: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm195, permute_dims602, out_dtype="void") + add689: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul601, model_decoder_layers_11_self_attn_q_proj_bias3) + reshape820: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add689, R.shape([batch_size, 1, 20, 64])) + permute_dims603: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_11_self_attn_k_proj_weight3, axes=None) + matmul602: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm195, permute_dims603, out_dtype="void") + reshape821: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul602, R.shape([batch_size, 1, 20, 64])) + permute_dims604: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_11_self_attn_v_proj_weight3, axes=None) + matmul603: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm195, permute_dims604, out_dtype="void") + add690: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul603, model_decoder_layers_11_self_attn_v_proj_bias3) + reshape822: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add690, R.shape([batch_size, 1, 20, 64])) + concat43: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape820, reshape821, reshape822), axis=2) + reshape823: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat43, R.shape([batch_size, 60, 64])) + lv156 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), reshape823), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape824: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv156, R.shape([batch_size, 1, 20, 64])) + reshape825: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape824, R.shape([batch_size, 1, 1280])) + permute_dims605: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_11_self_attn_out_proj_weight3, axes=None) + matmul604: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape825, permute_dims605, out_dtype="void") + add691: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul604, model_decoder_layers_11_self_attn_out_proj_bias3) + add692: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add688, add691) + layer_norm196: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add692, model_decoder_layers_11_encoder_attn_layer_norm_weight3, model_decoder_layers_11_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims606: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_11_encoder_attn_q_proj_weight3, axes=None) + matmul605: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm196, permute_dims606, out_dtype="void") + add693: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul605, model_decoder_layers_11_encoder_attn_q_proj_bias3) + reshape826: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add693, R.shape([batch_size, 1, 20, 64])) + reshape827: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape826, R.shape([batch_size, 20, 64])) + lv157 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), reshape827), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape828: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv157, R.shape([batch_size, 1, 20, 64])) + reshape829: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape828, R.shape([batch_size, 1, 1280])) + permute_dims607: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_11_encoder_attn_out_proj_weight3, axes=None) + matmul606: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape829, permute_dims607, out_dtype="void") + add694: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul606, model_decoder_layers_11_encoder_attn_out_proj_bias3) + add695: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add692, add694) + layer_norm197: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add695, model_decoder_layers_11_final_layer_norm_weight3, model_decoder_layers_11_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims608: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_11_fc1_weight3, axes=None) + matmul607: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm197, permute_dims608, out_dtype="void") + add696: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul607, model_decoder_layers_11_fc1_bias3) + gelu77: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add696) + permute_dims609: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_11_fc2_weight3, axes=None) + matmul608: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu77, permute_dims609, out_dtype="void") + add697: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul608, model_decoder_layers_11_fc2_bias3) + add698: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add695, add697) + layer_norm198: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add698, model_decoder_layers_12_self_attn_layer_norm_weight3, model_decoder_layers_12_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims610: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_12_self_attn_q_proj_weight3, axes=None) + matmul609: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm198, permute_dims610, out_dtype="void") + add699: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul609, model_decoder_layers_12_self_attn_q_proj_bias3) + reshape830: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add699, R.shape([batch_size, 1, 20, 64])) + permute_dims611: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_12_self_attn_k_proj_weight3, axes=None) + matmul610: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm198, permute_dims611, out_dtype="void") + reshape831: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul610, R.shape([batch_size, 1, 20, 64])) + permute_dims612: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_12_self_attn_v_proj_weight3, axes=None) + matmul611: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm198, permute_dims612, out_dtype="void") + add700: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul611, model_decoder_layers_12_self_attn_v_proj_bias3) + reshape832: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add700, R.shape([batch_size, 1, 20, 64])) + concat44: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape830, reshape831, reshape832), axis=2) + reshape833: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat44, R.shape([batch_size, 60, 64])) + lv158 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), reshape833), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape834: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv158, R.shape([batch_size, 1, 20, 64])) + reshape835: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape834, R.shape([batch_size, 1, 1280])) + permute_dims613: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_12_self_attn_out_proj_weight3, axes=None) + matmul612: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape835, permute_dims613, out_dtype="void") + add701: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul612, model_decoder_layers_12_self_attn_out_proj_bias3) + add702: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add698, add701) + layer_norm199: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add702, model_decoder_layers_12_encoder_attn_layer_norm_weight3, model_decoder_layers_12_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims614: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_12_encoder_attn_q_proj_weight3, axes=None) + matmul613: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm199, permute_dims614, out_dtype="void") + add703: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul613, model_decoder_layers_12_encoder_attn_q_proj_bias3) + reshape836: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add703, R.shape([batch_size, 1, 20, 64])) + reshape837: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape836, R.shape([batch_size, 20, 64])) + lv159 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), reshape837), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape838: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv159, R.shape([batch_size, 1, 20, 64])) + reshape839: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape838, R.shape([batch_size, 1, 1280])) + permute_dims615: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_12_encoder_attn_out_proj_weight3, axes=None) + matmul614: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape839, permute_dims615, out_dtype="void") + add704: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul614, model_decoder_layers_12_encoder_attn_out_proj_bias3) + add705: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add702, add704) + layer_norm200: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add705, model_decoder_layers_12_final_layer_norm_weight3, model_decoder_layers_12_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims616: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_12_fc1_weight3, axes=None) + matmul615: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm200, permute_dims616, out_dtype="void") + add706: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul615, model_decoder_layers_12_fc1_bias3) + gelu78: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add706) + permute_dims617: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_12_fc2_weight3, axes=None) + matmul616: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu78, permute_dims617, out_dtype="void") + add707: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul616, model_decoder_layers_12_fc2_bias3) + add708: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add705, add707) + layer_norm201: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add708, model_decoder_layers_13_self_attn_layer_norm_weight3, model_decoder_layers_13_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims618: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_13_self_attn_q_proj_weight3, axes=None) + matmul617: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm201, permute_dims618, out_dtype="void") + add709: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul617, model_decoder_layers_13_self_attn_q_proj_bias3) + reshape840: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add709, R.shape([batch_size, 1, 20, 64])) + permute_dims619: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_13_self_attn_k_proj_weight3, axes=None) + matmul618: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm201, permute_dims619, out_dtype="void") + reshape841: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul618, R.shape([batch_size, 1, 20, 64])) + permute_dims620: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_13_self_attn_v_proj_weight3, axes=None) + matmul619: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm201, permute_dims620, out_dtype="void") + add710: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul619, model_decoder_layers_13_self_attn_v_proj_bias3) + reshape842: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add710, R.shape([batch_size, 1, 20, 64])) + concat45: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape840, reshape841, reshape842), axis=2) + reshape843: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat45, R.shape([batch_size, 60, 64])) + lv160 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), reshape843), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape844: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv160, R.shape([batch_size, 1, 20, 64])) + reshape845: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape844, R.shape([batch_size, 1, 1280])) + permute_dims621: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_13_self_attn_out_proj_weight3, axes=None) + matmul620: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape845, permute_dims621, out_dtype="void") + add711: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul620, model_decoder_layers_13_self_attn_out_proj_bias3) + add712: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add708, add711) + layer_norm202: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add712, model_decoder_layers_13_encoder_attn_layer_norm_weight3, model_decoder_layers_13_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims622: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_13_encoder_attn_q_proj_weight3, axes=None) + matmul621: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm202, permute_dims622, out_dtype="void") + add713: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul621, model_decoder_layers_13_encoder_attn_q_proj_bias3) + reshape846: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add713, R.shape([batch_size, 1, 20, 64])) + reshape847: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape846, R.shape([batch_size, 20, 64])) + lv161 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), reshape847), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape848: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv161, R.shape([batch_size, 1, 20, 64])) + reshape849: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape848, R.shape([batch_size, 1, 1280])) + permute_dims623: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_13_encoder_attn_out_proj_weight3, axes=None) + matmul622: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape849, permute_dims623, out_dtype="void") + add714: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul622, model_decoder_layers_13_encoder_attn_out_proj_bias3) + add715: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add712, add714) + layer_norm203: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add715, model_decoder_layers_13_final_layer_norm_weight3, model_decoder_layers_13_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims624: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_13_fc1_weight3, axes=None) + matmul623: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm203, permute_dims624, out_dtype="void") + add716: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul623, model_decoder_layers_13_fc1_bias3) + gelu79: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add716) + permute_dims625: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_13_fc2_weight3, axes=None) + matmul624: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu79, permute_dims625, out_dtype="void") + add717: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul624, model_decoder_layers_13_fc2_bias3) + add718: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add715, add717) + layer_norm204: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add718, model_decoder_layers_14_self_attn_layer_norm_weight3, model_decoder_layers_14_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims626: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_14_self_attn_q_proj_weight3, axes=None) + matmul625: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm204, permute_dims626, out_dtype="void") + add719: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul625, model_decoder_layers_14_self_attn_q_proj_bias3) + reshape850: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add719, R.shape([batch_size, 1, 20, 64])) + permute_dims627: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_14_self_attn_k_proj_weight3, axes=None) + matmul626: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm204, permute_dims627, out_dtype="void") + reshape851: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul626, R.shape([batch_size, 1, 20, 64])) + permute_dims628: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_14_self_attn_v_proj_weight3, axes=None) + matmul627: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm204, permute_dims628, out_dtype="void") + add720: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul627, model_decoder_layers_14_self_attn_v_proj_bias3) + reshape852: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add720, R.shape([batch_size, 1, 20, 64])) + concat46: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape850, reshape851, reshape852), axis=2) + reshape853: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat46, R.shape([batch_size, 60, 64])) + lv162 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), reshape853), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape854: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv162, R.shape([batch_size, 1, 20, 64])) + reshape855: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape854, R.shape([batch_size, 1, 1280])) + permute_dims629: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_14_self_attn_out_proj_weight3, axes=None) + matmul628: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape855, permute_dims629, out_dtype="void") + add721: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul628, model_decoder_layers_14_self_attn_out_proj_bias3) + add722: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add718, add721) + layer_norm205: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add722, model_decoder_layers_14_encoder_attn_layer_norm_weight3, model_decoder_layers_14_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims630: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_14_encoder_attn_q_proj_weight3, axes=None) + matmul629: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm205, permute_dims630, out_dtype="void") + add723: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul629, model_decoder_layers_14_encoder_attn_q_proj_bias3) + reshape856: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add723, R.shape([batch_size, 1, 20, 64])) + reshape857: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape856, R.shape([batch_size, 20, 64])) + lv163 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), reshape857), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape858: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv163, R.shape([batch_size, 1, 20, 64])) + reshape859: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape858, R.shape([batch_size, 1, 1280])) + permute_dims631: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_14_encoder_attn_out_proj_weight3, axes=None) + matmul630: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape859, permute_dims631, out_dtype="void") + add724: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul630, model_decoder_layers_14_encoder_attn_out_proj_bias3) + add725: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add722, add724) + layer_norm206: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add725, model_decoder_layers_14_final_layer_norm_weight3, model_decoder_layers_14_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims632: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_14_fc1_weight3, axes=None) + matmul631: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm206, permute_dims632, out_dtype="void") + add726: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul631, model_decoder_layers_14_fc1_bias3) + gelu80: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add726) + permute_dims633: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_14_fc2_weight3, axes=None) + matmul632: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu80, permute_dims633, out_dtype="void") + add727: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul632, model_decoder_layers_14_fc2_bias3) + add728: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add725, add727) + layer_norm207: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add728, model_decoder_layers_15_self_attn_layer_norm_weight3, model_decoder_layers_15_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims634: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_15_self_attn_q_proj_weight3, axes=None) + matmul633: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm207, permute_dims634, out_dtype="void") + add729: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul633, model_decoder_layers_15_self_attn_q_proj_bias3) + reshape860: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add729, R.shape([batch_size, 1, 20, 64])) + permute_dims635: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_15_self_attn_k_proj_weight3, axes=None) + matmul634: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm207, permute_dims635, out_dtype="void") + reshape861: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul634, R.shape([batch_size, 1, 20, 64])) + permute_dims636: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_15_self_attn_v_proj_weight3, axes=None) + matmul635: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm207, permute_dims636, out_dtype="void") + add730: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul635, model_decoder_layers_15_self_attn_v_proj_bias3) + reshape862: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add730, R.shape([batch_size, 1, 20, 64])) + concat47: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape860, reshape861, reshape862), axis=2) + reshape863: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat47, R.shape([batch_size, 60, 64])) + lv164 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), reshape863), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape864: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv164, R.shape([batch_size, 1, 20, 64])) + reshape865: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape864, R.shape([batch_size, 1, 1280])) + permute_dims637: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_15_self_attn_out_proj_weight3, axes=None) + matmul636: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape865, permute_dims637, out_dtype="void") + add731: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul636, model_decoder_layers_15_self_attn_out_proj_bias3) + add732: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add728, add731) + layer_norm208: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add732, model_decoder_layers_15_encoder_attn_layer_norm_weight3, model_decoder_layers_15_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims638: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_15_encoder_attn_q_proj_weight3, axes=None) + matmul637: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm208, permute_dims638, out_dtype="void") + add733: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul637, model_decoder_layers_15_encoder_attn_q_proj_bias3) + reshape866: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add733, R.shape([batch_size, 1, 20, 64])) + reshape867: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape866, R.shape([batch_size, 20, 64])) + lv165 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), reshape867), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape868: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv165, R.shape([batch_size, 1, 20, 64])) + reshape869: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape868, R.shape([batch_size, 1, 1280])) + permute_dims639: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_15_encoder_attn_out_proj_weight3, axes=None) + matmul638: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape869, permute_dims639, out_dtype="void") + add734: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul638, model_decoder_layers_15_encoder_attn_out_proj_bias3) + add735: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add732, add734) + layer_norm209: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add735, model_decoder_layers_15_final_layer_norm_weight3, model_decoder_layers_15_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims640: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_15_fc1_weight3, axes=None) + matmul639: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm209, permute_dims640, out_dtype="void") + add736: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul639, model_decoder_layers_15_fc1_bias3) + gelu81: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add736) + permute_dims641: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_15_fc2_weight3, axes=None) + matmul640: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu81, permute_dims641, out_dtype="void") + add737: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul640, model_decoder_layers_15_fc2_bias3) + add738: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add735, add737) + layer_norm210: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add738, model_decoder_layers_16_self_attn_layer_norm_weight3, model_decoder_layers_16_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims642: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_16_self_attn_q_proj_weight3, axes=None) + matmul641: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm210, permute_dims642, out_dtype="void") + add739: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul641, model_decoder_layers_16_self_attn_q_proj_bias3) + reshape870: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add739, R.shape([batch_size, 1, 20, 64])) + permute_dims643: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_16_self_attn_k_proj_weight3, axes=None) + matmul642: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm210, permute_dims643, out_dtype="void") + reshape871: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul642, R.shape([batch_size, 1, 20, 64])) + permute_dims644: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_16_self_attn_v_proj_weight3, axes=None) + matmul643: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm210, permute_dims644, out_dtype="void") + add740: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul643, model_decoder_layers_16_self_attn_v_proj_bias3) + reshape872: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add740, R.shape([batch_size, 1, 20, 64])) + concat48: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape870, reshape871, reshape872), axis=2) + reshape873: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat48, R.shape([batch_size, 60, 64])) + lv166 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), reshape873), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape874: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv166, R.shape([batch_size, 1, 20, 64])) + reshape875: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape874, R.shape([batch_size, 1, 1280])) + permute_dims645: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_16_self_attn_out_proj_weight3, axes=None) + matmul644: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape875, permute_dims645, out_dtype="void") + add741: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul644, model_decoder_layers_16_self_attn_out_proj_bias3) + add742: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add738, add741) + layer_norm211: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add742, model_decoder_layers_16_encoder_attn_layer_norm_weight3, model_decoder_layers_16_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims646: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_16_encoder_attn_q_proj_weight3, axes=None) + matmul645: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm211, permute_dims646, out_dtype="void") + add743: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul645, model_decoder_layers_16_encoder_attn_q_proj_bias3) + reshape876: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add743, R.shape([batch_size, 1, 20, 64])) + reshape877: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape876, R.shape([batch_size, 20, 64])) + lv167 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), reshape877), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape878: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv167, R.shape([batch_size, 1, 20, 64])) + reshape879: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape878, R.shape([batch_size, 1, 1280])) + permute_dims647: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_16_encoder_attn_out_proj_weight3, axes=None) + matmul646: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape879, permute_dims647, out_dtype="void") + add744: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul646, model_decoder_layers_16_encoder_attn_out_proj_bias3) + add745: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add742, add744) + layer_norm212: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add745, model_decoder_layers_16_final_layer_norm_weight3, model_decoder_layers_16_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims648: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_16_fc1_weight3, axes=None) + matmul647: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm212, permute_dims648, out_dtype="void") + add746: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul647, model_decoder_layers_16_fc1_bias3) + gelu82: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add746) + permute_dims649: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_16_fc2_weight3, axes=None) + matmul648: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu82, permute_dims649, out_dtype="void") + add747: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul648, model_decoder_layers_16_fc2_bias3) + add748: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add745, add747) + layer_norm213: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add748, model_decoder_layers_17_self_attn_layer_norm_weight3, model_decoder_layers_17_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims650: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_17_self_attn_q_proj_weight3, axes=None) + matmul649: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm213, permute_dims650, out_dtype="void") + add749: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul649, model_decoder_layers_17_self_attn_q_proj_bias3) + reshape880: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add749, R.shape([batch_size, 1, 20, 64])) + permute_dims651: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_17_self_attn_k_proj_weight3, axes=None) + matmul650: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm213, permute_dims651, out_dtype="void") + reshape881: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul650, R.shape([batch_size, 1, 20, 64])) + permute_dims652: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_17_self_attn_v_proj_weight3, axes=None) + matmul651: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm213, permute_dims652, out_dtype="void") + add750: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul651, model_decoder_layers_17_self_attn_v_proj_bias3) + reshape882: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add750, R.shape([batch_size, 1, 20, 64])) + concat49: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape880, reshape881, reshape882), axis=2) + reshape883: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat49, R.shape([batch_size, 60, 64])) + lv168 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), reshape883), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape884: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv168, R.shape([batch_size, 1, 20, 64])) + reshape885: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape884, R.shape([batch_size, 1, 1280])) + permute_dims653: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_17_self_attn_out_proj_weight3, axes=None) + matmul652: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape885, permute_dims653, out_dtype="void") + add751: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul652, model_decoder_layers_17_self_attn_out_proj_bias3) + add752: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add748, add751) + layer_norm214: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add752, model_decoder_layers_17_encoder_attn_layer_norm_weight3, model_decoder_layers_17_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims654: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_17_encoder_attn_q_proj_weight3, axes=None) + matmul653: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm214, permute_dims654, out_dtype="void") + add753: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul653, model_decoder_layers_17_encoder_attn_q_proj_bias3) + reshape886: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add753, R.shape([batch_size, 1, 20, 64])) + reshape887: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape886, R.shape([batch_size, 20, 64])) + lv169 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), reshape887), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape888: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv169, R.shape([batch_size, 1, 20, 64])) + reshape889: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape888, R.shape([batch_size, 1, 1280])) + permute_dims655: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_17_encoder_attn_out_proj_weight3, axes=None) + matmul654: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape889, permute_dims655, out_dtype="void") + add754: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul654, model_decoder_layers_17_encoder_attn_out_proj_bias3) + add755: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add752, add754) + layer_norm215: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add755, model_decoder_layers_17_final_layer_norm_weight3, model_decoder_layers_17_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims656: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_17_fc1_weight3, axes=None) + matmul655: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm215, permute_dims656, out_dtype="void") + add756: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul655, model_decoder_layers_17_fc1_bias3) + gelu83: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add756) + permute_dims657: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_17_fc2_weight3, axes=None) + matmul656: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu83, permute_dims657, out_dtype="void") + add757: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul656, model_decoder_layers_17_fc2_bias3) + add758: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add755, add757) + layer_norm216: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add758, model_decoder_layers_18_self_attn_layer_norm_weight3, model_decoder_layers_18_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims658: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_18_self_attn_q_proj_weight3, axes=None) + matmul657: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm216, permute_dims658, out_dtype="void") + add759: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul657, model_decoder_layers_18_self_attn_q_proj_bias3) + reshape890: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add759, R.shape([batch_size, 1, 20, 64])) + permute_dims659: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_18_self_attn_k_proj_weight3, axes=None) + matmul658: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm216, permute_dims659, out_dtype="void") + reshape891: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul658, R.shape([batch_size, 1, 20, 64])) + permute_dims660: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_18_self_attn_v_proj_weight3, axes=None) + matmul659: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm216, permute_dims660, out_dtype="void") + add760: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul659, model_decoder_layers_18_self_attn_v_proj_bias3) + reshape892: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add760, R.shape([batch_size, 1, 20, 64])) + concat50: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape890, reshape891, reshape892), axis=2) + reshape893: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat50, R.shape([batch_size, 60, 64])) + lv170 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), reshape893), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape894: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv170, R.shape([batch_size, 1, 20, 64])) + reshape895: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape894, R.shape([batch_size, 1, 1280])) + permute_dims661: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_18_self_attn_out_proj_weight3, axes=None) + matmul660: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape895, permute_dims661, out_dtype="void") + add761: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul660, model_decoder_layers_18_self_attn_out_proj_bias3) + add762: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add758, add761) + layer_norm217: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add762, model_decoder_layers_18_encoder_attn_layer_norm_weight3, model_decoder_layers_18_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims662: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_18_encoder_attn_q_proj_weight3, axes=None) + matmul661: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm217, permute_dims662, out_dtype="void") + add763: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul661, model_decoder_layers_18_encoder_attn_q_proj_bias3) + reshape896: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add763, R.shape([batch_size, 1, 20, 64])) + reshape897: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape896, R.shape([batch_size, 20, 64])) + lv171 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), reshape897), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape898: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv171, R.shape([batch_size, 1, 20, 64])) + reshape899: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape898, R.shape([batch_size, 1, 1280])) + permute_dims663: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_18_encoder_attn_out_proj_weight3, axes=None) + matmul662: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape899, permute_dims663, out_dtype="void") + add764: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul662, model_decoder_layers_18_encoder_attn_out_proj_bias3) + add765: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add762, add764) + layer_norm218: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add765, model_decoder_layers_18_final_layer_norm_weight3, model_decoder_layers_18_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims664: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_18_fc1_weight3, axes=None) + matmul663: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm218, permute_dims664, out_dtype="void") + add766: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul663, model_decoder_layers_18_fc1_bias3) + gelu84: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add766) + permute_dims665: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_18_fc2_weight3, axes=None) + matmul664: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu84, permute_dims665, out_dtype="void") + add767: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul664, model_decoder_layers_18_fc2_bias3) + add768: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add765, add767) + layer_norm219: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add768, model_decoder_layers_19_self_attn_layer_norm_weight3, model_decoder_layers_19_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims666: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_19_self_attn_q_proj_weight3, axes=None) + matmul665: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm219, permute_dims666, out_dtype="void") + add769: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul665, model_decoder_layers_19_self_attn_q_proj_bias3) + reshape900: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add769, R.shape([batch_size, 1, 20, 64])) + permute_dims667: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_19_self_attn_k_proj_weight3, axes=None) + matmul666: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm219, permute_dims667, out_dtype="void") + reshape901: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul666, R.shape([batch_size, 1, 20, 64])) + permute_dims668: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_19_self_attn_v_proj_weight3, axes=None) + matmul667: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm219, permute_dims668, out_dtype="void") + add770: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul667, model_decoder_layers_19_self_attn_v_proj_bias3) + reshape902: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add770, R.shape([batch_size, 1, 20, 64])) + concat51: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape900, reshape901, reshape902), axis=2) + reshape903: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat51, R.shape([batch_size, 60, 64])) + lv172 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), reshape903), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape904: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv172, R.shape([batch_size, 1, 20, 64])) + reshape905: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape904, R.shape([batch_size, 1, 1280])) + permute_dims669: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_19_self_attn_out_proj_weight3, axes=None) + matmul668: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape905, permute_dims669, out_dtype="void") + add771: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul668, model_decoder_layers_19_self_attn_out_proj_bias3) + add772: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add768, add771) + layer_norm220: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add772, model_decoder_layers_19_encoder_attn_layer_norm_weight3, model_decoder_layers_19_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims670: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_19_encoder_attn_q_proj_weight3, axes=None) + matmul669: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm220, permute_dims670, out_dtype="void") + add773: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul669, model_decoder_layers_19_encoder_attn_q_proj_bias3) + reshape906: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add773, R.shape([batch_size, 1, 20, 64])) + reshape907: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape906, R.shape([batch_size, 20, 64])) + lv173 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), reshape907), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape908: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv173, R.shape([batch_size, 1, 20, 64])) + reshape909: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape908, R.shape([batch_size, 1, 1280])) + permute_dims671: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_19_encoder_attn_out_proj_weight3, axes=None) + matmul670: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape909, permute_dims671, out_dtype="void") + add774: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul670, model_decoder_layers_19_encoder_attn_out_proj_bias3) + add775: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add772, add774) + layer_norm221: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add775, model_decoder_layers_19_final_layer_norm_weight3, model_decoder_layers_19_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims672: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_19_fc1_weight3, axes=None) + matmul671: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm221, permute_dims672, out_dtype="void") + add776: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul671, model_decoder_layers_19_fc1_bias3) + gelu85: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add776) + permute_dims673: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_19_fc2_weight3, axes=None) + matmul672: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu85, permute_dims673, out_dtype="void") + add777: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul672, model_decoder_layers_19_fc2_bias3) + add778: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add775, add777) + layer_norm222: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add778, model_decoder_layers_20_self_attn_layer_norm_weight3, model_decoder_layers_20_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims674: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_20_self_attn_q_proj_weight3, axes=None) + matmul673: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm222, permute_dims674, out_dtype="void") + add779: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul673, model_decoder_layers_20_self_attn_q_proj_bias3) + reshape910: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add779, R.shape([batch_size, 1, 20, 64])) + permute_dims675: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_20_self_attn_k_proj_weight3, axes=None) + matmul674: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm222, permute_dims675, out_dtype="void") + reshape911: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul674, R.shape([batch_size, 1, 20, 64])) + permute_dims676: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_20_self_attn_v_proj_weight3, axes=None) + matmul675: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm222, permute_dims676, out_dtype="void") + add780: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul675, model_decoder_layers_20_self_attn_v_proj_bias3) + reshape912: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add780, R.shape([batch_size, 1, 20, 64])) + concat52: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape910, reshape911, reshape912), axis=2) + reshape913: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat52, R.shape([batch_size, 60, 64])) + lv174 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), reshape913), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape914: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv174, R.shape([batch_size, 1, 20, 64])) + reshape915: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape914, R.shape([batch_size, 1, 1280])) + permute_dims677: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_20_self_attn_out_proj_weight3, axes=None) + matmul676: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape915, permute_dims677, out_dtype="void") + add781: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul676, model_decoder_layers_20_self_attn_out_proj_bias3) + add782: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add778, add781) + layer_norm223: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add782, model_decoder_layers_20_encoder_attn_layer_norm_weight3, model_decoder_layers_20_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims678: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_20_encoder_attn_q_proj_weight3, axes=None) + matmul677: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm223, permute_dims678, out_dtype="void") + add783: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul677, model_decoder_layers_20_encoder_attn_q_proj_bias3) + reshape916: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add783, R.shape([batch_size, 1, 20, 64])) + reshape917: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape916, R.shape([batch_size, 20, 64])) + lv175 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), reshape917), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape918: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv175, R.shape([batch_size, 1, 20, 64])) + reshape919: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape918, R.shape([batch_size, 1, 1280])) + permute_dims679: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_20_encoder_attn_out_proj_weight3, axes=None) + matmul678: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape919, permute_dims679, out_dtype="void") + add784: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul678, model_decoder_layers_20_encoder_attn_out_proj_bias3) + add785: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add782, add784) + layer_norm224: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add785, model_decoder_layers_20_final_layer_norm_weight3, model_decoder_layers_20_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims680: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_20_fc1_weight3, axes=None) + matmul679: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm224, permute_dims680, out_dtype="void") + add786: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul679, model_decoder_layers_20_fc1_bias3) + gelu86: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add786) + permute_dims681: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_20_fc2_weight3, axes=None) + matmul680: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu86, permute_dims681, out_dtype="void") + add787: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul680, model_decoder_layers_20_fc2_bias3) + add788: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add785, add787) + layer_norm225: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add788, model_decoder_layers_21_self_attn_layer_norm_weight3, model_decoder_layers_21_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims682: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_21_self_attn_q_proj_weight3, axes=None) + matmul681: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm225, permute_dims682, out_dtype="void") + add789: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul681, model_decoder_layers_21_self_attn_q_proj_bias3) + reshape920: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add789, R.shape([batch_size, 1, 20, 64])) + permute_dims683: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_21_self_attn_k_proj_weight3, axes=None) + matmul682: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm225, permute_dims683, out_dtype="void") + reshape921: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul682, R.shape([batch_size, 1, 20, 64])) + permute_dims684: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_21_self_attn_v_proj_weight3, axes=None) + matmul683: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm225, permute_dims684, out_dtype="void") + add790: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul683, model_decoder_layers_21_self_attn_v_proj_bias3) + reshape922: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add790, R.shape([batch_size, 1, 20, 64])) + concat53: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape920, reshape921, reshape922), axis=2) + reshape923: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat53, R.shape([batch_size, 60, 64])) + lv176 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), reshape923), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape924: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv176, R.shape([batch_size, 1, 20, 64])) + reshape925: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape924, R.shape([batch_size, 1, 1280])) + permute_dims685: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_21_self_attn_out_proj_weight3, axes=None) + matmul684: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape925, permute_dims685, out_dtype="void") + add791: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul684, model_decoder_layers_21_self_attn_out_proj_bias3) + add792: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add788, add791) + layer_norm226: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add792, model_decoder_layers_21_encoder_attn_layer_norm_weight3, model_decoder_layers_21_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims686: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_21_encoder_attn_q_proj_weight3, axes=None) + matmul685: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm226, permute_dims686, out_dtype="void") + add793: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul685, model_decoder_layers_21_encoder_attn_q_proj_bias3) + reshape926: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add793, R.shape([batch_size, 1, 20, 64])) + reshape927: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape926, R.shape([batch_size, 20, 64])) + lv177 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), reshape927), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape928: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv177, R.shape([batch_size, 1, 20, 64])) + reshape929: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape928, R.shape([batch_size, 1, 1280])) + permute_dims687: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_21_encoder_attn_out_proj_weight3, axes=None) + matmul686: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape929, permute_dims687, out_dtype="void") + add794: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul686, model_decoder_layers_21_encoder_attn_out_proj_bias3) + add795: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add792, add794) + layer_norm227: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add795, model_decoder_layers_21_final_layer_norm_weight3, model_decoder_layers_21_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims688: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_21_fc1_weight3, axes=None) + matmul687: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm227, permute_dims688, out_dtype="void") + add796: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul687, model_decoder_layers_21_fc1_bias3) + gelu87: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add796) + permute_dims689: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_21_fc2_weight3, axes=None) + matmul688: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu87, permute_dims689, out_dtype="void") + add797: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul688, model_decoder_layers_21_fc2_bias3) + add798: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add795, add797) + layer_norm228: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add798, model_decoder_layers_22_self_attn_layer_norm_weight3, model_decoder_layers_22_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims690: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_22_self_attn_q_proj_weight3, axes=None) + matmul689: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm228, permute_dims690, out_dtype="void") + add799: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul689, model_decoder_layers_22_self_attn_q_proj_bias3) + reshape930: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add799, R.shape([batch_size, 1, 20, 64])) + permute_dims691: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_22_self_attn_k_proj_weight3, axes=None) + matmul690: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm228, permute_dims691, out_dtype="void") + reshape931: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul690, R.shape([batch_size, 1, 20, 64])) + permute_dims692: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_22_self_attn_v_proj_weight3, axes=None) + matmul691: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm228, permute_dims692, out_dtype="void") + add800: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul691, model_decoder_layers_22_self_attn_v_proj_bias3) + reshape932: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add800, R.shape([batch_size, 1, 20, 64])) + concat54: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape930, reshape931, reshape932), axis=2) + reshape933: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat54, R.shape([batch_size, 60, 64])) + lv178 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), reshape933), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape934: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv178, R.shape([batch_size, 1, 20, 64])) + reshape935: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape934, R.shape([batch_size, 1, 1280])) + permute_dims693: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_22_self_attn_out_proj_weight3, axes=None) + matmul692: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape935, permute_dims693, out_dtype="void") + add801: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul692, model_decoder_layers_22_self_attn_out_proj_bias3) + add802: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add798, add801) + layer_norm229: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add802, model_decoder_layers_22_encoder_attn_layer_norm_weight3, model_decoder_layers_22_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims694: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_22_encoder_attn_q_proj_weight3, axes=None) + matmul693: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm229, permute_dims694, out_dtype="void") + add803: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul693, model_decoder_layers_22_encoder_attn_q_proj_bias3) + reshape936: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add803, R.shape([batch_size, 1, 20, 64])) + reshape937: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape936, R.shape([batch_size, 20, 64])) + lv179 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), reshape937), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape938: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv179, R.shape([batch_size, 1, 20, 64])) + reshape939: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape938, R.shape([batch_size, 1, 1280])) + permute_dims695: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_22_encoder_attn_out_proj_weight3, axes=None) + matmul694: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape939, permute_dims695, out_dtype="void") + add804: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul694, model_decoder_layers_22_encoder_attn_out_proj_bias3) + add805: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add802, add804) + layer_norm230: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add805, model_decoder_layers_22_final_layer_norm_weight3, model_decoder_layers_22_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims696: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_22_fc1_weight3, axes=None) + matmul695: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm230, permute_dims696, out_dtype="void") + add806: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul695, model_decoder_layers_22_fc1_bias3) + gelu88: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add806) + permute_dims697: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_22_fc2_weight3, axes=None) + matmul696: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu88, permute_dims697, out_dtype="void") + add807: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul696, model_decoder_layers_22_fc2_bias3) + add808: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add805, add807) + layer_norm231: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add808, model_decoder_layers_23_self_attn_layer_norm_weight3, model_decoder_layers_23_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims698: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_23_self_attn_q_proj_weight3, axes=None) + matmul697: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm231, permute_dims698, out_dtype="void") + add809: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul697, model_decoder_layers_23_self_attn_q_proj_bias3) + reshape940: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add809, R.shape([batch_size, 1, 20, 64])) + permute_dims699: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_23_self_attn_k_proj_weight3, axes=None) + matmul698: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm231, permute_dims699, out_dtype="void") + reshape941: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul698, R.shape([batch_size, 1, 20, 64])) + permute_dims700: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_23_self_attn_v_proj_weight3, axes=None) + matmul699: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm231, permute_dims700, out_dtype="void") + add810: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul699, model_decoder_layers_23_self_attn_v_proj_bias3) + reshape942: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add810, R.shape([batch_size, 1, 20, 64])) + concat55: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape940, reshape941, reshape942), axis=2) + reshape943: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat55, R.shape([batch_size, 60, 64])) + lv180 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), reshape943), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape944: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv180, R.shape([batch_size, 1, 20, 64])) + reshape945: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape944, R.shape([batch_size, 1, 1280])) + permute_dims701: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_23_self_attn_out_proj_weight3, axes=None) + matmul700: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape945, permute_dims701, out_dtype="void") + add811: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul700, model_decoder_layers_23_self_attn_out_proj_bias3) + add812: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add808, add811) + layer_norm232: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add812, model_decoder_layers_23_encoder_attn_layer_norm_weight3, model_decoder_layers_23_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims702: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_23_encoder_attn_q_proj_weight3, axes=None) + matmul701: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm232, permute_dims702, out_dtype="void") + add813: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul701, model_decoder_layers_23_encoder_attn_q_proj_bias3) + reshape946: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add813, R.shape([batch_size, 1, 20, 64])) + reshape947: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape946, R.shape([batch_size, 20, 64])) + lv181 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), reshape947), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape948: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv181, R.shape([batch_size, 1, 20, 64])) + reshape949: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape948, R.shape([batch_size, 1, 1280])) + permute_dims703: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_23_encoder_attn_out_proj_weight3, axes=None) + matmul702: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape949, permute_dims703, out_dtype="void") + add814: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul702, model_decoder_layers_23_encoder_attn_out_proj_bias3) + add815: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add812, add814) + layer_norm233: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add815, model_decoder_layers_23_final_layer_norm_weight3, model_decoder_layers_23_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims704: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_23_fc1_weight3, axes=None) + matmul703: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm233, permute_dims704, out_dtype="void") + add816: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul703, model_decoder_layers_23_fc1_bias3) + gelu89: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add816) + permute_dims705: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_23_fc2_weight3, axes=None) + matmul704: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu89, permute_dims705, out_dtype="void") + add817: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul704, model_decoder_layers_23_fc2_bias3) + add818: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add815, add817) + layer_norm234: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add818, model_decoder_layers_24_self_attn_layer_norm_weight3, model_decoder_layers_24_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims706: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_24_self_attn_q_proj_weight3, axes=None) + matmul705: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm234, permute_dims706, out_dtype="void") + add819: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul705, model_decoder_layers_24_self_attn_q_proj_bias3) + reshape950: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add819, R.shape([batch_size, 1, 20, 64])) + permute_dims707: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_24_self_attn_k_proj_weight3, axes=None) + matmul706: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm234, permute_dims707, out_dtype="void") + reshape951: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul706, R.shape([batch_size, 1, 20, 64])) + permute_dims708: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_24_self_attn_v_proj_weight3, axes=None) + matmul707: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm234, permute_dims708, out_dtype="void") + add820: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul707, model_decoder_layers_24_self_attn_v_proj_bias3) + reshape952: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add820, R.shape([batch_size, 1, 20, 64])) + concat56: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape950, reshape951, reshape952), axis=2) + reshape953: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat56, R.shape([batch_size, 60, 64])) + lv182 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), reshape953), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape954: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv182, R.shape([batch_size, 1, 20, 64])) + reshape955: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape954, R.shape([batch_size, 1, 1280])) + permute_dims709: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_24_self_attn_out_proj_weight3, axes=None) + matmul708: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape955, permute_dims709, out_dtype="void") + add821: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul708, model_decoder_layers_24_self_attn_out_proj_bias3) + add822: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add818, add821) + layer_norm235: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add822, model_decoder_layers_24_encoder_attn_layer_norm_weight3, model_decoder_layers_24_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims710: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_24_encoder_attn_q_proj_weight3, axes=None) + matmul709: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm235, permute_dims710, out_dtype="void") + add823: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul709, model_decoder_layers_24_encoder_attn_q_proj_bias3) + reshape956: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add823, R.shape([batch_size, 1, 20, 64])) + reshape957: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape956, R.shape([batch_size, 20, 64])) + lv183 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), reshape957), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape958: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv183, R.shape([batch_size, 1, 20, 64])) + reshape959: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape958, R.shape([batch_size, 1, 1280])) + permute_dims711: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_24_encoder_attn_out_proj_weight3, axes=None) + matmul710: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape959, permute_dims711, out_dtype="void") + add824: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul710, model_decoder_layers_24_encoder_attn_out_proj_bias3) + add825: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add822, add824) + layer_norm236: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add825, model_decoder_layers_24_final_layer_norm_weight3, model_decoder_layers_24_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims712: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_24_fc1_weight3, axes=None) + matmul711: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm236, permute_dims712, out_dtype="void") + add826: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul711, model_decoder_layers_24_fc1_bias3) + gelu90: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add826) + permute_dims713: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_24_fc2_weight3, axes=None) + matmul712: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu90, permute_dims713, out_dtype="void") + add827: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul712, model_decoder_layers_24_fc2_bias3) + add828: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add825, add827) + layer_norm237: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add828, model_decoder_layers_25_self_attn_layer_norm_weight3, model_decoder_layers_25_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims714: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_25_self_attn_q_proj_weight3, axes=None) + matmul713: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm237, permute_dims714, out_dtype="void") + add829: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul713, model_decoder_layers_25_self_attn_q_proj_bias3) + reshape960: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add829, R.shape([batch_size, 1, 20, 64])) + permute_dims715: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_25_self_attn_k_proj_weight3, axes=None) + matmul714: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm237, permute_dims715, out_dtype="void") + reshape961: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul714, R.shape([batch_size, 1, 20, 64])) + permute_dims716: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_25_self_attn_v_proj_weight3, axes=None) + matmul715: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm237, permute_dims716, out_dtype="void") + add830: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul715, model_decoder_layers_25_self_attn_v_proj_bias3) + reshape962: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add830, R.shape([batch_size, 1, 20, 64])) + concat57: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape960, reshape961, reshape962), axis=2) + reshape963: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat57, R.shape([batch_size, 60, 64])) + lv184 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), reshape963), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape964: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv184, R.shape([batch_size, 1, 20, 64])) + reshape965: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape964, R.shape([batch_size, 1, 1280])) + permute_dims717: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_25_self_attn_out_proj_weight3, axes=None) + matmul716: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape965, permute_dims717, out_dtype="void") + add831: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul716, model_decoder_layers_25_self_attn_out_proj_bias3) + add832: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add828, add831) + layer_norm238: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add832, model_decoder_layers_25_encoder_attn_layer_norm_weight3, model_decoder_layers_25_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims718: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_25_encoder_attn_q_proj_weight3, axes=None) + matmul717: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm238, permute_dims718, out_dtype="void") + add833: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul717, model_decoder_layers_25_encoder_attn_q_proj_bias3) + reshape966: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add833, R.shape([batch_size, 1, 20, 64])) + reshape967: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape966, R.shape([batch_size, 20, 64])) + lv185 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), reshape967), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape968: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv185, R.shape([batch_size, 1, 20, 64])) + reshape969: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape968, R.shape([batch_size, 1, 1280])) + permute_dims719: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_25_encoder_attn_out_proj_weight3, axes=None) + matmul718: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape969, permute_dims719, out_dtype="void") + add834: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul718, model_decoder_layers_25_encoder_attn_out_proj_bias3) + add835: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add832, add834) + layer_norm239: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add835, model_decoder_layers_25_final_layer_norm_weight3, model_decoder_layers_25_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims720: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_25_fc1_weight3, axes=None) + matmul719: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm239, permute_dims720, out_dtype="void") + add836: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul719, model_decoder_layers_25_fc1_bias3) + gelu91: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add836) + permute_dims721: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_25_fc2_weight3, axes=None) + matmul720: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu91, permute_dims721, out_dtype="void") + add837: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul720, model_decoder_layers_25_fc2_bias3) + add838: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add835, add837) + layer_norm240: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add838, model_decoder_layers_26_self_attn_layer_norm_weight3, model_decoder_layers_26_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims722: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_26_self_attn_q_proj_weight3, axes=None) + matmul721: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm240, permute_dims722, out_dtype="void") + add839: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul721, model_decoder_layers_26_self_attn_q_proj_bias3) + reshape970: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add839, R.shape([batch_size, 1, 20, 64])) + permute_dims723: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_26_self_attn_k_proj_weight3, axes=None) + matmul722: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm240, permute_dims723, out_dtype="void") + reshape971: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul722, R.shape([batch_size, 1, 20, 64])) + permute_dims724: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_26_self_attn_v_proj_weight3, axes=None) + matmul723: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm240, permute_dims724, out_dtype="void") + add840: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul723, model_decoder_layers_26_self_attn_v_proj_bias3) + reshape972: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add840, R.shape([batch_size, 1, 20, 64])) + concat58: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape970, reshape971, reshape972), axis=2) + reshape973: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat58, R.shape([batch_size, 60, 64])) + lv186 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), reshape973), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape974: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv186, R.shape([batch_size, 1, 20, 64])) + reshape975: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape974, R.shape([batch_size, 1, 1280])) + permute_dims725: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_26_self_attn_out_proj_weight3, axes=None) + matmul724: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape975, permute_dims725, out_dtype="void") + add841: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul724, model_decoder_layers_26_self_attn_out_proj_bias3) + add842: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add838, add841) + layer_norm241: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add842, model_decoder_layers_26_encoder_attn_layer_norm_weight3, model_decoder_layers_26_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims726: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_26_encoder_attn_q_proj_weight3, axes=None) + matmul725: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm241, permute_dims726, out_dtype="void") + add843: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul725, model_decoder_layers_26_encoder_attn_q_proj_bias3) + reshape976: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add843, R.shape([batch_size, 1, 20, 64])) + reshape977: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape976, R.shape([batch_size, 20, 64])) + lv187 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), reshape977), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape978: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv187, R.shape([batch_size, 1, 20, 64])) + reshape979: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape978, R.shape([batch_size, 1, 1280])) + permute_dims727: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_26_encoder_attn_out_proj_weight3, axes=None) + matmul726: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape979, permute_dims727, out_dtype="void") + add844: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul726, model_decoder_layers_26_encoder_attn_out_proj_bias3) + add845: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add842, add844) + layer_norm242: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add845, model_decoder_layers_26_final_layer_norm_weight3, model_decoder_layers_26_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims728: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_26_fc1_weight3, axes=None) + matmul727: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm242, permute_dims728, out_dtype="void") + add846: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul727, model_decoder_layers_26_fc1_bias3) + gelu92: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add846) + permute_dims729: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_26_fc2_weight3, axes=None) + matmul728: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu92, permute_dims729, out_dtype="void") + add847: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul728, model_decoder_layers_26_fc2_bias3) + add848: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add845, add847) + layer_norm243: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add848, model_decoder_layers_27_self_attn_layer_norm_weight3, model_decoder_layers_27_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims730: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_27_self_attn_q_proj_weight3, axes=None) + matmul729: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm243, permute_dims730, out_dtype="void") + add849: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul729, model_decoder_layers_27_self_attn_q_proj_bias3) + reshape980: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add849, R.shape([batch_size, 1, 20, 64])) + permute_dims731: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_27_self_attn_k_proj_weight3, axes=None) + matmul730: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm243, permute_dims731, out_dtype="void") + reshape981: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul730, R.shape([batch_size, 1, 20, 64])) + permute_dims732: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_27_self_attn_v_proj_weight3, axes=None) + matmul731: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm243, permute_dims732, out_dtype="void") + add850: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul731, model_decoder_layers_27_self_attn_v_proj_bias3) + reshape982: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add850, R.shape([batch_size, 1, 20, 64])) + concat59: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape980, reshape981, reshape982), axis=2) + reshape983: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat59, R.shape([batch_size, 60, 64])) + lv188 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), reshape983), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape984: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv188, R.shape([batch_size, 1, 20, 64])) + reshape985: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape984, R.shape([batch_size, 1, 1280])) + permute_dims733: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_27_self_attn_out_proj_weight3, axes=None) + matmul732: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape985, permute_dims733, out_dtype="void") + add851: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul732, model_decoder_layers_27_self_attn_out_proj_bias3) + add852: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add848, add851) + layer_norm244: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add852, model_decoder_layers_27_encoder_attn_layer_norm_weight3, model_decoder_layers_27_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims734: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_27_encoder_attn_q_proj_weight3, axes=None) + matmul733: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm244, permute_dims734, out_dtype="void") + add853: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul733, model_decoder_layers_27_encoder_attn_q_proj_bias3) + reshape986: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add853, R.shape([batch_size, 1, 20, 64])) + reshape987: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape986, R.shape([batch_size, 20, 64])) + lv189 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), reshape987), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape988: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv189, R.shape([batch_size, 1, 20, 64])) + reshape989: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape988, R.shape([batch_size, 1, 1280])) + permute_dims735: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_27_encoder_attn_out_proj_weight3, axes=None) + matmul734: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape989, permute_dims735, out_dtype="void") + add854: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul734, model_decoder_layers_27_encoder_attn_out_proj_bias3) + add855: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add852, add854) + layer_norm245: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add855, model_decoder_layers_27_final_layer_norm_weight3, model_decoder_layers_27_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims736: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_27_fc1_weight3, axes=None) + matmul735: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm245, permute_dims736, out_dtype="void") + add856: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul735, model_decoder_layers_27_fc1_bias3) + gelu93: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add856) + permute_dims737: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_27_fc2_weight3, axes=None) + matmul736: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu93, permute_dims737, out_dtype="void") + add857: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul736, model_decoder_layers_27_fc2_bias3) + add858: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add855, add857) + layer_norm246: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add858, model_decoder_layers_28_self_attn_layer_norm_weight3, model_decoder_layers_28_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims738: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_28_self_attn_q_proj_weight3, axes=None) + matmul737: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm246, permute_dims738, out_dtype="void") + add859: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul737, model_decoder_layers_28_self_attn_q_proj_bias3) + reshape990: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add859, R.shape([batch_size, 1, 20, 64])) + permute_dims739: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_28_self_attn_k_proj_weight3, axes=None) + matmul738: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm246, permute_dims739, out_dtype="void") + reshape991: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul738, R.shape([batch_size, 1, 20, 64])) + permute_dims740: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_28_self_attn_v_proj_weight3, axes=None) + matmul739: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm246, permute_dims740, out_dtype="void") + add860: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul739, model_decoder_layers_28_self_attn_v_proj_bias3) + reshape992: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add860, R.shape([batch_size, 1, 20, 64])) + concat60: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape990, reshape991, reshape992), axis=2) + reshape993: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat60, R.shape([batch_size, 60, 64])) + lv190 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), reshape993), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape994: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv190, R.shape([batch_size, 1, 20, 64])) + reshape995: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape994, R.shape([batch_size, 1, 1280])) + permute_dims741: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_28_self_attn_out_proj_weight3, axes=None) + matmul740: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape995, permute_dims741, out_dtype="void") + add861: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul740, model_decoder_layers_28_self_attn_out_proj_bias3) + add862: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add858, add861) + layer_norm247: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add862, model_decoder_layers_28_encoder_attn_layer_norm_weight3, model_decoder_layers_28_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims742: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_28_encoder_attn_q_proj_weight3, axes=None) + matmul741: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm247, permute_dims742, out_dtype="void") + add863: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul741, model_decoder_layers_28_encoder_attn_q_proj_bias3) + reshape996: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add863, R.shape([batch_size, 1, 20, 64])) + reshape997: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape996, R.shape([batch_size, 20, 64])) + lv191 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), reshape997), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape998: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv191, R.shape([batch_size, 1, 20, 64])) + reshape999: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape998, R.shape([batch_size, 1, 1280])) + permute_dims743: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_28_encoder_attn_out_proj_weight3, axes=None) + matmul742: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape999, permute_dims743, out_dtype="void") + add864: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul742, model_decoder_layers_28_encoder_attn_out_proj_bias3) + add865: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add862, add864) + layer_norm248: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add865, model_decoder_layers_28_final_layer_norm_weight3, model_decoder_layers_28_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims744: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_28_fc1_weight3, axes=None) + matmul743: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm248, permute_dims744, out_dtype="void") + add866: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul743, model_decoder_layers_28_fc1_bias3) + gelu94: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add866) + permute_dims745: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_28_fc2_weight3, axes=None) + matmul744: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu94, permute_dims745, out_dtype="void") + add867: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul744, model_decoder_layers_28_fc2_bias3) + add868: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add865, add867) + layer_norm249: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add868, model_decoder_layers_29_self_attn_layer_norm_weight3, model_decoder_layers_29_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims746: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_29_self_attn_q_proj_weight3, axes=None) + matmul745: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm249, permute_dims746, out_dtype="void") + add869: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul745, model_decoder_layers_29_self_attn_q_proj_bias3) + reshape1000: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add869, R.shape([batch_size, 1, 20, 64])) + permute_dims747: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_29_self_attn_k_proj_weight3, axes=None) + matmul746: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm249, permute_dims747, out_dtype="void") + reshape1001: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul746, R.shape([batch_size, 1, 20, 64])) + permute_dims748: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_29_self_attn_v_proj_weight3, axes=None) + matmul747: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm249, permute_dims748, out_dtype="void") + add870: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul747, model_decoder_layers_29_self_attn_v_proj_bias3) + reshape1002: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add870, R.shape([batch_size, 1, 20, 64])) + concat61: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape1000, reshape1001, reshape1002), axis=2) + reshape1003: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat61, R.shape([batch_size, 60, 64])) + lv192 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), reshape1003), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape1004: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv192, R.shape([batch_size, 1, 20, 64])) + reshape1005: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape1004, R.shape([batch_size, 1, 1280])) + permute_dims749: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_29_self_attn_out_proj_weight3, axes=None) + matmul748: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape1005, permute_dims749, out_dtype="void") + add871: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul748, model_decoder_layers_29_self_attn_out_proj_bias3) + add872: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add868, add871) + layer_norm250: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add872, model_decoder_layers_29_encoder_attn_layer_norm_weight3, model_decoder_layers_29_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims750: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_29_encoder_attn_q_proj_weight3, axes=None) + matmul749: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm250, permute_dims750, out_dtype="void") + add873: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul749, model_decoder_layers_29_encoder_attn_q_proj_bias3) + reshape1006: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add873, R.shape([batch_size, 1, 20, 64])) + reshape1007: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape1006, R.shape([batch_size, 20, 64])) + lv193 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), reshape1007), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape1008: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv193, R.shape([batch_size, 1, 20, 64])) + reshape1009: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape1008, R.shape([batch_size, 1, 1280])) + permute_dims751: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_29_encoder_attn_out_proj_weight3, axes=None) + matmul750: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape1009, permute_dims751, out_dtype="void") + add874: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul750, model_decoder_layers_29_encoder_attn_out_proj_bias3) + add875: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add872, add874) + layer_norm251: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add875, model_decoder_layers_29_final_layer_norm_weight3, model_decoder_layers_29_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims752: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_29_fc1_weight3, axes=None) + matmul751: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm251, permute_dims752, out_dtype="void") + add876: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul751, model_decoder_layers_29_fc1_bias3) + gelu95: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add876) + permute_dims753: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_29_fc2_weight3, axes=None) + matmul752: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu95, permute_dims753, out_dtype="void") + add877: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul752, model_decoder_layers_29_fc2_bias3) + add878: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add875, add877) + layer_norm252: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add878, model_decoder_layers_30_self_attn_layer_norm_weight3, model_decoder_layers_30_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims754: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_30_self_attn_q_proj_weight3, axes=None) + matmul753: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm252, permute_dims754, out_dtype="void") + add879: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul753, model_decoder_layers_30_self_attn_q_proj_bias3) + reshape1010: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add879, R.shape([batch_size, 1, 20, 64])) + permute_dims755: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_30_self_attn_k_proj_weight3, axes=None) + matmul754: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm252, permute_dims755, out_dtype="void") + reshape1011: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul754, R.shape([batch_size, 1, 20, 64])) + permute_dims756: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_30_self_attn_v_proj_weight3, axes=None) + matmul755: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm252, permute_dims756, out_dtype="void") + add880: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul755, model_decoder_layers_30_self_attn_v_proj_bias3) + reshape1012: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add880, R.shape([batch_size, 1, 20, 64])) + concat62: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape1010, reshape1011, reshape1012), axis=2) + reshape1013: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat62, R.shape([batch_size, 60, 64])) + lv194 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), reshape1013), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape1014: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv194, R.shape([batch_size, 1, 20, 64])) + reshape1015: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape1014, R.shape([batch_size, 1, 1280])) + permute_dims757: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_30_self_attn_out_proj_weight3, axes=None) + matmul756: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape1015, permute_dims757, out_dtype="void") + add881: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul756, model_decoder_layers_30_self_attn_out_proj_bias3) + add882: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add878, add881) + layer_norm253: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add882, model_decoder_layers_30_encoder_attn_layer_norm_weight3, model_decoder_layers_30_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims758: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_30_encoder_attn_q_proj_weight3, axes=None) + matmul757: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm253, permute_dims758, out_dtype="void") + add883: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul757, model_decoder_layers_30_encoder_attn_q_proj_bias3) + reshape1016: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add883, R.shape([batch_size, 1, 20, 64])) + reshape1017: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape1016, R.shape([batch_size, 20, 64])) + lv195 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), reshape1017), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape1018: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv195, R.shape([batch_size, 1, 20, 64])) + reshape1019: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape1018, R.shape([batch_size, 1, 1280])) + permute_dims759: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_30_encoder_attn_out_proj_weight3, axes=None) + matmul758: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape1019, permute_dims759, out_dtype="void") + add884: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul758, model_decoder_layers_30_encoder_attn_out_proj_bias3) + add885: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add882, add884) + layer_norm254: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add885, model_decoder_layers_30_final_layer_norm_weight3, model_decoder_layers_30_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims760: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_30_fc1_weight3, axes=None) + matmul759: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm254, permute_dims760, out_dtype="void") + add886: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul759, model_decoder_layers_30_fc1_bias3) + gelu96: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add886) + permute_dims761: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_30_fc2_weight3, axes=None) + matmul760: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu96, permute_dims761, out_dtype="void") + add887: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul760, model_decoder_layers_30_fc2_bias3) + add888: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add885, add887) + layer_norm255: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add888, model_decoder_layers_31_self_attn_layer_norm_weight3, model_decoder_layers_31_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims762: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_31_self_attn_q_proj_weight3, axes=None) + matmul761: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm255, permute_dims762, out_dtype="void") + add889: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul761, model_decoder_layers_31_self_attn_q_proj_bias3) + reshape1020: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add889, R.shape([batch_size, 1, 20, 64])) + permute_dims763: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_31_self_attn_k_proj_weight3, axes=None) + matmul762: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm255, permute_dims763, out_dtype="void") + reshape1021: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul762, R.shape([batch_size, 1, 20, 64])) + permute_dims764: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_31_self_attn_v_proj_weight3, axes=None) + matmul763: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm255, permute_dims764, out_dtype="void") + add890: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul763, model_decoder_layers_31_self_attn_v_proj_bias3) + reshape1022: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add890, R.shape([batch_size, 1, 20, 64])) + concat63: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape1020, reshape1021, reshape1022), axis=2) + reshape1023: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat63, R.shape([batch_size, 60, 64])) + lv196 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), reshape1023), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape1024: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv196, R.shape([batch_size, 1, 20, 64])) + reshape1025: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape1024, R.shape([batch_size, 1, 1280])) + permute_dims765: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_31_self_attn_out_proj_weight3, axes=None) + matmul764: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape1025, permute_dims765, out_dtype="void") + add891: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul764, model_decoder_layers_31_self_attn_out_proj_bias3) + add892: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add888, add891) + layer_norm256: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add892, model_decoder_layers_31_encoder_attn_layer_norm_weight3, model_decoder_layers_31_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims766: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_31_encoder_attn_q_proj_weight3, axes=None) + matmul765: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm256, permute_dims766, out_dtype="void") + add893: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul765, model_decoder_layers_31_encoder_attn_q_proj_bias3) + reshape1026: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add893, R.shape([batch_size, 1, 20, 64])) + reshape1027: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape1026, R.shape([batch_size, 20, 64])) + lv197 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), reshape1027), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape1028: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv197, R.shape([batch_size, 1, 20, 64])) + reshape1029: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape1028, R.shape([batch_size, 1, 1280])) + permute_dims767: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_31_encoder_attn_out_proj_weight3, axes=None) + matmul766: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape1029, permute_dims767, out_dtype="void") + add894: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul766, model_decoder_layers_31_encoder_attn_out_proj_bias3) + add895: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add892, add894) + layer_norm257: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add895, model_decoder_layers_31_final_layer_norm_weight3, model_decoder_layers_31_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims768: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_31_fc1_weight3, axes=None) + matmul767: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm257, permute_dims768, out_dtype="void") + add896: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul767, model_decoder_layers_31_fc1_bias3) + gelu97: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add896) + permute_dims769: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_31_fc2_weight3, axes=None) + matmul768: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu97, permute_dims769, out_dtype="void") + add897: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul768, model_decoder_layers_31_fc2_bias3) + add898: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add895, add897) + layer_norm258: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add898, model_decoder_layer_norm_weight3, model_decoder_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims770: R.Tensor((1280, 51866), dtype="float16") = R.permute_dims(model_decoder_embed_tokens_weight3, axes=None) + matmul769: R.Tensor((batch_size, 1, 51866), dtype="float32") = R.matmul(layer_norm258, permute_dims770, out_dtype="float32") + gv3: R.Tensor((batch_size, 1, 51866), dtype="float32") = matmul769 + R.output(gv3) + return gv3 + + @R.function + def batch_encode(input_features: R.Tensor(("batch_size", 128, 3000), dtype="float16"), paged_kv_cache: R.Object, packed_params: R.Tuple(R.Tensor((1280, 128, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1500, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), 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R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"))) -> R.Tensor(("batch_size", 1500, 1280), dtype="float16"): + batch_size = T.int64() + R.func_attr({"num_input": 2, "relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "seq_len": 15000, "total_seq_len": 1500}}) + with R.dataflow(): + model_encoder_conv1_weight: R.Tensor((1280, 128, 3), dtype="float16") = packed_params[0] + model_encoder_conv1_bias: R.Tensor((1280,), dtype="float16") = packed_params[1] + model_encoder_conv2_weight: R.Tensor((1280, 1280, 3), dtype="float16") = packed_params[2] + model_encoder_conv2_bias: R.Tensor((1280,), dtype="float16") = packed_params[3] + model_encoder_embed_positions_weight: R.Tensor((1500, 1280), dtype="float16") = packed_params[4] + model_encoder_layers_0_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[5] + model_encoder_layers_0_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[6] + model_encoder_layers_0_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[7] + model_encoder_layers_0_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[8] + model_encoder_layers_0_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[9] + model_encoder_layers_0_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[10] + model_encoder_layers_0_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[11] + model_encoder_layers_0_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[12] + model_encoder_layers_0_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[13] + model_encoder_layers_0_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[14] + model_encoder_layers_0_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[15] + model_encoder_layers_0_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[16] + model_encoder_layers_0_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[17] + model_encoder_layers_0_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[18] + model_encoder_layers_0_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[19] + model_encoder_layers_1_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[20] + model_encoder_layers_1_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[21] + model_encoder_layers_1_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[22] + model_encoder_layers_1_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[23] + model_encoder_layers_1_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[24] + model_encoder_layers_1_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[25] + model_encoder_layers_1_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[26] + model_encoder_layers_1_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[27] + model_encoder_layers_1_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[28] + model_encoder_layers_1_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[29] + model_encoder_layers_1_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[30] + model_encoder_layers_1_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[31] + model_encoder_layers_1_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[32] + model_encoder_layers_1_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[33] + model_encoder_layers_1_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[34] + model_encoder_layers_2_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[35] + model_encoder_layers_2_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[36] + model_encoder_layers_2_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[37] + model_encoder_layers_2_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[38] + model_encoder_layers_2_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[39] + model_encoder_layers_2_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[40] + model_encoder_layers_2_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[41] + model_encoder_layers_2_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[42] + model_encoder_layers_2_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[43] + model_encoder_layers_2_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[44] + model_encoder_layers_2_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[45] + model_encoder_layers_2_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[46] + model_encoder_layers_2_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[47] + model_encoder_layers_2_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[48] + model_encoder_layers_2_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[49] + model_encoder_layers_3_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[50] + model_encoder_layers_3_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[51] + model_encoder_layers_3_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[52] + model_encoder_layers_3_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[53] + model_encoder_layers_3_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[54] + model_encoder_layers_3_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[55] + model_encoder_layers_3_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[56] + model_encoder_layers_3_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[57] + model_encoder_layers_3_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[58] + model_encoder_layers_3_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[59] + model_encoder_layers_3_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[60] + model_encoder_layers_3_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[61] + model_encoder_layers_3_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[62] + model_encoder_layers_3_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[63] + model_encoder_layers_3_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[64] + model_encoder_layers_4_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[65] + model_encoder_layers_4_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[66] + model_encoder_layers_4_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[67] + model_encoder_layers_4_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[68] + model_encoder_layers_4_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[69] + model_encoder_layers_4_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[70] + model_encoder_layers_4_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[71] + model_encoder_layers_4_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[72] + model_encoder_layers_4_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[73] + model_encoder_layers_4_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[74] + model_encoder_layers_4_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[75] + model_encoder_layers_4_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[76] + model_encoder_layers_4_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[77] + model_encoder_layers_4_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[78] + model_encoder_layers_4_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[79] + model_encoder_layers_5_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[80] + model_encoder_layers_5_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[81] + model_encoder_layers_5_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[82] + model_encoder_layers_5_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[83] + model_encoder_layers_5_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[84] + model_encoder_layers_5_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[85] + model_encoder_layers_5_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[86] + model_encoder_layers_5_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[87] + model_encoder_layers_5_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[88] + model_encoder_layers_5_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[89] + model_encoder_layers_5_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[90] + model_encoder_layers_5_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[91] + model_encoder_layers_5_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[92] + model_encoder_layers_5_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[93] + model_encoder_layers_5_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[94] + model_encoder_layers_6_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[95] + model_encoder_layers_6_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[96] + model_encoder_layers_6_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[97] + model_encoder_layers_6_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[98] + model_encoder_layers_6_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[99] + model_encoder_layers_6_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[100] + model_encoder_layers_6_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[101] + model_encoder_layers_6_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[102] + model_encoder_layers_6_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[103] + model_encoder_layers_6_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[104] + model_encoder_layers_6_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[105] + model_encoder_layers_6_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[106] + model_encoder_layers_6_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[107] + model_encoder_layers_6_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[108] + model_encoder_layers_6_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[109] + model_encoder_layers_7_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[110] + model_encoder_layers_7_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[111] + model_encoder_layers_7_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[112] + model_encoder_layers_7_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[113] + model_encoder_layers_7_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[114] + model_encoder_layers_7_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[115] + model_encoder_layers_7_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[116] + model_encoder_layers_7_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[117] + model_encoder_layers_7_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[118] + model_encoder_layers_7_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[119] + model_encoder_layers_7_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[120] + model_encoder_layers_7_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[121] + model_encoder_layers_7_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[122] + model_encoder_layers_7_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[123] + model_encoder_layers_7_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[124] + model_encoder_layers_8_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[125] + model_encoder_layers_8_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[126] + model_encoder_layers_8_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[127] + model_encoder_layers_8_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[128] + model_encoder_layers_8_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[129] + model_encoder_layers_8_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[130] + model_encoder_layers_8_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[131] + model_encoder_layers_8_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[132] + model_encoder_layers_8_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[133] + model_encoder_layers_8_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[134] + model_encoder_layers_8_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[135] + model_encoder_layers_8_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[136] + model_encoder_layers_8_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[137] + model_encoder_layers_8_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[138] + model_encoder_layers_8_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[139] + model_encoder_layers_9_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[140] + model_encoder_layers_9_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[141] + model_encoder_layers_9_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[142] + model_encoder_layers_9_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[143] + model_encoder_layers_9_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[144] + model_encoder_layers_9_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[145] + model_encoder_layers_9_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[146] + model_encoder_layers_9_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[147] + model_encoder_layers_9_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[148] + model_encoder_layers_9_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[149] + model_encoder_layers_9_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[150] + model_encoder_layers_9_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[151] + model_encoder_layers_9_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[152] + model_encoder_layers_9_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[153] + model_encoder_layers_9_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[154] + model_encoder_layers_10_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[155] + model_encoder_layers_10_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[156] + model_encoder_layers_10_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[157] + model_encoder_layers_10_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[158] + model_encoder_layers_10_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[159] + model_encoder_layers_10_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[160] + model_encoder_layers_10_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[161] + model_encoder_layers_10_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[162] + model_encoder_layers_10_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[163] + model_encoder_layers_10_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[164] + model_encoder_layers_10_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[165] + model_encoder_layers_10_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[166] + model_encoder_layers_10_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[167] + model_encoder_layers_10_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[168] + model_encoder_layers_10_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[169] + model_encoder_layers_11_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[170] + model_encoder_layers_11_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[171] + model_encoder_layers_11_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[172] + model_encoder_layers_11_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[173] + model_encoder_layers_11_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[174] + model_encoder_layers_11_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[175] + model_encoder_layers_11_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[176] + model_encoder_layers_11_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[177] + model_encoder_layers_11_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[178] + model_encoder_layers_11_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[179] + model_encoder_layers_11_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[180] + model_encoder_layers_11_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[181] + model_encoder_layers_11_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[182] + model_encoder_layers_11_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[183] + model_encoder_layers_11_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[184] + model_encoder_layers_12_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[185] + model_encoder_layers_12_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[186] + model_encoder_layers_12_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[187] + model_encoder_layers_12_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[188] + model_encoder_layers_12_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[189] + model_encoder_layers_12_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[190] + model_encoder_layers_12_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[191] + model_encoder_layers_12_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[192] + model_encoder_layers_12_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[193] + model_encoder_layers_12_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[194] + model_encoder_layers_12_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[195] + model_encoder_layers_12_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[196] + model_encoder_layers_12_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[197] + model_encoder_layers_12_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[198] + model_encoder_layers_12_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[199] + model_encoder_layers_13_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[200] + model_encoder_layers_13_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[201] + model_encoder_layers_13_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[202] + model_encoder_layers_13_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[203] + model_encoder_layers_13_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[204] + model_encoder_layers_13_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[205] + model_encoder_layers_13_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[206] + model_encoder_layers_13_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[207] + model_encoder_layers_13_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[208] + model_encoder_layers_13_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[209] + model_encoder_layers_13_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[210] + model_encoder_layers_13_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[211] + model_encoder_layers_13_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[212] + model_encoder_layers_13_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[213] + model_encoder_layers_13_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[214] + model_encoder_layers_14_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[215] + model_encoder_layers_14_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[216] + model_encoder_layers_14_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[217] + model_encoder_layers_14_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[218] + model_encoder_layers_14_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[219] + model_encoder_layers_14_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[220] + model_encoder_layers_14_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[221] + model_encoder_layers_14_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[222] + model_encoder_layers_14_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[223] + model_encoder_layers_14_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[224] + model_encoder_layers_14_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[225] + model_encoder_layers_14_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[226] + model_encoder_layers_14_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[227] + model_encoder_layers_14_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[228] + model_encoder_layers_14_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[229] + model_encoder_layers_15_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[230] + model_encoder_layers_15_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[231] + model_encoder_layers_15_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[232] + model_encoder_layers_15_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[233] + model_encoder_layers_15_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[234] + model_encoder_layers_15_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[235] + model_encoder_layers_15_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[236] + model_encoder_layers_15_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[237] + model_encoder_layers_15_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[238] + model_encoder_layers_15_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[239] + model_encoder_layers_15_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[240] + model_encoder_layers_15_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[241] + model_encoder_layers_15_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[242] + model_encoder_layers_15_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[243] + model_encoder_layers_15_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[244] + model_encoder_layers_16_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[245] + model_encoder_layers_16_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[246] + model_encoder_layers_16_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[247] + model_encoder_layers_16_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[248] + model_encoder_layers_16_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[249] + model_encoder_layers_16_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[250] + model_encoder_layers_16_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[251] + model_encoder_layers_16_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[252] + model_encoder_layers_16_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[253] + model_encoder_layers_16_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[254] + model_encoder_layers_16_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[255] + model_encoder_layers_16_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[256] + model_encoder_layers_16_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[257] + model_encoder_layers_16_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[258] + model_encoder_layers_16_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[259] + model_encoder_layers_17_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[260] + model_encoder_layers_17_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[261] + model_encoder_layers_17_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[262] + model_encoder_layers_17_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[263] + model_encoder_layers_17_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[264] + model_encoder_layers_17_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[265] + model_encoder_layers_17_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[266] + model_encoder_layers_17_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[267] + model_encoder_layers_17_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[268] + model_encoder_layers_17_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[269] + model_encoder_layers_17_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[270] + model_encoder_layers_17_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[271] + model_encoder_layers_17_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[272] + model_encoder_layers_17_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[273] + model_encoder_layers_17_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[274] + model_encoder_layers_18_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[275] + model_encoder_layers_18_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[276] + model_encoder_layers_18_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[277] + model_encoder_layers_18_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[278] + model_encoder_layers_18_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[279] + model_encoder_layers_18_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[280] + model_encoder_layers_18_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[281] + model_encoder_layers_18_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[282] + model_encoder_layers_18_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[283] + model_encoder_layers_18_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[284] + model_encoder_layers_18_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[285] + model_encoder_layers_18_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[286] + model_encoder_layers_18_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[287] + model_encoder_layers_18_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[288] + model_encoder_layers_18_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[289] + model_encoder_layers_19_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[290] + model_encoder_layers_19_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[291] + model_encoder_layers_19_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[292] + model_encoder_layers_19_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[293] + model_encoder_layers_19_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[294] + model_encoder_layers_19_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[295] + model_encoder_layers_19_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[296] + model_encoder_layers_19_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[297] + model_encoder_layers_19_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[298] + model_encoder_layers_19_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[299] + model_encoder_layers_19_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[300] + model_encoder_layers_19_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[301] + model_encoder_layers_19_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[302] + model_encoder_layers_19_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[303] + model_encoder_layers_19_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[304] + model_encoder_layers_20_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[305] + model_encoder_layers_20_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[306] + model_encoder_layers_20_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[307] + model_encoder_layers_20_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[308] + model_encoder_layers_20_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[309] + model_encoder_layers_20_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[310] + model_encoder_layers_20_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[311] + model_encoder_layers_20_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[312] + model_encoder_layers_20_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[313] + model_encoder_layers_20_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[314] + model_encoder_layers_20_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[315] + model_encoder_layers_20_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[316] + model_encoder_layers_20_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[317] + model_encoder_layers_20_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[318] + model_encoder_layers_20_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[319] + model_encoder_layers_21_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[320] + model_encoder_layers_21_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[321] + model_encoder_layers_21_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[322] + model_encoder_layers_21_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[323] + model_encoder_layers_21_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[324] + model_encoder_layers_21_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[325] + model_encoder_layers_21_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[326] + model_encoder_layers_21_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[327] + model_encoder_layers_21_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[328] + model_encoder_layers_21_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[329] + model_encoder_layers_21_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[330] + model_encoder_layers_21_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[331] + model_encoder_layers_21_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[332] + model_encoder_layers_21_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[333] + model_encoder_layers_21_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[334] + model_encoder_layers_22_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[335] + model_encoder_layers_22_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[336] + model_encoder_layers_22_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[337] + model_encoder_layers_22_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[338] + model_encoder_layers_22_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[339] + model_encoder_layers_22_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[340] + model_encoder_layers_22_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[341] + model_encoder_layers_22_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[342] + model_encoder_layers_22_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[343] + model_encoder_layers_22_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[344] + model_encoder_layers_22_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[345] + model_encoder_layers_22_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[346] + model_encoder_layers_22_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[347] + model_encoder_layers_22_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[348] + model_encoder_layers_22_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[349] + model_encoder_layers_23_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[350] + model_encoder_layers_23_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[351] + model_encoder_layers_23_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[352] + model_encoder_layers_23_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[353] + model_encoder_layers_23_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[354] + model_encoder_layers_23_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[355] + model_encoder_layers_23_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[356] + model_encoder_layers_23_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[357] + model_encoder_layers_23_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[358] + model_encoder_layers_23_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[359] + model_encoder_layers_23_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[360] + model_encoder_layers_23_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[361] + model_encoder_layers_23_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[362] + model_encoder_layers_23_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[363] + model_encoder_layers_23_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[364] + model_encoder_layers_24_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[365] + model_encoder_layers_24_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[366] + model_encoder_layers_24_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[367] + model_encoder_layers_24_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[368] + model_encoder_layers_24_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[369] + model_encoder_layers_24_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[370] + model_encoder_layers_24_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[371] + model_encoder_layers_24_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[372] + model_encoder_layers_24_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[373] + model_encoder_layers_24_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[374] + model_encoder_layers_24_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[375] + model_encoder_layers_24_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[376] + model_encoder_layers_24_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[377] + model_encoder_layers_24_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[378] + model_encoder_layers_24_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[379] + model_encoder_layers_25_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[380] + model_encoder_layers_25_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[381] + model_encoder_layers_25_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[382] + model_encoder_layers_25_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[383] + model_encoder_layers_25_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[384] + model_encoder_layers_25_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[385] + model_encoder_layers_25_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[386] + model_encoder_layers_25_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[387] + model_encoder_layers_25_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[388] + model_encoder_layers_25_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[389] + model_encoder_layers_25_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[390] + model_encoder_layers_25_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[391] + model_encoder_layers_25_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[392] + model_encoder_layers_25_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[393] + model_encoder_layers_25_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[394] + model_encoder_layers_26_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[395] + model_encoder_layers_26_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[396] + model_encoder_layers_26_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[397] + model_encoder_layers_26_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[398] + model_encoder_layers_26_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[399] + model_encoder_layers_26_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[400] + model_encoder_layers_26_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[401] + model_encoder_layers_26_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[402] + model_encoder_layers_26_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[403] + model_encoder_layers_26_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[404] + model_encoder_layers_26_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[405] + model_encoder_layers_26_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[406] + model_encoder_layers_26_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[407] + model_encoder_layers_26_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[408] + model_encoder_layers_26_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[409] + model_encoder_layers_27_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[410] + model_encoder_layers_27_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[411] + model_encoder_layers_27_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[412] + model_encoder_layers_27_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[413] + model_encoder_layers_27_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[414] + model_encoder_layers_27_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[415] + model_encoder_layers_27_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[416] + model_encoder_layers_27_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[417] + model_encoder_layers_27_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[418] + model_encoder_layers_27_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[419] + model_encoder_layers_27_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[420] + model_encoder_layers_27_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[421] + model_encoder_layers_27_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[422] + model_encoder_layers_27_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[423] + model_encoder_layers_27_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[424] + model_encoder_layers_28_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[425] + model_encoder_layers_28_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[426] + model_encoder_layers_28_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[427] + model_encoder_layers_28_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[428] + model_encoder_layers_28_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[429] + model_encoder_layers_28_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[430] + model_encoder_layers_28_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[431] + model_encoder_layers_28_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[432] + model_encoder_layers_28_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[433] + model_encoder_layers_28_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[434] + model_encoder_layers_28_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[435] + model_encoder_layers_28_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[436] + model_encoder_layers_28_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[437] + model_encoder_layers_28_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[438] + model_encoder_layers_28_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[439] + model_encoder_layers_29_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[440] + model_encoder_layers_29_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[441] + model_encoder_layers_29_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[442] + model_encoder_layers_29_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[443] + model_encoder_layers_29_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[444] + model_encoder_layers_29_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[445] + model_encoder_layers_29_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[446] + model_encoder_layers_29_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[447] + model_encoder_layers_29_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[448] + model_encoder_layers_29_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[449] + model_encoder_layers_29_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[450] + model_encoder_layers_29_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[451] + model_encoder_layers_29_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[452] + model_encoder_layers_29_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[453] + model_encoder_layers_29_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[454] + model_encoder_layers_30_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[455] + model_encoder_layers_30_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[456] + model_encoder_layers_30_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[457] + model_encoder_layers_30_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[458] + model_encoder_layers_30_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[459] + model_encoder_layers_30_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[460] + model_encoder_layers_30_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[461] + model_encoder_layers_30_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[462] + model_encoder_layers_30_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[463] + model_encoder_layers_30_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[464] + model_encoder_layers_30_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[465] + model_encoder_layers_30_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[466] + model_encoder_layers_30_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[467] + model_encoder_layers_30_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[468] + model_encoder_layers_30_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[469] + model_encoder_layers_31_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[470] + model_encoder_layers_31_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[471] + model_encoder_layers_31_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[472] + model_encoder_layers_31_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[473] + model_encoder_layers_31_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[474] + model_encoder_layers_31_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[475] + model_encoder_layers_31_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[476] + model_encoder_layers_31_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[477] + model_encoder_layers_31_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[478] + model_encoder_layers_31_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[479] + model_encoder_layers_31_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[480] + model_encoder_layers_31_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[481] + model_encoder_layers_31_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[482] + model_encoder_layers_31_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[483] + model_encoder_layers_31_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[484] + model_encoder_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[485] + model_encoder_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[486] + model_decoder_embed_tokens_weight: R.Tensor((51866, 1280), dtype="float16") = packed_params[487] + model_decoder_embed_positions_weight: R.Tensor((448, 1280), dtype="float16") = packed_params[488] + model_decoder_layers_0_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[489] + model_decoder_layers_0_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[490] + model_decoder_layers_0_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[491] + model_decoder_layers_0_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[492] + model_decoder_layers_0_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[493] + model_decoder_layers_0_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[494] + model_decoder_layers_0_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[495] + model_decoder_layers_0_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[496] + model_decoder_layers_0_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[497] + model_decoder_layers_0_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[498] + model_decoder_layers_0_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[499] + model_decoder_layers_0_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[500] + model_decoder_layers_0_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[501] + model_decoder_layers_0_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[502] + model_decoder_layers_0_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[503] + model_decoder_layers_0_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[504] + model_decoder_layers_0_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[505] + model_decoder_layers_0_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[506] + model_decoder_layers_0_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[507] + model_decoder_layers_0_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[508] + model_decoder_layers_0_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[509] + model_decoder_layers_0_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[510] + model_decoder_layers_0_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[511] + model_decoder_layers_0_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[512] + model_decoder_layers_1_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[513] + model_decoder_layers_1_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[514] + model_decoder_layers_1_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[515] + model_decoder_layers_1_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[516] + model_decoder_layers_1_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[517] + model_decoder_layers_1_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[518] + model_decoder_layers_1_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[519] + model_decoder_layers_1_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[520] + model_decoder_layers_1_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[521] + model_decoder_layers_1_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[522] + model_decoder_layers_1_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[523] + model_decoder_layers_1_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[524] + model_decoder_layers_1_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[525] + model_decoder_layers_1_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[526] + model_decoder_layers_1_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[527] + model_decoder_layers_1_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[528] + model_decoder_layers_1_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[529] + model_decoder_layers_1_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[530] + model_decoder_layers_1_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[531] + model_decoder_layers_1_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[532] + model_decoder_layers_1_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[533] + model_decoder_layers_1_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[534] + model_decoder_layers_1_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[535] + model_decoder_layers_1_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[536] + model_decoder_layers_2_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[537] + model_decoder_layers_2_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[538] + model_decoder_layers_2_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[539] + model_decoder_layers_2_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[540] + model_decoder_layers_2_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[541] + model_decoder_layers_2_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[542] + model_decoder_layers_2_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[543] + model_decoder_layers_2_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[544] + model_decoder_layers_2_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[545] + model_decoder_layers_2_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[546] + model_decoder_layers_2_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[547] + model_decoder_layers_2_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[548] + model_decoder_layers_2_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[549] + model_decoder_layers_2_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[550] + model_decoder_layers_2_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[551] + model_decoder_layers_2_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[552] + model_decoder_layers_2_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[553] + model_decoder_layers_2_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[554] + model_decoder_layers_2_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[555] + model_decoder_layers_2_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[556] + model_decoder_layers_2_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[557] + model_decoder_layers_2_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[558] + model_decoder_layers_2_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[559] + model_decoder_layers_2_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[560] + model_decoder_layers_3_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[561] + model_decoder_layers_3_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[562] + model_decoder_layers_3_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[563] + model_decoder_layers_3_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[564] + model_decoder_layers_3_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[565] + model_decoder_layers_3_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[566] + model_decoder_layers_3_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[567] + model_decoder_layers_3_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[568] + model_decoder_layers_3_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[569] + model_decoder_layers_3_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[570] + model_decoder_layers_3_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[571] + model_decoder_layers_3_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[572] + model_decoder_layers_3_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[573] + model_decoder_layers_3_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[574] + model_decoder_layers_3_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[575] + model_decoder_layers_3_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[576] + model_decoder_layers_3_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[577] + model_decoder_layers_3_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[578] + model_decoder_layers_3_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[579] + model_decoder_layers_3_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[580] + model_decoder_layers_3_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[581] + model_decoder_layers_3_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[582] + model_decoder_layers_3_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[583] + model_decoder_layers_3_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[584] + model_decoder_layers_4_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[585] + model_decoder_layers_4_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[586] + model_decoder_layers_4_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[587] + model_decoder_layers_4_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[588] + model_decoder_layers_4_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[589] + model_decoder_layers_4_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[590] + model_decoder_layers_4_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[591] + model_decoder_layers_4_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[592] + model_decoder_layers_4_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[593] + model_decoder_layers_4_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[594] + model_decoder_layers_4_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[595] + model_decoder_layers_4_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[596] + model_decoder_layers_4_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[597] + model_decoder_layers_4_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[598] + model_decoder_layers_4_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[599] + model_decoder_layers_4_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[600] + model_decoder_layers_4_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[601] + model_decoder_layers_4_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[602] + model_decoder_layers_4_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[603] + model_decoder_layers_4_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[604] + model_decoder_layers_4_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[605] + model_decoder_layers_4_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[606] + model_decoder_layers_4_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[607] + model_decoder_layers_4_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[608] + model_decoder_layers_5_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[609] + model_decoder_layers_5_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[610] + model_decoder_layers_5_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[611] + model_decoder_layers_5_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[612] + model_decoder_layers_5_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[613] + model_decoder_layers_5_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[614] + model_decoder_layers_5_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[615] + model_decoder_layers_5_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[616] + model_decoder_layers_5_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[617] + model_decoder_layers_5_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[618] + model_decoder_layers_5_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[619] + model_decoder_layers_5_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[620] + model_decoder_layers_5_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[621] + model_decoder_layers_5_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[622] + model_decoder_layers_5_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[623] + model_decoder_layers_5_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[624] + model_decoder_layers_5_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[625] + model_decoder_layers_5_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[626] + model_decoder_layers_5_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[627] + model_decoder_layers_5_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[628] + model_decoder_layers_5_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[629] + model_decoder_layers_5_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[630] + model_decoder_layers_5_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[631] + model_decoder_layers_5_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[632] + model_decoder_layers_6_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[633] + model_decoder_layers_6_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[634] + model_decoder_layers_6_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[635] + model_decoder_layers_6_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[636] + model_decoder_layers_6_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[637] + model_decoder_layers_6_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[638] + model_decoder_layers_6_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[639] + model_decoder_layers_6_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[640] + model_decoder_layers_6_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[641] + model_decoder_layers_6_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[642] + model_decoder_layers_6_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[643] + model_decoder_layers_6_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[644] + model_decoder_layers_6_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[645] + model_decoder_layers_6_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[646] + model_decoder_layers_6_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[647] + model_decoder_layers_6_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[648] + model_decoder_layers_6_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[649] + model_decoder_layers_6_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[650] + model_decoder_layers_6_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[651] + model_decoder_layers_6_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[652] + model_decoder_layers_6_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[653] + model_decoder_layers_6_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[654] + model_decoder_layers_6_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[655] + model_decoder_layers_6_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[656] + model_decoder_layers_7_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[657] + model_decoder_layers_7_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[658] + model_decoder_layers_7_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[659] + model_decoder_layers_7_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[660] + model_decoder_layers_7_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[661] + model_decoder_layers_7_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[662] + model_decoder_layers_7_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[663] + model_decoder_layers_7_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[664] + model_decoder_layers_7_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[665] + model_decoder_layers_7_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[666] + model_decoder_layers_7_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[667] + model_decoder_layers_7_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[668] + model_decoder_layers_7_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[669] + model_decoder_layers_7_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[670] + model_decoder_layers_7_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[671] + model_decoder_layers_7_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[672] + model_decoder_layers_7_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[673] + model_decoder_layers_7_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[674] + model_decoder_layers_7_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[675] + model_decoder_layers_7_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[676] + model_decoder_layers_7_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[677] + model_decoder_layers_7_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[678] + model_decoder_layers_7_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[679] + model_decoder_layers_7_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[680] + model_decoder_layers_8_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[681] + model_decoder_layers_8_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[682] + model_decoder_layers_8_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[683] + model_decoder_layers_8_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[684] + model_decoder_layers_8_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[685] + model_decoder_layers_8_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[686] + model_decoder_layers_8_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[687] + model_decoder_layers_8_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[688] + model_decoder_layers_8_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[689] + model_decoder_layers_8_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[690] + model_decoder_layers_8_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[691] + model_decoder_layers_8_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[692] + model_decoder_layers_8_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[693] + model_decoder_layers_8_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[694] + model_decoder_layers_8_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[695] + model_decoder_layers_8_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[696] + model_decoder_layers_8_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[697] + model_decoder_layers_8_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[698] + model_decoder_layers_8_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[699] + model_decoder_layers_8_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[700] + model_decoder_layers_8_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[701] + model_decoder_layers_8_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[702] + model_decoder_layers_8_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[703] + model_decoder_layers_8_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[704] + model_decoder_layers_9_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[705] + model_decoder_layers_9_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[706] + model_decoder_layers_9_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[707] + model_decoder_layers_9_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[708] + model_decoder_layers_9_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[709] + model_decoder_layers_9_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[710] + model_decoder_layers_9_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[711] + model_decoder_layers_9_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[712] + model_decoder_layers_9_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[713] + model_decoder_layers_9_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[714] + model_decoder_layers_9_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[715] + model_decoder_layers_9_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[716] + model_decoder_layers_9_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[717] + model_decoder_layers_9_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[718] + model_decoder_layers_9_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[719] + model_decoder_layers_9_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[720] + model_decoder_layers_9_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[721] + model_decoder_layers_9_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[722] + model_decoder_layers_9_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[723] + model_decoder_layers_9_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[724] + model_decoder_layers_9_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[725] + model_decoder_layers_9_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[726] + model_decoder_layers_9_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[727] + model_decoder_layers_9_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[728] + model_decoder_layers_10_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[729] + model_decoder_layers_10_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[730] + model_decoder_layers_10_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[731] + model_decoder_layers_10_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[732] + model_decoder_layers_10_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[733] + model_decoder_layers_10_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[734] + model_decoder_layers_10_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[735] + model_decoder_layers_10_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[736] + model_decoder_layers_10_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[737] + model_decoder_layers_10_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[738] + model_decoder_layers_10_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[739] + model_decoder_layers_10_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[740] + model_decoder_layers_10_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[741] + model_decoder_layers_10_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[742] + model_decoder_layers_10_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[743] + model_decoder_layers_10_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[744] + model_decoder_layers_10_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[745] + model_decoder_layers_10_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[746] + model_decoder_layers_10_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[747] + model_decoder_layers_10_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[748] + model_decoder_layers_10_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[749] + model_decoder_layers_10_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[750] + model_decoder_layers_10_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[751] + model_decoder_layers_10_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[752] + model_decoder_layers_11_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[753] + model_decoder_layers_11_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[754] + model_decoder_layers_11_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[755] + model_decoder_layers_11_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[756] + model_decoder_layers_11_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[757] + model_decoder_layers_11_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[758] + model_decoder_layers_11_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[759] + model_decoder_layers_11_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[760] + model_decoder_layers_11_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[761] + model_decoder_layers_11_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[762] + model_decoder_layers_11_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[763] + model_decoder_layers_11_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[764] + model_decoder_layers_11_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[765] + model_decoder_layers_11_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[766] + model_decoder_layers_11_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[767] + model_decoder_layers_11_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[768] + model_decoder_layers_11_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[769] + model_decoder_layers_11_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[770] + model_decoder_layers_11_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[771] + model_decoder_layers_11_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[772] + model_decoder_layers_11_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[773] + model_decoder_layers_11_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[774] + model_decoder_layers_11_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[775] + model_decoder_layers_11_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[776] + model_decoder_layers_12_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[777] + model_decoder_layers_12_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[778] + model_decoder_layers_12_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[779] + model_decoder_layers_12_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[780] + model_decoder_layers_12_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[781] + model_decoder_layers_12_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[782] + model_decoder_layers_12_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[783] + model_decoder_layers_12_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[784] + model_decoder_layers_12_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[785] + model_decoder_layers_12_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[786] + model_decoder_layers_12_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[787] + model_decoder_layers_12_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[788] + model_decoder_layers_12_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[789] + model_decoder_layers_12_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[790] + model_decoder_layers_12_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[791] + model_decoder_layers_12_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[792] + model_decoder_layers_12_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[793] + model_decoder_layers_12_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[794] + model_decoder_layers_12_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[795] + model_decoder_layers_12_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[796] + model_decoder_layers_12_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[797] + model_decoder_layers_12_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[798] + model_decoder_layers_12_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[799] + model_decoder_layers_12_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[800] + model_decoder_layers_13_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[801] + model_decoder_layers_13_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[802] + model_decoder_layers_13_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[803] + model_decoder_layers_13_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[804] + model_decoder_layers_13_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[805] + model_decoder_layers_13_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[806] + model_decoder_layers_13_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[807] + model_decoder_layers_13_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[808] + model_decoder_layers_13_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[809] + model_decoder_layers_13_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[810] + model_decoder_layers_13_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[811] + model_decoder_layers_13_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[812] + model_decoder_layers_13_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[813] + model_decoder_layers_13_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[814] + model_decoder_layers_13_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[815] + model_decoder_layers_13_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[816] + model_decoder_layers_13_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[817] + model_decoder_layers_13_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[818] + model_decoder_layers_13_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[819] + model_decoder_layers_13_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[820] + model_decoder_layers_13_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[821] + model_decoder_layers_13_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[822] + model_decoder_layers_13_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[823] + model_decoder_layers_13_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[824] + model_decoder_layers_14_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[825] + model_decoder_layers_14_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[826] + model_decoder_layers_14_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[827] + model_decoder_layers_14_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[828] + model_decoder_layers_14_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[829] + model_decoder_layers_14_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[830] + model_decoder_layers_14_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[831] + model_decoder_layers_14_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[832] + model_decoder_layers_14_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[833] + model_decoder_layers_14_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[834] + model_decoder_layers_14_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[835] + model_decoder_layers_14_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[836] + model_decoder_layers_14_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[837] + model_decoder_layers_14_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[838] + model_decoder_layers_14_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[839] + model_decoder_layers_14_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[840] + model_decoder_layers_14_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[841] + model_decoder_layers_14_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[842] + model_decoder_layers_14_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[843] + model_decoder_layers_14_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[844] + model_decoder_layers_14_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[845] + model_decoder_layers_14_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[846] + model_decoder_layers_14_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[847] + model_decoder_layers_14_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[848] + model_decoder_layers_15_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[849] + model_decoder_layers_15_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[850] + model_decoder_layers_15_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[851] + model_decoder_layers_15_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[852] + model_decoder_layers_15_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[853] + model_decoder_layers_15_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[854] + model_decoder_layers_15_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[855] + model_decoder_layers_15_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[856] + model_decoder_layers_15_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[857] + model_decoder_layers_15_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[858] + model_decoder_layers_15_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[859] + model_decoder_layers_15_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[860] + model_decoder_layers_15_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[861] + model_decoder_layers_15_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[862] + model_decoder_layers_15_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[863] + model_decoder_layers_15_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[864] + model_decoder_layers_15_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[865] + model_decoder_layers_15_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[866] + model_decoder_layers_15_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[867] + model_decoder_layers_15_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[868] + model_decoder_layers_15_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[869] + model_decoder_layers_15_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[870] + model_decoder_layers_15_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[871] + model_decoder_layers_15_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[872] + model_decoder_layers_16_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[873] + model_decoder_layers_16_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[874] + model_decoder_layers_16_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[875] + model_decoder_layers_16_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[876] + model_decoder_layers_16_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[877] + model_decoder_layers_16_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[878] + model_decoder_layers_16_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[879] + model_decoder_layers_16_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[880] + model_decoder_layers_16_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[881] + model_decoder_layers_16_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[882] + model_decoder_layers_16_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[883] + model_decoder_layers_16_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[884] + model_decoder_layers_16_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[885] + model_decoder_layers_16_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[886] + model_decoder_layers_16_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[887] + model_decoder_layers_16_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[888] + model_decoder_layers_16_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[889] + model_decoder_layers_16_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[890] + model_decoder_layers_16_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[891] + model_decoder_layers_16_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[892] + model_decoder_layers_16_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[893] + model_decoder_layers_16_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[894] + model_decoder_layers_16_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[895] + model_decoder_layers_16_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[896] + model_decoder_layers_17_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[897] + model_decoder_layers_17_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[898] + model_decoder_layers_17_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[899] + model_decoder_layers_17_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[900] + model_decoder_layers_17_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[901] + model_decoder_layers_17_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[902] + model_decoder_layers_17_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[903] + model_decoder_layers_17_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[904] + model_decoder_layers_17_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[905] + model_decoder_layers_17_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[906] + model_decoder_layers_17_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[907] + model_decoder_layers_17_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[908] + model_decoder_layers_17_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[909] + model_decoder_layers_17_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[910] + model_decoder_layers_17_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[911] + model_decoder_layers_17_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[912] + model_decoder_layers_17_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[913] + model_decoder_layers_17_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[914] + model_decoder_layers_17_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[915] + model_decoder_layers_17_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[916] + model_decoder_layers_17_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[917] + model_decoder_layers_17_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[918] + model_decoder_layers_17_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[919] + model_decoder_layers_17_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[920] + model_decoder_layers_18_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[921] + model_decoder_layers_18_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[922] + model_decoder_layers_18_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[923] + model_decoder_layers_18_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[924] + model_decoder_layers_18_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[925] + model_decoder_layers_18_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[926] + model_decoder_layers_18_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[927] + model_decoder_layers_18_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[928] + model_decoder_layers_18_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[929] + model_decoder_layers_18_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[930] + model_decoder_layers_18_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[931] + model_decoder_layers_18_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[932] + model_decoder_layers_18_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[933] + model_decoder_layers_18_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[934] + model_decoder_layers_18_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[935] + model_decoder_layers_18_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[936] + model_decoder_layers_18_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[937] + model_decoder_layers_18_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[938] + model_decoder_layers_18_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[939] + model_decoder_layers_18_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[940] + model_decoder_layers_18_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[941] + model_decoder_layers_18_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[942] + model_decoder_layers_18_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[943] + model_decoder_layers_18_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[944] + model_decoder_layers_19_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[945] + model_decoder_layers_19_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[946] + model_decoder_layers_19_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[947] + model_decoder_layers_19_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[948] + model_decoder_layers_19_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[949] + model_decoder_layers_19_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[950] + model_decoder_layers_19_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[951] + model_decoder_layers_19_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[952] + model_decoder_layers_19_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[953] + model_decoder_layers_19_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[954] + model_decoder_layers_19_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[955] + model_decoder_layers_19_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[956] + model_decoder_layers_19_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[957] + model_decoder_layers_19_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[958] + model_decoder_layers_19_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[959] + model_decoder_layers_19_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[960] + model_decoder_layers_19_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[961] + model_decoder_layers_19_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[962] + model_decoder_layers_19_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[963] + model_decoder_layers_19_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[964] + model_decoder_layers_19_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[965] + model_decoder_layers_19_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[966] + model_decoder_layers_19_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[967] + model_decoder_layers_19_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[968] + model_decoder_layers_20_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[969] + model_decoder_layers_20_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[970] + model_decoder_layers_20_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[971] + model_decoder_layers_20_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[972] + model_decoder_layers_20_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[973] + model_decoder_layers_20_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[974] + model_decoder_layers_20_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[975] + model_decoder_layers_20_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[976] + model_decoder_layers_20_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[977] + model_decoder_layers_20_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[978] + model_decoder_layers_20_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[979] + model_decoder_layers_20_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[980] + model_decoder_layers_20_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[981] + model_decoder_layers_20_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[982] + model_decoder_layers_20_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[983] + model_decoder_layers_20_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[984] + model_decoder_layers_20_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[985] + model_decoder_layers_20_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[986] + model_decoder_layers_20_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[987] + model_decoder_layers_20_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[988] + model_decoder_layers_20_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[989] + model_decoder_layers_20_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[990] + model_decoder_layers_20_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[991] + model_decoder_layers_20_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[992] + model_decoder_layers_21_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[993] + model_decoder_layers_21_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[994] + model_decoder_layers_21_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[995] + model_decoder_layers_21_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[996] + model_decoder_layers_21_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[997] + model_decoder_layers_21_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[998] + model_decoder_layers_21_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[999] + model_decoder_layers_21_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1000] + model_decoder_layers_21_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1001] + model_decoder_layers_21_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1002] + model_decoder_layers_21_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1003] + model_decoder_layers_21_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1004] + model_decoder_layers_21_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1005] + model_decoder_layers_21_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1006] + model_decoder_layers_21_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1007] + model_decoder_layers_21_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1008] + model_decoder_layers_21_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1009] + model_decoder_layers_21_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1010] + model_decoder_layers_21_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[1011] + model_decoder_layers_21_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[1012] + model_decoder_layers_21_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[1013] + model_decoder_layers_21_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[1014] + model_decoder_layers_21_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1015] + model_decoder_layers_21_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1016] + model_decoder_layers_22_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1017] + model_decoder_layers_22_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1018] + model_decoder_layers_22_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1019] + model_decoder_layers_22_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1020] + model_decoder_layers_22_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1021] + model_decoder_layers_22_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1022] + model_decoder_layers_22_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1023] + model_decoder_layers_22_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1024] + model_decoder_layers_22_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1025] + model_decoder_layers_22_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1026] + model_decoder_layers_22_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1027] + model_decoder_layers_22_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1028] + model_decoder_layers_22_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1029] + model_decoder_layers_22_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1030] + model_decoder_layers_22_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1031] + model_decoder_layers_22_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1032] + model_decoder_layers_22_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1033] + model_decoder_layers_22_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1034] + model_decoder_layers_22_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[1035] + model_decoder_layers_22_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[1036] + model_decoder_layers_22_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[1037] + model_decoder_layers_22_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[1038] + model_decoder_layers_22_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1039] + model_decoder_layers_22_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1040] + model_decoder_layers_23_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1041] + model_decoder_layers_23_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1042] + model_decoder_layers_23_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1043] + model_decoder_layers_23_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1044] + model_decoder_layers_23_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1045] + model_decoder_layers_23_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1046] + model_decoder_layers_23_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1047] + model_decoder_layers_23_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1048] + model_decoder_layers_23_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1049] + model_decoder_layers_23_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1050] + model_decoder_layers_23_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1051] + model_decoder_layers_23_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1052] + model_decoder_layers_23_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1053] + model_decoder_layers_23_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1054] + model_decoder_layers_23_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1055] + model_decoder_layers_23_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1056] + model_decoder_layers_23_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1057] + model_decoder_layers_23_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1058] + model_decoder_layers_23_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[1059] + model_decoder_layers_23_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[1060] + model_decoder_layers_23_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[1061] + model_decoder_layers_23_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[1062] + model_decoder_layers_23_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1063] + model_decoder_layers_23_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1064] + model_decoder_layers_24_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1065] + model_decoder_layers_24_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1066] + model_decoder_layers_24_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1067] + model_decoder_layers_24_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1068] + model_decoder_layers_24_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1069] + model_decoder_layers_24_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1070] + model_decoder_layers_24_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1071] + model_decoder_layers_24_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1072] + model_decoder_layers_24_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1073] + model_decoder_layers_24_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1074] + model_decoder_layers_24_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1075] + model_decoder_layers_24_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1076] + model_decoder_layers_24_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1077] + model_decoder_layers_24_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1078] + model_decoder_layers_24_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1079] + model_decoder_layers_24_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1080] + model_decoder_layers_24_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1081] + model_decoder_layers_24_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1082] + model_decoder_layers_24_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[1083] + model_decoder_layers_24_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[1084] + model_decoder_layers_24_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[1085] + model_decoder_layers_24_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[1086] + model_decoder_layers_24_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1087] + model_decoder_layers_24_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1088] + model_decoder_layers_25_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1089] + model_decoder_layers_25_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1090] + model_decoder_layers_25_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1091] + model_decoder_layers_25_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1092] + model_decoder_layers_25_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1093] + model_decoder_layers_25_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1094] + model_decoder_layers_25_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1095] + model_decoder_layers_25_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1096] + model_decoder_layers_25_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1097] + model_decoder_layers_25_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1098] + model_decoder_layers_25_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1099] + model_decoder_layers_25_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1100] + model_decoder_layers_25_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1101] + model_decoder_layers_25_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1102] + model_decoder_layers_25_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1103] + model_decoder_layers_25_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1104] + model_decoder_layers_25_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1105] + model_decoder_layers_25_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1106] + model_decoder_layers_25_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[1107] + model_decoder_layers_25_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[1108] + model_decoder_layers_25_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[1109] + model_decoder_layers_25_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[1110] + model_decoder_layers_25_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1111] + model_decoder_layers_25_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1112] + model_decoder_layers_26_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1113] + model_decoder_layers_26_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1114] + model_decoder_layers_26_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1115] + model_decoder_layers_26_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1116] + model_decoder_layers_26_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1117] + model_decoder_layers_26_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1118] + model_decoder_layers_26_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1119] + model_decoder_layers_26_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1120] + model_decoder_layers_26_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1121] + model_decoder_layers_26_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1122] + model_decoder_layers_26_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1123] + model_decoder_layers_26_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1124] + model_decoder_layers_26_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1125] + model_decoder_layers_26_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1126] + model_decoder_layers_26_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1127] + model_decoder_layers_26_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1128] + model_decoder_layers_26_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1129] + model_decoder_layers_26_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1130] + model_decoder_layers_26_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[1131] + model_decoder_layers_26_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[1132] + model_decoder_layers_26_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[1133] + model_decoder_layers_26_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[1134] + model_decoder_layers_26_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1135] + model_decoder_layers_26_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1136] + model_decoder_layers_27_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1137] + model_decoder_layers_27_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1138] + model_decoder_layers_27_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1139] + model_decoder_layers_27_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1140] + model_decoder_layers_27_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1141] + model_decoder_layers_27_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1142] + model_decoder_layers_27_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1143] + model_decoder_layers_27_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1144] + model_decoder_layers_27_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1145] + model_decoder_layers_27_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1146] + model_decoder_layers_27_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1147] + model_decoder_layers_27_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1148] + model_decoder_layers_27_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1149] + model_decoder_layers_27_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1150] + model_decoder_layers_27_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1151] + model_decoder_layers_27_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1152] + model_decoder_layers_27_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1153] + model_decoder_layers_27_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1154] + model_decoder_layers_27_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[1155] + model_decoder_layers_27_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[1156] + model_decoder_layers_27_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[1157] + model_decoder_layers_27_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[1158] + model_decoder_layers_27_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1159] + model_decoder_layers_27_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1160] + model_decoder_layers_28_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1161] + model_decoder_layers_28_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1162] + model_decoder_layers_28_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1163] + model_decoder_layers_28_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1164] + model_decoder_layers_28_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1165] + model_decoder_layers_28_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1166] + model_decoder_layers_28_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1167] + model_decoder_layers_28_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1168] + model_decoder_layers_28_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1169] + model_decoder_layers_28_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1170] + model_decoder_layers_28_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1171] + model_decoder_layers_28_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1172] + model_decoder_layers_28_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1173] + model_decoder_layers_28_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1174] + model_decoder_layers_28_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1175] + model_decoder_layers_28_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1176] + model_decoder_layers_28_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1177] + model_decoder_layers_28_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1178] + model_decoder_layers_28_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[1179] + model_decoder_layers_28_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[1180] + model_decoder_layers_28_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[1181] + model_decoder_layers_28_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[1182] + model_decoder_layers_28_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1183] + model_decoder_layers_28_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1184] + model_decoder_layers_29_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1185] + model_decoder_layers_29_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1186] + model_decoder_layers_29_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1187] + model_decoder_layers_29_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1188] + model_decoder_layers_29_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1189] + model_decoder_layers_29_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1190] + model_decoder_layers_29_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1191] + model_decoder_layers_29_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1192] + model_decoder_layers_29_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1193] + model_decoder_layers_29_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1194] + model_decoder_layers_29_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1195] + model_decoder_layers_29_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1196] + model_decoder_layers_29_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1197] + model_decoder_layers_29_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1198] + model_decoder_layers_29_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1199] + model_decoder_layers_29_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1200] + model_decoder_layers_29_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1201] + model_decoder_layers_29_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1202] + model_decoder_layers_29_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[1203] + model_decoder_layers_29_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[1204] + model_decoder_layers_29_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[1205] + model_decoder_layers_29_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[1206] + model_decoder_layers_29_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1207] + model_decoder_layers_29_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1208] + model_decoder_layers_30_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1209] + model_decoder_layers_30_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1210] + model_decoder_layers_30_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1211] + model_decoder_layers_30_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1212] + model_decoder_layers_30_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1213] + model_decoder_layers_30_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1214] + model_decoder_layers_30_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1215] + model_decoder_layers_30_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1216] + model_decoder_layers_30_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1217] + model_decoder_layers_30_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1218] + model_decoder_layers_30_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1219] + model_decoder_layers_30_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1220] + model_decoder_layers_30_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1221] + model_decoder_layers_30_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1222] + model_decoder_layers_30_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1223] + model_decoder_layers_30_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1224] + model_decoder_layers_30_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1225] + model_decoder_layers_30_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1226] + model_decoder_layers_30_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[1227] + model_decoder_layers_30_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[1228] + model_decoder_layers_30_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[1229] + model_decoder_layers_30_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[1230] + model_decoder_layers_30_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1231] + model_decoder_layers_30_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1232] + model_decoder_layers_31_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1233] + model_decoder_layers_31_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1234] + model_decoder_layers_31_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1235] + model_decoder_layers_31_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1236] + model_decoder_layers_31_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1237] + model_decoder_layers_31_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1238] + model_decoder_layers_31_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1239] + model_decoder_layers_31_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1240] + model_decoder_layers_31_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1241] + model_decoder_layers_31_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1242] + model_decoder_layers_31_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1243] + model_decoder_layers_31_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1244] + model_decoder_layers_31_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1245] + model_decoder_layers_31_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1246] + model_decoder_layers_31_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1247] + model_decoder_layers_31_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1248] + model_decoder_layers_31_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1249] + model_decoder_layers_31_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1250] + model_decoder_layers_31_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[1251] + model_decoder_layers_31_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[1252] + model_decoder_layers_31_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[1253] + model_decoder_layers_31_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[1254] + model_decoder_layers_31_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1255] + model_decoder_layers_31_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1256] + model_decoder_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1257] + model_decoder_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1258] + lv: R.Tensor((batch_size, 1280, 3000), dtype="float16") = R.nn.conv1d(input_features, model_encoder_conv1_weight, strides=[1], padding=[1, 1], dilation=[1], groups=1, data_layout="NCW", kernel_layout="OIW", out_layout="NCW", out_dtype="void") + lv1: R.Tensor((1, 1280, 1), dtype="float16") = R.reshape(model_encoder_conv1_bias, R.shape([1, 1280, 1])) + conv1d: R.Tensor((batch_size, 1280, 3000), dtype="float16") = R.add(lv, lv1) + gelu: R.Tensor((batch_size, 1280, 3000), dtype="float16") = R.nn.gelu(conv1d) + lv2: R.Tensor((batch_size, 1280, 1500), dtype="float16") = R.nn.conv1d(gelu, model_encoder_conv2_weight, strides=[2], padding=[1, 1], dilation=[1], groups=1, data_layout="NCW", kernel_layout="OIW", out_layout="NCW", out_dtype="void") + lv3: R.Tensor((1, 1280, 1), dtype="float16") = R.reshape(model_encoder_conv2_bias, R.shape([1, 1280, 1])) + conv1d1: R.Tensor((batch_size, 1280, 1500), dtype="float16") = R.add(lv2, lv3) + gelu1: R.Tensor((batch_size, 1280, 1500), dtype="float16") = R.nn.gelu(conv1d1) + permute_dims: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.permute_dims(gelu1, axes=[0, 2, 1]) + add: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(permute_dims, model_encoder_embed_positions_weight) + layer_norm: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add, model_encoder_layers_0_self_attn_layer_norm_weight, model_encoder_layers_0_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_0_self_attn_q_proj_weight, axes=None) + matmul: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm, permute_dims1, out_dtype="void") + add1: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul, model_encoder_layers_0_self_attn_q_proj_bias) + reshape: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add1, R.shape([batch_size, 1500, 20, 64])) + permute_dims2: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_0_self_attn_k_proj_weight, axes=None) + matmul1: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm, permute_dims2, out_dtype="void") + reshape1: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul1, R.shape([batch_size, 1500, 20, 64])) + permute_dims3: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_0_self_attn_v_proj_weight, axes=None) + matmul2: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm, permute_dims3, out_dtype="void") + add2: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul2, model_encoder_layers_0_self_attn_v_proj_bias) + reshape2: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add2, R.shape([batch_size, 1500, 20, 64])) + reshape3: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape, R.shape([batch_size * 1500, 20, 64])) + reshape4: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape1, R.shape([batch_size * 1500, 20, 64])) + reshape5: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape2, R.shape([batch_size * 1500, 20, 64])) + lv4 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), reshape3, reshape4, reshape5), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape6: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv4, R.shape([batch_size, 1500, 20, 64])) + reshape7: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape6, R.shape([batch_size, 1500, 1280])) + permute_dims4: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_0_self_attn_out_proj_weight, axes=None) + matmul3: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape7, permute_dims4, out_dtype="void") + add3: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul3, model_encoder_layers_0_self_attn_out_proj_bias) + add4: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add, add3) + layer_norm1: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add4, model_encoder_layers_0_final_layer_norm_weight, model_encoder_layers_0_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims5: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_0_fc1_weight, axes=None) + matmul4: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm1, permute_dims5, out_dtype="void") + add5: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul4, model_encoder_layers_0_fc1_bias) + gelu2: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add5) + permute_dims6: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_0_fc2_weight, axes=None) + matmul5: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu2, permute_dims6, out_dtype="void") + add6: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul5, model_encoder_layers_0_fc2_bias) + add7: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add4, add6) + maximum: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add7, R.const(-65504, "float16")) + minimum: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum, R.const(65504, "float16")) + layer_norm2: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum, model_encoder_layers_1_self_attn_layer_norm_weight, model_encoder_layers_1_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims7: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_1_self_attn_q_proj_weight, axes=None) + matmul6: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm2, permute_dims7, out_dtype="void") + add8: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul6, model_encoder_layers_1_self_attn_q_proj_bias) + reshape8: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add8, R.shape([batch_size, 1500, 20, 64])) + permute_dims8: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_1_self_attn_k_proj_weight, axes=None) + matmul7: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm2, permute_dims8, out_dtype="void") + reshape9: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul7, R.shape([batch_size, 1500, 20, 64])) + permute_dims9: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_1_self_attn_v_proj_weight, axes=None) + matmul8: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm2, permute_dims9, out_dtype="void") + add9: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul8, model_encoder_layers_1_self_attn_v_proj_bias) + reshape10: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add9, R.shape([batch_size, 1500, 20, 64])) + reshape11: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape8, R.shape([batch_size * 1500, 20, 64])) + reshape12: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape9, R.shape([batch_size * 1500, 20, 64])) + reshape13: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape10, R.shape([batch_size * 1500, 20, 64])) + lv5 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), reshape11, reshape12, reshape13), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape14: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv5, R.shape([batch_size, 1500, 20, 64])) + reshape15: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape14, R.shape([batch_size, 1500, 1280])) + permute_dims10: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_1_self_attn_out_proj_weight, axes=None) + matmul9: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape15, permute_dims10, out_dtype="void") + add10: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul9, model_encoder_layers_1_self_attn_out_proj_bias) + add11: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum, add10) + layer_norm3: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add11, model_encoder_layers_1_final_layer_norm_weight, model_encoder_layers_1_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims11: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_1_fc1_weight, axes=None) + matmul10: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm3, permute_dims11, out_dtype="void") + add12: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul10, model_encoder_layers_1_fc1_bias) + gelu3: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add12) + permute_dims12: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_1_fc2_weight, axes=None) + matmul11: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu3, permute_dims12, out_dtype="void") + add13: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul11, model_encoder_layers_1_fc2_bias) + add14: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add11, add13) + maximum1: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add14, R.const(-65504, "float16")) + minimum1: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum1, R.const(65504, "float16")) + layer_norm4: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum1, model_encoder_layers_2_self_attn_layer_norm_weight, model_encoder_layers_2_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims13: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_2_self_attn_q_proj_weight, axes=None) + matmul12: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm4, permute_dims13, out_dtype="void") + add15: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul12, model_encoder_layers_2_self_attn_q_proj_bias) + reshape16: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add15, R.shape([batch_size, 1500, 20, 64])) + permute_dims14: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_2_self_attn_k_proj_weight, axes=None) + matmul13: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm4, permute_dims14, out_dtype="void") + reshape17: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul13, R.shape([batch_size, 1500, 20, 64])) + permute_dims15: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_2_self_attn_v_proj_weight, axes=None) + matmul14: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm4, permute_dims15, out_dtype="void") + add16: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul14, model_encoder_layers_2_self_attn_v_proj_bias) + reshape18: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add16, R.shape([batch_size, 1500, 20, 64])) + reshape19: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape16, R.shape([batch_size * 1500, 20, 64])) + reshape20: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape17, R.shape([batch_size * 1500, 20, 64])) + reshape21: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape18, R.shape([batch_size * 1500, 20, 64])) + lv6 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), reshape19, reshape20, reshape21), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape22: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv6, R.shape([batch_size, 1500, 20, 64])) + reshape23: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape22, R.shape([batch_size, 1500, 1280])) + permute_dims16: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_2_self_attn_out_proj_weight, axes=None) + matmul15: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape23, permute_dims16, out_dtype="void") + add17: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul15, model_encoder_layers_2_self_attn_out_proj_bias) + add18: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum1, add17) + layer_norm5: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add18, model_encoder_layers_2_final_layer_norm_weight, model_encoder_layers_2_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims17: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_2_fc1_weight, axes=None) + matmul16: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm5, permute_dims17, out_dtype="void") + add19: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul16, model_encoder_layers_2_fc1_bias) + gelu4: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add19) + permute_dims18: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_2_fc2_weight, axes=None) + matmul17: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu4, permute_dims18, out_dtype="void") + add20: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul17, model_encoder_layers_2_fc2_bias) + add21: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add18, add20) + maximum2: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add21, R.const(-65504, "float16")) + minimum2: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum2, R.const(65504, "float16")) + layer_norm6: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum2, model_encoder_layers_3_self_attn_layer_norm_weight, model_encoder_layers_3_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims19: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_3_self_attn_q_proj_weight, axes=None) + matmul18: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm6, permute_dims19, out_dtype="void") + add22: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul18, model_encoder_layers_3_self_attn_q_proj_bias) + reshape24: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add22, R.shape([batch_size, 1500, 20, 64])) + permute_dims20: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_3_self_attn_k_proj_weight, axes=None) + matmul19: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm6, permute_dims20, out_dtype="void") + reshape25: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul19, R.shape([batch_size, 1500, 20, 64])) + permute_dims21: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_3_self_attn_v_proj_weight, axes=None) + matmul20: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm6, permute_dims21, out_dtype="void") + add23: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul20, model_encoder_layers_3_self_attn_v_proj_bias) + reshape26: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add23, R.shape([batch_size, 1500, 20, 64])) + reshape27: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape24, R.shape([batch_size * 1500, 20, 64])) + reshape28: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape25, R.shape([batch_size * 1500, 20, 64])) + reshape29: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape26, R.shape([batch_size * 1500, 20, 64])) + lv7 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), reshape27, reshape28, reshape29), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape30: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv7, R.shape([batch_size, 1500, 20, 64])) + reshape31: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape30, R.shape([batch_size, 1500, 1280])) + permute_dims22: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_3_self_attn_out_proj_weight, axes=None) + matmul21: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape31, permute_dims22, out_dtype="void") + add24: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul21, model_encoder_layers_3_self_attn_out_proj_bias) + add25: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum2, add24) + layer_norm7: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add25, model_encoder_layers_3_final_layer_norm_weight, model_encoder_layers_3_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims23: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_3_fc1_weight, axes=None) + matmul22: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm7, permute_dims23, out_dtype="void") + add26: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul22, model_encoder_layers_3_fc1_bias) + gelu5: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add26) + permute_dims24: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_3_fc2_weight, axes=None) + matmul23: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu5, permute_dims24, out_dtype="void") + add27: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul23, model_encoder_layers_3_fc2_bias) + add28: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add25, add27) + maximum3: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add28, R.const(-65504, "float16")) + minimum3: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum3, R.const(65504, "float16")) + layer_norm8: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum3, model_encoder_layers_4_self_attn_layer_norm_weight, model_encoder_layers_4_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims25: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_4_self_attn_q_proj_weight, axes=None) + matmul24: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm8, permute_dims25, out_dtype="void") + add29: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul24, model_encoder_layers_4_self_attn_q_proj_bias) + reshape32: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add29, R.shape([batch_size, 1500, 20, 64])) + permute_dims26: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_4_self_attn_k_proj_weight, axes=None) + matmul25: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm8, permute_dims26, out_dtype="void") + reshape33: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul25, R.shape([batch_size, 1500, 20, 64])) + permute_dims27: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_4_self_attn_v_proj_weight, axes=None) + matmul26: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm8, permute_dims27, out_dtype="void") + add30: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul26, model_encoder_layers_4_self_attn_v_proj_bias) + reshape34: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add30, R.shape([batch_size, 1500, 20, 64])) + reshape35: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape32, R.shape([batch_size * 1500, 20, 64])) + reshape36: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape33, R.shape([batch_size * 1500, 20, 64])) + reshape37: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape34, R.shape([batch_size * 1500, 20, 64])) + lv8 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), reshape35, reshape36, reshape37), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape38: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv8, R.shape([batch_size, 1500, 20, 64])) + reshape39: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape38, R.shape([batch_size, 1500, 1280])) + permute_dims28: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_4_self_attn_out_proj_weight, axes=None) + matmul27: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape39, permute_dims28, out_dtype="void") + add31: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul27, model_encoder_layers_4_self_attn_out_proj_bias) + add32: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum3, add31) + layer_norm9: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add32, model_encoder_layers_4_final_layer_norm_weight, model_encoder_layers_4_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims29: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_4_fc1_weight, axes=None) + matmul28: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm9, permute_dims29, out_dtype="void") + add33: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul28, model_encoder_layers_4_fc1_bias) + gelu6: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add33) + permute_dims30: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_4_fc2_weight, axes=None) + matmul29: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu6, permute_dims30, out_dtype="void") + add34: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul29, model_encoder_layers_4_fc2_bias) + add35: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add32, add34) + maximum4: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add35, R.const(-65504, "float16")) + minimum4: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum4, R.const(65504, "float16")) + layer_norm10: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum4, model_encoder_layers_5_self_attn_layer_norm_weight, model_encoder_layers_5_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims31: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_5_self_attn_q_proj_weight, axes=None) + matmul30: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm10, permute_dims31, out_dtype="void") + add36: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul30, model_encoder_layers_5_self_attn_q_proj_bias) + reshape40: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add36, R.shape([batch_size, 1500, 20, 64])) + permute_dims32: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_5_self_attn_k_proj_weight, axes=None) + matmul31: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm10, permute_dims32, out_dtype="void") + reshape41: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul31, R.shape([batch_size, 1500, 20, 64])) + permute_dims33: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_5_self_attn_v_proj_weight, axes=None) + matmul32: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm10, permute_dims33, out_dtype="void") + add37: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul32, model_encoder_layers_5_self_attn_v_proj_bias) + reshape42: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add37, R.shape([batch_size, 1500, 20, 64])) + reshape43: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape40, R.shape([batch_size * 1500, 20, 64])) + reshape44: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape41, R.shape([batch_size * 1500, 20, 64])) + reshape45: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape42, R.shape([batch_size * 1500, 20, 64])) + lv9 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), reshape43, reshape44, reshape45), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape46: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv9, R.shape([batch_size, 1500, 20, 64])) + reshape47: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape46, R.shape([batch_size, 1500, 1280])) + permute_dims34: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_5_self_attn_out_proj_weight, axes=None) + matmul33: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape47, permute_dims34, out_dtype="void") + add38: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul33, model_encoder_layers_5_self_attn_out_proj_bias) + add39: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum4, add38) + layer_norm11: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add39, model_encoder_layers_5_final_layer_norm_weight, model_encoder_layers_5_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims35: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_5_fc1_weight, axes=None) + matmul34: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm11, permute_dims35, out_dtype="void") + add40: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul34, model_encoder_layers_5_fc1_bias) + gelu7: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add40) + permute_dims36: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_5_fc2_weight, axes=None) + matmul35: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu7, permute_dims36, out_dtype="void") + add41: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul35, model_encoder_layers_5_fc2_bias) + add42: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add39, add41) + maximum5: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add42, R.const(-65504, "float16")) + minimum5: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum5, R.const(65504, "float16")) + layer_norm12: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum5, model_encoder_layers_6_self_attn_layer_norm_weight, model_encoder_layers_6_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims37: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_6_self_attn_q_proj_weight, axes=None) + matmul36: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm12, permute_dims37, out_dtype="void") + add43: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul36, model_encoder_layers_6_self_attn_q_proj_bias) + reshape48: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add43, R.shape([batch_size, 1500, 20, 64])) + permute_dims38: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_6_self_attn_k_proj_weight, axes=None) + matmul37: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm12, permute_dims38, out_dtype="void") + reshape49: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul37, R.shape([batch_size, 1500, 20, 64])) + permute_dims39: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_6_self_attn_v_proj_weight, axes=None) + matmul38: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm12, permute_dims39, out_dtype="void") + add44: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul38, model_encoder_layers_6_self_attn_v_proj_bias) + reshape50: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add44, R.shape([batch_size, 1500, 20, 64])) + reshape51: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape48, R.shape([batch_size * 1500, 20, 64])) + reshape52: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape49, R.shape([batch_size * 1500, 20, 64])) + reshape53: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape50, R.shape([batch_size * 1500, 20, 64])) + lv10 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), reshape51, reshape52, reshape53), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape54: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv10, R.shape([batch_size, 1500, 20, 64])) + reshape55: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape54, R.shape([batch_size, 1500, 1280])) + permute_dims40: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_6_self_attn_out_proj_weight, axes=None) + matmul39: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape55, permute_dims40, out_dtype="void") + add45: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul39, model_encoder_layers_6_self_attn_out_proj_bias) + add46: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum5, add45) + layer_norm13: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add46, model_encoder_layers_6_final_layer_norm_weight, model_encoder_layers_6_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims41: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_6_fc1_weight, axes=None) + matmul40: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm13, permute_dims41, out_dtype="void") + add47: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul40, model_encoder_layers_6_fc1_bias) + gelu8: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add47) + permute_dims42: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_6_fc2_weight, axes=None) + matmul41: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu8, permute_dims42, out_dtype="void") + add48: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul41, model_encoder_layers_6_fc2_bias) + add49: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add46, add48) + maximum6: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add49, R.const(-65504, "float16")) + minimum6: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum6, R.const(65504, "float16")) + layer_norm14: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum6, model_encoder_layers_7_self_attn_layer_norm_weight, model_encoder_layers_7_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims43: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_7_self_attn_q_proj_weight, axes=None) + matmul42: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm14, permute_dims43, out_dtype="void") + add50: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul42, model_encoder_layers_7_self_attn_q_proj_bias) + reshape56: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add50, R.shape([batch_size, 1500, 20, 64])) + permute_dims44: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_7_self_attn_k_proj_weight, axes=None) + matmul43: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm14, permute_dims44, out_dtype="void") + reshape57: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul43, R.shape([batch_size, 1500, 20, 64])) + permute_dims45: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_7_self_attn_v_proj_weight, axes=None) + matmul44: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm14, permute_dims45, out_dtype="void") + add51: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul44, model_encoder_layers_7_self_attn_v_proj_bias) + reshape58: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add51, R.shape([batch_size, 1500, 20, 64])) + reshape59: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape56, R.shape([batch_size * 1500, 20, 64])) + reshape60: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape57, R.shape([batch_size * 1500, 20, 64])) + reshape61: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape58, R.shape([batch_size * 1500, 20, 64])) + lv11 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), reshape59, reshape60, reshape61), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape62: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv11, R.shape([batch_size, 1500, 20, 64])) + reshape63: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape62, R.shape([batch_size, 1500, 1280])) + permute_dims46: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_7_self_attn_out_proj_weight, axes=None) + matmul45: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape63, permute_dims46, out_dtype="void") + add52: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul45, model_encoder_layers_7_self_attn_out_proj_bias) + add53: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum6, add52) + layer_norm15: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add53, model_encoder_layers_7_final_layer_norm_weight, model_encoder_layers_7_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims47: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_7_fc1_weight, axes=None) + matmul46: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm15, permute_dims47, out_dtype="void") + add54: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul46, model_encoder_layers_7_fc1_bias) + gelu9: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add54) + permute_dims48: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_7_fc2_weight, axes=None) + matmul47: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu9, permute_dims48, out_dtype="void") + add55: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul47, model_encoder_layers_7_fc2_bias) + add56: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add53, add55) + maximum7: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add56, R.const(-65504, "float16")) + minimum7: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum7, R.const(65504, "float16")) + layer_norm16: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum7, model_encoder_layers_8_self_attn_layer_norm_weight, model_encoder_layers_8_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims49: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_8_self_attn_q_proj_weight, axes=None) + matmul48: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm16, permute_dims49, out_dtype="void") + add57: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul48, model_encoder_layers_8_self_attn_q_proj_bias) + reshape64: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add57, R.shape([batch_size, 1500, 20, 64])) + permute_dims50: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_8_self_attn_k_proj_weight, axes=None) + matmul49: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm16, permute_dims50, out_dtype="void") + reshape65: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul49, R.shape([batch_size, 1500, 20, 64])) + permute_dims51: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_8_self_attn_v_proj_weight, axes=None) + matmul50: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm16, permute_dims51, out_dtype="void") + add58: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul50, model_encoder_layers_8_self_attn_v_proj_bias) + reshape66: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add58, R.shape([batch_size, 1500, 20, 64])) + reshape67: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape64, R.shape([batch_size * 1500, 20, 64])) + reshape68: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape65, R.shape([batch_size * 1500, 20, 64])) + reshape69: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape66, R.shape([batch_size * 1500, 20, 64])) + lv12 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), reshape67, reshape68, reshape69), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape70: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv12, R.shape([batch_size, 1500, 20, 64])) + reshape71: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape70, R.shape([batch_size, 1500, 1280])) + permute_dims52: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_8_self_attn_out_proj_weight, axes=None) + matmul51: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape71, permute_dims52, out_dtype="void") + add59: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul51, model_encoder_layers_8_self_attn_out_proj_bias) + add60: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum7, add59) + layer_norm17: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add60, model_encoder_layers_8_final_layer_norm_weight, model_encoder_layers_8_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims53: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_8_fc1_weight, axes=None) + matmul52: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm17, permute_dims53, out_dtype="void") + add61: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul52, model_encoder_layers_8_fc1_bias) + gelu10: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add61) + permute_dims54: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_8_fc2_weight, axes=None) + matmul53: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu10, permute_dims54, out_dtype="void") + add62: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul53, model_encoder_layers_8_fc2_bias) + add63: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add60, add62) + maximum8: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add63, R.const(-65504, "float16")) + minimum8: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum8, R.const(65504, "float16")) + layer_norm18: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum8, model_encoder_layers_9_self_attn_layer_norm_weight, model_encoder_layers_9_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims55: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_9_self_attn_q_proj_weight, axes=None) + matmul54: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm18, permute_dims55, out_dtype="void") + add64: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul54, model_encoder_layers_9_self_attn_q_proj_bias) + reshape72: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add64, R.shape([batch_size, 1500, 20, 64])) + permute_dims56: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_9_self_attn_k_proj_weight, axes=None) + matmul55: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm18, permute_dims56, out_dtype="void") + reshape73: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul55, R.shape([batch_size, 1500, 20, 64])) + permute_dims57: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_9_self_attn_v_proj_weight, axes=None) + matmul56: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm18, permute_dims57, out_dtype="void") + add65: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul56, model_encoder_layers_9_self_attn_v_proj_bias) + reshape74: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add65, R.shape([batch_size, 1500, 20, 64])) + reshape75: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape72, R.shape([batch_size * 1500, 20, 64])) + reshape76: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape73, R.shape([batch_size * 1500, 20, 64])) + reshape77: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape74, R.shape([batch_size * 1500, 20, 64])) + lv13 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), reshape75, reshape76, reshape77), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape78: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv13, R.shape([batch_size, 1500, 20, 64])) + reshape79: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape78, R.shape([batch_size, 1500, 1280])) + permute_dims58: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_9_self_attn_out_proj_weight, axes=None) + matmul57: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape79, permute_dims58, out_dtype="void") + add66: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul57, model_encoder_layers_9_self_attn_out_proj_bias) + add67: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum8, add66) + layer_norm19: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add67, model_encoder_layers_9_final_layer_norm_weight, model_encoder_layers_9_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims59: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_9_fc1_weight, axes=None) + matmul58: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm19, permute_dims59, out_dtype="void") + add68: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul58, model_encoder_layers_9_fc1_bias) + gelu11: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add68) + permute_dims60: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_9_fc2_weight, axes=None) + matmul59: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu11, permute_dims60, out_dtype="void") + add69: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul59, model_encoder_layers_9_fc2_bias) + add70: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add67, add69) + maximum9: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add70, R.const(-65504, "float16")) + minimum9: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum9, R.const(65504, "float16")) + layer_norm20: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum9, model_encoder_layers_10_self_attn_layer_norm_weight, model_encoder_layers_10_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims61: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_10_self_attn_q_proj_weight, axes=None) + matmul60: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm20, permute_dims61, out_dtype="void") + add71: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul60, model_encoder_layers_10_self_attn_q_proj_bias) + reshape80: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add71, R.shape([batch_size, 1500, 20, 64])) + permute_dims62: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_10_self_attn_k_proj_weight, axes=None) + matmul61: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm20, permute_dims62, out_dtype="void") + reshape81: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul61, R.shape([batch_size, 1500, 20, 64])) + permute_dims63: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_10_self_attn_v_proj_weight, axes=None) + matmul62: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm20, permute_dims63, out_dtype="void") + add72: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul62, model_encoder_layers_10_self_attn_v_proj_bias) + reshape82: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add72, R.shape([batch_size, 1500, 20, 64])) + reshape83: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape80, R.shape([batch_size * 1500, 20, 64])) + reshape84: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape81, R.shape([batch_size * 1500, 20, 64])) + reshape85: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape82, R.shape([batch_size * 1500, 20, 64])) + lv14 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), reshape83, reshape84, reshape85), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape86: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv14, R.shape([batch_size, 1500, 20, 64])) + reshape87: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape86, R.shape([batch_size, 1500, 1280])) + permute_dims64: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_10_self_attn_out_proj_weight, axes=None) + matmul63: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape87, permute_dims64, out_dtype="void") + add73: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul63, model_encoder_layers_10_self_attn_out_proj_bias) + add74: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum9, add73) + layer_norm21: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add74, model_encoder_layers_10_final_layer_norm_weight, model_encoder_layers_10_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims65: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_10_fc1_weight, axes=None) + matmul64: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm21, permute_dims65, out_dtype="void") + add75: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul64, model_encoder_layers_10_fc1_bias) + gelu12: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add75) + permute_dims66: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_10_fc2_weight, axes=None) + matmul65: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu12, permute_dims66, out_dtype="void") + add76: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul65, model_encoder_layers_10_fc2_bias) + add77: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add74, add76) + maximum10: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add77, R.const(-65504, "float16")) + minimum10: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum10, R.const(65504, "float16")) + layer_norm22: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum10, model_encoder_layers_11_self_attn_layer_norm_weight, model_encoder_layers_11_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims67: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_11_self_attn_q_proj_weight, axes=None) + matmul66: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm22, permute_dims67, out_dtype="void") + add78: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul66, model_encoder_layers_11_self_attn_q_proj_bias) + reshape88: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add78, R.shape([batch_size, 1500, 20, 64])) + permute_dims68: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_11_self_attn_k_proj_weight, axes=None) + matmul67: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm22, permute_dims68, out_dtype="void") + reshape89: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul67, R.shape([batch_size, 1500, 20, 64])) + permute_dims69: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_11_self_attn_v_proj_weight, axes=None) + matmul68: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm22, permute_dims69, out_dtype="void") + add79: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul68, model_encoder_layers_11_self_attn_v_proj_bias) + reshape90: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add79, R.shape([batch_size, 1500, 20, 64])) + reshape91: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape88, R.shape([batch_size * 1500, 20, 64])) + reshape92: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape89, R.shape([batch_size * 1500, 20, 64])) + reshape93: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape90, R.shape([batch_size * 1500, 20, 64])) + lv15 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), reshape91, reshape92, reshape93), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape94: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv15, R.shape([batch_size, 1500, 20, 64])) + reshape95: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape94, R.shape([batch_size, 1500, 1280])) + permute_dims70: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_11_self_attn_out_proj_weight, axes=None) + matmul69: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape95, permute_dims70, out_dtype="void") + add80: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul69, model_encoder_layers_11_self_attn_out_proj_bias) + add81: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum10, add80) + layer_norm23: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add81, model_encoder_layers_11_final_layer_norm_weight, model_encoder_layers_11_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims71: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_11_fc1_weight, axes=None) + matmul70: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm23, permute_dims71, out_dtype="void") + add82: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul70, model_encoder_layers_11_fc1_bias) + gelu13: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add82) + permute_dims72: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_11_fc2_weight, axes=None) + matmul71: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu13, permute_dims72, out_dtype="void") + add83: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul71, model_encoder_layers_11_fc2_bias) + add84: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add81, add83) + maximum11: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add84, R.const(-65504, "float16")) + minimum11: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum11, R.const(65504, "float16")) + layer_norm24: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum11, model_encoder_layers_12_self_attn_layer_norm_weight, model_encoder_layers_12_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims73: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_12_self_attn_q_proj_weight, axes=None) + matmul72: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm24, permute_dims73, out_dtype="void") + add85: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul72, model_encoder_layers_12_self_attn_q_proj_bias) + reshape96: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add85, R.shape([batch_size, 1500, 20, 64])) + permute_dims74: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_12_self_attn_k_proj_weight, axes=None) + matmul73: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm24, permute_dims74, out_dtype="void") + reshape97: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul73, R.shape([batch_size, 1500, 20, 64])) + permute_dims75: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_12_self_attn_v_proj_weight, axes=None) + matmul74: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm24, permute_dims75, out_dtype="void") + add86: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul74, model_encoder_layers_12_self_attn_v_proj_bias) + reshape98: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add86, R.shape([batch_size, 1500, 20, 64])) + reshape99: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape96, R.shape([batch_size * 1500, 20, 64])) + reshape100: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape97, R.shape([batch_size * 1500, 20, 64])) + reshape101: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape98, R.shape([batch_size * 1500, 20, 64])) + lv16 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), reshape99, reshape100, reshape101), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape102: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv16, R.shape([batch_size, 1500, 20, 64])) + reshape103: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape102, R.shape([batch_size, 1500, 1280])) + permute_dims76: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_12_self_attn_out_proj_weight, axes=None) + matmul75: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape103, permute_dims76, out_dtype="void") + add87: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul75, model_encoder_layers_12_self_attn_out_proj_bias) + add88: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum11, add87) + layer_norm25: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add88, model_encoder_layers_12_final_layer_norm_weight, model_encoder_layers_12_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims77: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_12_fc1_weight, axes=None) + matmul76: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm25, permute_dims77, out_dtype="void") + add89: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul76, model_encoder_layers_12_fc1_bias) + gelu14: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add89) + permute_dims78: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_12_fc2_weight, axes=None) + matmul77: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu14, permute_dims78, out_dtype="void") + add90: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul77, model_encoder_layers_12_fc2_bias) + add91: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add88, add90) + maximum12: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add91, R.const(-65504, "float16")) + minimum12: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum12, R.const(65504, "float16")) + layer_norm26: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum12, model_encoder_layers_13_self_attn_layer_norm_weight, model_encoder_layers_13_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims79: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_13_self_attn_q_proj_weight, axes=None) + matmul78: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm26, permute_dims79, out_dtype="void") + add92: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul78, model_encoder_layers_13_self_attn_q_proj_bias) + reshape104: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add92, R.shape([batch_size, 1500, 20, 64])) + permute_dims80: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_13_self_attn_k_proj_weight, axes=None) + matmul79: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm26, permute_dims80, out_dtype="void") + reshape105: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul79, R.shape([batch_size, 1500, 20, 64])) + permute_dims81: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_13_self_attn_v_proj_weight, axes=None) + matmul80: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm26, permute_dims81, out_dtype="void") + add93: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul80, model_encoder_layers_13_self_attn_v_proj_bias) + reshape106: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add93, R.shape([batch_size, 1500, 20, 64])) + reshape107: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape104, R.shape([batch_size * 1500, 20, 64])) + reshape108: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape105, R.shape([batch_size * 1500, 20, 64])) + reshape109: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape106, R.shape([batch_size * 1500, 20, 64])) + lv17 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), reshape107, reshape108, reshape109), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape110: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv17, R.shape([batch_size, 1500, 20, 64])) + reshape111: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape110, R.shape([batch_size, 1500, 1280])) + permute_dims82: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_13_self_attn_out_proj_weight, axes=None) + matmul81: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape111, permute_dims82, out_dtype="void") + add94: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul81, model_encoder_layers_13_self_attn_out_proj_bias) + add95: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum12, add94) + layer_norm27: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add95, model_encoder_layers_13_final_layer_norm_weight, model_encoder_layers_13_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims83: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_13_fc1_weight, axes=None) + matmul82: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm27, permute_dims83, out_dtype="void") + add96: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul82, model_encoder_layers_13_fc1_bias) + gelu15: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add96) + permute_dims84: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_13_fc2_weight, axes=None) + matmul83: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu15, permute_dims84, out_dtype="void") + add97: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul83, model_encoder_layers_13_fc2_bias) + add98: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add95, add97) + maximum13: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add98, R.const(-65504, "float16")) + minimum13: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum13, R.const(65504, "float16")) + layer_norm28: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum13, model_encoder_layers_14_self_attn_layer_norm_weight, model_encoder_layers_14_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims85: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_14_self_attn_q_proj_weight, axes=None) + matmul84: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm28, permute_dims85, out_dtype="void") + add99: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul84, model_encoder_layers_14_self_attn_q_proj_bias) + reshape112: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add99, R.shape([batch_size, 1500, 20, 64])) + permute_dims86: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_14_self_attn_k_proj_weight, axes=None) + matmul85: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm28, permute_dims86, out_dtype="void") + reshape113: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul85, R.shape([batch_size, 1500, 20, 64])) + permute_dims87: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_14_self_attn_v_proj_weight, axes=None) + matmul86: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm28, permute_dims87, out_dtype="void") + add100: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul86, model_encoder_layers_14_self_attn_v_proj_bias) + reshape114: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add100, R.shape([batch_size, 1500, 20, 64])) + reshape115: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape112, R.shape([batch_size * 1500, 20, 64])) + reshape116: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape113, R.shape([batch_size * 1500, 20, 64])) + reshape117: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape114, R.shape([batch_size * 1500, 20, 64])) + lv18 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), reshape115, reshape116, reshape117), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape118: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv18, R.shape([batch_size, 1500, 20, 64])) + reshape119: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape118, R.shape([batch_size, 1500, 1280])) + permute_dims88: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_14_self_attn_out_proj_weight, axes=None) + matmul87: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape119, permute_dims88, out_dtype="void") + add101: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul87, model_encoder_layers_14_self_attn_out_proj_bias) + add102: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum13, add101) + layer_norm29: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add102, model_encoder_layers_14_final_layer_norm_weight, model_encoder_layers_14_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims89: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_14_fc1_weight, axes=None) + matmul88: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm29, permute_dims89, out_dtype="void") + add103: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul88, model_encoder_layers_14_fc1_bias) + gelu16: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add103) + permute_dims90: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_14_fc2_weight, axes=None) + matmul89: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu16, permute_dims90, out_dtype="void") + add104: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul89, model_encoder_layers_14_fc2_bias) + add105: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add102, add104) + maximum14: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add105, R.const(-65504, "float16")) + minimum14: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum14, R.const(65504, "float16")) + layer_norm30: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum14, model_encoder_layers_15_self_attn_layer_norm_weight, model_encoder_layers_15_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims91: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_15_self_attn_q_proj_weight, axes=None) + matmul90: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm30, permute_dims91, out_dtype="void") + add106: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul90, model_encoder_layers_15_self_attn_q_proj_bias) + reshape120: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add106, R.shape([batch_size, 1500, 20, 64])) + permute_dims92: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_15_self_attn_k_proj_weight, axes=None) + matmul91: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm30, permute_dims92, out_dtype="void") + reshape121: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul91, R.shape([batch_size, 1500, 20, 64])) + permute_dims93: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_15_self_attn_v_proj_weight, axes=None) + matmul92: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm30, permute_dims93, out_dtype="void") + add107: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul92, model_encoder_layers_15_self_attn_v_proj_bias) + reshape122: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add107, R.shape([batch_size, 1500, 20, 64])) + reshape123: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape120, R.shape([batch_size * 1500, 20, 64])) + reshape124: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape121, R.shape([batch_size * 1500, 20, 64])) + reshape125: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape122, R.shape([batch_size * 1500, 20, 64])) + lv19 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), reshape123, reshape124, reshape125), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape126: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv19, R.shape([batch_size, 1500, 20, 64])) + reshape127: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape126, R.shape([batch_size, 1500, 1280])) + permute_dims94: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_15_self_attn_out_proj_weight, axes=None) + matmul93: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape127, permute_dims94, out_dtype="void") + add108: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul93, model_encoder_layers_15_self_attn_out_proj_bias) + add109: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum14, add108) + layer_norm31: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add109, model_encoder_layers_15_final_layer_norm_weight, model_encoder_layers_15_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims95: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_15_fc1_weight, axes=None) + matmul94: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm31, permute_dims95, out_dtype="void") + add110: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul94, model_encoder_layers_15_fc1_bias) + gelu17: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add110) + permute_dims96: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_15_fc2_weight, axes=None) + matmul95: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu17, permute_dims96, out_dtype="void") + add111: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul95, model_encoder_layers_15_fc2_bias) + add112: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add109, add111) + maximum15: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add112, R.const(-65504, "float16")) + minimum15: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum15, R.const(65504, "float16")) + layer_norm32: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum15, model_encoder_layers_16_self_attn_layer_norm_weight, model_encoder_layers_16_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims97: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_16_self_attn_q_proj_weight, axes=None) + matmul96: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm32, permute_dims97, out_dtype="void") + add113: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul96, model_encoder_layers_16_self_attn_q_proj_bias) + reshape128: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add113, R.shape([batch_size, 1500, 20, 64])) + permute_dims98: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_16_self_attn_k_proj_weight, axes=None) + matmul97: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm32, permute_dims98, out_dtype="void") + reshape129: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul97, R.shape([batch_size, 1500, 20, 64])) + permute_dims99: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_16_self_attn_v_proj_weight, axes=None) + matmul98: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm32, permute_dims99, out_dtype="void") + add114: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul98, model_encoder_layers_16_self_attn_v_proj_bias) + reshape130: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add114, R.shape([batch_size, 1500, 20, 64])) + reshape131: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape128, R.shape([batch_size * 1500, 20, 64])) + reshape132: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape129, R.shape([batch_size * 1500, 20, 64])) + reshape133: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape130, R.shape([batch_size * 1500, 20, 64])) + lv20 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), reshape131, reshape132, reshape133), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape134: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv20, R.shape([batch_size, 1500, 20, 64])) + reshape135: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape134, R.shape([batch_size, 1500, 1280])) + permute_dims100: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_16_self_attn_out_proj_weight, axes=None) + matmul99: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape135, permute_dims100, out_dtype="void") + add115: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul99, model_encoder_layers_16_self_attn_out_proj_bias) + add116: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum15, add115) + layer_norm33: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add116, model_encoder_layers_16_final_layer_norm_weight, model_encoder_layers_16_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims101: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_16_fc1_weight, axes=None) + matmul100: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm33, permute_dims101, out_dtype="void") + add117: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul100, model_encoder_layers_16_fc1_bias) + gelu18: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add117) + permute_dims102: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_16_fc2_weight, axes=None) + matmul101: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu18, permute_dims102, out_dtype="void") + add118: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul101, model_encoder_layers_16_fc2_bias) + add119: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add116, add118) + maximum16: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add119, R.const(-65504, "float16")) + minimum16: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum16, R.const(65504, "float16")) + layer_norm34: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum16, model_encoder_layers_17_self_attn_layer_norm_weight, model_encoder_layers_17_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims103: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_17_self_attn_q_proj_weight, axes=None) + matmul102: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm34, permute_dims103, out_dtype="void") + add120: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul102, model_encoder_layers_17_self_attn_q_proj_bias) + reshape136: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add120, R.shape([batch_size, 1500, 20, 64])) + permute_dims104: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_17_self_attn_k_proj_weight, axes=None) + matmul103: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm34, permute_dims104, out_dtype="void") + reshape137: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul103, R.shape([batch_size, 1500, 20, 64])) + permute_dims105: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_17_self_attn_v_proj_weight, axes=None) + matmul104: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm34, permute_dims105, out_dtype="void") + add121: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul104, model_encoder_layers_17_self_attn_v_proj_bias) + reshape138: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add121, R.shape([batch_size, 1500, 20, 64])) + reshape139: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape136, R.shape([batch_size * 1500, 20, 64])) + reshape140: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape137, R.shape([batch_size * 1500, 20, 64])) + reshape141: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape138, R.shape([batch_size * 1500, 20, 64])) + lv21 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), reshape139, reshape140, reshape141), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape142: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv21, R.shape([batch_size, 1500, 20, 64])) + reshape143: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape142, R.shape([batch_size, 1500, 1280])) + permute_dims106: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_17_self_attn_out_proj_weight, axes=None) + matmul105: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape143, permute_dims106, out_dtype="void") + add122: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul105, model_encoder_layers_17_self_attn_out_proj_bias) + add123: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum16, add122) + layer_norm35: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add123, model_encoder_layers_17_final_layer_norm_weight, model_encoder_layers_17_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims107: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_17_fc1_weight, axes=None) + matmul106: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm35, permute_dims107, out_dtype="void") + add124: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul106, model_encoder_layers_17_fc1_bias) + gelu19: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add124) + permute_dims108: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_17_fc2_weight, axes=None) + matmul107: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu19, permute_dims108, out_dtype="void") + add125: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul107, model_encoder_layers_17_fc2_bias) + add126: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add123, add125) + maximum17: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add126, R.const(-65504, "float16")) + minimum17: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum17, R.const(65504, "float16")) + layer_norm36: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum17, model_encoder_layers_18_self_attn_layer_norm_weight, model_encoder_layers_18_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims109: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_18_self_attn_q_proj_weight, axes=None) + matmul108: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm36, permute_dims109, out_dtype="void") + add127: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul108, model_encoder_layers_18_self_attn_q_proj_bias) + reshape144: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add127, R.shape([batch_size, 1500, 20, 64])) + permute_dims110: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_18_self_attn_k_proj_weight, axes=None) + matmul109: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm36, permute_dims110, out_dtype="void") + reshape145: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul109, R.shape([batch_size, 1500, 20, 64])) + permute_dims111: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_18_self_attn_v_proj_weight, axes=None) + matmul110: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm36, permute_dims111, out_dtype="void") + add128: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul110, model_encoder_layers_18_self_attn_v_proj_bias) + reshape146: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add128, R.shape([batch_size, 1500, 20, 64])) + reshape147: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape144, R.shape([batch_size * 1500, 20, 64])) + reshape148: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape145, R.shape([batch_size * 1500, 20, 64])) + reshape149: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape146, R.shape([batch_size * 1500, 20, 64])) + lv22 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), reshape147, reshape148, reshape149), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape150: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv22, R.shape([batch_size, 1500, 20, 64])) + reshape151: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape150, R.shape([batch_size, 1500, 1280])) + permute_dims112: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_18_self_attn_out_proj_weight, axes=None) + matmul111: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape151, permute_dims112, out_dtype="void") + add129: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul111, model_encoder_layers_18_self_attn_out_proj_bias) + add130: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum17, add129) + layer_norm37: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add130, model_encoder_layers_18_final_layer_norm_weight, model_encoder_layers_18_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims113: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_18_fc1_weight, axes=None) + matmul112: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm37, permute_dims113, out_dtype="void") + add131: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul112, model_encoder_layers_18_fc1_bias) + gelu20: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add131) + permute_dims114: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_18_fc2_weight, axes=None) + matmul113: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu20, permute_dims114, out_dtype="void") + add132: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul113, model_encoder_layers_18_fc2_bias) + add133: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add130, add132) + maximum18: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add133, R.const(-65504, "float16")) + minimum18: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum18, R.const(65504, "float16")) + layer_norm38: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum18, model_encoder_layers_19_self_attn_layer_norm_weight, model_encoder_layers_19_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims115: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_19_self_attn_q_proj_weight, axes=None) + matmul114: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm38, permute_dims115, out_dtype="void") + add134: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul114, model_encoder_layers_19_self_attn_q_proj_bias) + reshape152: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add134, R.shape([batch_size, 1500, 20, 64])) + permute_dims116: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_19_self_attn_k_proj_weight, axes=None) + matmul115: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm38, permute_dims116, out_dtype="void") + reshape153: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul115, R.shape([batch_size, 1500, 20, 64])) + permute_dims117: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_19_self_attn_v_proj_weight, axes=None) + matmul116: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm38, permute_dims117, out_dtype="void") + add135: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul116, model_encoder_layers_19_self_attn_v_proj_bias) + reshape154: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add135, R.shape([batch_size, 1500, 20, 64])) + reshape155: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape152, R.shape([batch_size * 1500, 20, 64])) + reshape156: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape153, R.shape([batch_size * 1500, 20, 64])) + reshape157: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape154, R.shape([batch_size * 1500, 20, 64])) + lv23 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), reshape155, reshape156, reshape157), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape158: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv23, R.shape([batch_size, 1500, 20, 64])) + reshape159: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape158, R.shape([batch_size, 1500, 1280])) + permute_dims118: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_19_self_attn_out_proj_weight, axes=None) + matmul117: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape159, permute_dims118, out_dtype="void") + add136: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul117, model_encoder_layers_19_self_attn_out_proj_bias) + add137: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum18, add136) + layer_norm39: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add137, model_encoder_layers_19_final_layer_norm_weight, model_encoder_layers_19_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims119: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_19_fc1_weight, axes=None) + matmul118: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm39, permute_dims119, out_dtype="void") + add138: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul118, model_encoder_layers_19_fc1_bias) + gelu21: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add138) + permute_dims120: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_19_fc2_weight, axes=None) + matmul119: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu21, permute_dims120, out_dtype="void") + add139: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul119, model_encoder_layers_19_fc2_bias) + add140: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add137, add139) + maximum19: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add140, R.const(-65504, "float16")) + minimum19: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum19, R.const(65504, "float16")) + layer_norm40: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum19, model_encoder_layers_20_self_attn_layer_norm_weight, model_encoder_layers_20_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims121: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_20_self_attn_q_proj_weight, axes=None) + matmul120: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm40, permute_dims121, out_dtype="void") + add141: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul120, model_encoder_layers_20_self_attn_q_proj_bias) + reshape160: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add141, R.shape([batch_size, 1500, 20, 64])) + permute_dims122: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_20_self_attn_k_proj_weight, axes=None) + matmul121: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm40, permute_dims122, out_dtype="void") + reshape161: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul121, R.shape([batch_size, 1500, 20, 64])) + permute_dims123: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_20_self_attn_v_proj_weight, axes=None) + matmul122: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm40, permute_dims123, out_dtype="void") + add142: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul122, model_encoder_layers_20_self_attn_v_proj_bias) + reshape162: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add142, R.shape([batch_size, 1500, 20, 64])) + reshape163: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape160, R.shape([batch_size * 1500, 20, 64])) + reshape164: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape161, R.shape([batch_size * 1500, 20, 64])) + reshape165: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape162, R.shape([batch_size * 1500, 20, 64])) + lv24 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), reshape163, reshape164, reshape165), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape166: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv24, R.shape([batch_size, 1500, 20, 64])) + reshape167: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape166, R.shape([batch_size, 1500, 1280])) + permute_dims124: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_20_self_attn_out_proj_weight, axes=None) + matmul123: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape167, permute_dims124, out_dtype="void") + add143: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul123, model_encoder_layers_20_self_attn_out_proj_bias) + add144: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum19, add143) + layer_norm41: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add144, model_encoder_layers_20_final_layer_norm_weight, model_encoder_layers_20_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims125: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_20_fc1_weight, axes=None) + matmul124: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm41, permute_dims125, out_dtype="void") + add145: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul124, model_encoder_layers_20_fc1_bias) + gelu22: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add145) + permute_dims126: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_20_fc2_weight, axes=None) + matmul125: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu22, permute_dims126, out_dtype="void") + add146: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul125, model_encoder_layers_20_fc2_bias) + add147: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add144, add146) + maximum20: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add147, R.const(-65504, "float16")) + minimum20: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum20, R.const(65504, "float16")) + layer_norm42: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum20, model_encoder_layers_21_self_attn_layer_norm_weight, model_encoder_layers_21_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims127: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_21_self_attn_q_proj_weight, axes=None) + matmul126: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm42, permute_dims127, out_dtype="void") + add148: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul126, model_encoder_layers_21_self_attn_q_proj_bias) + reshape168: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add148, R.shape([batch_size, 1500, 20, 64])) + permute_dims128: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_21_self_attn_k_proj_weight, axes=None) + matmul127: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm42, permute_dims128, out_dtype="void") + reshape169: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul127, R.shape([batch_size, 1500, 20, 64])) + permute_dims129: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_21_self_attn_v_proj_weight, axes=None) + matmul128: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm42, permute_dims129, out_dtype="void") + add149: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul128, model_encoder_layers_21_self_attn_v_proj_bias) + reshape170: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add149, R.shape([batch_size, 1500, 20, 64])) + reshape171: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape168, R.shape([batch_size * 1500, 20, 64])) + reshape172: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape169, R.shape([batch_size * 1500, 20, 64])) + reshape173: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape170, R.shape([batch_size * 1500, 20, 64])) + lv25 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), reshape171, reshape172, reshape173), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape174: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv25, R.shape([batch_size, 1500, 20, 64])) + reshape175: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape174, R.shape([batch_size, 1500, 1280])) + permute_dims130: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_21_self_attn_out_proj_weight, axes=None) + matmul129: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape175, permute_dims130, out_dtype="void") + add150: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul129, model_encoder_layers_21_self_attn_out_proj_bias) + add151: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum20, add150) + layer_norm43: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add151, model_encoder_layers_21_final_layer_norm_weight, model_encoder_layers_21_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims131: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_21_fc1_weight, axes=None) + matmul130: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm43, permute_dims131, out_dtype="void") + add152: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul130, model_encoder_layers_21_fc1_bias) + gelu23: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add152) + permute_dims132: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_21_fc2_weight, axes=None) + matmul131: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu23, permute_dims132, out_dtype="void") + add153: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul131, model_encoder_layers_21_fc2_bias) + add154: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add151, add153) + maximum21: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add154, R.const(-65504, "float16")) + minimum21: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum21, R.const(65504, "float16")) + layer_norm44: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum21, model_encoder_layers_22_self_attn_layer_norm_weight, model_encoder_layers_22_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims133: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_22_self_attn_q_proj_weight, axes=None) + matmul132: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm44, permute_dims133, out_dtype="void") + add155: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul132, model_encoder_layers_22_self_attn_q_proj_bias) + reshape176: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add155, R.shape([batch_size, 1500, 20, 64])) + permute_dims134: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_22_self_attn_k_proj_weight, axes=None) + matmul133: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm44, permute_dims134, out_dtype="void") + reshape177: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul133, R.shape([batch_size, 1500, 20, 64])) + permute_dims135: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_22_self_attn_v_proj_weight, axes=None) + matmul134: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm44, permute_dims135, out_dtype="void") + add156: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul134, model_encoder_layers_22_self_attn_v_proj_bias) + reshape178: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add156, R.shape([batch_size, 1500, 20, 64])) + reshape179: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape176, R.shape([batch_size * 1500, 20, 64])) + reshape180: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape177, R.shape([batch_size * 1500, 20, 64])) + reshape181: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape178, R.shape([batch_size * 1500, 20, 64])) + lv26 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), reshape179, reshape180, reshape181), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape182: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv26, R.shape([batch_size, 1500, 20, 64])) + reshape183: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape182, R.shape([batch_size, 1500, 1280])) + permute_dims136: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_22_self_attn_out_proj_weight, axes=None) + matmul135: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape183, permute_dims136, out_dtype="void") + add157: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul135, model_encoder_layers_22_self_attn_out_proj_bias) + add158: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum21, add157) + layer_norm45: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add158, model_encoder_layers_22_final_layer_norm_weight, model_encoder_layers_22_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims137: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_22_fc1_weight, axes=None) + matmul136: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm45, permute_dims137, out_dtype="void") + add159: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul136, model_encoder_layers_22_fc1_bias) + gelu24: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add159) + permute_dims138: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_22_fc2_weight, axes=None) + matmul137: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu24, permute_dims138, out_dtype="void") + add160: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul137, model_encoder_layers_22_fc2_bias) + add161: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add158, add160) + maximum22: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add161, R.const(-65504, "float16")) + minimum22: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum22, R.const(65504, "float16")) + layer_norm46: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum22, model_encoder_layers_23_self_attn_layer_norm_weight, model_encoder_layers_23_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims139: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_23_self_attn_q_proj_weight, axes=None) + matmul138: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm46, permute_dims139, out_dtype="void") + add162: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul138, model_encoder_layers_23_self_attn_q_proj_bias) + reshape184: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add162, R.shape([batch_size, 1500, 20, 64])) + permute_dims140: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_23_self_attn_k_proj_weight, axes=None) + matmul139: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm46, permute_dims140, out_dtype="void") + reshape185: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul139, R.shape([batch_size, 1500, 20, 64])) + permute_dims141: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_23_self_attn_v_proj_weight, axes=None) + matmul140: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm46, permute_dims141, out_dtype="void") + add163: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul140, model_encoder_layers_23_self_attn_v_proj_bias) + reshape186: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add163, R.shape([batch_size, 1500, 20, 64])) + reshape187: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape184, R.shape([batch_size * 1500, 20, 64])) + reshape188: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape185, R.shape([batch_size * 1500, 20, 64])) + reshape189: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape186, R.shape([batch_size * 1500, 20, 64])) + lv27 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), reshape187, reshape188, reshape189), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape190: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv27, R.shape([batch_size, 1500, 20, 64])) + reshape191: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape190, R.shape([batch_size, 1500, 1280])) + permute_dims142: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_23_self_attn_out_proj_weight, axes=None) + matmul141: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape191, permute_dims142, out_dtype="void") + add164: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul141, model_encoder_layers_23_self_attn_out_proj_bias) + add165: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum22, add164) + layer_norm47: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add165, model_encoder_layers_23_final_layer_norm_weight, model_encoder_layers_23_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims143: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_23_fc1_weight, axes=None) + matmul142: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm47, permute_dims143, out_dtype="void") + add166: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul142, model_encoder_layers_23_fc1_bias) + gelu25: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add166) + permute_dims144: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_23_fc2_weight, axes=None) + matmul143: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu25, permute_dims144, out_dtype="void") + add167: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul143, model_encoder_layers_23_fc2_bias) + add168: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add165, add167) + maximum23: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add168, R.const(-65504, "float16")) + minimum23: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum23, R.const(65504, "float16")) + layer_norm48: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum23, model_encoder_layers_24_self_attn_layer_norm_weight, model_encoder_layers_24_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims145: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_24_self_attn_q_proj_weight, axes=None) + matmul144: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm48, permute_dims145, out_dtype="void") + add169: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul144, model_encoder_layers_24_self_attn_q_proj_bias) + reshape192: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add169, R.shape([batch_size, 1500, 20, 64])) + permute_dims146: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_24_self_attn_k_proj_weight, axes=None) + matmul145: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm48, permute_dims146, out_dtype="void") + reshape193: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul145, R.shape([batch_size, 1500, 20, 64])) + permute_dims147: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_24_self_attn_v_proj_weight, axes=None) + matmul146: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm48, permute_dims147, out_dtype="void") + add170: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul146, model_encoder_layers_24_self_attn_v_proj_bias) + reshape194: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add170, R.shape([batch_size, 1500, 20, 64])) + reshape195: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape192, R.shape([batch_size * 1500, 20, 64])) + reshape196: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape193, R.shape([batch_size * 1500, 20, 64])) + reshape197: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape194, R.shape([batch_size * 1500, 20, 64])) + lv28 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), reshape195, reshape196, reshape197), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape198: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv28, R.shape([batch_size, 1500, 20, 64])) + reshape199: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape198, R.shape([batch_size, 1500, 1280])) + permute_dims148: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_24_self_attn_out_proj_weight, axes=None) + matmul147: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape199, permute_dims148, out_dtype="void") + add171: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul147, model_encoder_layers_24_self_attn_out_proj_bias) + add172: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum23, add171) + layer_norm49: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add172, model_encoder_layers_24_final_layer_norm_weight, model_encoder_layers_24_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims149: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_24_fc1_weight, axes=None) + matmul148: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm49, permute_dims149, out_dtype="void") + add173: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul148, model_encoder_layers_24_fc1_bias) + gelu26: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add173) + permute_dims150: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_24_fc2_weight, axes=None) + matmul149: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu26, permute_dims150, out_dtype="void") + add174: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul149, model_encoder_layers_24_fc2_bias) + add175: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add172, add174) + maximum24: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add175, R.const(-65504, "float16")) + minimum24: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum24, R.const(65504, "float16")) + layer_norm50: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum24, model_encoder_layers_25_self_attn_layer_norm_weight, model_encoder_layers_25_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims151: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_25_self_attn_q_proj_weight, axes=None) + matmul150: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm50, permute_dims151, out_dtype="void") + add176: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul150, model_encoder_layers_25_self_attn_q_proj_bias) + reshape200: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add176, R.shape([batch_size, 1500, 20, 64])) + permute_dims152: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_25_self_attn_k_proj_weight, axes=None) + matmul151: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm50, permute_dims152, out_dtype="void") + reshape201: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul151, R.shape([batch_size, 1500, 20, 64])) + permute_dims153: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_25_self_attn_v_proj_weight, axes=None) + matmul152: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm50, permute_dims153, out_dtype="void") + add177: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul152, model_encoder_layers_25_self_attn_v_proj_bias) + reshape202: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add177, R.shape([batch_size, 1500, 20, 64])) + reshape203: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape200, R.shape([batch_size * 1500, 20, 64])) + reshape204: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape201, R.shape([batch_size * 1500, 20, 64])) + reshape205: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape202, R.shape([batch_size * 1500, 20, 64])) + lv29 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), reshape203, reshape204, reshape205), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape206: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv29, R.shape([batch_size, 1500, 20, 64])) + reshape207: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape206, R.shape([batch_size, 1500, 1280])) + permute_dims154: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_25_self_attn_out_proj_weight, axes=None) + matmul153: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape207, permute_dims154, out_dtype="void") + add178: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul153, model_encoder_layers_25_self_attn_out_proj_bias) + add179: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum24, add178) + layer_norm51: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add179, model_encoder_layers_25_final_layer_norm_weight, model_encoder_layers_25_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims155: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_25_fc1_weight, axes=None) + matmul154: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm51, permute_dims155, out_dtype="void") + add180: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul154, model_encoder_layers_25_fc1_bias) + gelu27: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add180) + permute_dims156: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_25_fc2_weight, axes=None) + matmul155: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu27, permute_dims156, out_dtype="void") + add181: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul155, model_encoder_layers_25_fc2_bias) + add182: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add179, add181) + maximum25: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add182, R.const(-65504, "float16")) + minimum25: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum25, R.const(65504, "float16")) + layer_norm52: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum25, model_encoder_layers_26_self_attn_layer_norm_weight, model_encoder_layers_26_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims157: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_26_self_attn_q_proj_weight, axes=None) + matmul156: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm52, permute_dims157, out_dtype="void") + add183: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul156, model_encoder_layers_26_self_attn_q_proj_bias) + reshape208: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add183, R.shape([batch_size, 1500, 20, 64])) + permute_dims158: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_26_self_attn_k_proj_weight, axes=None) + matmul157: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm52, permute_dims158, out_dtype="void") + reshape209: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul157, R.shape([batch_size, 1500, 20, 64])) + permute_dims159: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_26_self_attn_v_proj_weight, axes=None) + matmul158: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm52, permute_dims159, out_dtype="void") + add184: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul158, model_encoder_layers_26_self_attn_v_proj_bias) + reshape210: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add184, R.shape([batch_size, 1500, 20, 64])) + reshape211: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape208, R.shape([batch_size * 1500, 20, 64])) + reshape212: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape209, R.shape([batch_size * 1500, 20, 64])) + reshape213: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape210, R.shape([batch_size * 1500, 20, 64])) + lv30 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), reshape211, reshape212, reshape213), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape214: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv30, R.shape([batch_size, 1500, 20, 64])) + reshape215: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape214, R.shape([batch_size, 1500, 1280])) + permute_dims160: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_26_self_attn_out_proj_weight, axes=None) + matmul159: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape215, permute_dims160, out_dtype="void") + add185: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul159, model_encoder_layers_26_self_attn_out_proj_bias) + add186: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum25, add185) + layer_norm53: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add186, model_encoder_layers_26_final_layer_norm_weight, model_encoder_layers_26_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims161: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_26_fc1_weight, axes=None) + matmul160: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm53, permute_dims161, out_dtype="void") + add187: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul160, model_encoder_layers_26_fc1_bias) + gelu28: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add187) + permute_dims162: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_26_fc2_weight, axes=None) + matmul161: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu28, permute_dims162, out_dtype="void") + add188: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul161, model_encoder_layers_26_fc2_bias) + add189: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add186, add188) + maximum26: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add189, R.const(-65504, "float16")) + minimum26: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum26, R.const(65504, "float16")) + layer_norm54: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum26, model_encoder_layers_27_self_attn_layer_norm_weight, model_encoder_layers_27_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims163: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_27_self_attn_q_proj_weight, axes=None) + matmul162: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm54, permute_dims163, out_dtype="void") + add190: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul162, model_encoder_layers_27_self_attn_q_proj_bias) + reshape216: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add190, R.shape([batch_size, 1500, 20, 64])) + permute_dims164: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_27_self_attn_k_proj_weight, axes=None) + matmul163: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm54, permute_dims164, out_dtype="void") + reshape217: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul163, R.shape([batch_size, 1500, 20, 64])) + permute_dims165: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_27_self_attn_v_proj_weight, axes=None) + matmul164: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm54, permute_dims165, out_dtype="void") + add191: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul164, model_encoder_layers_27_self_attn_v_proj_bias) + reshape218: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add191, R.shape([batch_size, 1500, 20, 64])) + reshape219: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape216, R.shape([batch_size * 1500, 20, 64])) + reshape220: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape217, R.shape([batch_size * 1500, 20, 64])) + reshape221: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape218, R.shape([batch_size * 1500, 20, 64])) + lv31 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), reshape219, reshape220, reshape221), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape222: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv31, R.shape([batch_size, 1500, 20, 64])) + reshape223: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape222, R.shape([batch_size, 1500, 1280])) + permute_dims166: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_27_self_attn_out_proj_weight, axes=None) + matmul165: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape223, permute_dims166, out_dtype="void") + add192: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul165, model_encoder_layers_27_self_attn_out_proj_bias) + add193: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum26, add192) + layer_norm55: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add193, model_encoder_layers_27_final_layer_norm_weight, model_encoder_layers_27_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims167: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_27_fc1_weight, axes=None) + matmul166: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm55, permute_dims167, out_dtype="void") + add194: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul166, model_encoder_layers_27_fc1_bias) + gelu29: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add194) + permute_dims168: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_27_fc2_weight, axes=None) + matmul167: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu29, permute_dims168, out_dtype="void") + add195: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul167, model_encoder_layers_27_fc2_bias) + add196: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add193, add195) + maximum27: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add196, R.const(-65504, "float16")) + minimum27: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum27, R.const(65504, "float16")) + layer_norm56: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum27, model_encoder_layers_28_self_attn_layer_norm_weight, model_encoder_layers_28_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims169: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_28_self_attn_q_proj_weight, axes=None) + matmul168: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm56, permute_dims169, out_dtype="void") + add197: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul168, model_encoder_layers_28_self_attn_q_proj_bias) + reshape224: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add197, R.shape([batch_size, 1500, 20, 64])) + permute_dims170: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_28_self_attn_k_proj_weight, axes=None) + matmul169: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm56, permute_dims170, out_dtype="void") + reshape225: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul169, R.shape([batch_size, 1500, 20, 64])) + permute_dims171: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_28_self_attn_v_proj_weight, axes=None) + matmul170: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm56, permute_dims171, out_dtype="void") + add198: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul170, model_encoder_layers_28_self_attn_v_proj_bias) + reshape226: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add198, R.shape([batch_size, 1500, 20, 64])) + reshape227: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape224, R.shape([batch_size * 1500, 20, 64])) + reshape228: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape225, R.shape([batch_size * 1500, 20, 64])) + reshape229: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape226, R.shape([batch_size * 1500, 20, 64])) + lv32 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), reshape227, reshape228, reshape229), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape230: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv32, R.shape([batch_size, 1500, 20, 64])) + reshape231: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape230, R.shape([batch_size, 1500, 1280])) + permute_dims172: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_28_self_attn_out_proj_weight, axes=None) + matmul171: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape231, permute_dims172, out_dtype="void") + add199: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul171, model_encoder_layers_28_self_attn_out_proj_bias) + add200: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum27, add199) + layer_norm57: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add200, model_encoder_layers_28_final_layer_norm_weight, model_encoder_layers_28_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims173: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_28_fc1_weight, axes=None) + matmul172: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm57, permute_dims173, out_dtype="void") + add201: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul172, model_encoder_layers_28_fc1_bias) + gelu30: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add201) + permute_dims174: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_28_fc2_weight, axes=None) + matmul173: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu30, permute_dims174, out_dtype="void") + add202: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul173, model_encoder_layers_28_fc2_bias) + add203: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add200, add202) + maximum28: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add203, R.const(-65504, "float16")) + minimum28: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum28, R.const(65504, "float16")) + layer_norm58: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum28, model_encoder_layers_29_self_attn_layer_norm_weight, model_encoder_layers_29_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims175: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_29_self_attn_q_proj_weight, axes=None) + matmul174: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm58, permute_dims175, out_dtype="void") + add204: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul174, model_encoder_layers_29_self_attn_q_proj_bias) + reshape232: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add204, R.shape([batch_size, 1500, 20, 64])) + permute_dims176: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_29_self_attn_k_proj_weight, axes=None) + matmul175: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm58, permute_dims176, out_dtype="void") + reshape233: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul175, R.shape([batch_size, 1500, 20, 64])) + permute_dims177: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_29_self_attn_v_proj_weight, axes=None) + matmul176: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm58, permute_dims177, out_dtype="void") + add205: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul176, model_encoder_layers_29_self_attn_v_proj_bias) + reshape234: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add205, R.shape([batch_size, 1500, 20, 64])) + reshape235: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape232, R.shape([batch_size * 1500, 20, 64])) + reshape236: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape233, R.shape([batch_size * 1500, 20, 64])) + reshape237: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape234, R.shape([batch_size * 1500, 20, 64])) + lv33 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), reshape235, reshape236, reshape237), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape238: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv33, R.shape([batch_size, 1500, 20, 64])) + reshape239: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape238, R.shape([batch_size, 1500, 1280])) + permute_dims178: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_29_self_attn_out_proj_weight, axes=None) + matmul177: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape239, permute_dims178, out_dtype="void") + add206: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul177, model_encoder_layers_29_self_attn_out_proj_bias) + add207: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum28, add206) + layer_norm59: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add207, model_encoder_layers_29_final_layer_norm_weight, model_encoder_layers_29_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims179: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_29_fc1_weight, axes=None) + matmul178: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm59, permute_dims179, out_dtype="void") + add208: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul178, model_encoder_layers_29_fc1_bias) + gelu31: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add208) + permute_dims180: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_29_fc2_weight, axes=None) + matmul179: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu31, permute_dims180, out_dtype="void") + add209: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul179, model_encoder_layers_29_fc2_bias) + add210: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add207, add209) + maximum29: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add210, R.const(-65504, "float16")) + minimum29: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum29, R.const(65504, "float16")) + layer_norm60: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum29, model_encoder_layers_30_self_attn_layer_norm_weight, model_encoder_layers_30_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims181: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_30_self_attn_q_proj_weight, axes=None) + matmul180: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm60, permute_dims181, out_dtype="void") + add211: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul180, model_encoder_layers_30_self_attn_q_proj_bias) + reshape240: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add211, R.shape([batch_size, 1500, 20, 64])) + permute_dims182: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_30_self_attn_k_proj_weight, axes=None) + matmul181: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm60, permute_dims182, out_dtype="void") + reshape241: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul181, R.shape([batch_size, 1500, 20, 64])) + permute_dims183: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_30_self_attn_v_proj_weight, axes=None) + matmul182: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm60, permute_dims183, out_dtype="void") + add212: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul182, model_encoder_layers_30_self_attn_v_proj_bias) + reshape242: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add212, R.shape([batch_size, 1500, 20, 64])) + reshape243: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape240, R.shape([batch_size * 1500, 20, 64])) + reshape244: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape241, R.shape([batch_size * 1500, 20, 64])) + reshape245: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape242, R.shape([batch_size * 1500, 20, 64])) + lv34 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), reshape243, reshape244, reshape245), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape246: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv34, R.shape([batch_size, 1500, 20, 64])) + reshape247: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape246, R.shape([batch_size, 1500, 1280])) + permute_dims184: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_30_self_attn_out_proj_weight, axes=None) + matmul183: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape247, permute_dims184, out_dtype="void") + add213: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul183, model_encoder_layers_30_self_attn_out_proj_bias) + add214: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum29, add213) + layer_norm61: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add214, model_encoder_layers_30_final_layer_norm_weight, model_encoder_layers_30_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims185: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_30_fc1_weight, axes=None) + matmul184: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm61, permute_dims185, out_dtype="void") + add215: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul184, model_encoder_layers_30_fc1_bias) + gelu32: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add215) + permute_dims186: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_30_fc2_weight, axes=None) + matmul185: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu32, permute_dims186, out_dtype="void") + add216: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul185, model_encoder_layers_30_fc2_bias) + add217: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add214, add216) + maximum30: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add217, R.const(-65504, "float16")) + minimum30: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum30, R.const(65504, "float16")) + layer_norm62: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum30, model_encoder_layers_31_self_attn_layer_norm_weight, model_encoder_layers_31_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims187: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_31_self_attn_q_proj_weight, axes=None) + matmul186: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm62, permute_dims187, out_dtype="void") + add218: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul186, model_encoder_layers_31_self_attn_q_proj_bias) + reshape248: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add218, R.shape([batch_size, 1500, 20, 64])) + permute_dims188: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_31_self_attn_k_proj_weight, axes=None) + matmul187: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm62, permute_dims188, out_dtype="void") + reshape249: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul187, R.shape([batch_size, 1500, 20, 64])) + permute_dims189: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_31_self_attn_v_proj_weight, axes=None) + matmul188: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm62, permute_dims189, out_dtype="void") + add219: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul188, model_encoder_layers_31_self_attn_v_proj_bias) + reshape250: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add219, R.shape([batch_size, 1500, 20, 64])) + reshape251: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape248, R.shape([batch_size * 1500, 20, 64])) + reshape252: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape249, R.shape([batch_size * 1500, 20, 64])) + reshape253: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape250, R.shape([batch_size * 1500, 20, 64])) + lv35 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), reshape251, reshape252, reshape253), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape254: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv35, R.shape([batch_size, 1500, 20, 64])) + reshape255: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape254, R.shape([batch_size, 1500, 1280])) + permute_dims190: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_31_self_attn_out_proj_weight, axes=None) + matmul189: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape255, permute_dims190, out_dtype="void") + add220: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul189, model_encoder_layers_31_self_attn_out_proj_bias) + add221: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum30, add220) + layer_norm63: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add221, model_encoder_layers_31_final_layer_norm_weight, model_encoder_layers_31_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims191: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_31_fc1_weight, axes=None) + matmul190: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm63, permute_dims191, out_dtype="void") + add222: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul190, model_encoder_layers_31_fc1_bias) + gelu33: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add222) + permute_dims192: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_31_fc2_weight, axes=None) + matmul191: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu33, permute_dims192, out_dtype="void") + add223: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul191, model_encoder_layers_31_fc2_bias) + add224: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add221, add223) + maximum31: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add224, R.const(-65504, "float16")) + minimum31: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum31, R.const(65504, "float16")) + layer_norm64: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum31, model_encoder_layer_norm_weight, model_encoder_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + gv: R.Tensor((batch_size, 1500, 1280), dtype="float16") = layer_norm64 + R.output(gv) + return gv + + @R.function + def batch_prefill(input_ids: R.Tensor((1, "seq_len"), dtype="int32"), logit_positions: R.Tensor(("batch_size",), dtype="int32"), paged_kv_cache: R.Object, packed_params: R.Tuple(R.Tensor((1280, 128, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1500, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), 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dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"))) -> R.Tensor((1, "batch_size", 51866), dtype="float32"): + batch_size = T.int64() + seq_len = T.int64() + R.func_attr({"num_input": 3, "relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "seq_len": 15000, "total_seq_len": 1500}}) + with R.dataflow(): + model_encoder_conv1_weight2: R.Tensor((1280, 128, 3), dtype="float16") = packed_params[0] + model_encoder_conv1_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1] + model_encoder_conv2_weight2: R.Tensor((1280, 1280, 3), dtype="float16") = packed_params[2] + model_encoder_conv2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[3] + model_encoder_embed_positions_weight2: R.Tensor((1500, 1280), dtype="float16") = packed_params[4] + model_encoder_layers_0_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[5] + model_encoder_layers_0_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[6] + model_encoder_layers_0_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[7] + model_encoder_layers_0_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[8] + model_encoder_layers_0_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[9] + model_encoder_layers_0_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[10] + model_encoder_layers_0_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[11] + model_encoder_layers_0_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[12] + model_encoder_layers_0_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[13] + model_encoder_layers_0_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[14] + model_encoder_layers_0_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[15] + model_encoder_layers_0_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[16] + model_encoder_layers_0_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[17] + model_encoder_layers_0_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[18] + model_encoder_layers_0_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[19] + model_encoder_layers_1_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[20] + model_encoder_layers_1_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[21] + model_encoder_layers_1_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[22] + model_encoder_layers_1_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[23] + model_encoder_layers_1_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[24] + model_encoder_layers_1_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[25] + model_encoder_layers_1_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[26] + model_encoder_layers_1_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[27] + model_encoder_layers_1_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[28] + model_encoder_layers_1_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[29] + model_encoder_layers_1_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[30] + model_encoder_layers_1_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[31] + model_encoder_layers_1_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[32] + model_encoder_layers_1_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[33] + model_encoder_layers_1_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[34] + model_encoder_layers_2_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[35] + model_encoder_layers_2_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[36] + model_encoder_layers_2_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[37] + model_encoder_layers_2_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[38] + model_encoder_layers_2_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[39] + model_encoder_layers_2_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[40] + model_encoder_layers_2_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[41] + model_encoder_layers_2_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[42] + model_encoder_layers_2_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[43] + model_encoder_layers_2_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[44] + model_encoder_layers_2_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[45] + model_encoder_layers_2_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[46] + model_encoder_layers_2_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[47] + model_encoder_layers_2_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[48] + model_encoder_layers_2_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[49] + model_encoder_layers_3_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[50] + model_encoder_layers_3_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[51] + model_encoder_layers_3_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[52] + model_encoder_layers_3_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[53] + model_encoder_layers_3_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[54] + model_encoder_layers_3_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[55] + model_encoder_layers_3_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[56] + model_encoder_layers_3_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[57] + model_encoder_layers_3_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[58] + model_encoder_layers_3_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[59] + model_encoder_layers_3_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[60] + model_encoder_layers_3_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[61] + model_encoder_layers_3_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[62] + model_encoder_layers_3_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[63] + model_encoder_layers_3_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[64] + model_encoder_layers_4_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[65] + model_encoder_layers_4_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[66] + model_encoder_layers_4_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[67] + model_encoder_layers_4_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[68] + model_encoder_layers_4_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[69] + model_encoder_layers_4_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[70] + model_encoder_layers_4_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[71] + model_encoder_layers_4_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[72] + model_encoder_layers_4_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[73] + model_encoder_layers_4_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[74] + model_encoder_layers_4_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[75] + model_encoder_layers_4_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[76] + model_encoder_layers_4_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[77] + model_encoder_layers_4_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[78] + model_encoder_layers_4_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[79] + model_encoder_layers_5_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[80] + model_encoder_layers_5_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[81] + model_encoder_layers_5_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[82] + model_encoder_layers_5_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[83] + model_encoder_layers_5_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[84] + model_encoder_layers_5_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[85] + model_encoder_layers_5_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[86] + model_encoder_layers_5_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[87] + model_encoder_layers_5_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[88] + model_encoder_layers_5_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[89] + model_encoder_layers_5_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[90] + model_encoder_layers_5_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[91] + model_encoder_layers_5_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[92] + model_encoder_layers_5_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[93] + model_encoder_layers_5_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[94] + model_encoder_layers_6_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[95] + model_encoder_layers_6_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[96] + model_encoder_layers_6_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[97] + model_encoder_layers_6_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[98] + model_encoder_layers_6_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[99] + model_encoder_layers_6_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[100] + model_encoder_layers_6_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[101] + model_encoder_layers_6_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[102] + model_encoder_layers_6_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[103] + model_encoder_layers_6_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[104] + model_encoder_layers_6_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[105] + model_encoder_layers_6_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[106] + model_encoder_layers_6_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[107] + model_encoder_layers_6_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[108] + model_encoder_layers_6_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[109] + model_encoder_layers_7_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[110] + model_encoder_layers_7_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[111] + model_encoder_layers_7_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[112] + model_encoder_layers_7_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[113] + model_encoder_layers_7_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[114] + model_encoder_layers_7_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[115] + model_encoder_layers_7_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[116] + model_encoder_layers_7_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[117] + model_encoder_layers_7_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[118] + model_encoder_layers_7_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[119] + model_encoder_layers_7_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[120] + model_encoder_layers_7_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[121] + model_encoder_layers_7_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[122] + model_encoder_layers_7_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[123] + model_encoder_layers_7_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[124] + model_encoder_layers_8_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[125] + model_encoder_layers_8_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[126] + model_encoder_layers_8_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[127] + model_encoder_layers_8_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[128] + model_encoder_layers_8_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[129] + model_encoder_layers_8_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[130] + model_encoder_layers_8_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[131] + model_encoder_layers_8_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[132] + model_encoder_layers_8_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[133] + model_encoder_layers_8_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[134] + model_encoder_layers_8_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[135] + model_encoder_layers_8_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[136] + model_encoder_layers_8_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[137] + model_encoder_layers_8_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[138] + model_encoder_layers_8_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[139] + model_encoder_layers_9_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[140] + model_encoder_layers_9_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[141] + model_encoder_layers_9_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[142] + model_encoder_layers_9_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[143] + model_encoder_layers_9_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[144] + model_encoder_layers_9_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[145] + model_encoder_layers_9_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[146] + model_encoder_layers_9_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[147] + model_encoder_layers_9_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[148] + model_encoder_layers_9_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[149] + model_encoder_layers_9_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[150] + model_encoder_layers_9_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[151] + model_encoder_layers_9_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[152] + model_encoder_layers_9_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[153] + model_encoder_layers_9_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[154] + model_encoder_layers_10_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[155] + model_encoder_layers_10_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[156] + model_encoder_layers_10_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[157] + model_encoder_layers_10_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[158] + model_encoder_layers_10_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[159] + model_encoder_layers_10_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[160] + model_encoder_layers_10_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[161] + model_encoder_layers_10_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[162] + model_encoder_layers_10_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[163] + model_encoder_layers_10_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[164] + model_encoder_layers_10_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[165] + model_encoder_layers_10_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[166] + model_encoder_layers_10_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[167] + model_encoder_layers_10_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[168] + model_encoder_layers_10_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[169] + model_encoder_layers_11_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[170] + model_encoder_layers_11_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[171] + model_encoder_layers_11_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[172] + model_encoder_layers_11_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[173] + model_encoder_layers_11_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[174] + model_encoder_layers_11_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[175] + model_encoder_layers_11_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[176] + model_encoder_layers_11_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[177] + model_encoder_layers_11_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[178] + model_encoder_layers_11_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[179] + model_encoder_layers_11_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[180] + model_encoder_layers_11_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[181] + model_encoder_layers_11_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[182] + model_encoder_layers_11_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[183] + model_encoder_layers_11_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[184] + model_encoder_layers_12_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[185] + model_encoder_layers_12_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[186] + model_encoder_layers_12_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[187] + model_encoder_layers_12_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[188] + model_encoder_layers_12_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[189] + model_encoder_layers_12_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[190] + model_encoder_layers_12_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[191] + model_encoder_layers_12_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[192] + model_encoder_layers_12_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[193] + model_encoder_layers_12_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[194] + model_encoder_layers_12_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[195] + model_encoder_layers_12_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[196] + model_encoder_layers_12_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[197] + model_encoder_layers_12_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[198] + model_encoder_layers_12_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[199] + model_encoder_layers_13_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[200] + model_encoder_layers_13_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[201] + model_encoder_layers_13_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[202] + model_encoder_layers_13_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[203] + model_encoder_layers_13_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[204] + model_encoder_layers_13_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[205] + model_encoder_layers_13_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[206] + model_encoder_layers_13_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[207] + model_encoder_layers_13_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[208] + model_encoder_layers_13_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[209] + model_encoder_layers_13_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[210] + model_encoder_layers_13_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[211] + model_encoder_layers_13_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[212] + model_encoder_layers_13_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[213] + model_encoder_layers_13_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[214] + model_encoder_layers_14_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[215] + model_encoder_layers_14_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[216] + model_encoder_layers_14_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[217] + model_encoder_layers_14_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[218] + model_encoder_layers_14_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[219] + model_encoder_layers_14_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[220] + model_encoder_layers_14_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[221] + model_encoder_layers_14_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[222] + model_encoder_layers_14_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[223] + model_encoder_layers_14_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[224] + model_encoder_layers_14_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[225] + model_encoder_layers_14_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[226] + model_encoder_layers_14_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[227] + model_encoder_layers_14_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[228] + model_encoder_layers_14_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[229] + model_encoder_layers_15_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[230] + model_encoder_layers_15_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[231] + model_encoder_layers_15_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[232] + model_encoder_layers_15_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[233] + model_encoder_layers_15_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[234] + model_encoder_layers_15_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[235] + model_encoder_layers_15_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[236] + model_encoder_layers_15_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[237] + model_encoder_layers_15_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[238] + model_encoder_layers_15_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[239] + model_encoder_layers_15_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[240] + model_encoder_layers_15_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[241] + model_encoder_layers_15_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[242] + model_encoder_layers_15_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[243] + model_encoder_layers_15_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[244] + model_encoder_layers_16_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[245] + model_encoder_layers_16_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[246] + model_encoder_layers_16_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[247] + model_encoder_layers_16_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[248] + model_encoder_layers_16_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[249] + model_encoder_layers_16_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[250] + model_encoder_layers_16_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[251] + model_encoder_layers_16_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[252] + model_encoder_layers_16_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[253] + model_encoder_layers_16_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[254] + model_encoder_layers_16_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[255] + model_encoder_layers_16_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[256] + model_encoder_layers_16_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[257] + model_encoder_layers_16_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[258] + model_encoder_layers_16_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[259] + model_encoder_layers_17_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[260] + model_encoder_layers_17_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[261] + model_encoder_layers_17_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[262] + model_encoder_layers_17_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[263] + model_encoder_layers_17_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[264] + model_encoder_layers_17_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[265] + model_encoder_layers_17_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[266] + model_encoder_layers_17_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[267] + model_encoder_layers_17_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[268] + model_encoder_layers_17_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[269] + model_encoder_layers_17_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[270] + model_encoder_layers_17_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[271] + model_encoder_layers_17_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[272] + model_encoder_layers_17_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[273] + model_encoder_layers_17_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[274] + model_encoder_layers_18_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[275] + model_encoder_layers_18_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[276] + model_encoder_layers_18_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[277] + model_encoder_layers_18_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[278] + model_encoder_layers_18_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[279] + model_encoder_layers_18_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[280] + model_encoder_layers_18_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[281] + model_encoder_layers_18_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[282] + model_encoder_layers_18_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[283] + model_encoder_layers_18_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[284] + model_encoder_layers_18_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[285] + model_encoder_layers_18_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[286] + model_encoder_layers_18_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[287] + model_encoder_layers_18_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[288] + model_encoder_layers_18_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[289] + model_encoder_layers_19_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[290] + model_encoder_layers_19_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[291] + model_encoder_layers_19_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[292] + model_encoder_layers_19_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[293] + model_encoder_layers_19_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[294] + model_encoder_layers_19_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[295] + model_encoder_layers_19_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[296] + model_encoder_layers_19_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[297] + model_encoder_layers_19_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[298] + model_encoder_layers_19_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[299] + model_encoder_layers_19_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[300] + model_encoder_layers_19_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[301] + model_encoder_layers_19_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[302] + model_encoder_layers_19_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[303] + model_encoder_layers_19_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[304] + model_encoder_layers_20_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[305] + model_encoder_layers_20_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[306] + model_encoder_layers_20_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[307] + model_encoder_layers_20_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[308] + model_encoder_layers_20_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[309] + model_encoder_layers_20_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[310] + model_encoder_layers_20_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[311] + model_encoder_layers_20_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[312] + model_encoder_layers_20_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[313] + model_encoder_layers_20_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[314] + model_encoder_layers_20_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[315] + model_encoder_layers_20_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[316] + model_encoder_layers_20_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[317] + model_encoder_layers_20_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[318] + model_encoder_layers_20_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[319] + model_encoder_layers_21_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[320] + model_encoder_layers_21_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[321] + model_encoder_layers_21_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[322] + model_encoder_layers_21_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[323] + model_encoder_layers_21_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[324] + model_encoder_layers_21_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[325] + model_encoder_layers_21_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[326] + model_encoder_layers_21_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[327] + model_encoder_layers_21_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[328] + model_encoder_layers_21_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[329] + model_encoder_layers_21_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[330] + model_encoder_layers_21_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[331] + model_encoder_layers_21_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[332] + model_encoder_layers_21_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[333] + model_encoder_layers_21_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[334] + model_encoder_layers_22_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[335] + model_encoder_layers_22_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[336] + model_encoder_layers_22_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[337] + model_encoder_layers_22_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[338] + model_encoder_layers_22_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[339] + model_encoder_layers_22_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[340] + model_encoder_layers_22_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[341] + model_encoder_layers_22_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[342] + model_encoder_layers_22_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[343] + model_encoder_layers_22_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[344] + model_encoder_layers_22_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[345] + model_encoder_layers_22_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[346] + model_encoder_layers_22_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[347] + model_encoder_layers_22_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[348] + model_encoder_layers_22_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[349] + model_encoder_layers_23_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[350] + model_encoder_layers_23_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[351] + model_encoder_layers_23_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[352] + model_encoder_layers_23_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[353] + model_encoder_layers_23_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[354] + model_encoder_layers_23_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[355] + model_encoder_layers_23_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[356] + model_encoder_layers_23_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[357] + model_encoder_layers_23_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[358] + model_encoder_layers_23_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[359] + model_encoder_layers_23_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[360] + model_encoder_layers_23_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[361] + model_encoder_layers_23_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[362] + model_encoder_layers_23_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[363] + model_encoder_layers_23_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[364] + model_encoder_layers_24_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[365] + model_encoder_layers_24_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[366] + model_encoder_layers_24_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[367] + model_encoder_layers_24_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[368] + model_encoder_layers_24_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[369] + model_encoder_layers_24_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[370] + model_encoder_layers_24_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[371] + model_encoder_layers_24_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[372] + model_encoder_layers_24_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[373] + model_encoder_layers_24_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[374] + model_encoder_layers_24_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[375] + model_encoder_layers_24_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[376] + model_encoder_layers_24_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[377] + model_encoder_layers_24_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[378] + model_encoder_layers_24_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[379] + model_encoder_layers_25_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[380] + model_encoder_layers_25_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[381] + model_encoder_layers_25_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[382] + model_encoder_layers_25_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[383] + model_encoder_layers_25_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[384] + model_encoder_layers_25_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[385] + model_encoder_layers_25_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[386] + model_encoder_layers_25_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[387] + model_encoder_layers_25_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[388] + model_encoder_layers_25_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[389] + model_encoder_layers_25_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[390] + model_encoder_layers_25_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[391] + model_encoder_layers_25_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[392] + model_encoder_layers_25_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[393] + model_encoder_layers_25_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[394] + model_encoder_layers_26_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[395] + model_encoder_layers_26_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[396] + model_encoder_layers_26_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[397] + model_encoder_layers_26_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[398] + model_encoder_layers_26_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[399] + model_encoder_layers_26_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[400] + model_encoder_layers_26_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[401] + model_encoder_layers_26_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[402] + model_encoder_layers_26_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[403] + model_encoder_layers_26_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[404] + model_encoder_layers_26_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[405] + model_encoder_layers_26_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[406] + model_encoder_layers_26_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[407] + model_encoder_layers_26_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[408] + model_encoder_layers_26_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[409] + model_encoder_layers_27_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[410] + model_encoder_layers_27_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[411] + model_encoder_layers_27_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[412] + model_encoder_layers_27_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[413] + model_encoder_layers_27_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[414] + model_encoder_layers_27_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[415] + model_encoder_layers_27_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[416] + model_encoder_layers_27_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[417] + model_encoder_layers_27_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[418] + model_encoder_layers_27_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[419] + model_encoder_layers_27_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[420] + model_encoder_layers_27_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[421] + model_encoder_layers_27_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[422] + model_encoder_layers_27_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[423] + model_encoder_layers_27_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[424] + model_encoder_layers_28_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[425] + model_encoder_layers_28_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[426] + model_encoder_layers_28_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[427] + model_encoder_layers_28_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[428] + model_encoder_layers_28_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[429] + model_encoder_layers_28_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[430] + model_encoder_layers_28_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[431] + model_encoder_layers_28_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[432] + model_encoder_layers_28_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[433] + model_encoder_layers_28_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[434] + model_encoder_layers_28_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[435] + model_encoder_layers_28_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[436] + model_encoder_layers_28_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[437] + model_encoder_layers_28_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[438] + model_encoder_layers_28_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[439] + model_encoder_layers_29_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[440] + model_encoder_layers_29_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[441] + model_encoder_layers_29_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[442] + model_encoder_layers_29_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[443] + model_encoder_layers_29_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[444] + model_encoder_layers_29_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[445] + model_encoder_layers_29_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[446] + model_encoder_layers_29_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[447] + model_encoder_layers_29_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[448] + model_encoder_layers_29_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[449] + model_encoder_layers_29_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[450] + model_encoder_layers_29_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[451] + model_encoder_layers_29_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[452] + model_encoder_layers_29_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[453] + model_encoder_layers_29_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[454] + model_encoder_layers_30_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[455] + model_encoder_layers_30_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[456] + model_encoder_layers_30_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[457] + model_encoder_layers_30_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[458] + model_encoder_layers_30_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[459] + model_encoder_layers_30_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[460] + model_encoder_layers_30_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[461] + model_encoder_layers_30_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[462] + model_encoder_layers_30_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[463] + model_encoder_layers_30_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[464] + model_encoder_layers_30_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[465] + model_encoder_layers_30_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[466] + model_encoder_layers_30_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[467] + model_encoder_layers_30_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[468] + model_encoder_layers_30_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[469] + model_encoder_layers_31_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[470] + model_encoder_layers_31_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[471] + model_encoder_layers_31_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[472] + model_encoder_layers_31_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[473] + model_encoder_layers_31_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[474] + model_encoder_layers_31_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[475] + model_encoder_layers_31_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[476] + model_encoder_layers_31_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[477] + model_encoder_layers_31_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[478] + model_encoder_layers_31_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[479] + model_encoder_layers_31_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[480] + model_encoder_layers_31_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[481] + model_encoder_layers_31_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[482] + model_encoder_layers_31_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[483] + model_encoder_layers_31_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[484] + model_encoder_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[485] + model_encoder_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[486] + model_decoder_embed_tokens_weight2: R.Tensor((51866, 1280), dtype="float16") = packed_params[487] + model_decoder_embed_positions_weight2: R.Tensor((448, 1280), dtype="float16") = packed_params[488] + model_decoder_layers_0_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[489] + model_decoder_layers_0_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[490] + model_decoder_layers_0_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[491] + model_decoder_layers_0_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[492] + model_decoder_layers_0_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[493] + model_decoder_layers_0_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[494] + model_decoder_layers_0_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[495] + model_decoder_layers_0_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[496] + model_decoder_layers_0_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[497] + model_decoder_layers_0_encoder_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[498] + model_decoder_layers_0_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[499] + model_decoder_layers_0_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[500] + model_decoder_layers_0_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[501] + model_decoder_layers_0_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[502] + model_decoder_layers_0_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[503] + model_decoder_layers_0_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[504] + model_decoder_layers_0_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[505] + model_decoder_layers_0_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[506] + model_decoder_layers_0_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[507] + model_decoder_layers_0_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[508] + model_decoder_layers_0_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[509] + model_decoder_layers_0_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[510] + model_decoder_layers_0_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[511] + model_decoder_layers_0_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[512] + model_decoder_layers_1_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[513] + model_decoder_layers_1_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[514] + model_decoder_layers_1_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[515] + model_decoder_layers_1_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[516] + model_decoder_layers_1_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[517] + model_decoder_layers_1_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[518] + model_decoder_layers_1_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[519] + model_decoder_layers_1_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[520] + model_decoder_layers_1_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[521] + model_decoder_layers_1_encoder_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[522] + model_decoder_layers_1_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[523] + model_decoder_layers_1_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[524] + model_decoder_layers_1_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[525] + model_decoder_layers_1_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[526] + model_decoder_layers_1_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[527] + model_decoder_layers_1_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[528] + model_decoder_layers_1_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[529] + model_decoder_layers_1_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[530] + model_decoder_layers_1_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[531] + model_decoder_layers_1_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[532] + model_decoder_layers_1_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[533] + model_decoder_layers_1_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[534] + model_decoder_layers_1_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[535] + model_decoder_layers_1_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[536] + model_decoder_layers_2_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[537] + model_decoder_layers_2_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[538] + model_decoder_layers_2_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[539] + model_decoder_layers_2_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[540] + model_decoder_layers_2_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[541] + model_decoder_layers_2_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[542] + model_decoder_layers_2_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[543] + model_decoder_layers_2_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[544] + model_decoder_layers_2_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[545] + model_decoder_layers_2_encoder_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[546] + model_decoder_layers_2_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[547] + model_decoder_layers_2_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[548] + model_decoder_layers_2_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[549] + model_decoder_layers_2_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[550] + model_decoder_layers_2_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[551] + model_decoder_layers_2_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[552] + model_decoder_layers_2_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[553] + model_decoder_layers_2_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[554] + model_decoder_layers_2_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[555] + model_decoder_layers_2_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[556] + model_decoder_layers_2_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[557] + model_decoder_layers_2_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[558] + model_decoder_layers_2_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[559] + model_decoder_layers_2_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[560] + model_decoder_layers_3_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[561] + model_decoder_layers_3_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[562] + model_decoder_layers_3_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[563] + model_decoder_layers_3_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[564] + model_decoder_layers_3_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[565] + model_decoder_layers_3_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[566] + model_decoder_layers_3_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[567] + model_decoder_layers_3_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[568] + model_decoder_layers_3_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[569] + model_decoder_layers_3_encoder_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[570] + model_decoder_layers_3_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[571] + model_decoder_layers_3_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[572] + model_decoder_layers_3_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[573] + model_decoder_layers_3_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[574] + model_decoder_layers_3_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[575] + model_decoder_layers_3_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[576] + model_decoder_layers_3_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[577] + model_decoder_layers_3_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[578] + model_decoder_layers_3_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[579] + model_decoder_layers_3_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[580] + model_decoder_layers_3_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[581] + model_decoder_layers_3_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[582] + model_decoder_layers_3_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[583] + model_decoder_layers_3_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[584] + model_decoder_layers_4_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[585] + model_decoder_layers_4_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[586] + model_decoder_layers_4_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[587] + model_decoder_layers_4_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[588] + model_decoder_layers_4_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[589] + model_decoder_layers_4_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[590] + model_decoder_layers_4_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[591] + model_decoder_layers_4_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[592] + model_decoder_layers_4_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[593] + model_decoder_layers_4_encoder_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[594] + model_decoder_layers_4_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[595] + model_decoder_layers_4_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[596] + model_decoder_layers_4_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[597] + model_decoder_layers_4_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[598] + model_decoder_layers_4_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[599] + model_decoder_layers_4_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[600] + model_decoder_layers_4_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[601] + model_decoder_layers_4_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[602] + model_decoder_layers_4_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[603] + model_decoder_layers_4_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[604] + model_decoder_layers_4_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[605] + model_decoder_layers_4_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[606] + model_decoder_layers_4_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[607] + model_decoder_layers_4_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[608] + model_decoder_layers_5_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[609] + model_decoder_layers_5_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[610] + model_decoder_layers_5_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[611] + model_decoder_layers_5_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[612] + model_decoder_layers_5_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[613] + model_decoder_layers_5_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[614] + model_decoder_layers_5_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[615] + model_decoder_layers_5_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[616] + model_decoder_layers_5_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[617] + model_decoder_layers_5_encoder_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[618] + model_decoder_layers_5_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[619] + model_decoder_layers_5_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[620] + model_decoder_layers_5_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[621] + model_decoder_layers_5_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[622] + model_decoder_layers_5_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[623] + model_decoder_layers_5_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[624] + model_decoder_layers_5_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[625] + model_decoder_layers_5_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[626] + model_decoder_layers_5_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[627] + model_decoder_layers_5_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[628] + model_decoder_layers_5_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[629] + model_decoder_layers_5_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[630] + model_decoder_layers_5_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[631] + model_decoder_layers_5_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[632] + model_decoder_layers_6_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[633] + model_decoder_layers_6_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[634] + model_decoder_layers_6_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[635] + model_decoder_layers_6_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[636] + model_decoder_layers_6_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[637] + model_decoder_layers_6_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[638] + model_decoder_layers_6_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[639] + model_decoder_layers_6_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[640] + model_decoder_layers_6_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[641] + model_decoder_layers_6_encoder_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[642] + model_decoder_layers_6_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[643] + model_decoder_layers_6_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[644] + model_decoder_layers_6_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[645] + model_decoder_layers_6_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[646] + model_decoder_layers_6_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[647] + model_decoder_layers_6_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[648] + model_decoder_layers_6_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[649] + model_decoder_layers_6_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[650] + model_decoder_layers_6_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[651] + model_decoder_layers_6_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[652] + model_decoder_layers_6_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[653] + model_decoder_layers_6_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[654] + model_decoder_layers_6_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[655] + model_decoder_layers_6_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[656] + model_decoder_layers_7_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[657] + model_decoder_layers_7_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[658] + model_decoder_layers_7_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[659] + model_decoder_layers_7_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[660] + model_decoder_layers_7_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[661] + model_decoder_layers_7_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[662] + model_decoder_layers_7_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[663] + model_decoder_layers_7_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[664] + model_decoder_layers_7_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[665] + model_decoder_layers_7_encoder_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[666] + model_decoder_layers_7_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[667] + model_decoder_layers_7_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[668] + model_decoder_layers_7_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[669] + model_decoder_layers_7_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[670] + model_decoder_layers_7_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[671] + model_decoder_layers_7_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[672] + model_decoder_layers_7_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[673] + model_decoder_layers_7_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[674] + model_decoder_layers_7_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[675] + model_decoder_layers_7_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[676] + model_decoder_layers_7_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[677] + model_decoder_layers_7_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[678] + model_decoder_layers_7_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[679] + model_decoder_layers_7_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[680] + model_decoder_layers_8_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[681] + model_decoder_layers_8_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[682] + model_decoder_layers_8_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[683] + model_decoder_layers_8_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[684] + model_decoder_layers_8_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[685] + model_decoder_layers_8_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[686] + model_decoder_layers_8_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[687] + model_decoder_layers_8_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[688] + model_decoder_layers_8_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[689] + model_decoder_layers_8_encoder_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[690] + model_decoder_layers_8_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[691] + model_decoder_layers_8_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[692] + model_decoder_layers_8_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[693] + model_decoder_layers_8_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[694] + model_decoder_layers_8_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[695] + model_decoder_layers_8_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[696] + model_decoder_layers_8_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[697] + model_decoder_layers_8_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[698] + model_decoder_layers_8_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[699] + model_decoder_layers_8_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[700] + model_decoder_layers_8_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[701] + model_decoder_layers_8_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[702] + model_decoder_layers_8_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[703] + model_decoder_layers_8_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[704] + model_decoder_layers_9_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[705] + model_decoder_layers_9_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[706] + model_decoder_layers_9_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[707] + model_decoder_layers_9_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[708] + model_decoder_layers_9_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[709] + model_decoder_layers_9_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[710] + model_decoder_layers_9_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[711] + model_decoder_layers_9_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[712] + model_decoder_layers_9_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[713] + model_decoder_layers_9_encoder_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[714] + model_decoder_layers_9_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[715] + model_decoder_layers_9_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[716] + model_decoder_layers_9_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[717] + model_decoder_layers_9_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[718] + model_decoder_layers_9_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[719] + model_decoder_layers_9_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[720] + model_decoder_layers_9_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[721] + model_decoder_layers_9_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[722] + model_decoder_layers_9_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[723] + model_decoder_layers_9_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[724] + model_decoder_layers_9_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[725] + model_decoder_layers_9_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[726] + model_decoder_layers_9_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[727] + model_decoder_layers_9_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[728] + model_decoder_layers_10_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[729] + model_decoder_layers_10_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[730] + model_decoder_layers_10_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[731] + model_decoder_layers_10_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[732] + model_decoder_layers_10_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[733] + model_decoder_layers_10_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[734] + model_decoder_layers_10_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[735] + model_decoder_layers_10_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[736] + model_decoder_layers_10_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[737] + model_decoder_layers_10_encoder_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[738] + model_decoder_layers_10_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[739] + model_decoder_layers_10_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[740] + model_decoder_layers_10_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[741] + model_decoder_layers_10_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[742] + model_decoder_layers_10_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[743] + model_decoder_layers_10_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[744] + model_decoder_layers_10_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[745] + model_decoder_layers_10_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[746] + model_decoder_layers_10_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[747] + model_decoder_layers_10_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[748] + model_decoder_layers_10_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[749] + model_decoder_layers_10_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[750] + model_decoder_layers_10_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[751] + model_decoder_layers_10_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[752] + model_decoder_layers_11_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[753] + model_decoder_layers_11_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[754] + model_decoder_layers_11_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[755] + model_decoder_layers_11_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[756] + model_decoder_layers_11_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[757] + model_decoder_layers_11_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[758] + model_decoder_layers_11_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[759] + model_decoder_layers_11_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[760] + model_decoder_layers_11_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[761] + model_decoder_layers_11_encoder_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[762] + model_decoder_layers_11_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[763] + model_decoder_layers_11_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[764] + model_decoder_layers_11_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[765] + model_decoder_layers_11_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[766] + model_decoder_layers_11_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[767] + model_decoder_layers_11_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[768] + model_decoder_layers_11_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[769] + model_decoder_layers_11_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[770] + model_decoder_layers_11_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[771] + model_decoder_layers_11_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[772] + model_decoder_layers_11_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[773] + model_decoder_layers_11_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[774] + model_decoder_layers_11_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[775] + model_decoder_layers_11_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[776] + model_decoder_layers_12_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[777] + model_decoder_layers_12_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[778] + model_decoder_layers_12_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[779] + model_decoder_layers_12_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[780] + model_decoder_layers_12_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[781] + model_decoder_layers_12_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[782] + model_decoder_layers_12_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[783] + model_decoder_layers_12_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[784] + model_decoder_layers_12_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[785] + model_decoder_layers_12_encoder_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[786] + model_decoder_layers_12_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[787] + model_decoder_layers_12_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[788] + model_decoder_layers_12_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[789] + model_decoder_layers_12_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[790] + model_decoder_layers_12_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[791] + model_decoder_layers_12_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[792] + model_decoder_layers_12_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[793] + model_decoder_layers_12_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[794] + model_decoder_layers_12_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[795] + model_decoder_layers_12_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[796] + model_decoder_layers_12_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[797] + model_decoder_layers_12_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[798] + model_decoder_layers_12_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[799] + model_decoder_layers_12_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[800] + model_decoder_layers_13_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[801] + model_decoder_layers_13_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[802] + model_decoder_layers_13_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[803] + model_decoder_layers_13_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[804] + model_decoder_layers_13_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[805] + model_decoder_layers_13_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[806] + model_decoder_layers_13_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[807] + model_decoder_layers_13_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[808] + model_decoder_layers_13_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[809] + model_decoder_layers_13_encoder_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[810] + model_decoder_layers_13_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[811] + model_decoder_layers_13_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[812] + model_decoder_layers_13_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[813] + model_decoder_layers_13_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[814] + model_decoder_layers_13_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[815] + model_decoder_layers_13_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[816] + model_decoder_layers_13_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[817] + model_decoder_layers_13_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[818] + model_decoder_layers_13_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[819] + model_decoder_layers_13_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[820] + model_decoder_layers_13_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[821] + model_decoder_layers_13_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[822] + model_decoder_layers_13_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[823] + model_decoder_layers_13_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[824] + model_decoder_layers_14_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[825] + model_decoder_layers_14_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[826] + model_decoder_layers_14_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[827] + model_decoder_layers_14_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[828] + model_decoder_layers_14_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[829] + model_decoder_layers_14_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[830] + model_decoder_layers_14_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[831] + model_decoder_layers_14_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[832] + model_decoder_layers_14_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[833] + model_decoder_layers_14_encoder_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[834] + model_decoder_layers_14_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[835] + model_decoder_layers_14_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[836] + model_decoder_layers_14_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[837] + model_decoder_layers_14_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[838] + model_decoder_layers_14_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[839] + model_decoder_layers_14_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[840] + model_decoder_layers_14_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[841] + model_decoder_layers_14_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[842] + model_decoder_layers_14_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[843] + model_decoder_layers_14_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[844] + model_decoder_layers_14_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[845] + model_decoder_layers_14_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[846] + model_decoder_layers_14_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[847] + model_decoder_layers_14_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[848] + model_decoder_layers_15_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[849] + model_decoder_layers_15_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[850] + model_decoder_layers_15_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[851] + model_decoder_layers_15_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[852] + model_decoder_layers_15_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[853] + model_decoder_layers_15_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[854] + model_decoder_layers_15_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[855] + model_decoder_layers_15_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[856] + model_decoder_layers_15_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[857] + model_decoder_layers_15_encoder_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[858] + model_decoder_layers_15_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[859] + model_decoder_layers_15_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[860] + model_decoder_layers_15_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[861] + model_decoder_layers_15_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[862] + model_decoder_layers_15_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[863] + model_decoder_layers_15_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[864] + model_decoder_layers_15_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[865] + model_decoder_layers_15_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[866] + model_decoder_layers_15_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[867] + model_decoder_layers_15_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[868] + model_decoder_layers_15_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[869] + model_decoder_layers_15_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[870] + model_decoder_layers_15_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[871] + model_decoder_layers_15_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[872] + model_decoder_layers_16_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[873] + model_decoder_layers_16_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[874] + model_decoder_layers_16_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[875] + model_decoder_layers_16_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[876] + model_decoder_layers_16_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[877] + model_decoder_layers_16_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[878] + model_decoder_layers_16_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[879] + model_decoder_layers_16_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[880] + model_decoder_layers_16_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[881] + model_decoder_layers_16_encoder_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[882] + model_decoder_layers_16_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[883] + model_decoder_layers_16_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[884] + model_decoder_layers_16_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[885] + model_decoder_layers_16_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[886] + model_decoder_layers_16_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[887] + model_decoder_layers_16_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[888] + model_decoder_layers_16_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[889] + model_decoder_layers_16_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[890] + model_decoder_layers_16_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[891] + model_decoder_layers_16_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[892] + model_decoder_layers_16_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[893] + model_decoder_layers_16_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[894] + model_decoder_layers_16_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[895] + model_decoder_layers_16_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[896] + model_decoder_layers_17_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[897] + model_decoder_layers_17_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[898] + model_decoder_layers_17_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[899] + model_decoder_layers_17_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[900] + model_decoder_layers_17_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[901] + model_decoder_layers_17_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[902] + model_decoder_layers_17_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[903] + model_decoder_layers_17_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[904] + model_decoder_layers_17_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[905] + model_decoder_layers_17_encoder_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[906] + model_decoder_layers_17_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[907] + model_decoder_layers_17_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[908] + model_decoder_layers_17_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[909] + model_decoder_layers_17_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[910] + model_decoder_layers_17_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[911] + model_decoder_layers_17_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[912] + model_decoder_layers_17_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[913] + model_decoder_layers_17_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[914] + model_decoder_layers_17_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[915] + model_decoder_layers_17_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[916] + model_decoder_layers_17_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[917] + model_decoder_layers_17_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[918] + model_decoder_layers_17_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[919] + model_decoder_layers_17_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[920] + model_decoder_layers_18_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[921] + model_decoder_layers_18_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[922] + model_decoder_layers_18_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[923] + model_decoder_layers_18_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[924] + model_decoder_layers_18_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[925] + model_decoder_layers_18_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[926] + model_decoder_layers_18_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[927] + model_decoder_layers_18_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[928] + model_decoder_layers_18_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[929] + model_decoder_layers_18_encoder_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[930] + model_decoder_layers_18_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[931] + model_decoder_layers_18_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[932] + model_decoder_layers_18_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[933] + model_decoder_layers_18_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[934] + model_decoder_layers_18_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[935] + model_decoder_layers_18_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[936] + model_decoder_layers_18_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[937] + model_decoder_layers_18_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[938] + model_decoder_layers_18_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[939] + model_decoder_layers_18_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[940] + model_decoder_layers_18_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[941] + model_decoder_layers_18_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[942] + model_decoder_layers_18_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[943] + model_decoder_layers_18_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[944] + model_decoder_layers_19_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[945] + model_decoder_layers_19_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[946] + model_decoder_layers_19_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[947] + model_decoder_layers_19_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[948] + model_decoder_layers_19_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[949] + model_decoder_layers_19_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[950] + model_decoder_layers_19_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[951] + model_decoder_layers_19_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[952] + model_decoder_layers_19_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[953] + model_decoder_layers_19_encoder_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[954] + model_decoder_layers_19_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[955] + model_decoder_layers_19_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[956] + model_decoder_layers_19_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[957] + model_decoder_layers_19_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[958] + model_decoder_layers_19_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[959] + model_decoder_layers_19_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[960] + model_decoder_layers_19_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[961] + model_decoder_layers_19_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[962] + model_decoder_layers_19_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[963] + model_decoder_layers_19_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[964] + model_decoder_layers_19_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[965] + model_decoder_layers_19_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[966] + model_decoder_layers_19_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[967] + model_decoder_layers_19_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[968] + model_decoder_layers_20_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[969] + model_decoder_layers_20_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[970] + model_decoder_layers_20_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[971] + model_decoder_layers_20_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[972] + model_decoder_layers_20_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[973] + model_decoder_layers_20_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[974] + model_decoder_layers_20_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[975] + model_decoder_layers_20_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[976] + model_decoder_layers_20_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[977] + model_decoder_layers_20_encoder_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[978] + model_decoder_layers_20_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[979] + model_decoder_layers_20_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[980] + model_decoder_layers_20_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[981] + model_decoder_layers_20_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[982] + model_decoder_layers_20_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[983] + model_decoder_layers_20_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[984] + model_decoder_layers_20_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[985] + model_decoder_layers_20_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[986] + model_decoder_layers_20_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[987] + model_decoder_layers_20_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[988] + model_decoder_layers_20_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[989] + model_decoder_layers_20_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[990] + model_decoder_layers_20_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[991] + model_decoder_layers_20_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[992] + model_decoder_layers_21_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[993] + model_decoder_layers_21_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[994] + model_decoder_layers_21_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[995] + model_decoder_layers_21_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[996] + model_decoder_layers_21_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[997] + model_decoder_layers_21_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[998] + model_decoder_layers_21_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[999] + model_decoder_layers_21_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1000] + model_decoder_layers_21_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1001] + model_decoder_layers_21_encoder_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1002] + model_decoder_layers_21_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1003] + model_decoder_layers_21_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1004] + model_decoder_layers_21_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1005] + model_decoder_layers_21_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1006] + model_decoder_layers_21_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1007] + model_decoder_layers_21_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1008] + model_decoder_layers_21_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1009] + model_decoder_layers_21_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1010] + model_decoder_layers_21_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1011] + model_decoder_layers_21_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1012] + model_decoder_layers_21_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1013] + model_decoder_layers_21_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1014] + model_decoder_layers_21_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1015] + model_decoder_layers_21_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1016] + model_decoder_layers_22_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1017] + model_decoder_layers_22_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1018] + model_decoder_layers_22_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1019] + model_decoder_layers_22_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1020] + model_decoder_layers_22_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1021] + model_decoder_layers_22_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1022] + model_decoder_layers_22_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1023] + model_decoder_layers_22_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1024] + model_decoder_layers_22_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1025] + model_decoder_layers_22_encoder_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1026] + model_decoder_layers_22_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1027] + model_decoder_layers_22_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1028] + model_decoder_layers_22_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1029] + model_decoder_layers_22_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1030] + model_decoder_layers_22_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1031] + model_decoder_layers_22_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1032] + model_decoder_layers_22_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1033] + model_decoder_layers_22_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1034] + model_decoder_layers_22_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1035] + model_decoder_layers_22_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1036] + model_decoder_layers_22_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1037] + model_decoder_layers_22_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1038] + model_decoder_layers_22_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1039] + model_decoder_layers_22_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1040] + model_decoder_layers_23_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1041] + model_decoder_layers_23_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1042] + model_decoder_layers_23_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1043] + model_decoder_layers_23_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1044] + model_decoder_layers_23_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1045] + model_decoder_layers_23_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1046] + model_decoder_layers_23_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1047] + model_decoder_layers_23_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1048] + model_decoder_layers_23_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1049] + model_decoder_layers_23_encoder_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1050] + model_decoder_layers_23_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1051] + model_decoder_layers_23_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1052] + model_decoder_layers_23_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1053] + model_decoder_layers_23_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1054] + model_decoder_layers_23_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1055] + model_decoder_layers_23_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1056] + model_decoder_layers_23_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1057] + model_decoder_layers_23_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1058] + model_decoder_layers_23_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1059] + model_decoder_layers_23_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1060] + model_decoder_layers_23_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1061] + model_decoder_layers_23_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1062] + model_decoder_layers_23_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1063] + model_decoder_layers_23_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1064] + model_decoder_layers_24_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1065] + model_decoder_layers_24_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1066] + model_decoder_layers_24_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1067] + model_decoder_layers_24_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1068] + model_decoder_layers_24_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1069] + model_decoder_layers_24_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1070] + model_decoder_layers_24_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1071] + model_decoder_layers_24_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1072] + model_decoder_layers_24_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1073] + model_decoder_layers_24_encoder_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1074] + model_decoder_layers_24_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1075] + model_decoder_layers_24_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1076] + model_decoder_layers_24_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1077] + model_decoder_layers_24_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1078] + model_decoder_layers_24_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1079] + model_decoder_layers_24_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1080] + model_decoder_layers_24_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1081] + model_decoder_layers_24_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1082] + model_decoder_layers_24_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1083] + model_decoder_layers_24_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1084] + model_decoder_layers_24_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1085] + model_decoder_layers_24_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1086] + model_decoder_layers_24_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1087] + model_decoder_layers_24_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1088] + model_decoder_layers_25_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1089] + model_decoder_layers_25_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1090] + model_decoder_layers_25_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1091] + model_decoder_layers_25_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1092] + model_decoder_layers_25_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1093] + model_decoder_layers_25_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1094] + model_decoder_layers_25_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1095] + model_decoder_layers_25_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1096] + model_decoder_layers_25_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1097] + model_decoder_layers_25_encoder_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1098] + model_decoder_layers_25_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1099] + model_decoder_layers_25_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1100] + model_decoder_layers_25_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1101] + model_decoder_layers_25_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1102] + model_decoder_layers_25_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1103] + model_decoder_layers_25_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1104] + model_decoder_layers_25_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1105] + model_decoder_layers_25_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1106] + model_decoder_layers_25_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1107] + model_decoder_layers_25_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1108] + model_decoder_layers_25_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1109] + model_decoder_layers_25_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1110] + model_decoder_layers_25_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1111] + model_decoder_layers_25_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1112] + model_decoder_layers_26_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1113] + model_decoder_layers_26_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1114] + model_decoder_layers_26_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1115] + model_decoder_layers_26_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1116] + model_decoder_layers_26_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1117] + model_decoder_layers_26_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1118] + model_decoder_layers_26_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1119] + model_decoder_layers_26_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1120] + model_decoder_layers_26_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1121] + model_decoder_layers_26_encoder_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1122] + model_decoder_layers_26_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1123] + model_decoder_layers_26_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1124] + model_decoder_layers_26_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1125] + model_decoder_layers_26_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1126] + model_decoder_layers_26_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1127] + model_decoder_layers_26_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1128] + model_decoder_layers_26_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1129] + model_decoder_layers_26_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1130] + model_decoder_layers_26_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1131] + model_decoder_layers_26_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1132] + model_decoder_layers_26_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1133] + model_decoder_layers_26_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1134] + model_decoder_layers_26_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1135] + model_decoder_layers_26_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1136] + model_decoder_layers_27_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1137] + model_decoder_layers_27_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1138] + model_decoder_layers_27_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1139] + model_decoder_layers_27_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1140] + model_decoder_layers_27_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1141] + model_decoder_layers_27_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1142] + model_decoder_layers_27_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1143] + model_decoder_layers_27_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1144] + model_decoder_layers_27_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1145] + model_decoder_layers_27_encoder_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1146] + model_decoder_layers_27_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1147] + model_decoder_layers_27_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1148] + model_decoder_layers_27_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1149] + model_decoder_layers_27_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1150] + model_decoder_layers_27_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1151] + model_decoder_layers_27_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1152] + model_decoder_layers_27_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1153] + model_decoder_layers_27_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1154] + model_decoder_layers_27_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1155] + model_decoder_layers_27_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1156] + model_decoder_layers_27_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1157] + model_decoder_layers_27_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1158] + model_decoder_layers_27_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1159] + model_decoder_layers_27_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1160] + model_decoder_layers_28_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1161] + model_decoder_layers_28_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1162] + model_decoder_layers_28_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1163] + model_decoder_layers_28_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1164] + model_decoder_layers_28_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1165] + model_decoder_layers_28_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1166] + model_decoder_layers_28_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1167] + model_decoder_layers_28_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1168] + model_decoder_layers_28_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1169] + model_decoder_layers_28_encoder_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1170] + model_decoder_layers_28_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1171] + model_decoder_layers_28_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1172] + model_decoder_layers_28_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1173] + model_decoder_layers_28_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1174] + model_decoder_layers_28_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1175] + model_decoder_layers_28_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1176] + model_decoder_layers_28_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1177] + model_decoder_layers_28_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1178] + model_decoder_layers_28_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1179] + model_decoder_layers_28_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1180] + model_decoder_layers_28_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1181] + model_decoder_layers_28_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1182] + model_decoder_layers_28_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1183] + model_decoder_layers_28_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1184] + model_decoder_layers_29_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1185] + model_decoder_layers_29_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1186] + model_decoder_layers_29_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1187] + model_decoder_layers_29_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1188] + model_decoder_layers_29_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1189] + model_decoder_layers_29_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1190] + model_decoder_layers_29_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1191] + model_decoder_layers_29_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1192] + model_decoder_layers_29_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1193] + model_decoder_layers_29_encoder_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1194] + model_decoder_layers_29_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1195] + model_decoder_layers_29_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1196] + model_decoder_layers_29_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1197] + model_decoder_layers_29_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1198] + model_decoder_layers_29_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1199] + model_decoder_layers_29_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1200] + model_decoder_layers_29_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1201] + model_decoder_layers_29_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1202] + model_decoder_layers_29_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1203] + model_decoder_layers_29_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1204] + model_decoder_layers_29_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1205] + model_decoder_layers_29_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1206] + model_decoder_layers_29_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1207] + model_decoder_layers_29_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1208] + model_decoder_layers_30_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1209] + model_decoder_layers_30_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1210] + model_decoder_layers_30_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1211] + model_decoder_layers_30_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1212] + model_decoder_layers_30_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1213] + model_decoder_layers_30_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1214] + model_decoder_layers_30_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1215] + model_decoder_layers_30_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1216] + model_decoder_layers_30_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1217] + model_decoder_layers_30_encoder_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1218] + model_decoder_layers_30_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1219] + model_decoder_layers_30_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1220] + model_decoder_layers_30_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1221] + model_decoder_layers_30_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1222] + model_decoder_layers_30_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1223] + model_decoder_layers_30_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1224] + model_decoder_layers_30_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1225] + model_decoder_layers_30_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1226] + model_decoder_layers_30_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1227] + model_decoder_layers_30_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1228] + model_decoder_layers_30_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1229] + model_decoder_layers_30_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1230] + model_decoder_layers_30_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1231] + model_decoder_layers_30_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1232] + model_decoder_layers_31_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1233] + model_decoder_layers_31_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1234] + model_decoder_layers_31_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1235] + model_decoder_layers_31_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1236] + model_decoder_layers_31_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1237] + model_decoder_layers_31_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1238] + model_decoder_layers_31_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1239] + model_decoder_layers_31_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1240] + model_decoder_layers_31_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1241] + model_decoder_layers_31_encoder_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1242] + model_decoder_layers_31_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1243] + model_decoder_layers_31_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1244] + model_decoder_layers_31_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1245] + model_decoder_layers_31_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1246] + model_decoder_layers_31_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1247] + model_decoder_layers_31_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1248] + model_decoder_layers_31_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1249] + model_decoder_layers_31_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1250] + model_decoder_layers_31_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1251] + model_decoder_layers_31_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1252] + model_decoder_layers_31_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1253] + model_decoder_layers_31_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1254] + model_decoder_layers_31_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1255] + model_decoder_layers_31_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1256] + model_decoder_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1257] + model_decoder_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1258] + reshape384: R.Tensor((seq_len,), dtype="int32") = R.reshape(input_ids, R.shape([seq_len])) + take: R.Tensor((seq_len, 1280), dtype="float16") = R.take(model_decoder_embed_tokens_weight2, reshape384, axis=0) + reshape385: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(take, R.shape([1, seq_len, 1280])) + lv68: R.Tensor((seq_len,), dtype="int32") = R.call_pure_packed("vm.builtin.attention_kv_cache_get_query_positions", paged_kv_cache, sinfo_args=(R.Tensor((seq_len,), dtype="int32"),)) + take1: R.Tensor((seq_len, 1280), dtype="float16") = R.take(model_decoder_embed_positions_weight2, lv68, axis=0) + reshape386: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(take1, R.shape([1, seq_len, 1280])) + add257: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(reshape385, reshape386) + layer_norm65: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add257, model_decoder_layers_0_self_attn_layer_norm_weight2, model_decoder_layers_0_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims257: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_self_attn_q_proj_weight2, axes=None) + matmul256: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm65, permute_dims257, out_dtype="void") + add258: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul256, model_decoder_layers_0_self_attn_q_proj_bias2) + reshape387: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add258, R.shape([1, seq_len, 20, 64])) + permute_dims258: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_self_attn_k_proj_weight2, axes=None) + matmul257: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm65, permute_dims258, out_dtype="void") + reshape388: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul257, R.shape([1, seq_len, 20, 64])) + permute_dims259: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_self_attn_v_proj_weight2, axes=None) + matmul258: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm65, permute_dims259, out_dtype="void") + add259: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul258, model_decoder_layers_0_self_attn_v_proj_bias2) + reshape389: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add259, R.shape([1, seq_len, 20, 64])) + concat: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape387, reshape388, reshape389), axis=2) + reshape390: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat, R.shape([seq_len, 60, 64])) + lv69 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), reshape390), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape391: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv69, R.shape([1, seq_len, 20, 64])) + reshape392: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape391, R.shape([1, seq_len, 1280])) + permute_dims260: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_self_attn_out_proj_weight2, axes=None) + matmul259: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape392, permute_dims260, out_dtype="void") + add260: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul259, model_decoder_layers_0_self_attn_out_proj_bias2) + add261: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add257, add260) + layer_norm66: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add261, model_decoder_layers_0_encoder_attn_layer_norm_weight2, model_decoder_layers_0_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims261: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_encoder_attn_q_proj_weight2, axes=None) + matmul260: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm66, permute_dims261, out_dtype="void") + add262: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul260, model_decoder_layers_0_encoder_attn_q_proj_bias2) + reshape393: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add262, R.shape([1, seq_len, 20, 64])) + reshape394: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape393, R.shape([seq_len, 20, 64])) + lv70 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), reshape394), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape395: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv70, R.shape([1, seq_len, 20, 64])) + reshape396: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape395, R.shape([1, seq_len, 1280])) + permute_dims262: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_encoder_attn_out_proj_weight2, axes=None) + matmul261: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape396, permute_dims262, out_dtype="void") + add263: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul261, model_decoder_layers_0_encoder_attn_out_proj_bias2) + add264: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add261, add263) + layer_norm67: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add264, model_decoder_layers_0_final_layer_norm_weight2, model_decoder_layers_0_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims263: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_0_fc1_weight2, axes=None) + matmul262: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm67, permute_dims263, out_dtype="void") + add265: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul262, model_decoder_layers_0_fc1_bias2) + gelu34: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add265) + permute_dims264: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_fc2_weight2, axes=None) + matmul263: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu34, permute_dims264, out_dtype="void") + add266: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul263, model_decoder_layers_0_fc2_bias2) + add267: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add264, add266) + layer_norm68: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add267, model_decoder_layers_1_self_attn_layer_norm_weight2, model_decoder_layers_1_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims265: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_1_self_attn_q_proj_weight2, axes=None) + matmul264: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm68, permute_dims265, out_dtype="void") + add268: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul264, model_decoder_layers_1_self_attn_q_proj_bias2) + reshape397: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add268, R.shape([1, seq_len, 20, 64])) + permute_dims266: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_1_self_attn_k_proj_weight2, axes=None) + matmul265: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm68, permute_dims266, out_dtype="void") + reshape398: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul265, R.shape([1, seq_len, 20, 64])) + permute_dims267: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_1_self_attn_v_proj_weight2, axes=None) + matmul266: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm68, permute_dims267, out_dtype="void") + add269: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul266, model_decoder_layers_1_self_attn_v_proj_bias2) + reshape399: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add269, R.shape([1, seq_len, 20, 64])) + concat1: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape397, reshape398, reshape399), axis=2) + reshape400: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat1, R.shape([seq_len, 60, 64])) + lv71 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), reshape400), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape401: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv71, R.shape([1, seq_len, 20, 64])) + reshape402: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape401, R.shape([1, seq_len, 1280])) + permute_dims268: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_1_self_attn_out_proj_weight2, axes=None) + matmul267: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape402, permute_dims268, out_dtype="void") + add270: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul267, model_decoder_layers_1_self_attn_out_proj_bias2) + add271: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add267, add270) + layer_norm69: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add271, model_decoder_layers_1_encoder_attn_layer_norm_weight2, model_decoder_layers_1_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims269: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_1_encoder_attn_q_proj_weight2, axes=None) + matmul268: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm69, permute_dims269, out_dtype="void") + add272: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul268, model_decoder_layers_1_encoder_attn_q_proj_bias2) + reshape403: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add272, R.shape([1, seq_len, 20, 64])) + reshape404: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape403, R.shape([seq_len, 20, 64])) + lv72 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), reshape404), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape405: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv72, R.shape([1, seq_len, 20, 64])) + reshape406: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape405, R.shape([1, seq_len, 1280])) + permute_dims270: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_1_encoder_attn_out_proj_weight2, axes=None) + matmul269: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape406, permute_dims270, out_dtype="void") + add273: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul269, model_decoder_layers_1_encoder_attn_out_proj_bias2) + add274: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add271, add273) + layer_norm70: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add274, model_decoder_layers_1_final_layer_norm_weight2, model_decoder_layers_1_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims271: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_1_fc1_weight2, axes=None) + matmul270: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm70, permute_dims271, out_dtype="void") + add275: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul270, model_decoder_layers_1_fc1_bias2) + gelu35: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add275) + permute_dims272: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_1_fc2_weight2, axes=None) + matmul271: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu35, permute_dims272, out_dtype="void") + add276: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul271, model_decoder_layers_1_fc2_bias2) + add277: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add274, add276) + layer_norm71: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add277, model_decoder_layers_2_self_attn_layer_norm_weight2, model_decoder_layers_2_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims273: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_2_self_attn_q_proj_weight2, axes=None) + matmul272: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm71, permute_dims273, out_dtype="void") + add278: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul272, model_decoder_layers_2_self_attn_q_proj_bias2) + reshape407: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add278, R.shape([1, seq_len, 20, 64])) + permute_dims274: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_2_self_attn_k_proj_weight2, axes=None) + matmul273: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm71, permute_dims274, out_dtype="void") + reshape408: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul273, R.shape([1, seq_len, 20, 64])) + permute_dims275: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_2_self_attn_v_proj_weight2, axes=None) + matmul274: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm71, permute_dims275, out_dtype="void") + add279: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul274, model_decoder_layers_2_self_attn_v_proj_bias2) + reshape409: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add279, R.shape([1, seq_len, 20, 64])) + concat2: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape407, reshape408, reshape409), axis=2) + reshape410: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat2, R.shape([seq_len, 60, 64])) + lv73 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), reshape410), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape411: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv73, R.shape([1, seq_len, 20, 64])) + reshape412: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape411, R.shape([1, seq_len, 1280])) + permute_dims276: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_2_self_attn_out_proj_weight2, axes=None) + matmul275: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape412, permute_dims276, out_dtype="void") + add280: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul275, model_decoder_layers_2_self_attn_out_proj_bias2) + add281: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add277, add280) + layer_norm72: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add281, model_decoder_layers_2_encoder_attn_layer_norm_weight2, model_decoder_layers_2_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims277: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_2_encoder_attn_q_proj_weight2, axes=None) + matmul276: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm72, permute_dims277, out_dtype="void") + add282: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul276, model_decoder_layers_2_encoder_attn_q_proj_bias2) + reshape413: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add282, R.shape([1, seq_len, 20, 64])) + reshape414: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape413, R.shape([seq_len, 20, 64])) + lv74 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), reshape414), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape415: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv74, R.shape([1, seq_len, 20, 64])) + reshape416: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape415, R.shape([1, seq_len, 1280])) + permute_dims278: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_2_encoder_attn_out_proj_weight2, axes=None) + matmul277: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape416, permute_dims278, out_dtype="void") + add283: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul277, model_decoder_layers_2_encoder_attn_out_proj_bias2) + add284: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add281, add283) + layer_norm73: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add284, model_decoder_layers_2_final_layer_norm_weight2, model_decoder_layers_2_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims279: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_2_fc1_weight2, axes=None) + matmul278: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm73, permute_dims279, out_dtype="void") + add285: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul278, model_decoder_layers_2_fc1_bias2) + gelu36: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add285) + permute_dims280: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_2_fc2_weight2, axes=None) + matmul279: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu36, permute_dims280, out_dtype="void") + add286: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul279, model_decoder_layers_2_fc2_bias2) + add287: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add284, add286) + layer_norm74: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add287, model_decoder_layers_3_self_attn_layer_norm_weight2, model_decoder_layers_3_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims281: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_3_self_attn_q_proj_weight2, axes=None) + matmul280: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm74, permute_dims281, out_dtype="void") + add288: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul280, model_decoder_layers_3_self_attn_q_proj_bias2) + reshape417: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add288, R.shape([1, seq_len, 20, 64])) + permute_dims282: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_3_self_attn_k_proj_weight2, axes=None) + matmul281: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm74, permute_dims282, out_dtype="void") + reshape418: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul281, R.shape([1, seq_len, 20, 64])) + permute_dims283: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_3_self_attn_v_proj_weight2, axes=None) + matmul282: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm74, permute_dims283, out_dtype="void") + add289: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul282, model_decoder_layers_3_self_attn_v_proj_bias2) + reshape419: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add289, R.shape([1, seq_len, 20, 64])) + concat3: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape417, reshape418, reshape419), axis=2) + reshape420: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat3, R.shape([seq_len, 60, 64])) + lv75 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), reshape420), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape421: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv75, R.shape([1, seq_len, 20, 64])) + reshape422: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape421, R.shape([1, seq_len, 1280])) + permute_dims284: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_3_self_attn_out_proj_weight2, axes=None) + matmul283: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape422, permute_dims284, out_dtype="void") + add290: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul283, model_decoder_layers_3_self_attn_out_proj_bias2) + add291: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add287, add290) + layer_norm75: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add291, model_decoder_layers_3_encoder_attn_layer_norm_weight2, model_decoder_layers_3_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims285: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_3_encoder_attn_q_proj_weight2, axes=None) + matmul284: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm75, permute_dims285, out_dtype="void") + add292: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul284, model_decoder_layers_3_encoder_attn_q_proj_bias2) + reshape423: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add292, R.shape([1, seq_len, 20, 64])) + reshape424: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape423, R.shape([seq_len, 20, 64])) + lv76 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), reshape424), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape425: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv76, R.shape([1, seq_len, 20, 64])) + reshape426: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape425, R.shape([1, seq_len, 1280])) + permute_dims286: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_3_encoder_attn_out_proj_weight2, axes=None) + matmul285: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape426, permute_dims286, out_dtype="void") + add293: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul285, model_decoder_layers_3_encoder_attn_out_proj_bias2) + add294: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add291, add293) + layer_norm76: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add294, model_decoder_layers_3_final_layer_norm_weight2, model_decoder_layers_3_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims287: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_3_fc1_weight2, axes=None) + matmul286: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm76, permute_dims287, out_dtype="void") + add295: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul286, model_decoder_layers_3_fc1_bias2) + gelu37: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add295) + permute_dims288: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_3_fc2_weight2, axes=None) + matmul287: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu37, permute_dims288, out_dtype="void") + add296: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul287, model_decoder_layers_3_fc2_bias2) + add297: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add294, add296) + layer_norm77: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add297, model_decoder_layers_4_self_attn_layer_norm_weight2, model_decoder_layers_4_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims289: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_4_self_attn_q_proj_weight2, axes=None) + matmul288: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm77, permute_dims289, out_dtype="void") + add298: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul288, model_decoder_layers_4_self_attn_q_proj_bias2) + reshape427: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add298, R.shape([1, seq_len, 20, 64])) + permute_dims290: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_4_self_attn_k_proj_weight2, axes=None) + matmul289: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm77, permute_dims290, out_dtype="void") + reshape428: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul289, R.shape([1, seq_len, 20, 64])) + permute_dims291: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_4_self_attn_v_proj_weight2, axes=None) + matmul290: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm77, permute_dims291, out_dtype="void") + add299: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul290, model_decoder_layers_4_self_attn_v_proj_bias2) + reshape429: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add299, R.shape([1, seq_len, 20, 64])) + concat4: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape427, reshape428, reshape429), axis=2) + reshape430: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat4, R.shape([seq_len, 60, 64])) + lv77 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), reshape430), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape431: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv77, R.shape([1, seq_len, 20, 64])) + reshape432: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape431, R.shape([1, seq_len, 1280])) + permute_dims292: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_4_self_attn_out_proj_weight2, axes=None) + matmul291: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape432, permute_dims292, out_dtype="void") + add300: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul291, model_decoder_layers_4_self_attn_out_proj_bias2) + add301: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add297, add300) + layer_norm78: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add301, model_decoder_layers_4_encoder_attn_layer_norm_weight2, model_decoder_layers_4_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims293: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_4_encoder_attn_q_proj_weight2, axes=None) + matmul292: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm78, permute_dims293, out_dtype="void") + add302: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul292, model_decoder_layers_4_encoder_attn_q_proj_bias2) + reshape433: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add302, R.shape([1, seq_len, 20, 64])) + reshape434: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape433, R.shape([seq_len, 20, 64])) + lv78 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), reshape434), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape435: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv78, R.shape([1, seq_len, 20, 64])) + reshape436: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape435, R.shape([1, seq_len, 1280])) + permute_dims294: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_4_encoder_attn_out_proj_weight2, axes=None) + matmul293: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape436, permute_dims294, out_dtype="void") + add303: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul293, model_decoder_layers_4_encoder_attn_out_proj_bias2) + add304: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add301, add303) + layer_norm79: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add304, model_decoder_layers_4_final_layer_norm_weight2, model_decoder_layers_4_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims295: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_4_fc1_weight2, axes=None) + matmul294: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm79, permute_dims295, out_dtype="void") + add305: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul294, model_decoder_layers_4_fc1_bias2) + gelu38: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add305) + permute_dims296: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_4_fc2_weight2, axes=None) + matmul295: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu38, permute_dims296, out_dtype="void") + add306: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul295, model_decoder_layers_4_fc2_bias2) + add307: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add304, add306) + layer_norm80: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add307, model_decoder_layers_5_self_attn_layer_norm_weight2, model_decoder_layers_5_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims297: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_5_self_attn_q_proj_weight2, axes=None) + matmul296: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm80, permute_dims297, out_dtype="void") + add308: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul296, model_decoder_layers_5_self_attn_q_proj_bias2) + reshape437: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add308, R.shape([1, seq_len, 20, 64])) + permute_dims298: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_5_self_attn_k_proj_weight2, axes=None) + matmul297: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm80, permute_dims298, out_dtype="void") + reshape438: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul297, R.shape([1, seq_len, 20, 64])) + permute_dims299: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_5_self_attn_v_proj_weight2, axes=None) + matmul298: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm80, permute_dims299, out_dtype="void") + add309: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul298, model_decoder_layers_5_self_attn_v_proj_bias2) + reshape439: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add309, R.shape([1, seq_len, 20, 64])) + concat5: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape437, reshape438, reshape439), axis=2) + reshape440: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat5, R.shape([seq_len, 60, 64])) + lv79 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), reshape440), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape441: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv79, R.shape([1, seq_len, 20, 64])) + reshape442: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape441, R.shape([1, seq_len, 1280])) + permute_dims300: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_5_self_attn_out_proj_weight2, axes=None) + matmul299: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape442, permute_dims300, out_dtype="void") + add310: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul299, model_decoder_layers_5_self_attn_out_proj_bias2) + add311: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add307, add310) + layer_norm81: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add311, model_decoder_layers_5_encoder_attn_layer_norm_weight2, model_decoder_layers_5_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims301: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_5_encoder_attn_q_proj_weight2, axes=None) + matmul300: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm81, permute_dims301, out_dtype="void") + add312: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul300, model_decoder_layers_5_encoder_attn_q_proj_bias2) + reshape443: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add312, R.shape([1, seq_len, 20, 64])) + reshape444: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape443, R.shape([seq_len, 20, 64])) + lv80 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), reshape444), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape445: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv80, R.shape([1, seq_len, 20, 64])) + reshape446: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape445, R.shape([1, seq_len, 1280])) + permute_dims302: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_5_encoder_attn_out_proj_weight2, axes=None) + matmul301: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape446, permute_dims302, out_dtype="void") + add313: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul301, model_decoder_layers_5_encoder_attn_out_proj_bias2) + add314: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add311, add313) + layer_norm82: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add314, model_decoder_layers_5_final_layer_norm_weight2, model_decoder_layers_5_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims303: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_5_fc1_weight2, axes=None) + matmul302: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm82, permute_dims303, out_dtype="void") + add315: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul302, model_decoder_layers_5_fc1_bias2) + gelu39: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add315) + permute_dims304: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_5_fc2_weight2, axes=None) + matmul303: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu39, permute_dims304, out_dtype="void") + add316: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul303, model_decoder_layers_5_fc2_bias2) + add317: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add314, add316) + layer_norm83: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add317, model_decoder_layers_6_self_attn_layer_norm_weight2, model_decoder_layers_6_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims305: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_6_self_attn_q_proj_weight2, axes=None) + matmul304: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm83, permute_dims305, out_dtype="void") + add318: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul304, model_decoder_layers_6_self_attn_q_proj_bias2) + reshape447: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add318, R.shape([1, seq_len, 20, 64])) + permute_dims306: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_6_self_attn_k_proj_weight2, axes=None) + matmul305: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm83, permute_dims306, out_dtype="void") + reshape448: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul305, R.shape([1, seq_len, 20, 64])) + permute_dims307: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_6_self_attn_v_proj_weight2, axes=None) + matmul306: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm83, permute_dims307, out_dtype="void") + add319: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul306, model_decoder_layers_6_self_attn_v_proj_bias2) + reshape449: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add319, R.shape([1, seq_len, 20, 64])) + concat6: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape447, reshape448, reshape449), axis=2) + reshape450: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat6, R.shape([seq_len, 60, 64])) + lv81 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), reshape450), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape451: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv81, R.shape([1, seq_len, 20, 64])) + reshape452: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape451, R.shape([1, seq_len, 1280])) + permute_dims308: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_6_self_attn_out_proj_weight2, axes=None) + matmul307: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape452, permute_dims308, out_dtype="void") + add320: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul307, model_decoder_layers_6_self_attn_out_proj_bias2) + add321: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add317, add320) + layer_norm84: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add321, model_decoder_layers_6_encoder_attn_layer_norm_weight2, model_decoder_layers_6_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims309: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_6_encoder_attn_q_proj_weight2, axes=None) + matmul308: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm84, permute_dims309, out_dtype="void") + add322: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul308, model_decoder_layers_6_encoder_attn_q_proj_bias2) + reshape453: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add322, R.shape([1, seq_len, 20, 64])) + reshape454: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape453, R.shape([seq_len, 20, 64])) + lv82 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), reshape454), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape455: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv82, R.shape([1, seq_len, 20, 64])) + reshape456: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape455, R.shape([1, seq_len, 1280])) + permute_dims310: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_6_encoder_attn_out_proj_weight2, axes=None) + matmul309: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape456, permute_dims310, out_dtype="void") + add323: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul309, model_decoder_layers_6_encoder_attn_out_proj_bias2) + add324: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add321, add323) + layer_norm85: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add324, model_decoder_layers_6_final_layer_norm_weight2, model_decoder_layers_6_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims311: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_6_fc1_weight2, axes=None) + matmul310: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm85, permute_dims311, out_dtype="void") + add325: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul310, model_decoder_layers_6_fc1_bias2) + gelu40: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add325) + permute_dims312: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_6_fc2_weight2, axes=None) + matmul311: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu40, permute_dims312, out_dtype="void") + add326: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul311, model_decoder_layers_6_fc2_bias2) + add327: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add324, add326) + layer_norm86: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add327, model_decoder_layers_7_self_attn_layer_norm_weight2, model_decoder_layers_7_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims313: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_7_self_attn_q_proj_weight2, axes=None) + matmul312: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm86, permute_dims313, out_dtype="void") + add328: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul312, model_decoder_layers_7_self_attn_q_proj_bias2) + reshape457: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add328, R.shape([1, seq_len, 20, 64])) + permute_dims314: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_7_self_attn_k_proj_weight2, axes=None) + matmul313: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm86, permute_dims314, out_dtype="void") + reshape458: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul313, R.shape([1, seq_len, 20, 64])) + permute_dims315: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_7_self_attn_v_proj_weight2, axes=None) + matmul314: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm86, permute_dims315, out_dtype="void") + add329: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul314, model_decoder_layers_7_self_attn_v_proj_bias2) + reshape459: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add329, R.shape([1, seq_len, 20, 64])) + concat7: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape457, reshape458, reshape459), axis=2) + reshape460: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat7, R.shape([seq_len, 60, 64])) + lv83 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), reshape460), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape461: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv83, R.shape([1, seq_len, 20, 64])) + reshape462: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape461, R.shape([1, seq_len, 1280])) + permute_dims316: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_7_self_attn_out_proj_weight2, axes=None) + matmul315: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape462, permute_dims316, out_dtype="void") + add330: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul315, model_decoder_layers_7_self_attn_out_proj_bias2) + add331: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add327, add330) + layer_norm87: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add331, model_decoder_layers_7_encoder_attn_layer_norm_weight2, model_decoder_layers_7_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims317: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_7_encoder_attn_q_proj_weight2, axes=None) + matmul316: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm87, permute_dims317, out_dtype="void") + add332: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul316, model_decoder_layers_7_encoder_attn_q_proj_bias2) + reshape463: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add332, R.shape([1, seq_len, 20, 64])) + reshape464: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape463, R.shape([seq_len, 20, 64])) + lv84 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), reshape464), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape465: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv84, R.shape([1, seq_len, 20, 64])) + reshape466: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape465, R.shape([1, seq_len, 1280])) + permute_dims318: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_7_encoder_attn_out_proj_weight2, axes=None) + matmul317: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape466, permute_dims318, out_dtype="void") + add333: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul317, model_decoder_layers_7_encoder_attn_out_proj_bias2) + add334: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add331, add333) + layer_norm88: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add334, model_decoder_layers_7_final_layer_norm_weight2, model_decoder_layers_7_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims319: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_7_fc1_weight2, axes=None) + matmul318: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm88, permute_dims319, out_dtype="void") + add335: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul318, model_decoder_layers_7_fc1_bias2) + gelu41: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add335) + permute_dims320: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_7_fc2_weight2, axes=None) + matmul319: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu41, permute_dims320, out_dtype="void") + add336: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul319, model_decoder_layers_7_fc2_bias2) + add337: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add334, add336) + layer_norm89: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add337, model_decoder_layers_8_self_attn_layer_norm_weight2, model_decoder_layers_8_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims321: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_8_self_attn_q_proj_weight2, axes=None) + matmul320: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm89, permute_dims321, out_dtype="void") + add338: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul320, model_decoder_layers_8_self_attn_q_proj_bias2) + reshape467: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add338, R.shape([1, seq_len, 20, 64])) + permute_dims322: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_8_self_attn_k_proj_weight2, axes=None) + matmul321: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm89, permute_dims322, out_dtype="void") + reshape468: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul321, R.shape([1, seq_len, 20, 64])) + permute_dims323: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_8_self_attn_v_proj_weight2, axes=None) + matmul322: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm89, permute_dims323, out_dtype="void") + add339: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul322, model_decoder_layers_8_self_attn_v_proj_bias2) + reshape469: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add339, R.shape([1, seq_len, 20, 64])) + concat8: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape467, reshape468, reshape469), axis=2) + reshape470: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat8, R.shape([seq_len, 60, 64])) + lv85 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), reshape470), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape471: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv85, R.shape([1, seq_len, 20, 64])) + reshape472: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape471, R.shape([1, seq_len, 1280])) + permute_dims324: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_8_self_attn_out_proj_weight2, axes=None) + matmul323: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape472, permute_dims324, out_dtype="void") + add340: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul323, model_decoder_layers_8_self_attn_out_proj_bias2) + add341: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add337, add340) + layer_norm90: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add341, model_decoder_layers_8_encoder_attn_layer_norm_weight2, model_decoder_layers_8_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims325: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_8_encoder_attn_q_proj_weight2, axes=None) + matmul324: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm90, permute_dims325, out_dtype="void") + add342: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul324, model_decoder_layers_8_encoder_attn_q_proj_bias2) + reshape473: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add342, R.shape([1, seq_len, 20, 64])) + reshape474: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape473, R.shape([seq_len, 20, 64])) + lv86 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), reshape474), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape475: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv86, R.shape([1, seq_len, 20, 64])) + reshape476: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape475, R.shape([1, seq_len, 1280])) + permute_dims326: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_8_encoder_attn_out_proj_weight2, axes=None) + matmul325: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape476, permute_dims326, out_dtype="void") + add343: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul325, model_decoder_layers_8_encoder_attn_out_proj_bias2) + add344: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add341, add343) + layer_norm91: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add344, model_decoder_layers_8_final_layer_norm_weight2, model_decoder_layers_8_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims327: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_8_fc1_weight2, axes=None) + matmul326: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm91, permute_dims327, out_dtype="void") + add345: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul326, model_decoder_layers_8_fc1_bias2) + gelu42: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add345) + permute_dims328: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_8_fc2_weight2, axes=None) + matmul327: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu42, permute_dims328, out_dtype="void") + add346: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul327, model_decoder_layers_8_fc2_bias2) + add347: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add344, add346) + layer_norm92: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add347, model_decoder_layers_9_self_attn_layer_norm_weight2, model_decoder_layers_9_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims329: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_9_self_attn_q_proj_weight2, axes=None) + matmul328: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm92, permute_dims329, out_dtype="void") + add348: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul328, model_decoder_layers_9_self_attn_q_proj_bias2) + reshape477: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add348, R.shape([1, seq_len, 20, 64])) + permute_dims330: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_9_self_attn_k_proj_weight2, axes=None) + matmul329: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm92, permute_dims330, out_dtype="void") + reshape478: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul329, R.shape([1, seq_len, 20, 64])) + permute_dims331: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_9_self_attn_v_proj_weight2, axes=None) + matmul330: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm92, permute_dims331, out_dtype="void") + add349: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul330, model_decoder_layers_9_self_attn_v_proj_bias2) + reshape479: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add349, R.shape([1, seq_len, 20, 64])) + concat9: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape477, reshape478, reshape479), axis=2) + reshape480: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat9, R.shape([seq_len, 60, 64])) + lv87 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), reshape480), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape481: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv87, R.shape([1, seq_len, 20, 64])) + reshape482: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape481, R.shape([1, seq_len, 1280])) + permute_dims332: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_9_self_attn_out_proj_weight2, axes=None) + matmul331: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape482, permute_dims332, out_dtype="void") + add350: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul331, model_decoder_layers_9_self_attn_out_proj_bias2) + add351: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add347, add350) + layer_norm93: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add351, model_decoder_layers_9_encoder_attn_layer_norm_weight2, model_decoder_layers_9_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims333: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_9_encoder_attn_q_proj_weight2, axes=None) + matmul332: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm93, permute_dims333, out_dtype="void") + add352: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul332, model_decoder_layers_9_encoder_attn_q_proj_bias2) + reshape483: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add352, R.shape([1, seq_len, 20, 64])) + reshape484: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape483, R.shape([seq_len, 20, 64])) + lv88 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), reshape484), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape485: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv88, R.shape([1, seq_len, 20, 64])) + reshape486: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape485, R.shape([1, seq_len, 1280])) + permute_dims334: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_9_encoder_attn_out_proj_weight2, axes=None) + matmul333: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape486, permute_dims334, out_dtype="void") + add353: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul333, model_decoder_layers_9_encoder_attn_out_proj_bias2) + add354: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add351, add353) + layer_norm94: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add354, model_decoder_layers_9_final_layer_norm_weight2, model_decoder_layers_9_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims335: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_9_fc1_weight2, axes=None) + matmul334: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm94, permute_dims335, out_dtype="void") + add355: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul334, model_decoder_layers_9_fc1_bias2) + gelu43: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add355) + permute_dims336: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_9_fc2_weight2, axes=None) + matmul335: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu43, permute_dims336, out_dtype="void") + add356: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul335, model_decoder_layers_9_fc2_bias2) + add357: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add354, add356) + layer_norm95: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add357, model_decoder_layers_10_self_attn_layer_norm_weight2, model_decoder_layers_10_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims337: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_10_self_attn_q_proj_weight2, axes=None) + matmul336: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm95, permute_dims337, out_dtype="void") + add358: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul336, model_decoder_layers_10_self_attn_q_proj_bias2) + reshape487: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add358, R.shape([1, seq_len, 20, 64])) + permute_dims338: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_10_self_attn_k_proj_weight2, axes=None) + matmul337: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm95, permute_dims338, out_dtype="void") + reshape488: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul337, R.shape([1, seq_len, 20, 64])) + permute_dims339: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_10_self_attn_v_proj_weight2, axes=None) + matmul338: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm95, permute_dims339, out_dtype="void") + add359: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul338, model_decoder_layers_10_self_attn_v_proj_bias2) + reshape489: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add359, R.shape([1, seq_len, 20, 64])) + concat10: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape487, reshape488, reshape489), axis=2) + reshape490: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat10, R.shape([seq_len, 60, 64])) + lv89 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), reshape490), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape491: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv89, R.shape([1, seq_len, 20, 64])) + reshape492: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape491, R.shape([1, seq_len, 1280])) + permute_dims340: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_10_self_attn_out_proj_weight2, axes=None) + matmul339: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape492, permute_dims340, out_dtype="void") + add360: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul339, model_decoder_layers_10_self_attn_out_proj_bias2) + add361: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add357, add360) + layer_norm96: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add361, model_decoder_layers_10_encoder_attn_layer_norm_weight2, model_decoder_layers_10_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims341: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_10_encoder_attn_q_proj_weight2, axes=None) + matmul340: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm96, permute_dims341, out_dtype="void") + add362: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul340, model_decoder_layers_10_encoder_attn_q_proj_bias2) + reshape493: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add362, R.shape([1, seq_len, 20, 64])) + reshape494: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape493, R.shape([seq_len, 20, 64])) + lv90 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), reshape494), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape495: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv90, R.shape([1, seq_len, 20, 64])) + reshape496: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape495, R.shape([1, seq_len, 1280])) + permute_dims342: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_10_encoder_attn_out_proj_weight2, axes=None) + matmul341: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape496, permute_dims342, out_dtype="void") + add363: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul341, model_decoder_layers_10_encoder_attn_out_proj_bias2) + add364: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add361, add363) + layer_norm97: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add364, model_decoder_layers_10_final_layer_norm_weight2, model_decoder_layers_10_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims343: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_10_fc1_weight2, axes=None) + matmul342: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm97, permute_dims343, out_dtype="void") + add365: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul342, model_decoder_layers_10_fc1_bias2) + gelu44: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add365) + permute_dims344: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_10_fc2_weight2, axes=None) + matmul343: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu44, permute_dims344, out_dtype="void") + add366: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul343, model_decoder_layers_10_fc2_bias2) + add367: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add364, add366) + layer_norm98: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add367, model_decoder_layers_11_self_attn_layer_norm_weight2, model_decoder_layers_11_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims345: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_11_self_attn_q_proj_weight2, axes=None) + matmul344: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm98, permute_dims345, out_dtype="void") + add368: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul344, model_decoder_layers_11_self_attn_q_proj_bias2) + reshape497: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add368, R.shape([1, seq_len, 20, 64])) + permute_dims346: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_11_self_attn_k_proj_weight2, axes=None) + matmul345: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm98, permute_dims346, out_dtype="void") + reshape498: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul345, R.shape([1, seq_len, 20, 64])) + permute_dims347: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_11_self_attn_v_proj_weight2, axes=None) + matmul346: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm98, permute_dims347, out_dtype="void") + add369: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul346, model_decoder_layers_11_self_attn_v_proj_bias2) + reshape499: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add369, R.shape([1, seq_len, 20, 64])) + concat11: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape497, reshape498, reshape499), axis=2) + reshape500: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat11, R.shape([seq_len, 60, 64])) + lv91 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), reshape500), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape501: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv91, R.shape([1, seq_len, 20, 64])) + reshape502: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape501, R.shape([1, seq_len, 1280])) + permute_dims348: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_11_self_attn_out_proj_weight2, axes=None) + matmul347: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape502, permute_dims348, out_dtype="void") + add370: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul347, model_decoder_layers_11_self_attn_out_proj_bias2) + add371: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add367, add370) + layer_norm99: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add371, model_decoder_layers_11_encoder_attn_layer_norm_weight2, model_decoder_layers_11_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims349: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_11_encoder_attn_q_proj_weight2, axes=None) + matmul348: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm99, permute_dims349, out_dtype="void") + add372: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul348, model_decoder_layers_11_encoder_attn_q_proj_bias2) + reshape503: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add372, R.shape([1, seq_len, 20, 64])) + reshape504: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape503, R.shape([seq_len, 20, 64])) + lv92 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), reshape504), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape505: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv92, R.shape([1, seq_len, 20, 64])) + reshape506: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape505, R.shape([1, seq_len, 1280])) + permute_dims350: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_11_encoder_attn_out_proj_weight2, axes=None) + matmul349: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape506, permute_dims350, out_dtype="void") + add373: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul349, model_decoder_layers_11_encoder_attn_out_proj_bias2) + add374: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add371, add373) + layer_norm100: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add374, model_decoder_layers_11_final_layer_norm_weight2, model_decoder_layers_11_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims351: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_11_fc1_weight2, axes=None) + matmul350: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm100, permute_dims351, out_dtype="void") + add375: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul350, model_decoder_layers_11_fc1_bias2) + gelu45: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add375) + permute_dims352: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_11_fc2_weight2, axes=None) + matmul351: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu45, permute_dims352, out_dtype="void") + add376: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul351, model_decoder_layers_11_fc2_bias2) + add377: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add374, add376) + layer_norm101: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add377, model_decoder_layers_12_self_attn_layer_norm_weight2, model_decoder_layers_12_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims353: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_12_self_attn_q_proj_weight2, axes=None) + matmul352: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm101, permute_dims353, out_dtype="void") + add378: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul352, model_decoder_layers_12_self_attn_q_proj_bias2) + reshape507: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add378, R.shape([1, seq_len, 20, 64])) + permute_dims354: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_12_self_attn_k_proj_weight2, axes=None) + matmul353: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm101, permute_dims354, out_dtype="void") + reshape508: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul353, R.shape([1, seq_len, 20, 64])) + permute_dims355: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_12_self_attn_v_proj_weight2, axes=None) + matmul354: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm101, permute_dims355, out_dtype="void") + add379: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul354, model_decoder_layers_12_self_attn_v_proj_bias2) + reshape509: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add379, R.shape([1, seq_len, 20, 64])) + concat12: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape507, reshape508, reshape509), axis=2) + reshape510: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat12, R.shape([seq_len, 60, 64])) + lv93 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), reshape510), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape511: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv93, R.shape([1, seq_len, 20, 64])) + reshape512: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape511, R.shape([1, seq_len, 1280])) + permute_dims356: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_12_self_attn_out_proj_weight2, axes=None) + matmul355: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape512, permute_dims356, out_dtype="void") + add380: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul355, model_decoder_layers_12_self_attn_out_proj_bias2) + add381: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add377, add380) + layer_norm102: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add381, model_decoder_layers_12_encoder_attn_layer_norm_weight2, model_decoder_layers_12_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims357: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_12_encoder_attn_q_proj_weight2, axes=None) + matmul356: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm102, permute_dims357, out_dtype="void") + add382: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul356, model_decoder_layers_12_encoder_attn_q_proj_bias2) + reshape513: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add382, R.shape([1, seq_len, 20, 64])) + reshape514: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape513, R.shape([seq_len, 20, 64])) + lv94 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), reshape514), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape515: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv94, R.shape([1, seq_len, 20, 64])) + reshape516: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape515, R.shape([1, seq_len, 1280])) + permute_dims358: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_12_encoder_attn_out_proj_weight2, axes=None) + matmul357: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape516, permute_dims358, out_dtype="void") + add383: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul357, model_decoder_layers_12_encoder_attn_out_proj_bias2) + add384: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add381, add383) + layer_norm103: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add384, model_decoder_layers_12_final_layer_norm_weight2, model_decoder_layers_12_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims359: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_12_fc1_weight2, axes=None) + matmul358: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm103, permute_dims359, out_dtype="void") + add385: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul358, model_decoder_layers_12_fc1_bias2) + gelu46: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add385) + permute_dims360: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_12_fc2_weight2, axes=None) + matmul359: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu46, permute_dims360, out_dtype="void") + add386: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul359, model_decoder_layers_12_fc2_bias2) + add387: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add384, add386) + layer_norm104: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add387, model_decoder_layers_13_self_attn_layer_norm_weight2, model_decoder_layers_13_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims361: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_13_self_attn_q_proj_weight2, axes=None) + matmul360: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm104, permute_dims361, out_dtype="void") + add388: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul360, model_decoder_layers_13_self_attn_q_proj_bias2) + reshape517: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add388, R.shape([1, seq_len, 20, 64])) + permute_dims362: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_13_self_attn_k_proj_weight2, axes=None) + matmul361: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm104, permute_dims362, out_dtype="void") + reshape518: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul361, R.shape([1, seq_len, 20, 64])) + permute_dims363: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_13_self_attn_v_proj_weight2, axes=None) + matmul362: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm104, permute_dims363, out_dtype="void") + add389: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul362, model_decoder_layers_13_self_attn_v_proj_bias2) + reshape519: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add389, R.shape([1, seq_len, 20, 64])) + concat13: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape517, reshape518, reshape519), axis=2) + reshape520: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat13, R.shape([seq_len, 60, 64])) + lv95 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), reshape520), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape521: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv95, R.shape([1, seq_len, 20, 64])) + reshape522: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape521, R.shape([1, seq_len, 1280])) + permute_dims364: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_13_self_attn_out_proj_weight2, axes=None) + matmul363: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape522, permute_dims364, out_dtype="void") + add390: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul363, model_decoder_layers_13_self_attn_out_proj_bias2) + add391: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add387, add390) + layer_norm105: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add391, model_decoder_layers_13_encoder_attn_layer_norm_weight2, model_decoder_layers_13_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims365: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_13_encoder_attn_q_proj_weight2, axes=None) + matmul364: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm105, permute_dims365, out_dtype="void") + add392: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul364, model_decoder_layers_13_encoder_attn_q_proj_bias2) + reshape523: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add392, R.shape([1, seq_len, 20, 64])) + reshape524: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape523, R.shape([seq_len, 20, 64])) + lv96 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), reshape524), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape525: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv96, R.shape([1, seq_len, 20, 64])) + reshape526: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape525, R.shape([1, seq_len, 1280])) + permute_dims366: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_13_encoder_attn_out_proj_weight2, axes=None) + matmul365: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape526, permute_dims366, out_dtype="void") + add393: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul365, model_decoder_layers_13_encoder_attn_out_proj_bias2) + add394: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add391, add393) + layer_norm106: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add394, model_decoder_layers_13_final_layer_norm_weight2, model_decoder_layers_13_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims367: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_13_fc1_weight2, axes=None) + matmul366: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm106, permute_dims367, out_dtype="void") + add395: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul366, model_decoder_layers_13_fc1_bias2) + gelu47: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add395) + permute_dims368: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_13_fc2_weight2, axes=None) + matmul367: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu47, permute_dims368, out_dtype="void") + add396: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul367, model_decoder_layers_13_fc2_bias2) + add397: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add394, add396) + layer_norm107: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add397, model_decoder_layers_14_self_attn_layer_norm_weight2, model_decoder_layers_14_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims369: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_14_self_attn_q_proj_weight2, axes=None) + matmul368: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm107, permute_dims369, out_dtype="void") + add398: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul368, model_decoder_layers_14_self_attn_q_proj_bias2) + reshape527: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add398, R.shape([1, seq_len, 20, 64])) + permute_dims370: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_14_self_attn_k_proj_weight2, axes=None) + matmul369: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm107, permute_dims370, out_dtype="void") + reshape528: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul369, R.shape([1, seq_len, 20, 64])) + permute_dims371: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_14_self_attn_v_proj_weight2, axes=None) + matmul370: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm107, permute_dims371, out_dtype="void") + add399: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul370, model_decoder_layers_14_self_attn_v_proj_bias2) + reshape529: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add399, R.shape([1, seq_len, 20, 64])) + concat14: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape527, reshape528, reshape529), axis=2) + reshape530: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat14, R.shape([seq_len, 60, 64])) + lv97 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), reshape530), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape531: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv97, R.shape([1, seq_len, 20, 64])) + reshape532: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape531, R.shape([1, seq_len, 1280])) + permute_dims372: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_14_self_attn_out_proj_weight2, axes=None) + matmul371: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape532, permute_dims372, out_dtype="void") + add400: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul371, model_decoder_layers_14_self_attn_out_proj_bias2) + add401: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add397, add400) + layer_norm108: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add401, model_decoder_layers_14_encoder_attn_layer_norm_weight2, model_decoder_layers_14_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims373: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_14_encoder_attn_q_proj_weight2, axes=None) + matmul372: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm108, permute_dims373, out_dtype="void") + add402: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul372, model_decoder_layers_14_encoder_attn_q_proj_bias2) + reshape533: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add402, R.shape([1, seq_len, 20, 64])) + reshape534: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape533, R.shape([seq_len, 20, 64])) + lv98 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), reshape534), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape535: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv98, R.shape([1, seq_len, 20, 64])) + reshape536: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape535, R.shape([1, seq_len, 1280])) + permute_dims374: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_14_encoder_attn_out_proj_weight2, axes=None) + matmul373: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape536, permute_dims374, out_dtype="void") + add403: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul373, model_decoder_layers_14_encoder_attn_out_proj_bias2) + add404: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add401, add403) + layer_norm109: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add404, model_decoder_layers_14_final_layer_norm_weight2, model_decoder_layers_14_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims375: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_14_fc1_weight2, axes=None) + matmul374: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm109, permute_dims375, out_dtype="void") + add405: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul374, model_decoder_layers_14_fc1_bias2) + gelu48: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add405) + permute_dims376: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_14_fc2_weight2, axes=None) + matmul375: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu48, permute_dims376, out_dtype="void") + add406: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul375, model_decoder_layers_14_fc2_bias2) + add407: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add404, add406) + layer_norm110: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add407, model_decoder_layers_15_self_attn_layer_norm_weight2, model_decoder_layers_15_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims377: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_15_self_attn_q_proj_weight2, axes=None) + matmul376: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm110, permute_dims377, out_dtype="void") + add408: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul376, model_decoder_layers_15_self_attn_q_proj_bias2) + reshape537: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add408, R.shape([1, seq_len, 20, 64])) + permute_dims378: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_15_self_attn_k_proj_weight2, axes=None) + matmul377: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm110, permute_dims378, out_dtype="void") + reshape538: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul377, R.shape([1, seq_len, 20, 64])) + permute_dims379: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_15_self_attn_v_proj_weight2, axes=None) + matmul378: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm110, permute_dims379, out_dtype="void") + add409: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul378, model_decoder_layers_15_self_attn_v_proj_bias2) + reshape539: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add409, R.shape([1, seq_len, 20, 64])) + concat15: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape537, reshape538, reshape539), axis=2) + reshape540: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat15, R.shape([seq_len, 60, 64])) + lv99 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), reshape540), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape541: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv99, R.shape([1, seq_len, 20, 64])) + reshape542: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape541, R.shape([1, seq_len, 1280])) + permute_dims380: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_15_self_attn_out_proj_weight2, axes=None) + matmul379: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape542, permute_dims380, out_dtype="void") + add410: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul379, model_decoder_layers_15_self_attn_out_proj_bias2) + add411: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add407, add410) + layer_norm111: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add411, model_decoder_layers_15_encoder_attn_layer_norm_weight2, model_decoder_layers_15_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims381: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_15_encoder_attn_q_proj_weight2, axes=None) + matmul380: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm111, permute_dims381, out_dtype="void") + add412: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul380, model_decoder_layers_15_encoder_attn_q_proj_bias2) + reshape543: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add412, R.shape([1, seq_len, 20, 64])) + reshape544: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape543, R.shape([seq_len, 20, 64])) + lv100 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), reshape544), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape545: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv100, R.shape([1, seq_len, 20, 64])) + reshape546: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape545, R.shape([1, seq_len, 1280])) + permute_dims382: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_15_encoder_attn_out_proj_weight2, axes=None) + matmul381: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape546, permute_dims382, out_dtype="void") + add413: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul381, model_decoder_layers_15_encoder_attn_out_proj_bias2) + add414: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add411, add413) + layer_norm112: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add414, model_decoder_layers_15_final_layer_norm_weight2, model_decoder_layers_15_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims383: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_15_fc1_weight2, axes=None) + matmul382: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm112, permute_dims383, out_dtype="void") + add415: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul382, model_decoder_layers_15_fc1_bias2) + gelu49: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add415) + permute_dims384: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_15_fc2_weight2, axes=None) + matmul383: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu49, permute_dims384, out_dtype="void") + add416: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul383, model_decoder_layers_15_fc2_bias2) + add417: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add414, add416) + layer_norm113: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add417, model_decoder_layers_16_self_attn_layer_norm_weight2, model_decoder_layers_16_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims385: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_16_self_attn_q_proj_weight2, axes=None) + matmul384: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm113, permute_dims385, out_dtype="void") + add418: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul384, model_decoder_layers_16_self_attn_q_proj_bias2) + reshape547: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add418, R.shape([1, seq_len, 20, 64])) + permute_dims386: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_16_self_attn_k_proj_weight2, axes=None) + matmul385: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm113, permute_dims386, out_dtype="void") + reshape548: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul385, R.shape([1, seq_len, 20, 64])) + permute_dims387: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_16_self_attn_v_proj_weight2, axes=None) + matmul386: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm113, permute_dims387, out_dtype="void") + add419: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul386, model_decoder_layers_16_self_attn_v_proj_bias2) + reshape549: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add419, R.shape([1, seq_len, 20, 64])) + concat16: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape547, reshape548, reshape549), axis=2) + reshape550: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat16, R.shape([seq_len, 60, 64])) + lv101 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), reshape550), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape551: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv101, R.shape([1, seq_len, 20, 64])) + reshape552: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape551, R.shape([1, seq_len, 1280])) + permute_dims388: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_16_self_attn_out_proj_weight2, axes=None) + matmul387: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape552, permute_dims388, out_dtype="void") + add420: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul387, model_decoder_layers_16_self_attn_out_proj_bias2) + add421: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add417, add420) + layer_norm114: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add421, model_decoder_layers_16_encoder_attn_layer_norm_weight2, model_decoder_layers_16_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims389: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_16_encoder_attn_q_proj_weight2, axes=None) + matmul388: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm114, permute_dims389, out_dtype="void") + add422: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul388, model_decoder_layers_16_encoder_attn_q_proj_bias2) + reshape553: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add422, R.shape([1, seq_len, 20, 64])) + reshape554: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape553, R.shape([seq_len, 20, 64])) + lv102 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), reshape554), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape555: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv102, R.shape([1, seq_len, 20, 64])) + reshape556: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape555, R.shape([1, seq_len, 1280])) + permute_dims390: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_16_encoder_attn_out_proj_weight2, axes=None) + matmul389: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape556, permute_dims390, out_dtype="void") + add423: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul389, model_decoder_layers_16_encoder_attn_out_proj_bias2) + add424: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add421, add423) + layer_norm115: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add424, model_decoder_layers_16_final_layer_norm_weight2, model_decoder_layers_16_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims391: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_16_fc1_weight2, axes=None) + matmul390: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm115, permute_dims391, out_dtype="void") + add425: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul390, model_decoder_layers_16_fc1_bias2) + gelu50: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add425) + permute_dims392: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_16_fc2_weight2, axes=None) + matmul391: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu50, permute_dims392, out_dtype="void") + add426: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul391, model_decoder_layers_16_fc2_bias2) + add427: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add424, add426) + layer_norm116: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add427, model_decoder_layers_17_self_attn_layer_norm_weight2, model_decoder_layers_17_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims393: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_17_self_attn_q_proj_weight2, axes=None) + matmul392: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm116, permute_dims393, out_dtype="void") + add428: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul392, model_decoder_layers_17_self_attn_q_proj_bias2) + reshape557: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add428, R.shape([1, seq_len, 20, 64])) + permute_dims394: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_17_self_attn_k_proj_weight2, axes=None) + matmul393: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm116, permute_dims394, out_dtype="void") + reshape558: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul393, R.shape([1, seq_len, 20, 64])) + permute_dims395: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_17_self_attn_v_proj_weight2, axes=None) + matmul394: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm116, permute_dims395, out_dtype="void") + add429: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul394, model_decoder_layers_17_self_attn_v_proj_bias2) + reshape559: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add429, R.shape([1, seq_len, 20, 64])) + concat17: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape557, reshape558, reshape559), axis=2) + reshape560: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat17, R.shape([seq_len, 60, 64])) + lv103 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), reshape560), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape561: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv103, R.shape([1, seq_len, 20, 64])) + reshape562: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape561, R.shape([1, seq_len, 1280])) + permute_dims396: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_17_self_attn_out_proj_weight2, axes=None) + matmul395: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape562, permute_dims396, out_dtype="void") + add430: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul395, model_decoder_layers_17_self_attn_out_proj_bias2) + add431: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add427, add430) + layer_norm117: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add431, model_decoder_layers_17_encoder_attn_layer_norm_weight2, model_decoder_layers_17_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims397: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_17_encoder_attn_q_proj_weight2, axes=None) + matmul396: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm117, permute_dims397, out_dtype="void") + add432: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul396, model_decoder_layers_17_encoder_attn_q_proj_bias2) + reshape563: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add432, R.shape([1, seq_len, 20, 64])) + reshape564: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape563, R.shape([seq_len, 20, 64])) + lv104 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), reshape564), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape565: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv104, R.shape([1, seq_len, 20, 64])) + reshape566: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape565, R.shape([1, seq_len, 1280])) + permute_dims398: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_17_encoder_attn_out_proj_weight2, axes=None) + matmul397: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape566, permute_dims398, out_dtype="void") + add433: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul397, model_decoder_layers_17_encoder_attn_out_proj_bias2) + add434: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add431, add433) + layer_norm118: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add434, model_decoder_layers_17_final_layer_norm_weight2, model_decoder_layers_17_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims399: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_17_fc1_weight2, axes=None) + matmul398: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm118, permute_dims399, out_dtype="void") + add435: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul398, model_decoder_layers_17_fc1_bias2) + gelu51: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add435) + permute_dims400: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_17_fc2_weight2, axes=None) + matmul399: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu51, permute_dims400, out_dtype="void") + add436: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul399, model_decoder_layers_17_fc2_bias2) + add437: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add434, add436) + layer_norm119: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add437, model_decoder_layers_18_self_attn_layer_norm_weight2, model_decoder_layers_18_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims401: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_18_self_attn_q_proj_weight2, axes=None) + matmul400: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm119, permute_dims401, out_dtype="void") + add438: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul400, model_decoder_layers_18_self_attn_q_proj_bias2) + reshape567: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add438, R.shape([1, seq_len, 20, 64])) + permute_dims402: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_18_self_attn_k_proj_weight2, axes=None) + matmul401: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm119, permute_dims402, out_dtype="void") + reshape568: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul401, R.shape([1, seq_len, 20, 64])) + permute_dims403: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_18_self_attn_v_proj_weight2, axes=None) + matmul402: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm119, permute_dims403, out_dtype="void") + add439: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul402, model_decoder_layers_18_self_attn_v_proj_bias2) + reshape569: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add439, R.shape([1, seq_len, 20, 64])) + concat18: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape567, reshape568, reshape569), axis=2) + reshape570: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat18, R.shape([seq_len, 60, 64])) + lv105 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), reshape570), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape571: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv105, R.shape([1, seq_len, 20, 64])) + reshape572: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape571, R.shape([1, seq_len, 1280])) + permute_dims404: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_18_self_attn_out_proj_weight2, axes=None) + matmul403: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape572, permute_dims404, out_dtype="void") + add440: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul403, model_decoder_layers_18_self_attn_out_proj_bias2) + add441: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add437, add440) + layer_norm120: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add441, model_decoder_layers_18_encoder_attn_layer_norm_weight2, model_decoder_layers_18_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims405: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_18_encoder_attn_q_proj_weight2, axes=None) + matmul404: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm120, permute_dims405, out_dtype="void") + add442: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul404, model_decoder_layers_18_encoder_attn_q_proj_bias2) + reshape573: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add442, R.shape([1, seq_len, 20, 64])) + reshape574: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape573, R.shape([seq_len, 20, 64])) + lv106 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), reshape574), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape575: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv106, R.shape([1, seq_len, 20, 64])) + reshape576: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape575, R.shape([1, seq_len, 1280])) + permute_dims406: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_18_encoder_attn_out_proj_weight2, axes=None) + matmul405: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape576, permute_dims406, out_dtype="void") + add443: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul405, model_decoder_layers_18_encoder_attn_out_proj_bias2) + add444: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add441, add443) + layer_norm121: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add444, model_decoder_layers_18_final_layer_norm_weight2, model_decoder_layers_18_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims407: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_18_fc1_weight2, axes=None) + matmul406: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm121, permute_dims407, out_dtype="void") + add445: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul406, model_decoder_layers_18_fc1_bias2) + gelu52: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add445) + permute_dims408: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_18_fc2_weight2, axes=None) + matmul407: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu52, permute_dims408, out_dtype="void") + add446: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul407, model_decoder_layers_18_fc2_bias2) + add447: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add444, add446) + layer_norm122: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add447, model_decoder_layers_19_self_attn_layer_norm_weight2, model_decoder_layers_19_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims409: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_19_self_attn_q_proj_weight2, axes=None) + matmul408: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm122, permute_dims409, out_dtype="void") + add448: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul408, model_decoder_layers_19_self_attn_q_proj_bias2) + reshape577: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add448, R.shape([1, seq_len, 20, 64])) + permute_dims410: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_19_self_attn_k_proj_weight2, axes=None) + matmul409: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm122, permute_dims410, out_dtype="void") + reshape578: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul409, R.shape([1, seq_len, 20, 64])) + permute_dims411: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_19_self_attn_v_proj_weight2, axes=None) + matmul410: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm122, permute_dims411, out_dtype="void") + add449: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul410, model_decoder_layers_19_self_attn_v_proj_bias2) + reshape579: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add449, R.shape([1, seq_len, 20, 64])) + concat19: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape577, reshape578, reshape579), axis=2) + reshape580: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat19, R.shape([seq_len, 60, 64])) + lv107 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), reshape580), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape581: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv107, R.shape([1, seq_len, 20, 64])) + reshape582: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape581, R.shape([1, seq_len, 1280])) + permute_dims412: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_19_self_attn_out_proj_weight2, axes=None) + matmul411: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape582, permute_dims412, out_dtype="void") + add450: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul411, model_decoder_layers_19_self_attn_out_proj_bias2) + add451: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add447, add450) + layer_norm123: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add451, model_decoder_layers_19_encoder_attn_layer_norm_weight2, model_decoder_layers_19_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims413: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_19_encoder_attn_q_proj_weight2, axes=None) + matmul412: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm123, permute_dims413, out_dtype="void") + add452: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul412, model_decoder_layers_19_encoder_attn_q_proj_bias2) + reshape583: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add452, R.shape([1, seq_len, 20, 64])) + reshape584: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape583, R.shape([seq_len, 20, 64])) + lv108 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), reshape584), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape585: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv108, R.shape([1, seq_len, 20, 64])) + reshape586: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape585, R.shape([1, seq_len, 1280])) + permute_dims414: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_19_encoder_attn_out_proj_weight2, axes=None) + matmul413: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape586, permute_dims414, out_dtype="void") + add453: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul413, model_decoder_layers_19_encoder_attn_out_proj_bias2) + add454: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add451, add453) + layer_norm124: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add454, model_decoder_layers_19_final_layer_norm_weight2, model_decoder_layers_19_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims415: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_19_fc1_weight2, axes=None) + matmul414: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm124, permute_dims415, out_dtype="void") + add455: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul414, model_decoder_layers_19_fc1_bias2) + gelu53: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add455) + permute_dims416: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_19_fc2_weight2, axes=None) + matmul415: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu53, permute_dims416, out_dtype="void") + add456: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul415, model_decoder_layers_19_fc2_bias2) + add457: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add454, add456) + layer_norm125: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add457, model_decoder_layers_20_self_attn_layer_norm_weight2, model_decoder_layers_20_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims417: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_20_self_attn_q_proj_weight2, axes=None) + matmul416: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm125, permute_dims417, out_dtype="void") + add458: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul416, model_decoder_layers_20_self_attn_q_proj_bias2) + reshape587: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add458, R.shape([1, seq_len, 20, 64])) + permute_dims418: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_20_self_attn_k_proj_weight2, axes=None) + matmul417: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm125, permute_dims418, out_dtype="void") + reshape588: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul417, R.shape([1, seq_len, 20, 64])) + permute_dims419: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_20_self_attn_v_proj_weight2, axes=None) + matmul418: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm125, permute_dims419, out_dtype="void") + add459: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul418, model_decoder_layers_20_self_attn_v_proj_bias2) + reshape589: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add459, R.shape([1, seq_len, 20, 64])) + concat20: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape587, reshape588, reshape589), axis=2) + reshape590: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat20, R.shape([seq_len, 60, 64])) + lv109 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), reshape590), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape591: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv109, R.shape([1, seq_len, 20, 64])) + reshape592: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape591, R.shape([1, seq_len, 1280])) + permute_dims420: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_20_self_attn_out_proj_weight2, axes=None) + matmul419: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape592, permute_dims420, out_dtype="void") + add460: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul419, model_decoder_layers_20_self_attn_out_proj_bias2) + add461: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add457, add460) + layer_norm126: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add461, model_decoder_layers_20_encoder_attn_layer_norm_weight2, model_decoder_layers_20_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims421: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_20_encoder_attn_q_proj_weight2, axes=None) + matmul420: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm126, permute_dims421, out_dtype="void") + add462: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul420, model_decoder_layers_20_encoder_attn_q_proj_bias2) + reshape593: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add462, R.shape([1, seq_len, 20, 64])) + reshape594: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape593, R.shape([seq_len, 20, 64])) + lv110 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), reshape594), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape595: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv110, R.shape([1, seq_len, 20, 64])) + reshape596: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape595, R.shape([1, seq_len, 1280])) + permute_dims422: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_20_encoder_attn_out_proj_weight2, axes=None) + matmul421: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape596, permute_dims422, out_dtype="void") + add463: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul421, model_decoder_layers_20_encoder_attn_out_proj_bias2) + add464: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add461, add463) + layer_norm127: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add464, model_decoder_layers_20_final_layer_norm_weight2, model_decoder_layers_20_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims423: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_20_fc1_weight2, axes=None) + matmul422: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm127, permute_dims423, out_dtype="void") + add465: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul422, model_decoder_layers_20_fc1_bias2) + gelu54: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add465) + permute_dims424: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_20_fc2_weight2, axes=None) + matmul423: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu54, permute_dims424, out_dtype="void") + add466: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul423, model_decoder_layers_20_fc2_bias2) + add467: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add464, add466) + layer_norm128: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add467, model_decoder_layers_21_self_attn_layer_norm_weight2, model_decoder_layers_21_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims425: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_21_self_attn_q_proj_weight2, axes=None) + matmul424: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm128, permute_dims425, out_dtype="void") + add468: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul424, model_decoder_layers_21_self_attn_q_proj_bias2) + reshape597: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add468, R.shape([1, seq_len, 20, 64])) + permute_dims426: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_21_self_attn_k_proj_weight2, axes=None) + matmul425: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm128, permute_dims426, out_dtype="void") + reshape598: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul425, R.shape([1, seq_len, 20, 64])) + permute_dims427: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_21_self_attn_v_proj_weight2, axes=None) + matmul426: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm128, permute_dims427, out_dtype="void") + add469: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul426, model_decoder_layers_21_self_attn_v_proj_bias2) + reshape599: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add469, R.shape([1, seq_len, 20, 64])) + concat21: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape597, reshape598, reshape599), axis=2) + reshape600: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat21, R.shape([seq_len, 60, 64])) + lv111 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), reshape600), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape601: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv111, R.shape([1, seq_len, 20, 64])) + reshape602: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape601, R.shape([1, seq_len, 1280])) + permute_dims428: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_21_self_attn_out_proj_weight2, axes=None) + matmul427: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape602, permute_dims428, out_dtype="void") + add470: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul427, model_decoder_layers_21_self_attn_out_proj_bias2) + add471: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add467, add470) + layer_norm129: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add471, model_decoder_layers_21_encoder_attn_layer_norm_weight2, model_decoder_layers_21_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims429: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_21_encoder_attn_q_proj_weight2, axes=None) + matmul428: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm129, permute_dims429, out_dtype="void") + add472: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul428, model_decoder_layers_21_encoder_attn_q_proj_bias2) + reshape603: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add472, R.shape([1, seq_len, 20, 64])) + reshape604: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape603, R.shape([seq_len, 20, 64])) + lv112 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), reshape604), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape605: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv112, R.shape([1, seq_len, 20, 64])) + reshape606: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape605, R.shape([1, seq_len, 1280])) + permute_dims430: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_21_encoder_attn_out_proj_weight2, axes=None) + matmul429: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape606, permute_dims430, out_dtype="void") + add473: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul429, model_decoder_layers_21_encoder_attn_out_proj_bias2) + add474: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add471, add473) + layer_norm130: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add474, model_decoder_layers_21_final_layer_norm_weight2, model_decoder_layers_21_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims431: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_21_fc1_weight2, axes=None) + matmul430: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm130, permute_dims431, out_dtype="void") + add475: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul430, model_decoder_layers_21_fc1_bias2) + gelu55: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add475) + permute_dims432: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_21_fc2_weight2, axes=None) + matmul431: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu55, permute_dims432, out_dtype="void") + add476: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul431, model_decoder_layers_21_fc2_bias2) + add477: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add474, add476) + layer_norm131: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add477, model_decoder_layers_22_self_attn_layer_norm_weight2, model_decoder_layers_22_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims433: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_22_self_attn_q_proj_weight2, axes=None) + matmul432: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm131, permute_dims433, out_dtype="void") + add478: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul432, model_decoder_layers_22_self_attn_q_proj_bias2) + reshape607: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add478, R.shape([1, seq_len, 20, 64])) + permute_dims434: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_22_self_attn_k_proj_weight2, axes=None) + matmul433: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm131, permute_dims434, out_dtype="void") + reshape608: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul433, R.shape([1, seq_len, 20, 64])) + permute_dims435: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_22_self_attn_v_proj_weight2, axes=None) + matmul434: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm131, permute_dims435, out_dtype="void") + add479: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul434, model_decoder_layers_22_self_attn_v_proj_bias2) + reshape609: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add479, R.shape([1, seq_len, 20, 64])) + concat22: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape607, reshape608, reshape609), axis=2) + reshape610: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat22, R.shape([seq_len, 60, 64])) + lv113 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), reshape610), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape611: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv113, R.shape([1, seq_len, 20, 64])) + reshape612: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape611, R.shape([1, seq_len, 1280])) + permute_dims436: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_22_self_attn_out_proj_weight2, axes=None) + matmul435: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape612, permute_dims436, out_dtype="void") + add480: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul435, model_decoder_layers_22_self_attn_out_proj_bias2) + add481: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add477, add480) + layer_norm132: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add481, model_decoder_layers_22_encoder_attn_layer_norm_weight2, model_decoder_layers_22_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims437: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_22_encoder_attn_q_proj_weight2, axes=None) + matmul436: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm132, permute_dims437, out_dtype="void") + add482: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul436, model_decoder_layers_22_encoder_attn_q_proj_bias2) + reshape613: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add482, R.shape([1, seq_len, 20, 64])) + reshape614: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape613, R.shape([seq_len, 20, 64])) + lv114 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), reshape614), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape615: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv114, R.shape([1, seq_len, 20, 64])) + reshape616: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape615, R.shape([1, seq_len, 1280])) + permute_dims438: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_22_encoder_attn_out_proj_weight2, axes=None) + matmul437: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape616, permute_dims438, out_dtype="void") + add483: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul437, model_decoder_layers_22_encoder_attn_out_proj_bias2) + add484: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add481, add483) + layer_norm133: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add484, model_decoder_layers_22_final_layer_norm_weight2, model_decoder_layers_22_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims439: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_22_fc1_weight2, axes=None) + matmul438: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm133, permute_dims439, out_dtype="void") + add485: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul438, model_decoder_layers_22_fc1_bias2) + gelu56: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add485) + permute_dims440: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_22_fc2_weight2, axes=None) + matmul439: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu56, permute_dims440, out_dtype="void") + add486: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul439, model_decoder_layers_22_fc2_bias2) + add487: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add484, add486) + layer_norm134: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add487, model_decoder_layers_23_self_attn_layer_norm_weight2, model_decoder_layers_23_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims441: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_23_self_attn_q_proj_weight2, axes=None) + matmul440: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm134, permute_dims441, out_dtype="void") + add488: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul440, model_decoder_layers_23_self_attn_q_proj_bias2) + reshape617: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add488, R.shape([1, seq_len, 20, 64])) + permute_dims442: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_23_self_attn_k_proj_weight2, axes=None) + matmul441: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm134, permute_dims442, out_dtype="void") + reshape618: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul441, R.shape([1, seq_len, 20, 64])) + permute_dims443: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_23_self_attn_v_proj_weight2, axes=None) + matmul442: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm134, permute_dims443, out_dtype="void") + add489: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul442, model_decoder_layers_23_self_attn_v_proj_bias2) + reshape619: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add489, R.shape([1, seq_len, 20, 64])) + concat23: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape617, reshape618, reshape619), axis=2) + reshape620: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat23, R.shape([seq_len, 60, 64])) + lv115 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), reshape620), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape621: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv115, R.shape([1, seq_len, 20, 64])) + reshape622: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape621, R.shape([1, seq_len, 1280])) + permute_dims444: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_23_self_attn_out_proj_weight2, axes=None) + matmul443: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape622, permute_dims444, out_dtype="void") + add490: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul443, model_decoder_layers_23_self_attn_out_proj_bias2) + add491: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add487, add490) + layer_norm135: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add491, model_decoder_layers_23_encoder_attn_layer_norm_weight2, model_decoder_layers_23_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims445: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_23_encoder_attn_q_proj_weight2, axes=None) + matmul444: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm135, permute_dims445, out_dtype="void") + add492: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul444, model_decoder_layers_23_encoder_attn_q_proj_bias2) + reshape623: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add492, R.shape([1, seq_len, 20, 64])) + reshape624: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape623, R.shape([seq_len, 20, 64])) + lv116 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), reshape624), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape625: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv116, R.shape([1, seq_len, 20, 64])) + reshape626: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape625, R.shape([1, seq_len, 1280])) + permute_dims446: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_23_encoder_attn_out_proj_weight2, axes=None) + matmul445: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape626, permute_dims446, out_dtype="void") + add493: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul445, model_decoder_layers_23_encoder_attn_out_proj_bias2) + add494: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add491, add493) + layer_norm136: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add494, model_decoder_layers_23_final_layer_norm_weight2, model_decoder_layers_23_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims447: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_23_fc1_weight2, axes=None) + matmul446: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm136, permute_dims447, out_dtype="void") + add495: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul446, model_decoder_layers_23_fc1_bias2) + gelu57: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add495) + permute_dims448: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_23_fc2_weight2, axes=None) + matmul447: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu57, permute_dims448, out_dtype="void") + add496: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul447, model_decoder_layers_23_fc2_bias2) + add497: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add494, add496) + layer_norm137: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add497, model_decoder_layers_24_self_attn_layer_norm_weight2, model_decoder_layers_24_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims449: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_24_self_attn_q_proj_weight2, axes=None) + matmul448: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm137, permute_dims449, out_dtype="void") + add498: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul448, model_decoder_layers_24_self_attn_q_proj_bias2) + reshape627: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add498, R.shape([1, seq_len, 20, 64])) + permute_dims450: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_24_self_attn_k_proj_weight2, axes=None) + matmul449: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm137, permute_dims450, out_dtype="void") + reshape628: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul449, R.shape([1, seq_len, 20, 64])) + permute_dims451: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_24_self_attn_v_proj_weight2, axes=None) + matmul450: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm137, permute_dims451, out_dtype="void") + add499: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul450, model_decoder_layers_24_self_attn_v_proj_bias2) + reshape629: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add499, R.shape([1, seq_len, 20, 64])) + concat24: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape627, reshape628, reshape629), axis=2) + reshape630: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat24, R.shape([seq_len, 60, 64])) + lv117 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), reshape630), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape631: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv117, R.shape([1, seq_len, 20, 64])) + reshape632: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape631, R.shape([1, seq_len, 1280])) + permute_dims452: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_24_self_attn_out_proj_weight2, axes=None) + matmul451: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape632, permute_dims452, out_dtype="void") + add500: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul451, model_decoder_layers_24_self_attn_out_proj_bias2) + add501: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add497, add500) + layer_norm138: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add501, model_decoder_layers_24_encoder_attn_layer_norm_weight2, model_decoder_layers_24_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims453: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_24_encoder_attn_q_proj_weight2, axes=None) + matmul452: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm138, permute_dims453, out_dtype="void") + add502: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul452, model_decoder_layers_24_encoder_attn_q_proj_bias2) + reshape633: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add502, R.shape([1, seq_len, 20, 64])) + reshape634: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape633, R.shape([seq_len, 20, 64])) + lv118 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), reshape634), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape635: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv118, R.shape([1, seq_len, 20, 64])) + reshape636: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape635, R.shape([1, seq_len, 1280])) + permute_dims454: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_24_encoder_attn_out_proj_weight2, axes=None) + matmul453: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape636, permute_dims454, out_dtype="void") + add503: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul453, model_decoder_layers_24_encoder_attn_out_proj_bias2) + add504: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add501, add503) + layer_norm139: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add504, model_decoder_layers_24_final_layer_norm_weight2, model_decoder_layers_24_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims455: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_24_fc1_weight2, axes=None) + matmul454: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm139, permute_dims455, out_dtype="void") + add505: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul454, model_decoder_layers_24_fc1_bias2) + gelu58: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add505) + permute_dims456: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_24_fc2_weight2, axes=None) + matmul455: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu58, permute_dims456, out_dtype="void") + add506: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul455, model_decoder_layers_24_fc2_bias2) + add507: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add504, add506) + layer_norm140: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add507, model_decoder_layers_25_self_attn_layer_norm_weight2, model_decoder_layers_25_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims457: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_25_self_attn_q_proj_weight2, axes=None) + matmul456: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm140, permute_dims457, out_dtype="void") + add508: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul456, model_decoder_layers_25_self_attn_q_proj_bias2) + reshape637: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add508, R.shape([1, seq_len, 20, 64])) + permute_dims458: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_25_self_attn_k_proj_weight2, axes=None) + matmul457: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm140, permute_dims458, out_dtype="void") + reshape638: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul457, R.shape([1, seq_len, 20, 64])) + permute_dims459: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_25_self_attn_v_proj_weight2, axes=None) + matmul458: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm140, permute_dims459, out_dtype="void") + add509: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul458, model_decoder_layers_25_self_attn_v_proj_bias2) + reshape639: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add509, R.shape([1, seq_len, 20, 64])) + concat25: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape637, reshape638, reshape639), axis=2) + reshape640: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat25, R.shape([seq_len, 60, 64])) + lv119 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), reshape640), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape641: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv119, R.shape([1, seq_len, 20, 64])) + reshape642: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape641, R.shape([1, seq_len, 1280])) + permute_dims460: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_25_self_attn_out_proj_weight2, axes=None) + matmul459: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape642, permute_dims460, out_dtype="void") + add510: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul459, model_decoder_layers_25_self_attn_out_proj_bias2) + add511: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add507, add510) + layer_norm141: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add511, model_decoder_layers_25_encoder_attn_layer_norm_weight2, model_decoder_layers_25_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims461: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_25_encoder_attn_q_proj_weight2, axes=None) + matmul460: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm141, permute_dims461, out_dtype="void") + add512: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul460, model_decoder_layers_25_encoder_attn_q_proj_bias2) + reshape643: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add512, R.shape([1, seq_len, 20, 64])) + reshape644: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape643, R.shape([seq_len, 20, 64])) + lv120 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), reshape644), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape645: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv120, R.shape([1, seq_len, 20, 64])) + reshape646: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape645, R.shape([1, seq_len, 1280])) + permute_dims462: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_25_encoder_attn_out_proj_weight2, axes=None) + matmul461: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape646, permute_dims462, out_dtype="void") + add513: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul461, model_decoder_layers_25_encoder_attn_out_proj_bias2) + add514: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add511, add513) + layer_norm142: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add514, model_decoder_layers_25_final_layer_norm_weight2, model_decoder_layers_25_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims463: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_25_fc1_weight2, axes=None) + matmul462: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm142, permute_dims463, out_dtype="void") + add515: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul462, model_decoder_layers_25_fc1_bias2) + gelu59: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add515) + permute_dims464: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_25_fc2_weight2, axes=None) + matmul463: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu59, permute_dims464, out_dtype="void") + add516: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul463, model_decoder_layers_25_fc2_bias2) + add517: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add514, add516) + layer_norm143: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add517, model_decoder_layers_26_self_attn_layer_norm_weight2, model_decoder_layers_26_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims465: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_26_self_attn_q_proj_weight2, axes=None) + matmul464: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm143, permute_dims465, out_dtype="void") + add518: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul464, model_decoder_layers_26_self_attn_q_proj_bias2) + reshape647: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add518, R.shape([1, seq_len, 20, 64])) + permute_dims466: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_26_self_attn_k_proj_weight2, axes=None) + matmul465: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm143, permute_dims466, out_dtype="void") + reshape648: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul465, R.shape([1, seq_len, 20, 64])) + permute_dims467: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_26_self_attn_v_proj_weight2, axes=None) + matmul466: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm143, permute_dims467, out_dtype="void") + add519: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul466, model_decoder_layers_26_self_attn_v_proj_bias2) + reshape649: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add519, R.shape([1, seq_len, 20, 64])) + concat26: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape647, reshape648, reshape649), axis=2) + reshape650: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat26, R.shape([seq_len, 60, 64])) + lv121 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), reshape650), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape651: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv121, R.shape([1, seq_len, 20, 64])) + reshape652: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape651, R.shape([1, seq_len, 1280])) + permute_dims468: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_26_self_attn_out_proj_weight2, axes=None) + matmul467: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape652, permute_dims468, out_dtype="void") + add520: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul467, model_decoder_layers_26_self_attn_out_proj_bias2) + add521: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add517, add520) + layer_norm144: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add521, model_decoder_layers_26_encoder_attn_layer_norm_weight2, model_decoder_layers_26_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims469: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_26_encoder_attn_q_proj_weight2, axes=None) + matmul468: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm144, permute_dims469, out_dtype="void") + add522: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul468, model_decoder_layers_26_encoder_attn_q_proj_bias2) + reshape653: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add522, R.shape([1, seq_len, 20, 64])) + reshape654: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape653, R.shape([seq_len, 20, 64])) + lv122 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), reshape654), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape655: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv122, R.shape([1, seq_len, 20, 64])) + reshape656: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape655, R.shape([1, seq_len, 1280])) + permute_dims470: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_26_encoder_attn_out_proj_weight2, axes=None) + matmul469: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape656, permute_dims470, out_dtype="void") + add523: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul469, model_decoder_layers_26_encoder_attn_out_proj_bias2) + add524: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add521, add523) + layer_norm145: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add524, model_decoder_layers_26_final_layer_norm_weight2, model_decoder_layers_26_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims471: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_26_fc1_weight2, axes=None) + matmul470: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm145, permute_dims471, out_dtype="void") + add525: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul470, model_decoder_layers_26_fc1_bias2) + gelu60: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add525) + permute_dims472: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_26_fc2_weight2, axes=None) + matmul471: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu60, permute_dims472, out_dtype="void") + add526: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul471, model_decoder_layers_26_fc2_bias2) + add527: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add524, add526) + layer_norm146: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add527, model_decoder_layers_27_self_attn_layer_norm_weight2, model_decoder_layers_27_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims473: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_27_self_attn_q_proj_weight2, axes=None) + matmul472: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm146, permute_dims473, out_dtype="void") + add528: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul472, model_decoder_layers_27_self_attn_q_proj_bias2) + reshape657: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add528, R.shape([1, seq_len, 20, 64])) + permute_dims474: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_27_self_attn_k_proj_weight2, axes=None) + matmul473: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm146, permute_dims474, out_dtype="void") + reshape658: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul473, R.shape([1, seq_len, 20, 64])) + permute_dims475: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_27_self_attn_v_proj_weight2, axes=None) + matmul474: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm146, permute_dims475, out_dtype="void") + add529: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul474, model_decoder_layers_27_self_attn_v_proj_bias2) + reshape659: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add529, R.shape([1, seq_len, 20, 64])) + concat27: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape657, reshape658, reshape659), axis=2) + reshape660: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat27, R.shape([seq_len, 60, 64])) + lv123 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), reshape660), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape661: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv123, R.shape([1, seq_len, 20, 64])) + reshape662: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape661, R.shape([1, seq_len, 1280])) + permute_dims476: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_27_self_attn_out_proj_weight2, axes=None) + matmul475: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape662, permute_dims476, out_dtype="void") + add530: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul475, model_decoder_layers_27_self_attn_out_proj_bias2) + add531: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add527, add530) + layer_norm147: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add531, model_decoder_layers_27_encoder_attn_layer_norm_weight2, model_decoder_layers_27_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims477: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_27_encoder_attn_q_proj_weight2, axes=None) + matmul476: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm147, permute_dims477, out_dtype="void") + add532: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul476, model_decoder_layers_27_encoder_attn_q_proj_bias2) + reshape663: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add532, R.shape([1, seq_len, 20, 64])) + reshape664: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape663, R.shape([seq_len, 20, 64])) + lv124 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), reshape664), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape665: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv124, R.shape([1, seq_len, 20, 64])) + reshape666: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape665, R.shape([1, seq_len, 1280])) + permute_dims478: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_27_encoder_attn_out_proj_weight2, axes=None) + matmul477: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape666, permute_dims478, out_dtype="void") + add533: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul477, model_decoder_layers_27_encoder_attn_out_proj_bias2) + add534: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add531, add533) + layer_norm148: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add534, model_decoder_layers_27_final_layer_norm_weight2, model_decoder_layers_27_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims479: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_27_fc1_weight2, axes=None) + matmul478: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm148, permute_dims479, out_dtype="void") + add535: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul478, model_decoder_layers_27_fc1_bias2) + gelu61: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add535) + permute_dims480: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_27_fc2_weight2, axes=None) + matmul479: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu61, permute_dims480, out_dtype="void") + add536: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul479, model_decoder_layers_27_fc2_bias2) + add537: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add534, add536) + layer_norm149: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add537, model_decoder_layers_28_self_attn_layer_norm_weight2, model_decoder_layers_28_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims481: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_28_self_attn_q_proj_weight2, axes=None) + matmul480: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm149, permute_dims481, out_dtype="void") + add538: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul480, model_decoder_layers_28_self_attn_q_proj_bias2) + reshape667: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add538, R.shape([1, seq_len, 20, 64])) + permute_dims482: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_28_self_attn_k_proj_weight2, axes=None) + matmul481: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm149, permute_dims482, out_dtype="void") + reshape668: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul481, R.shape([1, seq_len, 20, 64])) + permute_dims483: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_28_self_attn_v_proj_weight2, axes=None) + matmul482: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm149, permute_dims483, out_dtype="void") + add539: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul482, model_decoder_layers_28_self_attn_v_proj_bias2) + reshape669: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add539, R.shape([1, seq_len, 20, 64])) + concat28: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape667, reshape668, reshape669), axis=2) + reshape670: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat28, R.shape([seq_len, 60, 64])) + lv125 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), reshape670), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape671: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv125, R.shape([1, seq_len, 20, 64])) + reshape672: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape671, R.shape([1, seq_len, 1280])) + permute_dims484: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_28_self_attn_out_proj_weight2, axes=None) + matmul483: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape672, permute_dims484, out_dtype="void") + add540: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul483, model_decoder_layers_28_self_attn_out_proj_bias2) + add541: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add537, add540) + layer_norm150: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add541, model_decoder_layers_28_encoder_attn_layer_norm_weight2, model_decoder_layers_28_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims485: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_28_encoder_attn_q_proj_weight2, axes=None) + matmul484: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm150, permute_dims485, out_dtype="void") + add542: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul484, model_decoder_layers_28_encoder_attn_q_proj_bias2) + reshape673: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add542, R.shape([1, seq_len, 20, 64])) + reshape674: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape673, R.shape([seq_len, 20, 64])) + lv126 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), reshape674), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape675: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv126, R.shape([1, seq_len, 20, 64])) + reshape676: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape675, R.shape([1, seq_len, 1280])) + permute_dims486: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_28_encoder_attn_out_proj_weight2, axes=None) + matmul485: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape676, permute_dims486, out_dtype="void") + add543: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul485, model_decoder_layers_28_encoder_attn_out_proj_bias2) + add544: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add541, add543) + layer_norm151: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add544, model_decoder_layers_28_final_layer_norm_weight2, model_decoder_layers_28_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims487: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_28_fc1_weight2, axes=None) + matmul486: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm151, permute_dims487, out_dtype="void") + add545: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul486, model_decoder_layers_28_fc1_bias2) + gelu62: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add545) + permute_dims488: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_28_fc2_weight2, axes=None) + matmul487: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu62, permute_dims488, out_dtype="void") + add546: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul487, model_decoder_layers_28_fc2_bias2) + add547: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add544, add546) + layer_norm152: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add547, model_decoder_layers_29_self_attn_layer_norm_weight2, model_decoder_layers_29_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims489: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_29_self_attn_q_proj_weight2, axes=None) + matmul488: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm152, permute_dims489, out_dtype="void") + add548: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul488, model_decoder_layers_29_self_attn_q_proj_bias2) + reshape677: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add548, R.shape([1, seq_len, 20, 64])) + permute_dims490: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_29_self_attn_k_proj_weight2, axes=None) + matmul489: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm152, permute_dims490, out_dtype="void") + reshape678: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul489, R.shape([1, seq_len, 20, 64])) + permute_dims491: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_29_self_attn_v_proj_weight2, axes=None) + matmul490: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm152, permute_dims491, out_dtype="void") + add549: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul490, model_decoder_layers_29_self_attn_v_proj_bias2) + reshape679: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add549, R.shape([1, seq_len, 20, 64])) + concat29: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape677, reshape678, reshape679), axis=2) + reshape680: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat29, R.shape([seq_len, 60, 64])) + lv127 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), reshape680), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape681: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv127, R.shape([1, seq_len, 20, 64])) + reshape682: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape681, R.shape([1, seq_len, 1280])) + permute_dims492: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_29_self_attn_out_proj_weight2, axes=None) + matmul491: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape682, permute_dims492, out_dtype="void") + add550: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul491, model_decoder_layers_29_self_attn_out_proj_bias2) + add551: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add547, add550) + layer_norm153: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add551, model_decoder_layers_29_encoder_attn_layer_norm_weight2, model_decoder_layers_29_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims493: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_29_encoder_attn_q_proj_weight2, axes=None) + matmul492: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm153, permute_dims493, out_dtype="void") + add552: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul492, model_decoder_layers_29_encoder_attn_q_proj_bias2) + reshape683: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add552, R.shape([1, seq_len, 20, 64])) + reshape684: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape683, R.shape([seq_len, 20, 64])) + lv128 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), reshape684), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape685: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv128, R.shape([1, seq_len, 20, 64])) + reshape686: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape685, R.shape([1, seq_len, 1280])) + permute_dims494: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_29_encoder_attn_out_proj_weight2, axes=None) + matmul493: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape686, permute_dims494, out_dtype="void") + add553: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul493, model_decoder_layers_29_encoder_attn_out_proj_bias2) + add554: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add551, add553) + layer_norm154: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add554, model_decoder_layers_29_final_layer_norm_weight2, model_decoder_layers_29_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims495: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_29_fc1_weight2, axes=None) + matmul494: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm154, permute_dims495, out_dtype="void") + add555: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul494, model_decoder_layers_29_fc1_bias2) + gelu63: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add555) + permute_dims496: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_29_fc2_weight2, axes=None) + matmul495: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu63, permute_dims496, out_dtype="void") + add556: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul495, model_decoder_layers_29_fc2_bias2) + add557: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add554, add556) + layer_norm155: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add557, model_decoder_layers_30_self_attn_layer_norm_weight2, model_decoder_layers_30_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims497: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_30_self_attn_q_proj_weight2, axes=None) + matmul496: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm155, permute_dims497, out_dtype="void") + add558: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul496, model_decoder_layers_30_self_attn_q_proj_bias2) + reshape687: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add558, R.shape([1, seq_len, 20, 64])) + permute_dims498: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_30_self_attn_k_proj_weight2, axes=None) + matmul497: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm155, permute_dims498, out_dtype="void") + reshape688: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul497, R.shape([1, seq_len, 20, 64])) + permute_dims499: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_30_self_attn_v_proj_weight2, axes=None) + matmul498: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm155, permute_dims499, out_dtype="void") + add559: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul498, model_decoder_layers_30_self_attn_v_proj_bias2) + reshape689: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add559, R.shape([1, seq_len, 20, 64])) + concat30: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape687, reshape688, reshape689), axis=2) + reshape690: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat30, R.shape([seq_len, 60, 64])) + lv129 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), reshape690), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape691: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv129, R.shape([1, seq_len, 20, 64])) + reshape692: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape691, R.shape([1, seq_len, 1280])) + permute_dims500: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_30_self_attn_out_proj_weight2, axes=None) + matmul499: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape692, permute_dims500, out_dtype="void") + add560: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul499, model_decoder_layers_30_self_attn_out_proj_bias2) + add561: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add557, add560) + layer_norm156: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add561, model_decoder_layers_30_encoder_attn_layer_norm_weight2, model_decoder_layers_30_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims501: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_30_encoder_attn_q_proj_weight2, axes=None) + matmul500: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm156, permute_dims501, out_dtype="void") + add562: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul500, model_decoder_layers_30_encoder_attn_q_proj_bias2) + reshape693: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add562, R.shape([1, seq_len, 20, 64])) + reshape694: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape693, R.shape([seq_len, 20, 64])) + lv130 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), reshape694), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape695: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv130, R.shape([1, seq_len, 20, 64])) + reshape696: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape695, R.shape([1, seq_len, 1280])) + permute_dims502: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_30_encoder_attn_out_proj_weight2, axes=None) + matmul501: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape696, permute_dims502, out_dtype="void") + add563: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul501, model_decoder_layers_30_encoder_attn_out_proj_bias2) + add564: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add561, add563) + layer_norm157: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add564, model_decoder_layers_30_final_layer_norm_weight2, model_decoder_layers_30_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims503: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_30_fc1_weight2, axes=None) + matmul502: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm157, permute_dims503, out_dtype="void") + add565: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul502, model_decoder_layers_30_fc1_bias2) + gelu64: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add565) + permute_dims504: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_30_fc2_weight2, axes=None) + matmul503: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu64, permute_dims504, out_dtype="void") + add566: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul503, model_decoder_layers_30_fc2_bias2) + add567: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add564, add566) + layer_norm158: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add567, model_decoder_layers_31_self_attn_layer_norm_weight2, model_decoder_layers_31_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims505: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_31_self_attn_q_proj_weight2, axes=None) + matmul504: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm158, permute_dims505, out_dtype="void") + add568: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul504, model_decoder_layers_31_self_attn_q_proj_bias2) + reshape697: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add568, R.shape([1, seq_len, 20, 64])) + permute_dims506: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_31_self_attn_k_proj_weight2, axes=None) + matmul505: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm158, permute_dims506, out_dtype="void") + reshape698: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul505, R.shape([1, seq_len, 20, 64])) + permute_dims507: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_31_self_attn_v_proj_weight2, axes=None) + matmul506: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm158, permute_dims507, out_dtype="void") + add569: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul506, model_decoder_layers_31_self_attn_v_proj_bias2) + reshape699: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add569, R.shape([1, seq_len, 20, 64])) + concat31: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape697, reshape698, reshape699), axis=2) + reshape700: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat31, R.shape([seq_len, 60, 64])) + lv131 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), reshape700), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape701: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv131, R.shape([1, seq_len, 20, 64])) + reshape702: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape701, R.shape([1, seq_len, 1280])) + permute_dims508: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_31_self_attn_out_proj_weight2, axes=None) + matmul507: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape702, permute_dims508, out_dtype="void") + add570: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul507, model_decoder_layers_31_self_attn_out_proj_bias2) + add571: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add567, add570) + layer_norm159: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add571, model_decoder_layers_31_encoder_attn_layer_norm_weight2, model_decoder_layers_31_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims509: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_31_encoder_attn_q_proj_weight2, axes=None) + matmul508: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm159, permute_dims509, out_dtype="void") + add572: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul508, model_decoder_layers_31_encoder_attn_q_proj_bias2) + reshape703: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add572, R.shape([1, seq_len, 20, 64])) + reshape704: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape703, R.shape([seq_len, 20, 64])) + lv132 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), reshape704), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape705: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv132, R.shape([1, seq_len, 20, 64])) + reshape706: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape705, R.shape([1, seq_len, 1280])) + permute_dims510: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_31_encoder_attn_out_proj_weight2, axes=None) + matmul509: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape706, permute_dims510, out_dtype="void") + add573: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul509, model_decoder_layers_31_encoder_attn_out_proj_bias2) + add574: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add571, add573) + layer_norm160: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add574, model_decoder_layers_31_final_layer_norm_weight2, model_decoder_layers_31_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims511: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_31_fc1_weight2, axes=None) + matmul510: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm160, permute_dims511, out_dtype="void") + add575: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul510, model_decoder_layers_31_fc1_bias2) + gelu65: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add575) + permute_dims512: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_31_fc2_weight2, axes=None) + matmul511: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu65, permute_dims512, out_dtype="void") + add576: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul511, model_decoder_layers_31_fc2_bias2) + add577: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add574, add576) + layer_norm161: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add577, model_decoder_layer_norm_weight2, model_decoder_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + take2: R.Tensor((1, batch_size, 1280), dtype="float16") = R.take(layer_norm161, logit_positions, axis=1) + permute_dims513: R.Tensor((1280, 51866), dtype="float16") = R.permute_dims(model_decoder_embed_tokens_weight2, axes=None) + matmul512: R.Tensor((1, batch_size, 51866), dtype="float32") = R.matmul(take2, permute_dims513, out_dtype="float32") + gv2: R.Tensor((1, batch_size, 51866), dtype="float32") = matmul512 + R.output(gv2) + return gv2 + + @R.function + def create_tir_paged_kv_cache(max_batch_size_: R.Shape(["max_batch_size"]), max_total_seq_len_: R.Shape(["max_total_seq_len"]), prefill_chunk_size_: R.Shape(["prefill_chunk_size"]), page_size_: R.Shape(["page_size"]), support_sliding_window_: R.Shape(["support_sliding_window"])) -> R.Object: + max_batch_size = T.int64() + max_total_seq_len = T.int64() + prefill_chunk_size = T.int64() + page_size = T.int64() + support_sliding_window = T.int64() + R.func_attr({"relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "seq_len": 15000, "total_seq_len": 1500}}) + cls = Module + gv: R.Tensor((), dtype="float16") = R.zeros(R.shape([]), dtype="float16") + paged_kv_cache: R.Object = R.call_pure_packed("vm.builtin.paged_attention_kv_cache_create_reduced", R.shape([max_batch_size, max_total_seq_len, prefill_chunk_size, page_size, support_sliding_window]), R.prim_value(32), R.prim_value(20), R.prim_value(20), R.prim_value(64), R.prim_value(0), R.prim_value(1), R.prim_value(1), gv, cls.tir_kv_cache_transpose_append, cls.batch_prefill_paged_kv, cls.batch_decode_paged_kv, cls.batch_prefill_paged_kv_sliding_window, cls.batch_decode_paged_kv_sliding_window, cls.batch_prefill_ragged_kv, cls.merge_state_inplace, cls.fused_rope, cls.copy_single_page, cls.tir_kv_cache_debug_get_kv, cls.compact_kv_copy, cls.batch_tree_attn, sinfo_args=(R.Object,)) + return paged_kv_cache + + @R.function + def decode(input_ids: R.Tensor((1, 1), dtype="int32"), paged_kv_cache: R.Object, packed_params: R.Tuple(R.Tensor((1280, 128, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1500, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), 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R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"))) -> R.Tensor((1, 1, 51866), dtype="float32"): + R.func_attr({"num_input": 2, "relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "seq_len": 15000, "total_seq_len": 1500}}) + with R.dataflow(): + model_encoder_conv1_weight5: R.Tensor((1280, 128, 3), dtype="float16") = packed_params[0] + model_encoder_conv1_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1] + model_encoder_conv2_weight5: R.Tensor((1280, 1280, 3), dtype="float16") = packed_params[2] + model_encoder_conv2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[3] + model_encoder_embed_positions_weight5: R.Tensor((1500, 1280), dtype="float16") = packed_params[4] + model_encoder_layers_0_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[5] + model_encoder_layers_0_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[6] + model_encoder_layers_0_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[7] + model_encoder_layers_0_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[8] + model_encoder_layers_0_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[9] + model_encoder_layers_0_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[10] + model_encoder_layers_0_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[11] + model_encoder_layers_0_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[12] + model_encoder_layers_0_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[13] + model_encoder_layers_0_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[14] + model_encoder_layers_0_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[15] + model_encoder_layers_0_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[16] + model_encoder_layers_0_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[17] + model_encoder_layers_0_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[18] + model_encoder_layers_0_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[19] + model_encoder_layers_1_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[20] + model_encoder_layers_1_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[21] + model_encoder_layers_1_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[22] + model_encoder_layers_1_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[23] + model_encoder_layers_1_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[24] + model_encoder_layers_1_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[25] + model_encoder_layers_1_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[26] + model_encoder_layers_1_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[27] + model_encoder_layers_1_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[28] + model_encoder_layers_1_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[29] + model_encoder_layers_1_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[30] + model_encoder_layers_1_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[31] + model_encoder_layers_1_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[32] + model_encoder_layers_1_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[33] + model_encoder_layers_1_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[34] + model_encoder_layers_2_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[35] + model_encoder_layers_2_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[36] + model_encoder_layers_2_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[37] + model_encoder_layers_2_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[38] + model_encoder_layers_2_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[39] + model_encoder_layers_2_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[40] + model_encoder_layers_2_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[41] + model_encoder_layers_2_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[42] + model_encoder_layers_2_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[43] + model_encoder_layers_2_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[44] + model_encoder_layers_2_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[45] + model_encoder_layers_2_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[46] + model_encoder_layers_2_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[47] + model_encoder_layers_2_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[48] + model_encoder_layers_2_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[49] + model_encoder_layers_3_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[50] + model_encoder_layers_3_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[51] + model_encoder_layers_3_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[52] + model_encoder_layers_3_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[53] + model_encoder_layers_3_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[54] + model_encoder_layers_3_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[55] + model_encoder_layers_3_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[56] + model_encoder_layers_3_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[57] + model_encoder_layers_3_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[58] + model_encoder_layers_3_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[59] + model_encoder_layers_3_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[60] + model_encoder_layers_3_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[61] + model_encoder_layers_3_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[62] + model_encoder_layers_3_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[63] + model_encoder_layers_3_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[64] + model_encoder_layers_4_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[65] + model_encoder_layers_4_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[66] + model_encoder_layers_4_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[67] + model_encoder_layers_4_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[68] + model_encoder_layers_4_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[69] + model_encoder_layers_4_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[70] + model_encoder_layers_4_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[71] + model_encoder_layers_4_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[72] + model_encoder_layers_4_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[73] + model_encoder_layers_4_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[74] + model_encoder_layers_4_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[75] + model_encoder_layers_4_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[76] + model_encoder_layers_4_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[77] + model_encoder_layers_4_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[78] + model_encoder_layers_4_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[79] + model_encoder_layers_5_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[80] + model_encoder_layers_5_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[81] + model_encoder_layers_5_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[82] + model_encoder_layers_5_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[83] + model_encoder_layers_5_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[84] + model_encoder_layers_5_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[85] + model_encoder_layers_5_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[86] + model_encoder_layers_5_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[87] + model_encoder_layers_5_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[88] + model_encoder_layers_5_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[89] + model_encoder_layers_5_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[90] + model_encoder_layers_5_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[91] + model_encoder_layers_5_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[92] + model_encoder_layers_5_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[93] + model_encoder_layers_5_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[94] + model_encoder_layers_6_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[95] + model_encoder_layers_6_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[96] + model_encoder_layers_6_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[97] + model_encoder_layers_6_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[98] + model_encoder_layers_6_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[99] + model_encoder_layers_6_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[100] + model_encoder_layers_6_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[101] + model_encoder_layers_6_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[102] + model_encoder_layers_6_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[103] + model_encoder_layers_6_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[104] + model_encoder_layers_6_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[105] + model_encoder_layers_6_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[106] + model_encoder_layers_6_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[107] + model_encoder_layers_6_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[108] + model_encoder_layers_6_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[109] + model_encoder_layers_7_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[110] + model_encoder_layers_7_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[111] + model_encoder_layers_7_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[112] + model_encoder_layers_7_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[113] + model_encoder_layers_7_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[114] + model_encoder_layers_7_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[115] + model_encoder_layers_7_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[116] + model_encoder_layers_7_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[117] + model_encoder_layers_7_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[118] + model_encoder_layers_7_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[119] + model_encoder_layers_7_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[120] + model_encoder_layers_7_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[121] + model_encoder_layers_7_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[122] + model_encoder_layers_7_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[123] + model_encoder_layers_7_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[124] + model_encoder_layers_8_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[125] + model_encoder_layers_8_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[126] + model_encoder_layers_8_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[127] + model_encoder_layers_8_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[128] + model_encoder_layers_8_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[129] + model_encoder_layers_8_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[130] + model_encoder_layers_8_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[131] + model_encoder_layers_8_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[132] + model_encoder_layers_8_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[133] + model_encoder_layers_8_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[134] + model_encoder_layers_8_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[135] + model_encoder_layers_8_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[136] + model_encoder_layers_8_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[137] + model_encoder_layers_8_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[138] + model_encoder_layers_8_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[139] + model_encoder_layers_9_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[140] + model_encoder_layers_9_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[141] + model_encoder_layers_9_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[142] + model_encoder_layers_9_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[143] + model_encoder_layers_9_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[144] + model_encoder_layers_9_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[145] + model_encoder_layers_9_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[146] + model_encoder_layers_9_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[147] + model_encoder_layers_9_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[148] + model_encoder_layers_9_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[149] + model_encoder_layers_9_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[150] + model_encoder_layers_9_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[151] + model_encoder_layers_9_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[152] + model_encoder_layers_9_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[153] + model_encoder_layers_9_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[154] + model_encoder_layers_10_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[155] + model_encoder_layers_10_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[156] + model_encoder_layers_10_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[157] + model_encoder_layers_10_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[158] + model_encoder_layers_10_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[159] + model_encoder_layers_10_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[160] + model_encoder_layers_10_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[161] + model_encoder_layers_10_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[162] + model_encoder_layers_10_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[163] + model_encoder_layers_10_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[164] + model_encoder_layers_10_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[165] + model_encoder_layers_10_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[166] + model_encoder_layers_10_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[167] + model_encoder_layers_10_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[168] + model_encoder_layers_10_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[169] + model_encoder_layers_11_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[170] + model_encoder_layers_11_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[171] + model_encoder_layers_11_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[172] + model_encoder_layers_11_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[173] + model_encoder_layers_11_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[174] + model_encoder_layers_11_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[175] + model_encoder_layers_11_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[176] + model_encoder_layers_11_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[177] + model_encoder_layers_11_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[178] + model_encoder_layers_11_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[179] + model_encoder_layers_11_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[180] + model_encoder_layers_11_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[181] + model_encoder_layers_11_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[182] + model_encoder_layers_11_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[183] + model_encoder_layers_11_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[184] + model_encoder_layers_12_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[185] + model_encoder_layers_12_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[186] + model_encoder_layers_12_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[187] + model_encoder_layers_12_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[188] + model_encoder_layers_12_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[189] + model_encoder_layers_12_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[190] + model_encoder_layers_12_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[191] + model_encoder_layers_12_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[192] + model_encoder_layers_12_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[193] + model_encoder_layers_12_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[194] + model_encoder_layers_12_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[195] + model_encoder_layers_12_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[196] + model_encoder_layers_12_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[197] + model_encoder_layers_12_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[198] + model_encoder_layers_12_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[199] + model_encoder_layers_13_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[200] + model_encoder_layers_13_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[201] + model_encoder_layers_13_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[202] + model_encoder_layers_13_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[203] + model_encoder_layers_13_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[204] + model_encoder_layers_13_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[205] + model_encoder_layers_13_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[206] + model_encoder_layers_13_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[207] + model_encoder_layers_13_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[208] + model_encoder_layers_13_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[209] + model_encoder_layers_13_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[210] + model_encoder_layers_13_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[211] + model_encoder_layers_13_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[212] + model_encoder_layers_13_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[213] + model_encoder_layers_13_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[214] + model_encoder_layers_14_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[215] + model_encoder_layers_14_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[216] + model_encoder_layers_14_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[217] + model_encoder_layers_14_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[218] + model_encoder_layers_14_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[219] + model_encoder_layers_14_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[220] + model_encoder_layers_14_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[221] + model_encoder_layers_14_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[222] + model_encoder_layers_14_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[223] + model_encoder_layers_14_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[224] + model_encoder_layers_14_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[225] + model_encoder_layers_14_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[226] + model_encoder_layers_14_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[227] + model_encoder_layers_14_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[228] + model_encoder_layers_14_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[229] + model_encoder_layers_15_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[230] + model_encoder_layers_15_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[231] + model_encoder_layers_15_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[232] + model_encoder_layers_15_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[233] + model_encoder_layers_15_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[234] + model_encoder_layers_15_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[235] + model_encoder_layers_15_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[236] + model_encoder_layers_15_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[237] + model_encoder_layers_15_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[238] + model_encoder_layers_15_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[239] + model_encoder_layers_15_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[240] + model_encoder_layers_15_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[241] + model_encoder_layers_15_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[242] + model_encoder_layers_15_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[243] + model_encoder_layers_15_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[244] + model_encoder_layers_16_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[245] + model_encoder_layers_16_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[246] + model_encoder_layers_16_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[247] + model_encoder_layers_16_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[248] + model_encoder_layers_16_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[249] + model_encoder_layers_16_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[250] + model_encoder_layers_16_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[251] + model_encoder_layers_16_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[252] + model_encoder_layers_16_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[253] + model_encoder_layers_16_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[254] + model_encoder_layers_16_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[255] + model_encoder_layers_16_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[256] + model_encoder_layers_16_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[257] + model_encoder_layers_16_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[258] + model_encoder_layers_16_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[259] + model_encoder_layers_17_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[260] + model_encoder_layers_17_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[261] + model_encoder_layers_17_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[262] + model_encoder_layers_17_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[263] + model_encoder_layers_17_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[264] + model_encoder_layers_17_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[265] + model_encoder_layers_17_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[266] + model_encoder_layers_17_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[267] + model_encoder_layers_17_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[268] + model_encoder_layers_17_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[269] + model_encoder_layers_17_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[270] + model_encoder_layers_17_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[271] + model_encoder_layers_17_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[272] + model_encoder_layers_17_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[273] + model_encoder_layers_17_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[274] + model_encoder_layers_18_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[275] + model_encoder_layers_18_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[276] + model_encoder_layers_18_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[277] + model_encoder_layers_18_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[278] + model_encoder_layers_18_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[279] + model_encoder_layers_18_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[280] + model_encoder_layers_18_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[281] + model_encoder_layers_18_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[282] + model_encoder_layers_18_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[283] + model_encoder_layers_18_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[284] + model_encoder_layers_18_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[285] + model_encoder_layers_18_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[286] + model_encoder_layers_18_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[287] + model_encoder_layers_18_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[288] + model_encoder_layers_18_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[289] + model_encoder_layers_19_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[290] + model_encoder_layers_19_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[291] + model_encoder_layers_19_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[292] + model_encoder_layers_19_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[293] + model_encoder_layers_19_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[294] + model_encoder_layers_19_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[295] + model_encoder_layers_19_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[296] + model_encoder_layers_19_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[297] + model_encoder_layers_19_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[298] + model_encoder_layers_19_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[299] + model_encoder_layers_19_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[300] + model_encoder_layers_19_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[301] + model_encoder_layers_19_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[302] + model_encoder_layers_19_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[303] + model_encoder_layers_19_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[304] + model_encoder_layers_20_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[305] + model_encoder_layers_20_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[306] + model_encoder_layers_20_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[307] + model_encoder_layers_20_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[308] + model_encoder_layers_20_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[309] + model_encoder_layers_20_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[310] + model_encoder_layers_20_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[311] + model_encoder_layers_20_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[312] + model_encoder_layers_20_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[313] + model_encoder_layers_20_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[314] + model_encoder_layers_20_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[315] + model_encoder_layers_20_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[316] + model_encoder_layers_20_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[317] + model_encoder_layers_20_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[318] + model_encoder_layers_20_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[319] + model_encoder_layers_21_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[320] + model_encoder_layers_21_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[321] + model_encoder_layers_21_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[322] + model_encoder_layers_21_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[323] + model_encoder_layers_21_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[324] + model_encoder_layers_21_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[325] + model_encoder_layers_21_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[326] + model_encoder_layers_21_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[327] + model_encoder_layers_21_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[328] + model_encoder_layers_21_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[329] + model_encoder_layers_21_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[330] + model_encoder_layers_21_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[331] + model_encoder_layers_21_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[332] + model_encoder_layers_21_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[333] + model_encoder_layers_21_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[334] + model_encoder_layers_22_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[335] + model_encoder_layers_22_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[336] + model_encoder_layers_22_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[337] + model_encoder_layers_22_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[338] + model_encoder_layers_22_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[339] + model_encoder_layers_22_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[340] + model_encoder_layers_22_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[341] + model_encoder_layers_22_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[342] + model_encoder_layers_22_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[343] + model_encoder_layers_22_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[344] + model_encoder_layers_22_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[345] + model_encoder_layers_22_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[346] + model_encoder_layers_22_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[347] + model_encoder_layers_22_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[348] + model_encoder_layers_22_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[349] + model_encoder_layers_23_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[350] + model_encoder_layers_23_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[351] + model_encoder_layers_23_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[352] + model_encoder_layers_23_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[353] + model_encoder_layers_23_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[354] + model_encoder_layers_23_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[355] + model_encoder_layers_23_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[356] + model_encoder_layers_23_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[357] + model_encoder_layers_23_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[358] + model_encoder_layers_23_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[359] + model_encoder_layers_23_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[360] + model_encoder_layers_23_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[361] + model_encoder_layers_23_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[362] + model_encoder_layers_23_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[363] + model_encoder_layers_23_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[364] + model_encoder_layers_24_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[365] + model_encoder_layers_24_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[366] + model_encoder_layers_24_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[367] + model_encoder_layers_24_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[368] + model_encoder_layers_24_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[369] + model_encoder_layers_24_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[370] + model_encoder_layers_24_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[371] + model_encoder_layers_24_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[372] + model_encoder_layers_24_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[373] + model_encoder_layers_24_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[374] + model_encoder_layers_24_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[375] + model_encoder_layers_24_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[376] + model_encoder_layers_24_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[377] + model_encoder_layers_24_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[378] + model_encoder_layers_24_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[379] + model_encoder_layers_25_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[380] + model_encoder_layers_25_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[381] + model_encoder_layers_25_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[382] + model_encoder_layers_25_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[383] + model_encoder_layers_25_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[384] + model_encoder_layers_25_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[385] + model_encoder_layers_25_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[386] + model_encoder_layers_25_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[387] + model_encoder_layers_25_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[388] + model_encoder_layers_25_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[389] + model_encoder_layers_25_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[390] + model_encoder_layers_25_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[391] + model_encoder_layers_25_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[392] + model_encoder_layers_25_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[393] + model_encoder_layers_25_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[394] + model_encoder_layers_26_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[395] + model_encoder_layers_26_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[396] + model_encoder_layers_26_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[397] + model_encoder_layers_26_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[398] + model_encoder_layers_26_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[399] + model_encoder_layers_26_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[400] + model_encoder_layers_26_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[401] + model_encoder_layers_26_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[402] + model_encoder_layers_26_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[403] + model_encoder_layers_26_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[404] + model_encoder_layers_26_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[405] + model_encoder_layers_26_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[406] + model_encoder_layers_26_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[407] + model_encoder_layers_26_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[408] + model_encoder_layers_26_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[409] + model_encoder_layers_27_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[410] + model_encoder_layers_27_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[411] + model_encoder_layers_27_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[412] + model_encoder_layers_27_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[413] + model_encoder_layers_27_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[414] + model_encoder_layers_27_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[415] + model_encoder_layers_27_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[416] + model_encoder_layers_27_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[417] + model_encoder_layers_27_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[418] + model_encoder_layers_27_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[419] + model_encoder_layers_27_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[420] + model_encoder_layers_27_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[421] + model_encoder_layers_27_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[422] + model_encoder_layers_27_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[423] + model_encoder_layers_27_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[424] + model_encoder_layers_28_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[425] + model_encoder_layers_28_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[426] + model_encoder_layers_28_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[427] + model_encoder_layers_28_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[428] + model_encoder_layers_28_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[429] + model_encoder_layers_28_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[430] + model_encoder_layers_28_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[431] + model_encoder_layers_28_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[432] + model_encoder_layers_28_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[433] + model_encoder_layers_28_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[434] + model_encoder_layers_28_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[435] + model_encoder_layers_28_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[436] + model_encoder_layers_28_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[437] + model_encoder_layers_28_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[438] + model_encoder_layers_28_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[439] + model_encoder_layers_29_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[440] + model_encoder_layers_29_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[441] + model_encoder_layers_29_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[442] + model_encoder_layers_29_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[443] + model_encoder_layers_29_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[444] + model_encoder_layers_29_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[445] + model_encoder_layers_29_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[446] + model_encoder_layers_29_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[447] + model_encoder_layers_29_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[448] + model_encoder_layers_29_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[449] + model_encoder_layers_29_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[450] + model_encoder_layers_29_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[451] + model_encoder_layers_29_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[452] + model_encoder_layers_29_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[453] + model_encoder_layers_29_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[454] + model_encoder_layers_30_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[455] + model_encoder_layers_30_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[456] + model_encoder_layers_30_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[457] + model_encoder_layers_30_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[458] + model_encoder_layers_30_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[459] + model_encoder_layers_30_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[460] + model_encoder_layers_30_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[461] + model_encoder_layers_30_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[462] + model_encoder_layers_30_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[463] + model_encoder_layers_30_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[464] + model_encoder_layers_30_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[465] + model_encoder_layers_30_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[466] + model_encoder_layers_30_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[467] + model_encoder_layers_30_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[468] + model_encoder_layers_30_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[469] + model_encoder_layers_31_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[470] + model_encoder_layers_31_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[471] + model_encoder_layers_31_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[472] + model_encoder_layers_31_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[473] + model_encoder_layers_31_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[474] + model_encoder_layers_31_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[475] + model_encoder_layers_31_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[476] + model_encoder_layers_31_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[477] + model_encoder_layers_31_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[478] + model_encoder_layers_31_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[479] + model_encoder_layers_31_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[480] + model_encoder_layers_31_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[481] + model_encoder_layers_31_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[482] + model_encoder_layers_31_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[483] + model_encoder_layers_31_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[484] + model_encoder_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[485] + model_encoder_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[486] + model_decoder_embed_tokens_weight5: R.Tensor((51866, 1280), dtype="float16") = packed_params[487] + model_decoder_embed_positions_weight5: R.Tensor((448, 1280), dtype="float16") = packed_params[488] + model_decoder_layers_0_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[489] + model_decoder_layers_0_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[490] + model_decoder_layers_0_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[491] + model_decoder_layers_0_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[492] + model_decoder_layers_0_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[493] + model_decoder_layers_0_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[494] + model_decoder_layers_0_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[495] + model_decoder_layers_0_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[496] + model_decoder_layers_0_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[497] + model_decoder_layers_0_encoder_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[498] + model_decoder_layers_0_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[499] + model_decoder_layers_0_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[500] + model_decoder_layers_0_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[501] + model_decoder_layers_0_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[502] + model_decoder_layers_0_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[503] + model_decoder_layers_0_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[504] + model_decoder_layers_0_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[505] + model_decoder_layers_0_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[506] + model_decoder_layers_0_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[507] + model_decoder_layers_0_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[508] + model_decoder_layers_0_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[509] + model_decoder_layers_0_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[510] + model_decoder_layers_0_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[511] + model_decoder_layers_0_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[512] + model_decoder_layers_1_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[513] + model_decoder_layers_1_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[514] + model_decoder_layers_1_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[515] + model_decoder_layers_1_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[516] + model_decoder_layers_1_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[517] + model_decoder_layers_1_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[518] + model_decoder_layers_1_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[519] + model_decoder_layers_1_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[520] + model_decoder_layers_1_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[521] + model_decoder_layers_1_encoder_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[522] + model_decoder_layers_1_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[523] + model_decoder_layers_1_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[524] + model_decoder_layers_1_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[525] + model_decoder_layers_1_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[526] + model_decoder_layers_1_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[527] + model_decoder_layers_1_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[528] + model_decoder_layers_1_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[529] + model_decoder_layers_1_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[530] + model_decoder_layers_1_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[531] + model_decoder_layers_1_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[532] + model_decoder_layers_1_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[533] + model_decoder_layers_1_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[534] + model_decoder_layers_1_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[535] + model_decoder_layers_1_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[536] + model_decoder_layers_2_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[537] + model_decoder_layers_2_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[538] + model_decoder_layers_2_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[539] + model_decoder_layers_2_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[540] + model_decoder_layers_2_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[541] + model_decoder_layers_2_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[542] + model_decoder_layers_2_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[543] + model_decoder_layers_2_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[544] + model_decoder_layers_2_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[545] + model_decoder_layers_2_encoder_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[546] + model_decoder_layers_2_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[547] + model_decoder_layers_2_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[548] + model_decoder_layers_2_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[549] + model_decoder_layers_2_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[550] + model_decoder_layers_2_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[551] + model_decoder_layers_2_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[552] + model_decoder_layers_2_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[553] + model_decoder_layers_2_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[554] + model_decoder_layers_2_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[555] + model_decoder_layers_2_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[556] + model_decoder_layers_2_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[557] + model_decoder_layers_2_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[558] + model_decoder_layers_2_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[559] + model_decoder_layers_2_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[560] + model_decoder_layers_3_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[561] + model_decoder_layers_3_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[562] + model_decoder_layers_3_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[563] + model_decoder_layers_3_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[564] + model_decoder_layers_3_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[565] + model_decoder_layers_3_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[566] + model_decoder_layers_3_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[567] + model_decoder_layers_3_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[568] + model_decoder_layers_3_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[569] + model_decoder_layers_3_encoder_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[570] + model_decoder_layers_3_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[571] + model_decoder_layers_3_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[572] + model_decoder_layers_3_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[573] + model_decoder_layers_3_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[574] + model_decoder_layers_3_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[575] + model_decoder_layers_3_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[576] + model_decoder_layers_3_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[577] + model_decoder_layers_3_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[578] + model_decoder_layers_3_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[579] + model_decoder_layers_3_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[580] + model_decoder_layers_3_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[581] + model_decoder_layers_3_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[582] + model_decoder_layers_3_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[583] + model_decoder_layers_3_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[584] + model_decoder_layers_4_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[585] + model_decoder_layers_4_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[586] + model_decoder_layers_4_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[587] + model_decoder_layers_4_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[588] + model_decoder_layers_4_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[589] + model_decoder_layers_4_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[590] + model_decoder_layers_4_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[591] + model_decoder_layers_4_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[592] + model_decoder_layers_4_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[593] + model_decoder_layers_4_encoder_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[594] + model_decoder_layers_4_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[595] + model_decoder_layers_4_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[596] + model_decoder_layers_4_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[597] + model_decoder_layers_4_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[598] + model_decoder_layers_4_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[599] + model_decoder_layers_4_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[600] + model_decoder_layers_4_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[601] + model_decoder_layers_4_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[602] + model_decoder_layers_4_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[603] + model_decoder_layers_4_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[604] + model_decoder_layers_4_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[605] + model_decoder_layers_4_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[606] + model_decoder_layers_4_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[607] + model_decoder_layers_4_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[608] + model_decoder_layers_5_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[609] + model_decoder_layers_5_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[610] + model_decoder_layers_5_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[611] + model_decoder_layers_5_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[612] + model_decoder_layers_5_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[613] + model_decoder_layers_5_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[614] + model_decoder_layers_5_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[615] + model_decoder_layers_5_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[616] + model_decoder_layers_5_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[617] + model_decoder_layers_5_encoder_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[618] + model_decoder_layers_5_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[619] + model_decoder_layers_5_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[620] + model_decoder_layers_5_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[621] + model_decoder_layers_5_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[622] + model_decoder_layers_5_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[623] + model_decoder_layers_5_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[624] + model_decoder_layers_5_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[625] + model_decoder_layers_5_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[626] + model_decoder_layers_5_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[627] + model_decoder_layers_5_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[628] + model_decoder_layers_5_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[629] + model_decoder_layers_5_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[630] + model_decoder_layers_5_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[631] + model_decoder_layers_5_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[632] + model_decoder_layers_6_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[633] + model_decoder_layers_6_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[634] + model_decoder_layers_6_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[635] + model_decoder_layers_6_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[636] + model_decoder_layers_6_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[637] + model_decoder_layers_6_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[638] + model_decoder_layers_6_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[639] + model_decoder_layers_6_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[640] + model_decoder_layers_6_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[641] + model_decoder_layers_6_encoder_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[642] + model_decoder_layers_6_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[643] + model_decoder_layers_6_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[644] + model_decoder_layers_6_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[645] + model_decoder_layers_6_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[646] + model_decoder_layers_6_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[647] + model_decoder_layers_6_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[648] + model_decoder_layers_6_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[649] + model_decoder_layers_6_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[650] + model_decoder_layers_6_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[651] + model_decoder_layers_6_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[652] + model_decoder_layers_6_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[653] + model_decoder_layers_6_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[654] + model_decoder_layers_6_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[655] + model_decoder_layers_6_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[656] + model_decoder_layers_7_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[657] + model_decoder_layers_7_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[658] + model_decoder_layers_7_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[659] + model_decoder_layers_7_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[660] + model_decoder_layers_7_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[661] + model_decoder_layers_7_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[662] + model_decoder_layers_7_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[663] + model_decoder_layers_7_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[664] + model_decoder_layers_7_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[665] + model_decoder_layers_7_encoder_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[666] + model_decoder_layers_7_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[667] + model_decoder_layers_7_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[668] + model_decoder_layers_7_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[669] + model_decoder_layers_7_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[670] + model_decoder_layers_7_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[671] + model_decoder_layers_7_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[672] + model_decoder_layers_7_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[673] + model_decoder_layers_7_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[674] + model_decoder_layers_7_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[675] + model_decoder_layers_7_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[676] + model_decoder_layers_7_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[677] + model_decoder_layers_7_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[678] + model_decoder_layers_7_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[679] + model_decoder_layers_7_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[680] + model_decoder_layers_8_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[681] + model_decoder_layers_8_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[682] + model_decoder_layers_8_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[683] + model_decoder_layers_8_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[684] + model_decoder_layers_8_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[685] + model_decoder_layers_8_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[686] + model_decoder_layers_8_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[687] + model_decoder_layers_8_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[688] + model_decoder_layers_8_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[689] + model_decoder_layers_8_encoder_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[690] + model_decoder_layers_8_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[691] + model_decoder_layers_8_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[692] + model_decoder_layers_8_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[693] + model_decoder_layers_8_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[694] + model_decoder_layers_8_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[695] + model_decoder_layers_8_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[696] + model_decoder_layers_8_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[697] + model_decoder_layers_8_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[698] + model_decoder_layers_8_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[699] + model_decoder_layers_8_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[700] + model_decoder_layers_8_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[701] + model_decoder_layers_8_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[702] + model_decoder_layers_8_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[703] + model_decoder_layers_8_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[704] + model_decoder_layers_9_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[705] + model_decoder_layers_9_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[706] + model_decoder_layers_9_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[707] + model_decoder_layers_9_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[708] + model_decoder_layers_9_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[709] + model_decoder_layers_9_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[710] + model_decoder_layers_9_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[711] + model_decoder_layers_9_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[712] + model_decoder_layers_9_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[713] + model_decoder_layers_9_encoder_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[714] + model_decoder_layers_9_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[715] + model_decoder_layers_9_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[716] + model_decoder_layers_9_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[717] + model_decoder_layers_9_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[718] + model_decoder_layers_9_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[719] + model_decoder_layers_9_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[720] + model_decoder_layers_9_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[721] + model_decoder_layers_9_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[722] + model_decoder_layers_9_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[723] + model_decoder_layers_9_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[724] + model_decoder_layers_9_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[725] + model_decoder_layers_9_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[726] + model_decoder_layers_9_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[727] + model_decoder_layers_9_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[728] + model_decoder_layers_10_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[729] + model_decoder_layers_10_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[730] + model_decoder_layers_10_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[731] + model_decoder_layers_10_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[732] + model_decoder_layers_10_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[733] + model_decoder_layers_10_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[734] + model_decoder_layers_10_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[735] + model_decoder_layers_10_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[736] + model_decoder_layers_10_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[737] + model_decoder_layers_10_encoder_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[738] + model_decoder_layers_10_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[739] + model_decoder_layers_10_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[740] + model_decoder_layers_10_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[741] + model_decoder_layers_10_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[742] + model_decoder_layers_10_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[743] + model_decoder_layers_10_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[744] + model_decoder_layers_10_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[745] + model_decoder_layers_10_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[746] + model_decoder_layers_10_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[747] + model_decoder_layers_10_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[748] + model_decoder_layers_10_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[749] + model_decoder_layers_10_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[750] + model_decoder_layers_10_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[751] + model_decoder_layers_10_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[752] + model_decoder_layers_11_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[753] + model_decoder_layers_11_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[754] + model_decoder_layers_11_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[755] + model_decoder_layers_11_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[756] + model_decoder_layers_11_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[757] + model_decoder_layers_11_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[758] + model_decoder_layers_11_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[759] + model_decoder_layers_11_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[760] + model_decoder_layers_11_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[761] + model_decoder_layers_11_encoder_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[762] + model_decoder_layers_11_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[763] + model_decoder_layers_11_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[764] + model_decoder_layers_11_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[765] + model_decoder_layers_11_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[766] + model_decoder_layers_11_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[767] + model_decoder_layers_11_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[768] + model_decoder_layers_11_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[769] + model_decoder_layers_11_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[770] + model_decoder_layers_11_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[771] + model_decoder_layers_11_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[772] + model_decoder_layers_11_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[773] + model_decoder_layers_11_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[774] + model_decoder_layers_11_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[775] + model_decoder_layers_11_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[776] + model_decoder_layers_12_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[777] + model_decoder_layers_12_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[778] + model_decoder_layers_12_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[779] + model_decoder_layers_12_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[780] + model_decoder_layers_12_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[781] + model_decoder_layers_12_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[782] + model_decoder_layers_12_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[783] + model_decoder_layers_12_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[784] + model_decoder_layers_12_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[785] + model_decoder_layers_12_encoder_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[786] + model_decoder_layers_12_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[787] + model_decoder_layers_12_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[788] + model_decoder_layers_12_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[789] + model_decoder_layers_12_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[790] + model_decoder_layers_12_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[791] + model_decoder_layers_12_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[792] + model_decoder_layers_12_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[793] + model_decoder_layers_12_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[794] + model_decoder_layers_12_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[795] + model_decoder_layers_12_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[796] + model_decoder_layers_12_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[797] + model_decoder_layers_12_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[798] + model_decoder_layers_12_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[799] + model_decoder_layers_12_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[800] + model_decoder_layers_13_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[801] + model_decoder_layers_13_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[802] + model_decoder_layers_13_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[803] + model_decoder_layers_13_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[804] + model_decoder_layers_13_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[805] + model_decoder_layers_13_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[806] + model_decoder_layers_13_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[807] + model_decoder_layers_13_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[808] + model_decoder_layers_13_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[809] + model_decoder_layers_13_encoder_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[810] + model_decoder_layers_13_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[811] + model_decoder_layers_13_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[812] + model_decoder_layers_13_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[813] + model_decoder_layers_13_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[814] + model_decoder_layers_13_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[815] + model_decoder_layers_13_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[816] + model_decoder_layers_13_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[817] + model_decoder_layers_13_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[818] + model_decoder_layers_13_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[819] + model_decoder_layers_13_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[820] + model_decoder_layers_13_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[821] + model_decoder_layers_13_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[822] + model_decoder_layers_13_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[823] + model_decoder_layers_13_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[824] + model_decoder_layers_14_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[825] + model_decoder_layers_14_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[826] + model_decoder_layers_14_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[827] + model_decoder_layers_14_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[828] + model_decoder_layers_14_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[829] + model_decoder_layers_14_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[830] + model_decoder_layers_14_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[831] + model_decoder_layers_14_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[832] + model_decoder_layers_14_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[833] + model_decoder_layers_14_encoder_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[834] + model_decoder_layers_14_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[835] + model_decoder_layers_14_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[836] + model_decoder_layers_14_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[837] + model_decoder_layers_14_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[838] + model_decoder_layers_14_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[839] + model_decoder_layers_14_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[840] + model_decoder_layers_14_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[841] + model_decoder_layers_14_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[842] + model_decoder_layers_14_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[843] + model_decoder_layers_14_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[844] + model_decoder_layers_14_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[845] + model_decoder_layers_14_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[846] + model_decoder_layers_14_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[847] + model_decoder_layers_14_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[848] + model_decoder_layers_15_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[849] + model_decoder_layers_15_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[850] + model_decoder_layers_15_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[851] + model_decoder_layers_15_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[852] + model_decoder_layers_15_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[853] + model_decoder_layers_15_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[854] + model_decoder_layers_15_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[855] + model_decoder_layers_15_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[856] + model_decoder_layers_15_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[857] + model_decoder_layers_15_encoder_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[858] + model_decoder_layers_15_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[859] + model_decoder_layers_15_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[860] + model_decoder_layers_15_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[861] + model_decoder_layers_15_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[862] + model_decoder_layers_15_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[863] + model_decoder_layers_15_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[864] + model_decoder_layers_15_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[865] + model_decoder_layers_15_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[866] + model_decoder_layers_15_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[867] + model_decoder_layers_15_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[868] + model_decoder_layers_15_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[869] + model_decoder_layers_15_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[870] + model_decoder_layers_15_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[871] + model_decoder_layers_15_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[872] + model_decoder_layers_16_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[873] + model_decoder_layers_16_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[874] + model_decoder_layers_16_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[875] + model_decoder_layers_16_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[876] + model_decoder_layers_16_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[877] + model_decoder_layers_16_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[878] + model_decoder_layers_16_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[879] + model_decoder_layers_16_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[880] + model_decoder_layers_16_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[881] + model_decoder_layers_16_encoder_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[882] + model_decoder_layers_16_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[883] + model_decoder_layers_16_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[884] + model_decoder_layers_16_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[885] + model_decoder_layers_16_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[886] + model_decoder_layers_16_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[887] + model_decoder_layers_16_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[888] + model_decoder_layers_16_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[889] + model_decoder_layers_16_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[890] + model_decoder_layers_16_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[891] + model_decoder_layers_16_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[892] + model_decoder_layers_16_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[893] + model_decoder_layers_16_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[894] + model_decoder_layers_16_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[895] + model_decoder_layers_16_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[896] + model_decoder_layers_17_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[897] + model_decoder_layers_17_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[898] + model_decoder_layers_17_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[899] + model_decoder_layers_17_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[900] + model_decoder_layers_17_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[901] + model_decoder_layers_17_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[902] + model_decoder_layers_17_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[903] + model_decoder_layers_17_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[904] + model_decoder_layers_17_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[905] + model_decoder_layers_17_encoder_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[906] + model_decoder_layers_17_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[907] + model_decoder_layers_17_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[908] + model_decoder_layers_17_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[909] + model_decoder_layers_17_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[910] + model_decoder_layers_17_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[911] + model_decoder_layers_17_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[912] + model_decoder_layers_17_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[913] + model_decoder_layers_17_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[914] + model_decoder_layers_17_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[915] + model_decoder_layers_17_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[916] + model_decoder_layers_17_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[917] + model_decoder_layers_17_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[918] + model_decoder_layers_17_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[919] + model_decoder_layers_17_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[920] + model_decoder_layers_18_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[921] + model_decoder_layers_18_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[922] + model_decoder_layers_18_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[923] + model_decoder_layers_18_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[924] + model_decoder_layers_18_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[925] + model_decoder_layers_18_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[926] + model_decoder_layers_18_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[927] + model_decoder_layers_18_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[928] + model_decoder_layers_18_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[929] + model_decoder_layers_18_encoder_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[930] + model_decoder_layers_18_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[931] + model_decoder_layers_18_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[932] + model_decoder_layers_18_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[933] + model_decoder_layers_18_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[934] + model_decoder_layers_18_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[935] + model_decoder_layers_18_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[936] + model_decoder_layers_18_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[937] + model_decoder_layers_18_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[938] + model_decoder_layers_18_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[939] + model_decoder_layers_18_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[940] + model_decoder_layers_18_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[941] + model_decoder_layers_18_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[942] + model_decoder_layers_18_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[943] + model_decoder_layers_18_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[944] + model_decoder_layers_19_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[945] + model_decoder_layers_19_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[946] + model_decoder_layers_19_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[947] + model_decoder_layers_19_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[948] + model_decoder_layers_19_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[949] + model_decoder_layers_19_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[950] + model_decoder_layers_19_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[951] + model_decoder_layers_19_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[952] + model_decoder_layers_19_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[953] + model_decoder_layers_19_encoder_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[954] + model_decoder_layers_19_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[955] + model_decoder_layers_19_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[956] + model_decoder_layers_19_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[957] + model_decoder_layers_19_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[958] + model_decoder_layers_19_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[959] + model_decoder_layers_19_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[960] + model_decoder_layers_19_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[961] + model_decoder_layers_19_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[962] + model_decoder_layers_19_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[963] + model_decoder_layers_19_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[964] + model_decoder_layers_19_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[965] + model_decoder_layers_19_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[966] + model_decoder_layers_19_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[967] + model_decoder_layers_19_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[968] + model_decoder_layers_20_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[969] + model_decoder_layers_20_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[970] + model_decoder_layers_20_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[971] + model_decoder_layers_20_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[972] + model_decoder_layers_20_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[973] + model_decoder_layers_20_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[974] + model_decoder_layers_20_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[975] + model_decoder_layers_20_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[976] + model_decoder_layers_20_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[977] + model_decoder_layers_20_encoder_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[978] + model_decoder_layers_20_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[979] + model_decoder_layers_20_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[980] + model_decoder_layers_20_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[981] + model_decoder_layers_20_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[982] + model_decoder_layers_20_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[983] + model_decoder_layers_20_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[984] + model_decoder_layers_20_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[985] + model_decoder_layers_20_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[986] + model_decoder_layers_20_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[987] + model_decoder_layers_20_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[988] + model_decoder_layers_20_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[989] + model_decoder_layers_20_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[990] + model_decoder_layers_20_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[991] + model_decoder_layers_20_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[992] + model_decoder_layers_21_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[993] + model_decoder_layers_21_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[994] + model_decoder_layers_21_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[995] + model_decoder_layers_21_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[996] + model_decoder_layers_21_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[997] + model_decoder_layers_21_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[998] + model_decoder_layers_21_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[999] + model_decoder_layers_21_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1000] + model_decoder_layers_21_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1001] + model_decoder_layers_21_encoder_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1002] + model_decoder_layers_21_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1003] + model_decoder_layers_21_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1004] + model_decoder_layers_21_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1005] + model_decoder_layers_21_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1006] + model_decoder_layers_21_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1007] + model_decoder_layers_21_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1008] + model_decoder_layers_21_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1009] + model_decoder_layers_21_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1010] + model_decoder_layers_21_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1011] + model_decoder_layers_21_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1012] + model_decoder_layers_21_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1013] + model_decoder_layers_21_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1014] + model_decoder_layers_21_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1015] + model_decoder_layers_21_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1016] + model_decoder_layers_22_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1017] + model_decoder_layers_22_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1018] + model_decoder_layers_22_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1019] + model_decoder_layers_22_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1020] + model_decoder_layers_22_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1021] + model_decoder_layers_22_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1022] + model_decoder_layers_22_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1023] + model_decoder_layers_22_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1024] + model_decoder_layers_22_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1025] + model_decoder_layers_22_encoder_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1026] + model_decoder_layers_22_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1027] + model_decoder_layers_22_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1028] + model_decoder_layers_22_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1029] + model_decoder_layers_22_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1030] + model_decoder_layers_22_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1031] + model_decoder_layers_22_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1032] + model_decoder_layers_22_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1033] + model_decoder_layers_22_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1034] + model_decoder_layers_22_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1035] + model_decoder_layers_22_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1036] + model_decoder_layers_22_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1037] + model_decoder_layers_22_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1038] + model_decoder_layers_22_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1039] + model_decoder_layers_22_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1040] + model_decoder_layers_23_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1041] + model_decoder_layers_23_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1042] + model_decoder_layers_23_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1043] + model_decoder_layers_23_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1044] + model_decoder_layers_23_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1045] + model_decoder_layers_23_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1046] + model_decoder_layers_23_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1047] + model_decoder_layers_23_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1048] + model_decoder_layers_23_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1049] + model_decoder_layers_23_encoder_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1050] + model_decoder_layers_23_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1051] + model_decoder_layers_23_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1052] + model_decoder_layers_23_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1053] + model_decoder_layers_23_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1054] + model_decoder_layers_23_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1055] + model_decoder_layers_23_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1056] + model_decoder_layers_23_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1057] + model_decoder_layers_23_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1058] + model_decoder_layers_23_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1059] + model_decoder_layers_23_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1060] + model_decoder_layers_23_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1061] + model_decoder_layers_23_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1062] + model_decoder_layers_23_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1063] + model_decoder_layers_23_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1064] + model_decoder_layers_24_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1065] + model_decoder_layers_24_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1066] + model_decoder_layers_24_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1067] + model_decoder_layers_24_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1068] + model_decoder_layers_24_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1069] + model_decoder_layers_24_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1070] + model_decoder_layers_24_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1071] + model_decoder_layers_24_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1072] + model_decoder_layers_24_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1073] + model_decoder_layers_24_encoder_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1074] + model_decoder_layers_24_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1075] + model_decoder_layers_24_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1076] + model_decoder_layers_24_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1077] + model_decoder_layers_24_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1078] + model_decoder_layers_24_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1079] + model_decoder_layers_24_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1080] + model_decoder_layers_24_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1081] + model_decoder_layers_24_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1082] + model_decoder_layers_24_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1083] + model_decoder_layers_24_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1084] + model_decoder_layers_24_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1085] + model_decoder_layers_24_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1086] + model_decoder_layers_24_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1087] + model_decoder_layers_24_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1088] + model_decoder_layers_25_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1089] + model_decoder_layers_25_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1090] + model_decoder_layers_25_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1091] + model_decoder_layers_25_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1092] + model_decoder_layers_25_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1093] + model_decoder_layers_25_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1094] + model_decoder_layers_25_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1095] + model_decoder_layers_25_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1096] + model_decoder_layers_25_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1097] + model_decoder_layers_25_encoder_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1098] + model_decoder_layers_25_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1099] + model_decoder_layers_25_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1100] + model_decoder_layers_25_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1101] + model_decoder_layers_25_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1102] + model_decoder_layers_25_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1103] + model_decoder_layers_25_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1104] + model_decoder_layers_25_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1105] + model_decoder_layers_25_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1106] + model_decoder_layers_25_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1107] + model_decoder_layers_25_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1108] + model_decoder_layers_25_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1109] + model_decoder_layers_25_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1110] + model_decoder_layers_25_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1111] + model_decoder_layers_25_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1112] + model_decoder_layers_26_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1113] + model_decoder_layers_26_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1114] + model_decoder_layers_26_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1115] + model_decoder_layers_26_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1116] + model_decoder_layers_26_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1117] + model_decoder_layers_26_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1118] + model_decoder_layers_26_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1119] + model_decoder_layers_26_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1120] + model_decoder_layers_26_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1121] + model_decoder_layers_26_encoder_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1122] + model_decoder_layers_26_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1123] + model_decoder_layers_26_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1124] + model_decoder_layers_26_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1125] + model_decoder_layers_26_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1126] + model_decoder_layers_26_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1127] + model_decoder_layers_26_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1128] + model_decoder_layers_26_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1129] + model_decoder_layers_26_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1130] + model_decoder_layers_26_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1131] + model_decoder_layers_26_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1132] + model_decoder_layers_26_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1133] + model_decoder_layers_26_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1134] + model_decoder_layers_26_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1135] + model_decoder_layers_26_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1136] + model_decoder_layers_27_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1137] + model_decoder_layers_27_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1138] + model_decoder_layers_27_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1139] + model_decoder_layers_27_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1140] + model_decoder_layers_27_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1141] + model_decoder_layers_27_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1142] + model_decoder_layers_27_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1143] + model_decoder_layers_27_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1144] + model_decoder_layers_27_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1145] + model_decoder_layers_27_encoder_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1146] + model_decoder_layers_27_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1147] + model_decoder_layers_27_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1148] + model_decoder_layers_27_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1149] + model_decoder_layers_27_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1150] + model_decoder_layers_27_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1151] + model_decoder_layers_27_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1152] + model_decoder_layers_27_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1153] + model_decoder_layers_27_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1154] + model_decoder_layers_27_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1155] + model_decoder_layers_27_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1156] + model_decoder_layers_27_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1157] + model_decoder_layers_27_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1158] + model_decoder_layers_27_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1159] + model_decoder_layers_27_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1160] + model_decoder_layers_28_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1161] + model_decoder_layers_28_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1162] + model_decoder_layers_28_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1163] + model_decoder_layers_28_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1164] + model_decoder_layers_28_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1165] + model_decoder_layers_28_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1166] + model_decoder_layers_28_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1167] + model_decoder_layers_28_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1168] + model_decoder_layers_28_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1169] + model_decoder_layers_28_encoder_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1170] + model_decoder_layers_28_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1171] + model_decoder_layers_28_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1172] + model_decoder_layers_28_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1173] + model_decoder_layers_28_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1174] + model_decoder_layers_28_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1175] + model_decoder_layers_28_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1176] + model_decoder_layers_28_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1177] + model_decoder_layers_28_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1178] + model_decoder_layers_28_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1179] + model_decoder_layers_28_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1180] + model_decoder_layers_28_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1181] + model_decoder_layers_28_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1182] + model_decoder_layers_28_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1183] + model_decoder_layers_28_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1184] + model_decoder_layers_29_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1185] + model_decoder_layers_29_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1186] + model_decoder_layers_29_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1187] + model_decoder_layers_29_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1188] + model_decoder_layers_29_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1189] + model_decoder_layers_29_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1190] + model_decoder_layers_29_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1191] + model_decoder_layers_29_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1192] + model_decoder_layers_29_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1193] + model_decoder_layers_29_encoder_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1194] + model_decoder_layers_29_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1195] + model_decoder_layers_29_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1196] + model_decoder_layers_29_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1197] + model_decoder_layers_29_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1198] + model_decoder_layers_29_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1199] + model_decoder_layers_29_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1200] + model_decoder_layers_29_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1201] + model_decoder_layers_29_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1202] + model_decoder_layers_29_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1203] + model_decoder_layers_29_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1204] + model_decoder_layers_29_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1205] + model_decoder_layers_29_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1206] + model_decoder_layers_29_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1207] + model_decoder_layers_29_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1208] + model_decoder_layers_30_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1209] + model_decoder_layers_30_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1210] + model_decoder_layers_30_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1211] + model_decoder_layers_30_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1212] + model_decoder_layers_30_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1213] + model_decoder_layers_30_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1214] + model_decoder_layers_30_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1215] + model_decoder_layers_30_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1216] + model_decoder_layers_30_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1217] + model_decoder_layers_30_encoder_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1218] + model_decoder_layers_30_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1219] + model_decoder_layers_30_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1220] + model_decoder_layers_30_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1221] + model_decoder_layers_30_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1222] + model_decoder_layers_30_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1223] + model_decoder_layers_30_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1224] + model_decoder_layers_30_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1225] + model_decoder_layers_30_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1226] + model_decoder_layers_30_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1227] + model_decoder_layers_30_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1228] + model_decoder_layers_30_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1229] + model_decoder_layers_30_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1230] + model_decoder_layers_30_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1231] + model_decoder_layers_30_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1232] + model_decoder_layers_31_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1233] + model_decoder_layers_31_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1234] + model_decoder_layers_31_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1235] + model_decoder_layers_31_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1236] + model_decoder_layers_31_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1237] + model_decoder_layers_31_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1238] + model_decoder_layers_31_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1239] + model_decoder_layers_31_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1240] + model_decoder_layers_31_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1241] + model_decoder_layers_31_encoder_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1242] + model_decoder_layers_31_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1243] + model_decoder_layers_31_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1244] + model_decoder_layers_31_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1245] + model_decoder_layers_31_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1246] + model_decoder_layers_31_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1247] + model_decoder_layers_31_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1248] + model_decoder_layers_31_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1249] + model_decoder_layers_31_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1250] + model_decoder_layers_31_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1251] + model_decoder_layers_31_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1252] + model_decoder_layers_31_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1253] + model_decoder_layers_31_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1254] + model_decoder_layers_31_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1255] + model_decoder_layers_31_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1256] + model_decoder_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1257] + model_decoder_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1258] + reshape1353: R.Tensor((1,), dtype="int32") = R.reshape(input_ids, R.shape([1])) + take7: R.Tensor((1, 1280), dtype="float16") = R.take(model_decoder_embed_tokens_weight5, reshape1353, axis=0) + reshape1354: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(take7, R.shape([1, 1, 1280])) + lv264: R.Tensor((1,), dtype="int32") = R.call_pure_packed("vm.builtin.attention_kv_cache_get_query_positions", paged_kv_cache, sinfo_args=(R.Tensor((1,), dtype="int32"),)) + take8: R.Tensor((1, 1280), dtype="float16") = R.take(model_decoder_embed_positions_weight5, lv264, axis=0) + reshape1355: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(take8, R.shape([1, 1, 1280])) + add1220: R.Tensor((1, 1, 1280), dtype="float16") = R.add(reshape1354, reshape1355) + layer_norm356: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1220, model_decoder_layers_0_self_attn_layer_norm_weight5, model_decoder_layers_0_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1028: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_self_attn_q_proj_weight5, axes=None) + matmul1027: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm356, permute_dims1028, out_dtype="void") + add1221: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1027, model_decoder_layers_0_self_attn_q_proj_bias5) + reshape1356: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1221, R.shape([1, 1, 20, 64])) + permute_dims1029: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_self_attn_k_proj_weight5, axes=None) + matmul1028: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm356, permute_dims1029, out_dtype="void") + reshape1357: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1028, R.shape([1, 1, 20, 64])) + permute_dims1030: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_self_attn_v_proj_weight5, axes=None) + matmul1029: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm356, permute_dims1030, out_dtype="void") + add1222: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1029, model_decoder_layers_0_self_attn_v_proj_bias5) + reshape1358: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1222, R.shape([1, 1, 20, 64])) + concat96: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1356, reshape1357, reshape1358), axis=2) + reshape1359: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat96, R.shape([1, 60, 64])) + lv265 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), reshape1359), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1360: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv265, R.shape([1, 1, 20, 64])) + reshape1361: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1360, R.shape([1, 1, 1280])) + permute_dims1031: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_self_attn_out_proj_weight5, axes=None) + matmul1030: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1361, permute_dims1031, out_dtype="void") + add1223: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1030, model_decoder_layers_0_self_attn_out_proj_bias5) + add1224: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1220, add1223) + layer_norm357: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1224, model_decoder_layers_0_encoder_attn_layer_norm_weight5, model_decoder_layers_0_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1032: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_encoder_attn_q_proj_weight5, axes=None) + matmul1031: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm357, permute_dims1032, out_dtype="void") + add1225: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1031, model_decoder_layers_0_encoder_attn_q_proj_bias5) + reshape1362: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1225, R.shape([1, 1, 20, 64])) + reshape1363: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1362, R.shape([1, 20, 64])) + lv266 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), reshape1363), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1364: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv266, R.shape([1, 1, 20, 64])) + reshape1365: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1364, R.shape([1, 1, 1280])) + permute_dims1033: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_encoder_attn_out_proj_weight5, axes=None) + matmul1032: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1365, permute_dims1033, out_dtype="void") + add1226: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1032, model_decoder_layers_0_encoder_attn_out_proj_bias5) + add1227: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1224, add1226) + layer_norm358: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1227, model_decoder_layers_0_final_layer_norm_weight5, model_decoder_layers_0_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1034: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_0_fc1_weight5, axes=None) + matmul1033: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm358, permute_dims1034, out_dtype="void") + add1228: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1033, model_decoder_layers_0_fc1_bias5) + gelu130: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1228) + permute_dims1035: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_fc2_weight5, axes=None) + matmul1034: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu130, permute_dims1035, out_dtype="void") + add1229: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1034, model_decoder_layers_0_fc2_bias5) + add1230: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1227, add1229) + layer_norm359: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1230, model_decoder_layers_1_self_attn_layer_norm_weight5, model_decoder_layers_1_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1036: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_1_self_attn_q_proj_weight5, axes=None) + matmul1035: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm359, permute_dims1036, out_dtype="void") + add1231: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1035, model_decoder_layers_1_self_attn_q_proj_bias5) + reshape1366: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1231, R.shape([1, 1, 20, 64])) + permute_dims1037: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_1_self_attn_k_proj_weight5, axes=None) + matmul1036: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm359, permute_dims1037, out_dtype="void") + reshape1367: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1036, R.shape([1, 1, 20, 64])) + permute_dims1038: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_1_self_attn_v_proj_weight5, axes=None) + matmul1037: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm359, permute_dims1038, out_dtype="void") + add1232: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1037, model_decoder_layers_1_self_attn_v_proj_bias5) + reshape1368: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1232, R.shape([1, 1, 20, 64])) + concat97: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1366, reshape1367, reshape1368), axis=2) + reshape1369: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat97, R.shape([1, 60, 64])) + lv267 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), reshape1369), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1370: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv267, R.shape([1, 1, 20, 64])) + reshape1371: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1370, R.shape([1, 1, 1280])) + permute_dims1039: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_1_self_attn_out_proj_weight5, axes=None) + matmul1038: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1371, permute_dims1039, out_dtype="void") + add1233: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1038, model_decoder_layers_1_self_attn_out_proj_bias5) + add1234: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1230, add1233) + layer_norm360: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1234, model_decoder_layers_1_encoder_attn_layer_norm_weight5, model_decoder_layers_1_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1040: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_1_encoder_attn_q_proj_weight5, axes=None) + matmul1039: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm360, permute_dims1040, out_dtype="void") + add1235: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1039, model_decoder_layers_1_encoder_attn_q_proj_bias5) + reshape1372: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1235, R.shape([1, 1, 20, 64])) + reshape1373: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1372, R.shape([1, 20, 64])) + lv268 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), reshape1373), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1374: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv268, R.shape([1, 1, 20, 64])) + reshape1375: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1374, R.shape([1, 1, 1280])) + permute_dims1041: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_1_encoder_attn_out_proj_weight5, axes=None) + matmul1040: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1375, permute_dims1041, out_dtype="void") + add1236: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1040, model_decoder_layers_1_encoder_attn_out_proj_bias5) + add1237: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1234, add1236) + layer_norm361: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1237, model_decoder_layers_1_final_layer_norm_weight5, model_decoder_layers_1_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1042: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_1_fc1_weight5, axes=None) + matmul1041: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm361, permute_dims1042, out_dtype="void") + add1238: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1041, model_decoder_layers_1_fc1_bias5) + gelu131: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1238) + permute_dims1043: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_1_fc2_weight5, axes=None) + matmul1042: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu131, permute_dims1043, out_dtype="void") + add1239: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1042, model_decoder_layers_1_fc2_bias5) + add1240: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1237, add1239) + layer_norm362: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1240, model_decoder_layers_2_self_attn_layer_norm_weight5, model_decoder_layers_2_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1044: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_2_self_attn_q_proj_weight5, axes=None) + matmul1043: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm362, permute_dims1044, out_dtype="void") + add1241: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1043, model_decoder_layers_2_self_attn_q_proj_bias5) + reshape1376: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1241, R.shape([1, 1, 20, 64])) + permute_dims1045: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_2_self_attn_k_proj_weight5, axes=None) + matmul1044: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm362, permute_dims1045, out_dtype="void") + reshape1377: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1044, R.shape([1, 1, 20, 64])) + permute_dims1046: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_2_self_attn_v_proj_weight5, axes=None) + matmul1045: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm362, permute_dims1046, out_dtype="void") + add1242: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1045, model_decoder_layers_2_self_attn_v_proj_bias5) + reshape1378: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1242, R.shape([1, 1, 20, 64])) + concat98: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1376, reshape1377, reshape1378), axis=2) + reshape1379: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat98, R.shape([1, 60, 64])) + lv269 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), reshape1379), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1380: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv269, R.shape([1, 1, 20, 64])) + reshape1381: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1380, R.shape([1, 1, 1280])) + permute_dims1047: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_2_self_attn_out_proj_weight5, axes=None) + matmul1046: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1381, permute_dims1047, out_dtype="void") + add1243: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1046, model_decoder_layers_2_self_attn_out_proj_bias5) + add1244: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1240, add1243) + layer_norm363: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1244, model_decoder_layers_2_encoder_attn_layer_norm_weight5, model_decoder_layers_2_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1048: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_2_encoder_attn_q_proj_weight5, axes=None) + matmul1047: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm363, permute_dims1048, out_dtype="void") + add1245: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1047, model_decoder_layers_2_encoder_attn_q_proj_bias5) + reshape1382: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1245, R.shape([1, 1, 20, 64])) + reshape1383: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1382, R.shape([1, 20, 64])) + lv270 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), reshape1383), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1384: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv270, R.shape([1, 1, 20, 64])) + reshape1385: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1384, R.shape([1, 1, 1280])) + permute_dims1049: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_2_encoder_attn_out_proj_weight5, axes=None) + matmul1048: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1385, permute_dims1049, out_dtype="void") + add1246: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1048, model_decoder_layers_2_encoder_attn_out_proj_bias5) + add1247: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1244, add1246) + layer_norm364: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1247, model_decoder_layers_2_final_layer_norm_weight5, model_decoder_layers_2_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1050: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_2_fc1_weight5, axes=None) + matmul1049: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm364, permute_dims1050, out_dtype="void") + add1248: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1049, model_decoder_layers_2_fc1_bias5) + gelu132: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1248) + permute_dims1051: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_2_fc2_weight5, axes=None) + matmul1050: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu132, permute_dims1051, out_dtype="void") + add1249: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1050, model_decoder_layers_2_fc2_bias5) + add1250: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1247, add1249) + layer_norm365: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1250, model_decoder_layers_3_self_attn_layer_norm_weight5, model_decoder_layers_3_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1052: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_3_self_attn_q_proj_weight5, axes=None) + matmul1051: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm365, permute_dims1052, out_dtype="void") + add1251: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1051, model_decoder_layers_3_self_attn_q_proj_bias5) + reshape1386: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1251, R.shape([1, 1, 20, 64])) + permute_dims1053: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_3_self_attn_k_proj_weight5, axes=None) + matmul1052: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm365, permute_dims1053, out_dtype="void") + reshape1387: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1052, R.shape([1, 1, 20, 64])) + permute_dims1054: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_3_self_attn_v_proj_weight5, axes=None) + matmul1053: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm365, permute_dims1054, out_dtype="void") + add1252: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1053, model_decoder_layers_3_self_attn_v_proj_bias5) + reshape1388: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1252, R.shape([1, 1, 20, 64])) + concat99: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1386, reshape1387, reshape1388), axis=2) + reshape1389: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat99, R.shape([1, 60, 64])) + lv271 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), reshape1389), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1390: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv271, R.shape([1, 1, 20, 64])) + reshape1391: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1390, R.shape([1, 1, 1280])) + permute_dims1055: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_3_self_attn_out_proj_weight5, axes=None) + matmul1054: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1391, permute_dims1055, out_dtype="void") + add1253: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1054, model_decoder_layers_3_self_attn_out_proj_bias5) + add1254: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1250, add1253) + layer_norm366: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1254, model_decoder_layers_3_encoder_attn_layer_norm_weight5, model_decoder_layers_3_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1056: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_3_encoder_attn_q_proj_weight5, axes=None) + matmul1055: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm366, permute_dims1056, out_dtype="void") + add1255: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1055, model_decoder_layers_3_encoder_attn_q_proj_bias5) + reshape1392: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1255, R.shape([1, 1, 20, 64])) + reshape1393: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1392, R.shape([1, 20, 64])) + lv272 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), reshape1393), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1394: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv272, R.shape([1, 1, 20, 64])) + reshape1395: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1394, R.shape([1, 1, 1280])) + permute_dims1057: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_3_encoder_attn_out_proj_weight5, axes=None) + matmul1056: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1395, permute_dims1057, out_dtype="void") + add1256: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1056, model_decoder_layers_3_encoder_attn_out_proj_bias5) + add1257: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1254, add1256) + layer_norm367: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1257, model_decoder_layers_3_final_layer_norm_weight5, model_decoder_layers_3_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1058: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_3_fc1_weight5, axes=None) + matmul1057: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm367, permute_dims1058, out_dtype="void") + add1258: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1057, model_decoder_layers_3_fc1_bias5) + gelu133: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1258) + permute_dims1059: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_3_fc2_weight5, axes=None) + matmul1058: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu133, permute_dims1059, out_dtype="void") + add1259: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1058, model_decoder_layers_3_fc2_bias5) + add1260: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1257, add1259) + layer_norm368: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1260, model_decoder_layers_4_self_attn_layer_norm_weight5, model_decoder_layers_4_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1060: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_4_self_attn_q_proj_weight5, axes=None) + matmul1059: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm368, permute_dims1060, out_dtype="void") + add1261: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1059, model_decoder_layers_4_self_attn_q_proj_bias5) + reshape1396: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1261, R.shape([1, 1, 20, 64])) + permute_dims1061: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_4_self_attn_k_proj_weight5, axes=None) + matmul1060: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm368, permute_dims1061, out_dtype="void") + reshape1397: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1060, R.shape([1, 1, 20, 64])) + permute_dims1062: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_4_self_attn_v_proj_weight5, axes=None) + matmul1061: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm368, permute_dims1062, out_dtype="void") + add1262: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1061, model_decoder_layers_4_self_attn_v_proj_bias5) + reshape1398: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1262, R.shape([1, 1, 20, 64])) + concat100: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1396, reshape1397, reshape1398), axis=2) + reshape1399: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat100, R.shape([1, 60, 64])) + lv273 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), reshape1399), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1400: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv273, R.shape([1, 1, 20, 64])) + reshape1401: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1400, R.shape([1, 1, 1280])) + permute_dims1063: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_4_self_attn_out_proj_weight5, axes=None) + matmul1062: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1401, permute_dims1063, out_dtype="void") + add1263: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1062, model_decoder_layers_4_self_attn_out_proj_bias5) + add1264: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1260, add1263) + layer_norm369: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1264, model_decoder_layers_4_encoder_attn_layer_norm_weight5, model_decoder_layers_4_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1064: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_4_encoder_attn_q_proj_weight5, axes=None) + matmul1063: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm369, permute_dims1064, out_dtype="void") + add1265: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1063, model_decoder_layers_4_encoder_attn_q_proj_bias5) + reshape1402: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1265, R.shape([1, 1, 20, 64])) + reshape1403: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1402, R.shape([1, 20, 64])) + lv274 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), reshape1403), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1404: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv274, R.shape([1, 1, 20, 64])) + reshape1405: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1404, R.shape([1, 1, 1280])) + permute_dims1065: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_4_encoder_attn_out_proj_weight5, axes=None) + matmul1064: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1405, permute_dims1065, out_dtype="void") + add1266: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1064, model_decoder_layers_4_encoder_attn_out_proj_bias5) + add1267: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1264, add1266) + layer_norm370: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1267, model_decoder_layers_4_final_layer_norm_weight5, model_decoder_layers_4_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1066: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_4_fc1_weight5, axes=None) + matmul1065: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm370, permute_dims1066, out_dtype="void") + add1268: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1065, model_decoder_layers_4_fc1_bias5) + gelu134: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1268) + permute_dims1067: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_4_fc2_weight5, axes=None) + matmul1066: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu134, permute_dims1067, out_dtype="void") + add1269: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1066, model_decoder_layers_4_fc2_bias5) + add1270: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1267, add1269) + layer_norm371: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1270, model_decoder_layers_5_self_attn_layer_norm_weight5, model_decoder_layers_5_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1068: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_5_self_attn_q_proj_weight5, axes=None) + matmul1067: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm371, permute_dims1068, out_dtype="void") + add1271: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1067, model_decoder_layers_5_self_attn_q_proj_bias5) + reshape1406: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1271, R.shape([1, 1, 20, 64])) + permute_dims1069: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_5_self_attn_k_proj_weight5, axes=None) + matmul1068: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm371, permute_dims1069, out_dtype="void") + reshape1407: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1068, R.shape([1, 1, 20, 64])) + permute_dims1070: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_5_self_attn_v_proj_weight5, axes=None) + matmul1069: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm371, permute_dims1070, out_dtype="void") + add1272: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1069, model_decoder_layers_5_self_attn_v_proj_bias5) + reshape1408: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1272, R.shape([1, 1, 20, 64])) + concat101: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1406, reshape1407, reshape1408), axis=2) + reshape1409: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat101, R.shape([1, 60, 64])) + lv275 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), reshape1409), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1410: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv275, R.shape([1, 1, 20, 64])) + reshape1411: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1410, R.shape([1, 1, 1280])) + permute_dims1071: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_5_self_attn_out_proj_weight5, axes=None) + matmul1070: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1411, permute_dims1071, out_dtype="void") + add1273: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1070, model_decoder_layers_5_self_attn_out_proj_bias5) + add1274: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1270, add1273) + layer_norm372: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1274, model_decoder_layers_5_encoder_attn_layer_norm_weight5, model_decoder_layers_5_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1072: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_5_encoder_attn_q_proj_weight5, axes=None) + matmul1071: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm372, permute_dims1072, out_dtype="void") + add1275: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1071, model_decoder_layers_5_encoder_attn_q_proj_bias5) + reshape1412: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1275, R.shape([1, 1, 20, 64])) + reshape1413: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1412, R.shape([1, 20, 64])) + lv276 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), reshape1413), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1414: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv276, R.shape([1, 1, 20, 64])) + reshape1415: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1414, R.shape([1, 1, 1280])) + permute_dims1073: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_5_encoder_attn_out_proj_weight5, axes=None) + matmul1072: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1415, permute_dims1073, out_dtype="void") + add1276: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1072, model_decoder_layers_5_encoder_attn_out_proj_bias5) + add1277: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1274, add1276) + layer_norm373: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1277, model_decoder_layers_5_final_layer_norm_weight5, model_decoder_layers_5_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1074: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_5_fc1_weight5, axes=None) + matmul1073: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm373, permute_dims1074, out_dtype="void") + add1278: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1073, model_decoder_layers_5_fc1_bias5) + gelu135: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1278) + permute_dims1075: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_5_fc2_weight5, axes=None) + matmul1074: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu135, permute_dims1075, out_dtype="void") + add1279: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1074, model_decoder_layers_5_fc2_bias5) + add1280: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1277, add1279) + layer_norm374: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1280, model_decoder_layers_6_self_attn_layer_norm_weight5, model_decoder_layers_6_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1076: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_6_self_attn_q_proj_weight5, axes=None) + matmul1075: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm374, permute_dims1076, out_dtype="void") + add1281: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1075, model_decoder_layers_6_self_attn_q_proj_bias5) + reshape1416: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1281, R.shape([1, 1, 20, 64])) + permute_dims1077: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_6_self_attn_k_proj_weight5, axes=None) + matmul1076: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm374, permute_dims1077, out_dtype="void") + reshape1417: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1076, R.shape([1, 1, 20, 64])) + permute_dims1078: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_6_self_attn_v_proj_weight5, axes=None) + matmul1077: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm374, permute_dims1078, out_dtype="void") + add1282: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1077, model_decoder_layers_6_self_attn_v_proj_bias5) + reshape1418: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1282, R.shape([1, 1, 20, 64])) + concat102: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1416, reshape1417, reshape1418), axis=2) + reshape1419: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat102, R.shape([1, 60, 64])) + lv277 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), reshape1419), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1420: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv277, R.shape([1, 1, 20, 64])) + reshape1421: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1420, R.shape([1, 1, 1280])) + permute_dims1079: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_6_self_attn_out_proj_weight5, axes=None) + matmul1078: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1421, permute_dims1079, out_dtype="void") + add1283: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1078, model_decoder_layers_6_self_attn_out_proj_bias5) + add1284: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1280, add1283) + layer_norm375: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1284, model_decoder_layers_6_encoder_attn_layer_norm_weight5, model_decoder_layers_6_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1080: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_6_encoder_attn_q_proj_weight5, axes=None) + matmul1079: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm375, permute_dims1080, out_dtype="void") + add1285: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1079, model_decoder_layers_6_encoder_attn_q_proj_bias5) + reshape1422: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1285, R.shape([1, 1, 20, 64])) + reshape1423: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1422, R.shape([1, 20, 64])) + lv278 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), reshape1423), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1424: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv278, R.shape([1, 1, 20, 64])) + reshape1425: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1424, R.shape([1, 1, 1280])) + permute_dims1081: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_6_encoder_attn_out_proj_weight5, axes=None) + matmul1080: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1425, permute_dims1081, out_dtype="void") + add1286: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1080, model_decoder_layers_6_encoder_attn_out_proj_bias5) + add1287: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1284, add1286) + layer_norm376: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1287, model_decoder_layers_6_final_layer_norm_weight5, model_decoder_layers_6_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1082: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_6_fc1_weight5, axes=None) + matmul1081: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm376, permute_dims1082, out_dtype="void") + add1288: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1081, model_decoder_layers_6_fc1_bias5) + gelu136: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1288) + permute_dims1083: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_6_fc2_weight5, axes=None) + matmul1082: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu136, permute_dims1083, out_dtype="void") + add1289: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1082, model_decoder_layers_6_fc2_bias5) + add1290: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1287, add1289) + layer_norm377: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1290, model_decoder_layers_7_self_attn_layer_norm_weight5, model_decoder_layers_7_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1084: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_7_self_attn_q_proj_weight5, axes=None) + matmul1083: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm377, permute_dims1084, out_dtype="void") + add1291: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1083, model_decoder_layers_7_self_attn_q_proj_bias5) + reshape1426: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1291, R.shape([1, 1, 20, 64])) + permute_dims1085: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_7_self_attn_k_proj_weight5, axes=None) + matmul1084: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm377, permute_dims1085, out_dtype="void") + reshape1427: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1084, R.shape([1, 1, 20, 64])) + permute_dims1086: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_7_self_attn_v_proj_weight5, axes=None) + matmul1085: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm377, permute_dims1086, out_dtype="void") + add1292: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1085, model_decoder_layers_7_self_attn_v_proj_bias5) + reshape1428: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1292, R.shape([1, 1, 20, 64])) + concat103: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1426, reshape1427, reshape1428), axis=2) + reshape1429: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat103, R.shape([1, 60, 64])) + lv279 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), reshape1429), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1430: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv279, R.shape([1, 1, 20, 64])) + reshape1431: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1430, R.shape([1, 1, 1280])) + permute_dims1087: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_7_self_attn_out_proj_weight5, axes=None) + matmul1086: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1431, permute_dims1087, out_dtype="void") + add1293: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1086, model_decoder_layers_7_self_attn_out_proj_bias5) + add1294: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1290, add1293) + layer_norm378: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1294, model_decoder_layers_7_encoder_attn_layer_norm_weight5, model_decoder_layers_7_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1088: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_7_encoder_attn_q_proj_weight5, axes=None) + matmul1087: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm378, permute_dims1088, out_dtype="void") + add1295: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1087, model_decoder_layers_7_encoder_attn_q_proj_bias5) + reshape1432: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1295, R.shape([1, 1, 20, 64])) + reshape1433: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1432, R.shape([1, 20, 64])) + lv280 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), reshape1433), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1434: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv280, R.shape([1, 1, 20, 64])) + reshape1435: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1434, R.shape([1, 1, 1280])) + permute_dims1089: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_7_encoder_attn_out_proj_weight5, axes=None) + matmul1088: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1435, permute_dims1089, out_dtype="void") + add1296: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1088, model_decoder_layers_7_encoder_attn_out_proj_bias5) + add1297: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1294, add1296) + layer_norm379: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1297, model_decoder_layers_7_final_layer_norm_weight5, model_decoder_layers_7_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1090: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_7_fc1_weight5, axes=None) + matmul1089: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm379, permute_dims1090, out_dtype="void") + add1298: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1089, model_decoder_layers_7_fc1_bias5) + gelu137: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1298) + permute_dims1091: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_7_fc2_weight5, axes=None) + matmul1090: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu137, permute_dims1091, out_dtype="void") + add1299: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1090, model_decoder_layers_7_fc2_bias5) + add1300: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1297, add1299) + layer_norm380: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1300, model_decoder_layers_8_self_attn_layer_norm_weight5, model_decoder_layers_8_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1092: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_8_self_attn_q_proj_weight5, axes=None) + matmul1091: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm380, permute_dims1092, out_dtype="void") + add1301: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1091, model_decoder_layers_8_self_attn_q_proj_bias5) + reshape1436: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1301, R.shape([1, 1, 20, 64])) + permute_dims1093: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_8_self_attn_k_proj_weight5, axes=None) + matmul1092: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm380, permute_dims1093, out_dtype="void") + reshape1437: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1092, R.shape([1, 1, 20, 64])) + permute_dims1094: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_8_self_attn_v_proj_weight5, axes=None) + matmul1093: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm380, permute_dims1094, out_dtype="void") + add1302: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1093, model_decoder_layers_8_self_attn_v_proj_bias5) + reshape1438: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1302, R.shape([1, 1, 20, 64])) + concat104: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1436, reshape1437, reshape1438), axis=2) + reshape1439: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat104, R.shape([1, 60, 64])) + lv281 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), reshape1439), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1440: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv281, R.shape([1, 1, 20, 64])) + reshape1441: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1440, R.shape([1, 1, 1280])) + permute_dims1095: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_8_self_attn_out_proj_weight5, axes=None) + matmul1094: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1441, permute_dims1095, out_dtype="void") + add1303: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1094, model_decoder_layers_8_self_attn_out_proj_bias5) + add1304: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1300, add1303) + layer_norm381: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1304, model_decoder_layers_8_encoder_attn_layer_norm_weight5, model_decoder_layers_8_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1096: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_8_encoder_attn_q_proj_weight5, axes=None) + matmul1095: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm381, permute_dims1096, out_dtype="void") + add1305: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1095, model_decoder_layers_8_encoder_attn_q_proj_bias5) + reshape1442: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1305, R.shape([1, 1, 20, 64])) + reshape1443: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1442, R.shape([1, 20, 64])) + lv282 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), reshape1443), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1444: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv282, R.shape([1, 1, 20, 64])) + reshape1445: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1444, R.shape([1, 1, 1280])) + permute_dims1097: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_8_encoder_attn_out_proj_weight5, axes=None) + matmul1096: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1445, permute_dims1097, out_dtype="void") + add1306: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1096, model_decoder_layers_8_encoder_attn_out_proj_bias5) + add1307: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1304, add1306) + layer_norm382: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1307, model_decoder_layers_8_final_layer_norm_weight5, model_decoder_layers_8_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1098: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_8_fc1_weight5, axes=None) + matmul1097: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm382, permute_dims1098, out_dtype="void") + add1308: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1097, model_decoder_layers_8_fc1_bias5) + gelu138: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1308) + permute_dims1099: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_8_fc2_weight5, axes=None) + matmul1098: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu138, permute_dims1099, out_dtype="void") + add1309: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1098, model_decoder_layers_8_fc2_bias5) + add1310: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1307, add1309) + layer_norm383: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1310, model_decoder_layers_9_self_attn_layer_norm_weight5, model_decoder_layers_9_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1100: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_9_self_attn_q_proj_weight5, axes=None) + matmul1099: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm383, permute_dims1100, out_dtype="void") + add1311: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1099, model_decoder_layers_9_self_attn_q_proj_bias5) + reshape1446: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1311, R.shape([1, 1, 20, 64])) + permute_dims1101: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_9_self_attn_k_proj_weight5, axes=None) + matmul1100: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm383, permute_dims1101, out_dtype="void") + reshape1447: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1100, R.shape([1, 1, 20, 64])) + permute_dims1102: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_9_self_attn_v_proj_weight5, axes=None) + matmul1101: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm383, permute_dims1102, out_dtype="void") + add1312: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1101, model_decoder_layers_9_self_attn_v_proj_bias5) + reshape1448: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1312, R.shape([1, 1, 20, 64])) + concat105: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1446, reshape1447, reshape1448), axis=2) + reshape1449: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat105, R.shape([1, 60, 64])) + lv283 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), reshape1449), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1450: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv283, R.shape([1, 1, 20, 64])) + reshape1451: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1450, R.shape([1, 1, 1280])) + permute_dims1103: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_9_self_attn_out_proj_weight5, axes=None) + matmul1102: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1451, permute_dims1103, out_dtype="void") + add1313: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1102, model_decoder_layers_9_self_attn_out_proj_bias5) + add1314: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1310, add1313) + layer_norm384: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1314, model_decoder_layers_9_encoder_attn_layer_norm_weight5, model_decoder_layers_9_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1104: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_9_encoder_attn_q_proj_weight5, axes=None) + matmul1103: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm384, permute_dims1104, out_dtype="void") + add1315: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1103, model_decoder_layers_9_encoder_attn_q_proj_bias5) + reshape1452: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1315, R.shape([1, 1, 20, 64])) + reshape1453: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1452, R.shape([1, 20, 64])) + lv284 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), reshape1453), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1454: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv284, R.shape([1, 1, 20, 64])) + reshape1455: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1454, R.shape([1, 1, 1280])) + permute_dims1105: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_9_encoder_attn_out_proj_weight5, axes=None) + matmul1104: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1455, permute_dims1105, out_dtype="void") + add1316: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1104, model_decoder_layers_9_encoder_attn_out_proj_bias5) + add1317: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1314, add1316) + layer_norm385: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1317, model_decoder_layers_9_final_layer_norm_weight5, model_decoder_layers_9_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1106: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_9_fc1_weight5, axes=None) + matmul1105: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm385, permute_dims1106, out_dtype="void") + add1318: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1105, model_decoder_layers_9_fc1_bias5) + gelu139: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1318) + permute_dims1107: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_9_fc2_weight5, axes=None) + matmul1106: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu139, permute_dims1107, out_dtype="void") + add1319: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1106, model_decoder_layers_9_fc2_bias5) + add1320: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1317, add1319) + layer_norm386: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1320, model_decoder_layers_10_self_attn_layer_norm_weight5, model_decoder_layers_10_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1108: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_10_self_attn_q_proj_weight5, axes=None) + matmul1107: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm386, permute_dims1108, out_dtype="void") + add1321: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1107, model_decoder_layers_10_self_attn_q_proj_bias5) + reshape1456: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1321, R.shape([1, 1, 20, 64])) + permute_dims1109: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_10_self_attn_k_proj_weight5, axes=None) + matmul1108: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm386, permute_dims1109, out_dtype="void") + reshape1457: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1108, R.shape([1, 1, 20, 64])) + permute_dims1110: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_10_self_attn_v_proj_weight5, axes=None) + matmul1109: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm386, permute_dims1110, out_dtype="void") + add1322: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1109, model_decoder_layers_10_self_attn_v_proj_bias5) + reshape1458: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1322, R.shape([1, 1, 20, 64])) + concat106: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1456, reshape1457, reshape1458), axis=2) + reshape1459: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat106, R.shape([1, 60, 64])) + lv285 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), reshape1459), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1460: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv285, R.shape([1, 1, 20, 64])) + reshape1461: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1460, R.shape([1, 1, 1280])) + permute_dims1111: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_10_self_attn_out_proj_weight5, axes=None) + matmul1110: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1461, permute_dims1111, out_dtype="void") + add1323: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1110, model_decoder_layers_10_self_attn_out_proj_bias5) + add1324: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1320, add1323) + layer_norm387: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1324, model_decoder_layers_10_encoder_attn_layer_norm_weight5, model_decoder_layers_10_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1112: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_10_encoder_attn_q_proj_weight5, axes=None) + matmul1111: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm387, permute_dims1112, out_dtype="void") + add1325: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1111, model_decoder_layers_10_encoder_attn_q_proj_bias5) + reshape1462: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1325, R.shape([1, 1, 20, 64])) + reshape1463: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1462, R.shape([1, 20, 64])) + lv286 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), reshape1463), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1464: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv286, R.shape([1, 1, 20, 64])) + reshape1465: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1464, R.shape([1, 1, 1280])) + permute_dims1113: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_10_encoder_attn_out_proj_weight5, axes=None) + matmul1112: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1465, permute_dims1113, out_dtype="void") + add1326: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1112, model_decoder_layers_10_encoder_attn_out_proj_bias5) + add1327: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1324, add1326) + layer_norm388: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1327, model_decoder_layers_10_final_layer_norm_weight5, model_decoder_layers_10_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1114: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_10_fc1_weight5, axes=None) + matmul1113: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm388, permute_dims1114, out_dtype="void") + add1328: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1113, model_decoder_layers_10_fc1_bias5) + gelu140: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1328) + permute_dims1115: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_10_fc2_weight5, axes=None) + matmul1114: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu140, permute_dims1115, out_dtype="void") + add1329: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1114, model_decoder_layers_10_fc2_bias5) + add1330: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1327, add1329) + layer_norm389: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1330, model_decoder_layers_11_self_attn_layer_norm_weight5, model_decoder_layers_11_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1116: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_11_self_attn_q_proj_weight5, axes=None) + matmul1115: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm389, permute_dims1116, out_dtype="void") + add1331: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1115, model_decoder_layers_11_self_attn_q_proj_bias5) + reshape1466: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1331, R.shape([1, 1, 20, 64])) + permute_dims1117: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_11_self_attn_k_proj_weight5, axes=None) + matmul1116: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm389, permute_dims1117, out_dtype="void") + reshape1467: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1116, R.shape([1, 1, 20, 64])) + permute_dims1118: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_11_self_attn_v_proj_weight5, axes=None) + matmul1117: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm389, permute_dims1118, out_dtype="void") + add1332: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1117, model_decoder_layers_11_self_attn_v_proj_bias5) + reshape1468: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1332, R.shape([1, 1, 20, 64])) + concat107: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1466, reshape1467, reshape1468), axis=2) + reshape1469: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat107, R.shape([1, 60, 64])) + lv287 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), reshape1469), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1470: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv287, R.shape([1, 1, 20, 64])) + reshape1471: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1470, R.shape([1, 1, 1280])) + permute_dims1119: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_11_self_attn_out_proj_weight5, axes=None) + matmul1118: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1471, permute_dims1119, out_dtype="void") + add1333: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1118, model_decoder_layers_11_self_attn_out_proj_bias5) + add1334: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1330, add1333) + layer_norm390: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1334, model_decoder_layers_11_encoder_attn_layer_norm_weight5, model_decoder_layers_11_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1120: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_11_encoder_attn_q_proj_weight5, axes=None) + matmul1119: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm390, permute_dims1120, out_dtype="void") + add1335: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1119, model_decoder_layers_11_encoder_attn_q_proj_bias5) + reshape1472: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1335, R.shape([1, 1, 20, 64])) + reshape1473: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1472, R.shape([1, 20, 64])) + lv288 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), reshape1473), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1474: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv288, R.shape([1, 1, 20, 64])) + reshape1475: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1474, R.shape([1, 1, 1280])) + permute_dims1121: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_11_encoder_attn_out_proj_weight5, axes=None) + matmul1120: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1475, permute_dims1121, out_dtype="void") + add1336: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1120, model_decoder_layers_11_encoder_attn_out_proj_bias5) + add1337: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1334, add1336) + layer_norm391: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1337, model_decoder_layers_11_final_layer_norm_weight5, model_decoder_layers_11_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1122: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_11_fc1_weight5, axes=None) + matmul1121: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm391, permute_dims1122, out_dtype="void") + add1338: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1121, model_decoder_layers_11_fc1_bias5) + gelu141: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1338) + permute_dims1123: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_11_fc2_weight5, axes=None) + matmul1122: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu141, permute_dims1123, out_dtype="void") + add1339: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1122, model_decoder_layers_11_fc2_bias5) + add1340: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1337, add1339) + layer_norm392: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1340, model_decoder_layers_12_self_attn_layer_norm_weight5, model_decoder_layers_12_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1124: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_12_self_attn_q_proj_weight5, axes=None) + matmul1123: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm392, permute_dims1124, out_dtype="void") + add1341: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1123, model_decoder_layers_12_self_attn_q_proj_bias5) + reshape1476: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1341, R.shape([1, 1, 20, 64])) + permute_dims1125: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_12_self_attn_k_proj_weight5, axes=None) + matmul1124: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm392, permute_dims1125, out_dtype="void") + reshape1477: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1124, R.shape([1, 1, 20, 64])) + permute_dims1126: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_12_self_attn_v_proj_weight5, axes=None) + matmul1125: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm392, permute_dims1126, out_dtype="void") + add1342: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1125, model_decoder_layers_12_self_attn_v_proj_bias5) + reshape1478: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1342, R.shape([1, 1, 20, 64])) + concat108: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1476, reshape1477, reshape1478), axis=2) + reshape1479: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat108, R.shape([1, 60, 64])) + lv289 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), reshape1479), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1480: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv289, R.shape([1, 1, 20, 64])) + reshape1481: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1480, R.shape([1, 1, 1280])) + permute_dims1127: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_12_self_attn_out_proj_weight5, axes=None) + matmul1126: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1481, permute_dims1127, out_dtype="void") + add1343: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1126, model_decoder_layers_12_self_attn_out_proj_bias5) + add1344: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1340, add1343) + layer_norm393: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1344, model_decoder_layers_12_encoder_attn_layer_norm_weight5, model_decoder_layers_12_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1128: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_12_encoder_attn_q_proj_weight5, axes=None) + matmul1127: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm393, permute_dims1128, out_dtype="void") + add1345: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1127, model_decoder_layers_12_encoder_attn_q_proj_bias5) + reshape1482: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1345, R.shape([1, 1, 20, 64])) + reshape1483: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1482, R.shape([1, 20, 64])) + lv290 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), reshape1483), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1484: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv290, R.shape([1, 1, 20, 64])) + reshape1485: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1484, R.shape([1, 1, 1280])) + permute_dims1129: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_12_encoder_attn_out_proj_weight5, axes=None) + matmul1128: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1485, permute_dims1129, out_dtype="void") + add1346: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1128, model_decoder_layers_12_encoder_attn_out_proj_bias5) + add1347: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1344, add1346) + layer_norm394: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1347, model_decoder_layers_12_final_layer_norm_weight5, model_decoder_layers_12_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1130: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_12_fc1_weight5, axes=None) + matmul1129: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm394, permute_dims1130, out_dtype="void") + add1348: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1129, model_decoder_layers_12_fc1_bias5) + gelu142: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1348) + permute_dims1131: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_12_fc2_weight5, axes=None) + matmul1130: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu142, permute_dims1131, out_dtype="void") + add1349: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1130, model_decoder_layers_12_fc2_bias5) + add1350: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1347, add1349) + layer_norm395: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1350, model_decoder_layers_13_self_attn_layer_norm_weight5, model_decoder_layers_13_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1132: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_13_self_attn_q_proj_weight5, axes=None) + matmul1131: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm395, permute_dims1132, out_dtype="void") + add1351: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1131, model_decoder_layers_13_self_attn_q_proj_bias5) + reshape1486: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1351, R.shape([1, 1, 20, 64])) + permute_dims1133: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_13_self_attn_k_proj_weight5, axes=None) + matmul1132: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm395, permute_dims1133, out_dtype="void") + reshape1487: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1132, R.shape([1, 1, 20, 64])) + permute_dims1134: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_13_self_attn_v_proj_weight5, axes=None) + matmul1133: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm395, permute_dims1134, out_dtype="void") + add1352: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1133, model_decoder_layers_13_self_attn_v_proj_bias5) + reshape1488: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1352, R.shape([1, 1, 20, 64])) + concat109: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1486, reshape1487, reshape1488), axis=2) + reshape1489: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat109, R.shape([1, 60, 64])) + lv291 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), reshape1489), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1490: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv291, R.shape([1, 1, 20, 64])) + reshape1491: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1490, R.shape([1, 1, 1280])) + permute_dims1135: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_13_self_attn_out_proj_weight5, axes=None) + matmul1134: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1491, permute_dims1135, out_dtype="void") + add1353: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1134, model_decoder_layers_13_self_attn_out_proj_bias5) + add1354: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1350, add1353) + layer_norm396: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1354, model_decoder_layers_13_encoder_attn_layer_norm_weight5, model_decoder_layers_13_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1136: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_13_encoder_attn_q_proj_weight5, axes=None) + matmul1135: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm396, permute_dims1136, out_dtype="void") + add1355: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1135, model_decoder_layers_13_encoder_attn_q_proj_bias5) + reshape1492: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1355, R.shape([1, 1, 20, 64])) + reshape1493: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1492, R.shape([1, 20, 64])) + lv292 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), reshape1493), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1494: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv292, R.shape([1, 1, 20, 64])) + reshape1495: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1494, R.shape([1, 1, 1280])) + permute_dims1137: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_13_encoder_attn_out_proj_weight5, axes=None) + matmul1136: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1495, permute_dims1137, out_dtype="void") + add1356: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1136, model_decoder_layers_13_encoder_attn_out_proj_bias5) + add1357: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1354, add1356) + layer_norm397: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1357, model_decoder_layers_13_final_layer_norm_weight5, model_decoder_layers_13_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1138: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_13_fc1_weight5, axes=None) + matmul1137: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm397, permute_dims1138, out_dtype="void") + add1358: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1137, model_decoder_layers_13_fc1_bias5) + gelu143: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1358) + permute_dims1139: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_13_fc2_weight5, axes=None) + matmul1138: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu143, permute_dims1139, out_dtype="void") + add1359: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1138, model_decoder_layers_13_fc2_bias5) + add1360: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1357, add1359) + layer_norm398: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1360, model_decoder_layers_14_self_attn_layer_norm_weight5, model_decoder_layers_14_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1140: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_14_self_attn_q_proj_weight5, axes=None) + matmul1139: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm398, permute_dims1140, out_dtype="void") + add1361: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1139, model_decoder_layers_14_self_attn_q_proj_bias5) + reshape1496: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1361, R.shape([1, 1, 20, 64])) + permute_dims1141: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_14_self_attn_k_proj_weight5, axes=None) + matmul1140: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm398, permute_dims1141, out_dtype="void") + reshape1497: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1140, R.shape([1, 1, 20, 64])) + permute_dims1142: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_14_self_attn_v_proj_weight5, axes=None) + matmul1141: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm398, permute_dims1142, out_dtype="void") + add1362: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1141, model_decoder_layers_14_self_attn_v_proj_bias5) + reshape1498: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1362, R.shape([1, 1, 20, 64])) + concat110: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1496, reshape1497, reshape1498), axis=2) + reshape1499: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat110, R.shape([1, 60, 64])) + lv293 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), reshape1499), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1500: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv293, R.shape([1, 1, 20, 64])) + reshape1501: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1500, R.shape([1, 1, 1280])) + permute_dims1143: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_14_self_attn_out_proj_weight5, axes=None) + matmul1142: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1501, permute_dims1143, out_dtype="void") + add1363: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1142, model_decoder_layers_14_self_attn_out_proj_bias5) + add1364: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1360, add1363) + layer_norm399: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1364, model_decoder_layers_14_encoder_attn_layer_norm_weight5, model_decoder_layers_14_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1144: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_14_encoder_attn_q_proj_weight5, axes=None) + matmul1143: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm399, permute_dims1144, out_dtype="void") + add1365: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1143, model_decoder_layers_14_encoder_attn_q_proj_bias5) + reshape1502: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1365, R.shape([1, 1, 20, 64])) + reshape1503: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1502, R.shape([1, 20, 64])) + lv294 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), reshape1503), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1504: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv294, R.shape([1, 1, 20, 64])) + reshape1505: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1504, R.shape([1, 1, 1280])) + permute_dims1145: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_14_encoder_attn_out_proj_weight5, axes=None) + matmul1144: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1505, permute_dims1145, out_dtype="void") + add1366: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1144, model_decoder_layers_14_encoder_attn_out_proj_bias5) + add1367: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1364, add1366) + layer_norm400: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1367, model_decoder_layers_14_final_layer_norm_weight5, model_decoder_layers_14_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1146: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_14_fc1_weight5, axes=None) + matmul1145: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm400, permute_dims1146, out_dtype="void") + add1368: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1145, model_decoder_layers_14_fc1_bias5) + gelu144: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1368) + permute_dims1147: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_14_fc2_weight5, axes=None) + matmul1146: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu144, permute_dims1147, out_dtype="void") + add1369: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1146, model_decoder_layers_14_fc2_bias5) + add1370: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1367, add1369) + layer_norm401: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1370, model_decoder_layers_15_self_attn_layer_norm_weight5, model_decoder_layers_15_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1148: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_15_self_attn_q_proj_weight5, axes=None) + matmul1147: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm401, permute_dims1148, out_dtype="void") + add1371: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1147, model_decoder_layers_15_self_attn_q_proj_bias5) + reshape1506: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1371, R.shape([1, 1, 20, 64])) + permute_dims1149: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_15_self_attn_k_proj_weight5, axes=None) + matmul1148: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm401, permute_dims1149, out_dtype="void") + reshape1507: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1148, R.shape([1, 1, 20, 64])) + permute_dims1150: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_15_self_attn_v_proj_weight5, axes=None) + matmul1149: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm401, permute_dims1150, out_dtype="void") + add1372: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1149, model_decoder_layers_15_self_attn_v_proj_bias5) + reshape1508: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1372, R.shape([1, 1, 20, 64])) + concat111: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1506, reshape1507, reshape1508), axis=2) + reshape1509: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat111, R.shape([1, 60, 64])) + lv295 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), reshape1509), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1510: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv295, R.shape([1, 1, 20, 64])) + reshape1511: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1510, R.shape([1, 1, 1280])) + permute_dims1151: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_15_self_attn_out_proj_weight5, axes=None) + matmul1150: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1511, permute_dims1151, out_dtype="void") + add1373: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1150, model_decoder_layers_15_self_attn_out_proj_bias5) + add1374: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1370, add1373) + layer_norm402: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1374, model_decoder_layers_15_encoder_attn_layer_norm_weight5, model_decoder_layers_15_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1152: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_15_encoder_attn_q_proj_weight5, axes=None) + matmul1151: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm402, permute_dims1152, out_dtype="void") + add1375: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1151, model_decoder_layers_15_encoder_attn_q_proj_bias5) + reshape1512: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1375, R.shape([1, 1, 20, 64])) + reshape1513: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1512, R.shape([1, 20, 64])) + lv296 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), reshape1513), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1514: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv296, R.shape([1, 1, 20, 64])) + reshape1515: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1514, R.shape([1, 1, 1280])) + permute_dims1153: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_15_encoder_attn_out_proj_weight5, axes=None) + matmul1152: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1515, permute_dims1153, out_dtype="void") + add1376: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1152, model_decoder_layers_15_encoder_attn_out_proj_bias5) + add1377: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1374, add1376) + layer_norm403: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1377, model_decoder_layers_15_final_layer_norm_weight5, model_decoder_layers_15_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1154: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_15_fc1_weight5, axes=None) + matmul1153: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm403, permute_dims1154, out_dtype="void") + add1378: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1153, model_decoder_layers_15_fc1_bias5) + gelu145: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1378) + permute_dims1155: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_15_fc2_weight5, axes=None) + matmul1154: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu145, permute_dims1155, out_dtype="void") + add1379: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1154, model_decoder_layers_15_fc2_bias5) + add1380: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1377, add1379) + layer_norm404: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1380, model_decoder_layers_16_self_attn_layer_norm_weight5, model_decoder_layers_16_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1156: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_16_self_attn_q_proj_weight5, axes=None) + matmul1155: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm404, permute_dims1156, out_dtype="void") + add1381: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1155, model_decoder_layers_16_self_attn_q_proj_bias5) + reshape1516: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1381, R.shape([1, 1, 20, 64])) + permute_dims1157: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_16_self_attn_k_proj_weight5, axes=None) + matmul1156: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm404, permute_dims1157, out_dtype="void") + reshape1517: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1156, R.shape([1, 1, 20, 64])) + permute_dims1158: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_16_self_attn_v_proj_weight5, axes=None) + matmul1157: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm404, permute_dims1158, out_dtype="void") + add1382: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1157, model_decoder_layers_16_self_attn_v_proj_bias5) + reshape1518: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1382, R.shape([1, 1, 20, 64])) + concat112: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1516, reshape1517, reshape1518), axis=2) + reshape1519: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat112, R.shape([1, 60, 64])) + lv297 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), reshape1519), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1520: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv297, R.shape([1, 1, 20, 64])) + reshape1521: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1520, R.shape([1, 1, 1280])) + permute_dims1159: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_16_self_attn_out_proj_weight5, axes=None) + matmul1158: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1521, permute_dims1159, out_dtype="void") + add1383: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1158, model_decoder_layers_16_self_attn_out_proj_bias5) + add1384: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1380, add1383) + layer_norm405: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1384, model_decoder_layers_16_encoder_attn_layer_norm_weight5, model_decoder_layers_16_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1160: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_16_encoder_attn_q_proj_weight5, axes=None) + matmul1159: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm405, permute_dims1160, out_dtype="void") + add1385: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1159, model_decoder_layers_16_encoder_attn_q_proj_bias5) + reshape1522: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1385, R.shape([1, 1, 20, 64])) + reshape1523: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1522, R.shape([1, 20, 64])) + lv298 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), reshape1523), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1524: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv298, R.shape([1, 1, 20, 64])) + reshape1525: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1524, R.shape([1, 1, 1280])) + permute_dims1161: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_16_encoder_attn_out_proj_weight5, axes=None) + matmul1160: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1525, permute_dims1161, out_dtype="void") + add1386: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1160, model_decoder_layers_16_encoder_attn_out_proj_bias5) + add1387: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1384, add1386) + layer_norm406: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1387, model_decoder_layers_16_final_layer_norm_weight5, model_decoder_layers_16_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1162: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_16_fc1_weight5, axes=None) + matmul1161: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm406, permute_dims1162, out_dtype="void") + add1388: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1161, model_decoder_layers_16_fc1_bias5) + gelu146: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1388) + permute_dims1163: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_16_fc2_weight5, axes=None) + matmul1162: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu146, permute_dims1163, out_dtype="void") + add1389: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1162, model_decoder_layers_16_fc2_bias5) + add1390: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1387, add1389) + layer_norm407: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1390, model_decoder_layers_17_self_attn_layer_norm_weight5, model_decoder_layers_17_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1164: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_17_self_attn_q_proj_weight5, axes=None) + matmul1163: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm407, permute_dims1164, out_dtype="void") + add1391: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1163, model_decoder_layers_17_self_attn_q_proj_bias5) + reshape1526: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1391, R.shape([1, 1, 20, 64])) + permute_dims1165: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_17_self_attn_k_proj_weight5, axes=None) + matmul1164: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm407, permute_dims1165, out_dtype="void") + reshape1527: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1164, R.shape([1, 1, 20, 64])) + permute_dims1166: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_17_self_attn_v_proj_weight5, axes=None) + matmul1165: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm407, permute_dims1166, out_dtype="void") + add1392: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1165, model_decoder_layers_17_self_attn_v_proj_bias5) + reshape1528: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1392, R.shape([1, 1, 20, 64])) + concat113: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1526, reshape1527, reshape1528), axis=2) + reshape1529: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat113, R.shape([1, 60, 64])) + lv299 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), reshape1529), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1530: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv299, R.shape([1, 1, 20, 64])) + reshape1531: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1530, R.shape([1, 1, 1280])) + permute_dims1167: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_17_self_attn_out_proj_weight5, axes=None) + matmul1166: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1531, permute_dims1167, out_dtype="void") + add1393: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1166, model_decoder_layers_17_self_attn_out_proj_bias5) + add1394: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1390, add1393) + layer_norm408: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1394, model_decoder_layers_17_encoder_attn_layer_norm_weight5, model_decoder_layers_17_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1168: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_17_encoder_attn_q_proj_weight5, axes=None) + matmul1167: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm408, permute_dims1168, out_dtype="void") + add1395: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1167, model_decoder_layers_17_encoder_attn_q_proj_bias5) + reshape1532: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1395, R.shape([1, 1, 20, 64])) + reshape1533: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1532, R.shape([1, 20, 64])) + lv300 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), reshape1533), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1534: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv300, R.shape([1, 1, 20, 64])) + reshape1535: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1534, R.shape([1, 1, 1280])) + permute_dims1169: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_17_encoder_attn_out_proj_weight5, axes=None) + matmul1168: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1535, permute_dims1169, out_dtype="void") + add1396: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1168, model_decoder_layers_17_encoder_attn_out_proj_bias5) + add1397: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1394, add1396) + layer_norm409: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1397, model_decoder_layers_17_final_layer_norm_weight5, model_decoder_layers_17_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1170: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_17_fc1_weight5, axes=None) + matmul1169: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm409, permute_dims1170, out_dtype="void") + add1398: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1169, model_decoder_layers_17_fc1_bias5) + gelu147: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1398) + permute_dims1171: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_17_fc2_weight5, axes=None) + matmul1170: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu147, permute_dims1171, out_dtype="void") + add1399: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1170, model_decoder_layers_17_fc2_bias5) + add1400: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1397, add1399) + layer_norm410: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1400, model_decoder_layers_18_self_attn_layer_norm_weight5, model_decoder_layers_18_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1172: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_18_self_attn_q_proj_weight5, axes=None) + matmul1171: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm410, permute_dims1172, out_dtype="void") + add1401: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1171, model_decoder_layers_18_self_attn_q_proj_bias5) + reshape1536: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1401, R.shape([1, 1, 20, 64])) + permute_dims1173: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_18_self_attn_k_proj_weight5, axes=None) + matmul1172: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm410, permute_dims1173, out_dtype="void") + reshape1537: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1172, R.shape([1, 1, 20, 64])) + permute_dims1174: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_18_self_attn_v_proj_weight5, axes=None) + matmul1173: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm410, permute_dims1174, out_dtype="void") + add1402: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1173, model_decoder_layers_18_self_attn_v_proj_bias5) + reshape1538: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1402, R.shape([1, 1, 20, 64])) + concat114: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1536, reshape1537, reshape1538), axis=2) + reshape1539: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat114, R.shape([1, 60, 64])) + lv301 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), reshape1539), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1540: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv301, R.shape([1, 1, 20, 64])) + reshape1541: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1540, R.shape([1, 1, 1280])) + permute_dims1175: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_18_self_attn_out_proj_weight5, axes=None) + matmul1174: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1541, permute_dims1175, out_dtype="void") + add1403: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1174, model_decoder_layers_18_self_attn_out_proj_bias5) + add1404: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1400, add1403) + layer_norm411: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1404, model_decoder_layers_18_encoder_attn_layer_norm_weight5, model_decoder_layers_18_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1176: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_18_encoder_attn_q_proj_weight5, axes=None) + matmul1175: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm411, permute_dims1176, out_dtype="void") + add1405: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1175, model_decoder_layers_18_encoder_attn_q_proj_bias5) + reshape1542: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1405, R.shape([1, 1, 20, 64])) + reshape1543: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1542, R.shape([1, 20, 64])) + lv302 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), reshape1543), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1544: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv302, R.shape([1, 1, 20, 64])) + reshape1545: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1544, R.shape([1, 1, 1280])) + permute_dims1177: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_18_encoder_attn_out_proj_weight5, axes=None) + matmul1176: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1545, permute_dims1177, out_dtype="void") + add1406: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1176, model_decoder_layers_18_encoder_attn_out_proj_bias5) + add1407: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1404, add1406) + layer_norm412: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1407, model_decoder_layers_18_final_layer_norm_weight5, model_decoder_layers_18_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1178: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_18_fc1_weight5, axes=None) + matmul1177: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm412, permute_dims1178, out_dtype="void") + add1408: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1177, model_decoder_layers_18_fc1_bias5) + gelu148: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1408) + permute_dims1179: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_18_fc2_weight5, axes=None) + matmul1178: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu148, permute_dims1179, out_dtype="void") + add1409: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1178, model_decoder_layers_18_fc2_bias5) + add1410: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1407, add1409) + layer_norm413: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1410, model_decoder_layers_19_self_attn_layer_norm_weight5, model_decoder_layers_19_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1180: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_19_self_attn_q_proj_weight5, axes=None) + matmul1179: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm413, permute_dims1180, out_dtype="void") + add1411: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1179, model_decoder_layers_19_self_attn_q_proj_bias5) + reshape1546: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1411, R.shape([1, 1, 20, 64])) + permute_dims1181: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_19_self_attn_k_proj_weight5, axes=None) + matmul1180: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm413, permute_dims1181, out_dtype="void") + reshape1547: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1180, R.shape([1, 1, 20, 64])) + permute_dims1182: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_19_self_attn_v_proj_weight5, axes=None) + matmul1181: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm413, permute_dims1182, out_dtype="void") + add1412: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1181, model_decoder_layers_19_self_attn_v_proj_bias5) + reshape1548: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1412, R.shape([1, 1, 20, 64])) + concat115: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1546, reshape1547, reshape1548), axis=2) + reshape1549: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat115, R.shape([1, 60, 64])) + lv303 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), reshape1549), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1550: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv303, R.shape([1, 1, 20, 64])) + reshape1551: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1550, R.shape([1, 1, 1280])) + permute_dims1183: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_19_self_attn_out_proj_weight5, axes=None) + matmul1182: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1551, permute_dims1183, out_dtype="void") + add1413: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1182, model_decoder_layers_19_self_attn_out_proj_bias5) + add1414: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1410, add1413) + layer_norm414: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1414, model_decoder_layers_19_encoder_attn_layer_norm_weight5, model_decoder_layers_19_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1184: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_19_encoder_attn_q_proj_weight5, axes=None) + matmul1183: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm414, permute_dims1184, out_dtype="void") + add1415: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1183, model_decoder_layers_19_encoder_attn_q_proj_bias5) + reshape1552: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1415, R.shape([1, 1, 20, 64])) + reshape1553: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1552, R.shape([1, 20, 64])) + lv304 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), reshape1553), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1554: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv304, R.shape([1, 1, 20, 64])) + reshape1555: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1554, R.shape([1, 1, 1280])) + permute_dims1185: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_19_encoder_attn_out_proj_weight5, axes=None) + matmul1184: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1555, permute_dims1185, out_dtype="void") + add1416: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1184, model_decoder_layers_19_encoder_attn_out_proj_bias5) + add1417: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1414, add1416) + layer_norm415: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1417, model_decoder_layers_19_final_layer_norm_weight5, model_decoder_layers_19_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1186: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_19_fc1_weight5, axes=None) + matmul1185: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm415, permute_dims1186, out_dtype="void") + add1418: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1185, model_decoder_layers_19_fc1_bias5) + gelu149: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1418) + permute_dims1187: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_19_fc2_weight5, axes=None) + matmul1186: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu149, permute_dims1187, out_dtype="void") + add1419: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1186, model_decoder_layers_19_fc2_bias5) + add1420: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1417, add1419) + layer_norm416: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1420, model_decoder_layers_20_self_attn_layer_norm_weight5, model_decoder_layers_20_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1188: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_20_self_attn_q_proj_weight5, axes=None) + matmul1187: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm416, permute_dims1188, out_dtype="void") + add1421: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1187, model_decoder_layers_20_self_attn_q_proj_bias5) + reshape1556: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1421, R.shape([1, 1, 20, 64])) + permute_dims1189: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_20_self_attn_k_proj_weight5, axes=None) + matmul1188: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm416, permute_dims1189, out_dtype="void") + reshape1557: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1188, R.shape([1, 1, 20, 64])) + permute_dims1190: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_20_self_attn_v_proj_weight5, axes=None) + matmul1189: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm416, permute_dims1190, out_dtype="void") + add1422: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1189, model_decoder_layers_20_self_attn_v_proj_bias5) + reshape1558: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1422, R.shape([1, 1, 20, 64])) + concat116: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1556, reshape1557, reshape1558), axis=2) + reshape1559: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat116, R.shape([1, 60, 64])) + lv305 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), reshape1559), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1560: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv305, R.shape([1, 1, 20, 64])) + reshape1561: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1560, R.shape([1, 1, 1280])) + permute_dims1191: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_20_self_attn_out_proj_weight5, axes=None) + matmul1190: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1561, permute_dims1191, out_dtype="void") + add1423: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1190, model_decoder_layers_20_self_attn_out_proj_bias5) + add1424: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1420, add1423) + layer_norm417: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1424, model_decoder_layers_20_encoder_attn_layer_norm_weight5, model_decoder_layers_20_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1192: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_20_encoder_attn_q_proj_weight5, axes=None) + matmul1191: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm417, permute_dims1192, out_dtype="void") + add1425: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1191, model_decoder_layers_20_encoder_attn_q_proj_bias5) + reshape1562: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1425, R.shape([1, 1, 20, 64])) + reshape1563: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1562, R.shape([1, 20, 64])) + lv306 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), reshape1563), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1564: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv306, R.shape([1, 1, 20, 64])) + reshape1565: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1564, R.shape([1, 1, 1280])) + permute_dims1193: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_20_encoder_attn_out_proj_weight5, axes=None) + matmul1192: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1565, permute_dims1193, out_dtype="void") + add1426: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1192, model_decoder_layers_20_encoder_attn_out_proj_bias5) + add1427: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1424, add1426) + layer_norm418: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1427, model_decoder_layers_20_final_layer_norm_weight5, model_decoder_layers_20_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1194: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_20_fc1_weight5, axes=None) + matmul1193: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm418, permute_dims1194, out_dtype="void") + add1428: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1193, model_decoder_layers_20_fc1_bias5) + gelu150: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1428) + permute_dims1195: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_20_fc2_weight5, axes=None) + matmul1194: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu150, permute_dims1195, out_dtype="void") + add1429: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1194, model_decoder_layers_20_fc2_bias5) + add1430: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1427, add1429) + layer_norm419: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1430, model_decoder_layers_21_self_attn_layer_norm_weight5, model_decoder_layers_21_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1196: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_21_self_attn_q_proj_weight5, axes=None) + matmul1195: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm419, permute_dims1196, out_dtype="void") + add1431: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1195, model_decoder_layers_21_self_attn_q_proj_bias5) + reshape1566: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1431, R.shape([1, 1, 20, 64])) + permute_dims1197: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_21_self_attn_k_proj_weight5, axes=None) + matmul1196: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm419, permute_dims1197, out_dtype="void") + reshape1567: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1196, R.shape([1, 1, 20, 64])) + permute_dims1198: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_21_self_attn_v_proj_weight5, axes=None) + matmul1197: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm419, permute_dims1198, out_dtype="void") + add1432: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1197, model_decoder_layers_21_self_attn_v_proj_bias5) + reshape1568: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1432, R.shape([1, 1, 20, 64])) + concat117: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1566, reshape1567, reshape1568), axis=2) + reshape1569: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat117, R.shape([1, 60, 64])) + lv307 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), reshape1569), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1570: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv307, R.shape([1, 1, 20, 64])) + reshape1571: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1570, R.shape([1, 1, 1280])) + permute_dims1199: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_21_self_attn_out_proj_weight5, axes=None) + matmul1198: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1571, permute_dims1199, out_dtype="void") + add1433: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1198, model_decoder_layers_21_self_attn_out_proj_bias5) + add1434: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1430, add1433) + layer_norm420: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1434, model_decoder_layers_21_encoder_attn_layer_norm_weight5, model_decoder_layers_21_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1200: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_21_encoder_attn_q_proj_weight5, axes=None) + matmul1199: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm420, permute_dims1200, out_dtype="void") + add1435: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1199, model_decoder_layers_21_encoder_attn_q_proj_bias5) + reshape1572: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1435, R.shape([1, 1, 20, 64])) + reshape1573: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1572, R.shape([1, 20, 64])) + lv308 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), reshape1573), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1574: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv308, R.shape([1, 1, 20, 64])) + reshape1575: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1574, R.shape([1, 1, 1280])) + permute_dims1201: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_21_encoder_attn_out_proj_weight5, axes=None) + matmul1200: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1575, permute_dims1201, out_dtype="void") + add1436: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1200, model_decoder_layers_21_encoder_attn_out_proj_bias5) + add1437: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1434, add1436) + layer_norm421: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1437, model_decoder_layers_21_final_layer_norm_weight5, model_decoder_layers_21_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1202: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_21_fc1_weight5, axes=None) + matmul1201: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm421, permute_dims1202, out_dtype="void") + add1438: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1201, model_decoder_layers_21_fc1_bias5) + gelu151: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1438) + permute_dims1203: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_21_fc2_weight5, axes=None) + matmul1202: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu151, permute_dims1203, out_dtype="void") + add1439: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1202, model_decoder_layers_21_fc2_bias5) + add1440: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1437, add1439) + layer_norm422: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1440, model_decoder_layers_22_self_attn_layer_norm_weight5, model_decoder_layers_22_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1204: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_22_self_attn_q_proj_weight5, axes=None) + matmul1203: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm422, permute_dims1204, out_dtype="void") + add1441: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1203, model_decoder_layers_22_self_attn_q_proj_bias5) + reshape1576: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1441, R.shape([1, 1, 20, 64])) + permute_dims1205: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_22_self_attn_k_proj_weight5, axes=None) + matmul1204: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm422, permute_dims1205, out_dtype="void") + reshape1577: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1204, R.shape([1, 1, 20, 64])) + permute_dims1206: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_22_self_attn_v_proj_weight5, axes=None) + matmul1205: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm422, permute_dims1206, out_dtype="void") + add1442: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1205, model_decoder_layers_22_self_attn_v_proj_bias5) + reshape1578: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1442, R.shape([1, 1, 20, 64])) + concat118: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1576, reshape1577, reshape1578), axis=2) + reshape1579: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat118, R.shape([1, 60, 64])) + lv309 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), reshape1579), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1580: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv309, R.shape([1, 1, 20, 64])) + reshape1581: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1580, R.shape([1, 1, 1280])) + permute_dims1207: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_22_self_attn_out_proj_weight5, axes=None) + matmul1206: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1581, permute_dims1207, out_dtype="void") + add1443: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1206, model_decoder_layers_22_self_attn_out_proj_bias5) + add1444: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1440, add1443) + layer_norm423: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1444, model_decoder_layers_22_encoder_attn_layer_norm_weight5, model_decoder_layers_22_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1208: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_22_encoder_attn_q_proj_weight5, axes=None) + matmul1207: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm423, permute_dims1208, out_dtype="void") + add1445: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1207, model_decoder_layers_22_encoder_attn_q_proj_bias5) + reshape1582: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1445, R.shape([1, 1, 20, 64])) + reshape1583: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1582, R.shape([1, 20, 64])) + lv310 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), reshape1583), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1584: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv310, R.shape([1, 1, 20, 64])) + reshape1585: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1584, R.shape([1, 1, 1280])) + permute_dims1209: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_22_encoder_attn_out_proj_weight5, axes=None) + matmul1208: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1585, permute_dims1209, out_dtype="void") + add1446: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1208, model_decoder_layers_22_encoder_attn_out_proj_bias5) + add1447: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1444, add1446) + layer_norm424: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1447, model_decoder_layers_22_final_layer_norm_weight5, model_decoder_layers_22_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1210: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_22_fc1_weight5, axes=None) + matmul1209: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm424, permute_dims1210, out_dtype="void") + add1448: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1209, model_decoder_layers_22_fc1_bias5) + gelu152: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1448) + permute_dims1211: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_22_fc2_weight5, axes=None) + matmul1210: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu152, permute_dims1211, out_dtype="void") + add1449: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1210, model_decoder_layers_22_fc2_bias5) + add1450: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1447, add1449) + layer_norm425: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1450, model_decoder_layers_23_self_attn_layer_norm_weight5, model_decoder_layers_23_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1212: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_23_self_attn_q_proj_weight5, axes=None) + matmul1211: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm425, permute_dims1212, out_dtype="void") + add1451: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1211, model_decoder_layers_23_self_attn_q_proj_bias5) + reshape1586: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1451, R.shape([1, 1, 20, 64])) + permute_dims1213: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_23_self_attn_k_proj_weight5, axes=None) + matmul1212: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm425, permute_dims1213, out_dtype="void") + reshape1587: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1212, R.shape([1, 1, 20, 64])) + permute_dims1214: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_23_self_attn_v_proj_weight5, axes=None) + matmul1213: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm425, permute_dims1214, out_dtype="void") + add1452: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1213, model_decoder_layers_23_self_attn_v_proj_bias5) + reshape1588: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1452, R.shape([1, 1, 20, 64])) + concat119: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1586, reshape1587, reshape1588), axis=2) + reshape1589: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat119, R.shape([1, 60, 64])) + lv311 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), reshape1589), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1590: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv311, R.shape([1, 1, 20, 64])) + reshape1591: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1590, R.shape([1, 1, 1280])) + permute_dims1215: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_23_self_attn_out_proj_weight5, axes=None) + matmul1214: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1591, permute_dims1215, out_dtype="void") + add1453: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1214, model_decoder_layers_23_self_attn_out_proj_bias5) + add1454: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1450, add1453) + layer_norm426: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1454, model_decoder_layers_23_encoder_attn_layer_norm_weight5, model_decoder_layers_23_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1216: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_23_encoder_attn_q_proj_weight5, axes=None) + matmul1215: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm426, permute_dims1216, out_dtype="void") + add1455: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1215, model_decoder_layers_23_encoder_attn_q_proj_bias5) + reshape1592: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1455, R.shape([1, 1, 20, 64])) + reshape1593: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1592, R.shape([1, 20, 64])) + lv312 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), reshape1593), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1594: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv312, R.shape([1, 1, 20, 64])) + reshape1595: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1594, R.shape([1, 1, 1280])) + permute_dims1217: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_23_encoder_attn_out_proj_weight5, axes=None) + matmul1216: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1595, permute_dims1217, out_dtype="void") + add1456: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1216, model_decoder_layers_23_encoder_attn_out_proj_bias5) + add1457: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1454, add1456) + layer_norm427: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1457, model_decoder_layers_23_final_layer_norm_weight5, model_decoder_layers_23_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1218: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_23_fc1_weight5, axes=None) + matmul1217: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm427, permute_dims1218, out_dtype="void") + add1458: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1217, model_decoder_layers_23_fc1_bias5) + gelu153: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1458) + permute_dims1219: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_23_fc2_weight5, axes=None) + matmul1218: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu153, permute_dims1219, out_dtype="void") + add1459: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1218, model_decoder_layers_23_fc2_bias5) + add1460: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1457, add1459) + layer_norm428: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1460, model_decoder_layers_24_self_attn_layer_norm_weight5, model_decoder_layers_24_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1220: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_24_self_attn_q_proj_weight5, axes=None) + matmul1219: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm428, permute_dims1220, out_dtype="void") + add1461: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1219, model_decoder_layers_24_self_attn_q_proj_bias5) + reshape1596: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1461, R.shape([1, 1, 20, 64])) + permute_dims1221: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_24_self_attn_k_proj_weight5, axes=None) + matmul1220: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm428, permute_dims1221, out_dtype="void") + reshape1597: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1220, R.shape([1, 1, 20, 64])) + permute_dims1222: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_24_self_attn_v_proj_weight5, axes=None) + matmul1221: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm428, permute_dims1222, out_dtype="void") + add1462: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1221, model_decoder_layers_24_self_attn_v_proj_bias5) + reshape1598: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1462, R.shape([1, 1, 20, 64])) + concat120: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1596, reshape1597, reshape1598), axis=2) + reshape1599: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat120, R.shape([1, 60, 64])) + lv313 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), reshape1599), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1600: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv313, R.shape([1, 1, 20, 64])) + reshape1601: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1600, R.shape([1, 1, 1280])) + permute_dims1223: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_24_self_attn_out_proj_weight5, axes=None) + matmul1222: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1601, permute_dims1223, out_dtype="void") + add1463: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1222, model_decoder_layers_24_self_attn_out_proj_bias5) + add1464: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1460, add1463) + layer_norm429: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1464, model_decoder_layers_24_encoder_attn_layer_norm_weight5, model_decoder_layers_24_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1224: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_24_encoder_attn_q_proj_weight5, axes=None) + matmul1223: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm429, permute_dims1224, out_dtype="void") + add1465: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1223, model_decoder_layers_24_encoder_attn_q_proj_bias5) + reshape1602: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1465, R.shape([1, 1, 20, 64])) + reshape1603: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1602, R.shape([1, 20, 64])) + lv314 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), reshape1603), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1604: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv314, R.shape([1, 1, 20, 64])) + reshape1605: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1604, R.shape([1, 1, 1280])) + permute_dims1225: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_24_encoder_attn_out_proj_weight5, axes=None) + matmul1224: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1605, permute_dims1225, out_dtype="void") + add1466: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1224, model_decoder_layers_24_encoder_attn_out_proj_bias5) + add1467: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1464, add1466) + layer_norm430: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1467, model_decoder_layers_24_final_layer_norm_weight5, model_decoder_layers_24_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1226: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_24_fc1_weight5, axes=None) + matmul1225: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm430, permute_dims1226, out_dtype="void") + add1468: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1225, model_decoder_layers_24_fc1_bias5) + gelu154: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1468) + permute_dims1227: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_24_fc2_weight5, axes=None) + matmul1226: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu154, permute_dims1227, out_dtype="void") + add1469: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1226, model_decoder_layers_24_fc2_bias5) + add1470: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1467, add1469) + layer_norm431: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1470, model_decoder_layers_25_self_attn_layer_norm_weight5, model_decoder_layers_25_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1228: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_25_self_attn_q_proj_weight5, axes=None) + matmul1227: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm431, permute_dims1228, out_dtype="void") + add1471: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1227, model_decoder_layers_25_self_attn_q_proj_bias5) + reshape1606: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1471, R.shape([1, 1, 20, 64])) + permute_dims1229: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_25_self_attn_k_proj_weight5, axes=None) + matmul1228: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm431, permute_dims1229, out_dtype="void") + reshape1607: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1228, R.shape([1, 1, 20, 64])) + permute_dims1230: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_25_self_attn_v_proj_weight5, axes=None) + matmul1229: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm431, permute_dims1230, out_dtype="void") + add1472: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1229, model_decoder_layers_25_self_attn_v_proj_bias5) + reshape1608: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1472, R.shape([1, 1, 20, 64])) + concat121: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1606, reshape1607, reshape1608), axis=2) + reshape1609: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat121, R.shape([1, 60, 64])) + lv315 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), reshape1609), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1610: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv315, R.shape([1, 1, 20, 64])) + reshape1611: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1610, R.shape([1, 1, 1280])) + permute_dims1231: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_25_self_attn_out_proj_weight5, axes=None) + matmul1230: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1611, permute_dims1231, out_dtype="void") + add1473: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1230, model_decoder_layers_25_self_attn_out_proj_bias5) + add1474: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1470, add1473) + layer_norm432: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1474, model_decoder_layers_25_encoder_attn_layer_norm_weight5, model_decoder_layers_25_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1232: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_25_encoder_attn_q_proj_weight5, axes=None) + matmul1231: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm432, permute_dims1232, out_dtype="void") + add1475: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1231, model_decoder_layers_25_encoder_attn_q_proj_bias5) + reshape1612: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1475, R.shape([1, 1, 20, 64])) + reshape1613: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1612, R.shape([1, 20, 64])) + lv316 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), reshape1613), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1614: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv316, R.shape([1, 1, 20, 64])) + reshape1615: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1614, R.shape([1, 1, 1280])) + permute_dims1233: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_25_encoder_attn_out_proj_weight5, axes=None) + matmul1232: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1615, permute_dims1233, out_dtype="void") + add1476: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1232, model_decoder_layers_25_encoder_attn_out_proj_bias5) + add1477: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1474, add1476) + layer_norm433: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1477, model_decoder_layers_25_final_layer_norm_weight5, model_decoder_layers_25_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1234: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_25_fc1_weight5, axes=None) + matmul1233: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm433, permute_dims1234, out_dtype="void") + add1478: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1233, model_decoder_layers_25_fc1_bias5) + gelu155: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1478) + permute_dims1235: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_25_fc2_weight5, axes=None) + matmul1234: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu155, permute_dims1235, out_dtype="void") + add1479: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1234, model_decoder_layers_25_fc2_bias5) + add1480: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1477, add1479) + layer_norm434: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1480, model_decoder_layers_26_self_attn_layer_norm_weight5, model_decoder_layers_26_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1236: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_26_self_attn_q_proj_weight5, axes=None) + matmul1235: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm434, permute_dims1236, out_dtype="void") + add1481: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1235, model_decoder_layers_26_self_attn_q_proj_bias5) + reshape1616: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1481, R.shape([1, 1, 20, 64])) + permute_dims1237: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_26_self_attn_k_proj_weight5, axes=None) + matmul1236: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm434, permute_dims1237, out_dtype="void") + reshape1617: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1236, R.shape([1, 1, 20, 64])) + permute_dims1238: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_26_self_attn_v_proj_weight5, axes=None) + matmul1237: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm434, permute_dims1238, out_dtype="void") + add1482: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1237, model_decoder_layers_26_self_attn_v_proj_bias5) + reshape1618: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1482, R.shape([1, 1, 20, 64])) + concat122: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1616, reshape1617, reshape1618), axis=2) + reshape1619: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat122, R.shape([1, 60, 64])) + lv317 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), reshape1619), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1620: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv317, R.shape([1, 1, 20, 64])) + reshape1621: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1620, R.shape([1, 1, 1280])) + permute_dims1239: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_26_self_attn_out_proj_weight5, axes=None) + matmul1238: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1621, permute_dims1239, out_dtype="void") + add1483: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1238, model_decoder_layers_26_self_attn_out_proj_bias5) + add1484: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1480, add1483) + layer_norm435: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1484, model_decoder_layers_26_encoder_attn_layer_norm_weight5, model_decoder_layers_26_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1240: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_26_encoder_attn_q_proj_weight5, axes=None) + matmul1239: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm435, permute_dims1240, out_dtype="void") + add1485: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1239, model_decoder_layers_26_encoder_attn_q_proj_bias5) + reshape1622: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1485, R.shape([1, 1, 20, 64])) + reshape1623: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1622, R.shape([1, 20, 64])) + lv318 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), reshape1623), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1624: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv318, R.shape([1, 1, 20, 64])) + reshape1625: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1624, R.shape([1, 1, 1280])) + permute_dims1241: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_26_encoder_attn_out_proj_weight5, axes=None) + matmul1240: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1625, permute_dims1241, out_dtype="void") + add1486: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1240, model_decoder_layers_26_encoder_attn_out_proj_bias5) + add1487: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1484, add1486) + layer_norm436: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1487, model_decoder_layers_26_final_layer_norm_weight5, model_decoder_layers_26_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1242: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_26_fc1_weight5, axes=None) + matmul1241: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm436, permute_dims1242, out_dtype="void") + add1488: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1241, model_decoder_layers_26_fc1_bias5) + gelu156: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1488) + permute_dims1243: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_26_fc2_weight5, axes=None) + matmul1242: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu156, permute_dims1243, out_dtype="void") + add1489: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1242, model_decoder_layers_26_fc2_bias5) + add1490: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1487, add1489) + layer_norm437: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1490, model_decoder_layers_27_self_attn_layer_norm_weight5, model_decoder_layers_27_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1244: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_27_self_attn_q_proj_weight5, axes=None) + matmul1243: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm437, permute_dims1244, out_dtype="void") + add1491: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1243, model_decoder_layers_27_self_attn_q_proj_bias5) + reshape1626: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1491, R.shape([1, 1, 20, 64])) + permute_dims1245: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_27_self_attn_k_proj_weight5, axes=None) + matmul1244: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm437, permute_dims1245, out_dtype="void") + reshape1627: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1244, R.shape([1, 1, 20, 64])) + permute_dims1246: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_27_self_attn_v_proj_weight5, axes=None) + matmul1245: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm437, permute_dims1246, out_dtype="void") + add1492: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1245, model_decoder_layers_27_self_attn_v_proj_bias5) + reshape1628: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1492, R.shape([1, 1, 20, 64])) + concat123: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1626, reshape1627, reshape1628), axis=2) + reshape1629: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat123, R.shape([1, 60, 64])) + lv319 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), reshape1629), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1630: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv319, R.shape([1, 1, 20, 64])) + reshape1631: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1630, R.shape([1, 1, 1280])) + permute_dims1247: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_27_self_attn_out_proj_weight5, axes=None) + matmul1246: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1631, permute_dims1247, out_dtype="void") + add1493: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1246, model_decoder_layers_27_self_attn_out_proj_bias5) + add1494: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1490, add1493) + layer_norm438: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1494, model_decoder_layers_27_encoder_attn_layer_norm_weight5, model_decoder_layers_27_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1248: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_27_encoder_attn_q_proj_weight5, axes=None) + matmul1247: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm438, permute_dims1248, out_dtype="void") + add1495: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1247, model_decoder_layers_27_encoder_attn_q_proj_bias5) + reshape1632: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1495, R.shape([1, 1, 20, 64])) + reshape1633: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1632, R.shape([1, 20, 64])) + lv320 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), reshape1633), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1634: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv320, R.shape([1, 1, 20, 64])) + reshape1635: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1634, R.shape([1, 1, 1280])) + permute_dims1249: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_27_encoder_attn_out_proj_weight5, axes=None) + matmul1248: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1635, permute_dims1249, out_dtype="void") + add1496: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1248, model_decoder_layers_27_encoder_attn_out_proj_bias5) + add1497: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1494, add1496) + layer_norm439: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1497, model_decoder_layers_27_final_layer_norm_weight5, model_decoder_layers_27_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1250: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_27_fc1_weight5, axes=None) + matmul1249: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm439, permute_dims1250, out_dtype="void") + add1498: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1249, model_decoder_layers_27_fc1_bias5) + gelu157: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1498) + permute_dims1251: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_27_fc2_weight5, axes=None) + matmul1250: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu157, permute_dims1251, out_dtype="void") + add1499: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1250, model_decoder_layers_27_fc2_bias5) + add1500: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1497, add1499) + layer_norm440: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1500, model_decoder_layers_28_self_attn_layer_norm_weight5, model_decoder_layers_28_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1252: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_28_self_attn_q_proj_weight5, axes=None) + matmul1251: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm440, permute_dims1252, out_dtype="void") + add1501: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1251, model_decoder_layers_28_self_attn_q_proj_bias5) + reshape1636: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1501, R.shape([1, 1, 20, 64])) + permute_dims1253: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_28_self_attn_k_proj_weight5, axes=None) + matmul1252: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm440, permute_dims1253, out_dtype="void") + reshape1637: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1252, R.shape([1, 1, 20, 64])) + permute_dims1254: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_28_self_attn_v_proj_weight5, axes=None) + matmul1253: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm440, permute_dims1254, out_dtype="void") + add1502: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1253, model_decoder_layers_28_self_attn_v_proj_bias5) + reshape1638: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1502, R.shape([1, 1, 20, 64])) + concat124: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1636, reshape1637, reshape1638), axis=2) + reshape1639: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat124, R.shape([1, 60, 64])) + lv321 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), reshape1639), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1640: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv321, R.shape([1, 1, 20, 64])) + reshape1641: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1640, R.shape([1, 1, 1280])) + permute_dims1255: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_28_self_attn_out_proj_weight5, axes=None) + matmul1254: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1641, permute_dims1255, out_dtype="void") + add1503: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1254, model_decoder_layers_28_self_attn_out_proj_bias5) + add1504: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1500, add1503) + layer_norm441: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1504, model_decoder_layers_28_encoder_attn_layer_norm_weight5, model_decoder_layers_28_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1256: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_28_encoder_attn_q_proj_weight5, axes=None) + matmul1255: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm441, permute_dims1256, out_dtype="void") + add1505: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1255, model_decoder_layers_28_encoder_attn_q_proj_bias5) + reshape1642: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1505, R.shape([1, 1, 20, 64])) + reshape1643: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1642, R.shape([1, 20, 64])) + lv322 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), reshape1643), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1644: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv322, R.shape([1, 1, 20, 64])) + reshape1645: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1644, R.shape([1, 1, 1280])) + permute_dims1257: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_28_encoder_attn_out_proj_weight5, axes=None) + matmul1256: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1645, permute_dims1257, out_dtype="void") + add1506: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1256, model_decoder_layers_28_encoder_attn_out_proj_bias5) + add1507: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1504, add1506) + layer_norm442: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1507, model_decoder_layers_28_final_layer_norm_weight5, model_decoder_layers_28_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1258: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_28_fc1_weight5, axes=None) + matmul1257: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm442, permute_dims1258, out_dtype="void") + add1508: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1257, model_decoder_layers_28_fc1_bias5) + gelu158: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1508) + permute_dims1259: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_28_fc2_weight5, axes=None) + matmul1258: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu158, permute_dims1259, out_dtype="void") + add1509: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1258, model_decoder_layers_28_fc2_bias5) + add1510: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1507, add1509) + layer_norm443: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1510, model_decoder_layers_29_self_attn_layer_norm_weight5, model_decoder_layers_29_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1260: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_29_self_attn_q_proj_weight5, axes=None) + matmul1259: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm443, permute_dims1260, out_dtype="void") + add1511: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1259, model_decoder_layers_29_self_attn_q_proj_bias5) + reshape1646: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1511, R.shape([1, 1, 20, 64])) + permute_dims1261: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_29_self_attn_k_proj_weight5, axes=None) + matmul1260: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm443, permute_dims1261, out_dtype="void") + reshape1647: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1260, R.shape([1, 1, 20, 64])) + permute_dims1262: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_29_self_attn_v_proj_weight5, axes=None) + matmul1261: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm443, permute_dims1262, out_dtype="void") + add1512: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1261, model_decoder_layers_29_self_attn_v_proj_bias5) + reshape1648: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1512, R.shape([1, 1, 20, 64])) + concat125: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1646, reshape1647, reshape1648), axis=2) + reshape1649: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat125, R.shape([1, 60, 64])) + lv323 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), reshape1649), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1650: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv323, R.shape([1, 1, 20, 64])) + reshape1651: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1650, R.shape([1, 1, 1280])) + permute_dims1263: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_29_self_attn_out_proj_weight5, axes=None) + matmul1262: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1651, permute_dims1263, out_dtype="void") + add1513: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1262, model_decoder_layers_29_self_attn_out_proj_bias5) + add1514: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1510, add1513) + layer_norm444: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1514, model_decoder_layers_29_encoder_attn_layer_norm_weight5, model_decoder_layers_29_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1264: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_29_encoder_attn_q_proj_weight5, axes=None) + matmul1263: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm444, permute_dims1264, out_dtype="void") + add1515: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1263, model_decoder_layers_29_encoder_attn_q_proj_bias5) + reshape1652: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1515, R.shape([1, 1, 20, 64])) + reshape1653: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1652, R.shape([1, 20, 64])) + lv324 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), reshape1653), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1654: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv324, R.shape([1, 1, 20, 64])) + reshape1655: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1654, R.shape([1, 1, 1280])) + permute_dims1265: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_29_encoder_attn_out_proj_weight5, axes=None) + matmul1264: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1655, permute_dims1265, out_dtype="void") + add1516: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1264, model_decoder_layers_29_encoder_attn_out_proj_bias5) + add1517: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1514, add1516) + layer_norm445: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1517, model_decoder_layers_29_final_layer_norm_weight5, model_decoder_layers_29_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1266: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_29_fc1_weight5, axes=None) + matmul1265: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm445, permute_dims1266, out_dtype="void") + add1518: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1265, model_decoder_layers_29_fc1_bias5) + gelu159: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1518) + permute_dims1267: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_29_fc2_weight5, axes=None) + matmul1266: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu159, permute_dims1267, out_dtype="void") + add1519: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1266, model_decoder_layers_29_fc2_bias5) + add1520: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1517, add1519) + layer_norm446: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1520, model_decoder_layers_30_self_attn_layer_norm_weight5, model_decoder_layers_30_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1268: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_30_self_attn_q_proj_weight5, axes=None) + matmul1267: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm446, permute_dims1268, out_dtype="void") + add1521: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1267, model_decoder_layers_30_self_attn_q_proj_bias5) + reshape1656: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1521, R.shape([1, 1, 20, 64])) + permute_dims1269: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_30_self_attn_k_proj_weight5, axes=None) + matmul1268: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm446, permute_dims1269, out_dtype="void") + reshape1657: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1268, R.shape([1, 1, 20, 64])) + permute_dims1270: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_30_self_attn_v_proj_weight5, axes=None) + matmul1269: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm446, permute_dims1270, out_dtype="void") + add1522: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1269, model_decoder_layers_30_self_attn_v_proj_bias5) + reshape1658: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1522, R.shape([1, 1, 20, 64])) + concat126: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1656, reshape1657, reshape1658), axis=2) + reshape1659: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat126, R.shape([1, 60, 64])) + lv325 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), reshape1659), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1660: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv325, R.shape([1, 1, 20, 64])) + reshape1661: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1660, R.shape([1, 1, 1280])) + permute_dims1271: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_30_self_attn_out_proj_weight5, axes=None) + matmul1270: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1661, permute_dims1271, out_dtype="void") + add1523: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1270, model_decoder_layers_30_self_attn_out_proj_bias5) + add1524: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1520, add1523) + layer_norm447: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1524, model_decoder_layers_30_encoder_attn_layer_norm_weight5, model_decoder_layers_30_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1272: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_30_encoder_attn_q_proj_weight5, axes=None) + matmul1271: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm447, permute_dims1272, out_dtype="void") + add1525: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1271, model_decoder_layers_30_encoder_attn_q_proj_bias5) + reshape1662: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1525, R.shape([1, 1, 20, 64])) + reshape1663: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1662, R.shape([1, 20, 64])) + lv326 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), reshape1663), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1664: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv326, R.shape([1, 1, 20, 64])) + reshape1665: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1664, R.shape([1, 1, 1280])) + permute_dims1273: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_30_encoder_attn_out_proj_weight5, axes=None) + matmul1272: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1665, permute_dims1273, out_dtype="void") + add1526: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1272, model_decoder_layers_30_encoder_attn_out_proj_bias5) + add1527: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1524, add1526) + layer_norm448: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1527, model_decoder_layers_30_final_layer_norm_weight5, model_decoder_layers_30_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1274: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_30_fc1_weight5, axes=None) + matmul1273: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm448, permute_dims1274, out_dtype="void") + add1528: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1273, model_decoder_layers_30_fc1_bias5) + gelu160: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1528) + permute_dims1275: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_30_fc2_weight5, axes=None) + matmul1274: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu160, permute_dims1275, out_dtype="void") + add1529: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1274, model_decoder_layers_30_fc2_bias5) + add1530: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1527, add1529) + layer_norm449: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1530, model_decoder_layers_31_self_attn_layer_norm_weight5, model_decoder_layers_31_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1276: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_31_self_attn_q_proj_weight5, axes=None) + matmul1275: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm449, permute_dims1276, out_dtype="void") + add1531: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1275, model_decoder_layers_31_self_attn_q_proj_bias5) + reshape1666: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1531, R.shape([1, 1, 20, 64])) + permute_dims1277: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_31_self_attn_k_proj_weight5, axes=None) + matmul1276: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm449, permute_dims1277, out_dtype="void") + reshape1667: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1276, R.shape([1, 1, 20, 64])) + permute_dims1278: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_31_self_attn_v_proj_weight5, axes=None) + matmul1277: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm449, permute_dims1278, out_dtype="void") + add1532: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1277, model_decoder_layers_31_self_attn_v_proj_bias5) + reshape1668: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1532, R.shape([1, 1, 20, 64])) + concat127: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1666, reshape1667, reshape1668), axis=2) + reshape1669: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat127, R.shape([1, 60, 64])) + lv327 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), reshape1669), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1670: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv327, R.shape([1, 1, 20, 64])) + reshape1671: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1670, R.shape([1, 1, 1280])) + permute_dims1279: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_31_self_attn_out_proj_weight5, axes=None) + matmul1278: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1671, permute_dims1279, out_dtype="void") + add1533: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1278, model_decoder_layers_31_self_attn_out_proj_bias5) + add1534: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1530, add1533) + layer_norm450: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1534, model_decoder_layers_31_encoder_attn_layer_norm_weight5, model_decoder_layers_31_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1280: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_31_encoder_attn_q_proj_weight5, axes=None) + matmul1279: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm450, permute_dims1280, out_dtype="void") + add1535: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1279, model_decoder_layers_31_encoder_attn_q_proj_bias5) + reshape1672: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1535, R.shape([1, 1, 20, 64])) + reshape1673: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1672, R.shape([1, 20, 64])) + lv328 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), reshape1673), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1674: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv328, R.shape([1, 1, 20, 64])) + reshape1675: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1674, R.shape([1, 1, 1280])) + permute_dims1281: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_31_encoder_attn_out_proj_weight5, axes=None) + matmul1280: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1675, permute_dims1281, out_dtype="void") + add1536: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1280, model_decoder_layers_31_encoder_attn_out_proj_bias5) + add1537: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1534, add1536) + layer_norm451: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1537, model_decoder_layers_31_final_layer_norm_weight5, model_decoder_layers_31_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1282: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_31_fc1_weight5, axes=None) + matmul1281: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm451, permute_dims1282, out_dtype="void") + add1538: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1281, model_decoder_layers_31_fc1_bias5) + gelu161: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1538) + permute_dims1283: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_31_fc2_weight5, axes=None) + matmul1282: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu161, permute_dims1283, out_dtype="void") + add1539: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1282, model_decoder_layers_31_fc2_bias5) + add1540: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1537, add1539) + layer_norm452: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1540, model_decoder_layer_norm_weight5, model_decoder_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1284: R.Tensor((1280, 51866), dtype="float16") = R.permute_dims(model_decoder_embed_tokens_weight5, axes=None) + matmul1283: R.Tensor((1, 1, 51866), dtype="float32") = R.matmul(layer_norm452, permute_dims1284, out_dtype="float32") + gv5: R.Tensor((1, 1, 51866), dtype="float32") = matmul1283 + R.output(gv5) + return gv5 + + @R.function + def multinomial_from_uniform(probs: R.Tensor(("batch_size", "vocab_size"), dtype="float32"), uniform_samples: R.Tensor(("num_samples",), dtype="float32"), sample_indices: R.Tensor(("num_samples",), dtype="int32")) -> R.Tensor(("num_samples",), dtype="int32"): + num_samples = T.int64() + batch_size = T.int64() + vocab_size = T.int64() + R.func_attr({"relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "num_positions": 48, "num_samples": 8}}) + with R.dataflow(): + probs_1: R.Tensor((batch_size, vocab_size), dtype="float32") = probs + uniform_samples_1: R.Tensor((num_samples, 1), dtype="float32") = R.call_pure_packed("vm.builtin.reshape", uniform_samples, R.shape([num_samples, 1]), sinfo_args=(R.Tensor((num_samples, 1), dtype="float32"),)) + sample_indices_1: R.Tensor((num_samples, 1), dtype="int32") = R.call_pure_packed("vm.builtin.reshape", sample_indices, R.shape([num_samples, 1]), sinfo_args=(R.Tensor((num_samples, 1), dtype="int32"),)) + nn_multinomial_from_uniform: R.Tensor((num_samples, 1), dtype="int32") = R.multinomial_from_uniform(probs_1, uniform_samples_1, sample_indices_1, dtype="int32") + lv: R.Tensor((num_samples,), dtype="int32") = R.call_pure_packed("vm.builtin.reshape", nn_multinomial_from_uniform, R.shape([num_samples]), sinfo_args=(R.Tensor((num_samples,), dtype="int32"),)) + gv: R.Tensor((num_samples,), dtype="int32") = lv + R.output(gv) + return gv + + @R.function + def prefill(input_ids: R.Tensor((1, "seq_len"), dtype="int32"), paged_kv_cache: R.Object, packed_params: R.Tuple(R.Tensor((1280, 128, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1500, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), 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R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"))) -> R.Tensor((1, 1, 51866), dtype="float32"): + seq_len = T.int64() + R.func_attr({"num_input": 2, "relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "seq_len": 15000, "total_seq_len": 1500}}) + cls = Module + with R.dataflow(): + model_encoder_conv1_weight4: R.Tensor((1280, 128, 3), dtype="float16") = packed_params[0] + model_encoder_conv1_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1] + model_encoder_conv2_weight4: R.Tensor((1280, 1280, 3), dtype="float16") = packed_params[2] + model_encoder_conv2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[3] + model_encoder_embed_positions_weight4: R.Tensor((1500, 1280), dtype="float16") = packed_params[4] + model_encoder_layers_0_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[5] + model_encoder_layers_0_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[6] + model_encoder_layers_0_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[7] + model_encoder_layers_0_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[8] + model_encoder_layers_0_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[9] + model_encoder_layers_0_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[10] + model_encoder_layers_0_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[11] + model_encoder_layers_0_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[12] + model_encoder_layers_0_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[13] + model_encoder_layers_0_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[14] + model_encoder_layers_0_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[15] + model_encoder_layers_0_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[16] + model_encoder_layers_0_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[17] + model_encoder_layers_0_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[18] + model_encoder_layers_0_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[19] + model_encoder_layers_1_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[20] + model_encoder_layers_1_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[21] + model_encoder_layers_1_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[22] + model_encoder_layers_1_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[23] + model_encoder_layers_1_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[24] + model_encoder_layers_1_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[25] + model_encoder_layers_1_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[26] + model_encoder_layers_1_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[27] + model_encoder_layers_1_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[28] + model_encoder_layers_1_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[29] + model_encoder_layers_1_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[30] + model_encoder_layers_1_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[31] + model_encoder_layers_1_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[32] + model_encoder_layers_1_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[33] + model_encoder_layers_1_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[34] + model_encoder_layers_2_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[35] + model_encoder_layers_2_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[36] + model_encoder_layers_2_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[37] + model_encoder_layers_2_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[38] + model_encoder_layers_2_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[39] + model_encoder_layers_2_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[40] + model_encoder_layers_2_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[41] + model_encoder_layers_2_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[42] + model_encoder_layers_2_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[43] + model_encoder_layers_2_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[44] + model_encoder_layers_2_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[45] + model_encoder_layers_2_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[46] + model_encoder_layers_2_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[47] + model_encoder_layers_2_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[48] + model_encoder_layers_2_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[49] + model_encoder_layers_3_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[50] + model_encoder_layers_3_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[51] + model_encoder_layers_3_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[52] + model_encoder_layers_3_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[53] + model_encoder_layers_3_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[54] + model_encoder_layers_3_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[55] + model_encoder_layers_3_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[56] + model_encoder_layers_3_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[57] + model_encoder_layers_3_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[58] + model_encoder_layers_3_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[59] + model_encoder_layers_3_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[60] + model_encoder_layers_3_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[61] + model_encoder_layers_3_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[62] + model_encoder_layers_3_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[63] + model_encoder_layers_3_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[64] + model_encoder_layers_4_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[65] + model_encoder_layers_4_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[66] + model_encoder_layers_4_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[67] + model_encoder_layers_4_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[68] + model_encoder_layers_4_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[69] + model_encoder_layers_4_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[70] + model_encoder_layers_4_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[71] + model_encoder_layers_4_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[72] + model_encoder_layers_4_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[73] + model_encoder_layers_4_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[74] + model_encoder_layers_4_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[75] + model_encoder_layers_4_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[76] + model_encoder_layers_4_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[77] + model_encoder_layers_4_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[78] + model_encoder_layers_4_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[79] + model_encoder_layers_5_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[80] + model_encoder_layers_5_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[81] + model_encoder_layers_5_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[82] + model_encoder_layers_5_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[83] + model_encoder_layers_5_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[84] + model_encoder_layers_5_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[85] + model_encoder_layers_5_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[86] + model_encoder_layers_5_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[87] + model_encoder_layers_5_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[88] + model_encoder_layers_5_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[89] + model_encoder_layers_5_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[90] + model_encoder_layers_5_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[91] + model_encoder_layers_5_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[92] + model_encoder_layers_5_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[93] + model_encoder_layers_5_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[94] + model_encoder_layers_6_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[95] + model_encoder_layers_6_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[96] + model_encoder_layers_6_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[97] + model_encoder_layers_6_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[98] + model_encoder_layers_6_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[99] + model_encoder_layers_6_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[100] + model_encoder_layers_6_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[101] + model_encoder_layers_6_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[102] + model_encoder_layers_6_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[103] + model_encoder_layers_6_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[104] + model_encoder_layers_6_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[105] + model_encoder_layers_6_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[106] + model_encoder_layers_6_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[107] + model_encoder_layers_6_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[108] + model_encoder_layers_6_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[109] + model_encoder_layers_7_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[110] + model_encoder_layers_7_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[111] + model_encoder_layers_7_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[112] + model_encoder_layers_7_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[113] + model_encoder_layers_7_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[114] + model_encoder_layers_7_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[115] + model_encoder_layers_7_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[116] + model_encoder_layers_7_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[117] + model_encoder_layers_7_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[118] + model_encoder_layers_7_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[119] + model_encoder_layers_7_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[120] + model_encoder_layers_7_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[121] + model_encoder_layers_7_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[122] + model_encoder_layers_7_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[123] + model_encoder_layers_7_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[124] + model_encoder_layers_8_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[125] + model_encoder_layers_8_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[126] + model_encoder_layers_8_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[127] + model_encoder_layers_8_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[128] + model_encoder_layers_8_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[129] + model_encoder_layers_8_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[130] + model_encoder_layers_8_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[131] + model_encoder_layers_8_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[132] + model_encoder_layers_8_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[133] + model_encoder_layers_8_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[134] + model_encoder_layers_8_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[135] + model_encoder_layers_8_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[136] + model_encoder_layers_8_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[137] + model_encoder_layers_8_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[138] + model_encoder_layers_8_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[139] + model_encoder_layers_9_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[140] + model_encoder_layers_9_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[141] + model_encoder_layers_9_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[142] + model_encoder_layers_9_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[143] + model_encoder_layers_9_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[144] + model_encoder_layers_9_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[145] + model_encoder_layers_9_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[146] + model_encoder_layers_9_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[147] + model_encoder_layers_9_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[148] + model_encoder_layers_9_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[149] + model_encoder_layers_9_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[150] + model_encoder_layers_9_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[151] + model_encoder_layers_9_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[152] + model_encoder_layers_9_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[153] + model_encoder_layers_9_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[154] + model_encoder_layers_10_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[155] + model_encoder_layers_10_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[156] + model_encoder_layers_10_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[157] + model_encoder_layers_10_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[158] + model_encoder_layers_10_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[159] + model_encoder_layers_10_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[160] + model_encoder_layers_10_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[161] + model_encoder_layers_10_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[162] + model_encoder_layers_10_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[163] + model_encoder_layers_10_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[164] + model_encoder_layers_10_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[165] + model_encoder_layers_10_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[166] + model_encoder_layers_10_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[167] + model_encoder_layers_10_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[168] + model_encoder_layers_10_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[169] + model_encoder_layers_11_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[170] + model_encoder_layers_11_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[171] + model_encoder_layers_11_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[172] + model_encoder_layers_11_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[173] + model_encoder_layers_11_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[174] + model_encoder_layers_11_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[175] + model_encoder_layers_11_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[176] + model_encoder_layers_11_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[177] + model_encoder_layers_11_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[178] + model_encoder_layers_11_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[179] + model_encoder_layers_11_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[180] + model_encoder_layers_11_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[181] + model_encoder_layers_11_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[182] + model_encoder_layers_11_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[183] + model_encoder_layers_11_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[184] + model_encoder_layers_12_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[185] + model_encoder_layers_12_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[186] + model_encoder_layers_12_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[187] + model_encoder_layers_12_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[188] + model_encoder_layers_12_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[189] + model_encoder_layers_12_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[190] + model_encoder_layers_12_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[191] + model_encoder_layers_12_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[192] + model_encoder_layers_12_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[193] + model_encoder_layers_12_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[194] + model_encoder_layers_12_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[195] + model_encoder_layers_12_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[196] + model_encoder_layers_12_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[197] + model_encoder_layers_12_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[198] + model_encoder_layers_12_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[199] + model_encoder_layers_13_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[200] + model_encoder_layers_13_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[201] + model_encoder_layers_13_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[202] + model_encoder_layers_13_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[203] + model_encoder_layers_13_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[204] + model_encoder_layers_13_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[205] + model_encoder_layers_13_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[206] + model_encoder_layers_13_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[207] + model_encoder_layers_13_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[208] + model_encoder_layers_13_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[209] + model_encoder_layers_13_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[210] + model_encoder_layers_13_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[211] + model_encoder_layers_13_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[212] + model_encoder_layers_13_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[213] + model_encoder_layers_13_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[214] + model_encoder_layers_14_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[215] + model_encoder_layers_14_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[216] + model_encoder_layers_14_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[217] + model_encoder_layers_14_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[218] + model_encoder_layers_14_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[219] + model_encoder_layers_14_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[220] + model_encoder_layers_14_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[221] + model_encoder_layers_14_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[222] + model_encoder_layers_14_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[223] + model_encoder_layers_14_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[224] + model_encoder_layers_14_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[225] + model_encoder_layers_14_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[226] + model_encoder_layers_14_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[227] + model_encoder_layers_14_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[228] + model_encoder_layers_14_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[229] + model_encoder_layers_15_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[230] + model_encoder_layers_15_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[231] + model_encoder_layers_15_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[232] + model_encoder_layers_15_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[233] + model_encoder_layers_15_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[234] + model_encoder_layers_15_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[235] + model_encoder_layers_15_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[236] + model_encoder_layers_15_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[237] + model_encoder_layers_15_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[238] + model_encoder_layers_15_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[239] + model_encoder_layers_15_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[240] + model_encoder_layers_15_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[241] + model_encoder_layers_15_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[242] + model_encoder_layers_15_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[243] + model_encoder_layers_15_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[244] + model_encoder_layers_16_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[245] + model_encoder_layers_16_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[246] + model_encoder_layers_16_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[247] + model_encoder_layers_16_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[248] + model_encoder_layers_16_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[249] + model_encoder_layers_16_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[250] + model_encoder_layers_16_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[251] + model_encoder_layers_16_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[252] + model_encoder_layers_16_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[253] + model_encoder_layers_16_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[254] + model_encoder_layers_16_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[255] + model_encoder_layers_16_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[256] + model_encoder_layers_16_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[257] + model_encoder_layers_16_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[258] + model_encoder_layers_16_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[259] + model_encoder_layers_17_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[260] + model_encoder_layers_17_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[261] + model_encoder_layers_17_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[262] + model_encoder_layers_17_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[263] + model_encoder_layers_17_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[264] + model_encoder_layers_17_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[265] + model_encoder_layers_17_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[266] + model_encoder_layers_17_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[267] + model_encoder_layers_17_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[268] + model_encoder_layers_17_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[269] + model_encoder_layers_17_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[270] + model_encoder_layers_17_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[271] + model_encoder_layers_17_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[272] + model_encoder_layers_17_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[273] + model_encoder_layers_17_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[274] + model_encoder_layers_18_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[275] + model_encoder_layers_18_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[276] + model_encoder_layers_18_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[277] + model_encoder_layers_18_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[278] + model_encoder_layers_18_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[279] + model_encoder_layers_18_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[280] + model_encoder_layers_18_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[281] + model_encoder_layers_18_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[282] + model_encoder_layers_18_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[283] + model_encoder_layers_18_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[284] + model_encoder_layers_18_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[285] + model_encoder_layers_18_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[286] + model_encoder_layers_18_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[287] + model_encoder_layers_18_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[288] + model_encoder_layers_18_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[289] + model_encoder_layers_19_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[290] + model_encoder_layers_19_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[291] + model_encoder_layers_19_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[292] + model_encoder_layers_19_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[293] + model_encoder_layers_19_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[294] + model_encoder_layers_19_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[295] + model_encoder_layers_19_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[296] + model_encoder_layers_19_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[297] + model_encoder_layers_19_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[298] + model_encoder_layers_19_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[299] + model_encoder_layers_19_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[300] + model_encoder_layers_19_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[301] + model_encoder_layers_19_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[302] + model_encoder_layers_19_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[303] + model_encoder_layers_19_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[304] + model_encoder_layers_20_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[305] + model_encoder_layers_20_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[306] + model_encoder_layers_20_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[307] + model_encoder_layers_20_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[308] + model_encoder_layers_20_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[309] + model_encoder_layers_20_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[310] + model_encoder_layers_20_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[311] + model_encoder_layers_20_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[312] + model_encoder_layers_20_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[313] + model_encoder_layers_20_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[314] + model_encoder_layers_20_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[315] + model_encoder_layers_20_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[316] + model_encoder_layers_20_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[317] + model_encoder_layers_20_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[318] + model_encoder_layers_20_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[319] + model_encoder_layers_21_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[320] + model_encoder_layers_21_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[321] + model_encoder_layers_21_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[322] + model_encoder_layers_21_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[323] + model_encoder_layers_21_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[324] + model_encoder_layers_21_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[325] + model_encoder_layers_21_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[326] + model_encoder_layers_21_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[327] + model_encoder_layers_21_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[328] + model_encoder_layers_21_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[329] + model_encoder_layers_21_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[330] + model_encoder_layers_21_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[331] + model_encoder_layers_21_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[332] + model_encoder_layers_21_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[333] + model_encoder_layers_21_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[334] + model_encoder_layers_22_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[335] + model_encoder_layers_22_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[336] + model_encoder_layers_22_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[337] + model_encoder_layers_22_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[338] + model_encoder_layers_22_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[339] + model_encoder_layers_22_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[340] + model_encoder_layers_22_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[341] + model_encoder_layers_22_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[342] + model_encoder_layers_22_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[343] + model_encoder_layers_22_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[344] + model_encoder_layers_22_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[345] + model_encoder_layers_22_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[346] + model_encoder_layers_22_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[347] + model_encoder_layers_22_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[348] + model_encoder_layers_22_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[349] + model_encoder_layers_23_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[350] + model_encoder_layers_23_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[351] + model_encoder_layers_23_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[352] + model_encoder_layers_23_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[353] + model_encoder_layers_23_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[354] + model_encoder_layers_23_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[355] + model_encoder_layers_23_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[356] + model_encoder_layers_23_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[357] + model_encoder_layers_23_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[358] + model_encoder_layers_23_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[359] + model_encoder_layers_23_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[360] + model_encoder_layers_23_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[361] + model_encoder_layers_23_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[362] + model_encoder_layers_23_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[363] + model_encoder_layers_23_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[364] + model_encoder_layers_24_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[365] + model_encoder_layers_24_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[366] + model_encoder_layers_24_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[367] + model_encoder_layers_24_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[368] + model_encoder_layers_24_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[369] + model_encoder_layers_24_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[370] + model_encoder_layers_24_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[371] + model_encoder_layers_24_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[372] + model_encoder_layers_24_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[373] + model_encoder_layers_24_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[374] + model_encoder_layers_24_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[375] + model_encoder_layers_24_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[376] + model_encoder_layers_24_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[377] + model_encoder_layers_24_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[378] + model_encoder_layers_24_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[379] + model_encoder_layers_25_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[380] + model_encoder_layers_25_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[381] + model_encoder_layers_25_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[382] + model_encoder_layers_25_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[383] + model_encoder_layers_25_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[384] + model_encoder_layers_25_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[385] + model_encoder_layers_25_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[386] + model_encoder_layers_25_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[387] + model_encoder_layers_25_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[388] + model_encoder_layers_25_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[389] + model_encoder_layers_25_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[390] + model_encoder_layers_25_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[391] + model_encoder_layers_25_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[392] + model_encoder_layers_25_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[393] + model_encoder_layers_25_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[394] + model_encoder_layers_26_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[395] + model_encoder_layers_26_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[396] + model_encoder_layers_26_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[397] + model_encoder_layers_26_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[398] + model_encoder_layers_26_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[399] + model_encoder_layers_26_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[400] + model_encoder_layers_26_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[401] + model_encoder_layers_26_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[402] + model_encoder_layers_26_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[403] + model_encoder_layers_26_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[404] + model_encoder_layers_26_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[405] + model_encoder_layers_26_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[406] + model_encoder_layers_26_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[407] + model_encoder_layers_26_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[408] + model_encoder_layers_26_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[409] + model_encoder_layers_27_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[410] + model_encoder_layers_27_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[411] + model_encoder_layers_27_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[412] + model_encoder_layers_27_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[413] + model_encoder_layers_27_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[414] + model_encoder_layers_27_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[415] + model_encoder_layers_27_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[416] + model_encoder_layers_27_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[417] + model_encoder_layers_27_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[418] + model_encoder_layers_27_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[419] + model_encoder_layers_27_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[420] + model_encoder_layers_27_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[421] + model_encoder_layers_27_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[422] + model_encoder_layers_27_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[423] + model_encoder_layers_27_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[424] + model_encoder_layers_28_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[425] + model_encoder_layers_28_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[426] + model_encoder_layers_28_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[427] + model_encoder_layers_28_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[428] + model_encoder_layers_28_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[429] + model_encoder_layers_28_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[430] + model_encoder_layers_28_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[431] + model_encoder_layers_28_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[432] + model_encoder_layers_28_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[433] + model_encoder_layers_28_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[434] + model_encoder_layers_28_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[435] + model_encoder_layers_28_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[436] + model_encoder_layers_28_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[437] + model_encoder_layers_28_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[438] + model_encoder_layers_28_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[439] + model_encoder_layers_29_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[440] + model_encoder_layers_29_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[441] + model_encoder_layers_29_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[442] + model_encoder_layers_29_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[443] + model_encoder_layers_29_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[444] + model_encoder_layers_29_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[445] + model_encoder_layers_29_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[446] + model_encoder_layers_29_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[447] + model_encoder_layers_29_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[448] + model_encoder_layers_29_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[449] + model_encoder_layers_29_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[450] + model_encoder_layers_29_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[451] + model_encoder_layers_29_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[452] + model_encoder_layers_29_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[453] + model_encoder_layers_29_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[454] + model_encoder_layers_30_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[455] + model_encoder_layers_30_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[456] + model_encoder_layers_30_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[457] + model_encoder_layers_30_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[458] + model_encoder_layers_30_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[459] + model_encoder_layers_30_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[460] + model_encoder_layers_30_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[461] + model_encoder_layers_30_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[462] + model_encoder_layers_30_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[463] + model_encoder_layers_30_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[464] + model_encoder_layers_30_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[465] + model_encoder_layers_30_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[466] + model_encoder_layers_30_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[467] + model_encoder_layers_30_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[468] + model_encoder_layers_30_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[469] + model_encoder_layers_31_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[470] + model_encoder_layers_31_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[471] + model_encoder_layers_31_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[472] + model_encoder_layers_31_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[473] + model_encoder_layers_31_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[474] + model_encoder_layers_31_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[475] + model_encoder_layers_31_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[476] + model_encoder_layers_31_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[477] + model_encoder_layers_31_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[478] + model_encoder_layers_31_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[479] + model_encoder_layers_31_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[480] + model_encoder_layers_31_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[481] + model_encoder_layers_31_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[482] + model_encoder_layers_31_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[483] + model_encoder_layers_31_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[484] + model_encoder_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[485] + model_encoder_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[486] + model_decoder_embed_tokens_weight4: R.Tensor((51866, 1280), dtype="float16") = packed_params[487] + model_decoder_embed_positions_weight4: R.Tensor((448, 1280), dtype="float16") = packed_params[488] + model_decoder_layers_0_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[489] + model_decoder_layers_0_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[490] + model_decoder_layers_0_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[491] + model_decoder_layers_0_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[492] + model_decoder_layers_0_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[493] + model_decoder_layers_0_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[494] + model_decoder_layers_0_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[495] + model_decoder_layers_0_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[496] + model_decoder_layers_0_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[497] + model_decoder_layers_0_encoder_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[498] + model_decoder_layers_0_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[499] + model_decoder_layers_0_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[500] + model_decoder_layers_0_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[501] + model_decoder_layers_0_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[502] + model_decoder_layers_0_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[503] + model_decoder_layers_0_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[504] + model_decoder_layers_0_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[505] + model_decoder_layers_0_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[506] + model_decoder_layers_0_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[507] + model_decoder_layers_0_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[508] + model_decoder_layers_0_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[509] + model_decoder_layers_0_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[510] + model_decoder_layers_0_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[511] + model_decoder_layers_0_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[512] + model_decoder_layers_1_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[513] + model_decoder_layers_1_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[514] + model_decoder_layers_1_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[515] + model_decoder_layers_1_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[516] + model_decoder_layers_1_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[517] + model_decoder_layers_1_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[518] + model_decoder_layers_1_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[519] + model_decoder_layers_1_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[520] + model_decoder_layers_1_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[521] + model_decoder_layers_1_encoder_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[522] + model_decoder_layers_1_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[523] + model_decoder_layers_1_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[524] + model_decoder_layers_1_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[525] + model_decoder_layers_1_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[526] + model_decoder_layers_1_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[527] + model_decoder_layers_1_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[528] + model_decoder_layers_1_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[529] + model_decoder_layers_1_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[530] + model_decoder_layers_1_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[531] + model_decoder_layers_1_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[532] + model_decoder_layers_1_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[533] + model_decoder_layers_1_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[534] + model_decoder_layers_1_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[535] + model_decoder_layers_1_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[536] + model_decoder_layers_2_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[537] + model_decoder_layers_2_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[538] + model_decoder_layers_2_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[539] + model_decoder_layers_2_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[540] + model_decoder_layers_2_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[541] + model_decoder_layers_2_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[542] + model_decoder_layers_2_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[543] + model_decoder_layers_2_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[544] + model_decoder_layers_2_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[545] + model_decoder_layers_2_encoder_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[546] + model_decoder_layers_2_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[547] + model_decoder_layers_2_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[548] + model_decoder_layers_2_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[549] + model_decoder_layers_2_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[550] + model_decoder_layers_2_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[551] + model_decoder_layers_2_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[552] + model_decoder_layers_2_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[553] + model_decoder_layers_2_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[554] + model_decoder_layers_2_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[555] + model_decoder_layers_2_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[556] + model_decoder_layers_2_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[557] + model_decoder_layers_2_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[558] + model_decoder_layers_2_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[559] + model_decoder_layers_2_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[560] + model_decoder_layers_3_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[561] + model_decoder_layers_3_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[562] + model_decoder_layers_3_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[563] + model_decoder_layers_3_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[564] + model_decoder_layers_3_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[565] + model_decoder_layers_3_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[566] + model_decoder_layers_3_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[567] + model_decoder_layers_3_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[568] + model_decoder_layers_3_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[569] + model_decoder_layers_3_encoder_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[570] + model_decoder_layers_3_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[571] + model_decoder_layers_3_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[572] + model_decoder_layers_3_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[573] + model_decoder_layers_3_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[574] + model_decoder_layers_3_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[575] + model_decoder_layers_3_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[576] + model_decoder_layers_3_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[577] + model_decoder_layers_3_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[578] + model_decoder_layers_3_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[579] + model_decoder_layers_3_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[580] + model_decoder_layers_3_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[581] + model_decoder_layers_3_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[582] + model_decoder_layers_3_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[583] + model_decoder_layers_3_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[584] + model_decoder_layers_4_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[585] + model_decoder_layers_4_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[586] + model_decoder_layers_4_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[587] + model_decoder_layers_4_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[588] + model_decoder_layers_4_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[589] + model_decoder_layers_4_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[590] + model_decoder_layers_4_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[591] + model_decoder_layers_4_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[592] + model_decoder_layers_4_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[593] + model_decoder_layers_4_encoder_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[594] + model_decoder_layers_4_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[595] + model_decoder_layers_4_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[596] + model_decoder_layers_4_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[597] + model_decoder_layers_4_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[598] + model_decoder_layers_4_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[599] + model_decoder_layers_4_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[600] + model_decoder_layers_4_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[601] + model_decoder_layers_4_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[602] + model_decoder_layers_4_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[603] + model_decoder_layers_4_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[604] + model_decoder_layers_4_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[605] + model_decoder_layers_4_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[606] + model_decoder_layers_4_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[607] + model_decoder_layers_4_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[608] + model_decoder_layers_5_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[609] + model_decoder_layers_5_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[610] + model_decoder_layers_5_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[611] + model_decoder_layers_5_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[612] + model_decoder_layers_5_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[613] + model_decoder_layers_5_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[614] + model_decoder_layers_5_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[615] + model_decoder_layers_5_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[616] + model_decoder_layers_5_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[617] + model_decoder_layers_5_encoder_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[618] + model_decoder_layers_5_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[619] + model_decoder_layers_5_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[620] + model_decoder_layers_5_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[621] + model_decoder_layers_5_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[622] + model_decoder_layers_5_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[623] + model_decoder_layers_5_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[624] + model_decoder_layers_5_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[625] + model_decoder_layers_5_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[626] + model_decoder_layers_5_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[627] + model_decoder_layers_5_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[628] + model_decoder_layers_5_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[629] + model_decoder_layers_5_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[630] + model_decoder_layers_5_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[631] + model_decoder_layers_5_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[632] + model_decoder_layers_6_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[633] + model_decoder_layers_6_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[634] + model_decoder_layers_6_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[635] + model_decoder_layers_6_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[636] + model_decoder_layers_6_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[637] + model_decoder_layers_6_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[638] + model_decoder_layers_6_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[639] + model_decoder_layers_6_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[640] + model_decoder_layers_6_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[641] + model_decoder_layers_6_encoder_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[642] + model_decoder_layers_6_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[643] + model_decoder_layers_6_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[644] + model_decoder_layers_6_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[645] + model_decoder_layers_6_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[646] + model_decoder_layers_6_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[647] + model_decoder_layers_6_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[648] + model_decoder_layers_6_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[649] + model_decoder_layers_6_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[650] + model_decoder_layers_6_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[651] + model_decoder_layers_6_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[652] + model_decoder_layers_6_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[653] + model_decoder_layers_6_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[654] + model_decoder_layers_6_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[655] + model_decoder_layers_6_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[656] + model_decoder_layers_7_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[657] + model_decoder_layers_7_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[658] + model_decoder_layers_7_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[659] + model_decoder_layers_7_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[660] + model_decoder_layers_7_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[661] + model_decoder_layers_7_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[662] + model_decoder_layers_7_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[663] + model_decoder_layers_7_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[664] + model_decoder_layers_7_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[665] + model_decoder_layers_7_encoder_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[666] + model_decoder_layers_7_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[667] + model_decoder_layers_7_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[668] + model_decoder_layers_7_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[669] + model_decoder_layers_7_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[670] + model_decoder_layers_7_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[671] + model_decoder_layers_7_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[672] + model_decoder_layers_7_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[673] + model_decoder_layers_7_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[674] + model_decoder_layers_7_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[675] + model_decoder_layers_7_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[676] + model_decoder_layers_7_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[677] + model_decoder_layers_7_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[678] + model_decoder_layers_7_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[679] + model_decoder_layers_7_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[680] + model_decoder_layers_8_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[681] + model_decoder_layers_8_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[682] + model_decoder_layers_8_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[683] + model_decoder_layers_8_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[684] + model_decoder_layers_8_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[685] + model_decoder_layers_8_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[686] + model_decoder_layers_8_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[687] + model_decoder_layers_8_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[688] + model_decoder_layers_8_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[689] + model_decoder_layers_8_encoder_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[690] + model_decoder_layers_8_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[691] + model_decoder_layers_8_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[692] + model_decoder_layers_8_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[693] + model_decoder_layers_8_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[694] + model_decoder_layers_8_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[695] + model_decoder_layers_8_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[696] + model_decoder_layers_8_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[697] + model_decoder_layers_8_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[698] + model_decoder_layers_8_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[699] + model_decoder_layers_8_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[700] + model_decoder_layers_8_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[701] + model_decoder_layers_8_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[702] + model_decoder_layers_8_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[703] + model_decoder_layers_8_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[704] + model_decoder_layers_9_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[705] + model_decoder_layers_9_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[706] + model_decoder_layers_9_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[707] + model_decoder_layers_9_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[708] + model_decoder_layers_9_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[709] + model_decoder_layers_9_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[710] + model_decoder_layers_9_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[711] + model_decoder_layers_9_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[712] + model_decoder_layers_9_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[713] + model_decoder_layers_9_encoder_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[714] + model_decoder_layers_9_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[715] + model_decoder_layers_9_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[716] + model_decoder_layers_9_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[717] + model_decoder_layers_9_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[718] + model_decoder_layers_9_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[719] + model_decoder_layers_9_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[720] + model_decoder_layers_9_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[721] + model_decoder_layers_9_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[722] + model_decoder_layers_9_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[723] + model_decoder_layers_9_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[724] + model_decoder_layers_9_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[725] + model_decoder_layers_9_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[726] + model_decoder_layers_9_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[727] + model_decoder_layers_9_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[728] + model_decoder_layers_10_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[729] + model_decoder_layers_10_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[730] + model_decoder_layers_10_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[731] + model_decoder_layers_10_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[732] + model_decoder_layers_10_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[733] + model_decoder_layers_10_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[734] + model_decoder_layers_10_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[735] + model_decoder_layers_10_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[736] + model_decoder_layers_10_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[737] + model_decoder_layers_10_encoder_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[738] + model_decoder_layers_10_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[739] + model_decoder_layers_10_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[740] + model_decoder_layers_10_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[741] + model_decoder_layers_10_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[742] + model_decoder_layers_10_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[743] + model_decoder_layers_10_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[744] + model_decoder_layers_10_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[745] + model_decoder_layers_10_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[746] + model_decoder_layers_10_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[747] + model_decoder_layers_10_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[748] + model_decoder_layers_10_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[749] + model_decoder_layers_10_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[750] + model_decoder_layers_10_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[751] + model_decoder_layers_10_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[752] + model_decoder_layers_11_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[753] + model_decoder_layers_11_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[754] + model_decoder_layers_11_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[755] + model_decoder_layers_11_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[756] + model_decoder_layers_11_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[757] + model_decoder_layers_11_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[758] + model_decoder_layers_11_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[759] + model_decoder_layers_11_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[760] + model_decoder_layers_11_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[761] + model_decoder_layers_11_encoder_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[762] + model_decoder_layers_11_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[763] + model_decoder_layers_11_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[764] + model_decoder_layers_11_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[765] + model_decoder_layers_11_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[766] + model_decoder_layers_11_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[767] + model_decoder_layers_11_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[768] + model_decoder_layers_11_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[769] + model_decoder_layers_11_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[770] + model_decoder_layers_11_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[771] + model_decoder_layers_11_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[772] + model_decoder_layers_11_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[773] + model_decoder_layers_11_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[774] + model_decoder_layers_11_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[775] + model_decoder_layers_11_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[776] + model_decoder_layers_12_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[777] + model_decoder_layers_12_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[778] + model_decoder_layers_12_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[779] + model_decoder_layers_12_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[780] + model_decoder_layers_12_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[781] + model_decoder_layers_12_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[782] + model_decoder_layers_12_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[783] + model_decoder_layers_12_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[784] + model_decoder_layers_12_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[785] + model_decoder_layers_12_encoder_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[786] + model_decoder_layers_12_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[787] + model_decoder_layers_12_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[788] + model_decoder_layers_12_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[789] + model_decoder_layers_12_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[790] + model_decoder_layers_12_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[791] + model_decoder_layers_12_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[792] + model_decoder_layers_12_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[793] + model_decoder_layers_12_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[794] + model_decoder_layers_12_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[795] + model_decoder_layers_12_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[796] + model_decoder_layers_12_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[797] + model_decoder_layers_12_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[798] + model_decoder_layers_12_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[799] + model_decoder_layers_12_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[800] + model_decoder_layers_13_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[801] + model_decoder_layers_13_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[802] + model_decoder_layers_13_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[803] + model_decoder_layers_13_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[804] + model_decoder_layers_13_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[805] + model_decoder_layers_13_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[806] + model_decoder_layers_13_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[807] + model_decoder_layers_13_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[808] + model_decoder_layers_13_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[809] + model_decoder_layers_13_encoder_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[810] + model_decoder_layers_13_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[811] + model_decoder_layers_13_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[812] + model_decoder_layers_13_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[813] + model_decoder_layers_13_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[814] + model_decoder_layers_13_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[815] + model_decoder_layers_13_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[816] + model_decoder_layers_13_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[817] + model_decoder_layers_13_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[818] + model_decoder_layers_13_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[819] + model_decoder_layers_13_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[820] + model_decoder_layers_13_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[821] + model_decoder_layers_13_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[822] + model_decoder_layers_13_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[823] + model_decoder_layers_13_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[824] + model_decoder_layers_14_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[825] + model_decoder_layers_14_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[826] + model_decoder_layers_14_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[827] + model_decoder_layers_14_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[828] + model_decoder_layers_14_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[829] + model_decoder_layers_14_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[830] + model_decoder_layers_14_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[831] + model_decoder_layers_14_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[832] + model_decoder_layers_14_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[833] + model_decoder_layers_14_encoder_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[834] + model_decoder_layers_14_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[835] + model_decoder_layers_14_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[836] + model_decoder_layers_14_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[837] + model_decoder_layers_14_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[838] + model_decoder_layers_14_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[839] + model_decoder_layers_14_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[840] + model_decoder_layers_14_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[841] + model_decoder_layers_14_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[842] + model_decoder_layers_14_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[843] + model_decoder_layers_14_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[844] + model_decoder_layers_14_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[845] + model_decoder_layers_14_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[846] + model_decoder_layers_14_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[847] + model_decoder_layers_14_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[848] + model_decoder_layers_15_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[849] + model_decoder_layers_15_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[850] + model_decoder_layers_15_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[851] + model_decoder_layers_15_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[852] + model_decoder_layers_15_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[853] + model_decoder_layers_15_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[854] + model_decoder_layers_15_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[855] + model_decoder_layers_15_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[856] + model_decoder_layers_15_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[857] + model_decoder_layers_15_encoder_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[858] + model_decoder_layers_15_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[859] + model_decoder_layers_15_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[860] + model_decoder_layers_15_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[861] + model_decoder_layers_15_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[862] + model_decoder_layers_15_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[863] + model_decoder_layers_15_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[864] + model_decoder_layers_15_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[865] + model_decoder_layers_15_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[866] + model_decoder_layers_15_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[867] + model_decoder_layers_15_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[868] + model_decoder_layers_15_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[869] + model_decoder_layers_15_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[870] + model_decoder_layers_15_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[871] + model_decoder_layers_15_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[872] + model_decoder_layers_16_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[873] + model_decoder_layers_16_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[874] + model_decoder_layers_16_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[875] + model_decoder_layers_16_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[876] + model_decoder_layers_16_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[877] + model_decoder_layers_16_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[878] + model_decoder_layers_16_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[879] + model_decoder_layers_16_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[880] + model_decoder_layers_16_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[881] + model_decoder_layers_16_encoder_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[882] + model_decoder_layers_16_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[883] + model_decoder_layers_16_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[884] + model_decoder_layers_16_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[885] + model_decoder_layers_16_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[886] + model_decoder_layers_16_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[887] + model_decoder_layers_16_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[888] + model_decoder_layers_16_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[889] + model_decoder_layers_16_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[890] + model_decoder_layers_16_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[891] + model_decoder_layers_16_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[892] + model_decoder_layers_16_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[893] + model_decoder_layers_16_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[894] + model_decoder_layers_16_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[895] + model_decoder_layers_16_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[896] + model_decoder_layers_17_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[897] + model_decoder_layers_17_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[898] + model_decoder_layers_17_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[899] + model_decoder_layers_17_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[900] + model_decoder_layers_17_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[901] + model_decoder_layers_17_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[902] + model_decoder_layers_17_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[903] + model_decoder_layers_17_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[904] + model_decoder_layers_17_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[905] + model_decoder_layers_17_encoder_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[906] + model_decoder_layers_17_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[907] + model_decoder_layers_17_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[908] + model_decoder_layers_17_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[909] + model_decoder_layers_17_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[910] + model_decoder_layers_17_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[911] + model_decoder_layers_17_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[912] + model_decoder_layers_17_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[913] + model_decoder_layers_17_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[914] + model_decoder_layers_17_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[915] + model_decoder_layers_17_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[916] + model_decoder_layers_17_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[917] + model_decoder_layers_17_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[918] + model_decoder_layers_17_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[919] + model_decoder_layers_17_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[920] + model_decoder_layers_18_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[921] + model_decoder_layers_18_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[922] + model_decoder_layers_18_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[923] + model_decoder_layers_18_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[924] + model_decoder_layers_18_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[925] + model_decoder_layers_18_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[926] + model_decoder_layers_18_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[927] + model_decoder_layers_18_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[928] + model_decoder_layers_18_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[929] + model_decoder_layers_18_encoder_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[930] + model_decoder_layers_18_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[931] + model_decoder_layers_18_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[932] + model_decoder_layers_18_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[933] + model_decoder_layers_18_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[934] + model_decoder_layers_18_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[935] + model_decoder_layers_18_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[936] + model_decoder_layers_18_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[937] + model_decoder_layers_18_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[938] + model_decoder_layers_18_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[939] + model_decoder_layers_18_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[940] + model_decoder_layers_18_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[941] + model_decoder_layers_18_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[942] + model_decoder_layers_18_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[943] + model_decoder_layers_18_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[944] + model_decoder_layers_19_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[945] + model_decoder_layers_19_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[946] + model_decoder_layers_19_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[947] + model_decoder_layers_19_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[948] + model_decoder_layers_19_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[949] + model_decoder_layers_19_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[950] + model_decoder_layers_19_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[951] + model_decoder_layers_19_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[952] + model_decoder_layers_19_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[953] + model_decoder_layers_19_encoder_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[954] + model_decoder_layers_19_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[955] + model_decoder_layers_19_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[956] + model_decoder_layers_19_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[957] + model_decoder_layers_19_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[958] + model_decoder_layers_19_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[959] + model_decoder_layers_19_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[960] + model_decoder_layers_19_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[961] + model_decoder_layers_19_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[962] + model_decoder_layers_19_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[963] + model_decoder_layers_19_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[964] + model_decoder_layers_19_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[965] + model_decoder_layers_19_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[966] + model_decoder_layers_19_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[967] + model_decoder_layers_19_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[968] + model_decoder_layers_20_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[969] + model_decoder_layers_20_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[970] + model_decoder_layers_20_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[971] + model_decoder_layers_20_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[972] + model_decoder_layers_20_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[973] + model_decoder_layers_20_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[974] + model_decoder_layers_20_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[975] + model_decoder_layers_20_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[976] + model_decoder_layers_20_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[977] + model_decoder_layers_20_encoder_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[978] + model_decoder_layers_20_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[979] + model_decoder_layers_20_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[980] + model_decoder_layers_20_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[981] + model_decoder_layers_20_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[982] + model_decoder_layers_20_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[983] + model_decoder_layers_20_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[984] + model_decoder_layers_20_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[985] + model_decoder_layers_20_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[986] + model_decoder_layers_20_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[987] + model_decoder_layers_20_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[988] + model_decoder_layers_20_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[989] + model_decoder_layers_20_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[990] + model_decoder_layers_20_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[991] + model_decoder_layers_20_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[992] + model_decoder_layers_21_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[993] + model_decoder_layers_21_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[994] + model_decoder_layers_21_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[995] + model_decoder_layers_21_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[996] + model_decoder_layers_21_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[997] + model_decoder_layers_21_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[998] + model_decoder_layers_21_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[999] + model_decoder_layers_21_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1000] + model_decoder_layers_21_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1001] + model_decoder_layers_21_encoder_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1002] + model_decoder_layers_21_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1003] + model_decoder_layers_21_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1004] + model_decoder_layers_21_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1005] + model_decoder_layers_21_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1006] + model_decoder_layers_21_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1007] + model_decoder_layers_21_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1008] + model_decoder_layers_21_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1009] + model_decoder_layers_21_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1010] + model_decoder_layers_21_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1011] + model_decoder_layers_21_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1012] + model_decoder_layers_21_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1013] + model_decoder_layers_21_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1014] + model_decoder_layers_21_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1015] + model_decoder_layers_21_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1016] + model_decoder_layers_22_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1017] + model_decoder_layers_22_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1018] + model_decoder_layers_22_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1019] + model_decoder_layers_22_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1020] + model_decoder_layers_22_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1021] + model_decoder_layers_22_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1022] + model_decoder_layers_22_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1023] + model_decoder_layers_22_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1024] + model_decoder_layers_22_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1025] + model_decoder_layers_22_encoder_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1026] + model_decoder_layers_22_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1027] + model_decoder_layers_22_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1028] + model_decoder_layers_22_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1029] + model_decoder_layers_22_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1030] + model_decoder_layers_22_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1031] + model_decoder_layers_22_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1032] + model_decoder_layers_22_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1033] + model_decoder_layers_22_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1034] + model_decoder_layers_22_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1035] + model_decoder_layers_22_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1036] + model_decoder_layers_22_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1037] + model_decoder_layers_22_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1038] + model_decoder_layers_22_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1039] + model_decoder_layers_22_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1040] + model_decoder_layers_23_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1041] + model_decoder_layers_23_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1042] + model_decoder_layers_23_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1043] + model_decoder_layers_23_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1044] + model_decoder_layers_23_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1045] + model_decoder_layers_23_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1046] + model_decoder_layers_23_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1047] + model_decoder_layers_23_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1048] + model_decoder_layers_23_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1049] + model_decoder_layers_23_encoder_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1050] + model_decoder_layers_23_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1051] + model_decoder_layers_23_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1052] + model_decoder_layers_23_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1053] + model_decoder_layers_23_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1054] + model_decoder_layers_23_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1055] + model_decoder_layers_23_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1056] + model_decoder_layers_23_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1057] + model_decoder_layers_23_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1058] + model_decoder_layers_23_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1059] + model_decoder_layers_23_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1060] + model_decoder_layers_23_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1061] + model_decoder_layers_23_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1062] + model_decoder_layers_23_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1063] + model_decoder_layers_23_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1064] + model_decoder_layers_24_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1065] + model_decoder_layers_24_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1066] + model_decoder_layers_24_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1067] + model_decoder_layers_24_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1068] + model_decoder_layers_24_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1069] + model_decoder_layers_24_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1070] + model_decoder_layers_24_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1071] + model_decoder_layers_24_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1072] + model_decoder_layers_24_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1073] + model_decoder_layers_24_encoder_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1074] + model_decoder_layers_24_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1075] + model_decoder_layers_24_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1076] + model_decoder_layers_24_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1077] + model_decoder_layers_24_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1078] + model_decoder_layers_24_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1079] + model_decoder_layers_24_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1080] + model_decoder_layers_24_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1081] + model_decoder_layers_24_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1082] + model_decoder_layers_24_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1083] + model_decoder_layers_24_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1084] + model_decoder_layers_24_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1085] + model_decoder_layers_24_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1086] + model_decoder_layers_24_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1087] + model_decoder_layers_24_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1088] + model_decoder_layers_25_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1089] + model_decoder_layers_25_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1090] + model_decoder_layers_25_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1091] + model_decoder_layers_25_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1092] + model_decoder_layers_25_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1093] + model_decoder_layers_25_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1094] + model_decoder_layers_25_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1095] + model_decoder_layers_25_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1096] + model_decoder_layers_25_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1097] + model_decoder_layers_25_encoder_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1098] + model_decoder_layers_25_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1099] + model_decoder_layers_25_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1100] + model_decoder_layers_25_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1101] + model_decoder_layers_25_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1102] + model_decoder_layers_25_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1103] + model_decoder_layers_25_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1104] + model_decoder_layers_25_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1105] + model_decoder_layers_25_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1106] + model_decoder_layers_25_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1107] + model_decoder_layers_25_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1108] + model_decoder_layers_25_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1109] + model_decoder_layers_25_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1110] + model_decoder_layers_25_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1111] + model_decoder_layers_25_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1112] + model_decoder_layers_26_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1113] + model_decoder_layers_26_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1114] + model_decoder_layers_26_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1115] + model_decoder_layers_26_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1116] + model_decoder_layers_26_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1117] + model_decoder_layers_26_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1118] + model_decoder_layers_26_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1119] + model_decoder_layers_26_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1120] + model_decoder_layers_26_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1121] + model_decoder_layers_26_encoder_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1122] + model_decoder_layers_26_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1123] + model_decoder_layers_26_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1124] + model_decoder_layers_26_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1125] + model_decoder_layers_26_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1126] + model_decoder_layers_26_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1127] + model_decoder_layers_26_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1128] + model_decoder_layers_26_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1129] + model_decoder_layers_26_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1130] + model_decoder_layers_26_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1131] + model_decoder_layers_26_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1132] + model_decoder_layers_26_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1133] + model_decoder_layers_26_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1134] + model_decoder_layers_26_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1135] + model_decoder_layers_26_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1136] + model_decoder_layers_27_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1137] + model_decoder_layers_27_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1138] + model_decoder_layers_27_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1139] + model_decoder_layers_27_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1140] + model_decoder_layers_27_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1141] + model_decoder_layers_27_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1142] + model_decoder_layers_27_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1143] + model_decoder_layers_27_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1144] + model_decoder_layers_27_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1145] + model_decoder_layers_27_encoder_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1146] + model_decoder_layers_27_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1147] + model_decoder_layers_27_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1148] + model_decoder_layers_27_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1149] + model_decoder_layers_27_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1150] + model_decoder_layers_27_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1151] + model_decoder_layers_27_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1152] + model_decoder_layers_27_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1153] + model_decoder_layers_27_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1154] + model_decoder_layers_27_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1155] + model_decoder_layers_27_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1156] + model_decoder_layers_27_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1157] + model_decoder_layers_27_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1158] + model_decoder_layers_27_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1159] + model_decoder_layers_27_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1160] + model_decoder_layers_28_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1161] + model_decoder_layers_28_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1162] + model_decoder_layers_28_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1163] + model_decoder_layers_28_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1164] + model_decoder_layers_28_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1165] + model_decoder_layers_28_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1166] + model_decoder_layers_28_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1167] + model_decoder_layers_28_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1168] + model_decoder_layers_28_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1169] + model_decoder_layers_28_encoder_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1170] + model_decoder_layers_28_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1171] + model_decoder_layers_28_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1172] + model_decoder_layers_28_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1173] + model_decoder_layers_28_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1174] + model_decoder_layers_28_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1175] + model_decoder_layers_28_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1176] + model_decoder_layers_28_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1177] + model_decoder_layers_28_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1178] + model_decoder_layers_28_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1179] + model_decoder_layers_28_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1180] + model_decoder_layers_28_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1181] + model_decoder_layers_28_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1182] + model_decoder_layers_28_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1183] + model_decoder_layers_28_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1184] + model_decoder_layers_29_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1185] + model_decoder_layers_29_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1186] + model_decoder_layers_29_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1187] + model_decoder_layers_29_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1188] + model_decoder_layers_29_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1189] + model_decoder_layers_29_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1190] + model_decoder_layers_29_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1191] + model_decoder_layers_29_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1192] + model_decoder_layers_29_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1193] + model_decoder_layers_29_encoder_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1194] + model_decoder_layers_29_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1195] + model_decoder_layers_29_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1196] + model_decoder_layers_29_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1197] + model_decoder_layers_29_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1198] + model_decoder_layers_29_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1199] + model_decoder_layers_29_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1200] + model_decoder_layers_29_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1201] + model_decoder_layers_29_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1202] + model_decoder_layers_29_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1203] + model_decoder_layers_29_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1204] + model_decoder_layers_29_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1205] + model_decoder_layers_29_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1206] + model_decoder_layers_29_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1207] + model_decoder_layers_29_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1208] + model_decoder_layers_30_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1209] + model_decoder_layers_30_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1210] + model_decoder_layers_30_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1211] + model_decoder_layers_30_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1212] + model_decoder_layers_30_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1213] + model_decoder_layers_30_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1214] + model_decoder_layers_30_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1215] + model_decoder_layers_30_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1216] + model_decoder_layers_30_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1217] + model_decoder_layers_30_encoder_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1218] + model_decoder_layers_30_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1219] + model_decoder_layers_30_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1220] + model_decoder_layers_30_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1221] + model_decoder_layers_30_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1222] + model_decoder_layers_30_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1223] + model_decoder_layers_30_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1224] + model_decoder_layers_30_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1225] + model_decoder_layers_30_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1226] + model_decoder_layers_30_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1227] + model_decoder_layers_30_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1228] + model_decoder_layers_30_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1229] + model_decoder_layers_30_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1230] + model_decoder_layers_30_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1231] + model_decoder_layers_30_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1232] + model_decoder_layers_31_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1233] + model_decoder_layers_31_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1234] + model_decoder_layers_31_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1235] + model_decoder_layers_31_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1236] + model_decoder_layers_31_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1237] + model_decoder_layers_31_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1238] + model_decoder_layers_31_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1239] + model_decoder_layers_31_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1240] + model_decoder_layers_31_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1241] + model_decoder_layers_31_encoder_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1242] + model_decoder_layers_31_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1243] + model_decoder_layers_31_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1244] + model_decoder_layers_31_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1245] + model_decoder_layers_31_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1246] + model_decoder_layers_31_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1247] + model_decoder_layers_31_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1248] + model_decoder_layers_31_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1249] + model_decoder_layers_31_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1250] + model_decoder_layers_31_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1251] + model_decoder_layers_31_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1252] + model_decoder_layers_31_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1253] + model_decoder_layers_31_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1254] + model_decoder_layers_31_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1255] + model_decoder_layers_31_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1256] + model_decoder_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1257] + model_decoder_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1258] + reshape1030: R.Tensor((seq_len,), dtype="int32") = R.reshape(input_ids, R.shape([seq_len])) + take5: R.Tensor((seq_len, 1280), dtype="float16") = R.take(model_decoder_embed_tokens_weight4, reshape1030, axis=0) + reshape1031: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(take5, R.shape([1, seq_len, 1280])) + lv198: R.Tensor((seq_len,), dtype="int32") = R.call_pure_packed("vm.builtin.attention_kv_cache_get_query_positions", paged_kv_cache, sinfo_args=(R.Tensor((seq_len,), dtype="int32"),)) + take6: R.Tensor((seq_len, 1280), dtype="float16") = R.take(model_decoder_embed_positions_weight4, lv198, axis=0) + reshape1032: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(take6, R.shape([1, seq_len, 1280])) + add899: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(reshape1031, reshape1032) + layer_norm259: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add899, model_decoder_layers_0_self_attn_layer_norm_weight4, model_decoder_layers_0_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims771: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_self_attn_q_proj_weight4, axes=None) + matmul770: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm259, permute_dims771, out_dtype="void") + add900: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul770, model_decoder_layers_0_self_attn_q_proj_bias4) + reshape1033: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add900, R.shape([1, seq_len, 20, 64])) + permute_dims772: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_self_attn_k_proj_weight4, axes=None) + matmul771: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm259, permute_dims772, out_dtype="void") + reshape1034: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul771, R.shape([1, seq_len, 20, 64])) + permute_dims773: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_self_attn_v_proj_weight4, axes=None) + matmul772: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm259, permute_dims773, out_dtype="void") + add901: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul772, model_decoder_layers_0_self_attn_v_proj_bias4) + reshape1035: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add901, R.shape([1, seq_len, 20, 64])) + concat64: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1033, reshape1034, reshape1035), axis=2) + reshape1036: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat64, R.shape([seq_len, 60, 64])) + lv199 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), reshape1036), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1037: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv199, R.shape([1, seq_len, 20, 64])) + reshape1038: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1037, R.shape([1, seq_len, 1280])) + permute_dims774: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_self_attn_out_proj_weight4, axes=None) + matmul773: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1038, permute_dims774, out_dtype="void") + add902: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul773, model_decoder_layers_0_self_attn_out_proj_bias4) + add903: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add899, add902) + layer_norm260: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add903, model_decoder_layers_0_encoder_attn_layer_norm_weight4, model_decoder_layers_0_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims775: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_encoder_attn_q_proj_weight4, axes=None) + matmul774: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm260, permute_dims775, out_dtype="void") + add904: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul774, model_decoder_layers_0_encoder_attn_q_proj_bias4) + reshape1039: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add904, R.shape([1, seq_len, 20, 64])) + reshape1040: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1039, R.shape([seq_len, 20, 64])) + lv200 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), reshape1040), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1041: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv200, R.shape([1, seq_len, 20, 64])) + reshape1042: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1041, R.shape([1, seq_len, 1280])) + permute_dims776: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_encoder_attn_out_proj_weight4, axes=None) + matmul775: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1042, permute_dims776, out_dtype="void") + add905: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul775, model_decoder_layers_0_encoder_attn_out_proj_bias4) + add906: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add903, add905) + layer_norm261: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add906, model_decoder_layers_0_final_layer_norm_weight4, model_decoder_layers_0_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims777: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_0_fc1_weight4, axes=None) + matmul776: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm261, permute_dims777, out_dtype="void") + add907: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul776, model_decoder_layers_0_fc1_bias4) + gelu98: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add907) + permute_dims778: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_fc2_weight4, axes=None) + matmul777: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu98, permute_dims778, out_dtype="void") + add908: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul777, model_decoder_layers_0_fc2_bias4) + add909: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add906, add908) + layer_norm262: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add909, model_decoder_layers_1_self_attn_layer_norm_weight4, model_decoder_layers_1_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims779: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_1_self_attn_q_proj_weight4, axes=None) + matmul778: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm262, permute_dims779, out_dtype="void") + add910: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul778, model_decoder_layers_1_self_attn_q_proj_bias4) + reshape1043: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add910, R.shape([1, seq_len, 20, 64])) + permute_dims780: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_1_self_attn_k_proj_weight4, axes=None) + matmul779: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm262, permute_dims780, out_dtype="void") + reshape1044: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul779, R.shape([1, seq_len, 20, 64])) + permute_dims781: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_1_self_attn_v_proj_weight4, axes=None) + matmul780: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm262, permute_dims781, out_dtype="void") + add911: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul780, model_decoder_layers_1_self_attn_v_proj_bias4) + reshape1045: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add911, R.shape([1, seq_len, 20, 64])) + concat65: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1043, reshape1044, reshape1045), axis=2) + reshape1046: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat65, R.shape([seq_len, 60, 64])) + lv201 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), reshape1046), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1047: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv201, R.shape([1, seq_len, 20, 64])) + reshape1048: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1047, R.shape([1, seq_len, 1280])) + permute_dims782: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_1_self_attn_out_proj_weight4, axes=None) + matmul781: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1048, permute_dims782, out_dtype="void") + add912: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul781, model_decoder_layers_1_self_attn_out_proj_bias4) + add913: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add909, add912) + layer_norm263: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add913, model_decoder_layers_1_encoder_attn_layer_norm_weight4, model_decoder_layers_1_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims783: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_1_encoder_attn_q_proj_weight4, axes=None) + matmul782: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm263, permute_dims783, out_dtype="void") + add914: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul782, model_decoder_layers_1_encoder_attn_q_proj_bias4) + reshape1049: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add914, R.shape([1, seq_len, 20, 64])) + reshape1050: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1049, R.shape([seq_len, 20, 64])) + lv202 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), reshape1050), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1051: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv202, R.shape([1, seq_len, 20, 64])) + reshape1052: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1051, R.shape([1, seq_len, 1280])) + permute_dims784: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_1_encoder_attn_out_proj_weight4, axes=None) + matmul783: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1052, permute_dims784, out_dtype="void") + add915: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul783, model_decoder_layers_1_encoder_attn_out_proj_bias4) + add916: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add913, add915) + layer_norm264: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add916, model_decoder_layers_1_final_layer_norm_weight4, model_decoder_layers_1_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims785: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_1_fc1_weight4, axes=None) + matmul784: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm264, permute_dims785, out_dtype="void") + add917: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul784, model_decoder_layers_1_fc1_bias4) + gelu99: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add917) + permute_dims786: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_1_fc2_weight4, axes=None) + matmul785: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu99, permute_dims786, out_dtype="void") + add918: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul785, model_decoder_layers_1_fc2_bias4) + add919: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add916, add918) + layer_norm265: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add919, model_decoder_layers_2_self_attn_layer_norm_weight4, model_decoder_layers_2_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims787: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_2_self_attn_q_proj_weight4, axes=None) + matmul786: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm265, permute_dims787, out_dtype="void") + add920: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul786, model_decoder_layers_2_self_attn_q_proj_bias4) + reshape1053: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add920, R.shape([1, seq_len, 20, 64])) + permute_dims788: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_2_self_attn_k_proj_weight4, axes=None) + matmul787: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm265, permute_dims788, out_dtype="void") + reshape1054: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul787, R.shape([1, seq_len, 20, 64])) + permute_dims789: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_2_self_attn_v_proj_weight4, axes=None) + matmul788: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm265, permute_dims789, out_dtype="void") + add921: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul788, model_decoder_layers_2_self_attn_v_proj_bias4) + reshape1055: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add921, R.shape([1, seq_len, 20, 64])) + concat66: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1053, reshape1054, reshape1055), axis=2) + reshape1056: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat66, R.shape([seq_len, 60, 64])) + lv203 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), reshape1056), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1057: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv203, R.shape([1, seq_len, 20, 64])) + reshape1058: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1057, R.shape([1, seq_len, 1280])) + permute_dims790: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_2_self_attn_out_proj_weight4, axes=None) + matmul789: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1058, permute_dims790, out_dtype="void") + add922: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul789, model_decoder_layers_2_self_attn_out_proj_bias4) + add923: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add919, add922) + layer_norm266: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add923, model_decoder_layers_2_encoder_attn_layer_norm_weight4, model_decoder_layers_2_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims791: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_2_encoder_attn_q_proj_weight4, axes=None) + matmul790: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm266, permute_dims791, out_dtype="void") + add924: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul790, model_decoder_layers_2_encoder_attn_q_proj_bias4) + reshape1059: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add924, R.shape([1, seq_len, 20, 64])) + reshape1060: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1059, R.shape([seq_len, 20, 64])) + lv204 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), reshape1060), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1061: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv204, R.shape([1, seq_len, 20, 64])) + reshape1062: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1061, R.shape([1, seq_len, 1280])) + permute_dims792: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_2_encoder_attn_out_proj_weight4, axes=None) + matmul791: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1062, permute_dims792, out_dtype="void") + add925: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul791, model_decoder_layers_2_encoder_attn_out_proj_bias4) + add926: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add923, add925) + layer_norm267: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add926, model_decoder_layers_2_final_layer_norm_weight4, model_decoder_layers_2_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims793: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_2_fc1_weight4, axes=None) + matmul792: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm267, permute_dims793, out_dtype="void") + add927: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul792, model_decoder_layers_2_fc1_bias4) + gelu100: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add927) + permute_dims794: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_2_fc2_weight4, axes=None) + matmul793: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu100, permute_dims794, out_dtype="void") + add928: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul793, model_decoder_layers_2_fc2_bias4) + add929: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add926, add928) + layer_norm268: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add929, model_decoder_layers_3_self_attn_layer_norm_weight4, model_decoder_layers_3_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims795: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_3_self_attn_q_proj_weight4, axes=None) + matmul794: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm268, permute_dims795, out_dtype="void") + add930: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul794, model_decoder_layers_3_self_attn_q_proj_bias4) + reshape1063: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add930, R.shape([1, seq_len, 20, 64])) + permute_dims796: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_3_self_attn_k_proj_weight4, axes=None) + matmul795: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm268, permute_dims796, out_dtype="void") + reshape1064: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul795, R.shape([1, seq_len, 20, 64])) + permute_dims797: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_3_self_attn_v_proj_weight4, axes=None) + matmul796: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm268, permute_dims797, out_dtype="void") + add931: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul796, model_decoder_layers_3_self_attn_v_proj_bias4) + reshape1065: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add931, R.shape([1, seq_len, 20, 64])) + concat67: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1063, reshape1064, reshape1065), axis=2) + reshape1066: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat67, R.shape([seq_len, 60, 64])) + lv205 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), reshape1066), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1067: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv205, R.shape([1, seq_len, 20, 64])) + reshape1068: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1067, R.shape([1, seq_len, 1280])) + permute_dims798: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_3_self_attn_out_proj_weight4, axes=None) + matmul797: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1068, permute_dims798, out_dtype="void") + add932: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul797, model_decoder_layers_3_self_attn_out_proj_bias4) + add933: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add929, add932) + layer_norm269: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add933, model_decoder_layers_3_encoder_attn_layer_norm_weight4, model_decoder_layers_3_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims799: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_3_encoder_attn_q_proj_weight4, axes=None) + matmul798: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm269, permute_dims799, out_dtype="void") + add934: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul798, model_decoder_layers_3_encoder_attn_q_proj_bias4) + reshape1069: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add934, R.shape([1, seq_len, 20, 64])) + reshape1070: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1069, R.shape([seq_len, 20, 64])) + lv206 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), reshape1070), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1071: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv206, R.shape([1, seq_len, 20, 64])) + reshape1072: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1071, R.shape([1, seq_len, 1280])) + permute_dims800: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_3_encoder_attn_out_proj_weight4, axes=None) + matmul799: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1072, permute_dims800, out_dtype="void") + add935: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul799, model_decoder_layers_3_encoder_attn_out_proj_bias4) + add936: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add933, add935) + layer_norm270: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add936, model_decoder_layers_3_final_layer_norm_weight4, model_decoder_layers_3_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims801: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_3_fc1_weight4, axes=None) + matmul800: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm270, permute_dims801, out_dtype="void") + add937: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul800, model_decoder_layers_3_fc1_bias4) + gelu101: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add937) + permute_dims802: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_3_fc2_weight4, axes=None) + matmul801: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu101, permute_dims802, out_dtype="void") + add938: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul801, model_decoder_layers_3_fc2_bias4) + add939: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add936, add938) + layer_norm271: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add939, model_decoder_layers_4_self_attn_layer_norm_weight4, model_decoder_layers_4_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims803: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_4_self_attn_q_proj_weight4, axes=None) + matmul802: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm271, permute_dims803, out_dtype="void") + add940: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul802, model_decoder_layers_4_self_attn_q_proj_bias4) + reshape1073: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add940, R.shape([1, seq_len, 20, 64])) + permute_dims804: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_4_self_attn_k_proj_weight4, axes=None) + matmul803: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm271, permute_dims804, out_dtype="void") + reshape1074: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul803, R.shape([1, seq_len, 20, 64])) + permute_dims805: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_4_self_attn_v_proj_weight4, axes=None) + matmul804: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm271, permute_dims805, out_dtype="void") + add941: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul804, model_decoder_layers_4_self_attn_v_proj_bias4) + reshape1075: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add941, R.shape([1, seq_len, 20, 64])) + concat68: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1073, reshape1074, reshape1075), axis=2) + reshape1076: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat68, R.shape([seq_len, 60, 64])) + lv207 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), reshape1076), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1077: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv207, R.shape([1, seq_len, 20, 64])) + reshape1078: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1077, R.shape([1, seq_len, 1280])) + permute_dims806: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_4_self_attn_out_proj_weight4, axes=None) + matmul805: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1078, permute_dims806, out_dtype="void") + add942: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul805, model_decoder_layers_4_self_attn_out_proj_bias4) + add943: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add939, add942) + layer_norm272: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add943, model_decoder_layers_4_encoder_attn_layer_norm_weight4, model_decoder_layers_4_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims807: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_4_encoder_attn_q_proj_weight4, axes=None) + matmul806: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm272, permute_dims807, out_dtype="void") + add944: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul806, model_decoder_layers_4_encoder_attn_q_proj_bias4) + reshape1079: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add944, R.shape([1, seq_len, 20, 64])) + reshape1080: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1079, R.shape([seq_len, 20, 64])) + lv208 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), reshape1080), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1081: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv208, R.shape([1, seq_len, 20, 64])) + reshape1082: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1081, R.shape([1, seq_len, 1280])) + permute_dims808: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_4_encoder_attn_out_proj_weight4, axes=None) + matmul807: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1082, permute_dims808, out_dtype="void") + add945: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul807, model_decoder_layers_4_encoder_attn_out_proj_bias4) + add946: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add943, add945) + layer_norm273: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add946, model_decoder_layers_4_final_layer_norm_weight4, model_decoder_layers_4_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims809: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_4_fc1_weight4, axes=None) + matmul808: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm273, permute_dims809, out_dtype="void") + add947: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul808, model_decoder_layers_4_fc1_bias4) + gelu102: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add947) + permute_dims810: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_4_fc2_weight4, axes=None) + matmul809: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu102, permute_dims810, out_dtype="void") + add948: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul809, model_decoder_layers_4_fc2_bias4) + add949: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add946, add948) + layer_norm274: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add949, model_decoder_layers_5_self_attn_layer_norm_weight4, model_decoder_layers_5_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims811: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_5_self_attn_q_proj_weight4, axes=None) + matmul810: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm274, permute_dims811, out_dtype="void") + add950: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul810, model_decoder_layers_5_self_attn_q_proj_bias4) + reshape1083: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add950, R.shape([1, seq_len, 20, 64])) + permute_dims812: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_5_self_attn_k_proj_weight4, axes=None) + matmul811: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm274, permute_dims812, out_dtype="void") + reshape1084: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul811, R.shape([1, seq_len, 20, 64])) + permute_dims813: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_5_self_attn_v_proj_weight4, axes=None) + matmul812: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm274, permute_dims813, out_dtype="void") + add951: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul812, model_decoder_layers_5_self_attn_v_proj_bias4) + reshape1085: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add951, R.shape([1, seq_len, 20, 64])) + concat69: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1083, reshape1084, reshape1085), axis=2) + reshape1086: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat69, R.shape([seq_len, 60, 64])) + lv209 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), reshape1086), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1087: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv209, R.shape([1, seq_len, 20, 64])) + reshape1088: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1087, R.shape([1, seq_len, 1280])) + permute_dims814: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_5_self_attn_out_proj_weight4, axes=None) + matmul813: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1088, permute_dims814, out_dtype="void") + add952: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul813, model_decoder_layers_5_self_attn_out_proj_bias4) + add953: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add949, add952) + layer_norm275: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add953, model_decoder_layers_5_encoder_attn_layer_norm_weight4, model_decoder_layers_5_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims815: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_5_encoder_attn_q_proj_weight4, axes=None) + matmul814: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm275, permute_dims815, out_dtype="void") + add954: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul814, model_decoder_layers_5_encoder_attn_q_proj_bias4) + reshape1089: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add954, R.shape([1, seq_len, 20, 64])) + reshape1090: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1089, R.shape([seq_len, 20, 64])) + lv210 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), reshape1090), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1091: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv210, R.shape([1, seq_len, 20, 64])) + reshape1092: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1091, R.shape([1, seq_len, 1280])) + permute_dims816: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_5_encoder_attn_out_proj_weight4, axes=None) + matmul815: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1092, permute_dims816, out_dtype="void") + add955: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul815, model_decoder_layers_5_encoder_attn_out_proj_bias4) + add956: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add953, add955) + layer_norm276: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add956, model_decoder_layers_5_final_layer_norm_weight4, model_decoder_layers_5_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims817: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_5_fc1_weight4, axes=None) + matmul816: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm276, permute_dims817, out_dtype="void") + add957: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul816, model_decoder_layers_5_fc1_bias4) + gelu103: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add957) + permute_dims818: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_5_fc2_weight4, axes=None) + matmul817: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu103, permute_dims818, out_dtype="void") + add958: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul817, model_decoder_layers_5_fc2_bias4) + add959: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add956, add958) + layer_norm277: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add959, model_decoder_layers_6_self_attn_layer_norm_weight4, model_decoder_layers_6_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims819: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_6_self_attn_q_proj_weight4, axes=None) + matmul818: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm277, permute_dims819, out_dtype="void") + add960: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul818, model_decoder_layers_6_self_attn_q_proj_bias4) + reshape1093: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add960, R.shape([1, seq_len, 20, 64])) + permute_dims820: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_6_self_attn_k_proj_weight4, axes=None) + matmul819: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm277, permute_dims820, out_dtype="void") + reshape1094: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul819, R.shape([1, seq_len, 20, 64])) + permute_dims821: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_6_self_attn_v_proj_weight4, axes=None) + matmul820: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm277, permute_dims821, out_dtype="void") + add961: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul820, model_decoder_layers_6_self_attn_v_proj_bias4) + reshape1095: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add961, R.shape([1, seq_len, 20, 64])) + concat70: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1093, reshape1094, reshape1095), axis=2) + reshape1096: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat70, R.shape([seq_len, 60, 64])) + lv211 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), reshape1096), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1097: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv211, R.shape([1, seq_len, 20, 64])) + reshape1098: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1097, R.shape([1, seq_len, 1280])) + permute_dims822: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_6_self_attn_out_proj_weight4, axes=None) + matmul821: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1098, permute_dims822, out_dtype="void") + add962: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul821, model_decoder_layers_6_self_attn_out_proj_bias4) + add963: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add959, add962) + layer_norm278: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add963, model_decoder_layers_6_encoder_attn_layer_norm_weight4, model_decoder_layers_6_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims823: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_6_encoder_attn_q_proj_weight4, axes=None) + matmul822: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm278, permute_dims823, out_dtype="void") + add964: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul822, model_decoder_layers_6_encoder_attn_q_proj_bias4) + reshape1099: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add964, R.shape([1, seq_len, 20, 64])) + reshape1100: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1099, R.shape([seq_len, 20, 64])) + lv212 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), reshape1100), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1101: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv212, R.shape([1, seq_len, 20, 64])) + reshape1102: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1101, R.shape([1, seq_len, 1280])) + permute_dims824: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_6_encoder_attn_out_proj_weight4, axes=None) + matmul823: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1102, permute_dims824, out_dtype="void") + add965: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul823, model_decoder_layers_6_encoder_attn_out_proj_bias4) + add966: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add963, add965) + layer_norm279: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add966, model_decoder_layers_6_final_layer_norm_weight4, model_decoder_layers_6_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims825: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_6_fc1_weight4, axes=None) + matmul824: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm279, permute_dims825, out_dtype="void") + add967: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul824, model_decoder_layers_6_fc1_bias4) + gelu104: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add967) + permute_dims826: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_6_fc2_weight4, axes=None) + matmul825: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu104, permute_dims826, out_dtype="void") + add968: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul825, model_decoder_layers_6_fc2_bias4) + add969: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add966, add968) + layer_norm280: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add969, model_decoder_layers_7_self_attn_layer_norm_weight4, model_decoder_layers_7_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims827: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_7_self_attn_q_proj_weight4, axes=None) + matmul826: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm280, permute_dims827, out_dtype="void") + add970: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul826, model_decoder_layers_7_self_attn_q_proj_bias4) + reshape1103: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add970, R.shape([1, seq_len, 20, 64])) + permute_dims828: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_7_self_attn_k_proj_weight4, axes=None) + matmul827: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm280, permute_dims828, out_dtype="void") + reshape1104: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul827, R.shape([1, seq_len, 20, 64])) + permute_dims829: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_7_self_attn_v_proj_weight4, axes=None) + matmul828: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm280, permute_dims829, out_dtype="void") + add971: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul828, model_decoder_layers_7_self_attn_v_proj_bias4) + reshape1105: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add971, R.shape([1, seq_len, 20, 64])) + concat71: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1103, reshape1104, reshape1105), axis=2) + reshape1106: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat71, R.shape([seq_len, 60, 64])) + lv213 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), reshape1106), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1107: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv213, R.shape([1, seq_len, 20, 64])) + reshape1108: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1107, R.shape([1, seq_len, 1280])) + permute_dims830: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_7_self_attn_out_proj_weight4, axes=None) + matmul829: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1108, permute_dims830, out_dtype="void") + add972: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul829, model_decoder_layers_7_self_attn_out_proj_bias4) + add973: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add969, add972) + layer_norm281: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add973, model_decoder_layers_7_encoder_attn_layer_norm_weight4, model_decoder_layers_7_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims831: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_7_encoder_attn_q_proj_weight4, axes=None) + matmul830: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm281, permute_dims831, out_dtype="void") + add974: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul830, model_decoder_layers_7_encoder_attn_q_proj_bias4) + reshape1109: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add974, R.shape([1, seq_len, 20, 64])) + reshape1110: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1109, R.shape([seq_len, 20, 64])) + lv214 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), reshape1110), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1111: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv214, R.shape([1, seq_len, 20, 64])) + reshape1112: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1111, R.shape([1, seq_len, 1280])) + permute_dims832: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_7_encoder_attn_out_proj_weight4, axes=None) + matmul831: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1112, permute_dims832, out_dtype="void") + add975: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul831, model_decoder_layers_7_encoder_attn_out_proj_bias4) + add976: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add973, add975) + layer_norm282: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add976, model_decoder_layers_7_final_layer_norm_weight4, model_decoder_layers_7_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims833: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_7_fc1_weight4, axes=None) + matmul832: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm282, permute_dims833, out_dtype="void") + add977: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul832, model_decoder_layers_7_fc1_bias4) + gelu105: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add977) + permute_dims834: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_7_fc2_weight4, axes=None) + matmul833: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu105, permute_dims834, out_dtype="void") + add978: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul833, model_decoder_layers_7_fc2_bias4) + add979: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add976, add978) + layer_norm283: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add979, model_decoder_layers_8_self_attn_layer_norm_weight4, model_decoder_layers_8_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims835: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_8_self_attn_q_proj_weight4, axes=None) + matmul834: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm283, permute_dims835, out_dtype="void") + add980: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul834, model_decoder_layers_8_self_attn_q_proj_bias4) + reshape1113: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add980, R.shape([1, seq_len, 20, 64])) + permute_dims836: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_8_self_attn_k_proj_weight4, axes=None) + matmul835: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm283, permute_dims836, out_dtype="void") + reshape1114: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul835, R.shape([1, seq_len, 20, 64])) + permute_dims837: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_8_self_attn_v_proj_weight4, axes=None) + matmul836: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm283, permute_dims837, out_dtype="void") + add981: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul836, model_decoder_layers_8_self_attn_v_proj_bias4) + reshape1115: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add981, R.shape([1, seq_len, 20, 64])) + concat72: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1113, reshape1114, reshape1115), axis=2) + reshape1116: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat72, R.shape([seq_len, 60, 64])) + lv215 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), reshape1116), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1117: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv215, R.shape([1, seq_len, 20, 64])) + reshape1118: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1117, R.shape([1, seq_len, 1280])) + permute_dims838: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_8_self_attn_out_proj_weight4, axes=None) + matmul837: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1118, permute_dims838, out_dtype="void") + add982: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul837, model_decoder_layers_8_self_attn_out_proj_bias4) + add983: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add979, add982) + layer_norm284: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add983, model_decoder_layers_8_encoder_attn_layer_norm_weight4, model_decoder_layers_8_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims839: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_8_encoder_attn_q_proj_weight4, axes=None) + matmul838: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm284, permute_dims839, out_dtype="void") + add984: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul838, model_decoder_layers_8_encoder_attn_q_proj_bias4) + reshape1119: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add984, R.shape([1, seq_len, 20, 64])) + reshape1120: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1119, R.shape([seq_len, 20, 64])) + lv216 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), reshape1120), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1121: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv216, R.shape([1, seq_len, 20, 64])) + reshape1122: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1121, R.shape([1, seq_len, 1280])) + permute_dims840: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_8_encoder_attn_out_proj_weight4, axes=None) + matmul839: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1122, permute_dims840, out_dtype="void") + add985: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul839, model_decoder_layers_8_encoder_attn_out_proj_bias4) + add986: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add983, add985) + layer_norm285: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add986, model_decoder_layers_8_final_layer_norm_weight4, model_decoder_layers_8_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims841: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_8_fc1_weight4, axes=None) + matmul840: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm285, permute_dims841, out_dtype="void") + add987: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul840, model_decoder_layers_8_fc1_bias4) + gelu106: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add987) + permute_dims842: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_8_fc2_weight4, axes=None) + matmul841: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu106, permute_dims842, out_dtype="void") + add988: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul841, model_decoder_layers_8_fc2_bias4) + add989: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add986, add988) + layer_norm286: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add989, model_decoder_layers_9_self_attn_layer_norm_weight4, model_decoder_layers_9_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims843: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_9_self_attn_q_proj_weight4, axes=None) + matmul842: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm286, permute_dims843, out_dtype="void") + add990: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul842, model_decoder_layers_9_self_attn_q_proj_bias4) + reshape1123: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add990, R.shape([1, seq_len, 20, 64])) + permute_dims844: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_9_self_attn_k_proj_weight4, axes=None) + matmul843: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm286, permute_dims844, out_dtype="void") + reshape1124: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul843, R.shape([1, seq_len, 20, 64])) + permute_dims845: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_9_self_attn_v_proj_weight4, axes=None) + matmul844: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm286, permute_dims845, out_dtype="void") + add991: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul844, model_decoder_layers_9_self_attn_v_proj_bias4) + reshape1125: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add991, R.shape([1, seq_len, 20, 64])) + concat73: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1123, reshape1124, reshape1125), axis=2) + reshape1126: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat73, R.shape([seq_len, 60, 64])) + lv217 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), reshape1126), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1127: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv217, R.shape([1, seq_len, 20, 64])) + reshape1128: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1127, R.shape([1, seq_len, 1280])) + permute_dims846: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_9_self_attn_out_proj_weight4, axes=None) + matmul845: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1128, permute_dims846, out_dtype="void") + add992: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul845, model_decoder_layers_9_self_attn_out_proj_bias4) + add993: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add989, add992) + layer_norm287: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add993, model_decoder_layers_9_encoder_attn_layer_norm_weight4, model_decoder_layers_9_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims847: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_9_encoder_attn_q_proj_weight4, axes=None) + matmul846: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm287, permute_dims847, out_dtype="void") + add994: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul846, model_decoder_layers_9_encoder_attn_q_proj_bias4) + reshape1129: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add994, R.shape([1, seq_len, 20, 64])) + reshape1130: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1129, R.shape([seq_len, 20, 64])) + lv218 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), reshape1130), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1131: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv218, R.shape([1, seq_len, 20, 64])) + reshape1132: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1131, R.shape([1, seq_len, 1280])) + permute_dims848: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_9_encoder_attn_out_proj_weight4, axes=None) + matmul847: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1132, permute_dims848, out_dtype="void") + add995: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul847, model_decoder_layers_9_encoder_attn_out_proj_bias4) + add996: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add993, add995) + layer_norm288: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add996, model_decoder_layers_9_final_layer_norm_weight4, model_decoder_layers_9_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims849: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_9_fc1_weight4, axes=None) + matmul848: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm288, permute_dims849, out_dtype="void") + add997: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul848, model_decoder_layers_9_fc1_bias4) + gelu107: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add997) + permute_dims850: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_9_fc2_weight4, axes=None) + matmul849: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu107, permute_dims850, out_dtype="void") + add998: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul849, model_decoder_layers_9_fc2_bias4) + add999: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add996, add998) + layer_norm289: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add999, model_decoder_layers_10_self_attn_layer_norm_weight4, model_decoder_layers_10_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims851: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_10_self_attn_q_proj_weight4, axes=None) + matmul850: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm289, permute_dims851, out_dtype="void") + add1000: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul850, model_decoder_layers_10_self_attn_q_proj_bias4) + reshape1133: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1000, R.shape([1, seq_len, 20, 64])) + permute_dims852: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_10_self_attn_k_proj_weight4, axes=None) + matmul851: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm289, permute_dims852, out_dtype="void") + reshape1134: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul851, R.shape([1, seq_len, 20, 64])) + permute_dims853: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_10_self_attn_v_proj_weight4, axes=None) + matmul852: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm289, permute_dims853, out_dtype="void") + add1001: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul852, model_decoder_layers_10_self_attn_v_proj_bias4) + reshape1135: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1001, R.shape([1, seq_len, 20, 64])) + concat74: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1133, reshape1134, reshape1135), axis=2) + reshape1136: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat74, R.shape([seq_len, 60, 64])) + lv219 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), reshape1136), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1137: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv219, R.shape([1, seq_len, 20, 64])) + reshape1138: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1137, R.shape([1, seq_len, 1280])) + permute_dims854: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_10_self_attn_out_proj_weight4, axes=None) + matmul853: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1138, permute_dims854, out_dtype="void") + add1002: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul853, model_decoder_layers_10_self_attn_out_proj_bias4) + add1003: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add999, add1002) + layer_norm290: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1003, model_decoder_layers_10_encoder_attn_layer_norm_weight4, model_decoder_layers_10_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims855: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_10_encoder_attn_q_proj_weight4, axes=None) + matmul854: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm290, permute_dims855, out_dtype="void") + add1004: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul854, model_decoder_layers_10_encoder_attn_q_proj_bias4) + reshape1139: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1004, R.shape([1, seq_len, 20, 64])) + reshape1140: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1139, R.shape([seq_len, 20, 64])) + lv220 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), reshape1140), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1141: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv220, R.shape([1, seq_len, 20, 64])) + reshape1142: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1141, R.shape([1, seq_len, 1280])) + permute_dims856: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_10_encoder_attn_out_proj_weight4, axes=None) + matmul855: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1142, permute_dims856, out_dtype="void") + add1005: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul855, model_decoder_layers_10_encoder_attn_out_proj_bias4) + add1006: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1003, add1005) + layer_norm291: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1006, model_decoder_layers_10_final_layer_norm_weight4, model_decoder_layers_10_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims857: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_10_fc1_weight4, axes=None) + matmul856: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm291, permute_dims857, out_dtype="void") + add1007: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul856, model_decoder_layers_10_fc1_bias4) + gelu108: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add1007) + permute_dims858: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_10_fc2_weight4, axes=None) + matmul857: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu108, permute_dims858, out_dtype="void") + add1008: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul857, model_decoder_layers_10_fc2_bias4) + add1009: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1006, add1008) + layer_norm292: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1009, model_decoder_layers_11_self_attn_layer_norm_weight4, model_decoder_layers_11_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims859: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_11_self_attn_q_proj_weight4, axes=None) + matmul858: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm292, permute_dims859, out_dtype="void") + add1010: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul858, model_decoder_layers_11_self_attn_q_proj_bias4) + reshape1143: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1010, R.shape([1, seq_len, 20, 64])) + permute_dims860: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_11_self_attn_k_proj_weight4, axes=None) + matmul859: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm292, permute_dims860, out_dtype="void") + reshape1144: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul859, R.shape([1, seq_len, 20, 64])) + permute_dims861: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_11_self_attn_v_proj_weight4, axes=None) + matmul860: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm292, permute_dims861, out_dtype="void") + add1011: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul860, model_decoder_layers_11_self_attn_v_proj_bias4) + reshape1145: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1011, R.shape([1, seq_len, 20, 64])) + concat75: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1143, reshape1144, reshape1145), axis=2) + reshape1146: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat75, R.shape([seq_len, 60, 64])) + lv221 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), reshape1146), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1147: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv221, R.shape([1, seq_len, 20, 64])) + reshape1148: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1147, R.shape([1, seq_len, 1280])) + permute_dims862: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_11_self_attn_out_proj_weight4, axes=None) + matmul861: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1148, permute_dims862, out_dtype="void") + add1012: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul861, model_decoder_layers_11_self_attn_out_proj_bias4) + add1013: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1009, add1012) + layer_norm293: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1013, model_decoder_layers_11_encoder_attn_layer_norm_weight4, model_decoder_layers_11_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims863: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_11_encoder_attn_q_proj_weight4, axes=None) + matmul862: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm293, permute_dims863, out_dtype="void") + add1014: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul862, model_decoder_layers_11_encoder_attn_q_proj_bias4) + reshape1149: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1014, R.shape([1, seq_len, 20, 64])) + reshape1150: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1149, R.shape([seq_len, 20, 64])) + lv222 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), reshape1150), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1151: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv222, R.shape([1, seq_len, 20, 64])) + reshape1152: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1151, R.shape([1, seq_len, 1280])) + permute_dims864: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_11_encoder_attn_out_proj_weight4, axes=None) + matmul863: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1152, permute_dims864, out_dtype="void") + add1015: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul863, model_decoder_layers_11_encoder_attn_out_proj_bias4) + add1016: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1013, add1015) + layer_norm294: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1016, model_decoder_layers_11_final_layer_norm_weight4, model_decoder_layers_11_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims865: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_11_fc1_weight4, axes=None) + matmul864: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm294, permute_dims865, out_dtype="void") + add1017: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul864, model_decoder_layers_11_fc1_bias4) + gelu109: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add1017) + permute_dims866: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_11_fc2_weight4, axes=None) + matmul865: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu109, permute_dims866, out_dtype="void") + add1018: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul865, model_decoder_layers_11_fc2_bias4) + add1019: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1016, add1018) + layer_norm295: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1019, model_decoder_layers_12_self_attn_layer_norm_weight4, model_decoder_layers_12_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims867: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_12_self_attn_q_proj_weight4, axes=None) + matmul866: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm295, permute_dims867, out_dtype="void") + add1020: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul866, model_decoder_layers_12_self_attn_q_proj_bias4) + reshape1153: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1020, R.shape([1, seq_len, 20, 64])) + permute_dims868: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_12_self_attn_k_proj_weight4, axes=None) + matmul867: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm295, permute_dims868, out_dtype="void") + reshape1154: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul867, R.shape([1, seq_len, 20, 64])) + permute_dims869: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_12_self_attn_v_proj_weight4, axes=None) + matmul868: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm295, permute_dims869, out_dtype="void") + add1021: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul868, model_decoder_layers_12_self_attn_v_proj_bias4) + reshape1155: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1021, R.shape([1, seq_len, 20, 64])) + concat76: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1153, reshape1154, reshape1155), axis=2) + reshape1156: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat76, R.shape([seq_len, 60, 64])) + lv223 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), reshape1156), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1157: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv223, R.shape([1, seq_len, 20, 64])) + reshape1158: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1157, R.shape([1, seq_len, 1280])) + permute_dims870: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_12_self_attn_out_proj_weight4, axes=None) + matmul869: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1158, permute_dims870, out_dtype="void") + add1022: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul869, model_decoder_layers_12_self_attn_out_proj_bias4) + add1023: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1019, add1022) + layer_norm296: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1023, model_decoder_layers_12_encoder_attn_layer_norm_weight4, model_decoder_layers_12_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims871: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_12_encoder_attn_q_proj_weight4, axes=None) + matmul870: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm296, permute_dims871, out_dtype="void") + add1024: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul870, model_decoder_layers_12_encoder_attn_q_proj_bias4) + reshape1159: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1024, R.shape([1, seq_len, 20, 64])) + reshape1160: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1159, R.shape([seq_len, 20, 64])) + lv224 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), reshape1160), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1161: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv224, R.shape([1, seq_len, 20, 64])) + reshape1162: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1161, R.shape([1, seq_len, 1280])) + permute_dims872: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_12_encoder_attn_out_proj_weight4, axes=None) + matmul871: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1162, permute_dims872, out_dtype="void") + add1025: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul871, model_decoder_layers_12_encoder_attn_out_proj_bias4) + add1026: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1023, add1025) + layer_norm297: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1026, model_decoder_layers_12_final_layer_norm_weight4, model_decoder_layers_12_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims873: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_12_fc1_weight4, axes=None) + matmul872: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm297, permute_dims873, out_dtype="void") + add1027: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul872, model_decoder_layers_12_fc1_bias4) + gelu110: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add1027) + permute_dims874: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_12_fc2_weight4, axes=None) + matmul873: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu110, permute_dims874, out_dtype="void") + add1028: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul873, model_decoder_layers_12_fc2_bias4) + add1029: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1026, add1028) + layer_norm298: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1029, model_decoder_layers_13_self_attn_layer_norm_weight4, model_decoder_layers_13_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims875: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_13_self_attn_q_proj_weight4, axes=None) + matmul874: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm298, permute_dims875, out_dtype="void") + add1030: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul874, model_decoder_layers_13_self_attn_q_proj_bias4) + reshape1163: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1030, R.shape([1, seq_len, 20, 64])) + permute_dims876: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_13_self_attn_k_proj_weight4, axes=None) + matmul875: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm298, permute_dims876, out_dtype="void") + reshape1164: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul875, R.shape([1, seq_len, 20, 64])) + permute_dims877: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_13_self_attn_v_proj_weight4, axes=None) + matmul876: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm298, permute_dims877, out_dtype="void") + add1031: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul876, model_decoder_layers_13_self_attn_v_proj_bias4) + reshape1165: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1031, R.shape([1, seq_len, 20, 64])) + concat77: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1163, reshape1164, reshape1165), axis=2) + reshape1166: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat77, R.shape([seq_len, 60, 64])) + lv225 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), reshape1166), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1167: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv225, R.shape([1, seq_len, 20, 64])) + reshape1168: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1167, R.shape([1, seq_len, 1280])) + permute_dims878: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_13_self_attn_out_proj_weight4, axes=None) + matmul877: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1168, permute_dims878, out_dtype="void") + add1032: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul877, model_decoder_layers_13_self_attn_out_proj_bias4) + add1033: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1029, add1032) + layer_norm299: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1033, model_decoder_layers_13_encoder_attn_layer_norm_weight4, model_decoder_layers_13_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims879: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_13_encoder_attn_q_proj_weight4, axes=None) + matmul878: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm299, permute_dims879, out_dtype="void") + add1034: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul878, model_decoder_layers_13_encoder_attn_q_proj_bias4) + reshape1169: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1034, R.shape([1, seq_len, 20, 64])) + reshape1170: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1169, R.shape([seq_len, 20, 64])) + lv226 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), reshape1170), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1171: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv226, R.shape([1, seq_len, 20, 64])) + reshape1172: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1171, R.shape([1, seq_len, 1280])) + permute_dims880: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_13_encoder_attn_out_proj_weight4, axes=None) + matmul879: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1172, permute_dims880, out_dtype="void") + add1035: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul879, model_decoder_layers_13_encoder_attn_out_proj_bias4) + add1036: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1033, add1035) + layer_norm300: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1036, model_decoder_layers_13_final_layer_norm_weight4, model_decoder_layers_13_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims881: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_13_fc1_weight4, axes=None) + matmul880: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm300, permute_dims881, out_dtype="void") + add1037: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul880, model_decoder_layers_13_fc1_bias4) + gelu111: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add1037) + permute_dims882: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_13_fc2_weight4, axes=None) + matmul881: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu111, permute_dims882, out_dtype="void") + add1038: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul881, model_decoder_layers_13_fc2_bias4) + add1039: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1036, add1038) + layer_norm301: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1039, model_decoder_layers_14_self_attn_layer_norm_weight4, model_decoder_layers_14_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims883: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_14_self_attn_q_proj_weight4, axes=None) + matmul882: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm301, permute_dims883, out_dtype="void") + add1040: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul882, model_decoder_layers_14_self_attn_q_proj_bias4) + reshape1173: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1040, R.shape([1, seq_len, 20, 64])) + permute_dims884: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_14_self_attn_k_proj_weight4, axes=None) + matmul883: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm301, permute_dims884, out_dtype="void") + reshape1174: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul883, R.shape([1, seq_len, 20, 64])) + permute_dims885: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_14_self_attn_v_proj_weight4, axes=None) + matmul884: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm301, permute_dims885, out_dtype="void") + add1041: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul884, model_decoder_layers_14_self_attn_v_proj_bias4) + reshape1175: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1041, R.shape([1, seq_len, 20, 64])) + concat78: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1173, reshape1174, reshape1175), axis=2) + reshape1176: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat78, R.shape([seq_len, 60, 64])) + lv227 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), reshape1176), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1177: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv227, R.shape([1, seq_len, 20, 64])) + reshape1178: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1177, R.shape([1, seq_len, 1280])) + permute_dims886: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_14_self_attn_out_proj_weight4, axes=None) + matmul885: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1178, permute_dims886, out_dtype="void") + add1042: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul885, model_decoder_layers_14_self_attn_out_proj_bias4) + add1043: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1039, add1042) + layer_norm302: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1043, model_decoder_layers_14_encoder_attn_layer_norm_weight4, model_decoder_layers_14_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims887: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_14_encoder_attn_q_proj_weight4, axes=None) + matmul886: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm302, permute_dims887, out_dtype="void") + add1044: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul886, model_decoder_layers_14_encoder_attn_q_proj_bias4) + reshape1179: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1044, R.shape([1, seq_len, 20, 64])) + reshape1180: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1179, R.shape([seq_len, 20, 64])) + lv228 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), reshape1180), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1181: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv228, R.shape([1, seq_len, 20, 64])) + reshape1182: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1181, R.shape([1, seq_len, 1280])) + permute_dims888: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_14_encoder_attn_out_proj_weight4, axes=None) + matmul887: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1182, permute_dims888, out_dtype="void") + add1045: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul887, model_decoder_layers_14_encoder_attn_out_proj_bias4) + add1046: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1043, add1045) + layer_norm303: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1046, model_decoder_layers_14_final_layer_norm_weight4, model_decoder_layers_14_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims889: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_14_fc1_weight4, axes=None) + matmul888: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm303, permute_dims889, out_dtype="void") + add1047: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul888, model_decoder_layers_14_fc1_bias4) + gelu112: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add1047) + permute_dims890: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_14_fc2_weight4, axes=None) + matmul889: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu112, permute_dims890, out_dtype="void") + add1048: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul889, model_decoder_layers_14_fc2_bias4) + add1049: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1046, add1048) + layer_norm304: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1049, model_decoder_layers_15_self_attn_layer_norm_weight4, model_decoder_layers_15_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims891: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_15_self_attn_q_proj_weight4, axes=None) + matmul890: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm304, permute_dims891, out_dtype="void") + add1050: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul890, model_decoder_layers_15_self_attn_q_proj_bias4) + reshape1183: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1050, R.shape([1, seq_len, 20, 64])) + permute_dims892: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_15_self_attn_k_proj_weight4, axes=None) + matmul891: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm304, permute_dims892, out_dtype="void") + reshape1184: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul891, R.shape([1, seq_len, 20, 64])) + permute_dims893: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_15_self_attn_v_proj_weight4, axes=None) + matmul892: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm304, permute_dims893, out_dtype="void") + add1051: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul892, model_decoder_layers_15_self_attn_v_proj_bias4) + reshape1185: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1051, R.shape([1, seq_len, 20, 64])) + concat79: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1183, reshape1184, reshape1185), axis=2) + reshape1186: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat79, R.shape([seq_len, 60, 64])) + lv229 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), reshape1186), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1187: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv229, R.shape([1, seq_len, 20, 64])) + reshape1188: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1187, R.shape([1, seq_len, 1280])) + permute_dims894: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_15_self_attn_out_proj_weight4, axes=None) + matmul893: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1188, permute_dims894, out_dtype="void") + add1052: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul893, model_decoder_layers_15_self_attn_out_proj_bias4) + add1053: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1049, add1052) + layer_norm305: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1053, model_decoder_layers_15_encoder_attn_layer_norm_weight4, model_decoder_layers_15_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims895: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_15_encoder_attn_q_proj_weight4, axes=None) + matmul894: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm305, permute_dims895, out_dtype="void") + add1054: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul894, model_decoder_layers_15_encoder_attn_q_proj_bias4) + reshape1189: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1054, R.shape([1, seq_len, 20, 64])) + reshape1190: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1189, R.shape([seq_len, 20, 64])) + lv230 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), reshape1190), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1191: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv230, R.shape([1, seq_len, 20, 64])) + reshape1192: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1191, R.shape([1, seq_len, 1280])) + permute_dims896: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_15_encoder_attn_out_proj_weight4, axes=None) + matmul895: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1192, permute_dims896, out_dtype="void") + add1055: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul895, model_decoder_layers_15_encoder_attn_out_proj_bias4) + add1056: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1053, add1055) + layer_norm306: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1056, model_decoder_layers_15_final_layer_norm_weight4, model_decoder_layers_15_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims897: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_15_fc1_weight4, axes=None) + matmul896: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm306, permute_dims897, out_dtype="void") + add1057: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul896, model_decoder_layers_15_fc1_bias4) + gelu113: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add1057) + permute_dims898: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_15_fc2_weight4, axes=None) + matmul897: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu113, permute_dims898, out_dtype="void") + add1058: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul897, model_decoder_layers_15_fc2_bias4) + add1059: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1056, add1058) + layer_norm307: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1059, model_decoder_layers_16_self_attn_layer_norm_weight4, model_decoder_layers_16_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims899: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_16_self_attn_q_proj_weight4, axes=None) + matmul898: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm307, permute_dims899, out_dtype="void") + add1060: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul898, model_decoder_layers_16_self_attn_q_proj_bias4) + reshape1193: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1060, R.shape([1, seq_len, 20, 64])) + permute_dims900: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_16_self_attn_k_proj_weight4, axes=None) + matmul899: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm307, permute_dims900, out_dtype="void") + reshape1194: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul899, R.shape([1, seq_len, 20, 64])) + permute_dims901: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_16_self_attn_v_proj_weight4, axes=None) + matmul900: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm307, permute_dims901, out_dtype="void") + add1061: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul900, model_decoder_layers_16_self_attn_v_proj_bias4) + reshape1195: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1061, R.shape([1, seq_len, 20, 64])) + concat80: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1193, reshape1194, reshape1195), axis=2) + reshape1196: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat80, R.shape([seq_len, 60, 64])) + lv231 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), reshape1196), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1197: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv231, R.shape([1, seq_len, 20, 64])) + reshape1198: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1197, R.shape([1, seq_len, 1280])) + permute_dims902: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_16_self_attn_out_proj_weight4, axes=None) + matmul901: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1198, permute_dims902, out_dtype="void") + add1062: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul901, model_decoder_layers_16_self_attn_out_proj_bias4) + add1063: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1059, add1062) + layer_norm308: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1063, model_decoder_layers_16_encoder_attn_layer_norm_weight4, model_decoder_layers_16_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims903: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_16_encoder_attn_q_proj_weight4, axes=None) + matmul902: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm308, permute_dims903, out_dtype="void") + add1064: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul902, model_decoder_layers_16_encoder_attn_q_proj_bias4) + reshape1199: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1064, R.shape([1, seq_len, 20, 64])) + reshape1200: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1199, R.shape([seq_len, 20, 64])) + lv232 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), reshape1200), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1201: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv232, R.shape([1, seq_len, 20, 64])) + reshape1202: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1201, R.shape([1, seq_len, 1280])) + permute_dims904: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_16_encoder_attn_out_proj_weight4, axes=None) + matmul903: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1202, permute_dims904, out_dtype="void") + add1065: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul903, model_decoder_layers_16_encoder_attn_out_proj_bias4) + add1066: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1063, add1065) + layer_norm309: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1066, model_decoder_layers_16_final_layer_norm_weight4, model_decoder_layers_16_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims905: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_16_fc1_weight4, axes=None) + matmul904: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm309, permute_dims905, out_dtype="void") + add1067: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul904, model_decoder_layers_16_fc1_bias4) + gelu114: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add1067) + permute_dims906: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_16_fc2_weight4, axes=None) + matmul905: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu114, permute_dims906, out_dtype="void") + add1068: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul905, model_decoder_layers_16_fc2_bias4) + add1069: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1066, add1068) + layer_norm310: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1069, model_decoder_layers_17_self_attn_layer_norm_weight4, model_decoder_layers_17_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims907: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_17_self_attn_q_proj_weight4, axes=None) + matmul906: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm310, permute_dims907, out_dtype="void") + add1070: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul906, model_decoder_layers_17_self_attn_q_proj_bias4) + reshape1203: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1070, R.shape([1, seq_len, 20, 64])) + permute_dims908: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_17_self_attn_k_proj_weight4, axes=None) + matmul907: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm310, permute_dims908, out_dtype="void") + reshape1204: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul907, R.shape([1, seq_len, 20, 64])) + permute_dims909: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_17_self_attn_v_proj_weight4, axes=None) + matmul908: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm310, permute_dims909, out_dtype="void") + add1071: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul908, model_decoder_layers_17_self_attn_v_proj_bias4) + reshape1205: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1071, R.shape([1, seq_len, 20, 64])) + concat81: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1203, reshape1204, reshape1205), axis=2) + reshape1206: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat81, R.shape([seq_len, 60, 64])) + lv233 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), reshape1206), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1207: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv233, R.shape([1, seq_len, 20, 64])) + reshape1208: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1207, R.shape([1, seq_len, 1280])) + permute_dims910: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_17_self_attn_out_proj_weight4, axes=None) + matmul909: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1208, permute_dims910, out_dtype="void") + add1072: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul909, model_decoder_layers_17_self_attn_out_proj_bias4) + add1073: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1069, add1072) + layer_norm311: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1073, model_decoder_layers_17_encoder_attn_layer_norm_weight4, model_decoder_layers_17_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims911: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_17_encoder_attn_q_proj_weight4, axes=None) + matmul910: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm311, permute_dims911, out_dtype="void") + add1074: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul910, model_decoder_layers_17_encoder_attn_q_proj_bias4) + reshape1209: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1074, R.shape([1, seq_len, 20, 64])) + reshape1210: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1209, R.shape([seq_len, 20, 64])) + lv234 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), reshape1210), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1211: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv234, R.shape([1, seq_len, 20, 64])) + reshape1212: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1211, R.shape([1, seq_len, 1280])) + permute_dims912: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_17_encoder_attn_out_proj_weight4, axes=None) + matmul911: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1212, permute_dims912, out_dtype="void") + add1075: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul911, model_decoder_layers_17_encoder_attn_out_proj_bias4) + add1076: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1073, add1075) + layer_norm312: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1076, model_decoder_layers_17_final_layer_norm_weight4, model_decoder_layers_17_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims913: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_17_fc1_weight4, axes=None) + matmul912: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm312, permute_dims913, out_dtype="void") + add1077: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul912, model_decoder_layers_17_fc1_bias4) + gelu115: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add1077) + permute_dims914: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_17_fc2_weight4, axes=None) + matmul913: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu115, permute_dims914, out_dtype="void") + add1078: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul913, model_decoder_layers_17_fc2_bias4) + add1079: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1076, add1078) + layer_norm313: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1079, model_decoder_layers_18_self_attn_layer_norm_weight4, model_decoder_layers_18_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims915: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_18_self_attn_q_proj_weight4, axes=None) + matmul914: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm313, permute_dims915, out_dtype="void") + add1080: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul914, model_decoder_layers_18_self_attn_q_proj_bias4) + reshape1213: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1080, R.shape([1, seq_len, 20, 64])) + permute_dims916: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_18_self_attn_k_proj_weight4, axes=None) + matmul915: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm313, permute_dims916, out_dtype="void") + reshape1214: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul915, R.shape([1, seq_len, 20, 64])) + permute_dims917: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_18_self_attn_v_proj_weight4, axes=None) + matmul916: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm313, permute_dims917, out_dtype="void") + add1081: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul916, model_decoder_layers_18_self_attn_v_proj_bias4) + reshape1215: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1081, R.shape([1, seq_len, 20, 64])) + concat82: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1213, reshape1214, reshape1215), axis=2) + reshape1216: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat82, R.shape([seq_len, 60, 64])) + lv235 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), reshape1216), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1217: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv235, R.shape([1, seq_len, 20, 64])) + reshape1218: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1217, R.shape([1, seq_len, 1280])) + permute_dims918: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_18_self_attn_out_proj_weight4, axes=None) + matmul917: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1218, permute_dims918, out_dtype="void") + add1082: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul917, model_decoder_layers_18_self_attn_out_proj_bias4) + add1083: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1079, add1082) + layer_norm314: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1083, model_decoder_layers_18_encoder_attn_layer_norm_weight4, model_decoder_layers_18_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims919: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_18_encoder_attn_q_proj_weight4, axes=None) + matmul918: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm314, permute_dims919, out_dtype="void") + add1084: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul918, model_decoder_layers_18_encoder_attn_q_proj_bias4) + reshape1219: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1084, R.shape([1, seq_len, 20, 64])) + reshape1220: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1219, R.shape([seq_len, 20, 64])) + lv236 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), reshape1220), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1221: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv236, R.shape([1, seq_len, 20, 64])) + reshape1222: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1221, R.shape([1, seq_len, 1280])) + permute_dims920: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_18_encoder_attn_out_proj_weight4, axes=None) + matmul919: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1222, permute_dims920, out_dtype="void") + add1085: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul919, model_decoder_layers_18_encoder_attn_out_proj_bias4) + add1086: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1083, add1085) + layer_norm315: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1086, model_decoder_layers_18_final_layer_norm_weight4, model_decoder_layers_18_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims921: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_18_fc1_weight4, axes=None) + matmul920: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm315, permute_dims921, out_dtype="void") + add1087: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul920, model_decoder_layers_18_fc1_bias4) + gelu116: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add1087) + permute_dims922: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_18_fc2_weight4, axes=None) + matmul921: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu116, permute_dims922, out_dtype="void") + add1088: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul921, model_decoder_layers_18_fc2_bias4) + add1089: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1086, add1088) + layer_norm316: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1089, model_decoder_layers_19_self_attn_layer_norm_weight4, model_decoder_layers_19_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims923: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_19_self_attn_q_proj_weight4, axes=None) + matmul922: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm316, permute_dims923, out_dtype="void") + add1090: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul922, model_decoder_layers_19_self_attn_q_proj_bias4) + reshape1223: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1090, R.shape([1, seq_len, 20, 64])) + permute_dims924: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_19_self_attn_k_proj_weight4, axes=None) + matmul923: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm316, permute_dims924, out_dtype="void") + reshape1224: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul923, R.shape([1, seq_len, 20, 64])) + permute_dims925: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_19_self_attn_v_proj_weight4, axes=None) + matmul924: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm316, permute_dims925, out_dtype="void") + add1091: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul924, model_decoder_layers_19_self_attn_v_proj_bias4) + reshape1225: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1091, R.shape([1, seq_len, 20, 64])) + concat83: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1223, reshape1224, reshape1225), axis=2) + reshape1226: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat83, R.shape([seq_len, 60, 64])) + lv237 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), reshape1226), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1227: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv237, R.shape([1, seq_len, 20, 64])) + reshape1228: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1227, R.shape([1, seq_len, 1280])) + permute_dims926: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_19_self_attn_out_proj_weight4, axes=None) + matmul925: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1228, permute_dims926, out_dtype="void") + add1092: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul925, model_decoder_layers_19_self_attn_out_proj_bias4) + add1093: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1089, add1092) + layer_norm317: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1093, model_decoder_layers_19_encoder_attn_layer_norm_weight4, model_decoder_layers_19_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims927: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_19_encoder_attn_q_proj_weight4, axes=None) + matmul926: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm317, permute_dims927, out_dtype="void") + add1094: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul926, model_decoder_layers_19_encoder_attn_q_proj_bias4) + reshape1229: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1094, R.shape([1, seq_len, 20, 64])) + reshape1230: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1229, R.shape([seq_len, 20, 64])) + lv238 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), reshape1230), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1231: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv238, R.shape([1, seq_len, 20, 64])) + reshape1232: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1231, R.shape([1, seq_len, 1280])) + permute_dims928: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_19_encoder_attn_out_proj_weight4, axes=None) + matmul927: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1232, permute_dims928, out_dtype="void") + add1095: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul927, model_decoder_layers_19_encoder_attn_out_proj_bias4) + add1096: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1093, add1095) + layer_norm318: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1096, model_decoder_layers_19_final_layer_norm_weight4, model_decoder_layers_19_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims929: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_19_fc1_weight4, axes=None) + matmul928: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm318, permute_dims929, out_dtype="void") + add1097: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul928, model_decoder_layers_19_fc1_bias4) + gelu117: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add1097) + permute_dims930: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_19_fc2_weight4, axes=None) + matmul929: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu117, permute_dims930, out_dtype="void") + add1098: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul929, model_decoder_layers_19_fc2_bias4) + add1099: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1096, add1098) + layer_norm319: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1099, model_decoder_layers_20_self_attn_layer_norm_weight4, model_decoder_layers_20_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims931: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_20_self_attn_q_proj_weight4, axes=None) + matmul930: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm319, permute_dims931, out_dtype="void") + add1100: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul930, model_decoder_layers_20_self_attn_q_proj_bias4) + reshape1233: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1100, R.shape([1, seq_len, 20, 64])) + permute_dims932: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_20_self_attn_k_proj_weight4, axes=None) + matmul931: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm319, permute_dims932, out_dtype="void") + reshape1234: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul931, R.shape([1, seq_len, 20, 64])) + permute_dims933: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_20_self_attn_v_proj_weight4, axes=None) + matmul932: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm319, permute_dims933, out_dtype="void") + add1101: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul932, model_decoder_layers_20_self_attn_v_proj_bias4) + reshape1235: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1101, R.shape([1, seq_len, 20, 64])) + concat84: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1233, reshape1234, reshape1235), axis=2) + reshape1236: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat84, R.shape([seq_len, 60, 64])) + lv239 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), reshape1236), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1237: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv239, R.shape([1, seq_len, 20, 64])) + reshape1238: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1237, R.shape([1, seq_len, 1280])) + permute_dims934: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_20_self_attn_out_proj_weight4, axes=None) + matmul933: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1238, permute_dims934, out_dtype="void") + add1102: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul933, model_decoder_layers_20_self_attn_out_proj_bias4) + add1103: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1099, add1102) + layer_norm320: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1103, model_decoder_layers_20_encoder_attn_layer_norm_weight4, model_decoder_layers_20_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims935: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_20_encoder_attn_q_proj_weight4, axes=None) + matmul934: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm320, permute_dims935, out_dtype="void") + add1104: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul934, model_decoder_layers_20_encoder_attn_q_proj_bias4) + reshape1239: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1104, R.shape([1, seq_len, 20, 64])) + reshape1240: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1239, R.shape([seq_len, 20, 64])) + lv240 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), reshape1240), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1241: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv240, R.shape([1, seq_len, 20, 64])) + reshape1242: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1241, R.shape([1, seq_len, 1280])) + permute_dims936: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_20_encoder_attn_out_proj_weight4, axes=None) + matmul935: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1242, permute_dims936, out_dtype="void") + add1105: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul935, model_decoder_layers_20_encoder_attn_out_proj_bias4) + add1106: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1103, add1105) + layer_norm321: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1106, model_decoder_layers_20_final_layer_norm_weight4, model_decoder_layers_20_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims937: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_20_fc1_weight4, axes=None) + matmul936: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm321, permute_dims937, out_dtype="void") + add1107: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul936, model_decoder_layers_20_fc1_bias4) + gelu118: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add1107) + permute_dims938: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_20_fc2_weight4, axes=None) + matmul937: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu118, permute_dims938, out_dtype="void") + add1108: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul937, model_decoder_layers_20_fc2_bias4) + add1109: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1106, add1108) + layer_norm322: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1109, model_decoder_layers_21_self_attn_layer_norm_weight4, model_decoder_layers_21_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims939: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_21_self_attn_q_proj_weight4, axes=None) + matmul938: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm322, permute_dims939, out_dtype="void") + add1110: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul938, model_decoder_layers_21_self_attn_q_proj_bias4) + reshape1243: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1110, R.shape([1, seq_len, 20, 64])) + permute_dims940: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_21_self_attn_k_proj_weight4, axes=None) + matmul939: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm322, permute_dims940, out_dtype="void") + reshape1244: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul939, R.shape([1, seq_len, 20, 64])) + permute_dims941: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_21_self_attn_v_proj_weight4, axes=None) + matmul940: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm322, permute_dims941, out_dtype="void") + add1111: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul940, model_decoder_layers_21_self_attn_v_proj_bias4) + reshape1245: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1111, R.shape([1, seq_len, 20, 64])) + concat85: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1243, reshape1244, reshape1245), axis=2) + reshape1246: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat85, R.shape([seq_len, 60, 64])) + lv241 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), reshape1246), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1247: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv241, R.shape([1, seq_len, 20, 64])) + reshape1248: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1247, R.shape([1, seq_len, 1280])) + permute_dims942: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_21_self_attn_out_proj_weight4, axes=None) + matmul941: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1248, permute_dims942, out_dtype="void") + add1112: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul941, model_decoder_layers_21_self_attn_out_proj_bias4) + add1113: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1109, add1112) + layer_norm323: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1113, model_decoder_layers_21_encoder_attn_layer_norm_weight4, model_decoder_layers_21_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims943: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_21_encoder_attn_q_proj_weight4, axes=None) + matmul942: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm323, permute_dims943, out_dtype="void") + add1114: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul942, model_decoder_layers_21_encoder_attn_q_proj_bias4) + reshape1249: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1114, R.shape([1, seq_len, 20, 64])) + reshape1250: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1249, R.shape([seq_len, 20, 64])) + lv242 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), reshape1250), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1251: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv242, R.shape([1, seq_len, 20, 64])) + reshape1252: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1251, R.shape([1, seq_len, 1280])) + permute_dims944: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_21_encoder_attn_out_proj_weight4, axes=None) + matmul943: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1252, permute_dims944, out_dtype="void") + add1115: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul943, model_decoder_layers_21_encoder_attn_out_proj_bias4) + add1116: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1113, add1115) + layer_norm324: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1116, model_decoder_layers_21_final_layer_norm_weight4, model_decoder_layers_21_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims945: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_21_fc1_weight4, axes=None) + matmul944: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm324, permute_dims945, out_dtype="void") + add1117: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul944, model_decoder_layers_21_fc1_bias4) + gelu119: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add1117) + permute_dims946: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_21_fc2_weight4, axes=None) + matmul945: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu119, permute_dims946, out_dtype="void") + add1118: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul945, model_decoder_layers_21_fc2_bias4) + add1119: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1116, add1118) + layer_norm325: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1119, model_decoder_layers_22_self_attn_layer_norm_weight4, model_decoder_layers_22_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims947: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_22_self_attn_q_proj_weight4, axes=None) + matmul946: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm325, permute_dims947, out_dtype="void") + add1120: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul946, model_decoder_layers_22_self_attn_q_proj_bias4) + reshape1253: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1120, R.shape([1, seq_len, 20, 64])) + permute_dims948: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_22_self_attn_k_proj_weight4, axes=None) + matmul947: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm325, permute_dims948, out_dtype="void") + reshape1254: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul947, R.shape([1, seq_len, 20, 64])) + permute_dims949: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_22_self_attn_v_proj_weight4, axes=None) + matmul948: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm325, permute_dims949, out_dtype="void") + add1121: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul948, model_decoder_layers_22_self_attn_v_proj_bias4) + reshape1255: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1121, R.shape([1, seq_len, 20, 64])) + concat86: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1253, reshape1254, reshape1255), axis=2) + reshape1256: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat86, R.shape([seq_len, 60, 64])) + lv243 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), reshape1256), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1257: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv243, R.shape([1, seq_len, 20, 64])) + reshape1258: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1257, R.shape([1, seq_len, 1280])) + permute_dims950: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_22_self_attn_out_proj_weight4, axes=None) + matmul949: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1258, permute_dims950, out_dtype="void") + add1122: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul949, model_decoder_layers_22_self_attn_out_proj_bias4) + add1123: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1119, add1122) + layer_norm326: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1123, model_decoder_layers_22_encoder_attn_layer_norm_weight4, model_decoder_layers_22_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims951: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_22_encoder_attn_q_proj_weight4, axes=None) + matmul950: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm326, permute_dims951, out_dtype="void") + add1124: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul950, model_decoder_layers_22_encoder_attn_q_proj_bias4) + reshape1259: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1124, R.shape([1, seq_len, 20, 64])) + reshape1260: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1259, R.shape([seq_len, 20, 64])) + lv244 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), reshape1260), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1261: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv244, R.shape([1, seq_len, 20, 64])) + reshape1262: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1261, R.shape([1, seq_len, 1280])) + permute_dims952: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_22_encoder_attn_out_proj_weight4, axes=None) + matmul951: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1262, permute_dims952, out_dtype="void") + add1125: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul951, model_decoder_layers_22_encoder_attn_out_proj_bias4) + add1126: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1123, add1125) + layer_norm327: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1126, model_decoder_layers_22_final_layer_norm_weight4, model_decoder_layers_22_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims953: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_22_fc1_weight4, axes=None) + matmul952: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm327, permute_dims953, out_dtype="void") + add1127: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul952, model_decoder_layers_22_fc1_bias4) + gelu120: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add1127) + permute_dims954: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_22_fc2_weight4, axes=None) + matmul953: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu120, permute_dims954, out_dtype="void") + add1128: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul953, model_decoder_layers_22_fc2_bias4) + add1129: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1126, add1128) + layer_norm328: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1129, model_decoder_layers_23_self_attn_layer_norm_weight4, model_decoder_layers_23_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims955: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_23_self_attn_q_proj_weight4, axes=None) + matmul954: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm328, permute_dims955, out_dtype="void") + add1130: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul954, model_decoder_layers_23_self_attn_q_proj_bias4) + reshape1263: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1130, R.shape([1, seq_len, 20, 64])) + permute_dims956: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_23_self_attn_k_proj_weight4, axes=None) + matmul955: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm328, permute_dims956, out_dtype="void") + reshape1264: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul955, R.shape([1, seq_len, 20, 64])) + permute_dims957: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_23_self_attn_v_proj_weight4, axes=None) + matmul956: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm328, permute_dims957, out_dtype="void") + add1131: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul956, model_decoder_layers_23_self_attn_v_proj_bias4) + reshape1265: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1131, R.shape([1, seq_len, 20, 64])) + concat87: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1263, reshape1264, reshape1265), axis=2) + reshape1266: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat87, R.shape([seq_len, 60, 64])) + lv245 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), reshape1266), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1267: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv245, R.shape([1, seq_len, 20, 64])) + reshape1268: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1267, R.shape([1, seq_len, 1280])) + permute_dims958: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_23_self_attn_out_proj_weight4, axes=None) + matmul957: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1268, permute_dims958, out_dtype="void") + add1132: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul957, model_decoder_layers_23_self_attn_out_proj_bias4) + add1133: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1129, add1132) + layer_norm329: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1133, model_decoder_layers_23_encoder_attn_layer_norm_weight4, model_decoder_layers_23_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims959: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_23_encoder_attn_q_proj_weight4, axes=None) + matmul958: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm329, permute_dims959, out_dtype="void") + add1134: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul958, model_decoder_layers_23_encoder_attn_q_proj_bias4) + reshape1269: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1134, R.shape([1, seq_len, 20, 64])) + reshape1270: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1269, R.shape([seq_len, 20, 64])) + lv246 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), reshape1270), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1271: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv246, R.shape([1, seq_len, 20, 64])) + reshape1272: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1271, R.shape([1, seq_len, 1280])) + permute_dims960: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_23_encoder_attn_out_proj_weight4, axes=None) + matmul959: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1272, permute_dims960, out_dtype="void") + add1135: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul959, model_decoder_layers_23_encoder_attn_out_proj_bias4) + add1136: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1133, add1135) + layer_norm330: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1136, model_decoder_layers_23_final_layer_norm_weight4, model_decoder_layers_23_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims961: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_23_fc1_weight4, axes=None) + matmul960: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm330, permute_dims961, out_dtype="void") + add1137: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul960, model_decoder_layers_23_fc1_bias4) + gelu121: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add1137) + permute_dims962: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_23_fc2_weight4, axes=None) + matmul961: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu121, permute_dims962, out_dtype="void") + add1138: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul961, model_decoder_layers_23_fc2_bias4) + add1139: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1136, add1138) + layer_norm331: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1139, model_decoder_layers_24_self_attn_layer_norm_weight4, model_decoder_layers_24_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims963: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_24_self_attn_q_proj_weight4, axes=None) + matmul962: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm331, permute_dims963, out_dtype="void") + add1140: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul962, model_decoder_layers_24_self_attn_q_proj_bias4) + reshape1273: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1140, R.shape([1, seq_len, 20, 64])) + permute_dims964: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_24_self_attn_k_proj_weight4, axes=None) + matmul963: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm331, permute_dims964, out_dtype="void") + reshape1274: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul963, R.shape([1, seq_len, 20, 64])) + permute_dims965: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_24_self_attn_v_proj_weight4, axes=None) + matmul964: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm331, permute_dims965, out_dtype="void") + add1141: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul964, model_decoder_layers_24_self_attn_v_proj_bias4) + reshape1275: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1141, R.shape([1, seq_len, 20, 64])) + concat88: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1273, reshape1274, reshape1275), axis=2) + reshape1276: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat88, R.shape([seq_len, 60, 64])) + lv247 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), reshape1276), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1277: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv247, R.shape([1, seq_len, 20, 64])) + reshape1278: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1277, R.shape([1, seq_len, 1280])) + permute_dims966: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_24_self_attn_out_proj_weight4, axes=None) + matmul965: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1278, permute_dims966, out_dtype="void") + add1142: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul965, model_decoder_layers_24_self_attn_out_proj_bias4) + add1143: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1139, add1142) + layer_norm332: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1143, model_decoder_layers_24_encoder_attn_layer_norm_weight4, model_decoder_layers_24_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims967: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_24_encoder_attn_q_proj_weight4, axes=None) + matmul966: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm332, permute_dims967, out_dtype="void") + add1144: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul966, model_decoder_layers_24_encoder_attn_q_proj_bias4) + reshape1279: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1144, R.shape([1, seq_len, 20, 64])) + reshape1280: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1279, R.shape([seq_len, 20, 64])) + lv248 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), reshape1280), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1281: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv248, R.shape([1, seq_len, 20, 64])) + reshape1282: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1281, R.shape([1, seq_len, 1280])) + permute_dims968: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_24_encoder_attn_out_proj_weight4, axes=None) + matmul967: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1282, permute_dims968, out_dtype="void") + add1145: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul967, model_decoder_layers_24_encoder_attn_out_proj_bias4) + add1146: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1143, add1145) + layer_norm333: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1146, model_decoder_layers_24_final_layer_norm_weight4, model_decoder_layers_24_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims969: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_24_fc1_weight4, axes=None) + matmul968: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm333, permute_dims969, out_dtype="void") + add1147: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul968, model_decoder_layers_24_fc1_bias4) + gelu122: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add1147) + permute_dims970: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_24_fc2_weight4, axes=None) + matmul969: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu122, permute_dims970, out_dtype="void") + add1148: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul969, model_decoder_layers_24_fc2_bias4) + add1149: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1146, add1148) + layer_norm334: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1149, model_decoder_layers_25_self_attn_layer_norm_weight4, model_decoder_layers_25_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims971: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_25_self_attn_q_proj_weight4, axes=None) + matmul970: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm334, permute_dims971, out_dtype="void") + add1150: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul970, model_decoder_layers_25_self_attn_q_proj_bias4) + reshape1283: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1150, R.shape([1, seq_len, 20, 64])) + permute_dims972: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_25_self_attn_k_proj_weight4, axes=None) + matmul971: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm334, permute_dims972, out_dtype="void") + reshape1284: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul971, R.shape([1, seq_len, 20, 64])) + permute_dims973: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_25_self_attn_v_proj_weight4, axes=None) + matmul972: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm334, permute_dims973, out_dtype="void") + add1151: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul972, model_decoder_layers_25_self_attn_v_proj_bias4) + reshape1285: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1151, R.shape([1, seq_len, 20, 64])) + concat89: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1283, reshape1284, reshape1285), axis=2) + reshape1286: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat89, R.shape([seq_len, 60, 64])) + lv249 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), reshape1286), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1287: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv249, R.shape([1, seq_len, 20, 64])) + reshape1288: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1287, R.shape([1, seq_len, 1280])) + permute_dims974: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_25_self_attn_out_proj_weight4, axes=None) + matmul973: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1288, permute_dims974, out_dtype="void") + add1152: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul973, model_decoder_layers_25_self_attn_out_proj_bias4) + add1153: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1149, add1152) + layer_norm335: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1153, model_decoder_layers_25_encoder_attn_layer_norm_weight4, model_decoder_layers_25_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims975: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_25_encoder_attn_q_proj_weight4, axes=None) + matmul974: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm335, permute_dims975, out_dtype="void") + add1154: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul974, model_decoder_layers_25_encoder_attn_q_proj_bias4) + reshape1289: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1154, R.shape([1, seq_len, 20, 64])) + reshape1290: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1289, R.shape([seq_len, 20, 64])) + lv250 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), reshape1290), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1291: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv250, R.shape([1, seq_len, 20, 64])) + reshape1292: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1291, R.shape([1, seq_len, 1280])) + permute_dims976: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_25_encoder_attn_out_proj_weight4, axes=None) + matmul975: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1292, permute_dims976, out_dtype="void") + add1155: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul975, model_decoder_layers_25_encoder_attn_out_proj_bias4) + add1156: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1153, add1155) + layer_norm336: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1156, model_decoder_layers_25_final_layer_norm_weight4, model_decoder_layers_25_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims977: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_25_fc1_weight4, axes=None) + matmul976: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm336, permute_dims977, out_dtype="void") + add1157: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul976, model_decoder_layers_25_fc1_bias4) + gelu123: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add1157) + permute_dims978: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_25_fc2_weight4, axes=None) + matmul977: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu123, permute_dims978, out_dtype="void") + add1158: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul977, model_decoder_layers_25_fc2_bias4) + add1159: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1156, add1158) + layer_norm337: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1159, model_decoder_layers_26_self_attn_layer_norm_weight4, model_decoder_layers_26_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims979: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_26_self_attn_q_proj_weight4, axes=None) + matmul978: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm337, permute_dims979, out_dtype="void") + add1160: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul978, model_decoder_layers_26_self_attn_q_proj_bias4) + reshape1293: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1160, R.shape([1, seq_len, 20, 64])) + permute_dims980: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_26_self_attn_k_proj_weight4, axes=None) + matmul979: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm337, permute_dims980, out_dtype="void") + reshape1294: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul979, R.shape([1, seq_len, 20, 64])) + permute_dims981: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_26_self_attn_v_proj_weight4, axes=None) + matmul980: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm337, permute_dims981, out_dtype="void") + add1161: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul980, model_decoder_layers_26_self_attn_v_proj_bias4) + reshape1295: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1161, R.shape([1, seq_len, 20, 64])) + concat90: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1293, reshape1294, reshape1295), axis=2) + reshape1296: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat90, R.shape([seq_len, 60, 64])) + lv251 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), reshape1296), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1297: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv251, R.shape([1, seq_len, 20, 64])) + reshape1298: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1297, R.shape([1, seq_len, 1280])) + permute_dims982: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_26_self_attn_out_proj_weight4, axes=None) + matmul981: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1298, permute_dims982, out_dtype="void") + add1162: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul981, model_decoder_layers_26_self_attn_out_proj_bias4) + add1163: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1159, add1162) + layer_norm338: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1163, model_decoder_layers_26_encoder_attn_layer_norm_weight4, model_decoder_layers_26_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims983: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_26_encoder_attn_q_proj_weight4, axes=None) + matmul982: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm338, permute_dims983, out_dtype="void") + add1164: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul982, model_decoder_layers_26_encoder_attn_q_proj_bias4) + reshape1299: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1164, R.shape([1, seq_len, 20, 64])) + reshape1300: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1299, R.shape([seq_len, 20, 64])) + lv252 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), reshape1300), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1301: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv252, R.shape([1, seq_len, 20, 64])) + reshape1302: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1301, R.shape([1, seq_len, 1280])) + permute_dims984: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_26_encoder_attn_out_proj_weight4, axes=None) + matmul983: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1302, permute_dims984, out_dtype="void") + add1165: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul983, model_decoder_layers_26_encoder_attn_out_proj_bias4) + add1166: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1163, add1165) + layer_norm339: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1166, model_decoder_layers_26_final_layer_norm_weight4, model_decoder_layers_26_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims985: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_26_fc1_weight4, axes=None) + matmul984: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm339, permute_dims985, out_dtype="void") + add1167: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul984, model_decoder_layers_26_fc1_bias4) + gelu124: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add1167) + permute_dims986: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_26_fc2_weight4, axes=None) + matmul985: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu124, permute_dims986, out_dtype="void") + add1168: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul985, model_decoder_layers_26_fc2_bias4) + add1169: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1166, add1168) + layer_norm340: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1169, model_decoder_layers_27_self_attn_layer_norm_weight4, model_decoder_layers_27_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims987: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_27_self_attn_q_proj_weight4, axes=None) + matmul986: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm340, permute_dims987, out_dtype="void") + add1170: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul986, model_decoder_layers_27_self_attn_q_proj_bias4) + reshape1303: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1170, R.shape([1, seq_len, 20, 64])) + permute_dims988: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_27_self_attn_k_proj_weight4, axes=None) + matmul987: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm340, permute_dims988, out_dtype="void") + reshape1304: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul987, R.shape([1, seq_len, 20, 64])) + permute_dims989: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_27_self_attn_v_proj_weight4, axes=None) + matmul988: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm340, permute_dims989, out_dtype="void") + add1171: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul988, model_decoder_layers_27_self_attn_v_proj_bias4) + reshape1305: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1171, R.shape([1, seq_len, 20, 64])) + concat91: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1303, reshape1304, reshape1305), axis=2) + reshape1306: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat91, R.shape([seq_len, 60, 64])) + lv253 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), reshape1306), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1307: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv253, R.shape([1, seq_len, 20, 64])) + reshape1308: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1307, R.shape([1, seq_len, 1280])) + permute_dims990: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_27_self_attn_out_proj_weight4, axes=None) + matmul989: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1308, permute_dims990, out_dtype="void") + add1172: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul989, model_decoder_layers_27_self_attn_out_proj_bias4) + add1173: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1169, add1172) + layer_norm341: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1173, model_decoder_layers_27_encoder_attn_layer_norm_weight4, model_decoder_layers_27_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims991: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_27_encoder_attn_q_proj_weight4, axes=None) + matmul990: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm341, permute_dims991, out_dtype="void") + add1174: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul990, model_decoder_layers_27_encoder_attn_q_proj_bias4) + reshape1309: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1174, R.shape([1, seq_len, 20, 64])) + reshape1310: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1309, R.shape([seq_len, 20, 64])) + lv254 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), reshape1310), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1311: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv254, R.shape([1, seq_len, 20, 64])) + reshape1312: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1311, R.shape([1, seq_len, 1280])) + permute_dims992: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_27_encoder_attn_out_proj_weight4, axes=None) + matmul991: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1312, permute_dims992, out_dtype="void") + add1175: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul991, model_decoder_layers_27_encoder_attn_out_proj_bias4) + add1176: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1173, add1175) + layer_norm342: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1176, model_decoder_layers_27_final_layer_norm_weight4, model_decoder_layers_27_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims993: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_27_fc1_weight4, axes=None) + matmul992: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm342, permute_dims993, out_dtype="void") + add1177: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul992, model_decoder_layers_27_fc1_bias4) + gelu125: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add1177) + permute_dims994: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_27_fc2_weight4, axes=None) + matmul993: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu125, permute_dims994, out_dtype="void") + add1178: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul993, model_decoder_layers_27_fc2_bias4) + add1179: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1176, add1178) + layer_norm343: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1179, model_decoder_layers_28_self_attn_layer_norm_weight4, model_decoder_layers_28_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims995: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_28_self_attn_q_proj_weight4, axes=None) + matmul994: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm343, permute_dims995, out_dtype="void") + add1180: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul994, model_decoder_layers_28_self_attn_q_proj_bias4) + reshape1313: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1180, R.shape([1, seq_len, 20, 64])) + permute_dims996: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_28_self_attn_k_proj_weight4, axes=None) + matmul995: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm343, permute_dims996, out_dtype="void") + reshape1314: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul995, R.shape([1, seq_len, 20, 64])) + permute_dims997: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_28_self_attn_v_proj_weight4, axes=None) + matmul996: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm343, permute_dims997, out_dtype="void") + add1181: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul996, model_decoder_layers_28_self_attn_v_proj_bias4) + reshape1315: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1181, R.shape([1, seq_len, 20, 64])) + concat92: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1313, reshape1314, reshape1315), axis=2) + reshape1316: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat92, R.shape([seq_len, 60, 64])) + lv255 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), reshape1316), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1317: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv255, R.shape([1, seq_len, 20, 64])) + reshape1318: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1317, R.shape([1, seq_len, 1280])) + permute_dims998: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_28_self_attn_out_proj_weight4, axes=None) + matmul997: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1318, permute_dims998, out_dtype="void") + add1182: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul997, model_decoder_layers_28_self_attn_out_proj_bias4) + add1183: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1179, add1182) + layer_norm344: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1183, model_decoder_layers_28_encoder_attn_layer_norm_weight4, model_decoder_layers_28_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims999: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_28_encoder_attn_q_proj_weight4, axes=None) + matmul998: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm344, permute_dims999, out_dtype="void") + add1184: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul998, model_decoder_layers_28_encoder_attn_q_proj_bias4) + reshape1319: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1184, R.shape([1, seq_len, 20, 64])) + reshape1320: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1319, R.shape([seq_len, 20, 64])) + lv256 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), reshape1320), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1321: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv256, R.shape([1, seq_len, 20, 64])) + reshape1322: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1321, R.shape([1, seq_len, 1280])) + permute_dims1000: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_28_encoder_attn_out_proj_weight4, axes=None) + matmul999: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1322, permute_dims1000, out_dtype="void") + add1185: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul999, model_decoder_layers_28_encoder_attn_out_proj_bias4) + add1186: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1183, add1185) + layer_norm345: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1186, model_decoder_layers_28_final_layer_norm_weight4, model_decoder_layers_28_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1001: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_28_fc1_weight4, axes=None) + matmul1000: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm345, permute_dims1001, out_dtype="void") + add1187: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul1000, model_decoder_layers_28_fc1_bias4) + gelu126: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add1187) + permute_dims1002: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_28_fc2_weight4, axes=None) + matmul1001: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu126, permute_dims1002, out_dtype="void") + add1188: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul1001, model_decoder_layers_28_fc2_bias4) + add1189: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1186, add1188) + layer_norm346: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1189, model_decoder_layers_29_self_attn_layer_norm_weight4, model_decoder_layers_29_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1003: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_29_self_attn_q_proj_weight4, axes=None) + matmul1002: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm346, permute_dims1003, out_dtype="void") + add1190: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul1002, model_decoder_layers_29_self_attn_q_proj_bias4) + reshape1323: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1190, R.shape([1, seq_len, 20, 64])) + permute_dims1004: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_29_self_attn_k_proj_weight4, axes=None) + matmul1003: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm346, permute_dims1004, out_dtype="void") + reshape1324: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul1003, R.shape([1, seq_len, 20, 64])) + permute_dims1005: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_29_self_attn_v_proj_weight4, axes=None) + matmul1004: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm346, permute_dims1005, out_dtype="void") + add1191: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul1004, model_decoder_layers_29_self_attn_v_proj_bias4) + reshape1325: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1191, R.shape([1, seq_len, 20, 64])) + concat93: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1323, reshape1324, reshape1325), axis=2) + reshape1326: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat93, R.shape([seq_len, 60, 64])) + lv257 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), reshape1326), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1327: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv257, R.shape([1, seq_len, 20, 64])) + reshape1328: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1327, R.shape([1, seq_len, 1280])) + permute_dims1006: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_29_self_attn_out_proj_weight4, axes=None) + matmul1005: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1328, permute_dims1006, out_dtype="void") + add1192: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul1005, model_decoder_layers_29_self_attn_out_proj_bias4) + add1193: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1189, add1192) + layer_norm347: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1193, model_decoder_layers_29_encoder_attn_layer_norm_weight4, model_decoder_layers_29_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1007: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_29_encoder_attn_q_proj_weight4, axes=None) + matmul1006: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm347, permute_dims1007, out_dtype="void") + add1194: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul1006, model_decoder_layers_29_encoder_attn_q_proj_bias4) + reshape1329: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1194, R.shape([1, seq_len, 20, 64])) + reshape1330: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1329, R.shape([seq_len, 20, 64])) + lv258 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), reshape1330), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1331: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv258, R.shape([1, seq_len, 20, 64])) + reshape1332: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1331, R.shape([1, seq_len, 1280])) + permute_dims1008: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_29_encoder_attn_out_proj_weight4, axes=None) + matmul1007: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1332, permute_dims1008, out_dtype="void") + add1195: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul1007, model_decoder_layers_29_encoder_attn_out_proj_bias4) + add1196: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1193, add1195) + layer_norm348: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1196, model_decoder_layers_29_final_layer_norm_weight4, model_decoder_layers_29_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1009: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_29_fc1_weight4, axes=None) + matmul1008: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm348, permute_dims1009, out_dtype="void") + add1197: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul1008, model_decoder_layers_29_fc1_bias4) + gelu127: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add1197) + permute_dims1010: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_29_fc2_weight4, axes=None) + matmul1009: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu127, permute_dims1010, out_dtype="void") + add1198: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul1009, model_decoder_layers_29_fc2_bias4) + add1199: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1196, add1198) + layer_norm349: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1199, model_decoder_layers_30_self_attn_layer_norm_weight4, model_decoder_layers_30_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1011: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_30_self_attn_q_proj_weight4, axes=None) + matmul1010: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm349, permute_dims1011, out_dtype="void") + add1200: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul1010, model_decoder_layers_30_self_attn_q_proj_bias4) + reshape1333: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1200, R.shape([1, seq_len, 20, 64])) + permute_dims1012: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_30_self_attn_k_proj_weight4, axes=None) + matmul1011: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm349, permute_dims1012, out_dtype="void") + reshape1334: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul1011, R.shape([1, seq_len, 20, 64])) + permute_dims1013: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_30_self_attn_v_proj_weight4, axes=None) + matmul1012: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm349, permute_dims1013, out_dtype="void") + add1201: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul1012, model_decoder_layers_30_self_attn_v_proj_bias4) + reshape1335: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1201, R.shape([1, seq_len, 20, 64])) + concat94: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1333, reshape1334, reshape1335), axis=2) + reshape1336: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat94, R.shape([seq_len, 60, 64])) + lv259 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), reshape1336), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1337: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv259, R.shape([1, seq_len, 20, 64])) + reshape1338: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1337, R.shape([1, seq_len, 1280])) + permute_dims1014: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_30_self_attn_out_proj_weight4, axes=None) + matmul1013: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1338, permute_dims1014, out_dtype="void") + add1202: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul1013, model_decoder_layers_30_self_attn_out_proj_bias4) + add1203: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1199, add1202) + layer_norm350: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1203, model_decoder_layers_30_encoder_attn_layer_norm_weight4, model_decoder_layers_30_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1015: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_30_encoder_attn_q_proj_weight4, axes=None) + matmul1014: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm350, permute_dims1015, out_dtype="void") + add1204: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul1014, model_decoder_layers_30_encoder_attn_q_proj_bias4) + reshape1339: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1204, R.shape([1, seq_len, 20, 64])) + reshape1340: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1339, R.shape([seq_len, 20, 64])) + lv260 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), reshape1340), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1341: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv260, R.shape([1, seq_len, 20, 64])) + reshape1342: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1341, R.shape([1, seq_len, 1280])) + permute_dims1016: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_30_encoder_attn_out_proj_weight4, axes=None) + matmul1015: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1342, permute_dims1016, out_dtype="void") + add1205: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul1015, model_decoder_layers_30_encoder_attn_out_proj_bias4) + add1206: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1203, add1205) + layer_norm351: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1206, model_decoder_layers_30_final_layer_norm_weight4, model_decoder_layers_30_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1017: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_30_fc1_weight4, axes=None) + matmul1016: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm351, permute_dims1017, out_dtype="void") + add1207: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul1016, model_decoder_layers_30_fc1_bias4) + gelu128: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add1207) + permute_dims1018: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_30_fc2_weight4, axes=None) + matmul1017: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu128, permute_dims1018, out_dtype="void") + add1208: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul1017, model_decoder_layers_30_fc2_bias4) + add1209: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1206, add1208) + layer_norm352: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1209, model_decoder_layers_31_self_attn_layer_norm_weight4, model_decoder_layers_31_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1019: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_31_self_attn_q_proj_weight4, axes=None) + matmul1018: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm352, permute_dims1019, out_dtype="void") + add1210: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul1018, model_decoder_layers_31_self_attn_q_proj_bias4) + reshape1343: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1210, R.shape([1, seq_len, 20, 64])) + permute_dims1020: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_31_self_attn_k_proj_weight4, axes=None) + matmul1019: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm352, permute_dims1020, out_dtype="void") + reshape1344: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul1019, R.shape([1, seq_len, 20, 64])) + permute_dims1021: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_31_self_attn_v_proj_weight4, axes=None) + matmul1020: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm352, permute_dims1021, out_dtype="void") + add1211: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul1020, model_decoder_layers_31_self_attn_v_proj_bias4) + reshape1345: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1211, R.shape([1, seq_len, 20, 64])) + concat95: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1343, reshape1344, reshape1345), axis=2) + reshape1346: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat95, R.shape([seq_len, 60, 64])) + lv261 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), reshape1346), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1347: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv261, R.shape([1, seq_len, 20, 64])) + reshape1348: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1347, R.shape([1, seq_len, 1280])) + permute_dims1022: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_31_self_attn_out_proj_weight4, axes=None) + matmul1021: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1348, permute_dims1022, out_dtype="void") + add1212: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul1021, model_decoder_layers_31_self_attn_out_proj_bias4) + add1213: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1209, add1212) + layer_norm353: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1213, model_decoder_layers_31_encoder_attn_layer_norm_weight4, model_decoder_layers_31_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1023: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_31_encoder_attn_q_proj_weight4, axes=None) + matmul1022: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm353, permute_dims1023, out_dtype="void") + add1214: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul1022, model_decoder_layers_31_encoder_attn_q_proj_bias4) + reshape1349: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1214, R.shape([1, seq_len, 20, 64])) + reshape1350: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1349, R.shape([seq_len, 20, 64])) + lv262 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), reshape1350), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1351: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv262, R.shape([1, seq_len, 20, 64])) + reshape1352: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1351, R.shape([1, seq_len, 1280])) + permute_dims1024: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_31_encoder_attn_out_proj_weight4, axes=None) + matmul1023: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1352, permute_dims1024, out_dtype="void") + add1215: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul1023, model_decoder_layers_31_encoder_attn_out_proj_bias4) + add1216: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1213, add1215) + layer_norm354: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1216, model_decoder_layers_31_final_layer_norm_weight4, model_decoder_layers_31_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + permute_dims1025: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_31_fc1_weight4, axes=None) + matmul1024: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm354, permute_dims1025, out_dtype="void") + add1217: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul1024, model_decoder_layers_31_fc1_bias4) + gelu129: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add1217) + permute_dims1026: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_31_fc2_weight4, axes=None) + matmul1025: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu129, permute_dims1026, out_dtype="void") + add1218: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul1025, model_decoder_layers_31_fc2_bias4) + add1219: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1216, add1218) + layer_norm355: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1219, model_decoder_layer_norm_weight4, model_decoder_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv263 = R.call_tir(cls.index, (layer_norm355,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + permute_dims1027: R.Tensor((1280, 51866), dtype="float16") = R.permute_dims(model_decoder_embed_tokens_weight4, axes=None) + matmul1026: R.Tensor((1, 1, 51866), dtype="float32") = R.matmul(lv263, permute_dims1027, out_dtype="float32") + gv4: R.Tensor((1, 1, 51866), dtype="float32") = matmul1026 + R.output(gv4) + return gv4 + + @R.function + def renormalize_by_top_p(probs: R.Tensor(("batch_size", "vocab_size"), dtype="float32"), top_p: R.Tensor(("batch_size",), dtype="float32"), init_pivots: R.Tensor(("batch_size", 3), dtype="float32")) -> R.Tensor(("batch_size", "vocab_size"), dtype="float32"): + batch_size = T.int64() + vocab_size = T.int64() + R.func_attr({"relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "num_positions": 48, "num_samples": 8}}) + cls = Module + with R.dataflow(): + lv6 = R.call_tir(cls.top_p_pivot_cutoff, (probs, top_p, init_pivots), out_sinfo=[R.Tensor((batch_size,), dtype="float32"), R.Tensor((batch_size,), dtype="float32")]) + lv7: R.Tensor((batch_size,), dtype="float32") = lv6[0] + lv8: R.Tensor((batch_size,), dtype="float32") = lv6[1] + gv5 = R.call_tir(cls.top_p_renorm_after_cutoff, (probs, lv7, lv8), out_sinfo=R.Tensor((batch_size, vocab_size), dtype="float32")) + R.output(gv5) + return gv5 + + @R.function + def sample_with_top_p(sorted_probs: R.Tensor(("batch_size", "vocab_size"), dtype="float32"), sorted_indices: R.Tensor(("batch_size", "vocab_size"), dtype="int32"), uniform_samples: R.Tensor(("num_samples",), dtype="float32"), sample_indices: R.Tensor(("num_samples",), dtype="int32"), top_p: R.Tensor(("batch_size",), dtype="float32")) -> R.Tensor(("num_samples",), dtype="int32"): + num_samples = T.int64() + batch_size = T.int64() + vocab_size = T.int64() + R.func_attr({"relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "num_positions": 48, "num_samples": 8}}) + cls = Module + with R.dataflow(): + sorted_probs_1: R.Tensor((batch_size, vocab_size), dtype="float32") = sorted_probs + sorted_indices_1: R.Tensor((batch_size, vocab_size), dtype="int32") = sorted_indices + uniform_samples1: R.Tensor((num_samples, 1), dtype="float32") = R.call_pure_packed("vm.builtin.reshape", uniform_samples, R.shape([num_samples, 1]), sinfo_args=(R.Tensor((num_samples, 1), dtype="float32"),)) + sample_indices1: R.Tensor((num_samples, 1), dtype="int32") = R.call_pure_packed("vm.builtin.reshape", sample_indices, R.shape([num_samples, 1]), sinfo_args=(R.Tensor((num_samples, 1), dtype="int32"),)) + sample_indices2: R.Tensor((batch_size, 1), dtype="float32") = R.call_pure_packed("vm.builtin.reshape", top_p, R.shape([batch_size, 1]), sinfo_args=(R.Tensor((batch_size, 1), dtype="float32"),)) + lv3 = R.call_tir(cls.full, R.tuple(), out_sinfo=R.Tensor((batch_size, 1), dtype="int32"), tir_vars=R.shape([vocab_size])) + cumsum: R.Tensor((batch_size, vocab_size), dtype="float32") = R.cumsum(sorted_probs_1, axis=1, dtype="void", exclusive=None) + lv4 = R.call_tir(cls.get_renorm_prob, (cumsum, sample_indices2, lv3), out_sinfo=R.Tensor((batch_size, 1), dtype="float32")) + lv5 = R.call_tir(cls.get_index_from_sorted, (cumsum, sorted_indices_1, lv4, uniform_samples1, sample_indices1), out_sinfo=R.Tensor((num_samples, 1), dtype="int32")) + gv2: R.Tensor((num_samples,), dtype="int32") = R.call_pure_packed("vm.builtin.reshape", lv5, R.shape([num_samples]), sinfo_args=(R.Tensor((num_samples,), dtype="int32"),)) + R.output(gv2) + return gv2 + + @R.function + def sampler_take_probs(unsorted_probs: R.Tensor(("batch_size", "vocab_size"), dtype="float32"), sorted_indices: R.Tensor(("batch_size", "vocab_size"), dtype="int32"), sample_indices: R.Tensor(("num_samples",), dtype="int32"), sampling_result: R.Tensor(("num_samples",), dtype="int32"), lobprob_offsets: R.Tensor(("num_positions",), dtype="int32")) -> R.Tuple(R.Tensor(("num_samples",), dtype="float32"), R.Tensor(("num_positions",), dtype="float32"), R.Tensor(("num_positions",), dtype="int32")): + num_samples = T.int64() + num_positions = T.int64() + batch_size = T.int64() + vocab_size = T.int64() + R.func_attr({"relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "num_positions": 48, "num_samples": 8}}) + cls = Module + with R.dataflow(): + gv3 = R.call_tir(cls.sampler_take_probs_tir, (unsorted_probs, sorted_indices, sample_indices, sampling_result, lobprob_offsets), out_sinfo=[R.Tensor((num_samples,), dtype="float32"), R.Tensor((num_positions,), dtype="float32"), R.Tensor((num_positions,), dtype="int32")]) + R.output(gv3) + return gv3 + + @R.function + def sampler_verify_draft_tokens(draft_probs: R.Tensor(("num_nodes", "vocab_size"), dtype="float32"), draft_tokens: R.Tensor(("num_nodes",), dtype="int32"), model_probs: R.Tensor(("num_nodes", "vocab_size"), dtype="float32"), token_tree_first_child: R.Tensor(("num_nodes",), dtype="int32"), token_tree_next_sibling: R.Tensor(("num_nodes",), dtype="int32"), uniform_samples: R.Tensor(("num_nodes",), dtype="float32"), token_tree_parent_ptr: R.Tensor(("nbatch",), dtype="int32")) -> R.Tuple(R.Tensor(("num_nodes", "vocab_size"), dtype="float32"), R.Tensor(("nbatch",), dtype="int32")): + num_nodes = T.int64() + vocab_size = T.int64() + nbatch = T.int64() + R.func_attr({"relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "num_positions": 48, "num_samples": 8}}) + cls = Module + with R.dataflow(): + gv4: R.Tuple(R.Tensor((num_nodes, vocab_size), dtype="float32"), R.Tensor((nbatch,), dtype="int32")) = R.call_tir_inplace(cls.batch_verify_on_gpu_single_kernel, (draft_probs, draft_tokens, model_probs, token_tree_first_child, token_tree_next_sibling, uniform_samples, token_tree_parent_ptr), out_sinfo=[R.Tensor((num_nodes, vocab_size), dtype="float32"), R.Tensor((nbatch,), dtype="int32")], inplace_indices=[2, 6]) + R.output(gv4) + return gv4 + + @R.function + def softmax_with_temperature(logits: R.Tensor(("batch_size", 1, "vocab_size"), dtype="float32"), temperature: R.Tensor(("batch_size",), dtype="float32")) -> R.Tensor(("batch_size", 1, "vocab_size"), dtype="float32"): + batch_size = T.int64() + vocab_size = T.int64() + R.func_attr({"relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "seq_len": 15000, "total_seq_len": 1500}}) + cls = Module + with R.dataflow(): + lv: R.Tensor((batch_size, vocab_size), dtype="float32") = R.call_pure_packed("vm.builtin.reshape", logits, R.shape([batch_size, vocab_size]), sinfo_args=(R.Tensor((batch_size, vocab_size), dtype="float32"),)) + lv1 = R.call_tir(cls.chunk_lse, (lv, temperature), out_sinfo=[R.Tensor((batch_size, (vocab_size + 4096 - 1) // 4096), dtype="float32"), R.Tensor((batch_size, (vocab_size + 4096 - 1) // 4096), dtype="float32")]) + lv2: R.Tensor((batch_size, (vocab_size + 4096 - 1) // 4096), dtype="float32") = lv1[0] + lv3: R.Tensor((batch_size, (vocab_size + 4096 - 1) // 4096), dtype="float32") = lv1[1] + lv4 = R.call_tir(cls.softmax_with_chunked_sum, (lv, temperature, lv2, lv3), out_sinfo=R.Tensor((batch_size, vocab_size), dtype="float32")) + gv: R.Tensor((batch_size, 1, vocab_size), dtype="float32") = R.call_pure_packed("vm.builtin.reshape", lv4, R.shape([batch_size, 1, vocab_size]), sinfo_args=(R.Tensor((batch_size, 1, vocab_size), dtype="float32"),)) + R.output(gv) + return gv \ No newline at end of file diff --git a/debug/debug-phase1.py b/debug/debug-phase1.py new file mode 100644 index 0000000000000000000000000000000000000000..72af373766e36f9a2f7a59015871d244c19e10da --- /dev/null +++ b/debug/debug-phase1.py @@ -0,0 +1,10152 @@ +# from tvm.script import ir as I +# from tvm.script import tir as T +# from tvm.script import relax as R + +@I.ir_module +class Module: + I.module_attrs({"external_mods": [metadata["runtime.Module"][0], metadata["runtime.Module"][1], metadata["runtime.Module"][2], metadata["runtime.Module"][3], metadata["runtime.Module"][4], metadata["runtime.Module"][5], metadata["runtime.Module"][6], metadata["runtime.Module"][7], metadata["runtime.Module"][8], metadata["runtime.Module"][9], metadata["runtime.Module"][10], metadata["runtime.Module"][11], metadata["runtime.Module"][12], metadata["runtime.Module"][13], metadata["runtime.Module"][14]]}) + @T.prim_func(private=True) + def NT_matmul(layer_norm356: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16"), model_decoder_layers_0_self_attn_q_proj_weight5: T.Buffer((T.int64(1280), T.int64(1280)), "float16"), NT_matmul: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16")): + T.func_attr({"tir.noalias": T.bool(True)}) + # with T.block("root"): + for i0, i1, i2, k in T.grid(T.int64(1), T.int64(1), T.int64(1280), T.int64(1280)): + with T.block("NT_matmul"): + v_i0, v_i1, v_i2, v_k = T.axis.remap("SSSR", [i0, i1, i2, k]) + T.reads(layer_norm356[v_i0, v_i1, v_k], model_decoder_layers_0_self_attn_q_proj_weight5[v_i2, v_k]) + T.writes(NT_matmul[v_i0, v_i1, v_i2]) + with T.init(): + NT_matmul[v_i0, v_i1, v_i2] = T.float16(0) + NT_matmul[v_i0, v_i1, v_i2] = NT_matmul[v_i0, v_i1, v_i2] + layer_norm356[v_i0, v_i1, v_k] * model_decoder_layers_0_self_attn_q_proj_weight5[v_i2, v_k] + + @T.prim_func(private=True) + def NT_matmul1(layer_norm358: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16"), model_decoder_layers_0_fc1_weight5: T.Buffer((T.int64(5120), T.int64(1280)), "float16"), NT_matmul: T.Buffer((T.int64(1), T.int64(1), T.int64(5120)), "float16")): + T.func_attr({"tir.noalias": T.bool(True)}) + # with T.block("root"): + for i0, i1, i2, k in T.grid(T.int64(1), T.int64(1), T.int64(5120), T.int64(1280)): + with T.block("NT_matmul"): + v_i0, v_i1, v_i2, v_k = T.axis.remap("SSSR", [i0, i1, i2, k]) + T.reads(layer_norm358[v_i0, v_i1, v_k], model_decoder_layers_0_fc1_weight5[v_i2, v_k]) + T.writes(NT_matmul[v_i0, v_i1, v_i2]) + with T.init(): + NT_matmul[v_i0, v_i1, v_i2] = T.float16(0) + NT_matmul[v_i0, v_i1, v_i2] = NT_matmul[v_i0, v_i1, v_i2] + layer_norm358[v_i0, v_i1, v_k] * model_decoder_layers_0_fc1_weight5[v_i2, v_k] + + @T.prim_func(private=True) + def NT_matmul2(gelu130: T.Buffer((T.int64(1), T.int64(1), T.int64(5120)), "float16"), model_decoder_layers_0_fc2_weight5: T.Buffer((T.int64(1280), T.int64(5120)), "float16"), NT_matmul: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16")): + T.func_attr({"tir.noalias": T.bool(True)}) + # with T.block("root"): + for i0, i1, i2, k in T.grid(T.int64(1), T.int64(1), T.int64(1280), T.int64(5120)): + with T.block("NT_matmul"): + v_i0, v_i1, v_i2, v_k = T.axis.remap("SSSR", [i0, i1, i2, k]) + T.reads(gelu130[v_i0, v_i1, v_k], model_decoder_layers_0_fc2_weight5[v_i2, v_k]) + T.writes(NT_matmul[v_i0, v_i1, v_i2]) + with T.init(): + NT_matmul[v_i0, v_i1, v_i2] = T.float16(0) + NT_matmul[v_i0, v_i1, v_i2] = NT_matmul[v_i0, v_i1, v_i2] + gelu130[v_i0, v_i1, v_k] * model_decoder_layers_0_fc2_weight5[v_i2, v_k] + + @T.prim_func(private=True) + def NT_matmul3(layer_norm452: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16"), model_decoder_embed_tokens_weight5: T.Buffer((T.int64(51866), T.int64(1280)), "float16"), NT_matmul: T.Buffer((T.int64(1), T.int64(1), T.int64(51866)), "float32")): + T.func_attr({"tir.noalias": T.bool(True)}) + # with T.block("root"): + for i0, i1, i2, k in T.grid(T.int64(1), T.int64(1), T.int64(51866), T.int64(1280)): + with T.block("NT_matmul"): + v_i0, v_i1, v_i2, v_k = T.axis.remap("SSSR", [i0, i1, i2, k]) + T.reads(layer_norm452[v_i0, v_i1, v_k], model_decoder_embed_tokens_weight5[v_i2, v_k]) + T.writes(NT_matmul[v_i0, v_i1, v_i2]) + with T.init(): + NT_matmul[v_i0, v_i1, v_i2] = T.float32(0) + NT_matmul[v_i0, v_i1, v_i2] = NT_matmul[v_i0, v_i1, v_i2] + T.Cast("float32", layer_norm452[v_i0, v_i1, v_k]) * T.Cast("float32", model_decoder_embed_tokens_weight5[v_i2, v_k]) + + @T.prim_func + def apply_bitmask_inplace(var_logits: T.handle, var_seq_ids: T.handle, var_bitmask: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": T.bool(True), "tir.noalias": T.bool(True)}) + batch_size, vocab_size = T.int32(is_size_var=True), T.int32(is_size_var=True) + logits = T.match_buffer(var_logits, (batch_size, vocab_size)) + num_seq = T.int32(is_size_var=True) + seq_ids = T.match_buffer(var_seq_ids, (num_seq,), "int32") + bitmask = T.match_buffer(var_bitmask, (batch_size, (vocab_size + 31) // 32), "int32") + # with T.block("root"): + for fused_s_v_0 in T.thread_binding((num_seq * vocab_size + 1023) // 1024, thread="blockIdx.x"): + for fused_s_v_1 in T.thread_binding(1024, thread="threadIdx.x"): + with T.block("block"): + vs = T.axis.spatial(num_seq, (fused_s_v_0 * 1024 + fused_s_v_1) // vocab_size) + vv = T.axis.spatial(vocab_size, (fused_s_v_0 * 1024 + fused_s_v_1) % vocab_size) + T.where(fused_s_v_0 * 1024 + fused_s_v_1 < num_seq * vocab_size) + T.reads(bitmask[seq_ids[vs], vv // 32], seq_ids[vs], logits[seq_ids[vs], vv]) + T.writes(logits[seq_ids[vs], vv]) + logits[seq_ids[vs], vv] = T.if_then_else(T.bitwise_and(T.shift_right(bitmask[seq_ids[vs], vv // 32], vv % 32), 1) == 1, logits[seq_ids[vs], vv], T.float32(-3.4028234663852886e+38)) + + @T.prim_func + def apply_logit_bias_inplace(var_logits: T.handle, var_pos2seq_id: T.handle, var_token_ids: T.handle, var_logit_bias: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": T.bool(True), "tir.noalias": T.bool(True)}) + batch_size, vocab_size = T.int32(is_size_var=True), T.int32(is_size_var=True) + logits = T.match_buffer(var_logits, (batch_size, vocab_size)) + num_token = T.int32(is_size_var=True) + pos2seq_id = T.match_buffer(var_pos2seq_id, (num_token,), "int32") + token_ids = T.match_buffer(var_token_ids, (num_token,), "int32") + logit_bias = T.match_buffer(var_logit_bias, (num_token,)) + # with T.block("root"): + for p0 in T.thread_binding((num_token + 1023) // 1024, thread="blockIdx.x"): + for p1 in T.thread_binding(1024, thread="threadIdx.x"): + with T.block("block"): + vp = T.axis.spatial(num_token, p0 * 1024 + p1) + T.where(p0 * 1024 + p1 < num_token) + T.reads(logits[pos2seq_id[vp], token_ids[vp]], pos2seq_id[vp], token_ids[vp], logit_bias[vp]) + T.writes(logits[pos2seq_id[vp], token_ids[vp]]) + logits[pos2seq_id[vp], token_ids[vp]] = logits[pos2seq_id[vp], token_ids[vp]] + logit_bias[vp] + + @T.prim_func + def apply_penalty_inplace(var_logits: T.handle, var_seq_ids: T.handle, var_pos2seq_id: T.handle, var_token_ids: T.handle, var_token_cnt: T.handle, var_penalties: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": T.bool(True), "tir.noalias": T.bool(True)}) + batch_size, vocab_size = T.int32(is_size_var=True), T.int32(is_size_var=True) + logits = T.match_buffer(var_logits, (batch_size, vocab_size)) + num_seq = T.int32(is_size_var=True) + seq_ids = T.match_buffer(var_seq_ids, (num_seq,), "int32") + num_token = T.int32(is_size_var=True) + pos2seq_id = T.match_buffer(var_pos2seq_id, (num_token,), "int32") + token_ids = T.match_buffer(var_token_ids, (num_token,), "int32") + token_cnt = T.match_buffer(var_token_cnt, (num_token,), "int32") + penalties = T.match_buffer(var_penalties, (num_seq, 3)) + # with T.block("root"): + for p0 in T.thread_binding((num_token + 1023) // 1024, thread="blockIdx.x"): + for p1 in T.thread_binding(1024, thread="threadIdx.x"): + with T.block("block"): + vp = T.axis.spatial(num_token, p0 * 1024 + p1) + T.where(p0 * 1024 + p1 < num_token) + T.reads(logits[seq_ids[pos2seq_id[vp]], token_ids[vp]], seq_ids[pos2seq_id[vp]], pos2seq_id[vp], token_ids[vp], penalties[pos2seq_id[vp], 0:3], token_cnt[vp]) + T.writes(logits[seq_ids[pos2seq_id[vp]], token_ids[vp]]) + logits[seq_ids[pos2seq_id[vp]], token_ids[vp]] = logits[seq_ids[pos2seq_id[vp]], token_ids[vp]] - (penalties[pos2seq_id[vp], 0] + T.Cast("float32", token_cnt[vp]) * penalties[pos2seq_id[vp], 1]) + logits[seq_ids[pos2seq_id[vp]], token_ids[vp]] = T.if_then_else(logits[seq_ids[pos2seq_id[vp]], token_ids[vp]] > T.float32(0), logits[seq_ids[pos2seq_id[vp]], token_ids[vp]] * penalties[pos2seq_id[vp], 2], logits[seq_ids[pos2seq_id[vp]], token_ids[vp]] / penalties[pos2seq_id[vp], 2]) + + @T.prim_func + def batch_decode_paged_kv(_0: T.int32, Q_handle: T.handle, pages_handle: T.handle, page_table_indptr_handle: T.handle, page_table_values_handle: T.handle, var_length_info: T.handle, k_rope_pos_offset_handle: T.handle, q_rope_position_handle: T.handle, output_handle: T.handle, lse_handle: T.handle, rotary_mode: T.int32, rope_scale: T.float32, rope_theta: T.float32, attn_score_scaling_factor: T.float32): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) + B = T.int32(is_size_var=True) + Q = T.match_buffer(Q_handle, (B, 20, 64), "float16") + max_num_pages = T.int32(is_size_var=True) + pages = T.match_buffer(pages_handle, (max_num_pages, 2, 20, 16, 64), "float16") + page_table_indptr = T.match_buffer(page_table_indptr_handle, (B + 1,), "int32", offset_factor=1) + nnz_pages = T.int32(is_size_var=True) + page_table_values = T.match_buffer(page_table_values_handle, (nnz_pages,), "int32", offset_factor=1) + length_info = T.match_buffer(var_length_info, (B,), "int32", offset_factor=1) + k_rope_pos_offset = T.match_buffer(k_rope_pos_offset_handle, (B,), "int32", offset_factor=1) + q_rope_position = T.match_buffer(q_rope_position_handle, (B,), "int32", offset_factor=1) + output = T.match_buffer(output_handle, (B, 20, 64), "float16") + lse = T.match_buffer(lse_handle, (B, 20)) + # with T.block("root"): + sm_scale: T.float32 = T.float32(0.18033688011112042) + for bx in T.thread_binding(B, thread="blockIdx.x"): + for fused_by_bz in T.thread_binding(20, thread="blockIdx.y"): + for ty in T.thread_binding(1, thread="threadIdx.y"): + for tx in T.thread_binding(16, thread="threadIdx.x"): + for tz in T.thread_binding(32, thread="threadIdx.z"): + with T.block("attn"): + T.reads(page_table_indptr[bx:bx + 2], length_info[bx], q_rope_position[bx], Q[bx, fused_by_bz // 20 + ty + fused_by_bz % 20, tx * 4 - 32:tx * 4 - 32 + 68]) + T.writes(output[bx, fused_by_bz % 20 + fused_by_bz // 20 + ty, tx * 4:tx * 4 + 4], lse[bx, fused_by_bz % 20 + fused_by_bz // 20 + ty]) + Q_local = T.alloc_buffer((4,), "float16", scope="local") + kv_chunk_len = T.alloc_buffer((1,), "int32", scope="local") + K_smem = T.alloc_buffer((64, 64), "float16", scope="shared") + V_smem = T.alloc_buffer((64, 64), "float16", scope="shared") + O_allreduce = T.alloc_buffer((32, 1, 64), scope="shared") + md_allreduce = T.alloc_buffer((32, 1, 2), scope="shared") + S_reduce_local = T.alloc_buffer((1,), scope="local") + t0 = T.alloc_buffer((1,), scope="local") + S_local = T.alloc_buffer((2,), scope="local") + QK_local = T.alloc_buffer((4,), scope="local") + V_local = T.alloc_buffer((4,), "float16", scope="local") + m_prev = T.alloc_buffer((1,), scope="local") + d_prev = T.alloc_buffer((1,), scope="local") + other_m = T.alloc_buffer((1,), scope="local") + other_d = T.alloc_buffer((1,), scope="local") + exp_mprev = T.alloc_buffer((1,), scope="local") + exp_otherm = T.alloc_buffer((1,), scope="local") + other_o = T.alloc_buffer((4,), scope="local") + st_m = T.alloc_buffer((1,), scope="local") + st_d = T.alloc_buffer((1,), scope="local") + O_local = T.alloc_buffer((4,), scope="local") + by: T.int32 = fused_by_bz % 20 + bz: T.int32 = fused_by_bz // 20 + batch_idx: T.int32 = bx + cur_page_indptr_begin: T.int32 = page_table_indptr[batch_idx] + cur_page_indptr_end: T.int32 = page_table_indptr[batch_idx + 1] + kv_chunk_len[0] = T.if_then_else(cur_page_indptr_begin != cur_page_indptr_end, (cur_page_indptr_end - cur_page_indptr_begin - 1) * 16 + length_info[batch_idx], 0) + st_m[0] = T.float32(-50000) + st_d[0] = T.float32(1) + for vec in T.vectorized(4): + O_local[vec] = T.float32(0) + for vec in T.vectorized(4): + Q_local[vec] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", q_rope_position[batch_idx]) * rope_scale / T.pow(rope_theta, T.Cast("float32", (tx * 4 + vec) * 2 % 64) / T.float32(64))) * T.Cast("float32", Q[bx, by + bz + ty, tx * 4 + vec]) + T.sin(T.Cast("float32", q_rope_position[batch_idx]) * rope_scale / T.pow(rope_theta, T.Cast("float32", (tx * 4 + vec) * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(tx * 4 + vec < 32, Q[bx, by + bz + ty, tx * 4 + vec + 32] * T.float16(-1), Q[bx, by + bz + ty, tx * 4 + vec - 32]))), Q[bx, by + bz + ty, tx * 4 + vec]) + for iterator in range((kv_chunk_len[0] + 63) // 64): + tile_start_s: T.int32 = (tz + ty) * 2 + tile_start_g: T.int32 = (iterator * 32 + tz + ty) * 2 + for j in range(2): + with T.block("KV_load"): + T.reads() + T.writes() + row_g: T.int32 = tile_start_g + j + if row_g < kv_chunk_len[0]: + seq_offset: T.int32 = row_g + page_no: T.int32 = page_table_values[cur_page_indptr_begin + seq_offset // 16] + page_offset: T.int32 = seq_offset % 16 + for vec in T.vectorized(4): + K_smem[tile_start_s + j, tx * 4 + vec] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", k_rope_pos_offset[batch_idx] + row_g) * rope_scale / T.pow(rope_theta, T.Cast("float32", (tx * 4 + vec) * 2 % 64) / T.float32(64))) * T.Cast("float32", pages[page_no, 0, by, page_offset, tx * 4 + vec]) + T.sin(T.Cast("float32", k_rope_pos_offset[batch_idx] + row_g) * rope_scale / T.pow(rope_theta, T.Cast("float32", (tx * 4 + vec) * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(tx * 4 + vec < 32, pages[page_no, 0, by, page_offset, tx * 4 + vec + 32] * T.float16(-1), pages[page_no, 0, by, page_offset, tx * 4 + vec - 32]))), pages[page_no, 0, by, page_offset, tx * 4 + vec]) + V_smem[tile_start_s + j, tx * 4 + vec] = pages[page_no, 1, by, page_offset, tx * 4 + vec] + else: + for vec in T.vectorized(4): + K_smem[tile_start_s + j, tx * 4 + vec] = T.float16(0) + V_smem[tile_start_s + j, tx * 4 + vec] = T.float16(0) + T.tvm_storage_sync("shared") + m_prev[0] = st_m[0] + for j in range(2): + for vec in T.vectorized(4): + QK_local[vec] = T.Cast("float32", Q_local[vec]) * T.Cast("float32", K_smem[tz * 2 + j, tx * 4 + vec]) * attn_score_scaling_factor * sm_scale + S_reduce_local[0] = T.float32(0) + for vec in T.unroll(4): + S_reduce_local[0] = S_reduce_local[0] + QK_local[vec] + with T.block("block_cross_thread"): + T.reads(S_reduce_local[0]) + T.writes(t0[0]) + T.attr(T.comm_reducer(lambda x0, y0: x0 + y0, [T.float32(0)]), "reduce_scope", T.reinterpret("handle", T.uint64(0))) + T.tvm_thread_allreduce(T.uint32(1), S_reduce_local[0], T.bool(True), t0[0], tx) + S_local[j] = T.float32(-50000) + if (iterator * 32 + tz) * 2 + j < kv_chunk_len[0]: + S_local[j] = t0[0] + st_m[0] = T.max(st_m[0], S_local[j]) + o_scale: T.float32 = T.exp2(m_prev[0] - st_m[0]) + st_d[0] = st_d[0] * o_scale + for j in range(2): + S_local[j] = T.exp2(S_local[j] - st_m[0]) + st_d[0] = st_d[0] + S_local[j] + for j in T.vectorized(4): + O_local[j] = O_local[j] * o_scale + for j in range(2): + for vec in T.vectorized(4): + V_local[vec] = V_smem[tz * 2 + j, tx * 4 + vec] + for vec in T.vectorized(4): + O_local[vec] = O_local[vec] + T.Cast("float32", V_local[vec]) * S_local[j] + for vec in T.vectorized(4): + O_allreduce[tz, ty, tx * 4 + vec] = O_local[vec] + md_allreduce[tz, ty, 0] = st_m[0] + md_allreduce[tz, ty, 1] = st_d[0] + T.tvm_storage_sync("shared") + st_m[0] = T.float32(-50000) + st_d[0] = T.float32(1) + for vec in T.vectorized(4): + O_local[vec] = T.float32(0) + for j in range(32): + m_prev[0] = st_m[0] + d_prev[0] = st_d[0] + other_m[0] = md_allreduce[j, ty, 0] + other_d[0] = md_allreduce[j, ty, 1] + for vec in T.vectorized(4): + other_o[vec] = O_allreduce[j, ty, tx * 4 + vec] + st_m[0] = T.max(st_m[0], other_m[0]) + st_d[0] = d_prev[0] * T.exp2(m_prev[0] - st_m[0]) + other_d[0] * T.exp2(other_m[0] - st_m[0]) + exp_mprev[0] = T.exp2(m_prev[0] - st_m[0]) + exp_otherm[0] = T.exp2(other_m[0] - st_m[0]) + for vec in T.vectorized(4): + O_local[vec] = O_local[vec] * exp_mprev[0] + other_o[vec] * exp_otherm[0] + for vec in T.vectorized(4): + O_local[vec] = O_local[vec] / st_d[0] + for vec in T.vectorized(4): + output[batch_idx, by + bz + ty, tx * 4 + vec] = T.Cast("float16", O_local[vec]) + lse[batch_idx, by + bz + ty] = st_m[0] + T.log2(st_d[0]) + + @T.prim_func + def batch_decode_paged_kv_sliding_window(_0: T.int32, Q_handle: T.handle, pages_handle: T.handle, page_table_indptr_handle: T.handle, page_table_values_handle: T.handle, var_length_info: T.handle, k_rope_pos_offset_handle: T.handle, q_rope_position_handle: T.handle, output_handle: T.handle, lse_handle: T.handle, rotary_mode: T.int32, rope_scale: T.float32, rope_theta: T.float32, attn_score_scaling_factor: T.float32): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) + B = T.int32(is_size_var=True) + Q = T.match_buffer(Q_handle, (B, 20, 64), "float16") + max_num_pages = T.int32(is_size_var=True) + pages = T.match_buffer(pages_handle, (max_num_pages, 2, 20, 16, 64), "float16") + page_table_indptr = T.match_buffer(page_table_indptr_handle, (B + 1,), "int32", offset_factor=1) + nnz_pages = T.int32(is_size_var=True) + page_table_values = T.match_buffer(page_table_values_handle, (nnz_pages,), "int32", offset_factor=1) + length_info = T.match_buffer(var_length_info, (3, B), "int32", offset_factor=1) + k_rope_pos_offset = T.match_buffer(k_rope_pos_offset_handle, (B,), "int32", offset_factor=1) + q_rope_position = T.match_buffer(q_rope_position_handle, (B,), "int32", offset_factor=1) + output = T.match_buffer(output_handle, (B, 20, 64), "float16") + lse = T.match_buffer(lse_handle, (B, 20)) + # with T.block("root"): + sm_scale: T.float32 = T.float32(0.18033688011112042) + for bx in T.thread_binding(B, thread="blockIdx.x"): + for fused_by_bz in T.thread_binding(20, thread="blockIdx.y"): + for ty in T.thread_binding(1, thread="threadIdx.y"): + for tx in T.thread_binding(16, thread="threadIdx.x"): + for tz in T.thread_binding(32, thread="threadIdx.z"): + with T.block("attn"): + T.reads(page_table_indptr[bx:bx + 2], length_info[0:3, bx], q_rope_position[bx], Q[bx, fused_by_bz // 20 + ty + fused_by_bz % 20, tx * 4 - 32:tx * 4 - 32 + 68]) + T.writes(output[bx, fused_by_bz % 20 + fused_by_bz // 20 + ty, tx * 4:tx * 4 + 4], lse[bx, fused_by_bz % 20 + fused_by_bz // 20 + ty]) + Q_local = T.alloc_buffer((4,), "float16", scope="local") + kv_chunk_len = T.alloc_buffer((1,), "int32", scope="local") + K_smem = T.alloc_buffer((64, 64), "float16", scope="shared") + V_smem = T.alloc_buffer((64, 64), "float16", scope="shared") + O_allreduce = T.alloc_buffer((32, 1, 64), scope="shared") + md_allreduce = T.alloc_buffer((32, 1, 2), scope="shared") + S_reduce_local = T.alloc_buffer((1,), scope="local") + t0 = T.alloc_buffer((1,), scope="local") + S_local = T.alloc_buffer((2,), scope="local") + QK_local = T.alloc_buffer((4,), scope="local") + V_local = T.alloc_buffer((4,), "float16", scope="local") + m_prev = T.alloc_buffer((1,), scope="local") + d_prev = T.alloc_buffer((1,), scope="local") + other_m = T.alloc_buffer((1,), scope="local") + other_d = T.alloc_buffer((1,), scope="local") + exp_mprev = T.alloc_buffer((1,), scope="local") + exp_otherm = T.alloc_buffer((1,), scope="local") + other_o = T.alloc_buffer((4,), scope="local") + st_m = T.alloc_buffer((1,), scope="local") + st_d = T.alloc_buffer((1,), scope="local") + O_local = T.alloc_buffer((4,), scope="local") + by: T.int32 = fused_by_bz % 20 + bz: T.int32 = fused_by_bz // 20 + batch_idx: T.int32 = bx + cur_page_indptr_begin: T.int32 = page_table_indptr[batch_idx] + cur_page_indptr_end: T.int32 = page_table_indptr[batch_idx + 1] + kv_chunk_len[0] = T.if_then_else(cur_page_indptr_begin != cur_page_indptr_end, (cur_page_indptr_end - cur_page_indptr_begin - 1) * 16 + length_info[0, batch_idx] - length_info[1, batch_idx] + length_info[2, batch_idx], 0) + st_m[0] = T.float32(-50000) + st_d[0] = T.float32(1) + for vec in T.vectorized(4): + O_local[vec] = T.float32(0) + for vec in T.vectorized(4): + Q_local[vec] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", q_rope_position[batch_idx]) * rope_scale / T.pow(rope_theta, T.Cast("float32", (tx * 4 + vec) * 2 % 64) / T.float32(64))) * T.Cast("float32", Q[bx, by + bz + ty, tx * 4 + vec]) + T.sin(T.Cast("float32", q_rope_position[batch_idx]) * rope_scale / T.pow(rope_theta, T.Cast("float32", (tx * 4 + vec) * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(tx * 4 + vec < 32, Q[bx, by + bz + ty, tx * 4 + vec + 32] * T.float16(-1), Q[bx, by + bz + ty, tx * 4 + vec - 32]))), Q[bx, by + bz + ty, tx * 4 + vec]) + for iterator in range((kv_chunk_len[0] + 63) // 64): + tile_start_s: T.int32 = (tz + ty) * 2 + tile_start_g: T.int32 = (iterator * 32 + tz + ty) * 2 + for j in range(2): + with T.block("KV_load"): + T.reads() + T.writes() + row_g: T.int32 = tile_start_g + j + if row_g < kv_chunk_len[0]: + seq_offset: T.int32 = T.if_then_else(row_g < length_info[2, batch_idx], row_g, row_g - length_info[2, batch_idx] + length_info[1, batch_idx]) + page_no: T.int32 = page_table_values[cur_page_indptr_begin + seq_offset // 16] + page_offset: T.int32 = seq_offset % 16 + for vec in T.vectorized(4): + K_smem[tile_start_s + j, tx * 4 + vec] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", k_rope_pos_offset[batch_idx] + row_g) * rope_scale / T.pow(rope_theta, T.Cast("float32", (tx * 4 + vec) * 2 % 64) / T.float32(64))) * T.Cast("float32", pages[page_no, 0, by, page_offset, tx * 4 + vec]) + T.sin(T.Cast("float32", k_rope_pos_offset[batch_idx] + row_g) * rope_scale / T.pow(rope_theta, T.Cast("float32", (tx * 4 + vec) * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(tx * 4 + vec < 32, pages[page_no, 0, by, page_offset, tx * 4 + vec + 32] * T.float16(-1), pages[page_no, 0, by, page_offset, tx * 4 + vec - 32]))), pages[page_no, 0, by, page_offset, tx * 4 + vec]) + V_smem[tile_start_s + j, tx * 4 + vec] = pages[page_no, 1, by, page_offset, tx * 4 + vec] + else: + for vec in T.vectorized(4): + K_smem[tile_start_s + j, tx * 4 + vec] = T.float16(0) + V_smem[tile_start_s + j, tx * 4 + vec] = T.float16(0) + T.tvm_storage_sync("shared") + m_prev[0] = st_m[0] + for j in range(2): + for vec in T.vectorized(4): + QK_local[vec] = T.Cast("float32", Q_local[vec]) * T.Cast("float32", K_smem[tz * 2 + j, tx * 4 + vec]) * attn_score_scaling_factor * sm_scale + S_reduce_local[0] = T.float32(0) + for vec in T.unroll(4): + S_reduce_local[0] = S_reduce_local[0] + QK_local[vec] + with T.block("block_cross_thread"): + T.reads(S_reduce_local[0]) + T.writes(t0[0]) + T.attr(T.comm_reducer(lambda x0, y0: x0 + y0, [T.float32(0)]), "reduce_scope", T.reinterpret("handle", T.uint64(0))) + T.tvm_thread_allreduce(T.uint32(1), S_reduce_local[0], T.bool(True), t0[0], tx) + S_local[j] = T.float32(-50000) + if (iterator * 32 + tz) * 2 + j < kv_chunk_len[0]: + S_local[j] = t0[0] + st_m[0] = T.max(st_m[0], S_local[j]) + o_scale: T.float32 = T.exp2(m_prev[0] - st_m[0]) + st_d[0] = st_d[0] * o_scale + for j in range(2): + S_local[j] = T.exp2(S_local[j] - st_m[0]) + st_d[0] = st_d[0] + S_local[j] + for j in T.vectorized(4): + O_local[j] = O_local[j] * o_scale + for j in range(2): + for vec in T.vectorized(4): + V_local[vec] = V_smem[tz * 2 + j, tx * 4 + vec] + for vec in T.vectorized(4): + O_local[vec] = O_local[vec] + T.Cast("float32", V_local[vec]) * S_local[j] + for vec in T.vectorized(4): + O_allreduce[tz, ty, tx * 4 + vec] = O_local[vec] + md_allreduce[tz, ty, 0] = st_m[0] + md_allreduce[tz, ty, 1] = st_d[0] + T.tvm_storage_sync("shared") + st_m[0] = T.float32(-50000) + st_d[0] = T.float32(1) + for vec in T.vectorized(4): + O_local[vec] = T.float32(0) + for j in range(32): + m_prev[0] = st_m[0] + d_prev[0] = st_d[0] + other_m[0] = md_allreduce[j, ty, 0] + other_d[0] = md_allreduce[j, ty, 1] + for vec in T.vectorized(4): + other_o[vec] = O_allreduce[j, ty, tx * 4 + vec] + st_m[0] = T.max(st_m[0], other_m[0]) + st_d[0] = d_prev[0] * T.exp2(m_prev[0] - st_m[0]) + other_d[0] * T.exp2(other_m[0] - st_m[0]) + exp_mprev[0] = T.exp2(m_prev[0] - st_m[0]) + exp_otherm[0] = T.exp2(other_m[0] - st_m[0]) + for vec in T.vectorized(4): + O_local[vec] = O_local[vec] * exp_mprev[0] + other_o[vec] * exp_otherm[0] + for vec in T.vectorized(4): + O_local[vec] = O_local[vec] / st_d[0] + for vec in T.vectorized(4): + output[batch_idx, by + bz + ty, tx * 4 + vec] = T.Cast("float16", O_local[vec]) + lse[batch_idx, by + bz + ty] = st_m[0] + T.log2(st_d[0]) + + @T.prim_func + def batch_prefill_paged_kv(_0: T.int32, var_q: T.handle, var_q_indptr: T.handle, var_pages: T.handle, var_page_indptr: T.handle, var_page_values: T.handle, var_length_info: T.handle, var_k_rope_pos_offset: T.handle, var_q_rope_position: T.handle, var_output: T.handle, var_lse: T.handle, causal: T.int32, rotary_mode: T.int32, rope_scale: T.float32, rope_theta: T.float32, attn_score_scaling_factor: T.float32): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) + total_len = T.int32(is_size_var=True) + q = T.match_buffer(var_q, (total_len, 20, 64), "float16") + batch_size = T.int32(is_size_var=True) + q_indptr = T.match_buffer(var_q_indptr, (batch_size + 1,), "int32", offset_factor=1) + max_num_pages = T.int32(is_size_var=True) + pages = T.match_buffer(var_pages, (max_num_pages, 2, 20, 16, 64), "float16") + page_indptr = T.match_buffer(var_page_indptr, (batch_size + 1,), "int32", offset_factor=1) + nnz_pages = T.int32(is_size_var=True) + page_values = T.match_buffer(var_page_values, (nnz_pages,), "int32", offset_factor=1) + length_info = T.match_buffer(var_length_info, (batch_size,), "int32", offset_factor=1) + k_rope_pos_offset = T.match_buffer(var_k_rope_pos_offset, (batch_size,), "int32", offset_factor=1) + q_rope_position = T.match_buffer(var_q_rope_position, (total_len,), "int32", offset_factor=1) + output = T.match_buffer(var_output, (total_len, 20, 64), "float16") + lse = T.match_buffer(var_lse, (total_len, 20)) + # with T.block("root"): + for lbx in T.thread_binding(16, thread="blockIdx.x"): + for lby in T.thread_binding(20, thread="blockIdx.y"): + for lty in T.thread_binding(4, thread="threadIdx.y"): + for ltx in T.thread_binding(32, thread="threadIdx.x"): + with T.block("attn"): + bx, by, ty, tx = T.axis.remap("SSSS", [lbx, lby, lty, ltx]) + T.reads() + T.writes() + tile_id = T.alloc_buffer((1,), "int32", scope="local") + batch_idx = T.alloc_buffer((1,), "int32", scope="local") + batch_tiles = T.alloc_buffer((1,), "int32", scope="local") + batch_rows = T.alloc_buffer((1,), "int32", scope="local") + iterator = T.alloc_buffer((1,), "int32", scope="local") + kv_chunk_len = T.alloc_buffer((1,), "int32", scope="local") + Q_smem = T.alloc_buffer((32, 64), "float16", scope="shared") + K_smem = T.alloc_buffer((16, 64), "float16", scope="shared") + V_smem = T.alloc_buffer((16, 64), "float16", scope="shared") + S_smem = T.alloc_buffer((32, 16), scope="shared") + S_local = T.alloc_buffer((32, 16), scope="local") + O_local = T.alloc_buffer((32, 64), scope="local") + m_smem = T.alloc_buffer((32,), scope="shared") + m_prev_smem = T.alloc_buffer((32,), scope="shared") + d_smem = T.alloc_buffer((32,), scope="shared") + m_new = T.alloc_buffer((1,), scope="local") + m_prev = T.alloc_buffer((1,), scope="local") + d_new = T.alloc_buffer((1,), scope="local") + tile_id[0] = bx + batch_idx[0] = 0 + batch_rows[0] = q_indptr[1] - q_indptr[0] + batch_tiles[0] = (batch_rows[0] + 32 - 1) // 32 + while T.tvm_thread_invariant(batch_idx[0] < batch_size): + while tile_id[0] >= batch_tiles[0] and batch_idx[0] < batch_size: + tile_id[0] = tile_id[0] - batch_tiles[0] + batch_idx[0] = batch_idx[0] + 1 + if batch_idx[0] < batch_size: + b_idx: T.int32 = batch_idx[0] + batch_rows[0] = q_indptr[b_idx + 1] - q_indptr[b_idx] + batch_tiles[0] = (batch_rows[0] + 32 - 1) // 32 + if T.tvm_thread_invariant(batch_idx[0] < batch_size): + b_idx: T.int32 = batch_idx[0] + LH_start: T.int32 = tile_id[0] * 32 + q_indptr_val: T.int32 = q_indptr[b_idx] + cur_page_indptr_begin: T.int32 = page_indptr[b_idx] + cur_page_indptr_end: T.int32 = page_indptr[b_idx + 1] + kv_chunk_len[0] = T.if_then_else(cur_page_indptr_begin != cur_page_indptr_end, (cur_page_indptr_end - cur_page_indptr_begin - 1) * 16 + length_info[b_idx], 0) + T.tvm_storage_sync("shared") + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + m_smem[row] = T.float32(-50000) + d_smem[row] = T.float32(1) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(4, 4): + with T.block("O_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + T.reads() + T.writes(O_local[i, j]) + O_local[i, j] = T.float32(0) + T.tvm_storage_sync("shared") + for li_lj_fused_0 in range(4): + for li_lj_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for li_lj_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for li_lj_fused_3 in T.vectorized(4): + with T.block("Q_load"): + i = T.axis.spatial(32, (li_lj_fused_0 * 512 + li_lj_fused_1 * 128 + li_lj_fused_2 * 4 + li_lj_fused_3) // 64) + j = T.axis.spatial(64, (li_lj_fused_0 * 512 + li_lj_fused_1 * 128 + li_lj_fused_2 * 4 + li_lj_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = q_indptr_val + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + Q_smem[i, j] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", q[cur_L, cur_H_qo, j]) + T.sin(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(j < 32, q[cur_L, cur_H_qo, j + 32] * T.float16(-1), q[cur_L, cur_H_qo, j - 32]))), q[cur_L, cur_H_qo, j]) + else: + Q_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + for iterator_1 in range((kv_chunk_len[0] + 15) // 16): + L_kv_start: T.int32 = iterator_1 * 16 + for lz_ly_fused_0 in range(2): + for lz_ly_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for lz_ly_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for lz_ly_fused_3 in T.vectorized(4): + with T.block("K_load"): + i = T.axis.spatial(16, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) // 64) + j = T.axis.spatial(64, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = L_kv_start + i + if cur_L < kv_chunk_len[0]: + seq_offset: T.int32 = cur_L + page_no: T.int32 = page_values[cur_page_indptr_begin + seq_offset // 16] + page_offset: T.int32 = seq_offset % 16 + K_smem[i, j] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", k_rope_pos_offset[b_idx] + cur_L) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", pages[page_no, 0, by, page_offset, j]) + T.sin(T.Cast("float32", k_rope_pos_offset[b_idx] + cur_L) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(j < 32, pages[page_no, 0, by, page_offset, j + 32] * T.float16(-1), pages[page_no, 0, by, page_offset, j - 32]))), pages[page_no, 0, by, page_offset, j]) + else: + K_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + for lz_ly_fused_0 in range(2): + for lz_ly_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for lz_ly_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for lz_ly_fused_3 in T.vectorized(4): + with T.block("V_load"): + i = T.axis.spatial(16, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) // 64) + j = T.axis.spatial(64, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = L_kv_start + i + if cur_L < kv_chunk_len[0]: + seq_offset: T.int32 = cur_L + page_no: T.int32 = page_values[cur_page_indptr_begin + seq_offset // 16] + page_offset: T.int32 = seq_offset % 16 + V_smem[i, j] = pages[page_no, 1, by, page_offset, j] + else: + V_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + with T.block(""): + T.reads(Q_smem[0:32, 0:64], K_smem[0:16, 0:64]) + T.writes(S_local[0:32, 0:16]) + for li_0_lj_0_fused_0_init in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1_init in T.thread_binding(32, thread="threadIdx.x"): + for li_1_init, lj_1_init in T.grid(2, 2): + with T.block("S_gemm_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) // 8 * 2 + li_1_init) + j = T.axis.spatial(16, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) % 8 * 2 + lj_1_init) + T.reads() + T.writes(S_local[i, j]) + S_local[i, j] = T.float32(0) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for lk_0, li_1, lj_1, lk_1 in T.grid(8, 2, 2, 8): + with T.block("S_gemm_update"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 8 * 2 + li_1) + j = T.axis.spatial(16, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 8 * 2 + lj_1) + k = T.axis.reduce(64, lk_0 * 8 + lk_1) + T.reads(S_local[i, j], Q_smem[i, k], K_smem[j, k]) + T.writes(S_local[i, j]) + S_local[i, j] = S_local[i, j] + T.Cast("float32", Q_smem[i, k]) * T.Cast("float32", K_smem[j, k]) * attn_score_scaling_factor * T.float32(0.18033688011112042) + T.tvm_storage_sync("shared") + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(2, 2): + with T.block("S_store"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 8 * 2 + li_1) + j = T.axis.spatial(16, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 8 * 2 + lj_1) + T.reads(S_local[i, j]) + T.writes(S_smem[i, j]) + S_smem[i, j] = S_local[i, j] + T.tvm_storage_sync("shared") + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + with T.block("update1"): + T.reads(m_smem[row], kv_chunk_len[0], q_indptr[b_idx:b_idx + 2], m_new[i], S_smem[row, 0:16], d_smem[row], m_prev[i]) + T.writes(m_prev[i], m_new[i], d_new[i]) + m_prev[i] = m_smem[row] + m_new[i] = m_smem[row] + row_: T.int32 = LH_start + row + for j in range(16): + if T.if_then_else(causal > 0, L_kv_start + j < kv_chunk_len[0] - (q_indptr[b_idx + 1] - q_indptr[b_idx]) + row_ + 1, L_kv_start + j < kv_chunk_len[0]): + m_new[i] = T.max(m_new[i], S_smem[row, j]) + d_new[i] = d_smem[row] * T.exp2(m_prev[i] - m_new[i]) + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + with T.block("update"): + T.reads(kv_chunk_len[0], q_indptr[b_idx:b_idx + 2], S_smem[row, 0:16], m_new[i]) + T.writes(S_smem[row, 0:16]) + for j in range(16): + if row < 32: + row_: T.int32 = LH_start + row + if T.if_then_else(causal > 0, L_kv_start + j < kv_chunk_len[0] - (q_indptr[b_idx + 1] - q_indptr[b_idx]) + row_ + 1, L_kv_start + j < kv_chunk_len[0]): + S_smem[row, j] = T.exp2(S_smem[row, j] - m_new[i]) + else: + S_smem[row, j] = T.exp2(T.float32(-50000) - m_new[i]) + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + with T.block("update"): + T.reads(d_new[i], S_smem[row, 0:16], m_new[i], m_prev[i]) + T.writes(d_new[i], m_smem[row], d_smem[row], m_prev_smem[row]) + for j in range(16): + d_new[i] = d_new[i] + S_smem[row, j] + m_smem[row] = m_new[i] + d_smem[row] = d_new[i] + m_prev_smem[row] = m_prev[i] + T.tvm_storage_sync("shared") + with T.block(""): + T.reads(m_prev_smem[0:32], m_smem[0:32], S_smem[0:32, 0:16], V_smem[0:16, 0:64]) + T.writes(O_local[0:32, 0:64]) + for li_0_lj_0_fused_0_init in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1_init in T.thread_binding(32, thread="threadIdx.x"): + for li_1_init, lj_1_init in T.grid(4, 4): + with T.block("O_gemm_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) // 16 * 4 + li_1_init) + j = T.axis.spatial(64, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) % 16 * 4 + lj_1_init) + T.reads() + T.writes(O_local[i, j]) + O_local[i, j] = O_local[i, j] * T.exp2(m_prev_smem[i] - m_smem[i]) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for lk_0, lk_1, li_1, lj_1 in T.grid(2, 8, 4, 4): + with T.block("O_gemm_update"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + k = T.axis.reduce(16, lk_0 * 8 + lk_1) + T.reads(O_local[i, j], m_prev_smem[i], m_smem[i], S_smem[i, k], V_smem[k, j]) + T.writes(O_local[i, j]) + O_local[i, j] = O_local[i, j] + S_smem[i, k] * T.Cast("float32", V_smem[k, j]) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(4, 4): + with T.block("O_store"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + T.reads(q_indptr[b_idx:b_idx + 2], O_local[i, j], d_smem[i]) + T.writes(output[q_indptr[b_idx] + (LH_start + i), by, j]) + cur_L: T.int32 = q_indptr[b_idx] + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + output[cur_L, cur_H_qo, j] = T.Cast("float16", O_local[i, j] / d_smem[i]) + for li_0 in range(1): + for li_1 in T.thread_binding(4, thread="threadIdx.y"): + for li_2 in T.thread_binding(32, thread="threadIdx.x"): + with T.block("lse_store"): + i = T.axis.spatial(32, li_0 * 128 + li_1 * 32 + li_2) + T.where((li_0 * 4 + li_1) * 32 + li_2 < 32) + T.reads(q_indptr[b_idx:b_idx + 2], m_smem[i], d_smem[i]) + T.writes(lse[q_indptr[b_idx] + (LH_start + i), by]) + cur_L: T.int32 = q_indptr[b_idx] + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + lse[cur_L, cur_H_qo] = m_smem[i] + T.log2(d_smem[i]) + tile_id[0] = tile_id[0] + 16 + + @T.prim_func + def batch_prefill_paged_kv_sliding_window(_0: T.int32, var_q: T.handle, var_q_indptr: T.handle, var_pages: T.handle, var_page_indptr: T.handle, var_page_values: T.handle, var_length_info: T.handle, var_k_rope_pos_offset: T.handle, var_q_rope_position: T.handle, var_output: T.handle, var_lse: T.handle, causal: T.int32, rotary_mode: T.int32, rope_scale: T.float32, rope_theta: T.float32, attn_score_scaling_factor: T.float32): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) + total_len = T.int32(is_size_var=True) + q = T.match_buffer(var_q, (total_len, 20, 64), "float16") + batch_size = T.int32(is_size_var=True) + q_indptr = T.match_buffer(var_q_indptr, (batch_size + 1,), "int32", offset_factor=1) + max_num_pages = T.int32(is_size_var=True) + pages = T.match_buffer(var_pages, (max_num_pages, 2, 20, 16, 64), "float16") + page_indptr = T.match_buffer(var_page_indptr, (batch_size + 1,), "int32", offset_factor=1) + nnz_pages = T.int32(is_size_var=True) + page_values = T.match_buffer(var_page_values, (nnz_pages,), "int32", offset_factor=1) + length_info = T.match_buffer(var_length_info, (3, batch_size), "int32", offset_factor=1) + k_rope_pos_offset = T.match_buffer(var_k_rope_pos_offset, (batch_size,), "int32", offset_factor=1) + q_rope_position = T.match_buffer(var_q_rope_position, (total_len,), "int32", offset_factor=1) + output = T.match_buffer(var_output, (total_len, 20, 64), "float16") + lse = T.match_buffer(var_lse, (total_len, 20)) + # with T.block("root"): + for lbx in T.thread_binding(16, thread="blockIdx.x"): + for lby in T.thread_binding(20, thread="blockIdx.y"): + for lty in T.thread_binding(4, thread="threadIdx.y"): + for ltx in T.thread_binding(32, thread="threadIdx.x"): + with T.block("attn"): + bx, by, ty, tx = T.axis.remap("SSSS", [lbx, lby, lty, ltx]) + T.reads() + T.writes() + tile_id = T.alloc_buffer((1,), "int32", scope="local") + batch_idx = T.alloc_buffer((1,), "int32", scope="local") + batch_tiles = T.alloc_buffer((1,), "int32", scope="local") + batch_rows = T.alloc_buffer((1,), "int32", scope="local") + iterator = T.alloc_buffer((1,), "int32", scope="local") + kv_chunk_len = T.alloc_buffer((1,), "int32", scope="local") + Q_smem = T.alloc_buffer((32, 64), "float16", scope="shared") + K_smem = T.alloc_buffer((16, 64), "float16", scope="shared") + V_smem = T.alloc_buffer((16, 64), "float16", scope="shared") + S_smem = T.alloc_buffer((32, 16), scope="shared") + S_local = T.alloc_buffer((32, 16), scope="local") + O_local = T.alloc_buffer((32, 64), scope="local") + m_smem = T.alloc_buffer((32,), scope="shared") + m_prev_smem = T.alloc_buffer((32,), scope="shared") + d_smem = T.alloc_buffer((32,), scope="shared") + m_new = T.alloc_buffer((1,), scope="local") + m_prev = T.alloc_buffer((1,), scope="local") + d_new = T.alloc_buffer((1,), scope="local") + tile_id[0] = bx + batch_idx[0] = 0 + batch_rows[0] = q_indptr[1] - q_indptr[0] + batch_tiles[0] = (batch_rows[0] + 32 - 1) // 32 + while T.tvm_thread_invariant(batch_idx[0] < batch_size): + while tile_id[0] >= batch_tiles[0] and batch_idx[0] < batch_size: + tile_id[0] = tile_id[0] - batch_tiles[0] + batch_idx[0] = batch_idx[0] + 1 + if batch_idx[0] < batch_size: + b_idx: T.int32 = batch_idx[0] + batch_rows[0] = q_indptr[b_idx + 1] - q_indptr[b_idx] + batch_tiles[0] = (batch_rows[0] + 32 - 1) // 32 + if T.tvm_thread_invariant(batch_idx[0] < batch_size): + b_idx: T.int32 = batch_idx[0] + LH_start: T.int32 = tile_id[0] * 32 + q_indptr_val: T.int32 = q_indptr[b_idx] + cur_page_indptr_begin: T.int32 = page_indptr[b_idx] + cur_page_indptr_end: T.int32 = page_indptr[b_idx + 1] + kv_chunk_len[0] = T.if_then_else(cur_page_indptr_begin != cur_page_indptr_end, (cur_page_indptr_end - cur_page_indptr_begin - 1) * 16 + length_info[0, b_idx] - length_info[1, b_idx] + length_info[2, b_idx], 0) + T.tvm_storage_sync("shared") + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + m_smem[row] = T.float32(-50000) + d_smem[row] = T.float32(1) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(4, 4): + with T.block("O_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + T.reads() + T.writes(O_local[i, j]) + O_local[i, j] = T.float32(0) + T.tvm_storage_sync("shared") + for li_lj_fused_0 in range(4): + for li_lj_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for li_lj_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for li_lj_fused_3 in T.vectorized(4): + with T.block("Q_load"): + i = T.axis.spatial(32, (li_lj_fused_0 * 512 + li_lj_fused_1 * 128 + li_lj_fused_2 * 4 + li_lj_fused_3) // 64) + j = T.axis.spatial(64, (li_lj_fused_0 * 512 + li_lj_fused_1 * 128 + li_lj_fused_2 * 4 + li_lj_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = q_indptr_val + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + Q_smem[i, j] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", q[cur_L, cur_H_qo, j]) + T.sin(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(j < 32, q[cur_L, cur_H_qo, j + 32] * T.float16(-1), q[cur_L, cur_H_qo, j - 32]))), q[cur_L, cur_H_qo, j]) + else: + Q_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + for iterator_1 in range((kv_chunk_len[0] + 15) // 16): + L_kv_start: T.int32 = iterator_1 * 16 + for lz_ly_fused_0 in range(2): + for lz_ly_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for lz_ly_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for lz_ly_fused_3 in T.vectorized(4): + with T.block("K_load"): + i = T.axis.spatial(16, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) // 64) + j = T.axis.spatial(64, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = L_kv_start + i + if cur_L < kv_chunk_len[0]: + seq_offset: T.int32 = T.if_then_else(cur_L < length_info[2, b_idx], cur_L, cur_L - length_info[2, b_idx] + length_info[1, b_idx]) + page_no: T.int32 = page_values[cur_page_indptr_begin + seq_offset // 16] + page_offset: T.int32 = seq_offset % 16 + K_smem[i, j] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", k_rope_pos_offset[b_idx] + cur_L) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", pages[page_no, 0, by, page_offset, j]) + T.sin(T.Cast("float32", k_rope_pos_offset[b_idx] + cur_L) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(j < 32, pages[page_no, 0, by, page_offset, j + 32] * T.float16(-1), pages[page_no, 0, by, page_offset, j - 32]))), pages[page_no, 0, by, page_offset, j]) + else: + K_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + for lz_ly_fused_0 in range(2): + for lz_ly_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for lz_ly_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for lz_ly_fused_3 in T.vectorized(4): + with T.block("V_load"): + i = T.axis.spatial(16, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) // 64) + j = T.axis.spatial(64, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = L_kv_start + i + if cur_L < kv_chunk_len[0]: + seq_offset: T.int32 = T.if_then_else(cur_L < length_info[2, b_idx], cur_L, cur_L - length_info[2, b_idx] + length_info[1, b_idx]) + page_no: T.int32 = page_values[cur_page_indptr_begin + seq_offset // 16] + page_offset: T.int32 = seq_offset % 16 + V_smem[i, j] = pages[page_no, 1, by, page_offset, j] + else: + V_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + with T.block(""): + T.reads(Q_smem[0:32, 0:64], K_smem[0:16, 0:64]) + T.writes(S_local[0:32, 0:16]) + for li_0_lj_0_fused_0_init in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1_init in T.thread_binding(32, thread="threadIdx.x"): + for li_1_init, lj_1_init in T.grid(2, 2): + with T.block("S_gemm_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) // 8 * 2 + li_1_init) + j = T.axis.spatial(16, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) % 8 * 2 + lj_1_init) + T.reads() + T.writes(S_local[i, j]) + S_local[i, j] = T.float32(0) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for lk_0, li_1, lj_1, lk_1 in T.grid(8, 2, 2, 8): + with T.block("S_gemm_update"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 8 * 2 + li_1) + j = T.axis.spatial(16, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 8 * 2 + lj_1) + k = T.axis.reduce(64, lk_0 * 8 + lk_1) + T.reads(S_local[i, j], Q_smem[i, k], K_smem[j, k]) + T.writes(S_local[i, j]) + S_local[i, j] = S_local[i, j] + T.Cast("float32", Q_smem[i, k]) * T.Cast("float32", K_smem[j, k]) * attn_score_scaling_factor * T.float32(0.18033688011112042) + T.tvm_storage_sync("shared") + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(2, 2): + with T.block("S_store"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 8 * 2 + li_1) + j = T.axis.spatial(16, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 8 * 2 + lj_1) + T.reads(S_local[i, j]) + T.writes(S_smem[i, j]) + S_smem[i, j] = S_local[i, j] + T.tvm_storage_sync("shared") + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + with T.block("update1"): + T.reads(m_smem[row], kv_chunk_len[0], q_indptr[b_idx:b_idx + 2], m_new[i], S_smem[row, 0:16], d_smem[row], m_prev[i]) + T.writes(m_prev[i], m_new[i], d_new[i]) + m_prev[i] = m_smem[row] + m_new[i] = m_smem[row] + row_: T.int32 = LH_start + row + for j in range(16): + if T.if_then_else(causal > 0, L_kv_start + j < kv_chunk_len[0] - (q_indptr[b_idx + 1] - q_indptr[b_idx]) + row_ + 1, L_kv_start + j < kv_chunk_len[0]): + m_new[i] = T.max(m_new[i], S_smem[row, j]) + d_new[i] = d_smem[row] * T.exp2(m_prev[i] - m_new[i]) + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + with T.block("update"): + T.reads(kv_chunk_len[0], q_indptr[b_idx:b_idx + 2], S_smem[row, 0:16], m_new[i]) + T.writes(S_smem[row, 0:16]) + for j in range(16): + if row < 32: + row_: T.int32 = LH_start + row + if T.if_then_else(causal > 0, L_kv_start + j < kv_chunk_len[0] - (q_indptr[b_idx + 1] - q_indptr[b_idx]) + row_ + 1, L_kv_start + j < kv_chunk_len[0]): + S_smem[row, j] = T.exp2(S_smem[row, j] - m_new[i]) + else: + S_smem[row, j] = T.exp2(T.float32(-50000) - m_new[i]) + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + with T.block("update"): + T.reads(d_new[i], S_smem[row, 0:16], m_new[i], m_prev[i]) + T.writes(d_new[i], m_smem[row], d_smem[row], m_prev_smem[row]) + for j in range(16): + d_new[i] = d_new[i] + S_smem[row, j] + m_smem[row] = m_new[i] + d_smem[row] = d_new[i] + m_prev_smem[row] = m_prev[i] + T.tvm_storage_sync("shared") + with T.block(""): + T.reads(m_prev_smem[0:32], m_smem[0:32], S_smem[0:32, 0:16], V_smem[0:16, 0:64]) + T.writes(O_local[0:32, 0:64]) + for li_0_lj_0_fused_0_init in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1_init in T.thread_binding(32, thread="threadIdx.x"): + for li_1_init, lj_1_init in T.grid(4, 4): + with T.block("O_gemm_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) // 16 * 4 + li_1_init) + j = T.axis.spatial(64, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) % 16 * 4 + lj_1_init) + T.reads() + T.writes(O_local[i, j]) + O_local[i, j] = O_local[i, j] * T.exp2(m_prev_smem[i] - m_smem[i]) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for lk_0, lk_1, li_1, lj_1 in T.grid(2, 8, 4, 4): + with T.block("O_gemm_update"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + k = T.axis.reduce(16, lk_0 * 8 + lk_1) + T.reads(O_local[i, j], m_prev_smem[i], m_smem[i], S_smem[i, k], V_smem[k, j]) + T.writes(O_local[i, j]) + O_local[i, j] = O_local[i, j] + S_smem[i, k] * T.Cast("float32", V_smem[k, j]) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(4, 4): + with T.block("O_store"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + T.reads(q_indptr[b_idx:b_idx + 2], O_local[i, j], d_smem[i]) + T.writes(output[q_indptr[b_idx] + (LH_start + i), by, j]) + cur_L: T.int32 = q_indptr[b_idx] + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + output[cur_L, cur_H_qo, j] = T.Cast("float16", O_local[i, j] / d_smem[i]) + for li_0 in range(1): + for li_1 in T.thread_binding(4, thread="threadIdx.y"): + for li_2 in T.thread_binding(32, thread="threadIdx.x"): + with T.block("lse_store"): + i = T.axis.spatial(32, li_0 * 128 + li_1 * 32 + li_2) + T.where((li_0 * 4 + li_1) * 32 + li_2 < 32) + T.reads(q_indptr[b_idx:b_idx + 2], m_smem[i], d_smem[i]) + T.writes(lse[q_indptr[b_idx] + (LH_start + i), by]) + cur_L: T.int32 = q_indptr[b_idx] + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + lse[cur_L, cur_H_qo] = m_smem[i] + T.log2(d_smem[i]) + tile_id[0] = tile_id[0] + 16 + + @T.prim_func + def batch_prefill_ragged_kv(var_q: T.handle, var_q_indptr: T.handle, var_k: T.handle, var_v: T.handle, var_kv_indptr: T.handle, var_q_rope_position: T.handle, var_k_rope_pos_offset: T.handle, var_output: T.handle, var_lse: T.handle, causal: T.int32, rotary_mode: T.int32, rope_scale: T.float32, rope_theta: T.float32, attn_score_scaling_factor: T.float32): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) + qo_len = T.int32(is_size_var=True) + q = T.match_buffer(var_q, (qo_len, 20, 64), "float16") + batch_size = T.int32(is_size_var=True) + q_indptr = T.match_buffer(var_q_indptr, (batch_size + 1,), "int32", offset_factor=1) + kv_len = T.int32(is_size_var=True) + k = T.match_buffer(var_k, (kv_len, 20, 64), "float16") + v = T.match_buffer(var_v, (kv_len, 20, 64), "float16") + kv_indptr = T.match_buffer(var_kv_indptr, (batch_size + 1,), "int32", offset_factor=1) + q_rope_position = T.match_buffer(var_q_rope_position, (qo_len,), "int32", offset_factor=1) + k_rope_pos_offset = T.match_buffer(var_k_rope_pos_offset, (batch_size,), "int32", offset_factor=1) + output = T.match_buffer(var_output, (qo_len, 20, 64), "float16") + lse = T.match_buffer(var_lse, (qo_len, 20)) + # with T.block("root"): + for lbx in T.thread_binding(16, thread="blockIdx.x"): + for lby in T.thread_binding(20, thread="blockIdx.y"): + for lty in T.thread_binding(4, thread="threadIdx.y"): + for ltx in T.thread_binding(32, thread="threadIdx.x"): + with T.block("attn"): + bx, by, ty, tx = T.axis.remap("SSSS", [lbx, lby, lty, ltx]) + T.reads() + T.writes() + tile_id = T.alloc_buffer((1,), "int32", scope="local") + batch_idx = T.alloc_buffer((1,), "int32", scope="local") + batch_tiles = T.alloc_buffer((1,), "int32", scope="local") + batch_rows = T.alloc_buffer((1,), "int32", scope="local") + iterator = T.alloc_buffer((1,), "int32", scope="local") + kv_chunk_len = T.alloc_buffer((1,), "int32", scope="local") + Q_smem = T.alloc_buffer((32, 64), "float16", scope="shared") + K_smem = T.alloc_buffer((16, 64), "float16", scope="shared") + V_smem = T.alloc_buffer((16, 64), "float16", scope="shared") + S_smem = T.alloc_buffer((32, 16), scope="shared") + S_local = T.alloc_buffer((32, 16), scope="local") + O_local = T.alloc_buffer((32, 64), scope="local") + m_smem = T.alloc_buffer((32,), scope="shared") + m_prev_smem = T.alloc_buffer((32,), scope="shared") + d_smem = T.alloc_buffer((32,), scope="shared") + m_new = T.alloc_buffer((1,), scope="local") + m_prev = T.alloc_buffer((1,), scope="local") + d_new = T.alloc_buffer((1,), scope="local") + tile_id[0] = bx + batch_idx[0] = 0 + batch_rows[0] = q_indptr[1] - q_indptr[0] + batch_tiles[0] = (batch_rows[0] + 32 - 1) // 32 + while T.tvm_thread_invariant(batch_idx[0] < batch_size): + while tile_id[0] >= batch_tiles[0] and batch_idx[0] < batch_size: + tile_id[0] = tile_id[0] - batch_tiles[0] + batch_idx[0] = batch_idx[0] + 1 + if batch_idx[0] < batch_size: + b_idx: T.int32 = batch_idx[0] + batch_rows[0] = q_indptr[b_idx + 1] - q_indptr[b_idx] + batch_tiles[0] = (batch_rows[0] + 32 - 1) // 32 + if T.tvm_thread_invariant(batch_idx[0] < batch_size): + b_idx: T.int32 = batch_idx[0] + q_indptr_val: T.int32 = q_indptr[b_idx] + LH_start: T.int32 = tile_id[0] * 32 + kv_chunk_len[0] = kv_indptr[b_idx + 1] - kv_indptr[b_idx] + T.tvm_storage_sync("shared") + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + m_smem[row] = T.float32(-50000) + d_smem[row] = T.float32(1) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(4, 4): + with T.block("O_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + T.reads() + T.writes(O_local[i, j]) + O_local[i, j] = T.float32(0) + T.tvm_storage_sync("shared") + for li_lj_fused_0 in range(4): + for li_lj_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for li_lj_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for li_lj_fused_3 in T.vectorized(4): + with T.block("Q_load"): + i = T.axis.spatial(32, (li_lj_fused_0 * 512 + li_lj_fused_1 * 128 + li_lj_fused_2 * 4 + li_lj_fused_3) // 64) + j = T.axis.spatial(64, (li_lj_fused_0 * 512 + li_lj_fused_1 * 128 + li_lj_fused_2 * 4 + li_lj_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = q_indptr_val + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + Q_smem[i, j] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", q[cur_L, cur_H_qo, j]) + T.sin(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(j < 32, q[cur_L, cur_H_qo, j + 32] * T.float16(-1), q[cur_L, cur_H_qo, j - 32]))), q[cur_L, cur_H_qo, j]) + else: + Q_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + for iterator_1 in range((kv_chunk_len[0] + 15) // 16): + L_kv_start: T.int32 = iterator_1 * 16 + L_kv_base: T.int32 = kv_indptr[b_idx] + for lz_ly_fused_0 in range(2): + for lz_ly_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for lz_ly_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for lz_ly_fused_3 in T.vectorized(4): + with T.block("K_load"): + i = T.axis.spatial(16, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) // 64) + j = T.axis.spatial(64, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = L_kv_start + i + if cur_L < kv_chunk_len[0]: + K_smem[i, j] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", k_rope_pos_offset[b_idx] + cur_L) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", k[L_kv_base + cur_L, by, j]) + T.sin(T.Cast("float32", k_rope_pos_offset[b_idx] + cur_L) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(j < 32, k[L_kv_base + cur_L, by, j + 32] * T.float16(-1), k[L_kv_base + cur_L, by, j - 32]))), k[L_kv_base + cur_L, by, j]) + else: + K_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + for lz_ly_fused_0 in range(2): + for lz_ly_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for lz_ly_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for lz_ly_fused_3 in T.vectorized(4): + with T.block("V_load"): + i = T.axis.spatial(16, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) // 64) + j = T.axis.spatial(64, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = L_kv_start + i + if cur_L < kv_chunk_len[0]: + V_smem[i, j] = v[L_kv_base + cur_L, by, j] + else: + V_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + with T.block(""): + T.reads(Q_smem[0:32, 0:64], K_smem[0:16, 0:64]) + T.writes(S_local[0:32, 0:16]) + for li_0_lj_0_fused_0_init in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1_init in T.thread_binding(32, thread="threadIdx.x"): + for li_1_init, lj_1_init in T.grid(2, 2): + with T.block("S_gemm_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) // 8 * 2 + li_1_init) + j = T.axis.spatial(16, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) % 8 * 2 + lj_1_init) + T.reads() + T.writes(S_local[i, j]) + S_local[i, j] = T.float32(0) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for lk_0, li_1, lj_1, lk_1 in T.grid(8, 2, 2, 8): + with T.block("S_gemm_update"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 8 * 2 + li_1) + j = T.axis.spatial(16, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 8 * 2 + lj_1) + k_1 = T.axis.reduce(64, lk_0 * 8 + lk_1) + T.reads(S_local[i, j], Q_smem[i, k_1], K_smem[j, k_1]) + T.writes(S_local[i, j]) + S_local[i, j] = S_local[i, j] + T.Cast("float32", Q_smem[i, k_1]) * T.Cast("float32", K_smem[j, k_1]) * attn_score_scaling_factor * T.float32(0.18033688011112042) + T.tvm_storage_sync("shared") + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(2, 2): + with T.block("S_store"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 8 * 2 + li_1) + j = T.axis.spatial(16, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 8 * 2 + lj_1) + T.reads(S_local[i, j]) + T.writes(S_smem[i, j]) + S_smem[i, j] = S_local[i, j] + T.tvm_storage_sync("shared") + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + with T.block("update1"): + T.reads(m_smem[row], kv_chunk_len[0], q_indptr[b_idx:b_idx + 2], m_new[i], S_smem[row, 0:16], d_smem[row], m_prev[i]) + T.writes(m_prev[i], m_new[i], d_new[i]) + m_prev[i] = m_smem[row] + m_new[i] = m_smem[row] + row_: T.int32 = LH_start + row + for j in range(16): + if T.if_then_else(causal > 0, L_kv_start + j < kv_chunk_len[0] - (q_indptr[b_idx + 1] - q_indptr[b_idx]) + row_ + 1, L_kv_start + j < kv_chunk_len[0]): + m_new[i] = T.max(m_new[i], S_smem[row, j]) + d_new[i] = d_smem[row] * T.exp2(m_prev[i] - m_new[i]) + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + with T.block("update"): + T.reads(kv_chunk_len[0], q_indptr[b_idx:b_idx + 2], S_smem[row, 0:16], m_new[i]) + T.writes(S_smem[row, 0:16]) + for j in range(16): + if row < 32: + row_: T.int32 = LH_start + row + if T.if_then_else(causal > 0, L_kv_start + j < kv_chunk_len[0] - (q_indptr[b_idx + 1] - q_indptr[b_idx]) + row_ + 1, L_kv_start + j < kv_chunk_len[0]): + S_smem[row, j] = T.exp2(S_smem[row, j] - m_new[i]) + else: + S_smem[row, j] = T.exp2(T.float32(-50000) - m_new[i]) + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + with T.block("update"): + T.reads(d_new[i], S_smem[row, 0:16], m_new[i], m_prev[i]) + T.writes(d_new[i], m_smem[row], d_smem[row], m_prev_smem[row]) + for j in range(16): + d_new[i] = d_new[i] + S_smem[row, j] + m_smem[row] = m_new[i] + d_smem[row] = d_new[i] + m_prev_smem[row] = m_prev[i] + T.tvm_storage_sync("shared") + with T.block(""): + T.reads(m_prev_smem[0:32], m_smem[0:32], S_smem[0:32, 0:16], V_smem[0:16, 0:64]) + T.writes(O_local[0:32, 0:64]) + for li_0_lj_0_fused_0_init in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1_init in T.thread_binding(32, thread="threadIdx.x"): + for li_1_init, lj_1_init in T.grid(4, 4): + with T.block("O_gemm_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) // 16 * 4 + li_1_init) + j = T.axis.spatial(64, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) % 16 * 4 + lj_1_init) + T.reads() + T.writes(O_local[i, j]) + O_local[i, j] = O_local[i, j] * T.exp2(m_prev_smem[i] - m_smem[i]) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for lk_0, lk_1, li_1, lj_1 in T.grid(2, 8, 4, 4): + with T.block("O_gemm_update"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + k_1 = T.axis.reduce(16, lk_0 * 8 + lk_1) + T.reads(O_local[i, j], m_prev_smem[i], m_smem[i], S_smem[i, k_1], V_smem[k_1, j]) + T.writes(O_local[i, j]) + O_local[i, j] = O_local[i, j] + S_smem[i, k_1] * T.Cast("float32", V_smem[k_1, j]) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(4, 4): + with T.block("O_store"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + T.reads(q_indptr[b_idx:b_idx + 2], O_local[i, j], d_smem[i]) + T.writes(output[q_indptr[b_idx] + (LH_start + i), by, j]) + cur_L: T.int32 = q_indptr[b_idx] + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + output[cur_L, cur_H_qo, j] = T.Cast("float16", O_local[i, j] / d_smem[i]) + for li_0 in range(1): + for li_1 in T.thread_binding(4, thread="threadIdx.y"): + for li_2 in T.thread_binding(32, thread="threadIdx.x"): + with T.block("lse_store"): + i = T.axis.spatial(32, li_0 * 128 + li_1 * 32 + li_2) + T.where((li_0 * 4 + li_1) * 32 + li_2 < 32) + T.reads(q_indptr[b_idx:b_idx + 2], m_smem[i], d_smem[i]) + T.writes(lse[q_indptr[b_idx] + (LH_start + i), by]) + cur_L: T.int32 = q_indptr[b_idx] + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + lse[cur_L, cur_H_qo] = m_smem[i] + T.log2(d_smem[i]) + tile_id[0] = tile_id[0] + 16 + + @T.prim_func + def batch_tree_attn(var_q: T.handle, var_q_indptr: T.handle, var_k: T.handle, var_v: T.handle, var_kv_indptr: T.handle, var_q_rope_position: T.handle, var_mn_indptr: T.handle, var_mask: T.handle, var_output: T.handle, var_lse: T.handle, rotary_mode: T.int32, rope_scale: T.float32, rope_theta: T.float32, attn_score_scaling_factor: T.float32, batch_size: T.int32): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) + qo_len = T.int32(is_size_var=True) + q = T.match_buffer(var_q, (qo_len, 20, 64), "float16") + q_indptr = T.match_buffer(var_q_indptr, (batch_size + 1,), "int32", offset_factor=1) + kv_len = T.int32(is_size_var=True) + k = T.match_buffer(var_k, (kv_len, 20, 64), "float16") + v = T.match_buffer(var_v, (kv_len, 20, 64), "float16") + kv_indptr = T.match_buffer(var_kv_indptr, (batch_size + 1,), "int32", offset_factor=1) + q_rope_position = T.match_buffer(var_q_rope_position, (qo_len,), "int32", offset_factor=1) + mn_indptr = T.match_buffer(var_mn_indptr, (batch_size + 1,), "int32", offset_factor=1) + tree_size = T.int32(is_size_var=True) + mask = T.match_buffer(var_mask, (tree_size,), "int32", offset_factor=1) + output = T.match_buffer(var_output, (qo_len, 20, 64), "float16") + lse = T.match_buffer(var_lse, (qo_len, 20)) + # with T.block("root"): + for lbx in T.thread_binding(16, thread="blockIdx.x"): + for lby in T.thread_binding(20, thread="blockIdx.y"): + for lty in T.thread_binding(4, thread="threadIdx.y"): + for ltx in T.thread_binding(32, thread="threadIdx.x"): + with T.block("attn"): + bx, by, ty, tx = T.axis.remap("SSSS", [lbx, lby, lty, ltx]) + T.reads() + T.writes() + tile_id = T.alloc_buffer((1,), "int32", scope="local") + batch_idx = T.alloc_buffer((1,), "int32", scope="local") + batch_tiles = T.alloc_buffer((1,), "int32", scope="local") + batch_rows = T.alloc_buffer((1,), "int32", scope="local") + iterator = T.alloc_buffer((1,), "int32", scope="local") + kv_chunk_len = T.alloc_buffer((1,), "int32", scope="local") + Q_smem = T.alloc_buffer((32, 64), "float16", scope="shared") + K_smem = T.alloc_buffer((16, 64), "float16", scope="shared") + V_smem = T.alloc_buffer((16, 64), "float16", scope="shared") + S_smem = T.alloc_buffer((32, 16), scope="shared") + S_local = T.alloc_buffer((32, 16), scope="local") + O_local = T.alloc_buffer((32, 64), scope="local") + m_smem = T.alloc_buffer((32,), scope="shared") + m_prev_smem = T.alloc_buffer((32,), scope="shared") + d_smem = T.alloc_buffer((32,), scope="shared") + m_new = T.alloc_buffer((1,), scope="local") + m_prev = T.alloc_buffer((1,), scope="local") + d_new = T.alloc_buffer((1,), scope="local") + tile_id[0] = bx + batch_idx[0] = 0 + batch_rows[0] = q_indptr[1] - q_indptr[0] + batch_tiles[0] = (batch_rows[0] + 32 - 1) // 32 + while T.tvm_thread_invariant(batch_idx[0] < batch_size): + while tile_id[0] >= batch_tiles[0] and batch_idx[0] < batch_size: + tile_id[0] = tile_id[0] - batch_tiles[0] + batch_idx[0] = batch_idx[0] + 1 + if batch_idx[0] < batch_size: + b_idx: T.int32 = batch_idx[0] + batch_rows[0] = q_indptr[b_idx + 1] - q_indptr[b_idx] + batch_tiles[0] = (batch_rows[0] + 32 - 1) // 32 + if T.tvm_thread_invariant(batch_idx[0] < batch_size): + b_idx: T.int32 = batch_idx[0] + LH_start: T.int32 = tile_id[0] * 32 + q_indptr_val: T.int32 = q_indptr[b_idx] + kv_chunk_len[0] = kv_indptr[b_idx + 1] - kv_indptr[b_idx] + T.tvm_storage_sync("shared") + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + m_smem[row] = T.float32(-50000) + d_smem[row] = T.float32(1) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(4, 4): + with T.block("O_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + T.reads() + T.writes(O_local[i, j]) + O_local[i, j] = T.float32(0) + T.tvm_storage_sync("shared") + for li_lj_fused_0 in range(4): + for li_lj_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for li_lj_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for li_lj_fused_3 in T.vectorized(4): + with T.block("Q_load"): + i = T.axis.spatial(32, (li_lj_fused_0 * 512 + li_lj_fused_1 * 128 + li_lj_fused_2 * 4 + li_lj_fused_3) // 64) + j = T.axis.spatial(64, (li_lj_fused_0 * 512 + li_lj_fused_1 * 128 + li_lj_fused_2 * 4 + li_lj_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = q_indptr_val + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + Q_smem[i, j] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64)))) * q[cur_L, cur_H_qo, j] + T.Cast("float16", T.sin(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64)))) * T.if_then_else(j < 32, q[cur_L, cur_H_qo, j + 32] * T.float16(-1), q[cur_L, cur_H_qo, j - 32]), q[cur_L, cur_H_qo, j]) + else: + Q_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + for iterator_1 in range((kv_chunk_len[0] + 15) // 16): + L_kv_start: T.int32 = iterator_1 * 16 + L_kv_base: T.int32 = kv_indptr[b_idx] + for lz_ly_fused_0 in range(2): + for lz_ly_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for lz_ly_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for lz_ly_fused_3 in T.vectorized(4): + with T.block("KV_load"): + i = T.axis.spatial(16, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) // 64) + j = T.axis.spatial(64, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = L_kv_base + L_kv_start + i + if L_kv_start + i < kv_chunk_len[0]: + K_smem[i, j] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64)))) * k[cur_L, by, j] + T.Cast("float16", T.sin(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64)))) * T.if_then_else(j < 32, k[cur_L, by, j + 32] * T.float16(-1), k[cur_L, by, j - 32]), k[cur_L, by, j]) + V_smem[i, j] = v[cur_L, by, j] + else: + K_smem[i, j] = T.float16(0) + V_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + with T.block(""): + T.reads(Q_smem[0:32, 0:64], K_smem[0:16, 0:64]) + T.writes(S_local[0:32, 0:16]) + for li_0_lj_0_fused_0_init in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1_init in T.thread_binding(32, thread="threadIdx.x"): + for li_1_init, lj_1_init in T.grid(2, 2): + with T.block("S_gemm_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) // 8 * 2 + li_1_init) + j = T.axis.spatial(16, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) % 8 * 2 + lj_1_init) + T.reads() + T.writes(S_local[i, j]) + S_local[i, j] = T.float32(0) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for lk_0, li_1, lj_1, lk_1 in T.grid(8, 2, 2, 8): + with T.block("S_gemm_update"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 8 * 2 + li_1) + j = T.axis.spatial(16, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 8 * 2 + lj_1) + k_1 = T.axis.reduce(64, lk_0 * 8 + lk_1) + T.reads(S_local[i, j], Q_smem[i, k_1], K_smem[j, k_1]) + T.writes(S_local[i, j]) + S_local[i, j] = S_local[i, j] + T.Cast("float32", Q_smem[i, k_1]) * T.Cast("float32", K_smem[j, k_1]) * attn_score_scaling_factor * T.float32(0.18033688011112042) + T.tvm_storage_sync("shared") + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(2, 2): + with T.block("S_store"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 8 * 2 + li_1) + j = T.axis.spatial(16, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 8 * 2 + lj_1) + T.reads(S_local[i, j]) + T.writes(S_smem[i, j]) + S_smem[i, j] = S_local[i, j] + T.tvm_storage_sync("shared") + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + with T.block("update1"): + T.reads(m_smem[row], kv_chunk_len[0], mask[mn_indptr[b_idx] + (LH_start + row) * (q_indptr[b_idx + 1] - q_indptr[b_idx]) + L_kv_start:mn_indptr[b_idx] + (LH_start + row) * (q_indptr[b_idx + 1] - q_indptr[b_idx]) + L_kv_start + 16], mn_indptr[b_idx], q_indptr[b_idx:b_idx + 2], m_new[i], S_smem[row, 0:16], d_smem[row], m_prev[i]) + T.writes(m_prev[i], m_new[i], d_new[i]) + m_prev[i] = m_smem[row] + m_new[i] = m_smem[row] + row_: T.int32 = LH_start + row + for j in range(16): + if L_kv_start + j < kv_chunk_len[0] and mask[mn_indptr[b_idx] + row_ * (q_indptr[b_idx + 1] - q_indptr[b_idx]) + (L_kv_start + j)] == 1: + m_new[i] = T.max(m_new[i], S_smem[row, j]) + d_new[i] = d_smem[row] * T.exp2(m_prev[i] - m_new[i]) + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + with T.block("update"): + T.reads(kv_chunk_len[0], mask[mn_indptr[b_idx] + (LH_start + row) * (q_indptr[b_idx + 1] - q_indptr[b_idx]) + L_kv_start:mn_indptr[b_idx] + (LH_start + row) * (q_indptr[b_idx + 1] - q_indptr[b_idx]) + L_kv_start + 16], mn_indptr[b_idx], q_indptr[b_idx:b_idx + 2], S_smem[row, 0:16], m_new[i]) + T.writes(S_smem[row, 0:16]) + for j in range(16): + if row < 32: + row_: T.int32 = LH_start + row + if L_kv_start + j < kv_chunk_len[0] and mask[mn_indptr[b_idx] + row_ * (q_indptr[b_idx + 1] - q_indptr[b_idx]) + (L_kv_start + j)] == 1: + S_smem[row, j] = T.exp2(S_smem[row, j] - m_new[i]) + else: + S_smem[row, j] = T.exp2(T.float32(-50000) - m_new[i]) + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + with T.block("update"): + T.reads(d_new[i], S_smem[row, 0:16], m_new[i], m_prev[i]) + T.writes(d_new[i], m_smem[row], d_smem[row], m_prev_smem[row]) + for j in range(16): + d_new[i] = d_new[i] + S_smem[row, j] + m_smem[row] = m_new[i] + d_smem[row] = d_new[i] + m_prev_smem[row] = m_prev[i] + T.tvm_storage_sync("shared") + with T.block(""): + T.reads(m_prev_smem[0:32], m_smem[0:32], S_smem[0:32, 0:16], V_smem[0:16, 0:64]) + T.writes(O_local[0:32, 0:64]) + for li_0_lj_0_fused_0_init in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1_init in T.thread_binding(32, thread="threadIdx.x"): + for li_1_init, lj_1_init in T.grid(4, 4): + with T.block("O_gemm_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) // 16 * 4 + li_1_init) + j = T.axis.spatial(64, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) % 16 * 4 + lj_1_init) + T.reads() + T.writes(O_local[i, j]) + O_local[i, j] = O_local[i, j] * T.exp2(m_prev_smem[i] - m_smem[i]) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for lk_0, lk_1, li_1, lj_1 in T.grid(2, 8, 4, 4): + with T.block("O_gemm_update"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + k_1 = T.axis.reduce(16, lk_0 * 8 + lk_1) + T.reads(O_local[i, j], m_prev_smem[i], m_smem[i], S_smem[i, k_1], V_smem[k_1, j]) + T.writes(O_local[i, j]) + O_local[i, j] = O_local[i, j] + S_smem[i, k_1] * T.Cast("float32", V_smem[k_1, j]) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(4, 4): + with T.block("O_store"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + T.reads(q_indptr[b_idx:b_idx + 2], O_local[i, j], d_smem[i]) + T.writes(output[q_indptr[b_idx] + (LH_start + i), by, j]) + cur_L: T.int32 = q_indptr[b_idx] + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + output[cur_L, cur_H_qo, j] = T.Cast("float16", O_local[i, j] / d_smem[i]) + for li_0 in range(1): + for li_1 in T.thread_binding(4, thread="threadIdx.y"): + for li_2 in T.thread_binding(32, thread="threadIdx.x"): + with T.block("lse_store"): + i = T.axis.spatial(32, li_0 * 128 + li_1 * 32 + li_2) + T.where((li_0 * 4 + li_1) * 32 + li_2 < 32) + T.reads(q_indptr[b_idx:b_idx + 2], m_smem[i], d_smem[i]) + T.writes(lse[q_indptr[b_idx] + (LH_start + i), by]) + cur_L: T.int32 = q_indptr[b_idx] + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + lse[cur_L, cur_H_qo] = m_smem[i] + T.log2(d_smem[i]) + tile_id[0] = tile_id[0] + 16 + + @T.prim_func(private=True) + def batch_verify_on_gpu_single_kernel(var_draft_probs: T.handle, var_draft_tokens: T.handle, var_model_probs: T.handle, var_token_tree_first_child: T.handle, var_token_tree_next_sibling: T.handle, var_uniform_samples: T.handle, var_token_tree_parent_ptr: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + num_nodes, vocab_size = T.int32(is_size_var=True), T.int64() + draft_probs = T.match_buffer(var_draft_probs, (num_nodes, vocab_size)) + draft_tokens = T.match_buffer(var_draft_tokens, (num_nodes,), "int32") + model_probs = T.match_buffer(var_model_probs, (num_nodes, vocab_size)) + token_tree_first_child = T.match_buffer(var_token_tree_first_child, (num_nodes,), "int32") + token_tree_next_sibling = T.match_buffer(var_token_tree_next_sibling, (num_nodes,), "int32") + uniform_samples = T.match_buffer(var_uniform_samples, (num_nodes,)) + nbatch = T.int32(is_size_var=True) + token_tree_parent_ptr = T.match_buffer(var_token_tree_parent_ptr, (nbatch,), "int32") + # with T.block("root"): + child_ptr = T.alloc_buffer((1,), "int32", scope="local") + parent_ptr = T.alloc_buffer((1,), "int32", scope="local") + child_token = T.alloc_buffer((1,), "int32", scope="local") + done = T.alloc_buffer((1,), "bool", scope="local") + psum = T.alloc_buffer((1,), scope="local") + t0 = T.alloc_buffer((1,), scope="local") + model_prob_local = T.alloc_buffer((1,), scope="local") + draft_prob_local = T.alloc_buffer((1,), scope="local") + p_child = T.alloc_buffer((1,), scope="local") + q_child = T.alloc_buffer((1,), scope="local") + uniform_sample = T.alloc_buffer((1,), scope="local") + pred_shared = T.alloc_buffer((1,), "bool", scope="shared") + pred_local = T.alloc_buffer((1,), "bool", scope="local") + for _bx in T.thread_binding(nbatch, thread="blockIdx.x"): + for _tx in T.thread_binding(1024, thread="threadIdx.x"): + with T.block("CTA"): + b, tx = T.axis.remap("SS", [_bx, _tx]) + T.reads(token_tree_parent_ptr[b], token_tree_first_child[T.min(parent_ptr[0], child_ptr[0]):T.min(parent_ptr[0], child_ptr[0]) + (T.max(parent_ptr[0], child_ptr[0]) + 1 - T.min(parent_ptr[0], child_ptr[0]))], parent_ptr[0], done[0], child_ptr[0], draft_tokens[child_ptr[0]], model_probs[parent_ptr[0], T.min(T.Cast("int64", child_token[0]), T.Cast("int64", tx)):T.min(T.Cast("int64", child_token[0]), T.Cast("int64", tx)) + (T.max(T.Cast("int64", child_token[0]), (vocab_size + T.int64(1023)) // T.int64(1024) * T.int64(1024) + T.Cast("int64", tx) - T.int64(1024)) + T.int64(1) - T.min(T.Cast("int64", child_token[0]), T.Cast("int64", tx)))], child_token[0], draft_probs[child_ptr[0], T.min(T.Cast("int64", child_token[0]), T.Cast("int64", tx)):T.min(T.Cast("int64", child_token[0]), T.Cast("int64", tx)) + (T.max(T.Cast("int64", child_token[0]), (vocab_size + T.int64(1023)) // T.int64(1024) * T.int64(1024) + T.Cast("int64", tx) - T.int64(1024)) + T.int64(1) - T.min(T.Cast("int64", child_token[0]), T.Cast("int64", tx)))], uniform_samples[child_ptr[0]], p_child[0], uniform_sample[0], q_child[0], pred_shared[0], pred_local[0], model_prob_local[0], draft_prob_local[0], psum[0], t0[0], token_tree_next_sibling[child_ptr[0]]) + T.writes(parent_ptr[0], child_ptr[0], done[0], child_token[0], p_child[0], q_child[0], uniform_sample[0], pred_shared[0], pred_local[0], psum[0], model_prob_local[0], draft_prob_local[0], t0[0], model_probs[parent_ptr[0], T.Cast("int64", tx):T.Cast("int64", tx) + ((vocab_size + T.int64(1023)) // T.int64(1024) * T.int64(1024) - T.int64(1023))], token_tree_parent_ptr[b]) + parent_ptr[0] = token_tree_parent_ptr[b] + child_ptr[0] = token_tree_first_child[parent_ptr[0]] + done[0] = T.bool(False) + while not done[0]: + T.tvm_storage_sync("shared") + if child_ptr[0] == -1: + done[0] = T.bool(True) + T.tvm_storage_sync("shared") + else: + if tx == 0: + child_token[0] = draft_tokens[child_ptr[0]] + p_child[0] = model_probs[parent_ptr[0], child_token[0]] + q_child[0] = draft_probs[child_ptr[0], child_token[0]] + uniform_sample[0] = uniform_samples[child_ptr[0]] + pred_shared[0] = p_child[0] >= uniform_sample[0] * q_child[0] + T.tvm_storage_sync("shared") + pred_local[0] = pred_shared[0] + if pred_local[0]: + parent_ptr[0] = child_ptr[0] + child_ptr[0] = token_tree_first_child[child_ptr[0]] + else: + psum[0] = T.float32(0) + for i in range((vocab_size + T.int64(1023)) // T.int64(1024)): + if i * T.int64(1024) + T.Cast("int64", tx) < vocab_size: + model_prob_local[0] = model_probs[parent_ptr[0], i * T.int64(1024) + T.Cast("int64", tx)] + draft_prob_local[0] = draft_probs[child_ptr[0], i * T.int64(1024) + T.Cast("int64", tx)] + model_prob_local[0] = T.max(model_prob_local[0] - draft_prob_local[0], T.float32(0)) + psum[0] = psum[0] + model_prob_local[0] + with T.block("block_cross_thread"): + T.reads(psum[0]) + T.writes(t0[0]) + T.attr(T.comm_reducer(lambda x0, y0: x0 + y0, [T.float32(0)]), "reduce_scope", T.reinterpret("handle", T.uint64(0))) + T.tvm_thread_allreduce(T.uint32(1), psum[0], T.bool(True), t0[0], tx) + if t0[0] < T.float32(9.9999999999999995e-08): + parent_ptr[0] = child_ptr[0] + child_ptr[0] = token_tree_first_child[child_ptr[0]] + else: + for i in range((vocab_size + T.int64(1023)) // T.int64(1024)): + if i * T.int64(1024) + T.Cast("int64", tx) < vocab_size: + model_prob_local[0] = model_probs[parent_ptr[0], i * T.int64(1024) + T.Cast("int64", tx)] + draft_prob_local[0] = draft_probs[child_ptr[0], i * T.int64(1024) + T.Cast("int64", tx)] + model_prob_local[0] = T.max(model_prob_local[0] - draft_prob_local[0], T.float32(0)) + model_probs[parent_ptr[0], i * T.int64(1024) + T.Cast("int64", tx)] = model_prob_local[0] / t0[0] + child_ptr[0] = token_tree_next_sibling[child_ptr[0]] + if tx == 0: + token_tree_parent_ptr[b] = parent_ptr[0] + + @T.prim_func + def chunk_lse(var_A: T.handle, var_temperature: T.handle, var_chunked_sum: T.handle, var_chunked_max: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.noalias": T.bool(True)}) + batch_size, vocab_size = T.int64(is_size_var=True), T.int64(is_size_var=True) + A = T.match_buffer(var_A, (batch_size, vocab_size)) + temperature = T.match_buffer(var_temperature, (batch_size,)) + num_chunks = T.int64(is_size_var=True) + chunked_sum = T.match_buffer(var_chunked_sum, (batch_size, num_chunks)) + chunked_max = T.match_buffer(var_chunked_max, (batch_size, num_chunks)) + # with T.block("root"): + A_pad = T.alloc_buffer((batch_size, num_chunks, T.int64(4096))) + temp_max = T.alloc_buffer((batch_size, num_chunks)) + temp_sum = T.alloc_buffer((batch_size, num_chunks)) + for l0, l1, l2 in T.grid(batch_size, num_chunks, T.int64(4096)): + with T.block("pad"): + v0, v1, v2 = T.axis.remap("SSS", [l0, l1, l2]) + T.reads(temperature[v0], A[v0, v1 * T.int64(4096) + v2]) + T.writes(A_pad[v0, v1, v2]) + A_pad[v0, v1, v2] = T.if_then_else(v1 * T.int64(4096) + v2 < vocab_size, T.if_then_else(temperature[v0] > T.float32(1.0000000000000001e-05), A[v0, v1 * T.int64(4096) + v2] / temperature[v0], A[v0, v1 * T.int64(4096) + v2]), T.float32(-3.4028234663852886e+38)) + for l0, l1, l2 in T.grid(batch_size, num_chunks, T.int64(4096)): + with T.block("max"): + v0, v1, v2 = T.axis.remap("SSR", [l0, l1, l2]) + T.reads(A_pad[v0, v1, v2]) + T.writes(temp_max[v0, v1]) + with T.init(): + temp_max[v0, v1] = T.float32(-3.4028234663852886e+38) + temp_max[v0, v1] = T.max(temp_max[v0, v1], A_pad[v0, v1, v2]) + for l0, l1, l2 in T.grid(batch_size, num_chunks, T.int64(4096)): + with T.block("sum_exp"): + v0, v1, v2 = T.axis.remap("SSR", [l0, l1, l2]) + T.reads(temperature[v0], A_pad[v0, v1, v2], temp_max[v0, v1]) + T.writes(temp_sum[v0, v1]) + with T.init(): + temp_sum[v0, v1] = T.float32(0) + temp_sum[v0, v1] = temp_sum[v0, v1] + T.if_then_else(v1 * T.int64(4096) + v2 < vocab_size, T.Select(temperature[v0] > T.float32(1.0000000000000001e-05), T.exp(A_pad[v0, v1, v2] - temp_max[v0, v1]), T.Cast("float32", A_pad[v0, v1, v2] == temp_max[v0, v1])), T.float32(0)) + for l0, l1, l2 in T.grid(batch_size, num_chunks, T.int64(1)): + with T.block("log"): + v0, v1, v2 = T.axis.remap("SSS", [l0, l1, l2]) + T.reads(temperature[v0], temp_sum[v0, v1], temp_max[v0, v1]) + T.writes(chunked_sum[v0, v1], chunked_max[v0, v1]) + chunked_sum[v0, v1] = T.Select(temperature[v0] > T.float32(1.0000000000000001e-05), T.log(temp_sum[v0, v1]), temp_sum[v0, v1]) + chunked_max[v0, v1] = temp_max[v0, v1] + + @T.prim_func + def compact_kv_copy(var_pages: T.handle, var_copy_length_indptr: T.handle, var_copy_src_dst_pos: T.handle, batch_size: T.int32): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) + num_pages = T.int32() + pages = T.match_buffer(var_pages, (num_pages, 2, 20, 16, 64), "float16") + copy_length_indptr = T.match_buffer(var_copy_length_indptr, (batch_size + 1,), "int32", offset_factor=1) + total_copy_length = T.int32() + copy_src_dst_pos = T.match_buffer(var_copy_src_dst_pos, (2, total_copy_length), "int32", offset_factor=1) + with T.block("root"): + T.reads() + T.writes() + for bhd_o in T.thread_binding((batch_size * 1280 + 1023) // 1024, thread="blockIdx.x"): + for bhd_i in T.thread_binding(1024, thread="threadIdx.x"): + b: T.int32 = (bhd_o * 1024 + bhd_i) // 1280 + h: T.int32 = (bhd_o * 1024 + bhd_i) // 64 % 20 + d: T.int32 = (bhd_o * 1024 + bhd_i) % 64 + if bhd_o * 1024 + bhd_i < batch_size * 20 * 64: + for i in range(copy_length_indptr[b + 1] - copy_length_indptr[b]): + src_pos: T.int32 = copy_src_dst_pos[0, copy_length_indptr[b] + i] + dst_pos: T.int32 = copy_src_dst_pos[1, copy_length_indptr[b] + i] + pages[dst_pos // 16, 0, h, dst_pos % 16, d] = pages[src_pos // 16, 0, h, src_pos % 16, d] + pages[dst_pos // 16, 1, h, dst_pos % 16, d] = pages[src_pos // 16, 1, h, src_pos % 16, d] + + @T.prim_func + def copy_single_page(var_pages: T.handle, src_page_id: T.int64, tgt_page_id: T.int64, copy_length: T.int64): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) + num_pages, page_size = T.int32(), T.int64() + pages = T.match_buffer(var_pages, (num_pages, 2, 20, page_size, 64), "float16") + # with T.block("root"): + for b in T.thread_binding((copy_length * T.int64(1280) + T.int64(1023)) // T.int64(1024), thread="blockIdx.x"): + for t in T.thread_binding(1024, thread="threadIdx.x"): + with T.block("copy"): + vh = T.axis.spatial(20, T.Cast("int32", (b * T.int64(1024) + T.Cast("int64", t)) // (copy_length * T.int64(64)))) + vp = T.axis.spatial(copy_length, (b * T.int64(1024) + T.Cast("int64", t)) % (copy_length * T.int64(64)) // T.int64(64)) + vd = T.axis.spatial(64, T.Cast("int32", (b * T.int64(1024) + T.Cast("int64", t)) % T.int64(64))) + T.reads(pages[src_page_id, 0:2, vh, vp, vd]) + T.writes(pages[tgt_page_id, 0:2, vh, vp, vd]) + pages[tgt_page_id, 0, vh, vp, vd] = pages[src_page_id, 0, vh, vp, vd] + pages[tgt_page_id, 1, vh, vp, vd] = pages[src_page_id, 1, vh, vp, vd] + + @T.prim_func + def full(var_result: T.handle, value: T.int32): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32})}) + batch_size = T.int32(is_size_var=True) + result = T.match_buffer(var_result, (batch_size, 1), "int32") + # with T.block("root"): + for i in range(batch_size): + with T.block("block"): + vi = T.axis.spatial(batch_size, i) + T.reads() + T.writes(result[vi, 0]) + result[vi, 0] = value + + @T.prim_func + def fused_rope(var_qkv: T.handle, var_position_map: T.handle, var_q: T.handle, var_k: T.handle, var_v: T.handle, apply_rope: T.int32): + T.func_attr({"op_pattern": 8, "target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.noalias": T.bool(True)}) + seq_len = T.int64() + qkv = T.match_buffer(var_qkv, (seq_len, 60, 64), "float16") + position_map = T.match_buffer(var_position_map, (seq_len,), "int32", offset_factor=1) + q = T.match_buffer(var_q, (seq_len, 20, 64), "float16") + k = T.match_buffer(var_k, (seq_len, 20, 64), "float16") + v = T.match_buffer(var_v, (seq_len, 20, 64), "float16") + # with T.block("root"): + for iters_0, iters_1, iters_2 in T.grid(seq_len, 60, 64): + with T.block("llama_fused_rope"): + s, h, d = T.axis.remap("SSS", [iters_0, iters_1, iters_2]) + T.reads(position_map[s], qkv[s, h, d - 32:d - 32 + 65]) + T.writes(q[s, h, d], k[s, h - 20, d], v[s, h - 40, d]) + if h < 20: + q[s, h, d] = T.if_then_else(apply_rope > 0 and d < 64, T.Cast("float16", T.cos(T.Cast("float32", position_map[s]) / T.pow(T.float32(1), T.Cast("float32", d * 2 % 64) / T.float32(64))) * T.Cast("float32", qkv[s, h, d]) + T.sin(T.Cast("float32", position_map[s]) / T.pow(T.float32(1), T.Cast("float32", d * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(d < 32, qkv[s, h, d + 32] * T.float16(-1), qkv[s, h, d - 32]))), qkv[s, h, d]) + else: + if h < 40: + k[s, h - 20, d] = T.if_then_else(apply_rope > 0 and d < 64, T.Cast("float16", T.cos(T.Cast("float32", position_map[s]) / T.pow(T.float32(1), T.Cast("float32", d * 2 % 64) / T.float32(64))) * T.Cast("float32", qkv[s, h, d]) + T.sin(T.Cast("float32", position_map[s]) / T.pow(T.float32(1), T.Cast("float32", d * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(d < 32, qkv[s, h, d + 32] * T.float16(-1), qkv[s, h, d - 32]))), qkv[s, h, d]) + else: + v[s, h - 40, d] = qkv[s, h, d] + + @T.prim_func + def gather_probs(var_src: T.handle, var_indices: T.handle, var_dst: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.noalias": T.bool(True)}) + m, n = T.int32(is_size_var=True), T.int32(is_size_var=True) + src = T.match_buffer(var_src, (m, n)) + batch_size = T.int32(is_size_var=True) + indices = T.match_buffer(var_indices, (batch_size,), "int32") + dst = T.match_buffer(var_dst, (batch_size, n)) + # with T.block("root"): + for b, j in T.grid(batch_size, n): + with T.block("gather_2d"): + vb, vj = T.axis.remap("SS", [b, j]) + T.reads(src[indices[vb], vj], indices[vb]) + T.writes(dst[vb, vj]) + dst[vb, vj] = src[indices[vb], vj] + + @T.prim_func(private=True) + def get_index_from_sorted(A: T.handle, B: T.handle, C: T.handle, D: T.handle, E: T.handle, F: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32})}) + batch, vocab_size = T.int64(), T.int64() + cumsum_sorted = T.match_buffer(A, (batch, vocab_size)) + indices = T.match_buffer(B, (batch, vocab_size), "int32") + renorm_prob = T.match_buffer(C, (batch, 1)) + out_batch = T.int64() + usample = T.match_buffer(D, (out_batch, 1)) + sample_indices = T.match_buffer(E, (out_batch, 1), "int32") + output_index = T.match_buffer(F, (out_batch, 1), "int32") + # with T.block("root"): + for ax0, ax1 in T.grid(out_batch, vocab_size): + with T.block("T_get_index_from_sorted"): + v_ax0, v_ax1 = T.axis.remap("SS", [ax0, ax1]) + T.reads(usample[v_ax0, T.int64(0)], cumsum_sorted[sample_indices[v_ax0, T.int64(0)], v_ax1 - T.int64(1):v_ax1 - T.int64(1) + T.int64(2)], sample_indices[v_ax0, T.int64(0)], renorm_prob[sample_indices[v_ax0, T.int64(0)], 0], indices[sample_indices[v_ax0, T.int64(0)], T.min(T.int64(0), v_ax1):T.min(T.int64(0), v_ax1) + (T.max(T.int64(0), v_ax1) + T.int64(1) - T.min(T.int64(0), v_ax1))]) + T.writes(output_index[v_ax0, 0]) + if usample[v_ax0, T.int64(0)] < cumsum_sorted[sample_indices[v_ax0, T.int64(0)], v_ax1] / renorm_prob[sample_indices[v_ax0, T.int64(0)], 0] or v_ax1 + T.int64(1) == vocab_size: + if v_ax1 == T.int64(0): + output_index[v_ax0, 0] = indices[sample_indices[v_ax0, T.int64(0)], 0] + else: + if usample[v_ax0, T.int64(0)] >= cumsum_sorted[sample_indices[v_ax0, T.int64(0)], v_ax1 - T.int64(1)] / renorm_prob[sample_indices[v_ax0, T.int64(0)], 0]: + output_index[v_ax0, 0] = indices[sample_indices[v_ax0, T.int64(0)], v_ax1] + + @T.prim_func(private=True) + def get_renorm_prob(A: T.handle, B: T.handle, C: T.handle, D: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32})}) + batch, vocab_size = T.int64(), T.int64() + cumsum_sorted = T.match_buffer(A, (batch, vocab_size)) + top_p = T.match_buffer(B, (batch, 1)) + top_k = T.match_buffer(C, (batch, 1), "int32") + renorm_prob = T.match_buffer(D, (batch, 1)) + # with T.block("root"): + for ax0, ax1 in T.grid(batch, vocab_size): + with T.block("T_get_renorm_prob"): + v_ax0, v_ax1 = T.axis.remap("SS", [ax0, ax1]) + T.reads(cumsum_sorted[v_ax0, T.min(T.min(T.int64(0), v_ax1), v_ax1 + T.int64(1)):T.min(T.min(T.int64(0), v_ax1), v_ax1 + T.int64(1)) + (T.max(T.max(T.int64(0), v_ax1), v_ax1 + T.int64(1)) + T.int64(1) - T.min(T.min(T.int64(0), v_ax1), v_ax1 + T.int64(1)))], top_p[v_ax0, 0], top_k[v_ax0, 0]) + T.writes(renorm_prob[v_ax0, 0]) + if not (cumsum_sorted[v_ax0, 0] < top_p[v_ax0, 0] and top_k[v_ax0, 0] > 1): + renorm_prob[v_ax0, 0] = cumsum_sorted[v_ax0, 0] + else: + if cumsum_sorted[v_ax0, v_ax1] < top_p[v_ax0, 0] and v_ax1 + T.int64(1) < T.Cast("int64", top_k[v_ax0, 0]): + if v_ax1 + T.int64(1) == vocab_size: + renorm_prob[v_ax0, 0] = cumsum_sorted[v_ax0, v_ax1] + else: + if not (cumsum_sorted[v_ax0, v_ax1 + T.int64(1)] < top_p[v_ax0, 0] and v_ax1 + T.int64(1) + T.int64(1) < T.Cast("int64", top_k[v_ax0, 0])): + renorm_prob[v_ax0, 0] = cumsum_sorted[v_ax0, v_ax1 + T.int64(1)] + + @T.prim_func(private=True) + def index(var_layer_norm355: T.handle, index: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16")): + T.func_attr({"target": T.target({"arch": "sm_89", "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.noalias": T.bool(True)}) + seq_len = T.int64() + layer_norm355 = T.match_buffer(var_layer_norm355, (T.int64(1), seq_len, T.int64(1280)), "float16") + # with T.block("root"): + for i, _, k in T.grid(T.int64(1), T.int64(1), T.int64(1280)): + with T.block("index"): + v_i, v__, v_k = T.axis.remap("SSS", [i, _, k]) + T.reads(layer_norm355[v_i, seq_len - T.int64(1), v_k]) + T.writes(index[v_i, v__, v_k]) + index[v_i, v__, v_k] = layer_norm355[v_i, seq_len - T.int64(1), v_k] + + @T.prim_func + def merge_state_inplace(v: T.handle, s: T.handle, v_other: T.handle, s_other: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) + N, H, D = T.int32(is_size_var=True), T.int32(is_size_var=True), T.int32(is_size_var=True) + V = T.match_buffer(v, (N, H, D), "float16") + S = T.match_buffer(s, (N, H)) + V_other = T.match_buffer(v_other, (N, H, D), "float16") + S_other = T.match_buffer(s_other, (N, H)) + # with T.block("root"): + for bx in T.thread_binding(N, thread="blockIdx.x"): + for by in T.thread_binding(1, thread="blockIdx.y"): + for ty in T.thread_binding(20, thread="threadIdx.y"): + for tx in T.thread_binding(16, thread="threadIdx.x"): + with T.block("merge"): + T.reads(S[bx, ty + by * 20], S_other[bx, ty + by * 20], V[bx, ty + by * 20, tx * 4:tx * 4 + 4], V_other[bx, ty + by * 20, tx * 4:tx * 4 + 4]) + T.writes(V[bx, ty + by * 20, tx * 4:tx * 4 + 4], S[bx, ty + by * 20]) + s_val = T.alloc_buffer((1,), scope="local") + s_other_val = T.alloc_buffer((1,), scope="local") + s_max = T.alloc_buffer((1,), scope="local") + scale = T.alloc_buffer((1,), scope="local") + other_scale = T.alloc_buffer((1,), scope="local") + v_vec = T.alloc_buffer((4,), "float16", scope="local") + v_other_vec = T.alloc_buffer((4,), "float16", scope="local") + s_val[0] = S[bx, ty + by * 20] + s_other_val[0] = S_other[bx, ty + by * 20] + s_max[0] = T.max(s_val[0], s_other_val[0]) + s_val[0] = T.exp2(s_val[0] - s_max[0]) + s_other_val[0] = T.exp2(s_other_val[0] - s_max[0]) + scale[0] = s_val[0] / (s_val[0] + s_other_val[0]) + other_scale[0] = s_other_val[0] / (s_val[0] + s_other_val[0]) + for vec in T.vectorized(4): + v_vec[vec] = V[bx, ty + by * 20, tx * 4 + vec] + for vec in T.vectorized(4): + v_other_vec[vec] = V_other[bx, ty + by * 20, tx * 4 + vec] + for vec in range(4): + v_vec[vec] = T.Cast("float16", T.Cast("float32", v_vec[vec]) * scale[0] + T.Cast("float32", v_other_vec[vec]) * other_scale[0]) + for vec in T.vectorized(4): + V[bx, ty + by * 20, tx * 4 + vec] = v_vec[vec] + S[bx, ty + by * 20] = T.log2(s_val[0] + s_other_val[0]) + s_max[0] + + @T.prim_func + def sampler_take_probs_tir(var_unsorted_probs: T.handle, var_sorted_indices: T.handle, var_sample_indices: T.handle, var_sampling_results: T.handle, var_top_prob_offsets: T.handle, var_sampled_values: T.handle, var_top_prob_probs: T.handle, var_top_prob_indices: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32})}) + batch_size, vocab_size = T.int32(is_size_var=True), T.int32(is_size_var=True) + unsorted_probs = T.match_buffer(var_unsorted_probs, (batch_size, vocab_size)) + sorted_indices = T.match_buffer(var_sorted_indices, (batch_size, vocab_size), "int32") + num_samples = T.int32(is_size_var=True) + sample_indices = T.match_buffer(var_sample_indices, (num_samples,), "int32") + sampling_results = T.match_buffer(var_sampling_results, (num_samples,), "int32") + num_positions = T.int32(is_size_var=True) + top_prob_offsets = T.match_buffer(var_top_prob_offsets, (num_positions,), "int32") + sampled_values = T.match_buffer(var_sampled_values, (num_samples,)) + top_prob_probs = T.match_buffer(var_top_prob_probs, (num_positions,)) + top_prob_indices = T.match_buffer(var_top_prob_indices, (num_positions,), "int32") + # with T.block("root"): + for i in range(num_positions + num_samples): + with T.block("block"): + vi = T.axis.spatial(num_positions + num_samples, i) + T.reads(top_prob_offsets[vi], sorted_indices[top_prob_offsets[vi] // vocab_size, top_prob_offsets[vi] % vocab_size], unsorted_probs[T.min(top_prob_offsets[vi] // vocab_size, sample_indices[vi - num_positions]):T.min(top_prob_offsets[vi] // vocab_size, sample_indices[vi - num_positions]) + (T.max(top_prob_offsets[vi] // vocab_size, sample_indices[vi - num_positions]) + 1 - T.min(top_prob_offsets[vi] // vocab_size, sample_indices[vi - num_positions])), T.min(sorted_indices[top_prob_offsets[vi] // vocab_size, top_prob_offsets[vi] % vocab_size], sampling_results[vi - num_positions]):T.min(sorted_indices[top_prob_offsets[vi] // vocab_size, top_prob_offsets[vi] % vocab_size], sampling_results[vi - num_positions]) + (T.max(sorted_indices[top_prob_offsets[vi] // vocab_size, top_prob_offsets[vi] % vocab_size], sampling_results[vi - num_positions]) + 1 - T.min(sorted_indices[top_prob_offsets[vi] // vocab_size, top_prob_offsets[vi] % vocab_size], sampling_results[vi - num_positions]))], sample_indices[vi - num_positions], sampling_results[vi - num_positions]) + T.writes(top_prob_indices[vi], top_prob_probs[vi], sampled_values[vi - num_positions]) + if vi < num_positions: + row: T.int32 = top_prob_offsets[vi] // vocab_size + col: T.int32 = top_prob_offsets[vi] % vocab_size + top_prob_indices[vi] = sorted_indices[row, col] + top_prob_probs[vi] = unsorted_probs[row, sorted_indices[row, col]] + else: + vj: T.int32 = vi - num_positions + sampled_values[vj] = unsorted_probs[sample_indices[vj], sampling_results[vj]] + + @T.prim_func + def scatter_probs(var_src: T.handle, var_indices: T.handle, var_dst: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.noalias": T.bool(True)}) + batch_size, n = T.int32(is_size_var=True), T.int32(is_size_var=True) + src = T.match_buffer(var_src, (batch_size, n)) + indices = T.match_buffer(var_indices, (batch_size,), "int32") + m = T.int32(is_size_var=True) + dst = T.match_buffer(var_dst, (m, n)) + # with T.block("root"): + for b, j in T.grid(batch_size, n): + with T.block("scatter_2d"): + vb, vj = T.axis.remap("SS", [b, j]) + T.reads(src[vb, vj], indices[vb]) + T.writes(dst[indices[vb], vj]) + dst[indices[vb], vj] = src[vb, vj] + + @T.prim_func + def softmax_with_chunked_sum(var_A: T.handle, var_temperature: T.handle, var_chunked_sum: T.handle, var_chunked_max: T.handle, var_softmax: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + batch_size, vocab_size = T.int64(is_size_var=True), T.int64(is_size_var=True) + A = T.match_buffer(var_A, (batch_size, vocab_size)) + temperature = T.match_buffer(var_temperature, (batch_size,)) + num_chunks = T.int64(is_size_var=True) + chunked_sum = T.match_buffer(var_chunked_sum, (batch_size, num_chunks)) + chunked_max = T.match_buffer(var_chunked_max, (batch_size, num_chunks)) + softmax = T.match_buffer(var_softmax, (batch_size, vocab_size)) + # with T.block("root"): + temp_max_shared = T.alloc_buffer((batch_size,), scope="shared") + temp_sum_shared = T.alloc_buffer((batch_size,), scope="shared") + for l0_l1_fused in T.thread_binding(batch_size * num_chunks, thread="blockIdx.x"): + for ax0_1 in T.thread_binding(T.int64(32), thread="threadIdx.x"): + for ax0_0 in T.serial((num_chunks + T.int64(31)) // T.int64(32), annotations={"pragma_auto_unroll_max_step": 64, "pragma_unroll_explicit": 1}): + with T.block("max"): + v0 = T.axis.spatial(batch_size, l0_l1_fused % (num_chunks * batch_size) // num_chunks) + v1 = T.axis.reduce(num_chunks, ax0_0 * T.int64(32) + ax0_1) + T.where(ax0_0 * T.int64(32) + ax0_1 < num_chunks) + T.reads(chunked_max[v0, v1]) + T.writes(temp_max_shared[v0]) + with T.init(): + temp_max_shared[v0] = T.float32(-3.4028234663852886e+38) + temp_max_shared[v0] = T.max(temp_max_shared[v0], chunked_max[v0, v1]) + for ax0_1 in T.thread_binding(T.int64(32), thread="threadIdx.x"): + for ax0_0 in T.serial((num_chunks + T.int64(31)) // T.int64(32), annotations={"pragma_auto_unroll_max_step": 64, "pragma_unroll_explicit": 1}): + with T.block("sum_exp"): + v0 = T.axis.spatial(batch_size, l0_l1_fused % (num_chunks * batch_size) // num_chunks) + v1 = T.axis.reduce(num_chunks, ax0_0 * T.int64(32) + ax0_1) + T.where(ax0_0 * T.int64(32) + ax0_1 < num_chunks) + T.reads(temperature[v0], chunked_sum[v0, v1], chunked_max[v0, v1], temp_max_shared[v0]) + T.writes(temp_sum_shared[v0]) + with T.init(): + temp_sum_shared[v0] = T.float32(0) + temp_sum_shared[v0] = temp_sum_shared[v0] + T.Select(temperature[v0] > T.float32(1.0000000000000001e-05), T.exp(chunked_sum[v0, v1] + chunked_max[v0, v1] - temp_max_shared[v0]), T.Cast("float32", chunked_max[v0, v1] == temp_max_shared[v0]) * chunked_sum[v0, v1]) + for l2_0 in T.serial(T.int64(4), annotations={"pragma_auto_unroll_max_step": 64, "pragma_unroll_explicit": 1}): + for l2_1 in T.thread_binding(T.int64(32), thread="threadIdx.y"): + for l2_2 in T.thread_binding(T.int64(32), thread="threadIdx.x"): + with T.block("log_pad"): + v0 = T.axis.spatial(batch_size, l0_l1_fused % (num_chunks * batch_size) // num_chunks) + v1 = T.axis.spatial(num_chunks, l0_l1_fused % num_chunks) + v2 = T.axis.spatial(T.int64(4096), l2_0 * T.int64(1024) + l2_1 * T.int64(32) + l2_2) + T.reads(temperature[v0], A[v0, v1 * T.int64(4096) + v2], temp_sum_shared[v0], temp_max_shared[v0]) + T.writes(softmax[v0, v1 * T.int64(4096) + v2]) + if v1 * T.int64(4096) + v2 < vocab_size: + softmax[v0, v1 * T.int64(4096) + v2] = T.if_then_else(temperature[v0] > T.float32(1.0000000000000001e-05), T.exp(A[v0, v1 * T.int64(4096) + v2] / temperature[v0] - (T.log(temp_sum_shared[v0]) + temp_max_shared[v0])), T.Cast("float32", A[v0, v1 * T.int64(4096) + v2] == temp_max_shared[v0]) / temp_sum_shared[v0]) + + @T.prim_func(private=True) + def take_sorted_probs(var_probs: T.handle, var_lv1: T.handle, var_take_sorted_probs: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.noalias": T.bool(True)}) + batch_size, vocab_size = T.int64(), T.int64() + probs = T.match_buffer(var_probs, (batch_size, vocab_size)) + lv1 = T.match_buffer(var_lv1, (batch_size, vocab_size), "int32") + batch_size_1, vocab_size_1 = T.int64(), T.int64() + take_sorted_probs = T.match_buffer(var_take_sorted_probs, (batch_size_1, vocab_size_1)) + # with T.block("root"): + for i, j in T.grid(batch_size_1, vocab_size_1): + with T.block("take_sorted_probs"): + v_i, v_j = T.axis.remap("SS", [i, j]) + T.reads(probs[v_i, lv1[v_i, v_j]], lv1[v_i, v_j]) + T.writes(take_sorted_probs[v_i, v_j]) + take_sorted_probs[v_i, v_j] = probs[v_i, lv1[v_i, v_j]] + + @T.prim_func + def tir_kv_cache_debug_get_kv(var_pages: T.handle, var_position_map: T.handle, var_k_data: T.handle, var_v_data: T.handle, layer_id: T.int64): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.noalias": T.bool(True)}) + num_pages, page_size = T.int64(), T.int64(is_size_var=True) + pages = T.match_buffer(var_pages, (num_pages, 2, 20, page_size, 64), "float16") + seqlen = T.int64(is_size_var=True) + position_map = T.match_buffer(var_position_map, (seqlen,), "int32", offset_factor=1) + k_data = T.match_buffer(var_k_data, (32, seqlen, 20, 64), "float16") + v_data = T.match_buffer(var_v_data, (32, seqlen, 20, 64), "float16") + # with T.block("root"): + for p, h, d in T.grid(seqlen, 20, 64): + with T.block("copy0"): + vp, vh, vd = T.axis.remap("SSS", [p, h, d]) + T.reads(position_map[vp], pages[T.Cast("int64", position_map[vp]) // page_size, 0:2, vh, T.Cast("int64", position_map[vp]) % page_size, vd]) + T.writes(k_data[layer_id, vp, vh, vd], v_data[layer_id, vp, vh, vd]) + position: T.int32 = position_map[vp] + k_data[layer_id, vp, vh, vd] = pages[T.Cast("int64", position) // page_size, 0, vh, T.Cast("int64", position) % page_size, vd] + v_data[layer_id, vp, vh, vd] = pages[T.Cast("int64", position) // page_size, 1, vh, T.Cast("int64", position) % page_size, vd] + + @T.prim_func + def tir_kv_cache_transpose_append(var_pages: T.handle, var_k_data: T.handle, var_v_data: T.handle, var_position_map: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.noalias": T.bool(True)}) + num_pages = T.int64() + pages = T.match_buffer(var_pages, (num_pages, 2, 20, 16, 64), "float16") + ntoken = T.int64(is_size_var=True) + k_data = T.match_buffer(var_k_data, (ntoken, 20, 64), "float16") + v_data = T.match_buffer(var_v_data, (ntoken, 20, 64), "float16") + position_map = T.match_buffer(var_position_map, (ntoken,), "int32", offset_factor=1) + # with T.block("root"): + for global_pos, h, f in T.grid(ntoken, 20, 64): + if position_map[global_pos] != -1: + with T.block("k_transpose_append"): + vgpos, vh, vf = T.axis.remap("SSS", [global_pos, h, f]) + T.reads(position_map[vgpos], k_data[vgpos, vh, vf]) + T.writes(pages[position_map[vgpos] // 16, 0, vh, position_map[vgpos] % 16, vf]) + position: T.int32 = position_map[vgpos] + pages[position // 16, 0, vh, position % 16, vf] = k_data[vgpos, vh, vf] + with T.block("v_transpose_append"): + vgpos, vh, vf = T.axis.remap("SSS", [global_pos, h, f]) + T.reads(position_map[vgpos], v_data[vgpos, vh, vf]) + T.writes(pages[position_map[vgpos] // 16, 1, vh, position_map[vgpos] % 16, vf]) + position: T.int32 = position_map[vgpos] + pages[position // 16, 1, vh, position % 16, vf] = v_data[vgpos, vh, vf] + + @T.prim_func(private=True) + def top_p_pivot_cutoff(var_prob: T.handle, var_top_p_arr: T.handle, var_init_pivots: T.handle, var_final_pivot: T.handle, var_final_lsum: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + B, N = T.int32(), T.int32() + prob = T.match_buffer(var_prob, (B, N)) + top_p_arr = T.match_buffer(var_top_p_arr, (B,)) + init_pivots = T.match_buffer(var_init_pivots, (B, 3)) + final_pivot = T.match_buffer(var_final_pivot, (B,)) + final_lsum = T.match_buffer(var_final_lsum, (B,)) + # with T.block("root"): + pivot = T.alloc_buffer((3,), scope="local") + top_p = T.alloc_buffer((1,), scope="local") + L = T.alloc_buffer((1,), scope="shared") + R_1 = T.alloc_buffer((1,), scope="shared") + L_local = T.alloc_buffer((1,), scope="local") + R_local = T.alloc_buffer((1,), scope="local") + q = T.alloc_buffer((1,), scope="local") + lsum = T.alloc_buffer((3,), scope="local") + lmin_broadcast = T.alloc_buffer((1,), scope="shared") + lmin_broadcast_local = T.alloc_buffer((1,), scope="local") + lmin = T.alloc_buffer((3,), scope="local") + cmin = T.alloc_buffer((3,), "int32", scope="local") + total_sum = T.alloc_buffer((1,), scope="local") + it = T.alloc_buffer((1,), "int32", scope="local") + es_local = T.alloc_buffer((1,), "bool", scope="local") + es = T.alloc_buffer((1,), "bool", scope="shared") + find_pivot_local = T.alloc_buffer((1,), "bool", scope="local") + find_pivot = T.alloc_buffer((1,), "bool", scope="shared") + total_sum_reduce = T.alloc_buffer((1,), scope="local") + lsum_reduce = T.alloc_buffer((1,), scope="local") + lmin_reduce = T.alloc_buffer((1,), scope="local") + cmin_reduce = T.alloc_buffer((1,), "int32", scope="local") + for _bx in T.thread_binding(B, thread="blockIdx.x"): + for _tx in T.thread_binding(1024, thread="threadIdx.x"): + with T.block("CTA"): + b, tx = T.axis.remap("SS", [_bx, _tx]) + T.reads(top_p_arr[b], top_p[0], L[0], R_1[0], init_pivots[b, 0:3], L_local[0], R_local[0], find_pivot_local[0], it[0], es_local[0], prob[b, it[0] * 1024 + tx], total_sum[0], q[0], pivot[T.min(0, it[0]):T.min(0, it[0]) + (T.max(2, it[0]) + 1 - T.min(0, it[0]))], lsum[T.min(0, it[0]):T.min(0, it[0]) + (T.max(2, it[0]) + 1 - T.min(0, it[0]))], lmin[T.min(0, it[0]):T.min(0, it[0]) + (T.max(2, it[0]) + 1 - T.min(0, it[0]))], cmin[T.min(0, it[0]):T.min(0, it[0]) + (T.max(2, it[0]) + 1 - T.min(0, it[0]))], total_sum_reduce[0], es[0], lmin_reduce[0], lmin_broadcast[0], lmin_broadcast_local[0], lsum_reduce[0], cmin_reduce[0], find_pivot[0]) + T.writes(top_p[0], L[0], R_1[0], find_pivot[0], L_local[0], R_local[0], pivot[0:3], find_pivot_local[0], final_lsum[b], final_pivot[b], lsum[0:3], lmin[0:3], cmin[0:3], total_sum[0], it[0], es_local[0], q[0], total_sum_reduce[0], es[0], lsum_reduce[0], lmin_reduce[0], lmin_broadcast[0], lmin_broadcast_local[0], cmin_reduce[0]) + top_p[0] = top_p_arr[b] + if tx == 0: + L[0] = T.float32(1) - top_p[0] + R_1[0] = T.float32(9.9999999999999995e-08) + find_pivot[0] = T.bool(False) + T.tvm_storage_sync("shared") + L_local[0] = L[0] + R_local[0] = R_1[0] + for i in T.unroll(3): + pivot[i] = init_pivots[b, i] + find_pivot_local[0] = T.bool(False) + if L_local[0] - R_local[0] <= T.float32(9.9999999999999995e-08): + if tx == 0: + final_lsum[b] = T.float32(1) + final_pivot[b] = T.float32(0) + find_pivot_local[0] = T.bool(True) + while T.tvm_thread_invariant(L_local[0] - R_local[0] > T.float32(9.9999999999999995e-08) and not find_pivot_local[0]): + T.tvm_storage_sync("shared") + for pidx in T.unroll(3): + lsum[pidx] = T.float32(0) + lmin[pidx] = T.float32(3.4028234663852886e+38) + cmin[pidx] = 0 + total_sum[0] = T.float32(0) + it[0] = 0 + es_local[0] = T.bool(False) + while it[0] < (N + 1024 - 1) // 1024 and not es_local[0]: + q[0] = T.if_then_else(it[0] * 1024 + tx < N, prob[b, it[0] * 1024 + tx], T.float32(0)) + total_sum[0] = total_sum[0] + q[0] + for pidx in T.unroll(3): + if q[0] >= pivot[pidx]: + lsum[pidx] = lsum[pidx] + q[0] + if lmin[pidx] > q[0]: + lmin[pidx] = q[0] + cmin[pidx] = 1 + else: + if lmin[pidx] == q[0]: + cmin[pidx] = cmin[pidx] + 1 + it[0] = it[0] + 1 + if it[0] % 32 == 0: + with T.block("block_cross_thread"): + T.reads(total_sum[0]) + T.writes(total_sum_reduce[0]) + T.attr(T.comm_reducer(lambda x0, y0: x0 + y0, [T.float32(0)]), "reduce_scope", T.reinterpret("handle", T.uint64(0))) + T.tvm_thread_allreduce(T.uint32(1), total_sum[0], T.bool(True), total_sum_reduce[0], tx) + if tx == 0: + es[0] = T.float32(1) - total_sum_reduce[0] < pivot[2] + T.tvm_storage_sync("shared") + es_local[0] = es[0] + T.tvm_storage_sync("shared") + for pidx in range(3): + with T.block("block_cross_thread"): + T.reads(lsum[pidx]) + T.writes(lsum_reduce[0]) + T.attr(T.comm_reducer(lambda x0, y0: x0 + y0, [T.float32(0)]), "reduce_scope", T.reinterpret("handle", T.uint64(0))) + T.tvm_thread_allreduce(T.uint32(1), lsum[pidx], T.bool(True), lsum_reduce[0], tx) + with T.block("block_cross_thread"): + T.reads(lmin[pidx]) + T.writes(lmin_reduce[0]) + T.attr(T.comm_reducer(lambda x0, y0: T.min(x0, y0), [T.float32(0)]), "reduce_scope", T.reinterpret("handle", T.uint64(0))) + T.tvm_thread_allreduce(T.uint32(1), lmin[pidx], T.bool(True), lmin_reduce[0], tx) + if tx == 0: + lmin_broadcast[0] = lmin_reduce[0] + T.tvm_storage_sync("shared") + lmin_broadcast_local[0] = lmin_broadcast[0] + if lmin[pidx] > lmin_broadcast_local[0]: + cmin[pidx] = 0 + if tx == 0: + lsum[pidx] = lsum_reduce[0] + lmin[pidx] = lmin_reduce[0] + with T.block("block_cross_thread"): + T.reads(cmin[pidx]) + T.writes(cmin_reduce[0]) + T.attr(T.comm_reducer(lambda x0, y0: x0 + y0, [0]), "reduce_scope", T.reinterpret("handle", T.uint64(0))) + T.tvm_thread_allreduce(T.uint32(1), cmin[pidx], T.bool(True), cmin_reduce[0], tx) + if tx == 0: + cmin[pidx] = cmin_reduce[0] + T.tvm_storage_sync("shared") + if tx == 0: + it[0] = 0 + while it[0] < 3 and not find_pivot_local[0]: + if lsum[it[0]] >= top_p[0] and top_p[0] > lsum[it[0]] - T.Cast("float32", cmin[it[0]]) * lmin[it[0]]: + find_pivot[0] = T.bool(True) + find_pivot_local[0] = T.bool(True) + final_pivot[b] = pivot[it[0]] + final_lsum[b] = lsum[it[0]] + else: + if lsum[it[0]] - lmin[it[0]] * T.Cast("float32", cmin[it[0]]) >= top_p[0]: + R_1[0] = pivot[it[0]] + final_lsum[b] = lsum[it[0]] + else: + if lsum[it[0]] < top_p[0]: + L[0] = pivot[it[0]] + it[0] = it[0] + 1 + T.tvm_storage_sync("shared") + L_local[0] = L[0] + R_local[0] = R_1[0] + find_pivot_local[0] = find_pivot[0] + for pidx in T.unroll(3): + pivot[pidx] = L[0] - T.Cast("float32", pidx + 1) * (L_local[0] - R_local[0]) / T.float32(4) + if tx == 0: + if not find_pivot_local[0]: + final_pivot[b] = R_local[0] + if R_local[0] == T.float32(9.9999999999999995e-08): + final_lsum[b] = lsum[2] + + @T.prim_func(private=True) + def top_p_renorm_after_cutoff(var_prob: T.handle, var_final_pivot: T.handle, var_final_lsum: T.handle, var_renorm_prob: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + B, N = T.int32(), T.int32() + prob = T.match_buffer(var_prob, (B, N)) + final_pivot = T.match_buffer(var_final_pivot, (B,)) + final_lsum = T.match_buffer(var_final_lsum, (B,)) + renorm_prob = T.match_buffer(var_renorm_prob, (B, N)) + # with T.block("root"): + pivot = T.alloc_buffer((1,), scope="local") + lsum = T.alloc_buffer((1,), scope="local") + for _by in T.thread_binding(B, thread="blockIdx.y"): + for _bx in T.thread_binding((B + 511) // B, thread="blockIdx.x"): + for _tx in T.thread_binding(1024, thread="threadIdx.x"): + with T.block("CTA"): + by, bx, tx = T.axis.remap("SSS", [_by, _bx, _tx]) + T.reads(final_pivot[by], final_lsum[by], prob[by, T.Select(0 <= (B + 511) // B, 0, (((B + 511) // B * 1024 + N - 1) // ((B + 511) // B * 1024) - 1) * ((B + 511) // B)) * 1024 + bx * 1024 + tx:T.Select(0 <= (B + 511) // B, 0, (((B + 511) // B * 1024 + N - 1) // ((B + 511) // B * 1024) - 1) * ((B + 511) // B)) * 1024 + bx * 1024 + tx + (T.Select(0 <= (B + 511) // B, (N - 1) // ((B + 511) // B * 1024) * ((B + 511) // B), 0 - (((B + 511) // B * 1024 + N - 1) // ((B + 511) // B * 1024) - 1) * ((B + 511) // B)) * 1024 + 1)], pivot[0], lsum[0]) + T.writes(pivot[0], lsum[0], renorm_prob[by, T.Select(0 <= (B + 511) // B, 0, (((B + 511) // B * 1024 + N - 1) // ((B + 511) // B * 1024) - 1) * ((B + 511) // B)) * 1024 + bx * 1024 + tx:T.Select(0 <= (B + 511) // B, 0, (((B + 511) // B * 1024 + N - 1) // ((B + 511) // B * 1024) - 1) * ((B + 511) // B)) * 1024 + bx * 1024 + tx + (T.Select(0 <= (B + 511) // B, (N - 1) // ((B + 511) // B * 1024) * ((B + 511) // B), 0 - (((B + 511) // B * 1024 + N - 1) // ((B + 511) // B * 1024) - 1) * ((B + 511) // B)) * 1024 + 1)]) + pivot[0] = final_pivot[by] + lsum[0] = final_lsum[by] + for i in range(((B + 511) // B * 1024 + N - 1) // ((B + 511) // B * 1024)): + if i * ((512 + B - 1) // B) * 1024 + bx * 1024 + tx < N: + renorm_prob[by, i * ((512 + B - 1) // B) * 1024 + bx * 1024 + tx] = T.if_then_else(prob[by, i * ((512 + B - 1) // B) * 1024 + bx * 1024 + tx] >= pivot[0], prob[by, i * ((512 + B - 1) // B) * 1024 + bx * 1024 + tx] / lsum[0], T.float32(0)) + + @R.function + def argsort_probs(probs: R.Tensor(("batch_size", "vocab_size"), dtype="float32")) -> R.Tuple(R.Tensor(("batch_size", "vocab_size"), dtype="float32"), R.Tensor(("batch_size", "vocab_size"), dtype="int32")): + batch_size = T.int64() + vocab_size = T.int64() + R.func_attr({"relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "num_positions": 48, "num_samples": 8}}) + cls = Module + with R.dataflow(): + lv1: R.Tensor((batch_size, vocab_size), dtype="int32") = R.argsort(probs, axis=-1, descending=True, dtype="int32") + lv2 = R.call_tir(cls.take_sorted_probs, (probs, lv1), out_sinfo=R.Tensor((batch_size, vocab_size), dtype="float32")) + gv1: R.Tuple(R.Tensor((batch_size, vocab_size), dtype="float32"), R.Tensor((batch_size, vocab_size), dtype="int32")) = lv2, lv1 + R.output(gv1) + return gv1 + + @R.function + def batch_compute_cross_attn_kv(encoder_hidden_states: R.Tensor(("batch_size", 1500, 1280), dtype="float16"), paged_kv_cache: R.Object, packed_params: 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R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"))) -> R.Object: + batch_size = T.int64() + R.func_attr({"num_input": 2, "relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "seq_len": 15000, "total_seq_len": 1500}}) + with R.dataflow(): + model_decoder_layers_0_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[498] + model_decoder_layers_0_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[499] + model_decoder_layers_0_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[500] + model_decoder_layers_1_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[522] + model_decoder_layers_1_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[523] + model_decoder_layers_1_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[524] + model_decoder_layers_2_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[546] + model_decoder_layers_2_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[547] + model_decoder_layers_2_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[548] + model_decoder_layers_3_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[570] + model_decoder_layers_3_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[571] + model_decoder_layers_3_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[572] + model_decoder_layers_4_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[594] + model_decoder_layers_4_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[595] + model_decoder_layers_4_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[596] + model_decoder_layers_5_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[618] + model_decoder_layers_5_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[619] + model_decoder_layers_5_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[620] + model_decoder_layers_6_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[642] + model_decoder_layers_6_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[643] + model_decoder_layers_6_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[644] + model_decoder_layers_7_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[666] + model_decoder_layers_7_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[667] + model_decoder_layers_7_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[668] + model_decoder_layers_8_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[690] + model_decoder_layers_8_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[691] + model_decoder_layers_8_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[692] + model_decoder_layers_9_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[714] + model_decoder_layers_9_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[715] + model_decoder_layers_9_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[716] + model_decoder_layers_10_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[738] + model_decoder_layers_10_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[739] + model_decoder_layers_10_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[740] + model_decoder_layers_11_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[762] + model_decoder_layers_11_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[763] + model_decoder_layers_11_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[764] + model_decoder_layers_12_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[786] + model_decoder_layers_12_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[787] + model_decoder_layers_12_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[788] + model_decoder_layers_13_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[810] + model_decoder_layers_13_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[811] + model_decoder_layers_13_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[812] + model_decoder_layers_14_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[834] + model_decoder_layers_14_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[835] + model_decoder_layers_14_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[836] + model_decoder_layers_15_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[858] + model_decoder_layers_15_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[859] + model_decoder_layers_15_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[860] + model_decoder_layers_16_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[882] + model_decoder_layers_16_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[883] + model_decoder_layers_16_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[884] + model_decoder_layers_17_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[906] + model_decoder_layers_17_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[907] + model_decoder_layers_17_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[908] + model_decoder_layers_18_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[930] + model_decoder_layers_18_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[931] + model_decoder_layers_18_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[932] + model_decoder_layers_19_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[954] + model_decoder_layers_19_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[955] + model_decoder_layers_19_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[956] + model_decoder_layers_20_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[978] + model_decoder_layers_20_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[979] + model_decoder_layers_20_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[980] + model_decoder_layers_21_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1002] + model_decoder_layers_21_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1003] + model_decoder_layers_21_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1004] + model_decoder_layers_22_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1026] + model_decoder_layers_22_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1027] + model_decoder_layers_22_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1028] + model_decoder_layers_23_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1050] + model_decoder_layers_23_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1051] + model_decoder_layers_23_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1052] + model_decoder_layers_24_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1074] + model_decoder_layers_24_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1075] + model_decoder_layers_24_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1076] + model_decoder_layers_25_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1098] + model_decoder_layers_25_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1099] + model_decoder_layers_25_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1100] + model_decoder_layers_26_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1122] + model_decoder_layers_26_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1123] + model_decoder_layers_26_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1124] + model_decoder_layers_27_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1146] + model_decoder_layers_27_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1147] + model_decoder_layers_27_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1148] + model_decoder_layers_28_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1170] + model_decoder_layers_28_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1171] + model_decoder_layers_28_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1172] + model_decoder_layers_29_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1194] + model_decoder_layers_29_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1195] + model_decoder_layers_29_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1196] + model_decoder_layers_30_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1218] + model_decoder_layers_30_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1219] + model_decoder_layers_30_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1220] + model_decoder_layers_31_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1242] + model_decoder_layers_31_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1243] + model_decoder_layers_31_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1244] + lv = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_0_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape256: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv, R.shape([batch_size, 1500, 20, 64])) + lv_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_0_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_0_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape257: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv_1, R.shape([batch_size, 1500, 20, 64])) + reshape258: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape256, R.shape([batch_size * 1500, 20, 64])) + reshape259: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape257, R.shape([batch_size * 1500, 20, 64])) + lv36: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", paged_kv_cache, R.prim_value(0), reshape258, reshape259, sinfo_args=(R.Object,)) + lv1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_1_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape260: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv1, R.shape([batch_size, 1500, 20, 64])) + lv1_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_1_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_1_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape261: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv1_1, R.shape([batch_size, 1500, 20, 64])) + reshape262: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape260, R.shape([batch_size * 1500, 20, 64])) + reshape263: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape261, R.shape([batch_size * 1500, 20, 64])) + lv37: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv36, R.prim_value(1), reshape262, reshape263, sinfo_args=(R.Object,)) + lv2 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_2_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape264: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv2, R.shape([batch_size, 1500, 20, 64])) + lv2_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_2_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_2_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape265: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv2_1, R.shape([batch_size, 1500, 20, 64])) + reshape266: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape264, R.shape([batch_size * 1500, 20, 64])) + reshape267: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape265, R.shape([batch_size * 1500, 20, 64])) + lv38: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv37, R.prim_value(2), reshape266, reshape267, sinfo_args=(R.Object,)) + lv3 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_3_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape268: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv3, R.shape([batch_size, 1500, 20, 64])) + lv3_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_3_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_3_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape269: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv3_1, R.shape([batch_size, 1500, 20, 64])) + reshape270: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape268, R.shape([batch_size * 1500, 20, 64])) + reshape271: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape269, R.shape([batch_size * 1500, 20, 64])) + lv39: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv38, R.prim_value(3), reshape270, reshape271, sinfo_args=(R.Object,)) + lv4 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_4_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape272: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv4, R.shape([batch_size, 1500, 20, 64])) + lv4_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_4_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_4_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape273: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv4_1, R.shape([batch_size, 1500, 20, 64])) + reshape274: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape272, R.shape([batch_size * 1500, 20, 64])) + reshape275: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape273, R.shape([batch_size * 1500, 20, 64])) + lv40: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv39, R.prim_value(4), reshape274, reshape275, sinfo_args=(R.Object,)) + lv5 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_5_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape276: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv5, R.shape([batch_size, 1500, 20, 64])) + lv5_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_5_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_5_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape277: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv5_1, R.shape([batch_size, 1500, 20, 64])) + reshape278: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape276, R.shape([batch_size * 1500, 20, 64])) + reshape279: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape277, R.shape([batch_size * 1500, 20, 64])) + lv41: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv40, R.prim_value(5), reshape278, reshape279, sinfo_args=(R.Object,)) + lv6 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_6_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape280: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv6, R.shape([batch_size, 1500, 20, 64])) + lv6_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_6_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_6_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape281: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv6_1, R.shape([batch_size, 1500, 20, 64])) + reshape282: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape280, R.shape([batch_size * 1500, 20, 64])) + reshape283: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape281, R.shape([batch_size * 1500, 20, 64])) + lv42: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv41, R.prim_value(6), reshape282, reshape283, sinfo_args=(R.Object,)) + lv7 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_7_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape284: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv7, R.shape([batch_size, 1500, 20, 64])) + lv7_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_7_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_7_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape285: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv7_1, R.shape([batch_size, 1500, 20, 64])) + reshape286: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape284, R.shape([batch_size * 1500, 20, 64])) + reshape287: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape285, R.shape([batch_size * 1500, 20, 64])) + lv43: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv42, R.prim_value(7), reshape286, reshape287, sinfo_args=(R.Object,)) + lv8 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_8_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape288: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv8, R.shape([batch_size, 1500, 20, 64])) + lv8_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_8_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_8_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape289: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv8_1, R.shape([batch_size, 1500, 20, 64])) + reshape290: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape288, R.shape([batch_size * 1500, 20, 64])) + reshape291: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape289, R.shape([batch_size * 1500, 20, 64])) + lv44: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv43, R.prim_value(8), reshape290, reshape291, sinfo_args=(R.Object,)) + lv9 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_9_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape292: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv9, R.shape([batch_size, 1500, 20, 64])) + lv9_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_9_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_9_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape293: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv9_1, R.shape([batch_size, 1500, 20, 64])) + reshape294: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape292, R.shape([batch_size * 1500, 20, 64])) + reshape295: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape293, R.shape([batch_size * 1500, 20, 64])) + lv45: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv44, R.prim_value(9), reshape294, reshape295, sinfo_args=(R.Object,)) + lv10 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_10_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape296: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv10, R.shape([batch_size, 1500, 20, 64])) + lv10_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_10_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_10_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape297: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv10_1, R.shape([batch_size, 1500, 20, 64])) + reshape298: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape296, R.shape([batch_size * 1500, 20, 64])) + reshape299: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape297, R.shape([batch_size * 1500, 20, 64])) + lv46: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv45, R.prim_value(10), reshape298, reshape299, sinfo_args=(R.Object,)) + lv11 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_11_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape300: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv11, R.shape([batch_size, 1500, 20, 64])) + lv11_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_11_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_11_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape301: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv11_1, R.shape([batch_size, 1500, 20, 64])) + reshape302: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape300, R.shape([batch_size * 1500, 20, 64])) + reshape303: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape301, R.shape([batch_size * 1500, 20, 64])) + lv47: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv46, R.prim_value(11), reshape302, reshape303, sinfo_args=(R.Object,)) + lv12 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_12_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape304: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv12, R.shape([batch_size, 1500, 20, 64])) + lv12_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_12_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_12_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape305: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv12_1, R.shape([batch_size, 1500, 20, 64])) + reshape306: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape304, R.shape([batch_size * 1500, 20, 64])) + reshape307: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape305, R.shape([batch_size * 1500, 20, 64])) + lv48: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv47, R.prim_value(12), reshape306, reshape307, sinfo_args=(R.Object,)) + lv13 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_13_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape308: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv13, R.shape([batch_size, 1500, 20, 64])) + lv13_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_13_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_13_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape309: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv13_1, R.shape([batch_size, 1500, 20, 64])) + reshape310: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape308, R.shape([batch_size * 1500, 20, 64])) + reshape311: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape309, R.shape([batch_size * 1500, 20, 64])) + lv49: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv48, R.prim_value(13), reshape310, reshape311, sinfo_args=(R.Object,)) + lv14 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_14_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape312: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv14, R.shape([batch_size, 1500, 20, 64])) + lv14_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_14_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_14_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape313: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv14_1, R.shape([batch_size, 1500, 20, 64])) + reshape314: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape312, R.shape([batch_size * 1500, 20, 64])) + reshape315: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape313, R.shape([batch_size * 1500, 20, 64])) + lv50: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv49, R.prim_value(14), reshape314, reshape315, sinfo_args=(R.Object,)) + lv15 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_15_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape316: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv15, R.shape([batch_size, 1500, 20, 64])) + lv15_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_15_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_15_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape317: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv15_1, R.shape([batch_size, 1500, 20, 64])) + reshape318: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape316, R.shape([batch_size * 1500, 20, 64])) + reshape319: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape317, R.shape([batch_size * 1500, 20, 64])) + lv51: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv50, R.prim_value(15), reshape318, reshape319, sinfo_args=(R.Object,)) + lv16 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_16_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape320: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv16, R.shape([batch_size, 1500, 20, 64])) + lv16_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_16_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_16_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape321: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv16_1, R.shape([batch_size, 1500, 20, 64])) + reshape322: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape320, R.shape([batch_size * 1500, 20, 64])) + reshape323: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape321, R.shape([batch_size * 1500, 20, 64])) + lv52: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv51, R.prim_value(16), reshape322, reshape323, sinfo_args=(R.Object,)) + lv17 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_17_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape324: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv17, R.shape([batch_size, 1500, 20, 64])) + lv17_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_17_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_17_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape325: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv17_1, R.shape([batch_size, 1500, 20, 64])) + reshape326: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape324, R.shape([batch_size * 1500, 20, 64])) + reshape327: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape325, R.shape([batch_size * 1500, 20, 64])) + lv53: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv52, R.prim_value(17), reshape326, reshape327, sinfo_args=(R.Object,)) + lv18 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_18_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape328: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv18, R.shape([batch_size, 1500, 20, 64])) + lv18_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_18_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_18_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape329: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv18_1, R.shape([batch_size, 1500, 20, 64])) + reshape330: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape328, R.shape([batch_size * 1500, 20, 64])) + reshape331: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape329, R.shape([batch_size * 1500, 20, 64])) + lv54: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv53, R.prim_value(18), reshape330, reshape331, sinfo_args=(R.Object,)) + lv19 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_19_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape332: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv19, R.shape([batch_size, 1500, 20, 64])) + lv19_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_19_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_19_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape333: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv19_1, R.shape([batch_size, 1500, 20, 64])) + reshape334: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape332, R.shape([batch_size * 1500, 20, 64])) + reshape335: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape333, R.shape([batch_size * 1500, 20, 64])) + lv55: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv54, R.prim_value(19), reshape334, reshape335, sinfo_args=(R.Object,)) + lv20 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_20_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape336: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv20, R.shape([batch_size, 1500, 20, 64])) + lv20_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_20_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_20_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape337: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv20_1, R.shape([batch_size, 1500, 20, 64])) + reshape338: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape336, R.shape([batch_size * 1500, 20, 64])) + reshape339: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape337, R.shape([batch_size * 1500, 20, 64])) + lv56: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv55, R.prim_value(20), reshape338, reshape339, sinfo_args=(R.Object,)) + lv21 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_21_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape340: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv21, R.shape([batch_size, 1500, 20, 64])) + lv21_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_21_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_21_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape341: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv21_1, R.shape([batch_size, 1500, 20, 64])) + reshape342: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape340, R.shape([batch_size * 1500, 20, 64])) + reshape343: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape341, R.shape([batch_size * 1500, 20, 64])) + lv57: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv56, R.prim_value(21), reshape342, reshape343, sinfo_args=(R.Object,)) + lv22 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_22_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape344: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv22, R.shape([batch_size, 1500, 20, 64])) + lv22_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_22_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_22_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape345: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv22_1, R.shape([batch_size, 1500, 20, 64])) + reshape346: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape344, R.shape([batch_size * 1500, 20, 64])) + reshape347: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape345, R.shape([batch_size * 1500, 20, 64])) + lv58: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv57, R.prim_value(22), reshape346, reshape347, sinfo_args=(R.Object,)) + lv23 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_23_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape348: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv23, R.shape([batch_size, 1500, 20, 64])) + lv23_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_23_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_23_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape349: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv23_1, R.shape([batch_size, 1500, 20, 64])) + reshape350: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape348, R.shape([batch_size * 1500, 20, 64])) + reshape351: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape349, R.shape([batch_size * 1500, 20, 64])) + lv59: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv58, R.prim_value(23), reshape350, reshape351, sinfo_args=(R.Object,)) + lv24 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_24_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape352: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv24, R.shape([batch_size, 1500, 20, 64])) + lv24_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_24_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_24_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape353: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv24_1, R.shape([batch_size, 1500, 20, 64])) + reshape354: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape352, R.shape([batch_size * 1500, 20, 64])) + reshape355: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape353, R.shape([batch_size * 1500, 20, 64])) + lv60: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv59, R.prim_value(24), reshape354, reshape355, sinfo_args=(R.Object,)) + lv25 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_25_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape356: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv25, R.shape([batch_size, 1500, 20, 64])) + lv25_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_25_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_25_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape357: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv25_1, R.shape([batch_size, 1500, 20, 64])) + reshape358: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape356, R.shape([batch_size * 1500, 20, 64])) + reshape359: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape357, R.shape([batch_size * 1500, 20, 64])) + lv61: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv60, R.prim_value(25), reshape358, reshape359, sinfo_args=(R.Object,)) + lv26 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_26_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape360: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv26, R.shape([batch_size, 1500, 20, 64])) + lv26_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_26_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_26_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape361: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv26_1, R.shape([batch_size, 1500, 20, 64])) + reshape362: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape360, R.shape([batch_size * 1500, 20, 64])) + reshape363: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape361, R.shape([batch_size * 1500, 20, 64])) + lv62: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv61, R.prim_value(26), reshape362, reshape363, sinfo_args=(R.Object,)) + lv27 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_27_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape364: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv27, R.shape([batch_size, 1500, 20, 64])) + lv27_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_27_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_27_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape365: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv27_1, R.shape([batch_size, 1500, 20, 64])) + reshape366: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape364, R.shape([batch_size * 1500, 20, 64])) + reshape367: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape365, R.shape([batch_size * 1500, 20, 64])) + lv63: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv62, R.prim_value(27), reshape366, reshape367, sinfo_args=(R.Object,)) + lv28 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_28_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape368: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv28, R.shape([batch_size, 1500, 20, 64])) + lv28_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_28_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_28_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape369: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv28_1, R.shape([batch_size, 1500, 20, 64])) + reshape370: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape368, R.shape([batch_size * 1500, 20, 64])) + reshape371: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape369, R.shape([batch_size * 1500, 20, 64])) + lv64: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv63, R.prim_value(28), reshape370, reshape371, sinfo_args=(R.Object,)) + lv29 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_29_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape372: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv29, R.shape([batch_size, 1500, 20, 64])) + lv29_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_29_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_29_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape373: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv29_1, R.shape([batch_size, 1500, 20, 64])) + reshape374: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape372, R.shape([batch_size * 1500, 20, 64])) + reshape375: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape373, R.shape([batch_size * 1500, 20, 64])) + lv65: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv64, R.prim_value(29), reshape374, reshape375, sinfo_args=(R.Object,)) + lv30 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_30_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape376: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv30, R.shape([batch_size, 1500, 20, 64])) + lv30_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_30_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_30_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape377: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv30_1, R.shape([batch_size, 1500, 20, 64])) + reshape378: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape376, R.shape([batch_size * 1500, 20, 64])) + reshape379: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape377, R.shape([batch_size * 1500, 20, 64])) + lv66: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv65, R.prim_value(30), reshape378, reshape379, sinfo_args=(R.Object,)) + lv31 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_31_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape380: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv31, R.shape([batch_size, 1500, 20, 64])) + lv31_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_31_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_31_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape381: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv31_1, R.shape([batch_size, 1500, 20, 64])) + reshape382: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape380, R.shape([batch_size * 1500, 20, 64])) + reshape383: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape381, R.shape([batch_size * 1500, 20, 64])) + lv67: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv66, R.prim_value(31), reshape382, reshape383, sinfo_args=(R.Object,)) + gv1: R.Object = lv67 + R.output(gv1) + return gv1 + + @R.function + def batch_decode(input_ids: R.Tensor(("batch_size", 1), dtype="int32"), paged_kv_cache: R.Object, packed_params: R.Tuple(R.Tensor((1280, 128, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1500, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), 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R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"))) -> R.Tensor(("batch_size", 1, 51866), dtype="float32"): + batch_size = T.int64() + R.func_attr({"num_input": 2, "relax.memory_plan_dynamic_func_output": 1, "relax.rewrite_cuda_graph.capture_symbolic_vars": ["batch_size"], "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "seq_len": 15000, "total_seq_len": 1500}}) + with R.dataflow(): + model_decoder_embed_tokens_weight3: R.Tensor((51866, 1280), dtype="float16") = packed_params[487] + model_decoder_embed_positions_weight3: R.Tensor((448, 1280), dtype="float16") = packed_params[488] + model_decoder_layers_0_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[489] + model_decoder_layers_0_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[490] + model_decoder_layers_0_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[491] + model_decoder_layers_0_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[492] + model_decoder_layers_0_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[493] + model_decoder_layers_0_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[494] + model_decoder_layers_0_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[495] + model_decoder_layers_0_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[496] + model_decoder_layers_0_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[497] + model_decoder_layers_0_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[501] + model_decoder_layers_0_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[502] + model_decoder_layers_0_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[503] + model_decoder_layers_0_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[504] + model_decoder_layers_0_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[505] + model_decoder_layers_0_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[506] + model_decoder_layers_0_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[507] + model_decoder_layers_0_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[508] + model_decoder_layers_0_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[509] + model_decoder_layers_0_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[510] + model_decoder_layers_0_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[511] + model_decoder_layers_0_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[512] + model_decoder_layers_1_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[513] + model_decoder_layers_1_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[514] + model_decoder_layers_1_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[515] + model_decoder_layers_1_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[516] + model_decoder_layers_1_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[517] + model_decoder_layers_1_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[518] + model_decoder_layers_1_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[519] + model_decoder_layers_1_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[520] + model_decoder_layers_1_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[521] + model_decoder_layers_1_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[525] + model_decoder_layers_1_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[526] + model_decoder_layers_1_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[527] + model_decoder_layers_1_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[528] + model_decoder_layers_1_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[529] + model_decoder_layers_1_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[530] + model_decoder_layers_1_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[531] + model_decoder_layers_1_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[532] + model_decoder_layers_1_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[533] + model_decoder_layers_1_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[534] + model_decoder_layers_1_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[535] + model_decoder_layers_1_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[536] + model_decoder_layers_2_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[537] + model_decoder_layers_2_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[538] + model_decoder_layers_2_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[539] + model_decoder_layers_2_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[540] + model_decoder_layers_2_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[541] + model_decoder_layers_2_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[542] + model_decoder_layers_2_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[543] + model_decoder_layers_2_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[544] + model_decoder_layers_2_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[545] + model_decoder_layers_2_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[549] + model_decoder_layers_2_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[550] + model_decoder_layers_2_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[551] + model_decoder_layers_2_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[552] + model_decoder_layers_2_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[553] + model_decoder_layers_2_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[554] + model_decoder_layers_2_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[555] + model_decoder_layers_2_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[556] + model_decoder_layers_2_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[557] + model_decoder_layers_2_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[558] + model_decoder_layers_2_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[559] + model_decoder_layers_2_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[560] + model_decoder_layers_3_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[561] + model_decoder_layers_3_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[562] + model_decoder_layers_3_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[563] + model_decoder_layers_3_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[564] + model_decoder_layers_3_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[565] + model_decoder_layers_3_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[566] + model_decoder_layers_3_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[567] + model_decoder_layers_3_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[568] + model_decoder_layers_3_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[569] + model_decoder_layers_3_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[573] + model_decoder_layers_3_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[574] + model_decoder_layers_3_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[575] + model_decoder_layers_3_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[576] + model_decoder_layers_3_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[577] + model_decoder_layers_3_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[578] + model_decoder_layers_3_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[579] + model_decoder_layers_3_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[580] + model_decoder_layers_3_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[581] + model_decoder_layers_3_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[582] + model_decoder_layers_3_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[583] + model_decoder_layers_3_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[584] + model_decoder_layers_4_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[585] + model_decoder_layers_4_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[586] + model_decoder_layers_4_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[587] + model_decoder_layers_4_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[588] + model_decoder_layers_4_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[589] + model_decoder_layers_4_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[590] + model_decoder_layers_4_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[591] + model_decoder_layers_4_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[592] + model_decoder_layers_4_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[593] + model_decoder_layers_4_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[597] + model_decoder_layers_4_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[598] + model_decoder_layers_4_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[599] + model_decoder_layers_4_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[600] + model_decoder_layers_4_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[601] + model_decoder_layers_4_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[602] + model_decoder_layers_4_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[603] + model_decoder_layers_4_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[604] + model_decoder_layers_4_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[605] + model_decoder_layers_4_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[606] + model_decoder_layers_4_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[607] + model_decoder_layers_4_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[608] + model_decoder_layers_5_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[609] + model_decoder_layers_5_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[610] + model_decoder_layers_5_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[611] + model_decoder_layers_5_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[612] + model_decoder_layers_5_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[613] + model_decoder_layers_5_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[614] + model_decoder_layers_5_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[615] + model_decoder_layers_5_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[616] + model_decoder_layers_5_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[617] + model_decoder_layers_5_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[621] + model_decoder_layers_5_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[622] + model_decoder_layers_5_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[623] + model_decoder_layers_5_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[624] + model_decoder_layers_5_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[625] + model_decoder_layers_5_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[626] + model_decoder_layers_5_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[627] + model_decoder_layers_5_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[628] + model_decoder_layers_5_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[629] + model_decoder_layers_5_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[630] + model_decoder_layers_5_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[631] + model_decoder_layers_5_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[632] + model_decoder_layers_6_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[633] + model_decoder_layers_6_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[634] + model_decoder_layers_6_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[635] + model_decoder_layers_6_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[636] + model_decoder_layers_6_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[637] + model_decoder_layers_6_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[638] + model_decoder_layers_6_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[639] + model_decoder_layers_6_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[640] + model_decoder_layers_6_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[641] + model_decoder_layers_6_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[645] + model_decoder_layers_6_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[646] + model_decoder_layers_6_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[647] + model_decoder_layers_6_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[648] + model_decoder_layers_6_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[649] + model_decoder_layers_6_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[650] + model_decoder_layers_6_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[651] + model_decoder_layers_6_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[652] + model_decoder_layers_6_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[653] + model_decoder_layers_6_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[654] + model_decoder_layers_6_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[655] + model_decoder_layers_6_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[656] + model_decoder_layers_7_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[657] + model_decoder_layers_7_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[658] + model_decoder_layers_7_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[659] + model_decoder_layers_7_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[660] + model_decoder_layers_7_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[661] + model_decoder_layers_7_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[662] + model_decoder_layers_7_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[663] + model_decoder_layers_7_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[664] + model_decoder_layers_7_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[665] + model_decoder_layers_7_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[669] + model_decoder_layers_7_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[670] + model_decoder_layers_7_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[671] + model_decoder_layers_7_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[672] + model_decoder_layers_7_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[673] + model_decoder_layers_7_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[674] + model_decoder_layers_7_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[675] + model_decoder_layers_7_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[676] + model_decoder_layers_7_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[677] + model_decoder_layers_7_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[678] + model_decoder_layers_7_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[679] + model_decoder_layers_7_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[680] + model_decoder_layers_8_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[681] + model_decoder_layers_8_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[682] + model_decoder_layers_8_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[683] + model_decoder_layers_8_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[684] + model_decoder_layers_8_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[685] + model_decoder_layers_8_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[686] + model_decoder_layers_8_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[687] + model_decoder_layers_8_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[688] + model_decoder_layers_8_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[689] + model_decoder_layers_8_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[693] + model_decoder_layers_8_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[694] + model_decoder_layers_8_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[695] + model_decoder_layers_8_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[696] + model_decoder_layers_8_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[697] + model_decoder_layers_8_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[698] + model_decoder_layers_8_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[699] + model_decoder_layers_8_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[700] + model_decoder_layers_8_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[701] + model_decoder_layers_8_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[702] + model_decoder_layers_8_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[703] + model_decoder_layers_8_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[704] + model_decoder_layers_9_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[705] + model_decoder_layers_9_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[706] + model_decoder_layers_9_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[707] + model_decoder_layers_9_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[708] + model_decoder_layers_9_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[709] + model_decoder_layers_9_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[710] + model_decoder_layers_9_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[711] + model_decoder_layers_9_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[712] + model_decoder_layers_9_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[713] + model_decoder_layers_9_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[717] + model_decoder_layers_9_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[718] + model_decoder_layers_9_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[719] + model_decoder_layers_9_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[720] + model_decoder_layers_9_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[721] + model_decoder_layers_9_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[722] + model_decoder_layers_9_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[723] + model_decoder_layers_9_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[724] + model_decoder_layers_9_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[725] + model_decoder_layers_9_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[726] + model_decoder_layers_9_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[727] + model_decoder_layers_9_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[728] + model_decoder_layers_10_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[729] + model_decoder_layers_10_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[730] + model_decoder_layers_10_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[731] + model_decoder_layers_10_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[732] + model_decoder_layers_10_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[733] + model_decoder_layers_10_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[734] + model_decoder_layers_10_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[735] + model_decoder_layers_10_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[736] + model_decoder_layers_10_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[737] + model_decoder_layers_10_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[741] + model_decoder_layers_10_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[742] + model_decoder_layers_10_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[743] + model_decoder_layers_10_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[744] + model_decoder_layers_10_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[745] + model_decoder_layers_10_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[746] + model_decoder_layers_10_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[747] + model_decoder_layers_10_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[748] + model_decoder_layers_10_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[749] + model_decoder_layers_10_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[750] + model_decoder_layers_10_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[751] + model_decoder_layers_10_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[752] + model_decoder_layers_11_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[753] + model_decoder_layers_11_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[754] + model_decoder_layers_11_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[755] + model_decoder_layers_11_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[756] + model_decoder_layers_11_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[757] + model_decoder_layers_11_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[758] + model_decoder_layers_11_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[759] + model_decoder_layers_11_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[760] + model_decoder_layers_11_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[761] + model_decoder_layers_11_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[765] + model_decoder_layers_11_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[766] + model_decoder_layers_11_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[767] + model_decoder_layers_11_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[768] + model_decoder_layers_11_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[769] + model_decoder_layers_11_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[770] + model_decoder_layers_11_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[771] + model_decoder_layers_11_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[772] + model_decoder_layers_11_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[773] + model_decoder_layers_11_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[774] + model_decoder_layers_11_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[775] + model_decoder_layers_11_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[776] + model_decoder_layers_12_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[777] + model_decoder_layers_12_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[778] + model_decoder_layers_12_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[779] + model_decoder_layers_12_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[780] + model_decoder_layers_12_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[781] + model_decoder_layers_12_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[782] + model_decoder_layers_12_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[783] + model_decoder_layers_12_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[784] + model_decoder_layers_12_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[785] + model_decoder_layers_12_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[789] + model_decoder_layers_12_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[790] + model_decoder_layers_12_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[791] + model_decoder_layers_12_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[792] + model_decoder_layers_12_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[793] + model_decoder_layers_12_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[794] + model_decoder_layers_12_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[795] + model_decoder_layers_12_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[796] + model_decoder_layers_12_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[797] + model_decoder_layers_12_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[798] + model_decoder_layers_12_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[799] + model_decoder_layers_12_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[800] + model_decoder_layers_13_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[801] + model_decoder_layers_13_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[802] + model_decoder_layers_13_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[803] + model_decoder_layers_13_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[804] + model_decoder_layers_13_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[805] + model_decoder_layers_13_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[806] + model_decoder_layers_13_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[807] + model_decoder_layers_13_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[808] + model_decoder_layers_13_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[809] + model_decoder_layers_13_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[813] + model_decoder_layers_13_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[814] + model_decoder_layers_13_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[815] + model_decoder_layers_13_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[816] + model_decoder_layers_13_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[817] + model_decoder_layers_13_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[818] + model_decoder_layers_13_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[819] + model_decoder_layers_13_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[820] + model_decoder_layers_13_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[821] + model_decoder_layers_13_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[822] + model_decoder_layers_13_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[823] + model_decoder_layers_13_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[824] + model_decoder_layers_14_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[825] + model_decoder_layers_14_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[826] + model_decoder_layers_14_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[827] + model_decoder_layers_14_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[828] + model_decoder_layers_14_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[829] + model_decoder_layers_14_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[830] + model_decoder_layers_14_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[831] + model_decoder_layers_14_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[832] + model_decoder_layers_14_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[833] + model_decoder_layers_14_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[837] + model_decoder_layers_14_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[838] + model_decoder_layers_14_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[839] + model_decoder_layers_14_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[840] + model_decoder_layers_14_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[841] + model_decoder_layers_14_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[842] + model_decoder_layers_14_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[843] + model_decoder_layers_14_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[844] + model_decoder_layers_14_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[845] + model_decoder_layers_14_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[846] + model_decoder_layers_14_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[847] + model_decoder_layers_14_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[848] + model_decoder_layers_15_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[849] + model_decoder_layers_15_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[850] + model_decoder_layers_15_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[851] + model_decoder_layers_15_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[852] + model_decoder_layers_15_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[853] + model_decoder_layers_15_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[854] + model_decoder_layers_15_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[855] + model_decoder_layers_15_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[856] + model_decoder_layers_15_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[857] + model_decoder_layers_15_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[861] + model_decoder_layers_15_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[862] + model_decoder_layers_15_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[863] + model_decoder_layers_15_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[864] + model_decoder_layers_15_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[865] + model_decoder_layers_15_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[866] + model_decoder_layers_15_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[867] + model_decoder_layers_15_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[868] + model_decoder_layers_15_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[869] + model_decoder_layers_15_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[870] + model_decoder_layers_15_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[871] + model_decoder_layers_15_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[872] + model_decoder_layers_16_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[873] + model_decoder_layers_16_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[874] + model_decoder_layers_16_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[875] + model_decoder_layers_16_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[876] + model_decoder_layers_16_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[877] + model_decoder_layers_16_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[878] + model_decoder_layers_16_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[879] + model_decoder_layers_16_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[880] + model_decoder_layers_16_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[881] + model_decoder_layers_16_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[885] + model_decoder_layers_16_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[886] + model_decoder_layers_16_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[887] + model_decoder_layers_16_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[888] + model_decoder_layers_16_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[889] + model_decoder_layers_16_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[890] + model_decoder_layers_16_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[891] + model_decoder_layers_16_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[892] + model_decoder_layers_16_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[893] + model_decoder_layers_16_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[894] + model_decoder_layers_16_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[895] + model_decoder_layers_16_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[896] + model_decoder_layers_17_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[897] + model_decoder_layers_17_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[898] + model_decoder_layers_17_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[899] + model_decoder_layers_17_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[900] + model_decoder_layers_17_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[901] + model_decoder_layers_17_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[902] + model_decoder_layers_17_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[903] + model_decoder_layers_17_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[904] + model_decoder_layers_17_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[905] + model_decoder_layers_17_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[909] + model_decoder_layers_17_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[910] + model_decoder_layers_17_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[911] + model_decoder_layers_17_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[912] + model_decoder_layers_17_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[913] + model_decoder_layers_17_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[914] + model_decoder_layers_17_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[915] + model_decoder_layers_17_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[916] + model_decoder_layers_17_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[917] + model_decoder_layers_17_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[918] + model_decoder_layers_17_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[919] + model_decoder_layers_17_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[920] + model_decoder_layers_18_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[921] + model_decoder_layers_18_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[922] + model_decoder_layers_18_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[923] + model_decoder_layers_18_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[924] + model_decoder_layers_18_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[925] + model_decoder_layers_18_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[926] + model_decoder_layers_18_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[927] + model_decoder_layers_18_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[928] + model_decoder_layers_18_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[929] + model_decoder_layers_18_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[933] + model_decoder_layers_18_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[934] + model_decoder_layers_18_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[935] + model_decoder_layers_18_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[936] + model_decoder_layers_18_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[937] + model_decoder_layers_18_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[938] + model_decoder_layers_18_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[939] + model_decoder_layers_18_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[940] + model_decoder_layers_18_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[941] + model_decoder_layers_18_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[942] + model_decoder_layers_18_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[943] + model_decoder_layers_18_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[944] + model_decoder_layers_19_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[945] + model_decoder_layers_19_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[946] + model_decoder_layers_19_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[947] + model_decoder_layers_19_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[948] + model_decoder_layers_19_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[949] + model_decoder_layers_19_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[950] + model_decoder_layers_19_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[951] + model_decoder_layers_19_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[952] + model_decoder_layers_19_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[953] + model_decoder_layers_19_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[957] + model_decoder_layers_19_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[958] + model_decoder_layers_19_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[959] + model_decoder_layers_19_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[960] + model_decoder_layers_19_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[961] + model_decoder_layers_19_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[962] + model_decoder_layers_19_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[963] + model_decoder_layers_19_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[964] + model_decoder_layers_19_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[965] + model_decoder_layers_19_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[966] + model_decoder_layers_19_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[967] + model_decoder_layers_19_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[968] + model_decoder_layers_20_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[969] + model_decoder_layers_20_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[970] + model_decoder_layers_20_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[971] + model_decoder_layers_20_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[972] + model_decoder_layers_20_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[973] + model_decoder_layers_20_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[974] + model_decoder_layers_20_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[975] + model_decoder_layers_20_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[976] + model_decoder_layers_20_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[977] + model_decoder_layers_20_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[981] + model_decoder_layers_20_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[982] + model_decoder_layers_20_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[983] + model_decoder_layers_20_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[984] + model_decoder_layers_20_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[985] + model_decoder_layers_20_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[986] + model_decoder_layers_20_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[987] + model_decoder_layers_20_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[988] + model_decoder_layers_20_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[989] + model_decoder_layers_20_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[990] + model_decoder_layers_20_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[991] + model_decoder_layers_20_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[992] + model_decoder_layers_21_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[993] + model_decoder_layers_21_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[994] + model_decoder_layers_21_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[995] + model_decoder_layers_21_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[996] + model_decoder_layers_21_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[997] + model_decoder_layers_21_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[998] + model_decoder_layers_21_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[999] + model_decoder_layers_21_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1000] + model_decoder_layers_21_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1001] + model_decoder_layers_21_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1005] + model_decoder_layers_21_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1006] + model_decoder_layers_21_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1007] + model_decoder_layers_21_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1008] + model_decoder_layers_21_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1009] + model_decoder_layers_21_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1010] + model_decoder_layers_21_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1011] + model_decoder_layers_21_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1012] + model_decoder_layers_21_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1013] + model_decoder_layers_21_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1014] + model_decoder_layers_21_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1015] + model_decoder_layers_21_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1016] + model_decoder_layers_22_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1017] + model_decoder_layers_22_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1018] + model_decoder_layers_22_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1019] + model_decoder_layers_22_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1020] + model_decoder_layers_22_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1021] + model_decoder_layers_22_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1022] + model_decoder_layers_22_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1023] + model_decoder_layers_22_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1024] + model_decoder_layers_22_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1025] + model_decoder_layers_22_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1029] + model_decoder_layers_22_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1030] + model_decoder_layers_22_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1031] + model_decoder_layers_22_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1032] + model_decoder_layers_22_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1033] + model_decoder_layers_22_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1034] + model_decoder_layers_22_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1035] + model_decoder_layers_22_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1036] + model_decoder_layers_22_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1037] + model_decoder_layers_22_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1038] + model_decoder_layers_22_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1039] + model_decoder_layers_22_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1040] + model_decoder_layers_23_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1041] + model_decoder_layers_23_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1042] + model_decoder_layers_23_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1043] + model_decoder_layers_23_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1044] + model_decoder_layers_23_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1045] + model_decoder_layers_23_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1046] + model_decoder_layers_23_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1047] + model_decoder_layers_23_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1048] + model_decoder_layers_23_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1049] + model_decoder_layers_23_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1053] + model_decoder_layers_23_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1054] + model_decoder_layers_23_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1055] + model_decoder_layers_23_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1056] + model_decoder_layers_23_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1057] + model_decoder_layers_23_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1058] + model_decoder_layers_23_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1059] + model_decoder_layers_23_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1060] + model_decoder_layers_23_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1061] + model_decoder_layers_23_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1062] + model_decoder_layers_23_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1063] + model_decoder_layers_23_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1064] + model_decoder_layers_24_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1065] + model_decoder_layers_24_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1066] + model_decoder_layers_24_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1067] + model_decoder_layers_24_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1068] + model_decoder_layers_24_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1069] + model_decoder_layers_24_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1070] + model_decoder_layers_24_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1071] + model_decoder_layers_24_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1072] + model_decoder_layers_24_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1073] + model_decoder_layers_24_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1077] + model_decoder_layers_24_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1078] + model_decoder_layers_24_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1079] + model_decoder_layers_24_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1080] + model_decoder_layers_24_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1081] + model_decoder_layers_24_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1082] + model_decoder_layers_24_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1083] + model_decoder_layers_24_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1084] + model_decoder_layers_24_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1085] + model_decoder_layers_24_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1086] + model_decoder_layers_24_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1087] + model_decoder_layers_24_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1088] + model_decoder_layers_25_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1089] + model_decoder_layers_25_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1090] + model_decoder_layers_25_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1091] + model_decoder_layers_25_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1092] + model_decoder_layers_25_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1093] + model_decoder_layers_25_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1094] + model_decoder_layers_25_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1095] + model_decoder_layers_25_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1096] + model_decoder_layers_25_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1097] + model_decoder_layers_25_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1101] + model_decoder_layers_25_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1102] + model_decoder_layers_25_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1103] + model_decoder_layers_25_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1104] + model_decoder_layers_25_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1105] + model_decoder_layers_25_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1106] + model_decoder_layers_25_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1107] + model_decoder_layers_25_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1108] + model_decoder_layers_25_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1109] + model_decoder_layers_25_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1110] + model_decoder_layers_25_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1111] + model_decoder_layers_25_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1112] + model_decoder_layers_26_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1113] + model_decoder_layers_26_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1114] + model_decoder_layers_26_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1115] + model_decoder_layers_26_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1116] + model_decoder_layers_26_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1117] + model_decoder_layers_26_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1118] + model_decoder_layers_26_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1119] + model_decoder_layers_26_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1120] + model_decoder_layers_26_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1121] + model_decoder_layers_26_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1125] + model_decoder_layers_26_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1126] + model_decoder_layers_26_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1127] + model_decoder_layers_26_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1128] + model_decoder_layers_26_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1129] + model_decoder_layers_26_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1130] + model_decoder_layers_26_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1131] + model_decoder_layers_26_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1132] + model_decoder_layers_26_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1133] + model_decoder_layers_26_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1134] + model_decoder_layers_26_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1135] + model_decoder_layers_26_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1136] + model_decoder_layers_27_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1137] + model_decoder_layers_27_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1138] + model_decoder_layers_27_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1139] + model_decoder_layers_27_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1140] + model_decoder_layers_27_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1141] + model_decoder_layers_27_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1142] + model_decoder_layers_27_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1143] + model_decoder_layers_27_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1144] + model_decoder_layers_27_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1145] + model_decoder_layers_27_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1149] + model_decoder_layers_27_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1150] + model_decoder_layers_27_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1151] + model_decoder_layers_27_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1152] + model_decoder_layers_27_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1153] + model_decoder_layers_27_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1154] + model_decoder_layers_27_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1155] + model_decoder_layers_27_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1156] + model_decoder_layers_27_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1157] + model_decoder_layers_27_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1158] + model_decoder_layers_27_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1159] + model_decoder_layers_27_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1160] + model_decoder_layers_28_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1161] + model_decoder_layers_28_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1162] + model_decoder_layers_28_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1163] + model_decoder_layers_28_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1164] + model_decoder_layers_28_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1165] + model_decoder_layers_28_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1166] + model_decoder_layers_28_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1167] + model_decoder_layers_28_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1168] + model_decoder_layers_28_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1169] + model_decoder_layers_28_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1173] + model_decoder_layers_28_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1174] + model_decoder_layers_28_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1175] + model_decoder_layers_28_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1176] + model_decoder_layers_28_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1177] + model_decoder_layers_28_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1178] + model_decoder_layers_28_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1179] + model_decoder_layers_28_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1180] + model_decoder_layers_28_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1181] + model_decoder_layers_28_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1182] + model_decoder_layers_28_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1183] + model_decoder_layers_28_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1184] + model_decoder_layers_29_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1185] + model_decoder_layers_29_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1186] + model_decoder_layers_29_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1187] + model_decoder_layers_29_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1188] + model_decoder_layers_29_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1189] + model_decoder_layers_29_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1190] + model_decoder_layers_29_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1191] + model_decoder_layers_29_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1192] + model_decoder_layers_29_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1193] + model_decoder_layers_29_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1197] + model_decoder_layers_29_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1198] + model_decoder_layers_29_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1199] + model_decoder_layers_29_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1200] + model_decoder_layers_29_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1201] + model_decoder_layers_29_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1202] + model_decoder_layers_29_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1203] + model_decoder_layers_29_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1204] + model_decoder_layers_29_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1205] + model_decoder_layers_29_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1206] + model_decoder_layers_29_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1207] + model_decoder_layers_29_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1208] + model_decoder_layers_30_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1209] + model_decoder_layers_30_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1210] + model_decoder_layers_30_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1211] + model_decoder_layers_30_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1212] + model_decoder_layers_30_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1213] + model_decoder_layers_30_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1214] + model_decoder_layers_30_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1215] + model_decoder_layers_30_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1216] + model_decoder_layers_30_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1217] + model_decoder_layers_30_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1221] + model_decoder_layers_30_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1222] + model_decoder_layers_30_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1223] + model_decoder_layers_30_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1224] + model_decoder_layers_30_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1225] + model_decoder_layers_30_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1226] + model_decoder_layers_30_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1227] + model_decoder_layers_30_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1228] + model_decoder_layers_30_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1229] + model_decoder_layers_30_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1230] + model_decoder_layers_30_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1231] + model_decoder_layers_30_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1232] + model_decoder_layers_31_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1233] + model_decoder_layers_31_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1234] + model_decoder_layers_31_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1235] + model_decoder_layers_31_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1236] + model_decoder_layers_31_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1237] + model_decoder_layers_31_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1238] + model_decoder_layers_31_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1239] + model_decoder_layers_31_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1240] + model_decoder_layers_31_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1241] + model_decoder_layers_31_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1245] + model_decoder_layers_31_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1246] + model_decoder_layers_31_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1247] + model_decoder_layers_31_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1248] + model_decoder_layers_31_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1249] + model_decoder_layers_31_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1250] + model_decoder_layers_31_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1251] + model_decoder_layers_31_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1252] + model_decoder_layers_31_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1253] + model_decoder_layers_31_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1254] + model_decoder_layers_31_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1255] + model_decoder_layers_31_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1256] + model_decoder_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1257] + model_decoder_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1258] + reshape707: R.Tensor((batch_size,), dtype="int32") = R.reshape(input_ids, R.shape([batch_size])) + take3: R.Tensor((batch_size, 1280), dtype="float16") = R.take(model_decoder_embed_tokens_weight3, reshape707, axis=0) + reshape708: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(take3, R.shape([batch_size, 1, 1280])) + lv133: R.Tensor((batch_size,), dtype="int32") = R.call_pure_packed("vm.builtin.attention_kv_cache_get_query_positions", paged_kv_cache, sinfo_args=(R.Tensor((batch_size,), dtype="int32"),)) + take4: R.Tensor((batch_size, 1280), dtype="float16") = R.take(model_decoder_embed_positions_weight3, lv133, axis=0) + reshape709: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(take4, R.shape([batch_size, 1, 1280])) + add578: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(reshape708, reshape709) + layer_norm162: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add578, model_decoder_layers_0_self_attn_layer_norm_weight3, model_decoder_layers_0_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv224 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_0_self_attn_q_proj_weight3, layer_norm162, model_decoder_layers_0_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape710: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv224, R.shape([batch_size, 1, 20, 64])) + lv65 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_0_self_attn_k_proj_weight3, layer_norm162), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape711: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv65, R.shape([batch_size, 1, 20, 64])) + lv225 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_0_self_attn_v_proj_weight3, layer_norm162, model_decoder_layers_0_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape712: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv225, R.shape([batch_size, 1, 20, 64])) + concat32: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape710, reshape711, reshape712), axis=2) + reshape713: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat32, R.shape([batch_size, 60, 64])) + lv134 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), reshape713), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape714: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv134, R.shape([batch_size, 1, 20, 64])) + reshape715: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape714, R.shape([batch_size, 1, 1280])) + lv226 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_0_self_attn_out_proj_weight3, reshape715, model_decoder_layers_0_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add582: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add578, lv226) + layer_norm163: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add582, model_decoder_layers_0_encoder_attn_layer_norm_weight3, model_decoder_layers_0_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv227 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_0_encoder_attn_q_proj_weight3, layer_norm163, model_decoder_layers_0_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape716: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv227, R.shape([batch_size, 1, 20, 64])) + reshape717: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape716, R.shape([batch_size, 20, 64])) + lv135 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), reshape717), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape718: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv135, R.shape([batch_size, 1, 20, 64])) + reshape719: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape718, R.shape([batch_size, 1, 1280])) + lv228 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_0_encoder_attn_out_proj_weight3, reshape719, model_decoder_layers_0_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add585: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add582, lv228) + layer_norm164: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add585, model_decoder_layers_0_final_layer_norm_weight3, model_decoder_layers_0_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv32 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_0_fc1_weight3, layer_norm164, model_decoder_layers_0_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv229 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_0_fc2_weight3, lv32, model_decoder_layers_0_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add588: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add585, lv229) + layer_norm165: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add588, model_decoder_layers_1_self_attn_layer_norm_weight3, model_decoder_layers_1_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv230 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_1_self_attn_q_proj_weight3, layer_norm165, model_decoder_layers_1_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape720: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv230, R.shape([batch_size, 1, 20, 64])) + lv66 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_1_self_attn_k_proj_weight3, layer_norm165), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape721: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv66, R.shape([batch_size, 1, 20, 64])) + lv231 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_1_self_attn_v_proj_weight3, layer_norm165, model_decoder_layers_1_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape722: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv231, R.shape([batch_size, 1, 20, 64])) + concat33: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape720, reshape721, reshape722), axis=2) + reshape723: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat33, R.shape([batch_size, 60, 64])) + lv136 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), reshape723), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape724: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv136, R.shape([batch_size, 1, 20, 64])) + reshape725: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape724, R.shape([batch_size, 1, 1280])) + lv232 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_1_self_attn_out_proj_weight3, reshape725, model_decoder_layers_1_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add592: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add588, lv232) + layer_norm166: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add592, model_decoder_layers_1_encoder_attn_layer_norm_weight3, model_decoder_layers_1_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv233 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_1_encoder_attn_q_proj_weight3, layer_norm166, model_decoder_layers_1_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape726: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv233, R.shape([batch_size, 1, 20, 64])) + reshape727: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape726, R.shape([batch_size, 20, 64])) + lv137 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), reshape727), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape728: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv137, R.shape([batch_size, 1, 20, 64])) + reshape729: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape728, R.shape([batch_size, 1, 1280])) + lv234 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_1_encoder_attn_out_proj_weight3, reshape729, model_decoder_layers_1_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add595: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add592, lv234) + layer_norm167: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add595, model_decoder_layers_1_final_layer_norm_weight3, model_decoder_layers_1_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv33 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_1_fc1_weight3, layer_norm167, model_decoder_layers_1_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv235 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_1_fc2_weight3, lv33, model_decoder_layers_1_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add598: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add595, lv235) + layer_norm168: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add598, model_decoder_layers_2_self_attn_layer_norm_weight3, model_decoder_layers_2_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv236 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_2_self_attn_q_proj_weight3, layer_norm168, model_decoder_layers_2_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape730: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv236, R.shape([batch_size, 1, 20, 64])) + lv67 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_2_self_attn_k_proj_weight3, layer_norm168), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape731: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv67, R.shape([batch_size, 1, 20, 64])) + lv237 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_2_self_attn_v_proj_weight3, layer_norm168, model_decoder_layers_2_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape732: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv237, R.shape([batch_size, 1, 20, 64])) + concat34: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape730, reshape731, reshape732), axis=2) + reshape733: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat34, R.shape([batch_size, 60, 64])) + lv138 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), reshape733), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape734: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv138, R.shape([batch_size, 1, 20, 64])) + reshape735: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape734, R.shape([batch_size, 1, 1280])) + lv238 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_2_self_attn_out_proj_weight3, reshape735, model_decoder_layers_2_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add602: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add598, lv238) + layer_norm169: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add602, model_decoder_layers_2_encoder_attn_layer_norm_weight3, model_decoder_layers_2_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv239 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_2_encoder_attn_q_proj_weight3, layer_norm169, model_decoder_layers_2_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape736: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv239, R.shape([batch_size, 1, 20, 64])) + reshape737: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape736, R.shape([batch_size, 20, 64])) + lv139 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), reshape737), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape738: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv139, R.shape([batch_size, 1, 20, 64])) + reshape739: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape738, R.shape([batch_size, 1, 1280])) + lv240 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_2_encoder_attn_out_proj_weight3, reshape739, model_decoder_layers_2_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add605: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add602, lv240) + layer_norm170: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add605, model_decoder_layers_2_final_layer_norm_weight3, model_decoder_layers_2_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv34 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_2_fc1_weight3, layer_norm170, model_decoder_layers_2_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv241 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_2_fc2_weight3, lv34, model_decoder_layers_2_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add608: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add605, lv241) + layer_norm171: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add608, model_decoder_layers_3_self_attn_layer_norm_weight3, model_decoder_layers_3_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv242 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_3_self_attn_q_proj_weight3, layer_norm171, model_decoder_layers_3_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape740: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv242, R.shape([batch_size, 1, 20, 64])) + lv68 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_3_self_attn_k_proj_weight3, layer_norm171), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape741: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv68, R.shape([batch_size, 1, 20, 64])) + lv243 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_3_self_attn_v_proj_weight3, layer_norm171, model_decoder_layers_3_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape742: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv243, R.shape([batch_size, 1, 20, 64])) + concat35: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape740, reshape741, reshape742), axis=2) + reshape743: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat35, R.shape([batch_size, 60, 64])) + lv140 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), reshape743), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape744: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv140, R.shape([batch_size, 1, 20, 64])) + reshape745: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape744, R.shape([batch_size, 1, 1280])) + lv244 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_3_self_attn_out_proj_weight3, reshape745, model_decoder_layers_3_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add612: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add608, lv244) + layer_norm172: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add612, model_decoder_layers_3_encoder_attn_layer_norm_weight3, model_decoder_layers_3_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv245 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_3_encoder_attn_q_proj_weight3, layer_norm172, model_decoder_layers_3_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape746: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv245, R.shape([batch_size, 1, 20, 64])) + reshape747: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape746, R.shape([batch_size, 20, 64])) + lv141 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), reshape747), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape748: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv141, R.shape([batch_size, 1, 20, 64])) + reshape749: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape748, R.shape([batch_size, 1, 1280])) + lv246 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_3_encoder_attn_out_proj_weight3, reshape749, model_decoder_layers_3_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add615: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add612, lv246) + layer_norm173: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add615, model_decoder_layers_3_final_layer_norm_weight3, model_decoder_layers_3_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv35 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_3_fc1_weight3, layer_norm173, model_decoder_layers_3_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv247 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_3_fc2_weight3, lv35, model_decoder_layers_3_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add618: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add615, lv247) + layer_norm174: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add618, model_decoder_layers_4_self_attn_layer_norm_weight3, model_decoder_layers_4_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv248 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_4_self_attn_q_proj_weight3, layer_norm174, model_decoder_layers_4_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape750: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv248, R.shape([batch_size, 1, 20, 64])) + lv69 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_4_self_attn_k_proj_weight3, layer_norm174), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape751: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv69, R.shape([batch_size, 1, 20, 64])) + lv249 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_4_self_attn_v_proj_weight3, layer_norm174, model_decoder_layers_4_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape752: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv249, R.shape([batch_size, 1, 20, 64])) + concat36: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape750, reshape751, reshape752), axis=2) + reshape753: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat36, R.shape([batch_size, 60, 64])) + lv142 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), reshape753), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape754: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv142, R.shape([batch_size, 1, 20, 64])) + reshape755: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape754, R.shape([batch_size, 1, 1280])) + lv250 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_4_self_attn_out_proj_weight3, reshape755, model_decoder_layers_4_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add622: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add618, lv250) + layer_norm175: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add622, model_decoder_layers_4_encoder_attn_layer_norm_weight3, model_decoder_layers_4_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv251 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_4_encoder_attn_q_proj_weight3, layer_norm175, model_decoder_layers_4_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape756: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv251, R.shape([batch_size, 1, 20, 64])) + reshape757: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape756, R.shape([batch_size, 20, 64])) + lv143 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), reshape757), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape758: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv143, R.shape([batch_size, 1, 20, 64])) + reshape759: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape758, R.shape([batch_size, 1, 1280])) + lv252 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_4_encoder_attn_out_proj_weight3, reshape759, model_decoder_layers_4_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add625: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add622, lv252) + layer_norm176: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add625, model_decoder_layers_4_final_layer_norm_weight3, model_decoder_layers_4_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv36 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_4_fc1_weight3, layer_norm176, model_decoder_layers_4_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv253 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_4_fc2_weight3, lv36, model_decoder_layers_4_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add628: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add625, lv253) + layer_norm177: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add628, model_decoder_layers_5_self_attn_layer_norm_weight3, model_decoder_layers_5_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv254 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_5_self_attn_q_proj_weight3, layer_norm177, model_decoder_layers_5_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape760: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv254, R.shape([batch_size, 1, 20, 64])) + lv70 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_5_self_attn_k_proj_weight3, layer_norm177), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape761: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv70, R.shape([batch_size, 1, 20, 64])) + lv255 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_5_self_attn_v_proj_weight3, layer_norm177, model_decoder_layers_5_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape762: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv255, R.shape([batch_size, 1, 20, 64])) + concat37: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape760, reshape761, reshape762), axis=2) + reshape763: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat37, R.shape([batch_size, 60, 64])) + lv144 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), reshape763), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape764: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv144, R.shape([batch_size, 1, 20, 64])) + reshape765: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape764, R.shape([batch_size, 1, 1280])) + lv256 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_5_self_attn_out_proj_weight3, reshape765, model_decoder_layers_5_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add632: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add628, lv256) + layer_norm178: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add632, model_decoder_layers_5_encoder_attn_layer_norm_weight3, model_decoder_layers_5_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv257 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_5_encoder_attn_q_proj_weight3, layer_norm178, model_decoder_layers_5_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape766: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv257, R.shape([batch_size, 1, 20, 64])) + reshape767: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape766, R.shape([batch_size, 20, 64])) + lv145 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), reshape767), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape768: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv145, R.shape([batch_size, 1, 20, 64])) + reshape769: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape768, R.shape([batch_size, 1, 1280])) + lv258 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_5_encoder_attn_out_proj_weight3, reshape769, model_decoder_layers_5_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add635: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add632, lv258) + layer_norm179: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add635, model_decoder_layers_5_final_layer_norm_weight3, model_decoder_layers_5_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv37 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_5_fc1_weight3, layer_norm179, model_decoder_layers_5_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv259 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_5_fc2_weight3, lv37, model_decoder_layers_5_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add638: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add635, lv259) + layer_norm180: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add638, model_decoder_layers_6_self_attn_layer_norm_weight3, model_decoder_layers_6_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv260 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_6_self_attn_q_proj_weight3, layer_norm180, model_decoder_layers_6_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape770: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv260, R.shape([batch_size, 1, 20, 64])) + lv71 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_6_self_attn_k_proj_weight3, layer_norm180), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape771: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv71, R.shape([batch_size, 1, 20, 64])) + lv261 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_6_self_attn_v_proj_weight3, layer_norm180, model_decoder_layers_6_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape772: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv261, R.shape([batch_size, 1, 20, 64])) + concat38: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape770, reshape771, reshape772), axis=2) + reshape773: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat38, R.shape([batch_size, 60, 64])) + lv146 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), reshape773), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape774: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv146, R.shape([batch_size, 1, 20, 64])) + reshape775: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape774, R.shape([batch_size, 1, 1280])) + lv262 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_6_self_attn_out_proj_weight3, reshape775, model_decoder_layers_6_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add642: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add638, lv262) + layer_norm181: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add642, model_decoder_layers_6_encoder_attn_layer_norm_weight3, model_decoder_layers_6_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv263 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_6_encoder_attn_q_proj_weight3, layer_norm181, model_decoder_layers_6_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape776: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv263, R.shape([batch_size, 1, 20, 64])) + reshape777: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape776, R.shape([batch_size, 20, 64])) + lv147 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), reshape777), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape778: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv147, R.shape([batch_size, 1, 20, 64])) + reshape779: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape778, R.shape([batch_size, 1, 1280])) + lv264 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_6_encoder_attn_out_proj_weight3, reshape779, model_decoder_layers_6_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add645: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add642, lv264) + layer_norm182: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add645, model_decoder_layers_6_final_layer_norm_weight3, model_decoder_layers_6_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv38 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_6_fc1_weight3, layer_norm182, model_decoder_layers_6_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv265 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_6_fc2_weight3, lv38, model_decoder_layers_6_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add648: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add645, lv265) + layer_norm183: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add648, model_decoder_layers_7_self_attn_layer_norm_weight3, model_decoder_layers_7_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv266 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_7_self_attn_q_proj_weight3, layer_norm183, model_decoder_layers_7_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape780: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv266, R.shape([batch_size, 1, 20, 64])) + lv72 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_7_self_attn_k_proj_weight3, layer_norm183), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape781: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv72, R.shape([batch_size, 1, 20, 64])) + lv267 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_7_self_attn_v_proj_weight3, layer_norm183, model_decoder_layers_7_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape782: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv267, R.shape([batch_size, 1, 20, 64])) + concat39: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape780, reshape781, reshape782), axis=2) + reshape783: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat39, R.shape([batch_size, 60, 64])) + lv148 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), reshape783), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape784: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv148, R.shape([batch_size, 1, 20, 64])) + reshape785: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape784, R.shape([batch_size, 1, 1280])) + lv268 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_7_self_attn_out_proj_weight3, reshape785, model_decoder_layers_7_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add652: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add648, lv268) + layer_norm184: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add652, model_decoder_layers_7_encoder_attn_layer_norm_weight3, model_decoder_layers_7_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv269 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_7_encoder_attn_q_proj_weight3, layer_norm184, model_decoder_layers_7_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape786: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv269, R.shape([batch_size, 1, 20, 64])) + reshape787: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape786, R.shape([batch_size, 20, 64])) + lv149 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), reshape787), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape788: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv149, R.shape([batch_size, 1, 20, 64])) + reshape789: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape788, R.shape([batch_size, 1, 1280])) + lv270 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_7_encoder_attn_out_proj_weight3, reshape789, model_decoder_layers_7_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add655: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add652, lv270) + layer_norm185: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add655, model_decoder_layers_7_final_layer_norm_weight3, model_decoder_layers_7_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv39 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_7_fc1_weight3, layer_norm185, model_decoder_layers_7_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv271 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_7_fc2_weight3, lv39, model_decoder_layers_7_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add658: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add655, lv271) + layer_norm186: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add658, model_decoder_layers_8_self_attn_layer_norm_weight3, model_decoder_layers_8_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv272 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_8_self_attn_q_proj_weight3, layer_norm186, model_decoder_layers_8_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape790: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv272, R.shape([batch_size, 1, 20, 64])) + lv73 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_8_self_attn_k_proj_weight3, layer_norm186), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape791: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv73, R.shape([batch_size, 1, 20, 64])) + lv273 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_8_self_attn_v_proj_weight3, layer_norm186, model_decoder_layers_8_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape792: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv273, R.shape([batch_size, 1, 20, 64])) + concat40: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape790, reshape791, reshape792), axis=2) + reshape793: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat40, R.shape([batch_size, 60, 64])) + lv150 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), reshape793), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape794: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv150, R.shape([batch_size, 1, 20, 64])) + reshape795: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape794, R.shape([batch_size, 1, 1280])) + lv274 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_8_self_attn_out_proj_weight3, reshape795, model_decoder_layers_8_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add662: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add658, lv274) + layer_norm187: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add662, model_decoder_layers_8_encoder_attn_layer_norm_weight3, model_decoder_layers_8_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv275 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_8_encoder_attn_q_proj_weight3, layer_norm187, model_decoder_layers_8_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape796: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv275, R.shape([batch_size, 1, 20, 64])) + reshape797: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape796, R.shape([batch_size, 20, 64])) + lv151 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), reshape797), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape798: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv151, R.shape([batch_size, 1, 20, 64])) + reshape799: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape798, R.shape([batch_size, 1, 1280])) + lv276 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_8_encoder_attn_out_proj_weight3, reshape799, model_decoder_layers_8_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add665: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add662, lv276) + layer_norm188: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add665, model_decoder_layers_8_final_layer_norm_weight3, model_decoder_layers_8_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv40 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_8_fc1_weight3, layer_norm188, model_decoder_layers_8_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv277 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_8_fc2_weight3, lv40, model_decoder_layers_8_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add668: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add665, lv277) + layer_norm189: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add668, model_decoder_layers_9_self_attn_layer_norm_weight3, model_decoder_layers_9_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv278 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_9_self_attn_q_proj_weight3, layer_norm189, model_decoder_layers_9_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape800: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv278, R.shape([batch_size, 1, 20, 64])) + lv74 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_9_self_attn_k_proj_weight3, layer_norm189), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape801: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv74, R.shape([batch_size, 1, 20, 64])) + lv279 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_9_self_attn_v_proj_weight3, layer_norm189, model_decoder_layers_9_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape802: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv279, R.shape([batch_size, 1, 20, 64])) + concat41: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape800, reshape801, reshape802), axis=2) + reshape803: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat41, R.shape([batch_size, 60, 64])) + lv152 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), reshape803), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape804: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv152, R.shape([batch_size, 1, 20, 64])) + reshape805: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape804, R.shape([batch_size, 1, 1280])) + lv280 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_9_self_attn_out_proj_weight3, reshape805, model_decoder_layers_9_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add672: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add668, lv280) + layer_norm190: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add672, model_decoder_layers_9_encoder_attn_layer_norm_weight3, model_decoder_layers_9_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv281 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_9_encoder_attn_q_proj_weight3, layer_norm190, model_decoder_layers_9_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape806: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv281, R.shape([batch_size, 1, 20, 64])) + reshape807: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape806, R.shape([batch_size, 20, 64])) + lv153 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), reshape807), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape808: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv153, R.shape([batch_size, 1, 20, 64])) + reshape809: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape808, R.shape([batch_size, 1, 1280])) + lv282 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_9_encoder_attn_out_proj_weight3, reshape809, model_decoder_layers_9_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add675: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add672, lv282) + layer_norm191: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add675, model_decoder_layers_9_final_layer_norm_weight3, model_decoder_layers_9_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv41 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_9_fc1_weight3, layer_norm191, model_decoder_layers_9_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv283 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_9_fc2_weight3, lv41, model_decoder_layers_9_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add678: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add675, lv283) + layer_norm192: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add678, model_decoder_layers_10_self_attn_layer_norm_weight3, model_decoder_layers_10_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv284 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_10_self_attn_q_proj_weight3, layer_norm192, model_decoder_layers_10_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape810: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv284, R.shape([batch_size, 1, 20, 64])) + lv75 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_10_self_attn_k_proj_weight3, layer_norm192), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape811: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv75, R.shape([batch_size, 1, 20, 64])) + lv285 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_10_self_attn_v_proj_weight3, layer_norm192, model_decoder_layers_10_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape812: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv285, R.shape([batch_size, 1, 20, 64])) + concat42: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape810, reshape811, reshape812), axis=2) + reshape813: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat42, R.shape([batch_size, 60, 64])) + lv154 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), reshape813), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape814: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv154, R.shape([batch_size, 1, 20, 64])) + reshape815: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape814, R.shape([batch_size, 1, 1280])) + lv286 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_10_self_attn_out_proj_weight3, reshape815, model_decoder_layers_10_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add682: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add678, lv286) + layer_norm193: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add682, model_decoder_layers_10_encoder_attn_layer_norm_weight3, model_decoder_layers_10_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv287 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_10_encoder_attn_q_proj_weight3, layer_norm193, model_decoder_layers_10_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape816: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv287, R.shape([batch_size, 1, 20, 64])) + reshape817: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape816, R.shape([batch_size, 20, 64])) + lv155 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), reshape817), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape818: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv155, R.shape([batch_size, 1, 20, 64])) + reshape819: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape818, R.shape([batch_size, 1, 1280])) + lv288 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_10_encoder_attn_out_proj_weight3, reshape819, model_decoder_layers_10_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add685: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add682, lv288) + layer_norm194: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add685, model_decoder_layers_10_final_layer_norm_weight3, model_decoder_layers_10_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv42 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_10_fc1_weight3, layer_norm194, model_decoder_layers_10_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv289 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_10_fc2_weight3, lv42, model_decoder_layers_10_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add688: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add685, lv289) + layer_norm195: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add688, model_decoder_layers_11_self_attn_layer_norm_weight3, model_decoder_layers_11_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv290 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_11_self_attn_q_proj_weight3, layer_norm195, model_decoder_layers_11_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape820: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv290, R.shape([batch_size, 1, 20, 64])) + lv76 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_11_self_attn_k_proj_weight3, layer_norm195), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape821: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv76, R.shape([batch_size, 1, 20, 64])) + lv291 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_11_self_attn_v_proj_weight3, layer_norm195, model_decoder_layers_11_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape822: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv291, R.shape([batch_size, 1, 20, 64])) + concat43: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape820, reshape821, reshape822), axis=2) + reshape823: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat43, R.shape([batch_size, 60, 64])) + lv156 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), reshape823), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape824: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv156, R.shape([batch_size, 1, 20, 64])) + reshape825: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape824, R.shape([batch_size, 1, 1280])) + lv292 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_11_self_attn_out_proj_weight3, reshape825, model_decoder_layers_11_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add692: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add688, lv292) + layer_norm196: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add692, model_decoder_layers_11_encoder_attn_layer_norm_weight3, model_decoder_layers_11_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv293 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_11_encoder_attn_q_proj_weight3, layer_norm196, model_decoder_layers_11_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape826: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv293, R.shape([batch_size, 1, 20, 64])) + reshape827: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape826, R.shape([batch_size, 20, 64])) + lv157 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), reshape827), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape828: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv157, R.shape([batch_size, 1, 20, 64])) + reshape829: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape828, R.shape([batch_size, 1, 1280])) + lv294 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_11_encoder_attn_out_proj_weight3, reshape829, model_decoder_layers_11_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add695: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add692, lv294) + layer_norm197: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add695, model_decoder_layers_11_final_layer_norm_weight3, model_decoder_layers_11_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv43 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_11_fc1_weight3, layer_norm197, model_decoder_layers_11_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv295 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_11_fc2_weight3, lv43, model_decoder_layers_11_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add698: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add695, lv295) + layer_norm198: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add698, model_decoder_layers_12_self_attn_layer_norm_weight3, model_decoder_layers_12_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv296 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_12_self_attn_q_proj_weight3, layer_norm198, model_decoder_layers_12_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape830: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv296, R.shape([batch_size, 1, 20, 64])) + lv77 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_12_self_attn_k_proj_weight3, layer_norm198), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape831: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv77, R.shape([batch_size, 1, 20, 64])) + lv297 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_12_self_attn_v_proj_weight3, layer_norm198, model_decoder_layers_12_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape832: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv297, R.shape([batch_size, 1, 20, 64])) + concat44: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape830, reshape831, reshape832), axis=2) + reshape833: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat44, R.shape([batch_size, 60, 64])) + lv158 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), reshape833), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape834: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv158, R.shape([batch_size, 1, 20, 64])) + reshape835: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape834, R.shape([batch_size, 1, 1280])) + lv298 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_12_self_attn_out_proj_weight3, reshape835, model_decoder_layers_12_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add702: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add698, lv298) + layer_norm199: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add702, model_decoder_layers_12_encoder_attn_layer_norm_weight3, model_decoder_layers_12_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv299 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_12_encoder_attn_q_proj_weight3, layer_norm199, model_decoder_layers_12_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape836: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv299, R.shape([batch_size, 1, 20, 64])) + reshape837: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape836, R.shape([batch_size, 20, 64])) + lv159 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), reshape837), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape838: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv159, R.shape([batch_size, 1, 20, 64])) + reshape839: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape838, R.shape([batch_size, 1, 1280])) + lv300 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_12_encoder_attn_out_proj_weight3, reshape839, model_decoder_layers_12_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add705: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add702, lv300) + layer_norm200: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add705, model_decoder_layers_12_final_layer_norm_weight3, model_decoder_layers_12_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv44 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_12_fc1_weight3, layer_norm200, model_decoder_layers_12_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv301 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_12_fc2_weight3, lv44, model_decoder_layers_12_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add708: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add705, lv301) + layer_norm201: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add708, model_decoder_layers_13_self_attn_layer_norm_weight3, model_decoder_layers_13_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv302 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_13_self_attn_q_proj_weight3, layer_norm201, model_decoder_layers_13_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape840: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv302, R.shape([batch_size, 1, 20, 64])) + lv78 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_13_self_attn_k_proj_weight3, layer_norm201), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape841: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv78, R.shape([batch_size, 1, 20, 64])) + lv303 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_13_self_attn_v_proj_weight3, layer_norm201, model_decoder_layers_13_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape842: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv303, R.shape([batch_size, 1, 20, 64])) + concat45: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape840, reshape841, reshape842), axis=2) + reshape843: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat45, R.shape([batch_size, 60, 64])) + lv160 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), reshape843), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape844: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv160, R.shape([batch_size, 1, 20, 64])) + reshape845: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape844, R.shape([batch_size, 1, 1280])) + lv304 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_13_self_attn_out_proj_weight3, reshape845, model_decoder_layers_13_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add712: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add708, lv304) + layer_norm202: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add712, model_decoder_layers_13_encoder_attn_layer_norm_weight3, model_decoder_layers_13_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv305 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_13_encoder_attn_q_proj_weight3, layer_norm202, model_decoder_layers_13_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape846: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv305, R.shape([batch_size, 1, 20, 64])) + reshape847: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape846, R.shape([batch_size, 20, 64])) + lv161 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), reshape847), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape848: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv161, R.shape([batch_size, 1, 20, 64])) + reshape849: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape848, R.shape([batch_size, 1, 1280])) + lv306 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_13_encoder_attn_out_proj_weight3, reshape849, model_decoder_layers_13_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add715: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add712, lv306) + layer_norm203: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add715, model_decoder_layers_13_final_layer_norm_weight3, model_decoder_layers_13_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv45 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_13_fc1_weight3, layer_norm203, model_decoder_layers_13_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv307 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_13_fc2_weight3, lv45, model_decoder_layers_13_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add718: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add715, lv307) + layer_norm204: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add718, model_decoder_layers_14_self_attn_layer_norm_weight3, model_decoder_layers_14_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv308 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_14_self_attn_q_proj_weight3, layer_norm204, model_decoder_layers_14_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape850: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv308, R.shape([batch_size, 1, 20, 64])) + lv79 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_14_self_attn_k_proj_weight3, layer_norm204), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape851: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv79, R.shape([batch_size, 1, 20, 64])) + lv309 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_14_self_attn_v_proj_weight3, layer_norm204, model_decoder_layers_14_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape852: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv309, R.shape([batch_size, 1, 20, 64])) + concat46: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape850, reshape851, reshape852), axis=2) + reshape853: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat46, R.shape([batch_size, 60, 64])) + lv162 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), reshape853), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape854: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv162, R.shape([batch_size, 1, 20, 64])) + reshape855: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape854, R.shape([batch_size, 1, 1280])) + lv310 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_14_self_attn_out_proj_weight3, reshape855, model_decoder_layers_14_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add722: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add718, lv310) + layer_norm205: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add722, model_decoder_layers_14_encoder_attn_layer_norm_weight3, model_decoder_layers_14_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv311 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_14_encoder_attn_q_proj_weight3, layer_norm205, model_decoder_layers_14_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape856: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv311, R.shape([batch_size, 1, 20, 64])) + reshape857: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape856, R.shape([batch_size, 20, 64])) + lv163 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), reshape857), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape858: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv163, R.shape([batch_size, 1, 20, 64])) + reshape859: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape858, R.shape([batch_size, 1, 1280])) + lv312 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_14_encoder_attn_out_proj_weight3, reshape859, model_decoder_layers_14_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add725: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add722, lv312) + layer_norm206: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add725, model_decoder_layers_14_final_layer_norm_weight3, model_decoder_layers_14_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv46 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_14_fc1_weight3, layer_norm206, model_decoder_layers_14_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv313 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_14_fc2_weight3, lv46, model_decoder_layers_14_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add728: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add725, lv313) + layer_norm207: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add728, model_decoder_layers_15_self_attn_layer_norm_weight3, model_decoder_layers_15_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv314 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_15_self_attn_q_proj_weight3, layer_norm207, model_decoder_layers_15_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape860: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv314, R.shape([batch_size, 1, 20, 64])) + lv80 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_15_self_attn_k_proj_weight3, layer_norm207), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape861: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv80, R.shape([batch_size, 1, 20, 64])) + lv315 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_15_self_attn_v_proj_weight3, layer_norm207, model_decoder_layers_15_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape862: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv315, R.shape([batch_size, 1, 20, 64])) + concat47: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape860, reshape861, reshape862), axis=2) + reshape863: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat47, R.shape([batch_size, 60, 64])) + lv164 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), reshape863), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape864: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv164, R.shape([batch_size, 1, 20, 64])) + reshape865: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape864, R.shape([batch_size, 1, 1280])) + lv316 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_15_self_attn_out_proj_weight3, reshape865, model_decoder_layers_15_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add732: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add728, lv316) + layer_norm208: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add732, model_decoder_layers_15_encoder_attn_layer_norm_weight3, model_decoder_layers_15_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv317 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_15_encoder_attn_q_proj_weight3, layer_norm208, model_decoder_layers_15_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape866: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv317, R.shape([batch_size, 1, 20, 64])) + reshape867: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape866, R.shape([batch_size, 20, 64])) + lv165 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), reshape867), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape868: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv165, R.shape([batch_size, 1, 20, 64])) + reshape869: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape868, R.shape([batch_size, 1, 1280])) + lv318 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_15_encoder_attn_out_proj_weight3, reshape869, model_decoder_layers_15_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add735: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add732, lv318) + layer_norm209: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add735, model_decoder_layers_15_final_layer_norm_weight3, model_decoder_layers_15_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv47 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_15_fc1_weight3, layer_norm209, model_decoder_layers_15_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv319 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_15_fc2_weight3, lv47, model_decoder_layers_15_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add738: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add735, lv319) + layer_norm210: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add738, model_decoder_layers_16_self_attn_layer_norm_weight3, model_decoder_layers_16_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv320 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_16_self_attn_q_proj_weight3, layer_norm210, model_decoder_layers_16_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape870: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv320, R.shape([batch_size, 1, 20, 64])) + lv81 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_16_self_attn_k_proj_weight3, layer_norm210), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape871: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv81, R.shape([batch_size, 1, 20, 64])) + lv321 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_16_self_attn_v_proj_weight3, layer_norm210, model_decoder_layers_16_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape872: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv321, R.shape([batch_size, 1, 20, 64])) + concat48: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape870, reshape871, reshape872), axis=2) + reshape873: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat48, R.shape([batch_size, 60, 64])) + lv166 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), reshape873), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape874: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv166, R.shape([batch_size, 1, 20, 64])) + reshape875: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape874, R.shape([batch_size, 1, 1280])) + lv322 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_16_self_attn_out_proj_weight3, reshape875, model_decoder_layers_16_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add742: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add738, lv322) + layer_norm211: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add742, model_decoder_layers_16_encoder_attn_layer_norm_weight3, model_decoder_layers_16_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv323 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_16_encoder_attn_q_proj_weight3, layer_norm211, model_decoder_layers_16_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape876: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv323, R.shape([batch_size, 1, 20, 64])) + reshape877: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape876, R.shape([batch_size, 20, 64])) + lv167 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), reshape877), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape878: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv167, R.shape([batch_size, 1, 20, 64])) + reshape879: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape878, R.shape([batch_size, 1, 1280])) + lv324 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_16_encoder_attn_out_proj_weight3, reshape879, model_decoder_layers_16_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add745: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add742, lv324) + layer_norm212: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add745, model_decoder_layers_16_final_layer_norm_weight3, model_decoder_layers_16_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv48 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_16_fc1_weight3, layer_norm212, model_decoder_layers_16_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv325 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_16_fc2_weight3, lv48, model_decoder_layers_16_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add748: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add745, lv325) + layer_norm213: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add748, model_decoder_layers_17_self_attn_layer_norm_weight3, model_decoder_layers_17_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv326 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_17_self_attn_q_proj_weight3, layer_norm213, model_decoder_layers_17_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape880: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv326, R.shape([batch_size, 1, 20, 64])) + lv82 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_17_self_attn_k_proj_weight3, layer_norm213), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape881: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv82, R.shape([batch_size, 1, 20, 64])) + lv327 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_17_self_attn_v_proj_weight3, layer_norm213, model_decoder_layers_17_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape882: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv327, R.shape([batch_size, 1, 20, 64])) + concat49: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape880, reshape881, reshape882), axis=2) + reshape883: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat49, R.shape([batch_size, 60, 64])) + lv168 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), reshape883), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape884: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv168, R.shape([batch_size, 1, 20, 64])) + reshape885: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape884, R.shape([batch_size, 1, 1280])) + lv328 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_17_self_attn_out_proj_weight3, reshape885, model_decoder_layers_17_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add752: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add748, lv328) + layer_norm214: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add752, model_decoder_layers_17_encoder_attn_layer_norm_weight3, model_decoder_layers_17_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv329 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_17_encoder_attn_q_proj_weight3, layer_norm214, model_decoder_layers_17_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape886: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv329, R.shape([batch_size, 1, 20, 64])) + reshape887: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape886, R.shape([batch_size, 20, 64])) + lv169 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), reshape887), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape888: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv169, R.shape([batch_size, 1, 20, 64])) + reshape889: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape888, R.shape([batch_size, 1, 1280])) + lv330 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_17_encoder_attn_out_proj_weight3, reshape889, model_decoder_layers_17_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add755: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add752, lv330) + layer_norm215: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add755, model_decoder_layers_17_final_layer_norm_weight3, model_decoder_layers_17_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv49 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_17_fc1_weight3, layer_norm215, model_decoder_layers_17_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv331 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_17_fc2_weight3, lv49, model_decoder_layers_17_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add758: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add755, lv331) + layer_norm216: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add758, model_decoder_layers_18_self_attn_layer_norm_weight3, model_decoder_layers_18_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv332 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_18_self_attn_q_proj_weight3, layer_norm216, model_decoder_layers_18_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape890: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv332, R.shape([batch_size, 1, 20, 64])) + lv83 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_18_self_attn_k_proj_weight3, layer_norm216), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape891: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv83, R.shape([batch_size, 1, 20, 64])) + lv333 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_18_self_attn_v_proj_weight3, layer_norm216, model_decoder_layers_18_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape892: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv333, R.shape([batch_size, 1, 20, 64])) + concat50: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape890, reshape891, reshape892), axis=2) + reshape893: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat50, R.shape([batch_size, 60, 64])) + lv170 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), reshape893), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape894: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv170, R.shape([batch_size, 1, 20, 64])) + reshape895: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape894, R.shape([batch_size, 1, 1280])) + lv334 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_18_self_attn_out_proj_weight3, reshape895, model_decoder_layers_18_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add762: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add758, lv334) + layer_norm217: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add762, model_decoder_layers_18_encoder_attn_layer_norm_weight3, model_decoder_layers_18_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv335 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_18_encoder_attn_q_proj_weight3, layer_norm217, model_decoder_layers_18_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape896: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv335, R.shape([batch_size, 1, 20, 64])) + reshape897: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape896, R.shape([batch_size, 20, 64])) + lv171 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), reshape897), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape898: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv171, R.shape([batch_size, 1, 20, 64])) + reshape899: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape898, R.shape([batch_size, 1, 1280])) + lv336 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_18_encoder_attn_out_proj_weight3, reshape899, model_decoder_layers_18_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add765: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add762, lv336) + layer_norm218: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add765, model_decoder_layers_18_final_layer_norm_weight3, model_decoder_layers_18_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv50 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_18_fc1_weight3, layer_norm218, model_decoder_layers_18_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv337 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_18_fc2_weight3, lv50, model_decoder_layers_18_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add768: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add765, lv337) + layer_norm219: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add768, model_decoder_layers_19_self_attn_layer_norm_weight3, model_decoder_layers_19_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv338 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_19_self_attn_q_proj_weight3, layer_norm219, model_decoder_layers_19_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape900: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv338, R.shape([batch_size, 1, 20, 64])) + lv84 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_19_self_attn_k_proj_weight3, layer_norm219), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape901: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv84, R.shape([batch_size, 1, 20, 64])) + lv339 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_19_self_attn_v_proj_weight3, layer_norm219, model_decoder_layers_19_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape902: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv339, R.shape([batch_size, 1, 20, 64])) + concat51: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape900, reshape901, reshape902), axis=2) + reshape903: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat51, R.shape([batch_size, 60, 64])) + lv172 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), reshape903), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape904: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv172, R.shape([batch_size, 1, 20, 64])) + reshape905: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape904, R.shape([batch_size, 1, 1280])) + lv340 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_19_self_attn_out_proj_weight3, reshape905, model_decoder_layers_19_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add772: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add768, lv340) + layer_norm220: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add772, model_decoder_layers_19_encoder_attn_layer_norm_weight3, model_decoder_layers_19_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv341 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_19_encoder_attn_q_proj_weight3, layer_norm220, model_decoder_layers_19_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape906: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv341, R.shape([batch_size, 1, 20, 64])) + reshape907: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape906, R.shape([batch_size, 20, 64])) + lv173 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), reshape907), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape908: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv173, R.shape([batch_size, 1, 20, 64])) + reshape909: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape908, R.shape([batch_size, 1, 1280])) + lv342 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_19_encoder_attn_out_proj_weight3, reshape909, model_decoder_layers_19_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add775: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add772, lv342) + layer_norm221: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add775, model_decoder_layers_19_final_layer_norm_weight3, model_decoder_layers_19_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv51 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_19_fc1_weight3, layer_norm221, model_decoder_layers_19_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv343 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_19_fc2_weight3, lv51, model_decoder_layers_19_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add778: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add775, lv343) + layer_norm222: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add778, model_decoder_layers_20_self_attn_layer_norm_weight3, model_decoder_layers_20_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv344 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_20_self_attn_q_proj_weight3, layer_norm222, model_decoder_layers_20_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape910: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv344, R.shape([batch_size, 1, 20, 64])) + lv85 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_20_self_attn_k_proj_weight3, layer_norm222), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape911: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv85, R.shape([batch_size, 1, 20, 64])) + lv345 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_20_self_attn_v_proj_weight3, layer_norm222, model_decoder_layers_20_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape912: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv345, R.shape([batch_size, 1, 20, 64])) + concat52: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape910, reshape911, reshape912), axis=2) + reshape913: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat52, R.shape([batch_size, 60, 64])) + lv174 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), reshape913), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape914: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv174, R.shape([batch_size, 1, 20, 64])) + reshape915: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape914, R.shape([batch_size, 1, 1280])) + lv346 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_20_self_attn_out_proj_weight3, reshape915, model_decoder_layers_20_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add782: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add778, lv346) + layer_norm223: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add782, model_decoder_layers_20_encoder_attn_layer_norm_weight3, model_decoder_layers_20_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv347 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_20_encoder_attn_q_proj_weight3, layer_norm223, model_decoder_layers_20_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape916: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv347, R.shape([batch_size, 1, 20, 64])) + reshape917: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape916, R.shape([batch_size, 20, 64])) + lv175 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), reshape917), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape918: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv175, R.shape([batch_size, 1, 20, 64])) + reshape919: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape918, R.shape([batch_size, 1, 1280])) + lv348 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_20_encoder_attn_out_proj_weight3, reshape919, model_decoder_layers_20_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add785: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add782, lv348) + layer_norm224: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add785, model_decoder_layers_20_final_layer_norm_weight3, model_decoder_layers_20_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv52 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_20_fc1_weight3, layer_norm224, model_decoder_layers_20_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv349 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_20_fc2_weight3, lv52, model_decoder_layers_20_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add788: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add785, lv349) + layer_norm225: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add788, model_decoder_layers_21_self_attn_layer_norm_weight3, model_decoder_layers_21_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv350 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_21_self_attn_q_proj_weight3, layer_norm225, model_decoder_layers_21_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape920: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv350, R.shape([batch_size, 1, 20, 64])) + lv86 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_21_self_attn_k_proj_weight3, layer_norm225), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape921: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv86, R.shape([batch_size, 1, 20, 64])) + lv351 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_21_self_attn_v_proj_weight3, layer_norm225, model_decoder_layers_21_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape922: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv351, R.shape([batch_size, 1, 20, 64])) + concat53: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape920, reshape921, reshape922), axis=2) + reshape923: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat53, R.shape([batch_size, 60, 64])) + lv176 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), reshape923), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape924: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv176, R.shape([batch_size, 1, 20, 64])) + reshape925: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape924, R.shape([batch_size, 1, 1280])) + lv352 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_21_self_attn_out_proj_weight3, reshape925, model_decoder_layers_21_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add792: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add788, lv352) + layer_norm226: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add792, model_decoder_layers_21_encoder_attn_layer_norm_weight3, model_decoder_layers_21_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv353 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_21_encoder_attn_q_proj_weight3, layer_norm226, model_decoder_layers_21_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape926: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv353, R.shape([batch_size, 1, 20, 64])) + reshape927: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape926, R.shape([batch_size, 20, 64])) + lv177 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), reshape927), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape928: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv177, R.shape([batch_size, 1, 20, 64])) + reshape929: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape928, R.shape([batch_size, 1, 1280])) + lv354 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_21_encoder_attn_out_proj_weight3, reshape929, model_decoder_layers_21_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add795: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add792, lv354) + layer_norm227: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add795, model_decoder_layers_21_final_layer_norm_weight3, model_decoder_layers_21_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv53 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_21_fc1_weight3, layer_norm227, model_decoder_layers_21_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv355 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_21_fc2_weight3, lv53, model_decoder_layers_21_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add798: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add795, lv355) + layer_norm228: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add798, model_decoder_layers_22_self_attn_layer_norm_weight3, model_decoder_layers_22_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv356 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_22_self_attn_q_proj_weight3, layer_norm228, model_decoder_layers_22_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape930: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv356, R.shape([batch_size, 1, 20, 64])) + lv87 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_22_self_attn_k_proj_weight3, layer_norm228), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape931: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv87, R.shape([batch_size, 1, 20, 64])) + lv357 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_22_self_attn_v_proj_weight3, layer_norm228, model_decoder_layers_22_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape932: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv357, R.shape([batch_size, 1, 20, 64])) + concat54: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape930, reshape931, reshape932), axis=2) + reshape933: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat54, R.shape([batch_size, 60, 64])) + lv178 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), reshape933), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape934: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv178, R.shape([batch_size, 1, 20, 64])) + reshape935: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape934, R.shape([batch_size, 1, 1280])) + lv358 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_22_self_attn_out_proj_weight3, reshape935, model_decoder_layers_22_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add802: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add798, lv358) + layer_norm229: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add802, model_decoder_layers_22_encoder_attn_layer_norm_weight3, model_decoder_layers_22_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv359 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_22_encoder_attn_q_proj_weight3, layer_norm229, model_decoder_layers_22_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape936: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv359, R.shape([batch_size, 1, 20, 64])) + reshape937: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape936, R.shape([batch_size, 20, 64])) + lv179 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), reshape937), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape938: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv179, R.shape([batch_size, 1, 20, 64])) + reshape939: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape938, R.shape([batch_size, 1, 1280])) + lv360 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_22_encoder_attn_out_proj_weight3, reshape939, model_decoder_layers_22_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add805: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add802, lv360) + layer_norm230: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add805, model_decoder_layers_22_final_layer_norm_weight3, model_decoder_layers_22_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv54 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_22_fc1_weight3, layer_norm230, model_decoder_layers_22_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv361 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_22_fc2_weight3, lv54, model_decoder_layers_22_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add808: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add805, lv361) + layer_norm231: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add808, model_decoder_layers_23_self_attn_layer_norm_weight3, model_decoder_layers_23_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv362 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_23_self_attn_q_proj_weight3, layer_norm231, model_decoder_layers_23_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape940: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv362, R.shape([batch_size, 1, 20, 64])) + lv88 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_23_self_attn_k_proj_weight3, layer_norm231), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape941: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv88, R.shape([batch_size, 1, 20, 64])) + lv363 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_23_self_attn_v_proj_weight3, layer_norm231, model_decoder_layers_23_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape942: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv363, R.shape([batch_size, 1, 20, 64])) + concat55: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape940, reshape941, reshape942), axis=2) + reshape943: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat55, R.shape([batch_size, 60, 64])) + lv180 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), reshape943), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape944: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv180, R.shape([batch_size, 1, 20, 64])) + reshape945: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape944, R.shape([batch_size, 1, 1280])) + lv364 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_23_self_attn_out_proj_weight3, reshape945, model_decoder_layers_23_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add812: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add808, lv364) + layer_norm232: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add812, model_decoder_layers_23_encoder_attn_layer_norm_weight3, model_decoder_layers_23_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv365 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_23_encoder_attn_q_proj_weight3, layer_norm232, model_decoder_layers_23_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape946: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv365, R.shape([batch_size, 1, 20, 64])) + reshape947: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape946, R.shape([batch_size, 20, 64])) + lv181 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), reshape947), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape948: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv181, R.shape([batch_size, 1, 20, 64])) + reshape949: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape948, R.shape([batch_size, 1, 1280])) + lv366 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_23_encoder_attn_out_proj_weight3, reshape949, model_decoder_layers_23_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add815: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add812, lv366) + layer_norm233: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add815, model_decoder_layers_23_final_layer_norm_weight3, model_decoder_layers_23_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv55 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_23_fc1_weight3, layer_norm233, model_decoder_layers_23_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv367 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_23_fc2_weight3, lv55, model_decoder_layers_23_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add818: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add815, lv367) + layer_norm234: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add818, model_decoder_layers_24_self_attn_layer_norm_weight3, model_decoder_layers_24_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv368 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_24_self_attn_q_proj_weight3, layer_norm234, model_decoder_layers_24_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape950: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv368, R.shape([batch_size, 1, 20, 64])) + lv89 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_24_self_attn_k_proj_weight3, layer_norm234), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape951: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv89, R.shape([batch_size, 1, 20, 64])) + lv369 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_24_self_attn_v_proj_weight3, layer_norm234, model_decoder_layers_24_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape952: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv369, R.shape([batch_size, 1, 20, 64])) + concat56: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape950, reshape951, reshape952), axis=2) + reshape953: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat56, R.shape([batch_size, 60, 64])) + lv182 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), reshape953), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape954: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv182, R.shape([batch_size, 1, 20, 64])) + reshape955: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape954, R.shape([batch_size, 1, 1280])) + lv370 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_24_self_attn_out_proj_weight3, reshape955, model_decoder_layers_24_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add822: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add818, lv370) + layer_norm235: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add822, model_decoder_layers_24_encoder_attn_layer_norm_weight3, model_decoder_layers_24_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv371 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_24_encoder_attn_q_proj_weight3, layer_norm235, model_decoder_layers_24_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape956: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv371, R.shape([batch_size, 1, 20, 64])) + reshape957: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape956, R.shape([batch_size, 20, 64])) + lv183 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), reshape957), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape958: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv183, R.shape([batch_size, 1, 20, 64])) + reshape959: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape958, R.shape([batch_size, 1, 1280])) + lv372 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_24_encoder_attn_out_proj_weight3, reshape959, model_decoder_layers_24_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add825: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add822, lv372) + layer_norm236: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add825, model_decoder_layers_24_final_layer_norm_weight3, model_decoder_layers_24_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv56 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_24_fc1_weight3, layer_norm236, model_decoder_layers_24_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv373 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_24_fc2_weight3, lv56, model_decoder_layers_24_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add828: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add825, lv373) + layer_norm237: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add828, model_decoder_layers_25_self_attn_layer_norm_weight3, model_decoder_layers_25_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv374 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_25_self_attn_q_proj_weight3, layer_norm237, model_decoder_layers_25_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape960: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv374, R.shape([batch_size, 1, 20, 64])) + lv90 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_25_self_attn_k_proj_weight3, layer_norm237), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape961: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv90, R.shape([batch_size, 1, 20, 64])) + lv375 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_25_self_attn_v_proj_weight3, layer_norm237, model_decoder_layers_25_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape962: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv375, R.shape([batch_size, 1, 20, 64])) + concat57: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape960, reshape961, reshape962), axis=2) + reshape963: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat57, R.shape([batch_size, 60, 64])) + lv184 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), reshape963), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape964: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv184, R.shape([batch_size, 1, 20, 64])) + reshape965: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape964, R.shape([batch_size, 1, 1280])) + lv376 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_25_self_attn_out_proj_weight3, reshape965, model_decoder_layers_25_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add832: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add828, lv376) + layer_norm238: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add832, model_decoder_layers_25_encoder_attn_layer_norm_weight3, model_decoder_layers_25_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv377 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_25_encoder_attn_q_proj_weight3, layer_norm238, model_decoder_layers_25_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape966: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv377, R.shape([batch_size, 1, 20, 64])) + reshape967: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape966, R.shape([batch_size, 20, 64])) + lv185 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), reshape967), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape968: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv185, R.shape([batch_size, 1, 20, 64])) + reshape969: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape968, R.shape([batch_size, 1, 1280])) + lv378 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_25_encoder_attn_out_proj_weight3, reshape969, model_decoder_layers_25_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add835: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add832, lv378) + layer_norm239: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add835, model_decoder_layers_25_final_layer_norm_weight3, model_decoder_layers_25_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv57 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_25_fc1_weight3, layer_norm239, model_decoder_layers_25_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv379 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_25_fc2_weight3, lv57, model_decoder_layers_25_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add838: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add835, lv379) + layer_norm240: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add838, model_decoder_layers_26_self_attn_layer_norm_weight3, model_decoder_layers_26_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv380 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_26_self_attn_q_proj_weight3, layer_norm240, model_decoder_layers_26_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape970: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv380, R.shape([batch_size, 1, 20, 64])) + lv91 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_26_self_attn_k_proj_weight3, layer_norm240), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape971: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv91, R.shape([batch_size, 1, 20, 64])) + lv381 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_26_self_attn_v_proj_weight3, layer_norm240, model_decoder_layers_26_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape972: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv381, R.shape([batch_size, 1, 20, 64])) + concat58: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape970, reshape971, reshape972), axis=2) + reshape973: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat58, R.shape([batch_size, 60, 64])) + lv186 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), reshape973), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape974: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv186, R.shape([batch_size, 1, 20, 64])) + reshape975: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape974, R.shape([batch_size, 1, 1280])) + lv382 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_26_self_attn_out_proj_weight3, reshape975, model_decoder_layers_26_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add842: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add838, lv382) + layer_norm241: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add842, model_decoder_layers_26_encoder_attn_layer_norm_weight3, model_decoder_layers_26_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv383 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_26_encoder_attn_q_proj_weight3, layer_norm241, model_decoder_layers_26_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape976: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv383, R.shape([batch_size, 1, 20, 64])) + reshape977: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape976, R.shape([batch_size, 20, 64])) + lv187 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), reshape977), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape978: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv187, R.shape([batch_size, 1, 20, 64])) + reshape979: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape978, R.shape([batch_size, 1, 1280])) + lv384 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_26_encoder_attn_out_proj_weight3, reshape979, model_decoder_layers_26_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add845: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add842, lv384) + layer_norm242: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add845, model_decoder_layers_26_final_layer_norm_weight3, model_decoder_layers_26_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv58 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_26_fc1_weight3, layer_norm242, model_decoder_layers_26_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv385 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_26_fc2_weight3, lv58, model_decoder_layers_26_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add848: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add845, lv385) + layer_norm243: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add848, model_decoder_layers_27_self_attn_layer_norm_weight3, model_decoder_layers_27_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv386 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_27_self_attn_q_proj_weight3, layer_norm243, model_decoder_layers_27_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape980: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv386, R.shape([batch_size, 1, 20, 64])) + lv92 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_27_self_attn_k_proj_weight3, layer_norm243), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape981: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv92, R.shape([batch_size, 1, 20, 64])) + lv387 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_27_self_attn_v_proj_weight3, layer_norm243, model_decoder_layers_27_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape982: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv387, R.shape([batch_size, 1, 20, 64])) + concat59: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape980, reshape981, reshape982), axis=2) + reshape983: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat59, R.shape([batch_size, 60, 64])) + lv188 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), reshape983), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape984: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv188, R.shape([batch_size, 1, 20, 64])) + reshape985: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape984, R.shape([batch_size, 1, 1280])) + lv388 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_27_self_attn_out_proj_weight3, reshape985, model_decoder_layers_27_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add852: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add848, lv388) + layer_norm244: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add852, model_decoder_layers_27_encoder_attn_layer_norm_weight3, model_decoder_layers_27_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv389 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_27_encoder_attn_q_proj_weight3, layer_norm244, model_decoder_layers_27_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape986: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv389, R.shape([batch_size, 1, 20, 64])) + reshape987: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape986, R.shape([batch_size, 20, 64])) + lv189 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), reshape987), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape988: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv189, R.shape([batch_size, 1, 20, 64])) + reshape989: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape988, R.shape([batch_size, 1, 1280])) + lv390 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_27_encoder_attn_out_proj_weight3, reshape989, model_decoder_layers_27_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add855: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add852, lv390) + layer_norm245: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add855, model_decoder_layers_27_final_layer_norm_weight3, model_decoder_layers_27_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv59 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_27_fc1_weight3, layer_norm245, model_decoder_layers_27_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv391 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_27_fc2_weight3, lv59, model_decoder_layers_27_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add858: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add855, lv391) + layer_norm246: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add858, model_decoder_layers_28_self_attn_layer_norm_weight3, model_decoder_layers_28_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv392 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_28_self_attn_q_proj_weight3, layer_norm246, model_decoder_layers_28_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape990: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv392, R.shape([batch_size, 1, 20, 64])) + lv93 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_28_self_attn_k_proj_weight3, layer_norm246), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape991: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv93, R.shape([batch_size, 1, 20, 64])) + lv393 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_28_self_attn_v_proj_weight3, layer_norm246, model_decoder_layers_28_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape992: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv393, R.shape([batch_size, 1, 20, 64])) + concat60: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape990, reshape991, reshape992), axis=2) + reshape993: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat60, R.shape([batch_size, 60, 64])) + lv190 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), reshape993), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape994: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv190, R.shape([batch_size, 1, 20, 64])) + reshape995: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape994, R.shape([batch_size, 1, 1280])) + lv394 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_28_self_attn_out_proj_weight3, reshape995, model_decoder_layers_28_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add862: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add858, lv394) + layer_norm247: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add862, model_decoder_layers_28_encoder_attn_layer_norm_weight3, model_decoder_layers_28_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv395 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_28_encoder_attn_q_proj_weight3, layer_norm247, model_decoder_layers_28_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape996: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv395, R.shape([batch_size, 1, 20, 64])) + reshape997: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape996, R.shape([batch_size, 20, 64])) + lv191 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), reshape997), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape998: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv191, R.shape([batch_size, 1, 20, 64])) + reshape999: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape998, R.shape([batch_size, 1, 1280])) + lv396 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_28_encoder_attn_out_proj_weight3, reshape999, model_decoder_layers_28_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add865: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add862, lv396) + layer_norm248: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add865, model_decoder_layers_28_final_layer_norm_weight3, model_decoder_layers_28_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv60 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_28_fc1_weight3, layer_norm248, model_decoder_layers_28_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv397 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_28_fc2_weight3, lv60, model_decoder_layers_28_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add868: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add865, lv397) + layer_norm249: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add868, model_decoder_layers_29_self_attn_layer_norm_weight3, model_decoder_layers_29_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv398 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_29_self_attn_q_proj_weight3, layer_norm249, model_decoder_layers_29_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape1000: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv398, R.shape([batch_size, 1, 20, 64])) + lv94 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_29_self_attn_k_proj_weight3, layer_norm249), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape1001: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv94, R.shape([batch_size, 1, 20, 64])) + lv399 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_29_self_attn_v_proj_weight3, layer_norm249, model_decoder_layers_29_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape1002: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv399, R.shape([batch_size, 1, 20, 64])) + concat61: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape1000, reshape1001, reshape1002), axis=2) + reshape1003: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat61, R.shape([batch_size, 60, 64])) + lv192 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), reshape1003), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape1004: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv192, R.shape([batch_size, 1, 20, 64])) + reshape1005: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape1004, R.shape([batch_size, 1, 1280])) + lv400 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_29_self_attn_out_proj_weight3, reshape1005, model_decoder_layers_29_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add872: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add868, lv400) + layer_norm250: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add872, model_decoder_layers_29_encoder_attn_layer_norm_weight3, model_decoder_layers_29_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv401 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_29_encoder_attn_q_proj_weight3, layer_norm250, model_decoder_layers_29_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape1006: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv401, R.shape([batch_size, 1, 20, 64])) + reshape1007: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape1006, R.shape([batch_size, 20, 64])) + lv193 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), reshape1007), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape1008: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv193, R.shape([batch_size, 1, 20, 64])) + reshape1009: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape1008, R.shape([batch_size, 1, 1280])) + lv402 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_29_encoder_attn_out_proj_weight3, reshape1009, model_decoder_layers_29_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add875: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add872, lv402) + layer_norm251: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add875, model_decoder_layers_29_final_layer_norm_weight3, model_decoder_layers_29_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv61 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_29_fc1_weight3, layer_norm251, model_decoder_layers_29_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv403 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_29_fc2_weight3, lv61, model_decoder_layers_29_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add878: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add875, lv403) + layer_norm252: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add878, model_decoder_layers_30_self_attn_layer_norm_weight3, model_decoder_layers_30_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv404 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_30_self_attn_q_proj_weight3, layer_norm252, model_decoder_layers_30_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape1010: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv404, R.shape([batch_size, 1, 20, 64])) + lv95 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_30_self_attn_k_proj_weight3, layer_norm252), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape1011: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv95, R.shape([batch_size, 1, 20, 64])) + lv405 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_30_self_attn_v_proj_weight3, layer_norm252, model_decoder_layers_30_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape1012: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv405, R.shape([batch_size, 1, 20, 64])) + concat62: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape1010, reshape1011, reshape1012), axis=2) + reshape1013: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat62, R.shape([batch_size, 60, 64])) + lv194 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), reshape1013), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape1014: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv194, R.shape([batch_size, 1, 20, 64])) + reshape1015: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape1014, R.shape([batch_size, 1, 1280])) + lv406 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_30_self_attn_out_proj_weight3, reshape1015, model_decoder_layers_30_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add882: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add878, lv406) + layer_norm253: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add882, model_decoder_layers_30_encoder_attn_layer_norm_weight3, model_decoder_layers_30_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv407 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_30_encoder_attn_q_proj_weight3, layer_norm253, model_decoder_layers_30_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape1016: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv407, R.shape([batch_size, 1, 20, 64])) + reshape1017: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape1016, R.shape([batch_size, 20, 64])) + lv195 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), reshape1017), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape1018: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv195, R.shape([batch_size, 1, 20, 64])) + reshape1019: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape1018, R.shape([batch_size, 1, 1280])) + lv408 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_30_encoder_attn_out_proj_weight3, reshape1019, model_decoder_layers_30_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add885: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add882, lv408) + layer_norm254: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add885, model_decoder_layers_30_final_layer_norm_weight3, model_decoder_layers_30_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv62 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_30_fc1_weight3, layer_norm254, model_decoder_layers_30_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv409 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_30_fc2_weight3, lv62, model_decoder_layers_30_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add888: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add885, lv409) + layer_norm255: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add888, model_decoder_layers_31_self_attn_layer_norm_weight3, model_decoder_layers_31_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv410 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_31_self_attn_q_proj_weight3, layer_norm255, model_decoder_layers_31_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape1020: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv410, R.shape([batch_size, 1, 20, 64])) + lv96 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_31_self_attn_k_proj_weight3, layer_norm255), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape1021: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv96, R.shape([batch_size, 1, 20, 64])) + lv411 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_31_self_attn_v_proj_weight3, layer_norm255, model_decoder_layers_31_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape1022: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv411, R.shape([batch_size, 1, 20, 64])) + concat63: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape1020, reshape1021, reshape1022), axis=2) + reshape1023: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat63, R.shape([batch_size, 60, 64])) + lv196 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), reshape1023), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape1024: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv196, R.shape([batch_size, 1, 20, 64])) + reshape1025: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape1024, R.shape([batch_size, 1, 1280])) + lv412 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_31_self_attn_out_proj_weight3, reshape1025, model_decoder_layers_31_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add892: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add888, lv412) + layer_norm256: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add892, model_decoder_layers_31_encoder_attn_layer_norm_weight3, model_decoder_layers_31_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv413 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_31_encoder_attn_q_proj_weight3, layer_norm256, model_decoder_layers_31_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape1026: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv413, R.shape([batch_size, 1, 20, 64])) + reshape1027: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape1026, R.shape([batch_size, 20, 64])) + lv197 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), reshape1027), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape1028: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv197, R.shape([batch_size, 1, 20, 64])) + reshape1029: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape1028, R.shape([batch_size, 1, 1280])) + lv414 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_31_encoder_attn_out_proj_weight3, reshape1029, model_decoder_layers_31_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add895: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add892, lv414) + layer_norm257: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add895, model_decoder_layers_31_final_layer_norm_weight3, model_decoder_layers_31_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv63 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_31_fc1_weight3, layer_norm257, model_decoder_layers_31_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv415 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_31_fc2_weight3, lv63, model_decoder_layers_31_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add898: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add895, lv415) + layer_norm258: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add898, model_decoder_layer_norm_weight3, model_decoder_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv97 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul4_cublas", (model_decoder_embed_tokens_weight3, layer_norm258), out_sinfo=R.Tensor((batch_size, 1, 51866), dtype="float32")) + gv3: R.Tensor((batch_size, 1, 51866), dtype="float32") = lv97 + R.output(gv3) + return gv3 + + @R.function + def batch_encode(input_features: R.Tensor(("batch_size", 128, 3000), dtype="float16"), paged_kv_cache: R.Object, packed_params: R.Tuple(R.Tensor((1280, 128, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1500, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), 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dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"))) -> R.Tensor(("batch_size", 1500, 1280), dtype="float16"): + batch_size = T.int64() + R.func_attr({"num_input": 2, "relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "seq_len": 15000, "total_seq_len": 1500}}) + with R.dataflow(): + model_encoder_conv1_weight: R.Tensor((1280, 128, 3), dtype="float16") = packed_params[0] + model_encoder_conv1_bias: R.Tensor((1280,), dtype="float16") = packed_params[1] + model_encoder_conv2_weight: R.Tensor((1280, 1280, 3), dtype="float16") = packed_params[2] + model_encoder_conv2_bias: R.Tensor((1280,), dtype="float16") = packed_params[3] + model_encoder_embed_positions_weight: R.Tensor((1500, 1280), dtype="float16") = packed_params[4] + model_encoder_layers_0_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[5] + model_encoder_layers_0_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[6] + model_encoder_layers_0_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[7] + model_encoder_layers_0_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[8] + model_encoder_layers_0_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[9] + model_encoder_layers_0_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[10] + model_encoder_layers_0_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[11] + model_encoder_layers_0_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[12] + model_encoder_layers_0_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[13] + model_encoder_layers_0_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[14] + model_encoder_layers_0_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[15] + model_encoder_layers_0_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[16] + model_encoder_layers_0_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[17] + model_encoder_layers_0_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[18] + model_encoder_layers_0_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[19] + model_encoder_layers_1_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[20] + model_encoder_layers_1_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[21] + model_encoder_layers_1_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[22] + model_encoder_layers_1_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[23] + model_encoder_layers_1_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[24] + model_encoder_layers_1_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[25] + model_encoder_layers_1_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[26] + model_encoder_layers_1_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[27] + model_encoder_layers_1_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[28] + model_encoder_layers_1_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[29] + model_encoder_layers_1_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[30] + model_encoder_layers_1_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[31] + model_encoder_layers_1_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[32] + model_encoder_layers_1_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[33] + model_encoder_layers_1_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[34] + model_encoder_layers_2_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[35] + model_encoder_layers_2_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[36] + model_encoder_layers_2_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[37] + model_encoder_layers_2_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[38] + model_encoder_layers_2_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[39] + model_encoder_layers_2_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[40] + model_encoder_layers_2_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[41] + model_encoder_layers_2_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[42] + model_encoder_layers_2_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[43] + model_encoder_layers_2_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[44] + model_encoder_layers_2_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[45] + model_encoder_layers_2_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[46] + model_encoder_layers_2_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[47] + model_encoder_layers_2_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[48] + model_encoder_layers_2_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[49] + model_encoder_layers_3_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[50] + model_encoder_layers_3_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[51] + model_encoder_layers_3_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[52] + model_encoder_layers_3_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[53] + model_encoder_layers_3_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[54] + model_encoder_layers_3_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[55] + model_encoder_layers_3_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[56] + model_encoder_layers_3_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[57] + model_encoder_layers_3_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[58] + model_encoder_layers_3_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[59] + model_encoder_layers_3_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[60] + model_encoder_layers_3_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[61] + model_encoder_layers_3_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[62] + model_encoder_layers_3_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[63] + model_encoder_layers_3_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[64] + model_encoder_layers_4_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[65] + model_encoder_layers_4_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[66] + model_encoder_layers_4_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[67] + model_encoder_layers_4_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[68] + model_encoder_layers_4_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[69] + model_encoder_layers_4_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[70] + model_encoder_layers_4_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[71] + model_encoder_layers_4_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[72] + model_encoder_layers_4_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[73] + model_encoder_layers_4_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[74] + model_encoder_layers_4_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[75] + model_encoder_layers_4_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[76] + model_encoder_layers_4_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[77] + model_encoder_layers_4_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[78] + model_encoder_layers_4_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[79] + model_encoder_layers_5_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[80] + model_encoder_layers_5_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[81] + model_encoder_layers_5_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[82] + model_encoder_layers_5_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[83] + model_encoder_layers_5_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[84] + model_encoder_layers_5_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[85] + model_encoder_layers_5_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[86] + model_encoder_layers_5_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[87] + model_encoder_layers_5_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[88] + model_encoder_layers_5_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[89] + model_encoder_layers_5_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[90] + model_encoder_layers_5_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[91] + model_encoder_layers_5_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[92] + model_encoder_layers_5_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[93] + model_encoder_layers_5_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[94] + model_encoder_layers_6_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[95] + model_encoder_layers_6_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[96] + model_encoder_layers_6_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[97] + model_encoder_layers_6_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[98] + model_encoder_layers_6_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[99] + model_encoder_layers_6_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[100] + model_encoder_layers_6_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[101] + model_encoder_layers_6_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[102] + model_encoder_layers_6_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[103] + model_encoder_layers_6_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[104] + model_encoder_layers_6_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[105] + model_encoder_layers_6_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[106] + model_encoder_layers_6_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[107] + model_encoder_layers_6_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[108] + model_encoder_layers_6_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[109] + model_encoder_layers_7_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[110] + model_encoder_layers_7_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[111] + model_encoder_layers_7_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[112] + model_encoder_layers_7_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[113] + model_encoder_layers_7_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[114] + model_encoder_layers_7_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[115] + model_encoder_layers_7_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[116] + model_encoder_layers_7_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[117] + model_encoder_layers_7_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[118] + model_encoder_layers_7_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[119] + model_encoder_layers_7_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[120] + model_encoder_layers_7_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[121] + model_encoder_layers_7_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[122] + model_encoder_layers_7_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[123] + model_encoder_layers_7_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[124] + model_encoder_layers_8_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[125] + model_encoder_layers_8_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[126] + model_encoder_layers_8_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[127] + model_encoder_layers_8_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[128] + model_encoder_layers_8_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[129] + model_encoder_layers_8_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[130] + model_encoder_layers_8_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[131] + model_encoder_layers_8_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[132] + model_encoder_layers_8_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[133] + model_encoder_layers_8_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[134] + model_encoder_layers_8_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[135] + model_encoder_layers_8_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[136] + model_encoder_layers_8_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[137] + model_encoder_layers_8_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[138] + model_encoder_layers_8_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[139] + model_encoder_layers_9_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[140] + model_encoder_layers_9_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[141] + model_encoder_layers_9_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[142] + model_encoder_layers_9_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[143] + model_encoder_layers_9_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[144] + model_encoder_layers_9_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[145] + model_encoder_layers_9_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[146] + model_encoder_layers_9_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[147] + model_encoder_layers_9_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[148] + model_encoder_layers_9_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[149] + model_encoder_layers_9_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[150] + model_encoder_layers_9_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[151] + model_encoder_layers_9_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[152] + model_encoder_layers_9_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[153] + model_encoder_layers_9_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[154] + model_encoder_layers_10_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[155] + model_encoder_layers_10_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[156] + model_encoder_layers_10_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[157] + model_encoder_layers_10_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[158] + model_encoder_layers_10_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[159] + model_encoder_layers_10_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[160] + model_encoder_layers_10_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[161] + model_encoder_layers_10_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[162] + model_encoder_layers_10_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[163] + model_encoder_layers_10_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[164] + model_encoder_layers_10_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[165] + model_encoder_layers_10_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[166] + model_encoder_layers_10_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[167] + model_encoder_layers_10_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[168] + model_encoder_layers_10_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[169] + model_encoder_layers_11_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[170] + model_encoder_layers_11_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[171] + model_encoder_layers_11_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[172] + model_encoder_layers_11_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[173] + model_encoder_layers_11_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[174] + model_encoder_layers_11_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[175] + model_encoder_layers_11_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[176] + model_encoder_layers_11_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[177] + model_encoder_layers_11_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[178] + model_encoder_layers_11_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[179] + model_encoder_layers_11_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[180] + model_encoder_layers_11_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[181] + model_encoder_layers_11_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[182] + model_encoder_layers_11_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[183] + model_encoder_layers_11_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[184] + model_encoder_layers_12_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[185] + model_encoder_layers_12_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[186] + model_encoder_layers_12_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[187] + model_encoder_layers_12_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[188] + model_encoder_layers_12_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[189] + model_encoder_layers_12_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[190] + model_encoder_layers_12_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[191] + model_encoder_layers_12_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[192] + model_encoder_layers_12_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[193] + model_encoder_layers_12_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[194] + model_encoder_layers_12_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[195] + model_encoder_layers_12_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[196] + model_encoder_layers_12_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[197] + model_encoder_layers_12_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[198] + model_encoder_layers_12_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[199] + model_encoder_layers_13_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[200] + model_encoder_layers_13_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[201] + model_encoder_layers_13_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[202] + model_encoder_layers_13_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[203] + model_encoder_layers_13_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[204] + model_encoder_layers_13_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[205] + model_encoder_layers_13_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[206] + model_encoder_layers_13_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[207] + model_encoder_layers_13_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[208] + model_encoder_layers_13_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[209] + model_encoder_layers_13_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[210] + model_encoder_layers_13_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[211] + model_encoder_layers_13_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[212] + model_encoder_layers_13_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[213] + model_encoder_layers_13_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[214] + model_encoder_layers_14_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[215] + model_encoder_layers_14_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[216] + model_encoder_layers_14_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[217] + model_encoder_layers_14_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[218] + model_encoder_layers_14_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[219] + model_encoder_layers_14_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[220] + model_encoder_layers_14_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[221] + model_encoder_layers_14_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[222] + model_encoder_layers_14_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[223] + model_encoder_layers_14_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[224] + model_encoder_layers_14_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[225] + model_encoder_layers_14_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[226] + model_encoder_layers_14_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[227] + model_encoder_layers_14_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[228] + model_encoder_layers_14_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[229] + model_encoder_layers_15_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[230] + model_encoder_layers_15_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[231] + model_encoder_layers_15_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[232] + model_encoder_layers_15_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[233] + model_encoder_layers_15_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[234] + model_encoder_layers_15_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[235] + model_encoder_layers_15_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[236] + model_encoder_layers_15_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[237] + model_encoder_layers_15_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[238] + model_encoder_layers_15_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[239] + model_encoder_layers_15_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[240] + model_encoder_layers_15_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[241] + model_encoder_layers_15_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[242] + model_encoder_layers_15_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[243] + model_encoder_layers_15_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[244] + model_encoder_layers_16_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[245] + model_encoder_layers_16_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[246] + model_encoder_layers_16_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[247] + model_encoder_layers_16_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[248] + model_encoder_layers_16_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[249] + model_encoder_layers_16_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[250] + model_encoder_layers_16_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[251] + model_encoder_layers_16_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[252] + model_encoder_layers_16_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[253] + model_encoder_layers_16_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[254] + model_encoder_layers_16_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[255] + model_encoder_layers_16_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[256] + model_encoder_layers_16_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[257] + model_encoder_layers_16_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[258] + model_encoder_layers_16_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[259] + model_encoder_layers_17_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[260] + model_encoder_layers_17_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[261] + model_encoder_layers_17_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[262] + model_encoder_layers_17_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[263] + model_encoder_layers_17_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[264] + model_encoder_layers_17_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[265] + model_encoder_layers_17_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[266] + model_encoder_layers_17_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[267] + model_encoder_layers_17_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[268] + model_encoder_layers_17_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[269] + model_encoder_layers_17_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[270] + model_encoder_layers_17_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[271] + model_encoder_layers_17_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[272] + model_encoder_layers_17_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[273] + model_encoder_layers_17_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[274] + model_encoder_layers_18_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[275] + model_encoder_layers_18_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[276] + model_encoder_layers_18_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[277] + model_encoder_layers_18_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[278] + model_encoder_layers_18_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[279] + model_encoder_layers_18_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[280] + model_encoder_layers_18_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[281] + model_encoder_layers_18_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[282] + model_encoder_layers_18_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[283] + model_encoder_layers_18_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[284] + model_encoder_layers_18_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[285] + model_encoder_layers_18_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[286] + model_encoder_layers_18_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[287] + model_encoder_layers_18_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[288] + model_encoder_layers_18_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[289] + model_encoder_layers_19_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[290] + model_encoder_layers_19_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[291] + model_encoder_layers_19_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[292] + model_encoder_layers_19_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[293] + model_encoder_layers_19_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[294] + model_encoder_layers_19_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[295] + model_encoder_layers_19_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[296] + model_encoder_layers_19_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[297] + model_encoder_layers_19_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[298] + model_encoder_layers_19_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[299] + model_encoder_layers_19_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[300] + model_encoder_layers_19_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[301] + model_encoder_layers_19_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[302] + model_encoder_layers_19_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[303] + model_encoder_layers_19_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[304] + model_encoder_layers_20_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[305] + model_encoder_layers_20_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[306] + model_encoder_layers_20_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[307] + model_encoder_layers_20_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[308] + model_encoder_layers_20_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[309] + model_encoder_layers_20_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[310] + model_encoder_layers_20_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[311] + model_encoder_layers_20_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[312] + model_encoder_layers_20_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[313] + model_encoder_layers_20_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[314] + model_encoder_layers_20_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[315] + model_encoder_layers_20_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[316] + model_encoder_layers_20_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[317] + model_encoder_layers_20_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[318] + model_encoder_layers_20_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[319] + model_encoder_layers_21_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[320] + model_encoder_layers_21_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[321] + model_encoder_layers_21_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[322] + model_encoder_layers_21_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[323] + model_encoder_layers_21_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[324] + model_encoder_layers_21_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[325] + model_encoder_layers_21_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[326] + model_encoder_layers_21_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[327] + model_encoder_layers_21_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[328] + model_encoder_layers_21_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[329] + model_encoder_layers_21_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[330] + model_encoder_layers_21_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[331] + model_encoder_layers_21_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[332] + model_encoder_layers_21_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[333] + model_encoder_layers_21_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[334] + model_encoder_layers_22_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[335] + model_encoder_layers_22_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[336] + model_encoder_layers_22_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[337] + model_encoder_layers_22_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[338] + model_encoder_layers_22_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[339] + model_encoder_layers_22_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[340] + model_encoder_layers_22_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[341] + model_encoder_layers_22_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[342] + model_encoder_layers_22_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[343] + model_encoder_layers_22_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[344] + model_encoder_layers_22_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[345] + model_encoder_layers_22_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[346] + model_encoder_layers_22_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[347] + model_encoder_layers_22_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[348] + model_encoder_layers_22_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[349] + model_encoder_layers_23_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[350] + model_encoder_layers_23_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[351] + model_encoder_layers_23_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[352] + model_encoder_layers_23_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[353] + model_encoder_layers_23_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[354] + model_encoder_layers_23_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[355] + model_encoder_layers_23_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[356] + model_encoder_layers_23_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[357] + model_encoder_layers_23_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[358] + model_encoder_layers_23_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[359] + model_encoder_layers_23_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[360] + model_encoder_layers_23_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[361] + model_encoder_layers_23_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[362] + model_encoder_layers_23_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[363] + model_encoder_layers_23_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[364] + model_encoder_layers_24_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[365] + model_encoder_layers_24_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[366] + model_encoder_layers_24_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[367] + model_encoder_layers_24_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[368] + model_encoder_layers_24_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[369] + model_encoder_layers_24_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[370] + model_encoder_layers_24_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[371] + model_encoder_layers_24_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[372] + model_encoder_layers_24_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[373] + model_encoder_layers_24_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[374] + model_encoder_layers_24_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[375] + model_encoder_layers_24_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[376] + model_encoder_layers_24_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[377] + model_encoder_layers_24_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[378] + model_encoder_layers_24_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[379] + model_encoder_layers_25_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[380] + model_encoder_layers_25_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[381] + model_encoder_layers_25_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[382] + model_encoder_layers_25_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[383] + model_encoder_layers_25_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[384] + model_encoder_layers_25_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[385] + model_encoder_layers_25_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[386] + model_encoder_layers_25_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[387] + model_encoder_layers_25_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[388] + model_encoder_layers_25_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[389] + model_encoder_layers_25_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[390] + model_encoder_layers_25_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[391] + model_encoder_layers_25_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[392] + model_encoder_layers_25_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[393] + model_encoder_layers_25_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[394] + model_encoder_layers_26_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[395] + model_encoder_layers_26_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[396] + model_encoder_layers_26_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[397] + model_encoder_layers_26_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[398] + model_encoder_layers_26_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[399] + model_encoder_layers_26_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[400] + model_encoder_layers_26_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[401] + model_encoder_layers_26_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[402] + model_encoder_layers_26_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[403] + model_encoder_layers_26_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[404] + model_encoder_layers_26_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[405] + model_encoder_layers_26_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[406] + model_encoder_layers_26_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[407] + model_encoder_layers_26_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[408] + model_encoder_layers_26_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[409] + model_encoder_layers_27_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[410] + model_encoder_layers_27_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[411] + model_encoder_layers_27_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[412] + model_encoder_layers_27_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[413] + model_encoder_layers_27_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[414] + model_encoder_layers_27_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[415] + model_encoder_layers_27_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[416] + model_encoder_layers_27_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[417] + model_encoder_layers_27_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[418] + model_encoder_layers_27_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[419] + model_encoder_layers_27_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[420] + model_encoder_layers_27_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[421] + model_encoder_layers_27_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[422] + model_encoder_layers_27_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[423] + model_encoder_layers_27_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[424] + model_encoder_layers_28_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[425] + model_encoder_layers_28_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[426] + model_encoder_layers_28_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[427] + model_encoder_layers_28_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[428] + model_encoder_layers_28_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[429] + model_encoder_layers_28_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[430] + model_encoder_layers_28_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[431] + model_encoder_layers_28_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[432] + model_encoder_layers_28_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[433] + model_encoder_layers_28_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[434] + model_encoder_layers_28_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[435] + model_encoder_layers_28_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[436] + model_encoder_layers_28_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[437] + model_encoder_layers_28_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[438] + model_encoder_layers_28_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[439] + model_encoder_layers_29_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[440] + model_encoder_layers_29_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[441] + model_encoder_layers_29_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[442] + model_encoder_layers_29_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[443] + model_encoder_layers_29_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[444] + model_encoder_layers_29_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[445] + model_encoder_layers_29_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[446] + model_encoder_layers_29_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[447] + model_encoder_layers_29_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[448] + model_encoder_layers_29_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[449] + model_encoder_layers_29_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[450] + model_encoder_layers_29_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[451] + model_encoder_layers_29_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[452] + model_encoder_layers_29_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[453] + model_encoder_layers_29_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[454] + model_encoder_layers_30_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[455] + model_encoder_layers_30_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[456] + model_encoder_layers_30_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[457] + model_encoder_layers_30_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[458] + model_encoder_layers_30_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[459] + model_encoder_layers_30_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[460] + model_encoder_layers_30_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[461] + model_encoder_layers_30_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[462] + model_encoder_layers_30_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[463] + model_encoder_layers_30_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[464] + model_encoder_layers_30_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[465] + model_encoder_layers_30_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[466] + model_encoder_layers_30_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[467] + model_encoder_layers_30_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[468] + model_encoder_layers_30_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[469] + model_encoder_layers_31_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[470] + model_encoder_layers_31_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[471] + model_encoder_layers_31_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[472] + model_encoder_layers_31_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[473] + model_encoder_layers_31_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[474] + model_encoder_layers_31_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[475] + model_encoder_layers_31_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[476] + model_encoder_layers_31_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[477] + model_encoder_layers_31_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[478] + model_encoder_layers_31_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[479] + model_encoder_layers_31_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[480] + model_encoder_layers_31_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[481] + model_encoder_layers_31_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[482] + model_encoder_layers_31_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[483] + model_encoder_layers_31_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[484] + model_encoder_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[485] + model_encoder_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[486] + lv: R.Tensor((batch_size, 1280, 3000), dtype="float16") = R.nn.conv1d(input_features, model_encoder_conv1_weight, strides=[1], padding=[1, 1], dilation=[1], groups=1, data_layout="NCW", kernel_layout="OIW", out_layout="NCW", out_dtype="void") + lv1: R.Tensor((1, 1280, 1), dtype="float16") = R.reshape(model_encoder_conv1_bias, R.shape([1, 1280, 1])) + conv1d: R.Tensor((batch_size, 1280, 3000), dtype="float16") = R.add(lv, lv1) + gelu: R.Tensor((batch_size, 1280, 3000), dtype="float16") = R.nn.gelu(conv1d) + lv2: R.Tensor((batch_size, 1280, 1500), dtype="float16") = R.nn.conv1d(gelu, model_encoder_conv2_weight, strides=[2], padding=[1, 1], dilation=[1], groups=1, data_layout="NCW", kernel_layout="OIW", out_layout="NCW", out_dtype="void") + lv3: R.Tensor((1, 1280, 1), dtype="float16") = R.reshape(model_encoder_conv2_bias, R.shape([1, 1280, 1])) + conv1d1: R.Tensor((batch_size, 1280, 1500), dtype="float16") = R.add(lv2, lv3) + gelu1: R.Tensor((batch_size, 1280, 1500), dtype="float16") = R.nn.gelu(conv1d1) + permute_dims: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.permute_dims(gelu1, axes=[0, 2, 1]) + add: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(permute_dims, model_encoder_embed_positions_weight) + layer_norm: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add, model_encoder_layers_0_self_attn_layer_norm_weight, model_encoder_layers_0_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv608 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_0_self_attn_q_proj_weight, layer_norm, model_encoder_layers_0_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv608, R.shape([batch_size, 1500, 20, 64])) + lv131 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_0_self_attn_k_proj_weight, layer_norm), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape1: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv131, R.shape([batch_size, 1500, 20, 64])) + lv609 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_0_self_attn_v_proj_weight, layer_norm, model_encoder_layers_0_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape2: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv609, R.shape([batch_size, 1500, 20, 64])) + reshape3: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape, R.shape([batch_size * 1500, 20, 64])) + reshape4: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape1, R.shape([batch_size * 1500, 20, 64])) + reshape5: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape2, R.shape([batch_size * 1500, 20, 64])) + lv4 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), reshape3, reshape4, reshape5), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape6: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv4, R.shape([batch_size, 1500, 20, 64])) + reshape7: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape6, R.shape([batch_size, 1500, 1280])) + lv610 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_0_self_attn_out_proj_weight, reshape7, model_encoder_layers_0_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add4: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add, lv610) + layer_norm1: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add4, model_encoder_layers_0_final_layer_norm_weight, model_encoder_layers_0_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv96 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_0_fc1_weight, layer_norm1, model_encoder_layers_0_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv611 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_0_fc2_weight, lv96, model_encoder_layers_0_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add7: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add4, lv611) + maximum: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add7, R.const(-65504, "float16")) + minimum: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum, R.const(65504, "float16")) + layer_norm2: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum, model_encoder_layers_1_self_attn_layer_norm_weight, model_encoder_layers_1_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv612 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_1_self_attn_q_proj_weight, layer_norm2, model_encoder_layers_1_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape8: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv612, R.shape([batch_size, 1500, 20, 64])) + lv132 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_1_self_attn_k_proj_weight, layer_norm2), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape9: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv132, R.shape([batch_size, 1500, 20, 64])) + lv613 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_1_self_attn_v_proj_weight, layer_norm2, model_encoder_layers_1_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape10: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv613, R.shape([batch_size, 1500, 20, 64])) + reshape11: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape8, R.shape([batch_size * 1500, 20, 64])) + reshape12: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape9, R.shape([batch_size * 1500, 20, 64])) + reshape13: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape10, R.shape([batch_size * 1500, 20, 64])) + lv5 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), reshape11, reshape12, reshape13), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape14: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv5, R.shape([batch_size, 1500, 20, 64])) + reshape15: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape14, R.shape([batch_size, 1500, 1280])) + lv614 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_1_self_attn_out_proj_weight, reshape15, model_encoder_layers_1_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add11: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum, lv614) + layer_norm3: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add11, model_encoder_layers_1_final_layer_norm_weight, model_encoder_layers_1_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv97 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_1_fc1_weight, layer_norm3, model_encoder_layers_1_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv615 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_1_fc2_weight, lv97, model_encoder_layers_1_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add14: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add11, lv615) + maximum1: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add14, R.const(-65504, "float16")) + minimum1: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum1, R.const(65504, "float16")) + layer_norm4: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum1, model_encoder_layers_2_self_attn_layer_norm_weight, model_encoder_layers_2_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv616 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_2_self_attn_q_proj_weight, layer_norm4, model_encoder_layers_2_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape16: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv616, R.shape([batch_size, 1500, 20, 64])) + lv133 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_2_self_attn_k_proj_weight, layer_norm4), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape17: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv133, R.shape([batch_size, 1500, 20, 64])) + lv617 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_2_self_attn_v_proj_weight, layer_norm4, model_encoder_layers_2_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape18: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv617, R.shape([batch_size, 1500, 20, 64])) + reshape19: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape16, R.shape([batch_size * 1500, 20, 64])) + reshape20: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape17, R.shape([batch_size * 1500, 20, 64])) + reshape21: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape18, R.shape([batch_size * 1500, 20, 64])) + lv6 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), reshape19, reshape20, reshape21), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape22: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv6, R.shape([batch_size, 1500, 20, 64])) + reshape23: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape22, R.shape([batch_size, 1500, 1280])) + lv618 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_2_self_attn_out_proj_weight, reshape23, model_encoder_layers_2_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add18: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum1, lv618) + layer_norm5: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add18, model_encoder_layers_2_final_layer_norm_weight, model_encoder_layers_2_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv98 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_2_fc1_weight, layer_norm5, model_encoder_layers_2_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv619 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_2_fc2_weight, lv98, model_encoder_layers_2_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add21: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add18, lv619) + maximum2: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add21, R.const(-65504, "float16")) + minimum2: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum2, R.const(65504, "float16")) + layer_norm6: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum2, model_encoder_layers_3_self_attn_layer_norm_weight, model_encoder_layers_3_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv620 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_3_self_attn_q_proj_weight, layer_norm6, model_encoder_layers_3_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape24: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv620, R.shape([batch_size, 1500, 20, 64])) + lv134 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_3_self_attn_k_proj_weight, layer_norm6), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape25: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv134, R.shape([batch_size, 1500, 20, 64])) + lv621 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_3_self_attn_v_proj_weight, layer_norm6, model_encoder_layers_3_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape26: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv621, R.shape([batch_size, 1500, 20, 64])) + reshape27: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape24, R.shape([batch_size * 1500, 20, 64])) + reshape28: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape25, R.shape([batch_size * 1500, 20, 64])) + reshape29: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape26, R.shape([batch_size * 1500, 20, 64])) + lv7 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), reshape27, reshape28, reshape29), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape30: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv7, R.shape([batch_size, 1500, 20, 64])) + reshape31: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape30, R.shape([batch_size, 1500, 1280])) + lv622 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_3_self_attn_out_proj_weight, reshape31, model_encoder_layers_3_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add25: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum2, lv622) + layer_norm7: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add25, model_encoder_layers_3_final_layer_norm_weight, model_encoder_layers_3_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv99 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_3_fc1_weight, layer_norm7, model_encoder_layers_3_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv623 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_3_fc2_weight, lv99, model_encoder_layers_3_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add28: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add25, lv623) + maximum3: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add28, R.const(-65504, "float16")) + minimum3: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum3, R.const(65504, "float16")) + layer_norm8: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum3, model_encoder_layers_4_self_attn_layer_norm_weight, model_encoder_layers_4_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv624 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_4_self_attn_q_proj_weight, layer_norm8, model_encoder_layers_4_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape32: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv624, R.shape([batch_size, 1500, 20, 64])) + lv135 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_4_self_attn_k_proj_weight, layer_norm8), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape33: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv135, R.shape([batch_size, 1500, 20, 64])) + lv625 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_4_self_attn_v_proj_weight, layer_norm8, model_encoder_layers_4_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape34: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv625, R.shape([batch_size, 1500, 20, 64])) + reshape35: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape32, R.shape([batch_size * 1500, 20, 64])) + reshape36: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape33, R.shape([batch_size * 1500, 20, 64])) + reshape37: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape34, R.shape([batch_size * 1500, 20, 64])) + lv8 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), reshape35, reshape36, reshape37), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape38: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv8, R.shape([batch_size, 1500, 20, 64])) + reshape39: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape38, R.shape([batch_size, 1500, 1280])) + lv626 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_4_self_attn_out_proj_weight, reshape39, model_encoder_layers_4_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add32: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum3, lv626) + layer_norm9: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add32, model_encoder_layers_4_final_layer_norm_weight, model_encoder_layers_4_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv100 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_4_fc1_weight, layer_norm9, model_encoder_layers_4_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv627 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_4_fc2_weight, lv100, model_encoder_layers_4_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add35: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add32, lv627) + maximum4: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add35, R.const(-65504, "float16")) + minimum4: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum4, R.const(65504, "float16")) + layer_norm10: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum4, model_encoder_layers_5_self_attn_layer_norm_weight, model_encoder_layers_5_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv628 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_5_self_attn_q_proj_weight, layer_norm10, model_encoder_layers_5_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape40: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv628, R.shape([batch_size, 1500, 20, 64])) + lv136 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_5_self_attn_k_proj_weight, layer_norm10), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape41: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv136, R.shape([batch_size, 1500, 20, 64])) + lv629 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_5_self_attn_v_proj_weight, layer_norm10, model_encoder_layers_5_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape42: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv629, R.shape([batch_size, 1500, 20, 64])) + reshape43: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape40, R.shape([batch_size * 1500, 20, 64])) + reshape44: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape41, R.shape([batch_size * 1500, 20, 64])) + reshape45: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape42, R.shape([batch_size * 1500, 20, 64])) + lv9 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), reshape43, reshape44, reshape45), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape46: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv9, R.shape([batch_size, 1500, 20, 64])) + reshape47: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape46, R.shape([batch_size, 1500, 1280])) + lv630 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_5_self_attn_out_proj_weight, reshape47, model_encoder_layers_5_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add39: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum4, lv630) + layer_norm11: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add39, model_encoder_layers_5_final_layer_norm_weight, model_encoder_layers_5_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv101 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_5_fc1_weight, layer_norm11, model_encoder_layers_5_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv631 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_5_fc2_weight, lv101, model_encoder_layers_5_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add42: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add39, lv631) + maximum5: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add42, R.const(-65504, "float16")) + minimum5: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum5, R.const(65504, "float16")) + layer_norm12: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum5, model_encoder_layers_6_self_attn_layer_norm_weight, model_encoder_layers_6_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv632 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_6_self_attn_q_proj_weight, layer_norm12, model_encoder_layers_6_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape48: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv632, R.shape([batch_size, 1500, 20, 64])) + lv137 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_6_self_attn_k_proj_weight, layer_norm12), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape49: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv137, R.shape([batch_size, 1500, 20, 64])) + lv633 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_6_self_attn_v_proj_weight, layer_norm12, model_encoder_layers_6_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape50: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv633, R.shape([batch_size, 1500, 20, 64])) + reshape51: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape48, R.shape([batch_size * 1500, 20, 64])) + reshape52: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape49, R.shape([batch_size * 1500, 20, 64])) + reshape53: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape50, R.shape([batch_size * 1500, 20, 64])) + lv10 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), reshape51, reshape52, reshape53), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape54: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv10, R.shape([batch_size, 1500, 20, 64])) + reshape55: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape54, R.shape([batch_size, 1500, 1280])) + lv634 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_6_self_attn_out_proj_weight, reshape55, model_encoder_layers_6_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add46: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum5, lv634) + layer_norm13: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add46, model_encoder_layers_6_final_layer_norm_weight, model_encoder_layers_6_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv102 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_6_fc1_weight, layer_norm13, model_encoder_layers_6_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv635 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_6_fc2_weight, lv102, model_encoder_layers_6_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add49: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add46, lv635) + maximum6: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add49, R.const(-65504, "float16")) + minimum6: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum6, R.const(65504, "float16")) + layer_norm14: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum6, model_encoder_layers_7_self_attn_layer_norm_weight, model_encoder_layers_7_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv636 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_7_self_attn_q_proj_weight, layer_norm14, model_encoder_layers_7_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape56: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv636, R.shape([batch_size, 1500, 20, 64])) + lv138 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_7_self_attn_k_proj_weight, layer_norm14), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape57: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv138, R.shape([batch_size, 1500, 20, 64])) + lv637 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_7_self_attn_v_proj_weight, layer_norm14, model_encoder_layers_7_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape58: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv637, R.shape([batch_size, 1500, 20, 64])) + reshape59: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape56, R.shape([batch_size * 1500, 20, 64])) + reshape60: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape57, R.shape([batch_size * 1500, 20, 64])) + reshape61: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape58, R.shape([batch_size * 1500, 20, 64])) + lv11 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), reshape59, reshape60, reshape61), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape62: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv11, R.shape([batch_size, 1500, 20, 64])) + reshape63: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape62, R.shape([batch_size, 1500, 1280])) + lv638 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_7_self_attn_out_proj_weight, reshape63, model_encoder_layers_7_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add53: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum6, lv638) + layer_norm15: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add53, model_encoder_layers_7_final_layer_norm_weight, model_encoder_layers_7_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv103 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_7_fc1_weight, layer_norm15, model_encoder_layers_7_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv639 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_7_fc2_weight, lv103, model_encoder_layers_7_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add56: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add53, lv639) + maximum7: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add56, R.const(-65504, "float16")) + minimum7: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum7, R.const(65504, "float16")) + layer_norm16: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum7, model_encoder_layers_8_self_attn_layer_norm_weight, model_encoder_layers_8_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv640 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_8_self_attn_q_proj_weight, layer_norm16, model_encoder_layers_8_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape64: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv640, R.shape([batch_size, 1500, 20, 64])) + lv139 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_8_self_attn_k_proj_weight, layer_norm16), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape65: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv139, R.shape([batch_size, 1500, 20, 64])) + lv641 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_8_self_attn_v_proj_weight, layer_norm16, model_encoder_layers_8_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape66: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv641, R.shape([batch_size, 1500, 20, 64])) + reshape67: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape64, R.shape([batch_size * 1500, 20, 64])) + reshape68: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape65, R.shape([batch_size * 1500, 20, 64])) + reshape69: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape66, R.shape([batch_size * 1500, 20, 64])) + lv12 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), reshape67, reshape68, reshape69), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape70: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv12, R.shape([batch_size, 1500, 20, 64])) + reshape71: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape70, R.shape([batch_size, 1500, 1280])) + lv642 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_8_self_attn_out_proj_weight, reshape71, model_encoder_layers_8_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add60: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum7, lv642) + layer_norm17: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add60, model_encoder_layers_8_final_layer_norm_weight, model_encoder_layers_8_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv104 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_8_fc1_weight, layer_norm17, model_encoder_layers_8_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv643 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_8_fc2_weight, lv104, model_encoder_layers_8_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add63: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add60, lv643) + maximum8: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add63, R.const(-65504, "float16")) + minimum8: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum8, R.const(65504, "float16")) + layer_norm18: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum8, model_encoder_layers_9_self_attn_layer_norm_weight, model_encoder_layers_9_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv644 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_9_self_attn_q_proj_weight, layer_norm18, model_encoder_layers_9_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape72: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv644, R.shape([batch_size, 1500, 20, 64])) + lv140 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_9_self_attn_k_proj_weight, layer_norm18), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape73: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv140, R.shape([batch_size, 1500, 20, 64])) + lv645 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_9_self_attn_v_proj_weight, layer_norm18, model_encoder_layers_9_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape74: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv645, R.shape([batch_size, 1500, 20, 64])) + reshape75: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape72, R.shape([batch_size * 1500, 20, 64])) + reshape76: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape73, R.shape([batch_size * 1500, 20, 64])) + reshape77: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape74, R.shape([batch_size * 1500, 20, 64])) + lv13 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), reshape75, reshape76, reshape77), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape78: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv13, R.shape([batch_size, 1500, 20, 64])) + reshape79: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape78, R.shape([batch_size, 1500, 1280])) + lv646 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_9_self_attn_out_proj_weight, reshape79, model_encoder_layers_9_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add67: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum8, lv646) + layer_norm19: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add67, model_encoder_layers_9_final_layer_norm_weight, model_encoder_layers_9_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv105 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_9_fc1_weight, layer_norm19, model_encoder_layers_9_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv647 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_9_fc2_weight, lv105, model_encoder_layers_9_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add70: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add67, lv647) + maximum9: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add70, R.const(-65504, "float16")) + minimum9: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum9, R.const(65504, "float16")) + layer_norm20: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum9, model_encoder_layers_10_self_attn_layer_norm_weight, model_encoder_layers_10_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv648 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_10_self_attn_q_proj_weight, layer_norm20, model_encoder_layers_10_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape80: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv648, R.shape([batch_size, 1500, 20, 64])) + lv141 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_10_self_attn_k_proj_weight, layer_norm20), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape81: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv141, R.shape([batch_size, 1500, 20, 64])) + lv649 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_10_self_attn_v_proj_weight, layer_norm20, model_encoder_layers_10_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape82: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv649, R.shape([batch_size, 1500, 20, 64])) + reshape83: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape80, R.shape([batch_size * 1500, 20, 64])) + reshape84: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape81, R.shape([batch_size * 1500, 20, 64])) + reshape85: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape82, R.shape([batch_size * 1500, 20, 64])) + lv14 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), reshape83, reshape84, reshape85), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape86: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv14, R.shape([batch_size, 1500, 20, 64])) + reshape87: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape86, R.shape([batch_size, 1500, 1280])) + lv650 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_10_self_attn_out_proj_weight, reshape87, model_encoder_layers_10_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add74: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum9, lv650) + layer_norm21: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add74, model_encoder_layers_10_final_layer_norm_weight, model_encoder_layers_10_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv106 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_10_fc1_weight, layer_norm21, model_encoder_layers_10_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv651 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_10_fc2_weight, lv106, model_encoder_layers_10_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add77: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add74, lv651) + maximum10: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add77, R.const(-65504, "float16")) + minimum10: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum10, R.const(65504, "float16")) + layer_norm22: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum10, model_encoder_layers_11_self_attn_layer_norm_weight, model_encoder_layers_11_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv652 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_11_self_attn_q_proj_weight, layer_norm22, model_encoder_layers_11_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape88: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv652, R.shape([batch_size, 1500, 20, 64])) + lv142 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_11_self_attn_k_proj_weight, layer_norm22), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape89: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv142, R.shape([batch_size, 1500, 20, 64])) + lv653 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_11_self_attn_v_proj_weight, layer_norm22, model_encoder_layers_11_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape90: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv653, R.shape([batch_size, 1500, 20, 64])) + reshape91: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape88, R.shape([batch_size * 1500, 20, 64])) + reshape92: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape89, R.shape([batch_size * 1500, 20, 64])) + reshape93: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape90, R.shape([batch_size * 1500, 20, 64])) + lv15 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), reshape91, reshape92, reshape93), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape94: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv15, R.shape([batch_size, 1500, 20, 64])) + reshape95: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape94, R.shape([batch_size, 1500, 1280])) + lv654 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_11_self_attn_out_proj_weight, reshape95, model_encoder_layers_11_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add81: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum10, lv654) + layer_norm23: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add81, model_encoder_layers_11_final_layer_norm_weight, model_encoder_layers_11_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv107 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_11_fc1_weight, layer_norm23, model_encoder_layers_11_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv655 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_11_fc2_weight, lv107, model_encoder_layers_11_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add84: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add81, lv655) + maximum11: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add84, R.const(-65504, "float16")) + minimum11: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum11, R.const(65504, "float16")) + layer_norm24: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum11, model_encoder_layers_12_self_attn_layer_norm_weight, model_encoder_layers_12_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv656 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_12_self_attn_q_proj_weight, layer_norm24, model_encoder_layers_12_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape96: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv656, R.shape([batch_size, 1500, 20, 64])) + lv143 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_12_self_attn_k_proj_weight, layer_norm24), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape97: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv143, R.shape([batch_size, 1500, 20, 64])) + lv657 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_12_self_attn_v_proj_weight, layer_norm24, model_encoder_layers_12_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape98: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv657, R.shape([batch_size, 1500, 20, 64])) + reshape99: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape96, R.shape([batch_size * 1500, 20, 64])) + reshape100: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape97, R.shape([batch_size * 1500, 20, 64])) + reshape101: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape98, R.shape([batch_size * 1500, 20, 64])) + lv16 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), reshape99, reshape100, reshape101), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape102: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv16, R.shape([batch_size, 1500, 20, 64])) + reshape103: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape102, R.shape([batch_size, 1500, 1280])) + lv658 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_12_self_attn_out_proj_weight, reshape103, model_encoder_layers_12_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add88: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum11, lv658) + layer_norm25: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add88, model_encoder_layers_12_final_layer_norm_weight, model_encoder_layers_12_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv108 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_12_fc1_weight, layer_norm25, model_encoder_layers_12_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv659 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_12_fc2_weight, lv108, model_encoder_layers_12_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add91: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add88, lv659) + maximum12: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add91, R.const(-65504, "float16")) + minimum12: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum12, R.const(65504, "float16")) + layer_norm26: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum12, model_encoder_layers_13_self_attn_layer_norm_weight, model_encoder_layers_13_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv660 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_13_self_attn_q_proj_weight, layer_norm26, model_encoder_layers_13_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape104: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv660, R.shape([batch_size, 1500, 20, 64])) + lv144 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_13_self_attn_k_proj_weight, layer_norm26), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape105: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv144, R.shape([batch_size, 1500, 20, 64])) + lv661 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_13_self_attn_v_proj_weight, layer_norm26, model_encoder_layers_13_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape106: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv661, R.shape([batch_size, 1500, 20, 64])) + reshape107: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape104, R.shape([batch_size * 1500, 20, 64])) + reshape108: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape105, R.shape([batch_size * 1500, 20, 64])) + reshape109: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape106, R.shape([batch_size * 1500, 20, 64])) + lv17 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), reshape107, reshape108, reshape109), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape110: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv17, R.shape([batch_size, 1500, 20, 64])) + reshape111: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape110, R.shape([batch_size, 1500, 1280])) + lv662 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_13_self_attn_out_proj_weight, reshape111, model_encoder_layers_13_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add95: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum12, lv662) + layer_norm27: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add95, model_encoder_layers_13_final_layer_norm_weight, model_encoder_layers_13_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv109 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_13_fc1_weight, layer_norm27, model_encoder_layers_13_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv663 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_13_fc2_weight, lv109, model_encoder_layers_13_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add98: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add95, lv663) + maximum13: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add98, R.const(-65504, "float16")) + minimum13: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum13, R.const(65504, "float16")) + layer_norm28: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum13, model_encoder_layers_14_self_attn_layer_norm_weight, model_encoder_layers_14_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv664 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_14_self_attn_q_proj_weight, layer_norm28, model_encoder_layers_14_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape112: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv664, R.shape([batch_size, 1500, 20, 64])) + lv145 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_14_self_attn_k_proj_weight, layer_norm28), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape113: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv145, R.shape([batch_size, 1500, 20, 64])) + lv665 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_14_self_attn_v_proj_weight, layer_norm28, model_encoder_layers_14_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape114: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv665, R.shape([batch_size, 1500, 20, 64])) + reshape115: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape112, R.shape([batch_size * 1500, 20, 64])) + reshape116: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape113, R.shape([batch_size * 1500, 20, 64])) + reshape117: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape114, R.shape([batch_size * 1500, 20, 64])) + lv18 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), reshape115, reshape116, reshape117), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape118: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv18, R.shape([batch_size, 1500, 20, 64])) + reshape119: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape118, R.shape([batch_size, 1500, 1280])) + lv666 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_14_self_attn_out_proj_weight, reshape119, model_encoder_layers_14_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add102: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum13, lv666) + layer_norm29: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add102, model_encoder_layers_14_final_layer_norm_weight, model_encoder_layers_14_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv110 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_14_fc1_weight, layer_norm29, model_encoder_layers_14_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv667 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_14_fc2_weight, lv110, model_encoder_layers_14_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add105: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add102, lv667) + maximum14: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add105, R.const(-65504, "float16")) + minimum14: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum14, R.const(65504, "float16")) + layer_norm30: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum14, model_encoder_layers_15_self_attn_layer_norm_weight, model_encoder_layers_15_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv668 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_15_self_attn_q_proj_weight, layer_norm30, model_encoder_layers_15_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape120: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv668, R.shape([batch_size, 1500, 20, 64])) + lv146 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_15_self_attn_k_proj_weight, layer_norm30), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape121: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv146, R.shape([batch_size, 1500, 20, 64])) + lv669 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_15_self_attn_v_proj_weight, layer_norm30, model_encoder_layers_15_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape122: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv669, R.shape([batch_size, 1500, 20, 64])) + reshape123: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape120, R.shape([batch_size * 1500, 20, 64])) + reshape124: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape121, R.shape([batch_size * 1500, 20, 64])) + reshape125: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape122, R.shape([batch_size * 1500, 20, 64])) + lv19 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), reshape123, reshape124, reshape125), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape126: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv19, R.shape([batch_size, 1500, 20, 64])) + reshape127: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape126, R.shape([batch_size, 1500, 1280])) + lv670 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_15_self_attn_out_proj_weight, reshape127, model_encoder_layers_15_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add109: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum14, lv670) + layer_norm31: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add109, model_encoder_layers_15_final_layer_norm_weight, model_encoder_layers_15_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv111 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_15_fc1_weight, layer_norm31, model_encoder_layers_15_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv671 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_15_fc2_weight, lv111, model_encoder_layers_15_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add112: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add109, lv671) + maximum15: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add112, R.const(-65504, "float16")) + minimum15: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum15, R.const(65504, "float16")) + layer_norm32: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum15, model_encoder_layers_16_self_attn_layer_norm_weight, model_encoder_layers_16_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv672 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_16_self_attn_q_proj_weight, layer_norm32, model_encoder_layers_16_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape128: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv672, R.shape([batch_size, 1500, 20, 64])) + lv147 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_16_self_attn_k_proj_weight, layer_norm32), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape129: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv147, R.shape([batch_size, 1500, 20, 64])) + lv673 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_16_self_attn_v_proj_weight, layer_norm32, model_encoder_layers_16_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape130: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv673, R.shape([batch_size, 1500, 20, 64])) + reshape131: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape128, R.shape([batch_size * 1500, 20, 64])) + reshape132: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape129, R.shape([batch_size * 1500, 20, 64])) + reshape133: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape130, R.shape([batch_size * 1500, 20, 64])) + lv20 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), reshape131, reshape132, reshape133), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape134: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv20, R.shape([batch_size, 1500, 20, 64])) + reshape135: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape134, R.shape([batch_size, 1500, 1280])) + lv674 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_16_self_attn_out_proj_weight, reshape135, model_encoder_layers_16_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add116: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum15, lv674) + layer_norm33: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add116, model_encoder_layers_16_final_layer_norm_weight, model_encoder_layers_16_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv112 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_16_fc1_weight, layer_norm33, model_encoder_layers_16_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv675 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_16_fc2_weight, lv112, model_encoder_layers_16_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add119: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add116, lv675) + maximum16: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add119, R.const(-65504, "float16")) + minimum16: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum16, R.const(65504, "float16")) + layer_norm34: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum16, model_encoder_layers_17_self_attn_layer_norm_weight, model_encoder_layers_17_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv676 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_17_self_attn_q_proj_weight, layer_norm34, model_encoder_layers_17_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape136: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv676, R.shape([batch_size, 1500, 20, 64])) + lv148 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_17_self_attn_k_proj_weight, layer_norm34), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape137: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv148, R.shape([batch_size, 1500, 20, 64])) + lv677 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_17_self_attn_v_proj_weight, layer_norm34, model_encoder_layers_17_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape138: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv677, R.shape([batch_size, 1500, 20, 64])) + reshape139: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape136, R.shape([batch_size * 1500, 20, 64])) + reshape140: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape137, R.shape([batch_size * 1500, 20, 64])) + reshape141: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape138, R.shape([batch_size * 1500, 20, 64])) + lv21 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), reshape139, reshape140, reshape141), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape142: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv21, R.shape([batch_size, 1500, 20, 64])) + reshape143: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape142, R.shape([batch_size, 1500, 1280])) + lv678 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_17_self_attn_out_proj_weight, reshape143, model_encoder_layers_17_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add123: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum16, lv678) + layer_norm35: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add123, model_encoder_layers_17_final_layer_norm_weight, model_encoder_layers_17_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv113 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_17_fc1_weight, layer_norm35, model_encoder_layers_17_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv679 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_17_fc2_weight, lv113, model_encoder_layers_17_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add126: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add123, lv679) + maximum17: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add126, R.const(-65504, "float16")) + minimum17: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum17, R.const(65504, "float16")) + layer_norm36: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum17, model_encoder_layers_18_self_attn_layer_norm_weight, model_encoder_layers_18_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv680 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_18_self_attn_q_proj_weight, layer_norm36, model_encoder_layers_18_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape144: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv680, R.shape([batch_size, 1500, 20, 64])) + lv149 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_18_self_attn_k_proj_weight, layer_norm36), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape145: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv149, R.shape([batch_size, 1500, 20, 64])) + lv681 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_18_self_attn_v_proj_weight, layer_norm36, model_encoder_layers_18_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape146: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv681, R.shape([batch_size, 1500, 20, 64])) + reshape147: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape144, R.shape([batch_size * 1500, 20, 64])) + reshape148: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape145, R.shape([batch_size * 1500, 20, 64])) + reshape149: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape146, R.shape([batch_size * 1500, 20, 64])) + lv22 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), reshape147, reshape148, reshape149), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape150: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv22, R.shape([batch_size, 1500, 20, 64])) + reshape151: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape150, R.shape([batch_size, 1500, 1280])) + lv682 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_18_self_attn_out_proj_weight, reshape151, model_encoder_layers_18_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add130: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum17, lv682) + layer_norm37: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add130, model_encoder_layers_18_final_layer_norm_weight, model_encoder_layers_18_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv114 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_18_fc1_weight, layer_norm37, model_encoder_layers_18_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv683 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_18_fc2_weight, lv114, model_encoder_layers_18_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add133: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add130, lv683) + maximum18: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add133, R.const(-65504, "float16")) + minimum18: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum18, R.const(65504, "float16")) + layer_norm38: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum18, model_encoder_layers_19_self_attn_layer_norm_weight, model_encoder_layers_19_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv684 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_19_self_attn_q_proj_weight, layer_norm38, model_encoder_layers_19_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape152: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv684, R.shape([batch_size, 1500, 20, 64])) + lv150 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_19_self_attn_k_proj_weight, layer_norm38), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape153: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv150, R.shape([batch_size, 1500, 20, 64])) + lv685 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_19_self_attn_v_proj_weight, layer_norm38, model_encoder_layers_19_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape154: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv685, R.shape([batch_size, 1500, 20, 64])) + reshape155: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape152, R.shape([batch_size * 1500, 20, 64])) + reshape156: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape153, R.shape([batch_size * 1500, 20, 64])) + reshape157: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape154, R.shape([batch_size * 1500, 20, 64])) + lv23 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), reshape155, reshape156, reshape157), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape158: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv23, R.shape([batch_size, 1500, 20, 64])) + reshape159: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape158, R.shape([batch_size, 1500, 1280])) + lv686 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_19_self_attn_out_proj_weight, reshape159, model_encoder_layers_19_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add137: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum18, lv686) + layer_norm39: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add137, model_encoder_layers_19_final_layer_norm_weight, model_encoder_layers_19_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv115 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_19_fc1_weight, layer_norm39, model_encoder_layers_19_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv687 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_19_fc2_weight, lv115, model_encoder_layers_19_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add140: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add137, lv687) + maximum19: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add140, R.const(-65504, "float16")) + minimum19: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum19, R.const(65504, "float16")) + layer_norm40: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum19, model_encoder_layers_20_self_attn_layer_norm_weight, model_encoder_layers_20_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv688 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_20_self_attn_q_proj_weight, layer_norm40, model_encoder_layers_20_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape160: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv688, R.shape([batch_size, 1500, 20, 64])) + lv151 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_20_self_attn_k_proj_weight, layer_norm40), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape161: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv151, R.shape([batch_size, 1500, 20, 64])) + lv689 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_20_self_attn_v_proj_weight, layer_norm40, model_encoder_layers_20_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape162: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv689, R.shape([batch_size, 1500, 20, 64])) + reshape163: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape160, R.shape([batch_size * 1500, 20, 64])) + reshape164: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape161, R.shape([batch_size * 1500, 20, 64])) + reshape165: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape162, R.shape([batch_size * 1500, 20, 64])) + lv24 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), reshape163, reshape164, reshape165), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape166: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv24, R.shape([batch_size, 1500, 20, 64])) + reshape167: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape166, R.shape([batch_size, 1500, 1280])) + lv690 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_20_self_attn_out_proj_weight, reshape167, model_encoder_layers_20_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add144: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum19, lv690) + layer_norm41: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add144, model_encoder_layers_20_final_layer_norm_weight, model_encoder_layers_20_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv116 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_20_fc1_weight, layer_norm41, model_encoder_layers_20_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv691 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_20_fc2_weight, lv116, model_encoder_layers_20_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add147: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add144, lv691) + maximum20: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add147, R.const(-65504, "float16")) + minimum20: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum20, R.const(65504, "float16")) + layer_norm42: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum20, model_encoder_layers_21_self_attn_layer_norm_weight, model_encoder_layers_21_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv692 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_21_self_attn_q_proj_weight, layer_norm42, model_encoder_layers_21_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape168: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv692, R.shape([batch_size, 1500, 20, 64])) + lv152 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_21_self_attn_k_proj_weight, layer_norm42), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape169: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv152, R.shape([batch_size, 1500, 20, 64])) + lv693 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_21_self_attn_v_proj_weight, layer_norm42, model_encoder_layers_21_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape170: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv693, R.shape([batch_size, 1500, 20, 64])) + reshape171: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape168, R.shape([batch_size * 1500, 20, 64])) + reshape172: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape169, R.shape([batch_size * 1500, 20, 64])) + reshape173: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape170, R.shape([batch_size * 1500, 20, 64])) + lv25 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), reshape171, reshape172, reshape173), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape174: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv25, R.shape([batch_size, 1500, 20, 64])) + reshape175: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape174, R.shape([batch_size, 1500, 1280])) + lv694 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_21_self_attn_out_proj_weight, reshape175, model_encoder_layers_21_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add151: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum20, lv694) + layer_norm43: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add151, model_encoder_layers_21_final_layer_norm_weight, model_encoder_layers_21_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv117 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_21_fc1_weight, layer_norm43, model_encoder_layers_21_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv695 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_21_fc2_weight, lv117, model_encoder_layers_21_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add154: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add151, lv695) + maximum21: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add154, R.const(-65504, "float16")) + minimum21: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum21, R.const(65504, "float16")) + layer_norm44: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum21, model_encoder_layers_22_self_attn_layer_norm_weight, model_encoder_layers_22_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv696 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_22_self_attn_q_proj_weight, layer_norm44, model_encoder_layers_22_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape176: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv696, R.shape([batch_size, 1500, 20, 64])) + lv153 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_22_self_attn_k_proj_weight, layer_norm44), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape177: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv153, R.shape([batch_size, 1500, 20, 64])) + lv697 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_22_self_attn_v_proj_weight, layer_norm44, model_encoder_layers_22_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape178: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv697, R.shape([batch_size, 1500, 20, 64])) + reshape179: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape176, R.shape([batch_size * 1500, 20, 64])) + reshape180: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape177, R.shape([batch_size * 1500, 20, 64])) + reshape181: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape178, R.shape([batch_size * 1500, 20, 64])) + lv26 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), reshape179, reshape180, reshape181), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape182: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv26, R.shape([batch_size, 1500, 20, 64])) + reshape183: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape182, R.shape([batch_size, 1500, 1280])) + lv698 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_22_self_attn_out_proj_weight, reshape183, model_encoder_layers_22_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add158: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum21, lv698) + layer_norm45: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add158, model_encoder_layers_22_final_layer_norm_weight, model_encoder_layers_22_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv118 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_22_fc1_weight, layer_norm45, model_encoder_layers_22_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv699 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_22_fc2_weight, lv118, model_encoder_layers_22_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add161: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add158, lv699) + maximum22: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add161, R.const(-65504, "float16")) + minimum22: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum22, R.const(65504, "float16")) + layer_norm46: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum22, model_encoder_layers_23_self_attn_layer_norm_weight, model_encoder_layers_23_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv700 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_23_self_attn_q_proj_weight, layer_norm46, model_encoder_layers_23_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape184: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv700, R.shape([batch_size, 1500, 20, 64])) + lv154 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_23_self_attn_k_proj_weight, layer_norm46), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape185: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv154, R.shape([batch_size, 1500, 20, 64])) + lv701 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_23_self_attn_v_proj_weight, layer_norm46, model_encoder_layers_23_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape186: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv701, R.shape([batch_size, 1500, 20, 64])) + reshape187: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape184, R.shape([batch_size * 1500, 20, 64])) + reshape188: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape185, R.shape([batch_size * 1500, 20, 64])) + reshape189: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape186, R.shape([batch_size * 1500, 20, 64])) + lv27 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), reshape187, reshape188, reshape189), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape190: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv27, R.shape([batch_size, 1500, 20, 64])) + reshape191: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape190, R.shape([batch_size, 1500, 1280])) + lv702 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_23_self_attn_out_proj_weight, reshape191, model_encoder_layers_23_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add165: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum22, lv702) + layer_norm47: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add165, model_encoder_layers_23_final_layer_norm_weight, model_encoder_layers_23_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv119 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_23_fc1_weight, layer_norm47, model_encoder_layers_23_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv703 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_23_fc2_weight, lv119, model_encoder_layers_23_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add168: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add165, lv703) + maximum23: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add168, R.const(-65504, "float16")) + minimum23: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum23, R.const(65504, "float16")) + layer_norm48: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum23, model_encoder_layers_24_self_attn_layer_norm_weight, model_encoder_layers_24_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv704 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_24_self_attn_q_proj_weight, layer_norm48, model_encoder_layers_24_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape192: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv704, R.shape([batch_size, 1500, 20, 64])) + lv155 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_24_self_attn_k_proj_weight, layer_norm48), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape193: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv155, R.shape([batch_size, 1500, 20, 64])) + lv705 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_24_self_attn_v_proj_weight, layer_norm48, model_encoder_layers_24_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape194: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv705, R.shape([batch_size, 1500, 20, 64])) + reshape195: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape192, R.shape([batch_size * 1500, 20, 64])) + reshape196: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape193, R.shape([batch_size * 1500, 20, 64])) + reshape197: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape194, R.shape([batch_size * 1500, 20, 64])) + lv28 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), reshape195, reshape196, reshape197), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape198: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv28, R.shape([batch_size, 1500, 20, 64])) + reshape199: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape198, R.shape([batch_size, 1500, 1280])) + lv706 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_24_self_attn_out_proj_weight, reshape199, model_encoder_layers_24_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add172: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum23, lv706) + layer_norm49: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add172, model_encoder_layers_24_final_layer_norm_weight, model_encoder_layers_24_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv120 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_24_fc1_weight, layer_norm49, model_encoder_layers_24_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv707 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_24_fc2_weight, lv120, model_encoder_layers_24_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add175: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add172, lv707) + maximum24: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add175, R.const(-65504, "float16")) + minimum24: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum24, R.const(65504, "float16")) + layer_norm50: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum24, model_encoder_layers_25_self_attn_layer_norm_weight, model_encoder_layers_25_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv708 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_25_self_attn_q_proj_weight, layer_norm50, model_encoder_layers_25_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape200: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv708, R.shape([batch_size, 1500, 20, 64])) + lv156 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_25_self_attn_k_proj_weight, layer_norm50), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape201: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv156, R.shape([batch_size, 1500, 20, 64])) + lv709 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_25_self_attn_v_proj_weight, layer_norm50, model_encoder_layers_25_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape202: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv709, R.shape([batch_size, 1500, 20, 64])) + reshape203: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape200, R.shape([batch_size * 1500, 20, 64])) + reshape204: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape201, R.shape([batch_size * 1500, 20, 64])) + reshape205: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape202, R.shape([batch_size * 1500, 20, 64])) + lv29 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), reshape203, reshape204, reshape205), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape206: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv29, R.shape([batch_size, 1500, 20, 64])) + reshape207: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape206, R.shape([batch_size, 1500, 1280])) + lv710 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_25_self_attn_out_proj_weight, reshape207, model_encoder_layers_25_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add179: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum24, lv710) + layer_norm51: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add179, model_encoder_layers_25_final_layer_norm_weight, model_encoder_layers_25_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv121 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_25_fc1_weight, layer_norm51, model_encoder_layers_25_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv711 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_25_fc2_weight, lv121, model_encoder_layers_25_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add182: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add179, lv711) + maximum25: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add182, R.const(-65504, "float16")) + minimum25: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum25, R.const(65504, "float16")) + layer_norm52: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum25, model_encoder_layers_26_self_attn_layer_norm_weight, model_encoder_layers_26_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv712 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_26_self_attn_q_proj_weight, layer_norm52, model_encoder_layers_26_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape208: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv712, R.shape([batch_size, 1500, 20, 64])) + lv157 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_26_self_attn_k_proj_weight, layer_norm52), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape209: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv157, R.shape([batch_size, 1500, 20, 64])) + lv713 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_26_self_attn_v_proj_weight, layer_norm52, model_encoder_layers_26_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape210: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv713, R.shape([batch_size, 1500, 20, 64])) + reshape211: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape208, R.shape([batch_size * 1500, 20, 64])) + reshape212: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape209, R.shape([batch_size * 1500, 20, 64])) + reshape213: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape210, R.shape([batch_size * 1500, 20, 64])) + lv30 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), reshape211, reshape212, reshape213), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape214: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv30, R.shape([batch_size, 1500, 20, 64])) + reshape215: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape214, R.shape([batch_size, 1500, 1280])) + lv714 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_26_self_attn_out_proj_weight, reshape215, model_encoder_layers_26_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add186: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum25, lv714) + layer_norm53: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add186, model_encoder_layers_26_final_layer_norm_weight, model_encoder_layers_26_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv122 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_26_fc1_weight, layer_norm53, model_encoder_layers_26_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv715 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_26_fc2_weight, lv122, model_encoder_layers_26_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add189: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add186, lv715) + maximum26: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add189, R.const(-65504, "float16")) + minimum26: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum26, R.const(65504, "float16")) + layer_norm54: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum26, model_encoder_layers_27_self_attn_layer_norm_weight, model_encoder_layers_27_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv716 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_27_self_attn_q_proj_weight, layer_norm54, model_encoder_layers_27_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape216: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv716, R.shape([batch_size, 1500, 20, 64])) + lv158 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_27_self_attn_k_proj_weight, layer_norm54), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape217: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv158, R.shape([batch_size, 1500, 20, 64])) + lv717 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_27_self_attn_v_proj_weight, layer_norm54, model_encoder_layers_27_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape218: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv717, R.shape([batch_size, 1500, 20, 64])) + reshape219: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape216, R.shape([batch_size * 1500, 20, 64])) + reshape220: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape217, R.shape([batch_size * 1500, 20, 64])) + reshape221: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape218, R.shape([batch_size * 1500, 20, 64])) + lv31 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), reshape219, reshape220, reshape221), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape222: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv31, R.shape([batch_size, 1500, 20, 64])) + reshape223: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape222, R.shape([batch_size, 1500, 1280])) + lv718 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_27_self_attn_out_proj_weight, reshape223, model_encoder_layers_27_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add193: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum26, lv718) + layer_norm55: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add193, model_encoder_layers_27_final_layer_norm_weight, model_encoder_layers_27_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv123 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_27_fc1_weight, layer_norm55, model_encoder_layers_27_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv719 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_27_fc2_weight, lv123, model_encoder_layers_27_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add196: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add193, lv719) + maximum27: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add196, R.const(-65504, "float16")) + minimum27: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum27, R.const(65504, "float16")) + layer_norm56: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum27, model_encoder_layers_28_self_attn_layer_norm_weight, model_encoder_layers_28_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv720 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_28_self_attn_q_proj_weight, layer_norm56, model_encoder_layers_28_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape224: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv720, R.shape([batch_size, 1500, 20, 64])) + lv159 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_28_self_attn_k_proj_weight, layer_norm56), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape225: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv159, R.shape([batch_size, 1500, 20, 64])) + lv721 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_28_self_attn_v_proj_weight, layer_norm56, model_encoder_layers_28_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape226: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv721, R.shape([batch_size, 1500, 20, 64])) + reshape227: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape224, R.shape([batch_size * 1500, 20, 64])) + reshape228: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape225, R.shape([batch_size * 1500, 20, 64])) + reshape229: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape226, R.shape([batch_size * 1500, 20, 64])) + lv32 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), reshape227, reshape228, reshape229), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape230: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv32, R.shape([batch_size, 1500, 20, 64])) + reshape231: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape230, R.shape([batch_size, 1500, 1280])) + lv722 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_28_self_attn_out_proj_weight, reshape231, model_encoder_layers_28_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add200: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum27, lv722) + layer_norm57: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add200, model_encoder_layers_28_final_layer_norm_weight, model_encoder_layers_28_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv124 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_28_fc1_weight, layer_norm57, model_encoder_layers_28_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv723 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_28_fc2_weight, lv124, model_encoder_layers_28_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add203: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add200, lv723) + maximum28: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add203, R.const(-65504, "float16")) + minimum28: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum28, R.const(65504, "float16")) + layer_norm58: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum28, model_encoder_layers_29_self_attn_layer_norm_weight, model_encoder_layers_29_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv724 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_29_self_attn_q_proj_weight, layer_norm58, model_encoder_layers_29_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape232: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv724, R.shape([batch_size, 1500, 20, 64])) + lv160 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_29_self_attn_k_proj_weight, layer_norm58), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape233: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv160, R.shape([batch_size, 1500, 20, 64])) + lv725 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_29_self_attn_v_proj_weight, layer_norm58, model_encoder_layers_29_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape234: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv725, R.shape([batch_size, 1500, 20, 64])) + reshape235: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape232, R.shape([batch_size * 1500, 20, 64])) + reshape236: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape233, R.shape([batch_size * 1500, 20, 64])) + reshape237: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape234, R.shape([batch_size * 1500, 20, 64])) + lv33 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), reshape235, reshape236, reshape237), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape238: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv33, R.shape([batch_size, 1500, 20, 64])) + reshape239: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape238, R.shape([batch_size, 1500, 1280])) + lv726 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_29_self_attn_out_proj_weight, reshape239, model_encoder_layers_29_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add207: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum28, lv726) + layer_norm59: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add207, model_encoder_layers_29_final_layer_norm_weight, model_encoder_layers_29_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv125 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_29_fc1_weight, layer_norm59, model_encoder_layers_29_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv727 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_29_fc2_weight, lv125, model_encoder_layers_29_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add210: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add207, lv727) + maximum29: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add210, R.const(-65504, "float16")) + minimum29: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum29, R.const(65504, "float16")) + layer_norm60: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum29, model_encoder_layers_30_self_attn_layer_norm_weight, model_encoder_layers_30_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv728 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_30_self_attn_q_proj_weight, layer_norm60, model_encoder_layers_30_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape240: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv728, R.shape([batch_size, 1500, 20, 64])) + lv161 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_30_self_attn_k_proj_weight, layer_norm60), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape241: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv161, R.shape([batch_size, 1500, 20, 64])) + lv729 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_30_self_attn_v_proj_weight, layer_norm60, model_encoder_layers_30_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape242: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv729, R.shape([batch_size, 1500, 20, 64])) + reshape243: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape240, R.shape([batch_size * 1500, 20, 64])) + reshape244: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape241, R.shape([batch_size * 1500, 20, 64])) + reshape245: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape242, R.shape([batch_size * 1500, 20, 64])) + lv34 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), reshape243, reshape244, reshape245), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape246: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv34, R.shape([batch_size, 1500, 20, 64])) + reshape247: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape246, R.shape([batch_size, 1500, 1280])) + lv730 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_30_self_attn_out_proj_weight, reshape247, model_encoder_layers_30_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add214: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum29, lv730) + layer_norm61: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add214, model_encoder_layers_30_final_layer_norm_weight, model_encoder_layers_30_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv126 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_30_fc1_weight, layer_norm61, model_encoder_layers_30_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv731 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_30_fc2_weight, lv126, model_encoder_layers_30_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add217: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add214, lv731) + maximum30: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add217, R.const(-65504, "float16")) + minimum30: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum30, R.const(65504, "float16")) + layer_norm62: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum30, model_encoder_layers_31_self_attn_layer_norm_weight, model_encoder_layers_31_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv732 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_31_self_attn_q_proj_weight, layer_norm62, model_encoder_layers_31_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape248: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv732, R.shape([batch_size, 1500, 20, 64])) + lv162 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_31_self_attn_k_proj_weight, layer_norm62), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape249: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv162, R.shape([batch_size, 1500, 20, 64])) + lv733 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_31_self_attn_v_proj_weight, layer_norm62, model_encoder_layers_31_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape250: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv733, R.shape([batch_size, 1500, 20, 64])) + reshape251: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape248, R.shape([batch_size * 1500, 20, 64])) + reshape252: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape249, R.shape([batch_size * 1500, 20, 64])) + reshape253: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape250, R.shape([batch_size * 1500, 20, 64])) + lv35 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), reshape251, reshape252, reshape253), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape254: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv35, R.shape([batch_size, 1500, 20, 64])) + reshape255: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape254, R.shape([batch_size, 1500, 1280])) + lv734 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_31_self_attn_out_proj_weight, reshape255, model_encoder_layers_31_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add221: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum30, lv734) + layer_norm63: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add221, model_encoder_layers_31_final_layer_norm_weight, model_encoder_layers_31_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv127 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_31_fc1_weight, layer_norm63, model_encoder_layers_31_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv735 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_31_fc2_weight, lv127, model_encoder_layers_31_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add224: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add221, lv735) + maximum31: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add224, R.const(-65504, "float16")) + minimum31: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum31, R.const(65504, "float16")) + layer_norm64: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum31, model_encoder_layer_norm_weight, model_encoder_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + gv: R.Tensor((batch_size, 1500, 1280), dtype="float16") = layer_norm64 + R.output(gv) + return gv + + @R.function + def batch_prefill(input_ids: R.Tensor((1, "seq_len"), dtype="int32"), logit_positions: R.Tensor(("batch_size",), dtype="int32"), paged_kv_cache: R.Object, packed_params: R.Tuple(R.Tensor((1280, 128, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1500, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), 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R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"))) -> R.Tensor((1, "batch_size", 51866), dtype="float32"): + batch_size = T.int64() + seq_len = T.int64() + R.func_attr({"num_input": 3, "relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "seq_len": 15000, "total_seq_len": 1500}}) + with R.dataflow(): + model_decoder_embed_tokens_weight2: R.Tensor((51866, 1280), dtype="float16") = packed_params[487] + model_decoder_embed_positions_weight2: R.Tensor((448, 1280), dtype="float16") = packed_params[488] + model_decoder_layers_0_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[489] + model_decoder_layers_0_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[490] + model_decoder_layers_0_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[491] + model_decoder_layers_0_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[492] + model_decoder_layers_0_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[493] + model_decoder_layers_0_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[494] + model_decoder_layers_0_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[495] + model_decoder_layers_0_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[496] + model_decoder_layers_0_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[497] + model_decoder_layers_0_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[501] + model_decoder_layers_0_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[502] + model_decoder_layers_0_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[503] + model_decoder_layers_0_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[504] + model_decoder_layers_0_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[505] + model_decoder_layers_0_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[506] + model_decoder_layers_0_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[507] + model_decoder_layers_0_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[508] + model_decoder_layers_0_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[509] + model_decoder_layers_0_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[510] + model_decoder_layers_0_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[511] + model_decoder_layers_0_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[512] + model_decoder_layers_1_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[513] + model_decoder_layers_1_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[514] + model_decoder_layers_1_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[515] + model_decoder_layers_1_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[516] + model_decoder_layers_1_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[517] + model_decoder_layers_1_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[518] + model_decoder_layers_1_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[519] + model_decoder_layers_1_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[520] + model_decoder_layers_1_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[521] + model_decoder_layers_1_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[525] + model_decoder_layers_1_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[526] + model_decoder_layers_1_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[527] + model_decoder_layers_1_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[528] + model_decoder_layers_1_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[529] + model_decoder_layers_1_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[530] + model_decoder_layers_1_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[531] + model_decoder_layers_1_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[532] + model_decoder_layers_1_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[533] + model_decoder_layers_1_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[534] + model_decoder_layers_1_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[535] + model_decoder_layers_1_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[536] + model_decoder_layers_2_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[537] + model_decoder_layers_2_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[538] + model_decoder_layers_2_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[539] + model_decoder_layers_2_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[540] + model_decoder_layers_2_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[541] + model_decoder_layers_2_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[542] + model_decoder_layers_2_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[543] + model_decoder_layers_2_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[544] + model_decoder_layers_2_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[545] + model_decoder_layers_2_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[549] + model_decoder_layers_2_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[550] + model_decoder_layers_2_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[551] + model_decoder_layers_2_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[552] + model_decoder_layers_2_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[553] + model_decoder_layers_2_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[554] + model_decoder_layers_2_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[555] + model_decoder_layers_2_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[556] + model_decoder_layers_2_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[557] + model_decoder_layers_2_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[558] + model_decoder_layers_2_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[559] + model_decoder_layers_2_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[560] + model_decoder_layers_3_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[561] + model_decoder_layers_3_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[562] + model_decoder_layers_3_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[563] + model_decoder_layers_3_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[564] + model_decoder_layers_3_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[565] + model_decoder_layers_3_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[566] + model_decoder_layers_3_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[567] + model_decoder_layers_3_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[568] + model_decoder_layers_3_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[569] + model_decoder_layers_3_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[573] + model_decoder_layers_3_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[574] + model_decoder_layers_3_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[575] + model_decoder_layers_3_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[576] + model_decoder_layers_3_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[577] + model_decoder_layers_3_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[578] + model_decoder_layers_3_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[579] + model_decoder_layers_3_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[580] + model_decoder_layers_3_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[581] + model_decoder_layers_3_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[582] + model_decoder_layers_3_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[583] + model_decoder_layers_3_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[584] + model_decoder_layers_4_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[585] + model_decoder_layers_4_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[586] + model_decoder_layers_4_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[587] + model_decoder_layers_4_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[588] + model_decoder_layers_4_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[589] + model_decoder_layers_4_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[590] + model_decoder_layers_4_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[591] + model_decoder_layers_4_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[592] + model_decoder_layers_4_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[593] + model_decoder_layers_4_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[597] + model_decoder_layers_4_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[598] + model_decoder_layers_4_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[599] + model_decoder_layers_4_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[600] + model_decoder_layers_4_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[601] + model_decoder_layers_4_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[602] + model_decoder_layers_4_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[603] + model_decoder_layers_4_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[604] + model_decoder_layers_4_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[605] + model_decoder_layers_4_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[606] + model_decoder_layers_4_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[607] + model_decoder_layers_4_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[608] + model_decoder_layers_5_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[609] + model_decoder_layers_5_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[610] + model_decoder_layers_5_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[611] + model_decoder_layers_5_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[612] + model_decoder_layers_5_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[613] + model_decoder_layers_5_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[614] + model_decoder_layers_5_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[615] + model_decoder_layers_5_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[616] + model_decoder_layers_5_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[617] + model_decoder_layers_5_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[621] + model_decoder_layers_5_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[622] + model_decoder_layers_5_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[623] + model_decoder_layers_5_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[624] + model_decoder_layers_5_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[625] + model_decoder_layers_5_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[626] + model_decoder_layers_5_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[627] + model_decoder_layers_5_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[628] + model_decoder_layers_5_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[629] + model_decoder_layers_5_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[630] + model_decoder_layers_5_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[631] + model_decoder_layers_5_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[632] + model_decoder_layers_6_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[633] + model_decoder_layers_6_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[634] + model_decoder_layers_6_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[635] + model_decoder_layers_6_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[636] + model_decoder_layers_6_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[637] + model_decoder_layers_6_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[638] + model_decoder_layers_6_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[639] + model_decoder_layers_6_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[640] + model_decoder_layers_6_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[641] + model_decoder_layers_6_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[645] + model_decoder_layers_6_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[646] + model_decoder_layers_6_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[647] + model_decoder_layers_6_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[648] + model_decoder_layers_6_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[649] + model_decoder_layers_6_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[650] + model_decoder_layers_6_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[651] + model_decoder_layers_6_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[652] + model_decoder_layers_6_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[653] + model_decoder_layers_6_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[654] + model_decoder_layers_6_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[655] + model_decoder_layers_6_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[656] + model_decoder_layers_7_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[657] + model_decoder_layers_7_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[658] + model_decoder_layers_7_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[659] + model_decoder_layers_7_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[660] + model_decoder_layers_7_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[661] + model_decoder_layers_7_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[662] + model_decoder_layers_7_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[663] + model_decoder_layers_7_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[664] + model_decoder_layers_7_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[665] + model_decoder_layers_7_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[669] + model_decoder_layers_7_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[670] + model_decoder_layers_7_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[671] + model_decoder_layers_7_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[672] + model_decoder_layers_7_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[673] + model_decoder_layers_7_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[674] + model_decoder_layers_7_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[675] + model_decoder_layers_7_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[676] + model_decoder_layers_7_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[677] + model_decoder_layers_7_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[678] + model_decoder_layers_7_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[679] + model_decoder_layers_7_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[680] + model_decoder_layers_8_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[681] + model_decoder_layers_8_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[682] + model_decoder_layers_8_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[683] + model_decoder_layers_8_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[684] + model_decoder_layers_8_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[685] + model_decoder_layers_8_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[686] + model_decoder_layers_8_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[687] + model_decoder_layers_8_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[688] + model_decoder_layers_8_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[689] + model_decoder_layers_8_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[693] + model_decoder_layers_8_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[694] + model_decoder_layers_8_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[695] + model_decoder_layers_8_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[696] + model_decoder_layers_8_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[697] + model_decoder_layers_8_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[698] + model_decoder_layers_8_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[699] + model_decoder_layers_8_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[700] + model_decoder_layers_8_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[701] + model_decoder_layers_8_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[702] + model_decoder_layers_8_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[703] + model_decoder_layers_8_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[704] + model_decoder_layers_9_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[705] + model_decoder_layers_9_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[706] + model_decoder_layers_9_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[707] + model_decoder_layers_9_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[708] + model_decoder_layers_9_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[709] + model_decoder_layers_9_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[710] + model_decoder_layers_9_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[711] + model_decoder_layers_9_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[712] + model_decoder_layers_9_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[713] + model_decoder_layers_9_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[717] + model_decoder_layers_9_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[718] + model_decoder_layers_9_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[719] + model_decoder_layers_9_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[720] + model_decoder_layers_9_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[721] + model_decoder_layers_9_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[722] + model_decoder_layers_9_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[723] + model_decoder_layers_9_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[724] + model_decoder_layers_9_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[725] + model_decoder_layers_9_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[726] + model_decoder_layers_9_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[727] + model_decoder_layers_9_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[728] + model_decoder_layers_10_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[729] + model_decoder_layers_10_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[730] + model_decoder_layers_10_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[731] + model_decoder_layers_10_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[732] + model_decoder_layers_10_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[733] + model_decoder_layers_10_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[734] + model_decoder_layers_10_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[735] + model_decoder_layers_10_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[736] + model_decoder_layers_10_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[737] + model_decoder_layers_10_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[741] + model_decoder_layers_10_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[742] + model_decoder_layers_10_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[743] + model_decoder_layers_10_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[744] + model_decoder_layers_10_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[745] + model_decoder_layers_10_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[746] + model_decoder_layers_10_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[747] + model_decoder_layers_10_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[748] + model_decoder_layers_10_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[749] + model_decoder_layers_10_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[750] + model_decoder_layers_10_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[751] + model_decoder_layers_10_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[752] + model_decoder_layers_11_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[753] + model_decoder_layers_11_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[754] + model_decoder_layers_11_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[755] + model_decoder_layers_11_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[756] + model_decoder_layers_11_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[757] + model_decoder_layers_11_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[758] + model_decoder_layers_11_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[759] + model_decoder_layers_11_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[760] + model_decoder_layers_11_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[761] + model_decoder_layers_11_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[765] + model_decoder_layers_11_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[766] + model_decoder_layers_11_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[767] + model_decoder_layers_11_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[768] + model_decoder_layers_11_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[769] + model_decoder_layers_11_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[770] + model_decoder_layers_11_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[771] + model_decoder_layers_11_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[772] + model_decoder_layers_11_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[773] + model_decoder_layers_11_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[774] + model_decoder_layers_11_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[775] + model_decoder_layers_11_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[776] + model_decoder_layers_12_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[777] + model_decoder_layers_12_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[778] + model_decoder_layers_12_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[779] + model_decoder_layers_12_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[780] + model_decoder_layers_12_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[781] + model_decoder_layers_12_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[782] + model_decoder_layers_12_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[783] + model_decoder_layers_12_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[784] + model_decoder_layers_12_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[785] + model_decoder_layers_12_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[789] + model_decoder_layers_12_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[790] + model_decoder_layers_12_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[791] + model_decoder_layers_12_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[792] + model_decoder_layers_12_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[793] + model_decoder_layers_12_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[794] + model_decoder_layers_12_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[795] + model_decoder_layers_12_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[796] + model_decoder_layers_12_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[797] + model_decoder_layers_12_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[798] + model_decoder_layers_12_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[799] + model_decoder_layers_12_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[800] + model_decoder_layers_13_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[801] + model_decoder_layers_13_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[802] + model_decoder_layers_13_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[803] + model_decoder_layers_13_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[804] + model_decoder_layers_13_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[805] + model_decoder_layers_13_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[806] + model_decoder_layers_13_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[807] + model_decoder_layers_13_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[808] + model_decoder_layers_13_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[809] + model_decoder_layers_13_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[813] + model_decoder_layers_13_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[814] + model_decoder_layers_13_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[815] + model_decoder_layers_13_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[816] + model_decoder_layers_13_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[817] + model_decoder_layers_13_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[818] + model_decoder_layers_13_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[819] + model_decoder_layers_13_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[820] + model_decoder_layers_13_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[821] + model_decoder_layers_13_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[822] + model_decoder_layers_13_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[823] + model_decoder_layers_13_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[824] + model_decoder_layers_14_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[825] + model_decoder_layers_14_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[826] + model_decoder_layers_14_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[827] + model_decoder_layers_14_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[828] + model_decoder_layers_14_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[829] + model_decoder_layers_14_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[830] + model_decoder_layers_14_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[831] + model_decoder_layers_14_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[832] + model_decoder_layers_14_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[833] + model_decoder_layers_14_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[837] + model_decoder_layers_14_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[838] + model_decoder_layers_14_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[839] + model_decoder_layers_14_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[840] + model_decoder_layers_14_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[841] + model_decoder_layers_14_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[842] + model_decoder_layers_14_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[843] + model_decoder_layers_14_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[844] + model_decoder_layers_14_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[845] + model_decoder_layers_14_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[846] + model_decoder_layers_14_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[847] + model_decoder_layers_14_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[848] + model_decoder_layers_15_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[849] + model_decoder_layers_15_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[850] + model_decoder_layers_15_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[851] + model_decoder_layers_15_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[852] + model_decoder_layers_15_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[853] + model_decoder_layers_15_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[854] + model_decoder_layers_15_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[855] + model_decoder_layers_15_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[856] + model_decoder_layers_15_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[857] + model_decoder_layers_15_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[861] + model_decoder_layers_15_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[862] + model_decoder_layers_15_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[863] + model_decoder_layers_15_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[864] + model_decoder_layers_15_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[865] + model_decoder_layers_15_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[866] + model_decoder_layers_15_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[867] + model_decoder_layers_15_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[868] + model_decoder_layers_15_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[869] + model_decoder_layers_15_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[870] + model_decoder_layers_15_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[871] + model_decoder_layers_15_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[872] + model_decoder_layers_16_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[873] + model_decoder_layers_16_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[874] + model_decoder_layers_16_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[875] + model_decoder_layers_16_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[876] + model_decoder_layers_16_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[877] + model_decoder_layers_16_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[878] + model_decoder_layers_16_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[879] + model_decoder_layers_16_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[880] + model_decoder_layers_16_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[881] + model_decoder_layers_16_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[885] + model_decoder_layers_16_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[886] + model_decoder_layers_16_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[887] + model_decoder_layers_16_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[888] + model_decoder_layers_16_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[889] + model_decoder_layers_16_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[890] + model_decoder_layers_16_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[891] + model_decoder_layers_16_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[892] + model_decoder_layers_16_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[893] + model_decoder_layers_16_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[894] + model_decoder_layers_16_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[895] + model_decoder_layers_16_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[896] + model_decoder_layers_17_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[897] + model_decoder_layers_17_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[898] + model_decoder_layers_17_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[899] + model_decoder_layers_17_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[900] + model_decoder_layers_17_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[901] + model_decoder_layers_17_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[902] + model_decoder_layers_17_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[903] + model_decoder_layers_17_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[904] + model_decoder_layers_17_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[905] + model_decoder_layers_17_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[909] + model_decoder_layers_17_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[910] + model_decoder_layers_17_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[911] + model_decoder_layers_17_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[912] + model_decoder_layers_17_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[913] + model_decoder_layers_17_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[914] + model_decoder_layers_17_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[915] + model_decoder_layers_17_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[916] + model_decoder_layers_17_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[917] + model_decoder_layers_17_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[918] + model_decoder_layers_17_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[919] + model_decoder_layers_17_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[920] + model_decoder_layers_18_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[921] + model_decoder_layers_18_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[922] + model_decoder_layers_18_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[923] + model_decoder_layers_18_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[924] + model_decoder_layers_18_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[925] + model_decoder_layers_18_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[926] + model_decoder_layers_18_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[927] + model_decoder_layers_18_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[928] + model_decoder_layers_18_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[929] + model_decoder_layers_18_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[933] + model_decoder_layers_18_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[934] + model_decoder_layers_18_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[935] + model_decoder_layers_18_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[936] + model_decoder_layers_18_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[937] + model_decoder_layers_18_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[938] + model_decoder_layers_18_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[939] + model_decoder_layers_18_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[940] + model_decoder_layers_18_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[941] + model_decoder_layers_18_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[942] + model_decoder_layers_18_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[943] + model_decoder_layers_18_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[944] + model_decoder_layers_19_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[945] + model_decoder_layers_19_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[946] + model_decoder_layers_19_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[947] + model_decoder_layers_19_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[948] + model_decoder_layers_19_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[949] + model_decoder_layers_19_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[950] + model_decoder_layers_19_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[951] + model_decoder_layers_19_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[952] + model_decoder_layers_19_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[953] + model_decoder_layers_19_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[957] + model_decoder_layers_19_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[958] + model_decoder_layers_19_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[959] + model_decoder_layers_19_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[960] + model_decoder_layers_19_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[961] + model_decoder_layers_19_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[962] + model_decoder_layers_19_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[963] + model_decoder_layers_19_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[964] + model_decoder_layers_19_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[965] + model_decoder_layers_19_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[966] + model_decoder_layers_19_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[967] + model_decoder_layers_19_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[968] + model_decoder_layers_20_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[969] + model_decoder_layers_20_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[970] + model_decoder_layers_20_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[971] + model_decoder_layers_20_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[972] + model_decoder_layers_20_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[973] + model_decoder_layers_20_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[974] + model_decoder_layers_20_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[975] + model_decoder_layers_20_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[976] + model_decoder_layers_20_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[977] + model_decoder_layers_20_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[981] + model_decoder_layers_20_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[982] + model_decoder_layers_20_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[983] + model_decoder_layers_20_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[984] + model_decoder_layers_20_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[985] + model_decoder_layers_20_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[986] + model_decoder_layers_20_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[987] + model_decoder_layers_20_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[988] + model_decoder_layers_20_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[989] + model_decoder_layers_20_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[990] + model_decoder_layers_20_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[991] + model_decoder_layers_20_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[992] + model_decoder_layers_21_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[993] + model_decoder_layers_21_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[994] + model_decoder_layers_21_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[995] + model_decoder_layers_21_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[996] + model_decoder_layers_21_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[997] + model_decoder_layers_21_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[998] + model_decoder_layers_21_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[999] + model_decoder_layers_21_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1000] + model_decoder_layers_21_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1001] + model_decoder_layers_21_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1005] + model_decoder_layers_21_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1006] + model_decoder_layers_21_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1007] + model_decoder_layers_21_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1008] + model_decoder_layers_21_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1009] + model_decoder_layers_21_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1010] + model_decoder_layers_21_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1011] + model_decoder_layers_21_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1012] + model_decoder_layers_21_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1013] + model_decoder_layers_21_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1014] + model_decoder_layers_21_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1015] + model_decoder_layers_21_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1016] + model_decoder_layers_22_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1017] + model_decoder_layers_22_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1018] + model_decoder_layers_22_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1019] + model_decoder_layers_22_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1020] + model_decoder_layers_22_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1021] + model_decoder_layers_22_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1022] + model_decoder_layers_22_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1023] + model_decoder_layers_22_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1024] + model_decoder_layers_22_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1025] + model_decoder_layers_22_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1029] + model_decoder_layers_22_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1030] + model_decoder_layers_22_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1031] + model_decoder_layers_22_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1032] + model_decoder_layers_22_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1033] + model_decoder_layers_22_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1034] + model_decoder_layers_22_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1035] + model_decoder_layers_22_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1036] + model_decoder_layers_22_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1037] + model_decoder_layers_22_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1038] + model_decoder_layers_22_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1039] + model_decoder_layers_22_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1040] + model_decoder_layers_23_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1041] + model_decoder_layers_23_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1042] + model_decoder_layers_23_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1043] + model_decoder_layers_23_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1044] + model_decoder_layers_23_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1045] + model_decoder_layers_23_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1046] + model_decoder_layers_23_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1047] + model_decoder_layers_23_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1048] + model_decoder_layers_23_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1049] + model_decoder_layers_23_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1053] + model_decoder_layers_23_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1054] + model_decoder_layers_23_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1055] + model_decoder_layers_23_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1056] + model_decoder_layers_23_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1057] + model_decoder_layers_23_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1058] + model_decoder_layers_23_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1059] + model_decoder_layers_23_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1060] + model_decoder_layers_23_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1061] + model_decoder_layers_23_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1062] + model_decoder_layers_23_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1063] + model_decoder_layers_23_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1064] + model_decoder_layers_24_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1065] + model_decoder_layers_24_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1066] + model_decoder_layers_24_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1067] + model_decoder_layers_24_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1068] + model_decoder_layers_24_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1069] + model_decoder_layers_24_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1070] + model_decoder_layers_24_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1071] + model_decoder_layers_24_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1072] + model_decoder_layers_24_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1073] + model_decoder_layers_24_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1077] + model_decoder_layers_24_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1078] + model_decoder_layers_24_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1079] + model_decoder_layers_24_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1080] + model_decoder_layers_24_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1081] + model_decoder_layers_24_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1082] + model_decoder_layers_24_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1083] + model_decoder_layers_24_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1084] + model_decoder_layers_24_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1085] + model_decoder_layers_24_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1086] + model_decoder_layers_24_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1087] + model_decoder_layers_24_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1088] + model_decoder_layers_25_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1089] + model_decoder_layers_25_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1090] + model_decoder_layers_25_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1091] + model_decoder_layers_25_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1092] + model_decoder_layers_25_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1093] + model_decoder_layers_25_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1094] + model_decoder_layers_25_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1095] + model_decoder_layers_25_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1096] + model_decoder_layers_25_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1097] + model_decoder_layers_25_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1101] + model_decoder_layers_25_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1102] + model_decoder_layers_25_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1103] + model_decoder_layers_25_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1104] + model_decoder_layers_25_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1105] + model_decoder_layers_25_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1106] + model_decoder_layers_25_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1107] + model_decoder_layers_25_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1108] + model_decoder_layers_25_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1109] + model_decoder_layers_25_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1110] + model_decoder_layers_25_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1111] + model_decoder_layers_25_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1112] + model_decoder_layers_26_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1113] + model_decoder_layers_26_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1114] + model_decoder_layers_26_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1115] + model_decoder_layers_26_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1116] + model_decoder_layers_26_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1117] + model_decoder_layers_26_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1118] + model_decoder_layers_26_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1119] + model_decoder_layers_26_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1120] + model_decoder_layers_26_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1121] + model_decoder_layers_26_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1125] + model_decoder_layers_26_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1126] + model_decoder_layers_26_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1127] + model_decoder_layers_26_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1128] + model_decoder_layers_26_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1129] + model_decoder_layers_26_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1130] + model_decoder_layers_26_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1131] + model_decoder_layers_26_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1132] + model_decoder_layers_26_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1133] + model_decoder_layers_26_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1134] + model_decoder_layers_26_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1135] + model_decoder_layers_26_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1136] + model_decoder_layers_27_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1137] + model_decoder_layers_27_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1138] + model_decoder_layers_27_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1139] + model_decoder_layers_27_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1140] + model_decoder_layers_27_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1141] + model_decoder_layers_27_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1142] + model_decoder_layers_27_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1143] + model_decoder_layers_27_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1144] + model_decoder_layers_27_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1145] + model_decoder_layers_27_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1149] + model_decoder_layers_27_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1150] + model_decoder_layers_27_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1151] + model_decoder_layers_27_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1152] + model_decoder_layers_27_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1153] + model_decoder_layers_27_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1154] + model_decoder_layers_27_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1155] + model_decoder_layers_27_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1156] + model_decoder_layers_27_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1157] + model_decoder_layers_27_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1158] + model_decoder_layers_27_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1159] + model_decoder_layers_27_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1160] + model_decoder_layers_28_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1161] + model_decoder_layers_28_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1162] + model_decoder_layers_28_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1163] + model_decoder_layers_28_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1164] + model_decoder_layers_28_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1165] + model_decoder_layers_28_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1166] + model_decoder_layers_28_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1167] + model_decoder_layers_28_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1168] + model_decoder_layers_28_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1169] + model_decoder_layers_28_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1173] + model_decoder_layers_28_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1174] + model_decoder_layers_28_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1175] + model_decoder_layers_28_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1176] + model_decoder_layers_28_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1177] + model_decoder_layers_28_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1178] + model_decoder_layers_28_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1179] + model_decoder_layers_28_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1180] + model_decoder_layers_28_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1181] + model_decoder_layers_28_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1182] + model_decoder_layers_28_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1183] + model_decoder_layers_28_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1184] + model_decoder_layers_29_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1185] + model_decoder_layers_29_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1186] + model_decoder_layers_29_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1187] + model_decoder_layers_29_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1188] + model_decoder_layers_29_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1189] + model_decoder_layers_29_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1190] + model_decoder_layers_29_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1191] + model_decoder_layers_29_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1192] + model_decoder_layers_29_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1193] + model_decoder_layers_29_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1197] + model_decoder_layers_29_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1198] + model_decoder_layers_29_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1199] + model_decoder_layers_29_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1200] + model_decoder_layers_29_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1201] + model_decoder_layers_29_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1202] + model_decoder_layers_29_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1203] + model_decoder_layers_29_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1204] + model_decoder_layers_29_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1205] + model_decoder_layers_29_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1206] + model_decoder_layers_29_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1207] + model_decoder_layers_29_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1208] + model_decoder_layers_30_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1209] + model_decoder_layers_30_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1210] + model_decoder_layers_30_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1211] + model_decoder_layers_30_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1212] + model_decoder_layers_30_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1213] + model_decoder_layers_30_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1214] + model_decoder_layers_30_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1215] + model_decoder_layers_30_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1216] + model_decoder_layers_30_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1217] + model_decoder_layers_30_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1221] + model_decoder_layers_30_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1222] + model_decoder_layers_30_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1223] + model_decoder_layers_30_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1224] + model_decoder_layers_30_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1225] + model_decoder_layers_30_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1226] + model_decoder_layers_30_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1227] + model_decoder_layers_30_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1228] + model_decoder_layers_30_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1229] + model_decoder_layers_30_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1230] + model_decoder_layers_30_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1231] + model_decoder_layers_30_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1232] + model_decoder_layers_31_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1233] + model_decoder_layers_31_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1234] + model_decoder_layers_31_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1235] + model_decoder_layers_31_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1236] + model_decoder_layers_31_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1237] + model_decoder_layers_31_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1238] + model_decoder_layers_31_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1239] + model_decoder_layers_31_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1240] + model_decoder_layers_31_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1241] + model_decoder_layers_31_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1245] + model_decoder_layers_31_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1246] + model_decoder_layers_31_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1247] + model_decoder_layers_31_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1248] + model_decoder_layers_31_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1249] + model_decoder_layers_31_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1250] + model_decoder_layers_31_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1251] + model_decoder_layers_31_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1252] + model_decoder_layers_31_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1253] + model_decoder_layers_31_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1254] + model_decoder_layers_31_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1255] + model_decoder_layers_31_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1256] + model_decoder_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1257] + model_decoder_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1258] + reshape384: R.Tensor((seq_len,), dtype="int32") = R.reshape(input_ids, R.shape([seq_len])) + take: R.Tensor((seq_len, 1280), dtype="float16") = R.take(model_decoder_embed_tokens_weight2, reshape384, axis=0) + reshape385: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(take, R.shape([1, seq_len, 1280])) + lv68: R.Tensor((seq_len,), dtype="int32") = R.call_pure_packed("vm.builtin.attention_kv_cache_get_query_positions", paged_kv_cache, sinfo_args=(R.Tensor((seq_len,), dtype="int32"),)) + take1: R.Tensor((seq_len, 1280), dtype="float16") = R.take(model_decoder_embed_positions_weight2, lv68, axis=0) + reshape386: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(take1, R.shape([1, seq_len, 1280])) + add257: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(reshape385, reshape386) + layer_norm65: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add257, model_decoder_layers_0_self_attn_layer_norm_weight2, model_decoder_layers_0_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv416 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_0_self_attn_q_proj_weight2, layer_norm65, model_decoder_layers_0_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape387: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv416, R.shape([1, seq_len, 20, 64])) + lv98 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_0_self_attn_k_proj_weight2, layer_norm65), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape388: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv98, R.shape([1, seq_len, 20, 64])) + lv417 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_0_self_attn_v_proj_weight2, layer_norm65, model_decoder_layers_0_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape389: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv417, R.shape([1, seq_len, 20, 64])) + concat: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape387, reshape388, reshape389), axis=2) + reshape390: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat, R.shape([seq_len, 60, 64])) + lv69 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), reshape390), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape391: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv69, R.shape([1, seq_len, 20, 64])) + reshape392: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape391, R.shape([1, seq_len, 1280])) + lv418 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_0_self_attn_out_proj_weight2, reshape392, model_decoder_layers_0_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add261: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add257, lv418) + layer_norm66: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add261, model_decoder_layers_0_encoder_attn_layer_norm_weight2, model_decoder_layers_0_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv419 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_0_encoder_attn_q_proj_weight2, layer_norm66, model_decoder_layers_0_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape393: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv419, R.shape([1, seq_len, 20, 64])) + reshape394: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape393, R.shape([seq_len, 20, 64])) + lv70 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), reshape394), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape395: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv70, R.shape([1, seq_len, 20, 64])) + reshape396: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape395, R.shape([1, seq_len, 1280])) + lv420 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_0_encoder_attn_out_proj_weight2, reshape396, model_decoder_layers_0_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add264: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add261, lv420) + layer_norm67: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add264, model_decoder_layers_0_final_layer_norm_weight2, model_decoder_layers_0_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv64 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_0_fc1_weight2, layer_norm67, model_decoder_layers_0_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv421 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_0_fc2_weight2, lv64, model_decoder_layers_0_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add267: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add264, lv421) + layer_norm68: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add267, model_decoder_layers_1_self_attn_layer_norm_weight2, model_decoder_layers_1_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv422 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_1_self_attn_q_proj_weight2, layer_norm68, model_decoder_layers_1_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape397: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv422, R.shape([1, seq_len, 20, 64])) + lv99 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_1_self_attn_k_proj_weight2, layer_norm68), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape398: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv99, R.shape([1, seq_len, 20, 64])) + lv423 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_1_self_attn_v_proj_weight2, layer_norm68, model_decoder_layers_1_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape399: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv423, R.shape([1, seq_len, 20, 64])) + concat1: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape397, reshape398, reshape399), axis=2) + reshape400: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat1, R.shape([seq_len, 60, 64])) + lv71 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), reshape400), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape401: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv71, R.shape([1, seq_len, 20, 64])) + reshape402: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape401, R.shape([1, seq_len, 1280])) + lv424 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_1_self_attn_out_proj_weight2, reshape402, model_decoder_layers_1_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add271: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add267, lv424) + layer_norm69: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add271, model_decoder_layers_1_encoder_attn_layer_norm_weight2, model_decoder_layers_1_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv425 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_1_encoder_attn_q_proj_weight2, layer_norm69, model_decoder_layers_1_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape403: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv425, R.shape([1, seq_len, 20, 64])) + reshape404: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape403, R.shape([seq_len, 20, 64])) + lv72 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), reshape404), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape405: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv72, R.shape([1, seq_len, 20, 64])) + reshape406: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape405, R.shape([1, seq_len, 1280])) + lv426 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_1_encoder_attn_out_proj_weight2, reshape406, model_decoder_layers_1_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add274: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add271, lv426) + layer_norm70: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add274, model_decoder_layers_1_final_layer_norm_weight2, model_decoder_layers_1_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv65 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_1_fc1_weight2, layer_norm70, model_decoder_layers_1_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv427 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_1_fc2_weight2, lv65, model_decoder_layers_1_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add277: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add274, lv427) + layer_norm71: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add277, model_decoder_layers_2_self_attn_layer_norm_weight2, model_decoder_layers_2_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv428 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_2_self_attn_q_proj_weight2, layer_norm71, model_decoder_layers_2_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape407: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv428, R.shape([1, seq_len, 20, 64])) + lv100 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_2_self_attn_k_proj_weight2, layer_norm71), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape408: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv100, R.shape([1, seq_len, 20, 64])) + lv429 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_2_self_attn_v_proj_weight2, layer_norm71, model_decoder_layers_2_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape409: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv429, R.shape([1, seq_len, 20, 64])) + concat2: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape407, reshape408, reshape409), axis=2) + reshape410: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat2, R.shape([seq_len, 60, 64])) + lv73 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), reshape410), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape411: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv73, R.shape([1, seq_len, 20, 64])) + reshape412: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape411, R.shape([1, seq_len, 1280])) + lv430 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_2_self_attn_out_proj_weight2, reshape412, model_decoder_layers_2_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add281: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add277, lv430) + layer_norm72: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add281, model_decoder_layers_2_encoder_attn_layer_norm_weight2, model_decoder_layers_2_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv431 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_2_encoder_attn_q_proj_weight2, layer_norm72, model_decoder_layers_2_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape413: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv431, R.shape([1, seq_len, 20, 64])) + reshape414: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape413, R.shape([seq_len, 20, 64])) + lv74 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), reshape414), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape415: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv74, R.shape([1, seq_len, 20, 64])) + reshape416: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape415, R.shape([1, seq_len, 1280])) + lv432 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_2_encoder_attn_out_proj_weight2, reshape416, model_decoder_layers_2_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add284: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add281, lv432) + layer_norm73: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add284, model_decoder_layers_2_final_layer_norm_weight2, model_decoder_layers_2_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv66 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_2_fc1_weight2, layer_norm73, model_decoder_layers_2_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv433 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_2_fc2_weight2, lv66, model_decoder_layers_2_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add287: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add284, lv433) + layer_norm74: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add287, model_decoder_layers_3_self_attn_layer_norm_weight2, model_decoder_layers_3_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv434 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_3_self_attn_q_proj_weight2, layer_norm74, model_decoder_layers_3_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape417: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv434, R.shape([1, seq_len, 20, 64])) + lv101 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_3_self_attn_k_proj_weight2, layer_norm74), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape418: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv101, R.shape([1, seq_len, 20, 64])) + lv435 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_3_self_attn_v_proj_weight2, layer_norm74, model_decoder_layers_3_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape419: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv435, R.shape([1, seq_len, 20, 64])) + concat3: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape417, reshape418, reshape419), axis=2) + reshape420: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat3, R.shape([seq_len, 60, 64])) + lv75 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), reshape420), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape421: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv75, R.shape([1, seq_len, 20, 64])) + reshape422: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape421, R.shape([1, seq_len, 1280])) + lv436 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_3_self_attn_out_proj_weight2, reshape422, model_decoder_layers_3_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add291: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add287, lv436) + layer_norm75: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add291, model_decoder_layers_3_encoder_attn_layer_norm_weight2, model_decoder_layers_3_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv437 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_3_encoder_attn_q_proj_weight2, layer_norm75, model_decoder_layers_3_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape423: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv437, R.shape([1, seq_len, 20, 64])) + reshape424: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape423, R.shape([seq_len, 20, 64])) + lv76 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), reshape424), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape425: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv76, R.shape([1, seq_len, 20, 64])) + reshape426: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape425, R.shape([1, seq_len, 1280])) + lv438 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_3_encoder_attn_out_proj_weight2, reshape426, model_decoder_layers_3_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add294: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add291, lv438) + layer_norm76: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add294, model_decoder_layers_3_final_layer_norm_weight2, model_decoder_layers_3_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv67 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_3_fc1_weight2, layer_norm76, model_decoder_layers_3_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv439 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_3_fc2_weight2, lv67, model_decoder_layers_3_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add297: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add294, lv439) + layer_norm77: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add297, model_decoder_layers_4_self_attn_layer_norm_weight2, model_decoder_layers_4_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv440 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_4_self_attn_q_proj_weight2, layer_norm77, model_decoder_layers_4_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape427: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv440, R.shape([1, seq_len, 20, 64])) + lv102 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_4_self_attn_k_proj_weight2, layer_norm77), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape428: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv102, R.shape([1, seq_len, 20, 64])) + lv441 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_4_self_attn_v_proj_weight2, layer_norm77, model_decoder_layers_4_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape429: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv441, R.shape([1, seq_len, 20, 64])) + concat4: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape427, reshape428, reshape429), axis=2) + reshape430: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat4, R.shape([seq_len, 60, 64])) + lv77 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), reshape430), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape431: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv77, R.shape([1, seq_len, 20, 64])) + reshape432: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape431, R.shape([1, seq_len, 1280])) + lv442 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_4_self_attn_out_proj_weight2, reshape432, model_decoder_layers_4_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add301: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add297, lv442) + layer_norm78: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add301, model_decoder_layers_4_encoder_attn_layer_norm_weight2, model_decoder_layers_4_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv443 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_4_encoder_attn_q_proj_weight2, layer_norm78, model_decoder_layers_4_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape433: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv443, R.shape([1, seq_len, 20, 64])) + reshape434: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape433, R.shape([seq_len, 20, 64])) + lv78 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), reshape434), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape435: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv78, R.shape([1, seq_len, 20, 64])) + reshape436: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape435, R.shape([1, seq_len, 1280])) + lv444 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_4_encoder_attn_out_proj_weight2, reshape436, model_decoder_layers_4_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add304: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add301, lv444) + layer_norm79: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add304, model_decoder_layers_4_final_layer_norm_weight2, model_decoder_layers_4_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv68_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_4_fc1_weight2, layer_norm79, model_decoder_layers_4_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv445 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_4_fc2_weight2, lv68_1, model_decoder_layers_4_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add307: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add304, lv445) + layer_norm80: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add307, model_decoder_layers_5_self_attn_layer_norm_weight2, model_decoder_layers_5_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv446 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_5_self_attn_q_proj_weight2, layer_norm80, model_decoder_layers_5_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape437: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv446, R.shape([1, seq_len, 20, 64])) + lv103 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_5_self_attn_k_proj_weight2, layer_norm80), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape438: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv103, R.shape([1, seq_len, 20, 64])) + lv447 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_5_self_attn_v_proj_weight2, layer_norm80, model_decoder_layers_5_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape439: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv447, R.shape([1, seq_len, 20, 64])) + concat5: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape437, reshape438, reshape439), axis=2) + reshape440: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat5, R.shape([seq_len, 60, 64])) + lv79 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), reshape440), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape441: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv79, R.shape([1, seq_len, 20, 64])) + reshape442: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape441, R.shape([1, seq_len, 1280])) + lv448 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_5_self_attn_out_proj_weight2, reshape442, model_decoder_layers_5_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add311: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add307, lv448) + layer_norm81: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add311, model_decoder_layers_5_encoder_attn_layer_norm_weight2, model_decoder_layers_5_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv449 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_5_encoder_attn_q_proj_weight2, layer_norm81, model_decoder_layers_5_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape443: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv449, R.shape([1, seq_len, 20, 64])) + reshape444: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape443, R.shape([seq_len, 20, 64])) + lv80 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), reshape444), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape445: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv80, R.shape([1, seq_len, 20, 64])) + reshape446: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape445, R.shape([1, seq_len, 1280])) + lv450 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_5_encoder_attn_out_proj_weight2, reshape446, model_decoder_layers_5_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add314: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add311, lv450) + layer_norm82: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add314, model_decoder_layers_5_final_layer_norm_weight2, model_decoder_layers_5_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv69_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_5_fc1_weight2, layer_norm82, model_decoder_layers_5_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv451 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_5_fc2_weight2, lv69_1, model_decoder_layers_5_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add317: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add314, lv451) + layer_norm83: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add317, model_decoder_layers_6_self_attn_layer_norm_weight2, model_decoder_layers_6_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv452 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_6_self_attn_q_proj_weight2, layer_norm83, model_decoder_layers_6_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape447: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv452, R.shape([1, seq_len, 20, 64])) + lv104 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_6_self_attn_k_proj_weight2, layer_norm83), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape448: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv104, R.shape([1, seq_len, 20, 64])) + lv453 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_6_self_attn_v_proj_weight2, layer_norm83, model_decoder_layers_6_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape449: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv453, R.shape([1, seq_len, 20, 64])) + concat6: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape447, reshape448, reshape449), axis=2) + reshape450: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat6, R.shape([seq_len, 60, 64])) + lv81 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), reshape450), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape451: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv81, R.shape([1, seq_len, 20, 64])) + reshape452: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape451, R.shape([1, seq_len, 1280])) + lv454 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_6_self_attn_out_proj_weight2, reshape452, model_decoder_layers_6_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add321: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add317, lv454) + layer_norm84: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add321, model_decoder_layers_6_encoder_attn_layer_norm_weight2, model_decoder_layers_6_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv455 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_6_encoder_attn_q_proj_weight2, layer_norm84, model_decoder_layers_6_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape453: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv455, R.shape([1, seq_len, 20, 64])) + reshape454: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape453, R.shape([seq_len, 20, 64])) + lv82 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), reshape454), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape455: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv82, R.shape([1, seq_len, 20, 64])) + reshape456: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape455, R.shape([1, seq_len, 1280])) + lv456 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_6_encoder_attn_out_proj_weight2, reshape456, model_decoder_layers_6_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add324: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add321, lv456) + layer_norm85: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add324, model_decoder_layers_6_final_layer_norm_weight2, model_decoder_layers_6_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv70_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_6_fc1_weight2, layer_norm85, model_decoder_layers_6_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv457 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_6_fc2_weight2, lv70_1, model_decoder_layers_6_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add327: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add324, lv457) + layer_norm86: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add327, model_decoder_layers_7_self_attn_layer_norm_weight2, model_decoder_layers_7_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv458 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_7_self_attn_q_proj_weight2, layer_norm86, model_decoder_layers_7_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape457: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv458, R.shape([1, seq_len, 20, 64])) + lv105 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_7_self_attn_k_proj_weight2, layer_norm86), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape458: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv105, R.shape([1, seq_len, 20, 64])) + lv459 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_7_self_attn_v_proj_weight2, layer_norm86, model_decoder_layers_7_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape459: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv459, R.shape([1, seq_len, 20, 64])) + concat7: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape457, reshape458, reshape459), axis=2) + reshape460: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat7, R.shape([seq_len, 60, 64])) + lv83 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), reshape460), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape461: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv83, R.shape([1, seq_len, 20, 64])) + reshape462: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape461, R.shape([1, seq_len, 1280])) + lv460 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_7_self_attn_out_proj_weight2, reshape462, model_decoder_layers_7_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add331: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add327, lv460) + layer_norm87: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add331, model_decoder_layers_7_encoder_attn_layer_norm_weight2, model_decoder_layers_7_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv461 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_7_encoder_attn_q_proj_weight2, layer_norm87, model_decoder_layers_7_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape463: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv461, R.shape([1, seq_len, 20, 64])) + reshape464: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape463, R.shape([seq_len, 20, 64])) + lv84 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), reshape464), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape465: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv84, R.shape([1, seq_len, 20, 64])) + reshape466: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape465, R.shape([1, seq_len, 1280])) + lv462 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_7_encoder_attn_out_proj_weight2, reshape466, model_decoder_layers_7_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add334: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add331, lv462) + layer_norm88: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add334, model_decoder_layers_7_final_layer_norm_weight2, model_decoder_layers_7_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv71_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_7_fc1_weight2, layer_norm88, model_decoder_layers_7_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv463 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_7_fc2_weight2, lv71_1, model_decoder_layers_7_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add337: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add334, lv463) + layer_norm89: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add337, model_decoder_layers_8_self_attn_layer_norm_weight2, model_decoder_layers_8_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv464 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_8_self_attn_q_proj_weight2, layer_norm89, model_decoder_layers_8_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape467: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv464, R.shape([1, seq_len, 20, 64])) + lv106 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_8_self_attn_k_proj_weight2, layer_norm89), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape468: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv106, R.shape([1, seq_len, 20, 64])) + lv465 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_8_self_attn_v_proj_weight2, layer_norm89, model_decoder_layers_8_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape469: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv465, R.shape([1, seq_len, 20, 64])) + concat8: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape467, reshape468, reshape469), axis=2) + reshape470: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat8, R.shape([seq_len, 60, 64])) + lv85 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), reshape470), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape471: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv85, R.shape([1, seq_len, 20, 64])) + reshape472: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape471, R.shape([1, seq_len, 1280])) + lv466 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_8_self_attn_out_proj_weight2, reshape472, model_decoder_layers_8_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add341: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add337, lv466) + layer_norm90: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add341, model_decoder_layers_8_encoder_attn_layer_norm_weight2, model_decoder_layers_8_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv467 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_8_encoder_attn_q_proj_weight2, layer_norm90, model_decoder_layers_8_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape473: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv467, R.shape([1, seq_len, 20, 64])) + reshape474: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape473, R.shape([seq_len, 20, 64])) + lv86 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), reshape474), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape475: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv86, R.shape([1, seq_len, 20, 64])) + reshape476: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape475, R.shape([1, seq_len, 1280])) + lv468 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_8_encoder_attn_out_proj_weight2, reshape476, model_decoder_layers_8_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add344: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add341, lv468) + layer_norm91: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add344, model_decoder_layers_8_final_layer_norm_weight2, model_decoder_layers_8_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv72_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_8_fc1_weight2, layer_norm91, model_decoder_layers_8_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv469 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_8_fc2_weight2, lv72_1, model_decoder_layers_8_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add347: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add344, lv469) + layer_norm92: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add347, model_decoder_layers_9_self_attn_layer_norm_weight2, model_decoder_layers_9_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv470 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_9_self_attn_q_proj_weight2, layer_norm92, model_decoder_layers_9_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape477: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv470, R.shape([1, seq_len, 20, 64])) + lv107 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_9_self_attn_k_proj_weight2, layer_norm92), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape478: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv107, R.shape([1, seq_len, 20, 64])) + lv471 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_9_self_attn_v_proj_weight2, layer_norm92, model_decoder_layers_9_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape479: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv471, R.shape([1, seq_len, 20, 64])) + concat9: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape477, reshape478, reshape479), axis=2) + reshape480: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat9, R.shape([seq_len, 60, 64])) + lv87 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), reshape480), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape481: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv87, R.shape([1, seq_len, 20, 64])) + reshape482: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape481, R.shape([1, seq_len, 1280])) + lv472 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_9_self_attn_out_proj_weight2, reshape482, model_decoder_layers_9_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add351: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add347, lv472) + layer_norm93: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add351, model_decoder_layers_9_encoder_attn_layer_norm_weight2, model_decoder_layers_9_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv473 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_9_encoder_attn_q_proj_weight2, layer_norm93, model_decoder_layers_9_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape483: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv473, R.shape([1, seq_len, 20, 64])) + reshape484: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape483, R.shape([seq_len, 20, 64])) + lv88 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), reshape484), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape485: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv88, R.shape([1, seq_len, 20, 64])) + reshape486: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape485, R.shape([1, seq_len, 1280])) + lv474 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_9_encoder_attn_out_proj_weight2, reshape486, model_decoder_layers_9_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add354: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add351, lv474) + layer_norm94: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add354, model_decoder_layers_9_final_layer_norm_weight2, model_decoder_layers_9_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv73_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_9_fc1_weight2, layer_norm94, model_decoder_layers_9_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv475 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_9_fc2_weight2, lv73_1, model_decoder_layers_9_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add357: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add354, lv475) + layer_norm95: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add357, model_decoder_layers_10_self_attn_layer_norm_weight2, model_decoder_layers_10_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv476 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_10_self_attn_q_proj_weight2, layer_norm95, model_decoder_layers_10_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape487: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv476, R.shape([1, seq_len, 20, 64])) + lv108 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_10_self_attn_k_proj_weight2, layer_norm95), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape488: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv108, R.shape([1, seq_len, 20, 64])) + lv477 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_10_self_attn_v_proj_weight2, layer_norm95, model_decoder_layers_10_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape489: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv477, R.shape([1, seq_len, 20, 64])) + concat10: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape487, reshape488, reshape489), axis=2) + reshape490: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat10, R.shape([seq_len, 60, 64])) + lv89 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), reshape490), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape491: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv89, R.shape([1, seq_len, 20, 64])) + reshape492: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape491, R.shape([1, seq_len, 1280])) + lv478 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_10_self_attn_out_proj_weight2, reshape492, model_decoder_layers_10_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add361: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add357, lv478) + layer_norm96: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add361, model_decoder_layers_10_encoder_attn_layer_norm_weight2, model_decoder_layers_10_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv479 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_10_encoder_attn_q_proj_weight2, layer_norm96, model_decoder_layers_10_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape493: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv479, R.shape([1, seq_len, 20, 64])) + reshape494: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape493, R.shape([seq_len, 20, 64])) + lv90 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), reshape494), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape495: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv90, R.shape([1, seq_len, 20, 64])) + reshape496: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape495, R.shape([1, seq_len, 1280])) + lv480 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_10_encoder_attn_out_proj_weight2, reshape496, model_decoder_layers_10_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add364: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add361, lv480) + layer_norm97: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add364, model_decoder_layers_10_final_layer_norm_weight2, model_decoder_layers_10_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv74_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_10_fc1_weight2, layer_norm97, model_decoder_layers_10_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv481 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_10_fc2_weight2, lv74_1, model_decoder_layers_10_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add367: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add364, lv481) + layer_norm98: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add367, model_decoder_layers_11_self_attn_layer_norm_weight2, model_decoder_layers_11_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv482 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_11_self_attn_q_proj_weight2, layer_norm98, model_decoder_layers_11_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape497: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv482, R.shape([1, seq_len, 20, 64])) + lv109 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_11_self_attn_k_proj_weight2, layer_norm98), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape498: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv109, R.shape([1, seq_len, 20, 64])) + lv483 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_11_self_attn_v_proj_weight2, layer_norm98, model_decoder_layers_11_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape499: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv483, R.shape([1, seq_len, 20, 64])) + concat11: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape497, reshape498, reshape499), axis=2) + reshape500: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat11, R.shape([seq_len, 60, 64])) + lv91 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), reshape500), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape501: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv91, R.shape([1, seq_len, 20, 64])) + reshape502: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape501, R.shape([1, seq_len, 1280])) + lv484 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_11_self_attn_out_proj_weight2, reshape502, model_decoder_layers_11_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add371: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add367, lv484) + layer_norm99: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add371, model_decoder_layers_11_encoder_attn_layer_norm_weight2, model_decoder_layers_11_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv485 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_11_encoder_attn_q_proj_weight2, layer_norm99, model_decoder_layers_11_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape503: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv485, R.shape([1, seq_len, 20, 64])) + reshape504: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape503, R.shape([seq_len, 20, 64])) + lv92 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), reshape504), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape505: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv92, R.shape([1, seq_len, 20, 64])) + reshape506: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape505, R.shape([1, seq_len, 1280])) + lv486 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_11_encoder_attn_out_proj_weight2, reshape506, model_decoder_layers_11_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add374: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add371, lv486) + layer_norm100: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add374, model_decoder_layers_11_final_layer_norm_weight2, model_decoder_layers_11_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv75_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_11_fc1_weight2, layer_norm100, model_decoder_layers_11_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv487 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_11_fc2_weight2, lv75_1, model_decoder_layers_11_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add377: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add374, lv487) + layer_norm101: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add377, model_decoder_layers_12_self_attn_layer_norm_weight2, model_decoder_layers_12_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv488 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_12_self_attn_q_proj_weight2, layer_norm101, model_decoder_layers_12_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape507: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv488, R.shape([1, seq_len, 20, 64])) + lv110 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_12_self_attn_k_proj_weight2, layer_norm101), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape508: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv110, R.shape([1, seq_len, 20, 64])) + lv489 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_12_self_attn_v_proj_weight2, layer_norm101, model_decoder_layers_12_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape509: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv489, R.shape([1, seq_len, 20, 64])) + concat12: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape507, reshape508, reshape509), axis=2) + reshape510: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat12, R.shape([seq_len, 60, 64])) + lv93 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), reshape510), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape511: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv93, R.shape([1, seq_len, 20, 64])) + reshape512: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape511, R.shape([1, seq_len, 1280])) + lv490 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_12_self_attn_out_proj_weight2, reshape512, model_decoder_layers_12_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add381: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add377, lv490) + layer_norm102: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add381, model_decoder_layers_12_encoder_attn_layer_norm_weight2, model_decoder_layers_12_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv491 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_12_encoder_attn_q_proj_weight2, layer_norm102, model_decoder_layers_12_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape513: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv491, R.shape([1, seq_len, 20, 64])) + reshape514: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape513, R.shape([seq_len, 20, 64])) + lv94 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), reshape514), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape515: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv94, R.shape([1, seq_len, 20, 64])) + reshape516: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape515, R.shape([1, seq_len, 1280])) + lv492 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_12_encoder_attn_out_proj_weight2, reshape516, model_decoder_layers_12_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add384: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add381, lv492) + layer_norm103: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add384, model_decoder_layers_12_final_layer_norm_weight2, model_decoder_layers_12_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv76_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_12_fc1_weight2, layer_norm103, model_decoder_layers_12_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv493 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_12_fc2_weight2, lv76_1, model_decoder_layers_12_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add387: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add384, lv493) + layer_norm104: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add387, model_decoder_layers_13_self_attn_layer_norm_weight2, model_decoder_layers_13_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv494 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_13_self_attn_q_proj_weight2, layer_norm104, model_decoder_layers_13_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape517: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv494, R.shape([1, seq_len, 20, 64])) + lv111 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_13_self_attn_k_proj_weight2, layer_norm104), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape518: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv111, R.shape([1, seq_len, 20, 64])) + lv495 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_13_self_attn_v_proj_weight2, layer_norm104, model_decoder_layers_13_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape519: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv495, R.shape([1, seq_len, 20, 64])) + concat13: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape517, reshape518, reshape519), axis=2) + reshape520: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat13, R.shape([seq_len, 60, 64])) + lv95 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), reshape520), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape521: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv95, R.shape([1, seq_len, 20, 64])) + reshape522: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape521, R.shape([1, seq_len, 1280])) + lv496 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_13_self_attn_out_proj_weight2, reshape522, model_decoder_layers_13_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add391: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add387, lv496) + layer_norm105: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add391, model_decoder_layers_13_encoder_attn_layer_norm_weight2, model_decoder_layers_13_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv497 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_13_encoder_attn_q_proj_weight2, layer_norm105, model_decoder_layers_13_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape523: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv497, R.shape([1, seq_len, 20, 64])) + reshape524: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape523, R.shape([seq_len, 20, 64])) + lv96 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), reshape524), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape525: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv96, R.shape([1, seq_len, 20, 64])) + reshape526: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape525, R.shape([1, seq_len, 1280])) + lv498 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_13_encoder_attn_out_proj_weight2, reshape526, model_decoder_layers_13_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add394: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add391, lv498) + layer_norm106: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add394, model_decoder_layers_13_final_layer_norm_weight2, model_decoder_layers_13_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv77_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_13_fc1_weight2, layer_norm106, model_decoder_layers_13_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv499 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_13_fc2_weight2, lv77_1, model_decoder_layers_13_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add397: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add394, lv499) + layer_norm107: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add397, model_decoder_layers_14_self_attn_layer_norm_weight2, model_decoder_layers_14_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv500 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_14_self_attn_q_proj_weight2, layer_norm107, model_decoder_layers_14_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape527: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv500, R.shape([1, seq_len, 20, 64])) + lv112 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_14_self_attn_k_proj_weight2, layer_norm107), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape528: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv112, R.shape([1, seq_len, 20, 64])) + lv501 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_14_self_attn_v_proj_weight2, layer_norm107, model_decoder_layers_14_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape529: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv501, R.shape([1, seq_len, 20, 64])) + concat14: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape527, reshape528, reshape529), axis=2) + reshape530: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat14, R.shape([seq_len, 60, 64])) + lv97 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), reshape530), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape531: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv97, R.shape([1, seq_len, 20, 64])) + reshape532: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape531, R.shape([1, seq_len, 1280])) + lv502 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_14_self_attn_out_proj_weight2, reshape532, model_decoder_layers_14_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add401: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add397, lv502) + layer_norm108: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add401, model_decoder_layers_14_encoder_attn_layer_norm_weight2, model_decoder_layers_14_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv503 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_14_encoder_attn_q_proj_weight2, layer_norm108, model_decoder_layers_14_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape533: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv503, R.shape([1, seq_len, 20, 64])) + reshape534: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape533, R.shape([seq_len, 20, 64])) + lv98_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), reshape534), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape535: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv98_1, R.shape([1, seq_len, 20, 64])) + reshape536: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape535, R.shape([1, seq_len, 1280])) + lv504 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_14_encoder_attn_out_proj_weight2, reshape536, model_decoder_layers_14_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add404: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add401, lv504) + layer_norm109: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add404, model_decoder_layers_14_final_layer_norm_weight2, model_decoder_layers_14_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv78_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_14_fc1_weight2, layer_norm109, model_decoder_layers_14_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv505 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_14_fc2_weight2, lv78_1, model_decoder_layers_14_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add407: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add404, lv505) + layer_norm110: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add407, model_decoder_layers_15_self_attn_layer_norm_weight2, model_decoder_layers_15_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv506 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_15_self_attn_q_proj_weight2, layer_norm110, model_decoder_layers_15_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape537: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv506, R.shape([1, seq_len, 20, 64])) + lv113 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_15_self_attn_k_proj_weight2, layer_norm110), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape538: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv113, R.shape([1, seq_len, 20, 64])) + lv507 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_15_self_attn_v_proj_weight2, layer_norm110, model_decoder_layers_15_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape539: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv507, R.shape([1, seq_len, 20, 64])) + concat15: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape537, reshape538, reshape539), axis=2) + reshape540: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat15, R.shape([seq_len, 60, 64])) + lv99_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), reshape540), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape541: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv99_1, R.shape([1, seq_len, 20, 64])) + reshape542: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape541, R.shape([1, seq_len, 1280])) + lv508 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_15_self_attn_out_proj_weight2, reshape542, model_decoder_layers_15_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add411: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add407, lv508) + layer_norm111: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add411, model_decoder_layers_15_encoder_attn_layer_norm_weight2, model_decoder_layers_15_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv509 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_15_encoder_attn_q_proj_weight2, layer_norm111, model_decoder_layers_15_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape543: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv509, R.shape([1, seq_len, 20, 64])) + reshape544: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape543, R.shape([seq_len, 20, 64])) + lv100_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), reshape544), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape545: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv100_1, R.shape([1, seq_len, 20, 64])) + reshape546: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape545, R.shape([1, seq_len, 1280])) + lv510 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_15_encoder_attn_out_proj_weight2, reshape546, model_decoder_layers_15_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add414: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add411, lv510) + layer_norm112: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add414, model_decoder_layers_15_final_layer_norm_weight2, model_decoder_layers_15_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv79_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_15_fc1_weight2, layer_norm112, model_decoder_layers_15_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv511 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_15_fc2_weight2, lv79_1, model_decoder_layers_15_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add417: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add414, lv511) + layer_norm113: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add417, model_decoder_layers_16_self_attn_layer_norm_weight2, model_decoder_layers_16_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv512 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_16_self_attn_q_proj_weight2, layer_norm113, model_decoder_layers_16_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape547: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv512, R.shape([1, seq_len, 20, 64])) + lv114 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_16_self_attn_k_proj_weight2, layer_norm113), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape548: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv114, R.shape([1, seq_len, 20, 64])) + lv513 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_16_self_attn_v_proj_weight2, layer_norm113, model_decoder_layers_16_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape549: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv513, R.shape([1, seq_len, 20, 64])) + concat16: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape547, reshape548, reshape549), axis=2) + reshape550: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat16, R.shape([seq_len, 60, 64])) + lv101_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), reshape550), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape551: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv101_1, R.shape([1, seq_len, 20, 64])) + reshape552: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape551, R.shape([1, seq_len, 1280])) + lv514 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_16_self_attn_out_proj_weight2, reshape552, model_decoder_layers_16_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add421: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add417, lv514) + layer_norm114: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add421, model_decoder_layers_16_encoder_attn_layer_norm_weight2, model_decoder_layers_16_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv515 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_16_encoder_attn_q_proj_weight2, layer_norm114, model_decoder_layers_16_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape553: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv515, R.shape([1, seq_len, 20, 64])) + reshape554: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape553, R.shape([seq_len, 20, 64])) + lv102_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), reshape554), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape555: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv102_1, R.shape([1, seq_len, 20, 64])) + reshape556: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape555, R.shape([1, seq_len, 1280])) + lv516 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_16_encoder_attn_out_proj_weight2, reshape556, model_decoder_layers_16_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add424: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add421, lv516) + layer_norm115: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add424, model_decoder_layers_16_final_layer_norm_weight2, model_decoder_layers_16_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv80_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_16_fc1_weight2, layer_norm115, model_decoder_layers_16_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv517 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_16_fc2_weight2, lv80_1, model_decoder_layers_16_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add427: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add424, lv517) + layer_norm116: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add427, model_decoder_layers_17_self_attn_layer_norm_weight2, model_decoder_layers_17_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv518 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_17_self_attn_q_proj_weight2, layer_norm116, model_decoder_layers_17_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape557: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv518, R.shape([1, seq_len, 20, 64])) + lv115 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_17_self_attn_k_proj_weight2, layer_norm116), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape558: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv115, R.shape([1, seq_len, 20, 64])) + lv519 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_17_self_attn_v_proj_weight2, layer_norm116, model_decoder_layers_17_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape559: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv519, R.shape([1, seq_len, 20, 64])) + concat17: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape557, reshape558, reshape559), axis=2) + reshape560: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat17, R.shape([seq_len, 60, 64])) + lv103_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), reshape560), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape561: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv103_1, R.shape([1, seq_len, 20, 64])) + reshape562: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape561, R.shape([1, seq_len, 1280])) + lv520 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_17_self_attn_out_proj_weight2, reshape562, model_decoder_layers_17_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add431: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add427, lv520) + layer_norm117: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add431, model_decoder_layers_17_encoder_attn_layer_norm_weight2, model_decoder_layers_17_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv521 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_17_encoder_attn_q_proj_weight2, layer_norm117, model_decoder_layers_17_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape563: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv521, R.shape([1, seq_len, 20, 64])) + reshape564: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape563, R.shape([seq_len, 20, 64])) + lv104_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), reshape564), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape565: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv104_1, R.shape([1, seq_len, 20, 64])) + reshape566: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape565, R.shape([1, seq_len, 1280])) + lv522 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_17_encoder_attn_out_proj_weight2, reshape566, model_decoder_layers_17_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add434: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add431, lv522) + layer_norm118: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add434, model_decoder_layers_17_final_layer_norm_weight2, model_decoder_layers_17_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv81_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_17_fc1_weight2, layer_norm118, model_decoder_layers_17_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv523 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_17_fc2_weight2, lv81_1, model_decoder_layers_17_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add437: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add434, lv523) + layer_norm119: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add437, model_decoder_layers_18_self_attn_layer_norm_weight2, model_decoder_layers_18_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv524 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_18_self_attn_q_proj_weight2, layer_norm119, model_decoder_layers_18_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape567: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv524, R.shape([1, seq_len, 20, 64])) + lv116 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_18_self_attn_k_proj_weight2, layer_norm119), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape568: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv116, R.shape([1, seq_len, 20, 64])) + lv525 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_18_self_attn_v_proj_weight2, layer_norm119, model_decoder_layers_18_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape569: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv525, R.shape([1, seq_len, 20, 64])) + concat18: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape567, reshape568, reshape569), axis=2) + reshape570: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat18, R.shape([seq_len, 60, 64])) + lv105_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), reshape570), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape571: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv105_1, R.shape([1, seq_len, 20, 64])) + reshape572: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape571, R.shape([1, seq_len, 1280])) + lv526 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_18_self_attn_out_proj_weight2, reshape572, model_decoder_layers_18_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add441: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add437, lv526) + layer_norm120: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add441, model_decoder_layers_18_encoder_attn_layer_norm_weight2, model_decoder_layers_18_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv527 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_18_encoder_attn_q_proj_weight2, layer_norm120, model_decoder_layers_18_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape573: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv527, R.shape([1, seq_len, 20, 64])) + reshape574: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape573, R.shape([seq_len, 20, 64])) + lv106_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), reshape574), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape575: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv106_1, R.shape([1, seq_len, 20, 64])) + reshape576: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape575, R.shape([1, seq_len, 1280])) + lv528 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_18_encoder_attn_out_proj_weight2, reshape576, model_decoder_layers_18_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add444: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add441, lv528) + layer_norm121: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add444, model_decoder_layers_18_final_layer_norm_weight2, model_decoder_layers_18_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv82_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_18_fc1_weight2, layer_norm121, model_decoder_layers_18_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv529 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_18_fc2_weight2, lv82_1, model_decoder_layers_18_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add447: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add444, lv529) + layer_norm122: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add447, model_decoder_layers_19_self_attn_layer_norm_weight2, model_decoder_layers_19_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv530 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_19_self_attn_q_proj_weight2, layer_norm122, model_decoder_layers_19_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape577: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv530, R.shape([1, seq_len, 20, 64])) + lv117 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_19_self_attn_k_proj_weight2, layer_norm122), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape578: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv117, R.shape([1, seq_len, 20, 64])) + lv531 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_19_self_attn_v_proj_weight2, layer_norm122, model_decoder_layers_19_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape579: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv531, R.shape([1, seq_len, 20, 64])) + concat19: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape577, reshape578, reshape579), axis=2) + reshape580: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat19, R.shape([seq_len, 60, 64])) + lv107_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), reshape580), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape581: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv107_1, R.shape([1, seq_len, 20, 64])) + reshape582: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape581, R.shape([1, seq_len, 1280])) + lv532 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_19_self_attn_out_proj_weight2, reshape582, model_decoder_layers_19_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add451: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add447, lv532) + layer_norm123: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add451, model_decoder_layers_19_encoder_attn_layer_norm_weight2, model_decoder_layers_19_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv533 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_19_encoder_attn_q_proj_weight2, layer_norm123, model_decoder_layers_19_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape583: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv533, R.shape([1, seq_len, 20, 64])) + reshape584: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape583, R.shape([seq_len, 20, 64])) + lv108_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), reshape584), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape585: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv108_1, R.shape([1, seq_len, 20, 64])) + reshape586: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape585, R.shape([1, seq_len, 1280])) + lv534 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_19_encoder_attn_out_proj_weight2, reshape586, model_decoder_layers_19_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add454: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add451, lv534) + layer_norm124: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add454, model_decoder_layers_19_final_layer_norm_weight2, model_decoder_layers_19_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv83_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_19_fc1_weight2, layer_norm124, model_decoder_layers_19_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv535 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_19_fc2_weight2, lv83_1, model_decoder_layers_19_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add457: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add454, lv535) + layer_norm125: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add457, model_decoder_layers_20_self_attn_layer_norm_weight2, model_decoder_layers_20_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv536 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_20_self_attn_q_proj_weight2, layer_norm125, model_decoder_layers_20_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape587: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv536, R.shape([1, seq_len, 20, 64])) + lv118 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_20_self_attn_k_proj_weight2, layer_norm125), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape588: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv118, R.shape([1, seq_len, 20, 64])) + lv537 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_20_self_attn_v_proj_weight2, layer_norm125, model_decoder_layers_20_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape589: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv537, R.shape([1, seq_len, 20, 64])) + concat20: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape587, reshape588, reshape589), axis=2) + reshape590: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat20, R.shape([seq_len, 60, 64])) + lv109_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), reshape590), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape591: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv109_1, R.shape([1, seq_len, 20, 64])) + reshape592: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape591, R.shape([1, seq_len, 1280])) + lv538 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_20_self_attn_out_proj_weight2, reshape592, model_decoder_layers_20_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add461: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add457, lv538) + layer_norm126: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add461, model_decoder_layers_20_encoder_attn_layer_norm_weight2, model_decoder_layers_20_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv539 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_20_encoder_attn_q_proj_weight2, layer_norm126, model_decoder_layers_20_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape593: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv539, R.shape([1, seq_len, 20, 64])) + reshape594: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape593, R.shape([seq_len, 20, 64])) + lv110_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), reshape594), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape595: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv110_1, R.shape([1, seq_len, 20, 64])) + reshape596: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape595, R.shape([1, seq_len, 1280])) + lv540 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_20_encoder_attn_out_proj_weight2, reshape596, model_decoder_layers_20_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add464: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add461, lv540) + layer_norm127: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add464, model_decoder_layers_20_final_layer_norm_weight2, model_decoder_layers_20_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv84_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_20_fc1_weight2, layer_norm127, model_decoder_layers_20_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv541 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_20_fc2_weight2, lv84_1, model_decoder_layers_20_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add467: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add464, lv541) + layer_norm128: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add467, model_decoder_layers_21_self_attn_layer_norm_weight2, model_decoder_layers_21_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv542 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_21_self_attn_q_proj_weight2, layer_norm128, model_decoder_layers_21_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape597: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv542, R.shape([1, seq_len, 20, 64])) + lv119 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_21_self_attn_k_proj_weight2, layer_norm128), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape598: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv119, R.shape([1, seq_len, 20, 64])) + lv543 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_21_self_attn_v_proj_weight2, layer_norm128, model_decoder_layers_21_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape599: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv543, R.shape([1, seq_len, 20, 64])) + concat21: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape597, reshape598, reshape599), axis=2) + reshape600: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat21, R.shape([seq_len, 60, 64])) + lv111_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), reshape600), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape601: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv111_1, R.shape([1, seq_len, 20, 64])) + reshape602: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape601, R.shape([1, seq_len, 1280])) + lv544 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_21_self_attn_out_proj_weight2, reshape602, model_decoder_layers_21_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add471: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add467, lv544) + layer_norm129: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add471, model_decoder_layers_21_encoder_attn_layer_norm_weight2, model_decoder_layers_21_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv545 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_21_encoder_attn_q_proj_weight2, layer_norm129, model_decoder_layers_21_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape603: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv545, R.shape([1, seq_len, 20, 64])) + reshape604: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape603, R.shape([seq_len, 20, 64])) + lv112_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), reshape604), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape605: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv112_1, R.shape([1, seq_len, 20, 64])) + reshape606: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape605, R.shape([1, seq_len, 1280])) + lv546 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_21_encoder_attn_out_proj_weight2, reshape606, model_decoder_layers_21_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add474: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add471, lv546) + layer_norm130: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add474, model_decoder_layers_21_final_layer_norm_weight2, model_decoder_layers_21_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv85_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_21_fc1_weight2, layer_norm130, model_decoder_layers_21_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv547 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_21_fc2_weight2, lv85_1, model_decoder_layers_21_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add477: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add474, lv547) + layer_norm131: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add477, model_decoder_layers_22_self_attn_layer_norm_weight2, model_decoder_layers_22_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv548 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_22_self_attn_q_proj_weight2, layer_norm131, model_decoder_layers_22_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape607: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv548, R.shape([1, seq_len, 20, 64])) + lv120 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_22_self_attn_k_proj_weight2, layer_norm131), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape608: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv120, R.shape([1, seq_len, 20, 64])) + lv549 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_22_self_attn_v_proj_weight2, layer_norm131, model_decoder_layers_22_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape609: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv549, R.shape([1, seq_len, 20, 64])) + concat22: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape607, reshape608, reshape609), axis=2) + reshape610: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat22, R.shape([seq_len, 60, 64])) + lv113_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), reshape610), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape611: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv113_1, R.shape([1, seq_len, 20, 64])) + reshape612: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape611, R.shape([1, seq_len, 1280])) + lv550 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_22_self_attn_out_proj_weight2, reshape612, model_decoder_layers_22_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add481: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add477, lv550) + layer_norm132: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add481, model_decoder_layers_22_encoder_attn_layer_norm_weight2, model_decoder_layers_22_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv551 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_22_encoder_attn_q_proj_weight2, layer_norm132, model_decoder_layers_22_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape613: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv551, R.shape([1, seq_len, 20, 64])) + reshape614: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape613, R.shape([seq_len, 20, 64])) + lv114_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), reshape614), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape615: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv114_1, R.shape([1, seq_len, 20, 64])) + reshape616: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape615, R.shape([1, seq_len, 1280])) + lv552 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_22_encoder_attn_out_proj_weight2, reshape616, model_decoder_layers_22_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add484: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add481, lv552) + layer_norm133: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add484, model_decoder_layers_22_final_layer_norm_weight2, model_decoder_layers_22_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv86_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_22_fc1_weight2, layer_norm133, model_decoder_layers_22_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv553 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_22_fc2_weight2, lv86_1, model_decoder_layers_22_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add487: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add484, lv553) + layer_norm134: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add487, model_decoder_layers_23_self_attn_layer_norm_weight2, model_decoder_layers_23_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv554 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_23_self_attn_q_proj_weight2, layer_norm134, model_decoder_layers_23_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape617: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv554, R.shape([1, seq_len, 20, 64])) + lv121 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_23_self_attn_k_proj_weight2, layer_norm134), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape618: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv121, R.shape([1, seq_len, 20, 64])) + lv555 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_23_self_attn_v_proj_weight2, layer_norm134, model_decoder_layers_23_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape619: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv555, R.shape([1, seq_len, 20, 64])) + concat23: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape617, reshape618, reshape619), axis=2) + reshape620: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat23, R.shape([seq_len, 60, 64])) + lv115_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), reshape620), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape621: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv115_1, R.shape([1, seq_len, 20, 64])) + reshape622: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape621, R.shape([1, seq_len, 1280])) + lv556 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_23_self_attn_out_proj_weight2, reshape622, model_decoder_layers_23_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add491: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add487, lv556) + layer_norm135: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add491, model_decoder_layers_23_encoder_attn_layer_norm_weight2, model_decoder_layers_23_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv557 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_23_encoder_attn_q_proj_weight2, layer_norm135, model_decoder_layers_23_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape623: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv557, R.shape([1, seq_len, 20, 64])) + reshape624: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape623, R.shape([seq_len, 20, 64])) + lv116_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), reshape624), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape625: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv116_1, R.shape([1, seq_len, 20, 64])) + reshape626: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape625, R.shape([1, seq_len, 1280])) + lv558 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_23_encoder_attn_out_proj_weight2, reshape626, model_decoder_layers_23_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add494: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add491, lv558) + layer_norm136: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add494, model_decoder_layers_23_final_layer_norm_weight2, model_decoder_layers_23_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv87_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_23_fc1_weight2, layer_norm136, model_decoder_layers_23_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv559 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_23_fc2_weight2, lv87_1, model_decoder_layers_23_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add497: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add494, lv559) + layer_norm137: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add497, model_decoder_layers_24_self_attn_layer_norm_weight2, model_decoder_layers_24_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv560 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_24_self_attn_q_proj_weight2, layer_norm137, model_decoder_layers_24_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape627: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv560, R.shape([1, seq_len, 20, 64])) + lv122 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_24_self_attn_k_proj_weight2, layer_norm137), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape628: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv122, R.shape([1, seq_len, 20, 64])) + lv561 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_24_self_attn_v_proj_weight2, layer_norm137, model_decoder_layers_24_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape629: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv561, R.shape([1, seq_len, 20, 64])) + concat24: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape627, reshape628, reshape629), axis=2) + reshape630: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat24, R.shape([seq_len, 60, 64])) + lv117_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), reshape630), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape631: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv117_1, R.shape([1, seq_len, 20, 64])) + reshape632: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape631, R.shape([1, seq_len, 1280])) + lv562 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_24_self_attn_out_proj_weight2, reshape632, model_decoder_layers_24_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add501: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add497, lv562) + layer_norm138: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add501, model_decoder_layers_24_encoder_attn_layer_norm_weight2, model_decoder_layers_24_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv563 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_24_encoder_attn_q_proj_weight2, layer_norm138, model_decoder_layers_24_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape633: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv563, R.shape([1, seq_len, 20, 64])) + reshape634: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape633, R.shape([seq_len, 20, 64])) + lv118_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), reshape634), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape635: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv118_1, R.shape([1, seq_len, 20, 64])) + reshape636: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape635, R.shape([1, seq_len, 1280])) + lv564 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_24_encoder_attn_out_proj_weight2, reshape636, model_decoder_layers_24_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add504: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add501, lv564) + layer_norm139: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add504, model_decoder_layers_24_final_layer_norm_weight2, model_decoder_layers_24_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv88_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_24_fc1_weight2, layer_norm139, model_decoder_layers_24_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv565 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_24_fc2_weight2, lv88_1, model_decoder_layers_24_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add507: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add504, lv565) + layer_norm140: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add507, model_decoder_layers_25_self_attn_layer_norm_weight2, model_decoder_layers_25_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv566 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_25_self_attn_q_proj_weight2, layer_norm140, model_decoder_layers_25_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape637: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv566, R.shape([1, seq_len, 20, 64])) + lv123 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_25_self_attn_k_proj_weight2, layer_norm140), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape638: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv123, R.shape([1, seq_len, 20, 64])) + lv567 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_25_self_attn_v_proj_weight2, layer_norm140, model_decoder_layers_25_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape639: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv567, R.shape([1, seq_len, 20, 64])) + concat25: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape637, reshape638, reshape639), axis=2) + reshape640: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat25, R.shape([seq_len, 60, 64])) + lv119_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), reshape640), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape641: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv119_1, R.shape([1, seq_len, 20, 64])) + reshape642: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape641, R.shape([1, seq_len, 1280])) + lv568 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_25_self_attn_out_proj_weight2, reshape642, model_decoder_layers_25_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add511: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add507, lv568) + layer_norm141: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add511, model_decoder_layers_25_encoder_attn_layer_norm_weight2, model_decoder_layers_25_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv569 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_25_encoder_attn_q_proj_weight2, layer_norm141, model_decoder_layers_25_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape643: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv569, R.shape([1, seq_len, 20, 64])) + reshape644: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape643, R.shape([seq_len, 20, 64])) + lv120_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), reshape644), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape645: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv120_1, R.shape([1, seq_len, 20, 64])) + reshape646: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape645, R.shape([1, seq_len, 1280])) + lv570 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_25_encoder_attn_out_proj_weight2, reshape646, model_decoder_layers_25_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add514: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add511, lv570) + layer_norm142: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add514, model_decoder_layers_25_final_layer_norm_weight2, model_decoder_layers_25_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv89_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_25_fc1_weight2, layer_norm142, model_decoder_layers_25_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv571 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_25_fc2_weight2, lv89_1, model_decoder_layers_25_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add517: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add514, lv571) + layer_norm143: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add517, model_decoder_layers_26_self_attn_layer_norm_weight2, model_decoder_layers_26_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv572 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_26_self_attn_q_proj_weight2, layer_norm143, model_decoder_layers_26_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape647: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv572, R.shape([1, seq_len, 20, 64])) + lv124 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_26_self_attn_k_proj_weight2, layer_norm143), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape648: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv124, R.shape([1, seq_len, 20, 64])) + lv573 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_26_self_attn_v_proj_weight2, layer_norm143, model_decoder_layers_26_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape649: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv573, R.shape([1, seq_len, 20, 64])) + concat26: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape647, reshape648, reshape649), axis=2) + reshape650: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat26, R.shape([seq_len, 60, 64])) + lv121_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), reshape650), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape651: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv121_1, R.shape([1, seq_len, 20, 64])) + reshape652: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape651, R.shape([1, seq_len, 1280])) + lv574 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_26_self_attn_out_proj_weight2, reshape652, model_decoder_layers_26_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add521: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add517, lv574) + layer_norm144: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add521, model_decoder_layers_26_encoder_attn_layer_norm_weight2, model_decoder_layers_26_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv575 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_26_encoder_attn_q_proj_weight2, layer_norm144, model_decoder_layers_26_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape653: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv575, R.shape([1, seq_len, 20, 64])) + reshape654: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape653, R.shape([seq_len, 20, 64])) + lv122_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), reshape654), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape655: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv122_1, R.shape([1, seq_len, 20, 64])) + reshape656: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape655, R.shape([1, seq_len, 1280])) + lv576 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_26_encoder_attn_out_proj_weight2, reshape656, model_decoder_layers_26_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add524: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add521, lv576) + layer_norm145: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add524, model_decoder_layers_26_final_layer_norm_weight2, model_decoder_layers_26_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv90_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_26_fc1_weight2, layer_norm145, model_decoder_layers_26_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv577 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_26_fc2_weight2, lv90_1, model_decoder_layers_26_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add527: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add524, lv577) + layer_norm146: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add527, model_decoder_layers_27_self_attn_layer_norm_weight2, model_decoder_layers_27_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv578 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_27_self_attn_q_proj_weight2, layer_norm146, model_decoder_layers_27_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape657: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv578, R.shape([1, seq_len, 20, 64])) + lv125 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_27_self_attn_k_proj_weight2, layer_norm146), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape658: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv125, R.shape([1, seq_len, 20, 64])) + lv579 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_27_self_attn_v_proj_weight2, layer_norm146, model_decoder_layers_27_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape659: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv579, R.shape([1, seq_len, 20, 64])) + concat27: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape657, reshape658, reshape659), axis=2) + reshape660: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat27, R.shape([seq_len, 60, 64])) + lv123_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), reshape660), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape661: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv123_1, R.shape([1, seq_len, 20, 64])) + reshape662: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape661, R.shape([1, seq_len, 1280])) + lv580 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_27_self_attn_out_proj_weight2, reshape662, model_decoder_layers_27_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add531: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add527, lv580) + layer_norm147: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add531, model_decoder_layers_27_encoder_attn_layer_norm_weight2, model_decoder_layers_27_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv581 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_27_encoder_attn_q_proj_weight2, layer_norm147, model_decoder_layers_27_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape663: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv581, R.shape([1, seq_len, 20, 64])) + reshape664: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape663, R.shape([seq_len, 20, 64])) + lv124_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), reshape664), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape665: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv124_1, R.shape([1, seq_len, 20, 64])) + reshape666: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape665, R.shape([1, seq_len, 1280])) + lv582 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_27_encoder_attn_out_proj_weight2, reshape666, model_decoder_layers_27_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add534: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add531, lv582) + layer_norm148: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add534, model_decoder_layers_27_final_layer_norm_weight2, model_decoder_layers_27_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv91_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_27_fc1_weight2, layer_norm148, model_decoder_layers_27_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv583 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_27_fc2_weight2, lv91_1, model_decoder_layers_27_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add537: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add534, lv583) + layer_norm149: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add537, model_decoder_layers_28_self_attn_layer_norm_weight2, model_decoder_layers_28_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv584 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_28_self_attn_q_proj_weight2, layer_norm149, model_decoder_layers_28_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape667: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv584, R.shape([1, seq_len, 20, 64])) + lv126 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_28_self_attn_k_proj_weight2, layer_norm149), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape668: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv126, R.shape([1, seq_len, 20, 64])) + lv585 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_28_self_attn_v_proj_weight2, layer_norm149, model_decoder_layers_28_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape669: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv585, R.shape([1, seq_len, 20, 64])) + concat28: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape667, reshape668, reshape669), axis=2) + reshape670: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat28, R.shape([seq_len, 60, 64])) + lv125_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), reshape670), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape671: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv125_1, R.shape([1, seq_len, 20, 64])) + reshape672: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape671, R.shape([1, seq_len, 1280])) + lv586 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_28_self_attn_out_proj_weight2, reshape672, model_decoder_layers_28_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add541: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add537, lv586) + layer_norm150: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add541, model_decoder_layers_28_encoder_attn_layer_norm_weight2, model_decoder_layers_28_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv587 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_28_encoder_attn_q_proj_weight2, layer_norm150, model_decoder_layers_28_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape673: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv587, R.shape([1, seq_len, 20, 64])) + reshape674: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape673, R.shape([seq_len, 20, 64])) + lv126_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), reshape674), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape675: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv126_1, R.shape([1, seq_len, 20, 64])) + reshape676: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape675, R.shape([1, seq_len, 1280])) + lv588 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_28_encoder_attn_out_proj_weight2, reshape676, model_decoder_layers_28_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add544: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add541, lv588) + layer_norm151: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add544, model_decoder_layers_28_final_layer_norm_weight2, model_decoder_layers_28_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv92_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_28_fc1_weight2, layer_norm151, model_decoder_layers_28_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv589 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_28_fc2_weight2, lv92_1, model_decoder_layers_28_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add547: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add544, lv589) + layer_norm152: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add547, model_decoder_layers_29_self_attn_layer_norm_weight2, model_decoder_layers_29_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv590 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_29_self_attn_q_proj_weight2, layer_norm152, model_decoder_layers_29_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape677: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv590, R.shape([1, seq_len, 20, 64])) + lv127 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_29_self_attn_k_proj_weight2, layer_norm152), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape678: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv127, R.shape([1, seq_len, 20, 64])) + lv591 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_29_self_attn_v_proj_weight2, layer_norm152, model_decoder_layers_29_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape679: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv591, R.shape([1, seq_len, 20, 64])) + concat29: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape677, reshape678, reshape679), axis=2) + reshape680: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat29, R.shape([seq_len, 60, 64])) + lv127_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), reshape680), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape681: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv127_1, R.shape([1, seq_len, 20, 64])) + reshape682: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape681, R.shape([1, seq_len, 1280])) + lv592 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_29_self_attn_out_proj_weight2, reshape682, model_decoder_layers_29_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add551: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add547, lv592) + layer_norm153: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add551, model_decoder_layers_29_encoder_attn_layer_norm_weight2, model_decoder_layers_29_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv593 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_29_encoder_attn_q_proj_weight2, layer_norm153, model_decoder_layers_29_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape683: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv593, R.shape([1, seq_len, 20, 64])) + reshape684: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape683, R.shape([seq_len, 20, 64])) + lv128 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), reshape684), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape685: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv128, R.shape([1, seq_len, 20, 64])) + reshape686: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape685, R.shape([1, seq_len, 1280])) + lv594 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_29_encoder_attn_out_proj_weight2, reshape686, model_decoder_layers_29_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add554: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add551, lv594) + layer_norm154: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add554, model_decoder_layers_29_final_layer_norm_weight2, model_decoder_layers_29_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv93_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_29_fc1_weight2, layer_norm154, model_decoder_layers_29_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv595 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_29_fc2_weight2, lv93_1, model_decoder_layers_29_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add557: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add554, lv595) + layer_norm155: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add557, model_decoder_layers_30_self_attn_layer_norm_weight2, model_decoder_layers_30_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv596 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_30_self_attn_q_proj_weight2, layer_norm155, model_decoder_layers_30_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape687: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv596, R.shape([1, seq_len, 20, 64])) + lv128_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_30_self_attn_k_proj_weight2, layer_norm155), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape688: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv128_1, R.shape([1, seq_len, 20, 64])) + lv597 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_30_self_attn_v_proj_weight2, layer_norm155, model_decoder_layers_30_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape689: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv597, R.shape([1, seq_len, 20, 64])) + concat30: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape687, reshape688, reshape689), axis=2) + reshape690: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat30, R.shape([seq_len, 60, 64])) + lv129 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), reshape690), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape691: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv129, R.shape([1, seq_len, 20, 64])) + reshape692: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape691, R.shape([1, seq_len, 1280])) + lv598 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_30_self_attn_out_proj_weight2, reshape692, model_decoder_layers_30_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add561: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add557, lv598) + layer_norm156: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add561, model_decoder_layers_30_encoder_attn_layer_norm_weight2, model_decoder_layers_30_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv599 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_30_encoder_attn_q_proj_weight2, layer_norm156, model_decoder_layers_30_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape693: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv599, R.shape([1, seq_len, 20, 64])) + reshape694: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape693, R.shape([seq_len, 20, 64])) + lv130 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), reshape694), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape695: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv130, R.shape([1, seq_len, 20, 64])) + reshape696: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape695, R.shape([1, seq_len, 1280])) + lv600 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_30_encoder_attn_out_proj_weight2, reshape696, model_decoder_layers_30_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add564: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add561, lv600) + layer_norm157: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add564, model_decoder_layers_30_final_layer_norm_weight2, model_decoder_layers_30_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv94_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_30_fc1_weight2, layer_norm157, model_decoder_layers_30_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv601 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_30_fc2_weight2, lv94_1, model_decoder_layers_30_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add567: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add564, lv601) + layer_norm158: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add567, model_decoder_layers_31_self_attn_layer_norm_weight2, model_decoder_layers_31_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv602 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_31_self_attn_q_proj_weight2, layer_norm158, model_decoder_layers_31_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape697: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv602, R.shape([1, seq_len, 20, 64])) + lv129_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_31_self_attn_k_proj_weight2, layer_norm158), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape698: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv129_1, R.shape([1, seq_len, 20, 64])) + lv603 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_31_self_attn_v_proj_weight2, layer_norm158, model_decoder_layers_31_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape699: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv603, R.shape([1, seq_len, 20, 64])) + concat31: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape697, reshape698, reshape699), axis=2) + reshape700: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat31, R.shape([seq_len, 60, 64])) + lv131 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), reshape700), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape701: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv131, R.shape([1, seq_len, 20, 64])) + reshape702: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape701, R.shape([1, seq_len, 1280])) + lv604 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_31_self_attn_out_proj_weight2, reshape702, model_decoder_layers_31_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add571: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add567, lv604) + layer_norm159: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add571, model_decoder_layers_31_encoder_attn_layer_norm_weight2, model_decoder_layers_31_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv605 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_31_encoder_attn_q_proj_weight2, layer_norm159, model_decoder_layers_31_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape703: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv605, R.shape([1, seq_len, 20, 64])) + reshape704: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape703, R.shape([seq_len, 20, 64])) + lv132 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), reshape704), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape705: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv132, R.shape([1, seq_len, 20, 64])) + reshape706: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape705, R.shape([1, seq_len, 1280])) + lv606 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_31_encoder_attn_out_proj_weight2, reshape706, model_decoder_layers_31_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add574: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add571, lv606) + layer_norm160: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add574, model_decoder_layers_31_final_layer_norm_weight2, model_decoder_layers_31_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv95_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_31_fc1_weight2, layer_norm160, model_decoder_layers_31_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv607 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_31_fc2_weight2, lv95_1, model_decoder_layers_31_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add577: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add574, lv607) + layer_norm161: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add577, model_decoder_layer_norm_weight2, model_decoder_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + take2: R.Tensor((1, batch_size, 1280), dtype="float16") = R.take(layer_norm161, logit_positions, axis=1) + lv130_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul5_cublas", (model_decoder_embed_tokens_weight2, take2), out_sinfo=R.Tensor((1, batch_size, 51866), dtype="float32")) + gv2: R.Tensor((1, batch_size, 51866), dtype="float32") = lv130_1 + R.output(gv2) + return gv2 + + @R.function + def create_tir_paged_kv_cache(max_batch_size_: R.Shape(["max_batch_size"]), max_total_seq_len_: R.Shape(["max_total_seq_len"]), prefill_chunk_size_: R.Shape(["prefill_chunk_size"]), page_size_: R.Shape(["page_size"]), support_sliding_window_: R.Shape(["support_sliding_window"])) -> R.Object: + max_batch_size = T.int64() + max_total_seq_len = T.int64() + prefill_chunk_size = T.int64() + page_size = T.int64() + support_sliding_window = T.int64() + R.func_attr({"relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "seq_len": 15000, "total_seq_len": 1500}}) + cls = Module + gv: R.Tensor((), dtype="float16") = R.zeros(R.shape([]), dtype="float16") + paged_kv_cache: R.Object = R.call_pure_packed("vm.builtin.paged_attention_kv_cache_create_reduced", R.shape([max_batch_size, max_total_seq_len, prefill_chunk_size, page_size, support_sliding_window]), R.prim_value(32), R.prim_value(20), R.prim_value(20), R.prim_value(64), R.prim_value(0), R.prim_value(1), R.prim_value(1), gv, cls.tir_kv_cache_transpose_append, cls.batch_prefill_paged_kv, cls.batch_decode_paged_kv, cls.batch_prefill_paged_kv_sliding_window, cls.batch_decode_paged_kv_sliding_window, cls.batch_prefill_ragged_kv, cls.merge_state_inplace, cls.fused_rope, cls.copy_single_page, cls.tir_kv_cache_debug_get_kv, cls.compact_kv_copy, cls.batch_tree_attn, sinfo_args=(R.Object,)) + return paged_kv_cache + + @R.function + def decode(input_ids: R.Tensor((1, 1), dtype="int32"), paged_kv_cache: R.Object, packed_params: R.Tuple(R.Tensor((1280, 128, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1500, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), 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R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"))) -> R.Tensor((1, 1, 51866), dtype="float32"): + R.func_attr({"num_input": 2, "relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "seq_len": 15000, "total_seq_len": 1500}}) + cls = Module + with R.dataflow(): + model_decoder_embed_tokens_weight5: R.Tensor((51866, 1280), dtype="float16") = packed_params[487] + model_decoder_embed_positions_weight5: R.Tensor((448, 1280), dtype="float16") = packed_params[488] + model_decoder_layers_0_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[489] + model_decoder_layers_0_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[490] + model_decoder_layers_0_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[491] + model_decoder_layers_0_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[492] + model_decoder_layers_0_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[493] + model_decoder_layers_0_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[494] + model_decoder_layers_0_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[495] + model_decoder_layers_0_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[496] + model_decoder_layers_0_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[497] + model_decoder_layers_0_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[501] + model_decoder_layers_0_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[502] + model_decoder_layers_0_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[503] + model_decoder_layers_0_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[504] + model_decoder_layers_0_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[505] + model_decoder_layers_0_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[506] + model_decoder_layers_0_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[507] + model_decoder_layers_0_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[508] + model_decoder_layers_0_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[509] + model_decoder_layers_0_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[510] + model_decoder_layers_0_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[511] + model_decoder_layers_0_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[512] + model_decoder_layers_1_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[513] + model_decoder_layers_1_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[514] + model_decoder_layers_1_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[515] + model_decoder_layers_1_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[516] + model_decoder_layers_1_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[517] + model_decoder_layers_1_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[518] + model_decoder_layers_1_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[519] + model_decoder_layers_1_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[520] + model_decoder_layers_1_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[521] + model_decoder_layers_1_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[525] + model_decoder_layers_1_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[526] + model_decoder_layers_1_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[527] + model_decoder_layers_1_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[528] + model_decoder_layers_1_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[529] + model_decoder_layers_1_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[530] + model_decoder_layers_1_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[531] + model_decoder_layers_1_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[532] + model_decoder_layers_1_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[533] + model_decoder_layers_1_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[534] + model_decoder_layers_1_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[535] + model_decoder_layers_1_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[536] + model_decoder_layers_2_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[537] + model_decoder_layers_2_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[538] + model_decoder_layers_2_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[539] + model_decoder_layers_2_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[540] + model_decoder_layers_2_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[541] + model_decoder_layers_2_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[542] + model_decoder_layers_2_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[543] + model_decoder_layers_2_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[544] + model_decoder_layers_2_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[545] + model_decoder_layers_2_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[549] + model_decoder_layers_2_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[550] + model_decoder_layers_2_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[551] + model_decoder_layers_2_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[552] + model_decoder_layers_2_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[553] + model_decoder_layers_2_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[554] + model_decoder_layers_2_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[555] + model_decoder_layers_2_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[556] + model_decoder_layers_2_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[557] + model_decoder_layers_2_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[558] + model_decoder_layers_2_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[559] + model_decoder_layers_2_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[560] + model_decoder_layers_3_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[561] + model_decoder_layers_3_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[562] + model_decoder_layers_3_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[563] + model_decoder_layers_3_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[564] + model_decoder_layers_3_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[565] + model_decoder_layers_3_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[566] + model_decoder_layers_3_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[567] + model_decoder_layers_3_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[568] + model_decoder_layers_3_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[569] + model_decoder_layers_3_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[573] + model_decoder_layers_3_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[574] + model_decoder_layers_3_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[575] + model_decoder_layers_3_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[576] + model_decoder_layers_3_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[577] + model_decoder_layers_3_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[578] + model_decoder_layers_3_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[579] + model_decoder_layers_3_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[580] + model_decoder_layers_3_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[581] + model_decoder_layers_3_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[582] + model_decoder_layers_3_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[583] + model_decoder_layers_3_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[584] + model_decoder_layers_4_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[585] + model_decoder_layers_4_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[586] + model_decoder_layers_4_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[587] + model_decoder_layers_4_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[588] + model_decoder_layers_4_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[589] + model_decoder_layers_4_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[590] + model_decoder_layers_4_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[591] + model_decoder_layers_4_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[592] + model_decoder_layers_4_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[593] + model_decoder_layers_4_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[597] + model_decoder_layers_4_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[598] + model_decoder_layers_4_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[599] + model_decoder_layers_4_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[600] + model_decoder_layers_4_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[601] + model_decoder_layers_4_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[602] + model_decoder_layers_4_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[603] + model_decoder_layers_4_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[604] + model_decoder_layers_4_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[605] + model_decoder_layers_4_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[606] + model_decoder_layers_4_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[607] + model_decoder_layers_4_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[608] + model_decoder_layers_5_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[609] + model_decoder_layers_5_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[610] + model_decoder_layers_5_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[611] + model_decoder_layers_5_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[612] + model_decoder_layers_5_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[613] + model_decoder_layers_5_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[614] + model_decoder_layers_5_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[615] + model_decoder_layers_5_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[616] + model_decoder_layers_5_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[617] + model_decoder_layers_5_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[621] + model_decoder_layers_5_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[622] + model_decoder_layers_5_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[623] + model_decoder_layers_5_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[624] + model_decoder_layers_5_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[625] + model_decoder_layers_5_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[626] + model_decoder_layers_5_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[627] + model_decoder_layers_5_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[628] + model_decoder_layers_5_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[629] + model_decoder_layers_5_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[630] + model_decoder_layers_5_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[631] + model_decoder_layers_5_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[632] + model_decoder_layers_6_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[633] + model_decoder_layers_6_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[634] + model_decoder_layers_6_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[635] + model_decoder_layers_6_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[636] + model_decoder_layers_6_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[637] + model_decoder_layers_6_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[638] + model_decoder_layers_6_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[639] + model_decoder_layers_6_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[640] + model_decoder_layers_6_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[641] + model_decoder_layers_6_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[645] + model_decoder_layers_6_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[646] + model_decoder_layers_6_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[647] + model_decoder_layers_6_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[648] + model_decoder_layers_6_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[649] + model_decoder_layers_6_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[650] + model_decoder_layers_6_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[651] + model_decoder_layers_6_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[652] + model_decoder_layers_6_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[653] + model_decoder_layers_6_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[654] + model_decoder_layers_6_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[655] + model_decoder_layers_6_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[656] + model_decoder_layers_7_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[657] + model_decoder_layers_7_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[658] + model_decoder_layers_7_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[659] + model_decoder_layers_7_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[660] + model_decoder_layers_7_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[661] + model_decoder_layers_7_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[662] + model_decoder_layers_7_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[663] + model_decoder_layers_7_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[664] + model_decoder_layers_7_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[665] + model_decoder_layers_7_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[669] + model_decoder_layers_7_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[670] + model_decoder_layers_7_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[671] + model_decoder_layers_7_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[672] + model_decoder_layers_7_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[673] + model_decoder_layers_7_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[674] + model_decoder_layers_7_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[675] + model_decoder_layers_7_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[676] + model_decoder_layers_7_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[677] + model_decoder_layers_7_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[678] + model_decoder_layers_7_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[679] + model_decoder_layers_7_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[680] + model_decoder_layers_8_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[681] + model_decoder_layers_8_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[682] + model_decoder_layers_8_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[683] + model_decoder_layers_8_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[684] + model_decoder_layers_8_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[685] + model_decoder_layers_8_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[686] + model_decoder_layers_8_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[687] + model_decoder_layers_8_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[688] + model_decoder_layers_8_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[689] + model_decoder_layers_8_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[693] + model_decoder_layers_8_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[694] + model_decoder_layers_8_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[695] + model_decoder_layers_8_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[696] + model_decoder_layers_8_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[697] + model_decoder_layers_8_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[698] + model_decoder_layers_8_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[699] + model_decoder_layers_8_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[700] + model_decoder_layers_8_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[701] + model_decoder_layers_8_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[702] + model_decoder_layers_8_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[703] + model_decoder_layers_8_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[704] + model_decoder_layers_9_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[705] + model_decoder_layers_9_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[706] + model_decoder_layers_9_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[707] + model_decoder_layers_9_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[708] + model_decoder_layers_9_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[709] + model_decoder_layers_9_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[710] + model_decoder_layers_9_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[711] + model_decoder_layers_9_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[712] + model_decoder_layers_9_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[713] + model_decoder_layers_9_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[717] + model_decoder_layers_9_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[718] + model_decoder_layers_9_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[719] + model_decoder_layers_9_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[720] + model_decoder_layers_9_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[721] + model_decoder_layers_9_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[722] + model_decoder_layers_9_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[723] + model_decoder_layers_9_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[724] + model_decoder_layers_9_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[725] + model_decoder_layers_9_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[726] + model_decoder_layers_9_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[727] + model_decoder_layers_9_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[728] + model_decoder_layers_10_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[729] + model_decoder_layers_10_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[730] + model_decoder_layers_10_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[731] + model_decoder_layers_10_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[732] + model_decoder_layers_10_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[733] + model_decoder_layers_10_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[734] + model_decoder_layers_10_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[735] + model_decoder_layers_10_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[736] + model_decoder_layers_10_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[737] + model_decoder_layers_10_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[741] + model_decoder_layers_10_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[742] + model_decoder_layers_10_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[743] + model_decoder_layers_10_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[744] + model_decoder_layers_10_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[745] + model_decoder_layers_10_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[746] + model_decoder_layers_10_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[747] + model_decoder_layers_10_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[748] + model_decoder_layers_10_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[749] + model_decoder_layers_10_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[750] + model_decoder_layers_10_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[751] + model_decoder_layers_10_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[752] + model_decoder_layers_11_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[753] + model_decoder_layers_11_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[754] + model_decoder_layers_11_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[755] + model_decoder_layers_11_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[756] + model_decoder_layers_11_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[757] + model_decoder_layers_11_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[758] + model_decoder_layers_11_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[759] + model_decoder_layers_11_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[760] + model_decoder_layers_11_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[761] + model_decoder_layers_11_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[765] + model_decoder_layers_11_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[766] + model_decoder_layers_11_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[767] + model_decoder_layers_11_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[768] + model_decoder_layers_11_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[769] + model_decoder_layers_11_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[770] + model_decoder_layers_11_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[771] + model_decoder_layers_11_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[772] + model_decoder_layers_11_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[773] + model_decoder_layers_11_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[774] + model_decoder_layers_11_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[775] + model_decoder_layers_11_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[776] + model_decoder_layers_12_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[777] + model_decoder_layers_12_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[778] + model_decoder_layers_12_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[779] + model_decoder_layers_12_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[780] + model_decoder_layers_12_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[781] + model_decoder_layers_12_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[782] + model_decoder_layers_12_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[783] + model_decoder_layers_12_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[784] + model_decoder_layers_12_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[785] + model_decoder_layers_12_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[789] + model_decoder_layers_12_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[790] + model_decoder_layers_12_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[791] + model_decoder_layers_12_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[792] + model_decoder_layers_12_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[793] + model_decoder_layers_12_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[794] + model_decoder_layers_12_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[795] + model_decoder_layers_12_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[796] + model_decoder_layers_12_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[797] + model_decoder_layers_12_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[798] + model_decoder_layers_12_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[799] + model_decoder_layers_12_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[800] + model_decoder_layers_13_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[801] + model_decoder_layers_13_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[802] + model_decoder_layers_13_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[803] + model_decoder_layers_13_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[804] + model_decoder_layers_13_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[805] + model_decoder_layers_13_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[806] + model_decoder_layers_13_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[807] + model_decoder_layers_13_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[808] + model_decoder_layers_13_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[809] + model_decoder_layers_13_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[813] + model_decoder_layers_13_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[814] + model_decoder_layers_13_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[815] + model_decoder_layers_13_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[816] + model_decoder_layers_13_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[817] + model_decoder_layers_13_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[818] + model_decoder_layers_13_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[819] + model_decoder_layers_13_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[820] + model_decoder_layers_13_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[821] + model_decoder_layers_13_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[822] + model_decoder_layers_13_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[823] + model_decoder_layers_13_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[824] + model_decoder_layers_14_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[825] + model_decoder_layers_14_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[826] + model_decoder_layers_14_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[827] + model_decoder_layers_14_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[828] + model_decoder_layers_14_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[829] + model_decoder_layers_14_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[830] + model_decoder_layers_14_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[831] + model_decoder_layers_14_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[832] + model_decoder_layers_14_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[833] + model_decoder_layers_14_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[837] + model_decoder_layers_14_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[838] + model_decoder_layers_14_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[839] + model_decoder_layers_14_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[840] + model_decoder_layers_14_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[841] + model_decoder_layers_14_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[842] + model_decoder_layers_14_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[843] + model_decoder_layers_14_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[844] + model_decoder_layers_14_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[845] + model_decoder_layers_14_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[846] + model_decoder_layers_14_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[847] + model_decoder_layers_14_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[848] + model_decoder_layers_15_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[849] + model_decoder_layers_15_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[850] + model_decoder_layers_15_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[851] + model_decoder_layers_15_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[852] + model_decoder_layers_15_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[853] + model_decoder_layers_15_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[854] + model_decoder_layers_15_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[855] + model_decoder_layers_15_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[856] + model_decoder_layers_15_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[857] + model_decoder_layers_15_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[861] + model_decoder_layers_15_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[862] + model_decoder_layers_15_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[863] + model_decoder_layers_15_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[864] + model_decoder_layers_15_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[865] + model_decoder_layers_15_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[866] + model_decoder_layers_15_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[867] + model_decoder_layers_15_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[868] + model_decoder_layers_15_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[869] + model_decoder_layers_15_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[870] + model_decoder_layers_15_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[871] + model_decoder_layers_15_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[872] + model_decoder_layers_16_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[873] + model_decoder_layers_16_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[874] + model_decoder_layers_16_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[875] + model_decoder_layers_16_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[876] + model_decoder_layers_16_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[877] + model_decoder_layers_16_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[878] + model_decoder_layers_16_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[879] + model_decoder_layers_16_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[880] + model_decoder_layers_16_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[881] + model_decoder_layers_16_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[885] + model_decoder_layers_16_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[886] + model_decoder_layers_16_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[887] + model_decoder_layers_16_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[888] + model_decoder_layers_16_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[889] + model_decoder_layers_16_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[890] + model_decoder_layers_16_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[891] + model_decoder_layers_16_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[892] + model_decoder_layers_16_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[893] + model_decoder_layers_16_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[894] + model_decoder_layers_16_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[895] + model_decoder_layers_16_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[896] + model_decoder_layers_17_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[897] + model_decoder_layers_17_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[898] + model_decoder_layers_17_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[899] + model_decoder_layers_17_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[900] + model_decoder_layers_17_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[901] + model_decoder_layers_17_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[902] + model_decoder_layers_17_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[903] + model_decoder_layers_17_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[904] + model_decoder_layers_17_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[905] + model_decoder_layers_17_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[909] + model_decoder_layers_17_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[910] + model_decoder_layers_17_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[911] + model_decoder_layers_17_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[912] + model_decoder_layers_17_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[913] + model_decoder_layers_17_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[914] + model_decoder_layers_17_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[915] + model_decoder_layers_17_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[916] + model_decoder_layers_17_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[917] + model_decoder_layers_17_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[918] + model_decoder_layers_17_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[919] + model_decoder_layers_17_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[920] + model_decoder_layers_18_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[921] + model_decoder_layers_18_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[922] + model_decoder_layers_18_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[923] + model_decoder_layers_18_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[924] + model_decoder_layers_18_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[925] + model_decoder_layers_18_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[926] + model_decoder_layers_18_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[927] + model_decoder_layers_18_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[928] + model_decoder_layers_18_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[929] + model_decoder_layers_18_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[933] + model_decoder_layers_18_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[934] + model_decoder_layers_18_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[935] + model_decoder_layers_18_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[936] + model_decoder_layers_18_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[937] + model_decoder_layers_18_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[938] + model_decoder_layers_18_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[939] + model_decoder_layers_18_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[940] + model_decoder_layers_18_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[941] + model_decoder_layers_18_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[942] + model_decoder_layers_18_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[943] + model_decoder_layers_18_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[944] + model_decoder_layers_19_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[945] + model_decoder_layers_19_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[946] + model_decoder_layers_19_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[947] + model_decoder_layers_19_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[948] + model_decoder_layers_19_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[949] + model_decoder_layers_19_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[950] + model_decoder_layers_19_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[951] + model_decoder_layers_19_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[952] + model_decoder_layers_19_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[953] + model_decoder_layers_19_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[957] + model_decoder_layers_19_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[958] + model_decoder_layers_19_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[959] + model_decoder_layers_19_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[960] + model_decoder_layers_19_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[961] + model_decoder_layers_19_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[962] + model_decoder_layers_19_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[963] + model_decoder_layers_19_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[964] + model_decoder_layers_19_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[965] + model_decoder_layers_19_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[966] + model_decoder_layers_19_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[967] + model_decoder_layers_19_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[968] + model_decoder_layers_20_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[969] + model_decoder_layers_20_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[970] + model_decoder_layers_20_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[971] + model_decoder_layers_20_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[972] + model_decoder_layers_20_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[973] + model_decoder_layers_20_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[974] + model_decoder_layers_20_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[975] + model_decoder_layers_20_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[976] + model_decoder_layers_20_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[977] + model_decoder_layers_20_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[981] + model_decoder_layers_20_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[982] + model_decoder_layers_20_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[983] + model_decoder_layers_20_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[984] + model_decoder_layers_20_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[985] + model_decoder_layers_20_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[986] + model_decoder_layers_20_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[987] + model_decoder_layers_20_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[988] + model_decoder_layers_20_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[989] + model_decoder_layers_20_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[990] + model_decoder_layers_20_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[991] + model_decoder_layers_20_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[992] + model_decoder_layers_21_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[993] + model_decoder_layers_21_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[994] + model_decoder_layers_21_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[995] + model_decoder_layers_21_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[996] + model_decoder_layers_21_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[997] + model_decoder_layers_21_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[998] + model_decoder_layers_21_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[999] + model_decoder_layers_21_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1000] + model_decoder_layers_21_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1001] + model_decoder_layers_21_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1005] + model_decoder_layers_21_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1006] + model_decoder_layers_21_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1007] + model_decoder_layers_21_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1008] + model_decoder_layers_21_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1009] + model_decoder_layers_21_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1010] + model_decoder_layers_21_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1011] + model_decoder_layers_21_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1012] + model_decoder_layers_21_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1013] + model_decoder_layers_21_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1014] + model_decoder_layers_21_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1015] + model_decoder_layers_21_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1016] + model_decoder_layers_22_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1017] + model_decoder_layers_22_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1018] + model_decoder_layers_22_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1019] + model_decoder_layers_22_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1020] + model_decoder_layers_22_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1021] + model_decoder_layers_22_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1022] + model_decoder_layers_22_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1023] + model_decoder_layers_22_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1024] + model_decoder_layers_22_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1025] + model_decoder_layers_22_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1029] + model_decoder_layers_22_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1030] + model_decoder_layers_22_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1031] + model_decoder_layers_22_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1032] + model_decoder_layers_22_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1033] + model_decoder_layers_22_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1034] + model_decoder_layers_22_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1035] + model_decoder_layers_22_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1036] + model_decoder_layers_22_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1037] + model_decoder_layers_22_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1038] + model_decoder_layers_22_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1039] + model_decoder_layers_22_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1040] + model_decoder_layers_23_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1041] + model_decoder_layers_23_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1042] + model_decoder_layers_23_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1043] + model_decoder_layers_23_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1044] + model_decoder_layers_23_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1045] + model_decoder_layers_23_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1046] + model_decoder_layers_23_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1047] + model_decoder_layers_23_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1048] + model_decoder_layers_23_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1049] + model_decoder_layers_23_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1053] + model_decoder_layers_23_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1054] + model_decoder_layers_23_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1055] + model_decoder_layers_23_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1056] + model_decoder_layers_23_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1057] + model_decoder_layers_23_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1058] + model_decoder_layers_23_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1059] + model_decoder_layers_23_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1060] + model_decoder_layers_23_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1061] + model_decoder_layers_23_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1062] + model_decoder_layers_23_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1063] + model_decoder_layers_23_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1064] + model_decoder_layers_24_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1065] + model_decoder_layers_24_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1066] + model_decoder_layers_24_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1067] + model_decoder_layers_24_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1068] + model_decoder_layers_24_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1069] + model_decoder_layers_24_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1070] + model_decoder_layers_24_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1071] + model_decoder_layers_24_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1072] + model_decoder_layers_24_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1073] + model_decoder_layers_24_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1077] + model_decoder_layers_24_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1078] + model_decoder_layers_24_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1079] + model_decoder_layers_24_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1080] + model_decoder_layers_24_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1081] + model_decoder_layers_24_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1082] + model_decoder_layers_24_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1083] + model_decoder_layers_24_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1084] + model_decoder_layers_24_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1085] + model_decoder_layers_24_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1086] + model_decoder_layers_24_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1087] + model_decoder_layers_24_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1088] + model_decoder_layers_25_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1089] + model_decoder_layers_25_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1090] + model_decoder_layers_25_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1091] + model_decoder_layers_25_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1092] + model_decoder_layers_25_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1093] + model_decoder_layers_25_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1094] + model_decoder_layers_25_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1095] + model_decoder_layers_25_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1096] + model_decoder_layers_25_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1097] + model_decoder_layers_25_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1101] + model_decoder_layers_25_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1102] + model_decoder_layers_25_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1103] + model_decoder_layers_25_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1104] + model_decoder_layers_25_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1105] + model_decoder_layers_25_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1106] + model_decoder_layers_25_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1107] + model_decoder_layers_25_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1108] + model_decoder_layers_25_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1109] + model_decoder_layers_25_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1110] + model_decoder_layers_25_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1111] + model_decoder_layers_25_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1112] + model_decoder_layers_26_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1113] + model_decoder_layers_26_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1114] + model_decoder_layers_26_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1115] + model_decoder_layers_26_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1116] + model_decoder_layers_26_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1117] + model_decoder_layers_26_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1118] + model_decoder_layers_26_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1119] + model_decoder_layers_26_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1120] + model_decoder_layers_26_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1121] + model_decoder_layers_26_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1125] + model_decoder_layers_26_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1126] + model_decoder_layers_26_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1127] + model_decoder_layers_26_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1128] + model_decoder_layers_26_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1129] + model_decoder_layers_26_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1130] + model_decoder_layers_26_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1131] + model_decoder_layers_26_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1132] + model_decoder_layers_26_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1133] + model_decoder_layers_26_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1134] + model_decoder_layers_26_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1135] + model_decoder_layers_26_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1136] + model_decoder_layers_27_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1137] + model_decoder_layers_27_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1138] + model_decoder_layers_27_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1139] + model_decoder_layers_27_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1140] + model_decoder_layers_27_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1141] + model_decoder_layers_27_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1142] + model_decoder_layers_27_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1143] + model_decoder_layers_27_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1144] + model_decoder_layers_27_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1145] + model_decoder_layers_27_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1149] + model_decoder_layers_27_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1150] + model_decoder_layers_27_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1151] + model_decoder_layers_27_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1152] + model_decoder_layers_27_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1153] + model_decoder_layers_27_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1154] + model_decoder_layers_27_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1155] + model_decoder_layers_27_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1156] + model_decoder_layers_27_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1157] + model_decoder_layers_27_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1158] + model_decoder_layers_27_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1159] + model_decoder_layers_27_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1160] + model_decoder_layers_28_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1161] + model_decoder_layers_28_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1162] + model_decoder_layers_28_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1163] + model_decoder_layers_28_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1164] + model_decoder_layers_28_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1165] + model_decoder_layers_28_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1166] + model_decoder_layers_28_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1167] + model_decoder_layers_28_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1168] + model_decoder_layers_28_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1169] + model_decoder_layers_28_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1173] + model_decoder_layers_28_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1174] + model_decoder_layers_28_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1175] + model_decoder_layers_28_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1176] + model_decoder_layers_28_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1177] + model_decoder_layers_28_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1178] + model_decoder_layers_28_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1179] + model_decoder_layers_28_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1180] + model_decoder_layers_28_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1181] + model_decoder_layers_28_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1182] + model_decoder_layers_28_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1183] + model_decoder_layers_28_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1184] + model_decoder_layers_29_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1185] + model_decoder_layers_29_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1186] + model_decoder_layers_29_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1187] + model_decoder_layers_29_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1188] + model_decoder_layers_29_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1189] + model_decoder_layers_29_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1190] + model_decoder_layers_29_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1191] + model_decoder_layers_29_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1192] + model_decoder_layers_29_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1193] + model_decoder_layers_29_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1197] + model_decoder_layers_29_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1198] + model_decoder_layers_29_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1199] + model_decoder_layers_29_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1200] + model_decoder_layers_29_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1201] + model_decoder_layers_29_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1202] + model_decoder_layers_29_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1203] + model_decoder_layers_29_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1204] + model_decoder_layers_29_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1205] + model_decoder_layers_29_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1206] + model_decoder_layers_29_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1207] + model_decoder_layers_29_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1208] + model_decoder_layers_30_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1209] + model_decoder_layers_30_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1210] + model_decoder_layers_30_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1211] + model_decoder_layers_30_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1212] + model_decoder_layers_30_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1213] + model_decoder_layers_30_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1214] + model_decoder_layers_30_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1215] + model_decoder_layers_30_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1216] + model_decoder_layers_30_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1217] + model_decoder_layers_30_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1221] + model_decoder_layers_30_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1222] + model_decoder_layers_30_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1223] + model_decoder_layers_30_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1224] + model_decoder_layers_30_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1225] + model_decoder_layers_30_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1226] + model_decoder_layers_30_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1227] + model_decoder_layers_30_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1228] + model_decoder_layers_30_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1229] + model_decoder_layers_30_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1230] + model_decoder_layers_30_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1231] + model_decoder_layers_30_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1232] + model_decoder_layers_31_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1233] + model_decoder_layers_31_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1234] + model_decoder_layers_31_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1235] + model_decoder_layers_31_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1236] + model_decoder_layers_31_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1237] + model_decoder_layers_31_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1238] + model_decoder_layers_31_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1239] + model_decoder_layers_31_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1240] + model_decoder_layers_31_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1241] + model_decoder_layers_31_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1245] + model_decoder_layers_31_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1246] + model_decoder_layers_31_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1247] + model_decoder_layers_31_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1248] + model_decoder_layers_31_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1249] + model_decoder_layers_31_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1250] + model_decoder_layers_31_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1251] + model_decoder_layers_31_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1252] + model_decoder_layers_31_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1253] + model_decoder_layers_31_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1254] + model_decoder_layers_31_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1255] + model_decoder_layers_31_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1256] + model_decoder_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1257] + model_decoder_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1258] + reshape1353: R.Tensor((1,), dtype="int32") = R.reshape(input_ids, R.shape([1])) + take7: R.Tensor((1, 1280), dtype="float16") = R.take(model_decoder_embed_tokens_weight5, reshape1353, axis=0) + reshape1354: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(take7, R.shape([1, 1, 1280])) + lv264: R.Tensor((1,), dtype="int32") = R.call_pure_packed("vm.builtin.attention_kv_cache_get_query_positions", paged_kv_cache, sinfo_args=(R.Tensor((1,), dtype="int32"),)) + take8: R.Tensor((1, 1280), dtype="float16") = R.take(model_decoder_embed_positions_weight5, lv264, axis=0) + reshape1355: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(take8, R.shape([1, 1, 1280])) + add1220: R.Tensor((1, 1, 1280), dtype="float16") = R.add(reshape1354, reshape1355) + layer_norm356: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1220, model_decoder_layers_0_self_attn_layer_norm_weight5, model_decoder_layers_0_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv = R.call_tir(cls.NT_matmul, (layer_norm356, model_decoder_layers_0_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1221: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv, model_decoder_layers_0_self_attn_q_proj_bias5) + reshape1356: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1221, R.shape([1, 1, 20, 64])) + lv1 = R.call_tir(cls.NT_matmul, (layer_norm356, model_decoder_layers_0_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + reshape1357: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv1, R.shape([1, 1, 20, 64])) + lv2 = R.call_tir(cls.NT_matmul, (layer_norm356, model_decoder_layers_0_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1222: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv2, model_decoder_layers_0_self_attn_v_proj_bias5) + reshape1358: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1222, R.shape([1, 1, 20, 64])) + concat96: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1356, reshape1357, reshape1358), axis=2) + reshape1359: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat96, R.shape([1, 60, 64])) + lv265 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), reshape1359), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1360: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv265, R.shape([1, 1, 20, 64])) + reshape1361: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1360, R.shape([1, 1, 1280])) + lv3 = R.call_tir(cls.NT_matmul, (reshape1361, model_decoder_layers_0_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1223: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv3, model_decoder_layers_0_self_attn_out_proj_bias5) + add1224: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1220, add1223) + layer_norm357: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1224, model_decoder_layers_0_encoder_attn_layer_norm_weight5, model_decoder_layers_0_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv4 = R.call_tir(cls.NT_matmul, (layer_norm357, model_decoder_layers_0_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1225: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv4, model_decoder_layers_0_encoder_attn_q_proj_bias5) + reshape1362: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1225, R.shape([1, 1, 20, 64])) + reshape1363: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1362, R.shape([1, 20, 64])) + lv266 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), reshape1363), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1364: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv266, R.shape([1, 1, 20, 64])) + reshape1365: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1364, R.shape([1, 1, 1280])) + lv5 = R.call_tir(cls.NT_matmul, (reshape1365, model_decoder_layers_0_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1226: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv5, model_decoder_layers_0_encoder_attn_out_proj_bias5) + add1227: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1224, add1226) + layer_norm358: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1227, model_decoder_layers_0_final_layer_norm_weight5, model_decoder_layers_0_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv6 = R.call_tir(cls.NT_matmul1, (layer_norm358, model_decoder_layers_0_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + add1228: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv6, model_decoder_layers_0_fc1_bias5) + gelu130: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1228) + lv7 = R.call_tir(cls.NT_matmul2, (gelu130, model_decoder_layers_0_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1229: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv7, model_decoder_layers_0_fc2_bias5) + add1230: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1227, add1229) + layer_norm359: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1230, model_decoder_layers_1_self_attn_layer_norm_weight5, model_decoder_layers_1_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv8 = R.call_tir(cls.NT_matmul, (layer_norm359, model_decoder_layers_1_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1231: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv8, model_decoder_layers_1_self_attn_q_proj_bias5) + reshape1366: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1231, R.shape([1, 1, 20, 64])) + lv9 = R.call_tir(cls.NT_matmul, (layer_norm359, model_decoder_layers_1_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + reshape1367: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv9, R.shape([1, 1, 20, 64])) + lv10 = R.call_tir(cls.NT_matmul, (layer_norm359, model_decoder_layers_1_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1232: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv10, model_decoder_layers_1_self_attn_v_proj_bias5) + reshape1368: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1232, R.shape([1, 1, 20, 64])) + concat97: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1366, reshape1367, reshape1368), axis=2) + reshape1369: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat97, R.shape([1, 60, 64])) + lv267 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), reshape1369), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1370: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv267, R.shape([1, 1, 20, 64])) + reshape1371: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1370, R.shape([1, 1, 1280])) + lv11 = R.call_tir(cls.NT_matmul, (reshape1371, model_decoder_layers_1_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1233: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv11, model_decoder_layers_1_self_attn_out_proj_bias5) + add1234: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1230, add1233) + layer_norm360: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1234, model_decoder_layers_1_encoder_attn_layer_norm_weight5, model_decoder_layers_1_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv12 = R.call_tir(cls.NT_matmul, (layer_norm360, model_decoder_layers_1_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1235: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv12, model_decoder_layers_1_encoder_attn_q_proj_bias5) + reshape1372: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1235, R.shape([1, 1, 20, 64])) + reshape1373: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1372, R.shape([1, 20, 64])) + lv268 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), reshape1373), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1374: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv268, R.shape([1, 1, 20, 64])) + reshape1375: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1374, R.shape([1, 1, 1280])) + lv13 = R.call_tir(cls.NT_matmul, (reshape1375, model_decoder_layers_1_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1236: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv13, model_decoder_layers_1_encoder_attn_out_proj_bias5) + add1237: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1234, add1236) + layer_norm361: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1237, model_decoder_layers_1_final_layer_norm_weight5, model_decoder_layers_1_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv14 = R.call_tir(cls.NT_matmul1, (layer_norm361, model_decoder_layers_1_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + add1238: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv14, model_decoder_layers_1_fc1_bias5) + gelu131: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1238) + lv15 = R.call_tir(cls.NT_matmul2, (gelu131, model_decoder_layers_1_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1239: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv15, model_decoder_layers_1_fc2_bias5) + add1240: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1237, add1239) + layer_norm362: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1240, model_decoder_layers_2_self_attn_layer_norm_weight5, model_decoder_layers_2_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv16 = R.call_tir(cls.NT_matmul, (layer_norm362, model_decoder_layers_2_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1241: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv16, model_decoder_layers_2_self_attn_q_proj_bias5) + reshape1376: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1241, R.shape([1, 1, 20, 64])) + lv17 = R.call_tir(cls.NT_matmul, (layer_norm362, model_decoder_layers_2_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + reshape1377: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv17, R.shape([1, 1, 20, 64])) + lv18 = R.call_tir(cls.NT_matmul, (layer_norm362, model_decoder_layers_2_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1242: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv18, model_decoder_layers_2_self_attn_v_proj_bias5) + reshape1378: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1242, R.shape([1, 1, 20, 64])) + concat98: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1376, reshape1377, reshape1378), axis=2) + reshape1379: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat98, R.shape([1, 60, 64])) + lv269 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), reshape1379), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1380: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv269, R.shape([1, 1, 20, 64])) + reshape1381: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1380, R.shape([1, 1, 1280])) + lv19 = R.call_tir(cls.NT_matmul, (reshape1381, model_decoder_layers_2_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1243: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv19, model_decoder_layers_2_self_attn_out_proj_bias5) + add1244: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1240, add1243) + layer_norm363: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1244, model_decoder_layers_2_encoder_attn_layer_norm_weight5, model_decoder_layers_2_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv20 = R.call_tir(cls.NT_matmul, (layer_norm363, model_decoder_layers_2_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1245: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv20, model_decoder_layers_2_encoder_attn_q_proj_bias5) + reshape1382: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1245, R.shape([1, 1, 20, 64])) + reshape1383: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1382, R.shape([1, 20, 64])) + lv270 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), reshape1383), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1384: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv270, R.shape([1, 1, 20, 64])) + reshape1385: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1384, R.shape([1, 1, 1280])) + lv21 = R.call_tir(cls.NT_matmul, (reshape1385, model_decoder_layers_2_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1246: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv21, model_decoder_layers_2_encoder_attn_out_proj_bias5) + add1247: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1244, add1246) + layer_norm364: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1247, model_decoder_layers_2_final_layer_norm_weight5, model_decoder_layers_2_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv22 = R.call_tir(cls.NT_matmul1, (layer_norm364, model_decoder_layers_2_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + add1248: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv22, model_decoder_layers_2_fc1_bias5) + gelu132: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1248) + lv23 = R.call_tir(cls.NT_matmul2, (gelu132, model_decoder_layers_2_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1249: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv23, model_decoder_layers_2_fc2_bias5) + add1250: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1247, add1249) + layer_norm365: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1250, model_decoder_layers_3_self_attn_layer_norm_weight5, model_decoder_layers_3_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv24 = R.call_tir(cls.NT_matmul, (layer_norm365, model_decoder_layers_3_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1251: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv24, model_decoder_layers_3_self_attn_q_proj_bias5) + reshape1386: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1251, R.shape([1, 1, 20, 64])) + lv25 = R.call_tir(cls.NT_matmul, (layer_norm365, model_decoder_layers_3_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + reshape1387: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv25, R.shape([1, 1, 20, 64])) + lv26 = R.call_tir(cls.NT_matmul, (layer_norm365, model_decoder_layers_3_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1252: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv26, model_decoder_layers_3_self_attn_v_proj_bias5) + reshape1388: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1252, R.shape([1, 1, 20, 64])) + concat99: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1386, reshape1387, reshape1388), axis=2) + reshape1389: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat99, R.shape([1, 60, 64])) + lv271 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), reshape1389), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1390: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv271, R.shape([1, 1, 20, 64])) + reshape1391: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1390, R.shape([1, 1, 1280])) + lv27 = R.call_tir(cls.NT_matmul, (reshape1391, model_decoder_layers_3_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1253: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv27, model_decoder_layers_3_self_attn_out_proj_bias5) + add1254: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1250, add1253) + layer_norm366: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1254, model_decoder_layers_3_encoder_attn_layer_norm_weight5, model_decoder_layers_3_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv28 = R.call_tir(cls.NT_matmul, (layer_norm366, model_decoder_layers_3_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1255: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv28, model_decoder_layers_3_encoder_attn_q_proj_bias5) + reshape1392: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1255, R.shape([1, 1, 20, 64])) + reshape1393: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1392, R.shape([1, 20, 64])) + lv272 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), reshape1393), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1394: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv272, R.shape([1, 1, 20, 64])) + reshape1395: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1394, R.shape([1, 1, 1280])) + lv29 = R.call_tir(cls.NT_matmul, (reshape1395, model_decoder_layers_3_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1256: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv29, model_decoder_layers_3_encoder_attn_out_proj_bias5) + add1257: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1254, add1256) + layer_norm367: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1257, model_decoder_layers_3_final_layer_norm_weight5, model_decoder_layers_3_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv30 = R.call_tir(cls.NT_matmul1, (layer_norm367, model_decoder_layers_3_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + add1258: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv30, model_decoder_layers_3_fc1_bias5) + gelu133: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1258) + lv31 = R.call_tir(cls.NT_matmul2, (gelu133, model_decoder_layers_3_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1259: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv31, model_decoder_layers_3_fc2_bias5) + add1260: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1257, add1259) + layer_norm368: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1260, model_decoder_layers_4_self_attn_layer_norm_weight5, model_decoder_layers_4_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv32 = R.call_tir(cls.NT_matmul, (layer_norm368, model_decoder_layers_4_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1261: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv32, model_decoder_layers_4_self_attn_q_proj_bias5) + reshape1396: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1261, R.shape([1, 1, 20, 64])) + lv33 = R.call_tir(cls.NT_matmul, (layer_norm368, model_decoder_layers_4_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + reshape1397: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv33, R.shape([1, 1, 20, 64])) + lv34 = R.call_tir(cls.NT_matmul, (layer_norm368, model_decoder_layers_4_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1262: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv34, model_decoder_layers_4_self_attn_v_proj_bias5) + reshape1398: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1262, R.shape([1, 1, 20, 64])) + concat100: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1396, reshape1397, reshape1398), axis=2) + reshape1399: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat100, R.shape([1, 60, 64])) + lv273 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), reshape1399), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1400: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv273, R.shape([1, 1, 20, 64])) + reshape1401: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1400, R.shape([1, 1, 1280])) + lv35 = R.call_tir(cls.NT_matmul, (reshape1401, model_decoder_layers_4_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1263: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv35, model_decoder_layers_4_self_attn_out_proj_bias5) + add1264: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1260, add1263) + layer_norm369: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1264, model_decoder_layers_4_encoder_attn_layer_norm_weight5, model_decoder_layers_4_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv36 = R.call_tir(cls.NT_matmul, (layer_norm369, model_decoder_layers_4_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1265: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv36, model_decoder_layers_4_encoder_attn_q_proj_bias5) + reshape1402: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1265, R.shape([1, 1, 20, 64])) + reshape1403: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1402, R.shape([1, 20, 64])) + lv274 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), reshape1403), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1404: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv274, R.shape([1, 1, 20, 64])) + reshape1405: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1404, R.shape([1, 1, 1280])) + lv37 = R.call_tir(cls.NT_matmul, (reshape1405, model_decoder_layers_4_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1266: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv37, model_decoder_layers_4_encoder_attn_out_proj_bias5) + add1267: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1264, add1266) + layer_norm370: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1267, model_decoder_layers_4_final_layer_norm_weight5, model_decoder_layers_4_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv38 = R.call_tir(cls.NT_matmul1, (layer_norm370, model_decoder_layers_4_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + add1268: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv38, model_decoder_layers_4_fc1_bias5) + gelu134: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1268) + lv39 = R.call_tir(cls.NT_matmul2, (gelu134, model_decoder_layers_4_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1269: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv39, model_decoder_layers_4_fc2_bias5) + add1270: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1267, add1269) + layer_norm371: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1270, model_decoder_layers_5_self_attn_layer_norm_weight5, model_decoder_layers_5_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv40 = R.call_tir(cls.NT_matmul, (layer_norm371, model_decoder_layers_5_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1271: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv40, model_decoder_layers_5_self_attn_q_proj_bias5) + reshape1406: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1271, R.shape([1, 1, 20, 64])) + lv41 = R.call_tir(cls.NT_matmul, (layer_norm371, model_decoder_layers_5_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + reshape1407: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv41, R.shape([1, 1, 20, 64])) + lv42 = R.call_tir(cls.NT_matmul, (layer_norm371, model_decoder_layers_5_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1272: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv42, model_decoder_layers_5_self_attn_v_proj_bias5) + reshape1408: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1272, R.shape([1, 1, 20, 64])) + concat101: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1406, reshape1407, reshape1408), axis=2) + reshape1409: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat101, R.shape([1, 60, 64])) + lv275 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), reshape1409), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1410: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv275, R.shape([1, 1, 20, 64])) + reshape1411: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1410, R.shape([1, 1, 1280])) + lv43 = R.call_tir(cls.NT_matmul, (reshape1411, model_decoder_layers_5_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1273: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv43, model_decoder_layers_5_self_attn_out_proj_bias5) + add1274: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1270, add1273) + layer_norm372: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1274, model_decoder_layers_5_encoder_attn_layer_norm_weight5, model_decoder_layers_5_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv44 = R.call_tir(cls.NT_matmul, (layer_norm372, model_decoder_layers_5_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1275: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv44, model_decoder_layers_5_encoder_attn_q_proj_bias5) + reshape1412: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1275, R.shape([1, 1, 20, 64])) + reshape1413: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1412, R.shape([1, 20, 64])) + lv276 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), reshape1413), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1414: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv276, R.shape([1, 1, 20, 64])) + reshape1415: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1414, R.shape([1, 1, 1280])) + lv45 = R.call_tir(cls.NT_matmul, (reshape1415, model_decoder_layers_5_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1276: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv45, model_decoder_layers_5_encoder_attn_out_proj_bias5) + add1277: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1274, add1276) + layer_norm373: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1277, model_decoder_layers_5_final_layer_norm_weight5, model_decoder_layers_5_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv46 = R.call_tir(cls.NT_matmul1, (layer_norm373, model_decoder_layers_5_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + add1278: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv46, model_decoder_layers_5_fc1_bias5) + gelu135: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1278) + lv47 = R.call_tir(cls.NT_matmul2, (gelu135, model_decoder_layers_5_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1279: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv47, model_decoder_layers_5_fc2_bias5) + add1280: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1277, add1279) + layer_norm374: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1280, model_decoder_layers_6_self_attn_layer_norm_weight5, model_decoder_layers_6_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv48 = R.call_tir(cls.NT_matmul, (layer_norm374, model_decoder_layers_6_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1281: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv48, model_decoder_layers_6_self_attn_q_proj_bias5) + reshape1416: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1281, R.shape([1, 1, 20, 64])) + lv49 = R.call_tir(cls.NT_matmul, (layer_norm374, model_decoder_layers_6_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + reshape1417: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv49, R.shape([1, 1, 20, 64])) + lv50 = R.call_tir(cls.NT_matmul, (layer_norm374, model_decoder_layers_6_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1282: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv50, model_decoder_layers_6_self_attn_v_proj_bias5) + reshape1418: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1282, R.shape([1, 1, 20, 64])) + concat102: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1416, reshape1417, reshape1418), axis=2) + reshape1419: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat102, R.shape([1, 60, 64])) + lv277 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), reshape1419), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1420: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv277, R.shape([1, 1, 20, 64])) + reshape1421: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1420, R.shape([1, 1, 1280])) + lv51 = R.call_tir(cls.NT_matmul, (reshape1421, model_decoder_layers_6_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1283: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv51, model_decoder_layers_6_self_attn_out_proj_bias5) + add1284: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1280, add1283) + layer_norm375: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1284, model_decoder_layers_6_encoder_attn_layer_norm_weight5, model_decoder_layers_6_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv52 = R.call_tir(cls.NT_matmul, (layer_norm375, model_decoder_layers_6_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1285: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv52, model_decoder_layers_6_encoder_attn_q_proj_bias5) + reshape1422: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1285, R.shape([1, 1, 20, 64])) + reshape1423: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1422, R.shape([1, 20, 64])) + lv278 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), reshape1423), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1424: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv278, R.shape([1, 1, 20, 64])) + reshape1425: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1424, R.shape([1, 1, 1280])) + lv53 = R.call_tir(cls.NT_matmul, (reshape1425, model_decoder_layers_6_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1286: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv53, model_decoder_layers_6_encoder_attn_out_proj_bias5) + add1287: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1284, add1286) + layer_norm376: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1287, model_decoder_layers_6_final_layer_norm_weight5, model_decoder_layers_6_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv54 = R.call_tir(cls.NT_matmul1, (layer_norm376, model_decoder_layers_6_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + add1288: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv54, model_decoder_layers_6_fc1_bias5) + gelu136: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1288) + lv55 = R.call_tir(cls.NT_matmul2, (gelu136, model_decoder_layers_6_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1289: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv55, model_decoder_layers_6_fc2_bias5) + add1290: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1287, add1289) + layer_norm377: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1290, model_decoder_layers_7_self_attn_layer_norm_weight5, model_decoder_layers_7_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv56 = R.call_tir(cls.NT_matmul, (layer_norm377, model_decoder_layers_7_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1291: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv56, model_decoder_layers_7_self_attn_q_proj_bias5) + reshape1426: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1291, R.shape([1, 1, 20, 64])) + lv57 = R.call_tir(cls.NT_matmul, (layer_norm377, model_decoder_layers_7_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + reshape1427: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv57, R.shape([1, 1, 20, 64])) + lv58 = R.call_tir(cls.NT_matmul, (layer_norm377, model_decoder_layers_7_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1292: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv58, model_decoder_layers_7_self_attn_v_proj_bias5) + reshape1428: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1292, R.shape([1, 1, 20, 64])) + concat103: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1426, reshape1427, reshape1428), axis=2) + reshape1429: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat103, R.shape([1, 60, 64])) + lv279 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), reshape1429), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1430: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv279, R.shape([1, 1, 20, 64])) + reshape1431: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1430, R.shape([1, 1, 1280])) + lv59 = R.call_tir(cls.NT_matmul, (reshape1431, model_decoder_layers_7_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1293: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv59, model_decoder_layers_7_self_attn_out_proj_bias5) + add1294: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1290, add1293) + layer_norm378: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1294, model_decoder_layers_7_encoder_attn_layer_norm_weight5, model_decoder_layers_7_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv60 = R.call_tir(cls.NT_matmul, (layer_norm378, model_decoder_layers_7_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1295: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv60, model_decoder_layers_7_encoder_attn_q_proj_bias5) + reshape1432: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1295, R.shape([1, 1, 20, 64])) + reshape1433: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1432, R.shape([1, 20, 64])) + lv280 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), reshape1433), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1434: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv280, R.shape([1, 1, 20, 64])) + reshape1435: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1434, R.shape([1, 1, 1280])) + lv61 = R.call_tir(cls.NT_matmul, (reshape1435, model_decoder_layers_7_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1296: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv61, model_decoder_layers_7_encoder_attn_out_proj_bias5) + add1297: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1294, add1296) + layer_norm379: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1297, model_decoder_layers_7_final_layer_norm_weight5, model_decoder_layers_7_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv62 = R.call_tir(cls.NT_matmul1, (layer_norm379, model_decoder_layers_7_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + add1298: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv62, model_decoder_layers_7_fc1_bias5) + gelu137: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1298) + lv63 = R.call_tir(cls.NT_matmul2, (gelu137, model_decoder_layers_7_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1299: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv63, model_decoder_layers_7_fc2_bias5) + add1300: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1297, add1299) + layer_norm380: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1300, model_decoder_layers_8_self_attn_layer_norm_weight5, model_decoder_layers_8_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv64 = R.call_tir(cls.NT_matmul, (layer_norm380, model_decoder_layers_8_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1301: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv64, model_decoder_layers_8_self_attn_q_proj_bias5) + reshape1436: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1301, R.shape([1, 1, 20, 64])) + lv65 = R.call_tir(cls.NT_matmul, (layer_norm380, model_decoder_layers_8_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + reshape1437: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv65, R.shape([1, 1, 20, 64])) + lv66 = R.call_tir(cls.NT_matmul, (layer_norm380, model_decoder_layers_8_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1302: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv66, model_decoder_layers_8_self_attn_v_proj_bias5) + reshape1438: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1302, R.shape([1, 1, 20, 64])) + concat104: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1436, reshape1437, reshape1438), axis=2) + reshape1439: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat104, R.shape([1, 60, 64])) + lv281 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), reshape1439), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1440: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv281, R.shape([1, 1, 20, 64])) + reshape1441: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1440, R.shape([1, 1, 1280])) + lv67 = R.call_tir(cls.NT_matmul, (reshape1441, model_decoder_layers_8_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1303: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv67, model_decoder_layers_8_self_attn_out_proj_bias5) + add1304: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1300, add1303) + layer_norm381: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1304, model_decoder_layers_8_encoder_attn_layer_norm_weight5, model_decoder_layers_8_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv68 = R.call_tir(cls.NT_matmul, (layer_norm381, model_decoder_layers_8_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1305: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv68, model_decoder_layers_8_encoder_attn_q_proj_bias5) + reshape1442: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1305, R.shape([1, 1, 20, 64])) + reshape1443: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1442, R.shape([1, 20, 64])) + lv282 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), reshape1443), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1444: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv282, R.shape([1, 1, 20, 64])) + reshape1445: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1444, R.shape([1, 1, 1280])) + lv69 = R.call_tir(cls.NT_matmul, (reshape1445, model_decoder_layers_8_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1306: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv69, model_decoder_layers_8_encoder_attn_out_proj_bias5) + add1307: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1304, add1306) + layer_norm382: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1307, model_decoder_layers_8_final_layer_norm_weight5, model_decoder_layers_8_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv70 = R.call_tir(cls.NT_matmul1, (layer_norm382, model_decoder_layers_8_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + add1308: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv70, model_decoder_layers_8_fc1_bias5) + gelu138: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1308) + lv71 = R.call_tir(cls.NT_matmul2, (gelu138, model_decoder_layers_8_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1309: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv71, model_decoder_layers_8_fc2_bias5) + add1310: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1307, add1309) + layer_norm383: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1310, model_decoder_layers_9_self_attn_layer_norm_weight5, model_decoder_layers_9_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv72 = R.call_tir(cls.NT_matmul, (layer_norm383, model_decoder_layers_9_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1311: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv72, model_decoder_layers_9_self_attn_q_proj_bias5) + reshape1446: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1311, R.shape([1, 1, 20, 64])) + lv73 = R.call_tir(cls.NT_matmul, (layer_norm383, model_decoder_layers_9_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + reshape1447: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv73, R.shape([1, 1, 20, 64])) + lv74 = R.call_tir(cls.NT_matmul, (layer_norm383, model_decoder_layers_9_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1312: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv74, model_decoder_layers_9_self_attn_v_proj_bias5) + reshape1448: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1312, R.shape([1, 1, 20, 64])) + concat105: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1446, reshape1447, reshape1448), axis=2) + reshape1449: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat105, R.shape([1, 60, 64])) + lv283 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), reshape1449), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1450: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv283, R.shape([1, 1, 20, 64])) + reshape1451: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1450, R.shape([1, 1, 1280])) + lv75 = R.call_tir(cls.NT_matmul, (reshape1451, model_decoder_layers_9_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1313: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv75, model_decoder_layers_9_self_attn_out_proj_bias5) + add1314: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1310, add1313) + layer_norm384: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1314, model_decoder_layers_9_encoder_attn_layer_norm_weight5, model_decoder_layers_9_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv76 = R.call_tir(cls.NT_matmul, (layer_norm384, model_decoder_layers_9_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1315: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv76, model_decoder_layers_9_encoder_attn_q_proj_bias5) + reshape1452: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1315, R.shape([1, 1, 20, 64])) + reshape1453: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1452, R.shape([1, 20, 64])) + lv284 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), reshape1453), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1454: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv284, R.shape([1, 1, 20, 64])) + reshape1455: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1454, R.shape([1, 1, 1280])) + lv77 = R.call_tir(cls.NT_matmul, (reshape1455, model_decoder_layers_9_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1316: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv77, model_decoder_layers_9_encoder_attn_out_proj_bias5) + add1317: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1314, add1316) + layer_norm385: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1317, model_decoder_layers_9_final_layer_norm_weight5, model_decoder_layers_9_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv78 = R.call_tir(cls.NT_matmul1, (layer_norm385, model_decoder_layers_9_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + add1318: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv78, model_decoder_layers_9_fc1_bias5) + gelu139: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1318) + lv79 = R.call_tir(cls.NT_matmul2, (gelu139, model_decoder_layers_9_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1319: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv79, model_decoder_layers_9_fc2_bias5) + add1320: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1317, add1319) + layer_norm386: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1320, model_decoder_layers_10_self_attn_layer_norm_weight5, model_decoder_layers_10_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv80 = R.call_tir(cls.NT_matmul, (layer_norm386, model_decoder_layers_10_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1321: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv80, model_decoder_layers_10_self_attn_q_proj_bias5) + reshape1456: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1321, R.shape([1, 1, 20, 64])) + lv81 = R.call_tir(cls.NT_matmul, (layer_norm386, model_decoder_layers_10_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + reshape1457: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv81, R.shape([1, 1, 20, 64])) + lv82 = R.call_tir(cls.NT_matmul, (layer_norm386, model_decoder_layers_10_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1322: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv82, model_decoder_layers_10_self_attn_v_proj_bias5) + reshape1458: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1322, R.shape([1, 1, 20, 64])) + concat106: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1456, reshape1457, reshape1458), axis=2) + reshape1459: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat106, R.shape([1, 60, 64])) + lv285 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), reshape1459), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1460: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv285, R.shape([1, 1, 20, 64])) + reshape1461: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1460, R.shape([1, 1, 1280])) + lv83 = R.call_tir(cls.NT_matmul, (reshape1461, model_decoder_layers_10_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1323: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv83, model_decoder_layers_10_self_attn_out_proj_bias5) + add1324: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1320, add1323) + layer_norm387: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1324, model_decoder_layers_10_encoder_attn_layer_norm_weight5, model_decoder_layers_10_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv84 = R.call_tir(cls.NT_matmul, (layer_norm387, model_decoder_layers_10_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1325: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv84, model_decoder_layers_10_encoder_attn_q_proj_bias5) + reshape1462: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1325, R.shape([1, 1, 20, 64])) + reshape1463: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1462, R.shape([1, 20, 64])) + lv286 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), reshape1463), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1464: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv286, R.shape([1, 1, 20, 64])) + reshape1465: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1464, R.shape([1, 1, 1280])) + lv85 = R.call_tir(cls.NT_matmul, (reshape1465, model_decoder_layers_10_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1326: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv85, model_decoder_layers_10_encoder_attn_out_proj_bias5) + add1327: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1324, add1326) + layer_norm388: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1327, model_decoder_layers_10_final_layer_norm_weight5, model_decoder_layers_10_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv86 = R.call_tir(cls.NT_matmul1, (layer_norm388, model_decoder_layers_10_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + add1328: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv86, model_decoder_layers_10_fc1_bias5) + gelu140: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1328) + lv87 = R.call_tir(cls.NT_matmul2, (gelu140, model_decoder_layers_10_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1329: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv87, model_decoder_layers_10_fc2_bias5) + add1330: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1327, add1329) + layer_norm389: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1330, model_decoder_layers_11_self_attn_layer_norm_weight5, model_decoder_layers_11_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv88 = R.call_tir(cls.NT_matmul, (layer_norm389, model_decoder_layers_11_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1331: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv88, model_decoder_layers_11_self_attn_q_proj_bias5) + reshape1466: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1331, R.shape([1, 1, 20, 64])) + lv89 = R.call_tir(cls.NT_matmul, (layer_norm389, model_decoder_layers_11_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + reshape1467: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv89, R.shape([1, 1, 20, 64])) + lv90 = R.call_tir(cls.NT_matmul, (layer_norm389, model_decoder_layers_11_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1332: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv90, model_decoder_layers_11_self_attn_v_proj_bias5) + reshape1468: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1332, R.shape([1, 1, 20, 64])) + concat107: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1466, reshape1467, reshape1468), axis=2) + reshape1469: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat107, R.shape([1, 60, 64])) + lv287 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), reshape1469), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1470: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv287, R.shape([1, 1, 20, 64])) + reshape1471: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1470, R.shape([1, 1, 1280])) + lv91 = R.call_tir(cls.NT_matmul, (reshape1471, model_decoder_layers_11_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1333: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv91, model_decoder_layers_11_self_attn_out_proj_bias5) + add1334: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1330, add1333) + layer_norm390: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1334, model_decoder_layers_11_encoder_attn_layer_norm_weight5, model_decoder_layers_11_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv92 = R.call_tir(cls.NT_matmul, (layer_norm390, model_decoder_layers_11_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1335: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv92, model_decoder_layers_11_encoder_attn_q_proj_bias5) + reshape1472: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1335, R.shape([1, 1, 20, 64])) + reshape1473: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1472, R.shape([1, 20, 64])) + lv288 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), reshape1473), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1474: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv288, R.shape([1, 1, 20, 64])) + reshape1475: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1474, R.shape([1, 1, 1280])) + lv93 = R.call_tir(cls.NT_matmul, (reshape1475, model_decoder_layers_11_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1336: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv93, model_decoder_layers_11_encoder_attn_out_proj_bias5) + add1337: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1334, add1336) + layer_norm391: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1337, model_decoder_layers_11_final_layer_norm_weight5, model_decoder_layers_11_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv94 = R.call_tir(cls.NT_matmul1, (layer_norm391, model_decoder_layers_11_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + add1338: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv94, model_decoder_layers_11_fc1_bias5) + gelu141: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1338) + lv95 = R.call_tir(cls.NT_matmul2, (gelu141, model_decoder_layers_11_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1339: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv95, model_decoder_layers_11_fc2_bias5) + add1340: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1337, add1339) + layer_norm392: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1340, model_decoder_layers_12_self_attn_layer_norm_weight5, model_decoder_layers_12_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv96 = R.call_tir(cls.NT_matmul, (layer_norm392, model_decoder_layers_12_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1341: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv96, model_decoder_layers_12_self_attn_q_proj_bias5) + reshape1476: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1341, R.shape([1, 1, 20, 64])) + lv97 = R.call_tir(cls.NT_matmul, (layer_norm392, model_decoder_layers_12_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + reshape1477: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv97, R.shape([1, 1, 20, 64])) + lv98 = R.call_tir(cls.NT_matmul, (layer_norm392, model_decoder_layers_12_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1342: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv98, model_decoder_layers_12_self_attn_v_proj_bias5) + reshape1478: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1342, R.shape([1, 1, 20, 64])) + concat108: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1476, reshape1477, reshape1478), axis=2) + reshape1479: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat108, R.shape([1, 60, 64])) + lv289 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), reshape1479), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1480: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv289, R.shape([1, 1, 20, 64])) + reshape1481: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1480, R.shape([1, 1, 1280])) + lv99 = R.call_tir(cls.NT_matmul, (reshape1481, model_decoder_layers_12_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1343: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv99, model_decoder_layers_12_self_attn_out_proj_bias5) + add1344: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1340, add1343) + layer_norm393: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1344, model_decoder_layers_12_encoder_attn_layer_norm_weight5, model_decoder_layers_12_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv100 = R.call_tir(cls.NT_matmul, (layer_norm393, model_decoder_layers_12_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1345: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv100, model_decoder_layers_12_encoder_attn_q_proj_bias5) + reshape1482: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1345, R.shape([1, 1, 20, 64])) + reshape1483: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1482, R.shape([1, 20, 64])) + lv290 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), reshape1483), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1484: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv290, R.shape([1, 1, 20, 64])) + reshape1485: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1484, R.shape([1, 1, 1280])) + lv101 = R.call_tir(cls.NT_matmul, (reshape1485, model_decoder_layers_12_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1346: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv101, model_decoder_layers_12_encoder_attn_out_proj_bias5) + add1347: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1344, add1346) + layer_norm394: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1347, model_decoder_layers_12_final_layer_norm_weight5, model_decoder_layers_12_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv102 = R.call_tir(cls.NT_matmul1, (layer_norm394, model_decoder_layers_12_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + add1348: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv102, model_decoder_layers_12_fc1_bias5) + gelu142: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1348) + lv103 = R.call_tir(cls.NT_matmul2, (gelu142, model_decoder_layers_12_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1349: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv103, model_decoder_layers_12_fc2_bias5) + add1350: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1347, add1349) + layer_norm395: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1350, model_decoder_layers_13_self_attn_layer_norm_weight5, model_decoder_layers_13_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv104 = R.call_tir(cls.NT_matmul, (layer_norm395, model_decoder_layers_13_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1351: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv104, model_decoder_layers_13_self_attn_q_proj_bias5) + reshape1486: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1351, R.shape([1, 1, 20, 64])) + lv105 = R.call_tir(cls.NT_matmul, (layer_norm395, model_decoder_layers_13_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + reshape1487: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv105, R.shape([1, 1, 20, 64])) + lv106 = R.call_tir(cls.NT_matmul, (layer_norm395, model_decoder_layers_13_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1352: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv106, model_decoder_layers_13_self_attn_v_proj_bias5) + reshape1488: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1352, R.shape([1, 1, 20, 64])) + concat109: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1486, reshape1487, reshape1488), axis=2) + reshape1489: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat109, R.shape([1, 60, 64])) + lv291 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), reshape1489), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1490: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv291, R.shape([1, 1, 20, 64])) + reshape1491: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1490, R.shape([1, 1, 1280])) + lv107 = R.call_tir(cls.NT_matmul, (reshape1491, model_decoder_layers_13_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1353: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv107, model_decoder_layers_13_self_attn_out_proj_bias5) + add1354: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1350, add1353) + layer_norm396: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1354, model_decoder_layers_13_encoder_attn_layer_norm_weight5, model_decoder_layers_13_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv108 = R.call_tir(cls.NT_matmul, (layer_norm396, model_decoder_layers_13_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1355: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv108, model_decoder_layers_13_encoder_attn_q_proj_bias5) + reshape1492: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1355, R.shape([1, 1, 20, 64])) + reshape1493: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1492, R.shape([1, 20, 64])) + lv292 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), reshape1493), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1494: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv292, R.shape([1, 1, 20, 64])) + reshape1495: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1494, R.shape([1, 1, 1280])) + lv109 = R.call_tir(cls.NT_matmul, (reshape1495, model_decoder_layers_13_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1356: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv109, model_decoder_layers_13_encoder_attn_out_proj_bias5) + add1357: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1354, add1356) + layer_norm397: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1357, model_decoder_layers_13_final_layer_norm_weight5, model_decoder_layers_13_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv110 = R.call_tir(cls.NT_matmul1, (layer_norm397, model_decoder_layers_13_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + add1358: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv110, model_decoder_layers_13_fc1_bias5) + gelu143: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1358) + lv111 = R.call_tir(cls.NT_matmul2, (gelu143, model_decoder_layers_13_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1359: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv111, model_decoder_layers_13_fc2_bias5) + add1360: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1357, add1359) + layer_norm398: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1360, model_decoder_layers_14_self_attn_layer_norm_weight5, model_decoder_layers_14_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv112 = R.call_tir(cls.NT_matmul, (layer_norm398, model_decoder_layers_14_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1361: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv112, model_decoder_layers_14_self_attn_q_proj_bias5) + reshape1496: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1361, R.shape([1, 1, 20, 64])) + lv113 = R.call_tir(cls.NT_matmul, (layer_norm398, model_decoder_layers_14_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + reshape1497: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv113, R.shape([1, 1, 20, 64])) + lv114 = R.call_tir(cls.NT_matmul, (layer_norm398, model_decoder_layers_14_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1362: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv114, model_decoder_layers_14_self_attn_v_proj_bias5) + reshape1498: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1362, R.shape([1, 1, 20, 64])) + concat110: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1496, reshape1497, reshape1498), axis=2) + reshape1499: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat110, R.shape([1, 60, 64])) + lv293 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), reshape1499), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1500: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv293, R.shape([1, 1, 20, 64])) + reshape1501: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1500, R.shape([1, 1, 1280])) + lv115 = R.call_tir(cls.NT_matmul, (reshape1501, model_decoder_layers_14_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1363: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv115, model_decoder_layers_14_self_attn_out_proj_bias5) + add1364: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1360, add1363) + layer_norm399: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1364, model_decoder_layers_14_encoder_attn_layer_norm_weight5, model_decoder_layers_14_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv116 = R.call_tir(cls.NT_matmul, (layer_norm399, model_decoder_layers_14_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1365: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv116, model_decoder_layers_14_encoder_attn_q_proj_bias5) + reshape1502: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1365, R.shape([1, 1, 20, 64])) + reshape1503: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1502, R.shape([1, 20, 64])) + lv294 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), reshape1503), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1504: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv294, R.shape([1, 1, 20, 64])) + reshape1505: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1504, R.shape([1, 1, 1280])) + lv117 = R.call_tir(cls.NT_matmul, (reshape1505, model_decoder_layers_14_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1366: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv117, model_decoder_layers_14_encoder_attn_out_proj_bias5) + add1367: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1364, add1366) + layer_norm400: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1367, model_decoder_layers_14_final_layer_norm_weight5, model_decoder_layers_14_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv118 = R.call_tir(cls.NT_matmul1, (layer_norm400, model_decoder_layers_14_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + add1368: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv118, model_decoder_layers_14_fc1_bias5) + gelu144: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1368) + lv119 = R.call_tir(cls.NT_matmul2, (gelu144, model_decoder_layers_14_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1369: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv119, model_decoder_layers_14_fc2_bias5) + add1370: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1367, add1369) + layer_norm401: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1370, model_decoder_layers_15_self_attn_layer_norm_weight5, model_decoder_layers_15_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv120 = R.call_tir(cls.NT_matmul, (layer_norm401, model_decoder_layers_15_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1371: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv120, model_decoder_layers_15_self_attn_q_proj_bias5) + reshape1506: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1371, R.shape([1, 1, 20, 64])) + lv121 = R.call_tir(cls.NT_matmul, (layer_norm401, model_decoder_layers_15_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + reshape1507: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv121, R.shape([1, 1, 20, 64])) + lv122 = R.call_tir(cls.NT_matmul, (layer_norm401, model_decoder_layers_15_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1372: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv122, model_decoder_layers_15_self_attn_v_proj_bias5) + reshape1508: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1372, R.shape([1, 1, 20, 64])) + concat111: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1506, reshape1507, reshape1508), axis=2) + reshape1509: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat111, R.shape([1, 60, 64])) + lv295 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), reshape1509), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1510: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv295, R.shape([1, 1, 20, 64])) + reshape1511: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1510, R.shape([1, 1, 1280])) + lv123 = R.call_tir(cls.NT_matmul, (reshape1511, model_decoder_layers_15_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1373: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv123, model_decoder_layers_15_self_attn_out_proj_bias5) + add1374: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1370, add1373) + layer_norm402: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1374, model_decoder_layers_15_encoder_attn_layer_norm_weight5, model_decoder_layers_15_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv124 = R.call_tir(cls.NT_matmul, (layer_norm402, model_decoder_layers_15_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1375: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv124, model_decoder_layers_15_encoder_attn_q_proj_bias5) + reshape1512: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1375, R.shape([1, 1, 20, 64])) + reshape1513: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1512, R.shape([1, 20, 64])) + lv296 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), reshape1513), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1514: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv296, R.shape([1, 1, 20, 64])) + reshape1515: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1514, R.shape([1, 1, 1280])) + lv125 = R.call_tir(cls.NT_matmul, (reshape1515, model_decoder_layers_15_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1376: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv125, model_decoder_layers_15_encoder_attn_out_proj_bias5) + add1377: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1374, add1376) + layer_norm403: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1377, model_decoder_layers_15_final_layer_norm_weight5, model_decoder_layers_15_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv126 = R.call_tir(cls.NT_matmul1, (layer_norm403, model_decoder_layers_15_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + add1378: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv126, model_decoder_layers_15_fc1_bias5) + gelu145: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1378) + lv127 = R.call_tir(cls.NT_matmul2, (gelu145, model_decoder_layers_15_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1379: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv127, model_decoder_layers_15_fc2_bias5) + add1380: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1377, add1379) + layer_norm404: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1380, model_decoder_layers_16_self_attn_layer_norm_weight5, model_decoder_layers_16_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv128 = R.call_tir(cls.NT_matmul, (layer_norm404, model_decoder_layers_16_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1381: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv128, model_decoder_layers_16_self_attn_q_proj_bias5) + reshape1516: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1381, R.shape([1, 1, 20, 64])) + lv129 = R.call_tir(cls.NT_matmul, (layer_norm404, model_decoder_layers_16_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + reshape1517: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv129, R.shape([1, 1, 20, 64])) + lv130 = R.call_tir(cls.NT_matmul, (layer_norm404, model_decoder_layers_16_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1382: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv130, model_decoder_layers_16_self_attn_v_proj_bias5) + reshape1518: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1382, R.shape([1, 1, 20, 64])) + concat112: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1516, reshape1517, reshape1518), axis=2) + reshape1519: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat112, R.shape([1, 60, 64])) + lv297 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), reshape1519), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1520: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv297, R.shape([1, 1, 20, 64])) + reshape1521: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1520, R.shape([1, 1, 1280])) + lv131 = R.call_tir(cls.NT_matmul, (reshape1521, model_decoder_layers_16_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1383: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv131, model_decoder_layers_16_self_attn_out_proj_bias5) + add1384: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1380, add1383) + layer_norm405: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1384, model_decoder_layers_16_encoder_attn_layer_norm_weight5, model_decoder_layers_16_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv132 = R.call_tir(cls.NT_matmul, (layer_norm405, model_decoder_layers_16_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1385: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv132, model_decoder_layers_16_encoder_attn_q_proj_bias5) + reshape1522: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1385, R.shape([1, 1, 20, 64])) + reshape1523: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1522, R.shape([1, 20, 64])) + lv298 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), reshape1523), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1524: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv298, R.shape([1, 1, 20, 64])) + reshape1525: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1524, R.shape([1, 1, 1280])) + lv133 = R.call_tir(cls.NT_matmul, (reshape1525, model_decoder_layers_16_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1386: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv133, model_decoder_layers_16_encoder_attn_out_proj_bias5) + add1387: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1384, add1386) + layer_norm406: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1387, model_decoder_layers_16_final_layer_norm_weight5, model_decoder_layers_16_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv134 = R.call_tir(cls.NT_matmul1, (layer_norm406, model_decoder_layers_16_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + add1388: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv134, model_decoder_layers_16_fc1_bias5) + gelu146: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1388) + lv135 = R.call_tir(cls.NT_matmul2, (gelu146, model_decoder_layers_16_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1389: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv135, model_decoder_layers_16_fc2_bias5) + add1390: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1387, add1389) + layer_norm407: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1390, model_decoder_layers_17_self_attn_layer_norm_weight5, model_decoder_layers_17_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv136 = R.call_tir(cls.NT_matmul, (layer_norm407, model_decoder_layers_17_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1391: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv136, model_decoder_layers_17_self_attn_q_proj_bias5) + reshape1526: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1391, R.shape([1, 1, 20, 64])) + lv137 = R.call_tir(cls.NT_matmul, (layer_norm407, model_decoder_layers_17_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + reshape1527: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv137, R.shape([1, 1, 20, 64])) + lv138 = R.call_tir(cls.NT_matmul, (layer_norm407, model_decoder_layers_17_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1392: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv138, model_decoder_layers_17_self_attn_v_proj_bias5) + reshape1528: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1392, R.shape([1, 1, 20, 64])) + concat113: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1526, reshape1527, reshape1528), axis=2) + reshape1529: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat113, R.shape([1, 60, 64])) + lv299 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), reshape1529), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1530: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv299, R.shape([1, 1, 20, 64])) + reshape1531: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1530, R.shape([1, 1, 1280])) + lv139 = R.call_tir(cls.NT_matmul, (reshape1531, model_decoder_layers_17_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1393: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv139, model_decoder_layers_17_self_attn_out_proj_bias5) + add1394: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1390, add1393) + layer_norm408: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1394, model_decoder_layers_17_encoder_attn_layer_norm_weight5, model_decoder_layers_17_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv140 = R.call_tir(cls.NT_matmul, (layer_norm408, model_decoder_layers_17_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1395: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv140, model_decoder_layers_17_encoder_attn_q_proj_bias5) + reshape1532: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1395, R.shape([1, 1, 20, 64])) + reshape1533: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1532, R.shape([1, 20, 64])) + lv300 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), reshape1533), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1534: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv300, R.shape([1, 1, 20, 64])) + reshape1535: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1534, R.shape([1, 1, 1280])) + lv141 = R.call_tir(cls.NT_matmul, (reshape1535, model_decoder_layers_17_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1396: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv141, model_decoder_layers_17_encoder_attn_out_proj_bias5) + add1397: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1394, add1396) + layer_norm409: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1397, model_decoder_layers_17_final_layer_norm_weight5, model_decoder_layers_17_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv142 = R.call_tir(cls.NT_matmul1, (layer_norm409, model_decoder_layers_17_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + add1398: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv142, model_decoder_layers_17_fc1_bias5) + gelu147: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1398) + lv143 = R.call_tir(cls.NT_matmul2, (gelu147, model_decoder_layers_17_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1399: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv143, model_decoder_layers_17_fc2_bias5) + add1400: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1397, add1399) + layer_norm410: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1400, model_decoder_layers_18_self_attn_layer_norm_weight5, model_decoder_layers_18_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv144 = R.call_tir(cls.NT_matmul, (layer_norm410, model_decoder_layers_18_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1401: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv144, model_decoder_layers_18_self_attn_q_proj_bias5) + reshape1536: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1401, R.shape([1, 1, 20, 64])) + lv145 = R.call_tir(cls.NT_matmul, (layer_norm410, model_decoder_layers_18_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + reshape1537: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv145, R.shape([1, 1, 20, 64])) + lv146 = R.call_tir(cls.NT_matmul, (layer_norm410, model_decoder_layers_18_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1402: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv146, model_decoder_layers_18_self_attn_v_proj_bias5) + reshape1538: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1402, R.shape([1, 1, 20, 64])) + concat114: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1536, reshape1537, reshape1538), axis=2) + reshape1539: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat114, R.shape([1, 60, 64])) + lv301 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), reshape1539), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1540: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv301, R.shape([1, 1, 20, 64])) + reshape1541: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1540, R.shape([1, 1, 1280])) + lv147 = R.call_tir(cls.NT_matmul, (reshape1541, model_decoder_layers_18_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1403: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv147, model_decoder_layers_18_self_attn_out_proj_bias5) + add1404: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1400, add1403) + layer_norm411: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1404, model_decoder_layers_18_encoder_attn_layer_norm_weight5, model_decoder_layers_18_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv148 = R.call_tir(cls.NT_matmul, (layer_norm411, model_decoder_layers_18_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1405: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv148, model_decoder_layers_18_encoder_attn_q_proj_bias5) + reshape1542: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1405, R.shape([1, 1, 20, 64])) + reshape1543: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1542, R.shape([1, 20, 64])) + lv302 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), reshape1543), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1544: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv302, R.shape([1, 1, 20, 64])) + reshape1545: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1544, R.shape([1, 1, 1280])) + lv149 = R.call_tir(cls.NT_matmul, (reshape1545, model_decoder_layers_18_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1406: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv149, model_decoder_layers_18_encoder_attn_out_proj_bias5) + add1407: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1404, add1406) + layer_norm412: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1407, model_decoder_layers_18_final_layer_norm_weight5, model_decoder_layers_18_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv150 = R.call_tir(cls.NT_matmul1, (layer_norm412, model_decoder_layers_18_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + add1408: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv150, model_decoder_layers_18_fc1_bias5) + gelu148: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1408) + lv151 = R.call_tir(cls.NT_matmul2, (gelu148, model_decoder_layers_18_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1409: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv151, model_decoder_layers_18_fc2_bias5) + add1410: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1407, add1409) + layer_norm413: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1410, model_decoder_layers_19_self_attn_layer_norm_weight5, model_decoder_layers_19_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv152 = R.call_tir(cls.NT_matmul, (layer_norm413, model_decoder_layers_19_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1411: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv152, model_decoder_layers_19_self_attn_q_proj_bias5) + reshape1546: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1411, R.shape([1, 1, 20, 64])) + lv153 = R.call_tir(cls.NT_matmul, (layer_norm413, model_decoder_layers_19_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + reshape1547: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv153, R.shape([1, 1, 20, 64])) + lv154 = R.call_tir(cls.NT_matmul, (layer_norm413, model_decoder_layers_19_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1412: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv154, model_decoder_layers_19_self_attn_v_proj_bias5) + reshape1548: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1412, R.shape([1, 1, 20, 64])) + concat115: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1546, reshape1547, reshape1548), axis=2) + reshape1549: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat115, R.shape([1, 60, 64])) + lv303 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), reshape1549), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1550: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv303, R.shape([1, 1, 20, 64])) + reshape1551: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1550, R.shape([1, 1, 1280])) + lv155 = R.call_tir(cls.NT_matmul, (reshape1551, model_decoder_layers_19_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1413: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv155, model_decoder_layers_19_self_attn_out_proj_bias5) + add1414: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1410, add1413) + layer_norm414: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1414, model_decoder_layers_19_encoder_attn_layer_norm_weight5, model_decoder_layers_19_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv156 = R.call_tir(cls.NT_matmul, (layer_norm414, model_decoder_layers_19_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1415: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv156, model_decoder_layers_19_encoder_attn_q_proj_bias5) + reshape1552: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1415, R.shape([1, 1, 20, 64])) + reshape1553: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1552, R.shape([1, 20, 64])) + lv304 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), reshape1553), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1554: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv304, R.shape([1, 1, 20, 64])) + reshape1555: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1554, R.shape([1, 1, 1280])) + lv157 = R.call_tir(cls.NT_matmul, (reshape1555, model_decoder_layers_19_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1416: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv157, model_decoder_layers_19_encoder_attn_out_proj_bias5) + add1417: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1414, add1416) + layer_norm415: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1417, model_decoder_layers_19_final_layer_norm_weight5, model_decoder_layers_19_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv158 = R.call_tir(cls.NT_matmul1, (layer_norm415, model_decoder_layers_19_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + add1418: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv158, model_decoder_layers_19_fc1_bias5) + gelu149: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1418) + lv159 = R.call_tir(cls.NT_matmul2, (gelu149, model_decoder_layers_19_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1419: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv159, model_decoder_layers_19_fc2_bias5) + add1420: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1417, add1419) + layer_norm416: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1420, model_decoder_layers_20_self_attn_layer_norm_weight5, model_decoder_layers_20_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv160 = R.call_tir(cls.NT_matmul, (layer_norm416, model_decoder_layers_20_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1421: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv160, model_decoder_layers_20_self_attn_q_proj_bias5) + reshape1556: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1421, R.shape([1, 1, 20, 64])) + lv161 = R.call_tir(cls.NT_matmul, (layer_norm416, model_decoder_layers_20_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + reshape1557: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv161, R.shape([1, 1, 20, 64])) + lv162 = R.call_tir(cls.NT_matmul, (layer_norm416, model_decoder_layers_20_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1422: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv162, model_decoder_layers_20_self_attn_v_proj_bias5) + reshape1558: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1422, R.shape([1, 1, 20, 64])) + concat116: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1556, reshape1557, reshape1558), axis=2) + reshape1559: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat116, R.shape([1, 60, 64])) + lv305 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), reshape1559), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1560: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv305, R.shape([1, 1, 20, 64])) + reshape1561: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1560, R.shape([1, 1, 1280])) + lv163 = R.call_tir(cls.NT_matmul, (reshape1561, model_decoder_layers_20_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1423: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv163, model_decoder_layers_20_self_attn_out_proj_bias5) + add1424: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1420, add1423) + layer_norm417: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1424, model_decoder_layers_20_encoder_attn_layer_norm_weight5, model_decoder_layers_20_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv164 = R.call_tir(cls.NT_matmul, (layer_norm417, model_decoder_layers_20_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1425: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv164, model_decoder_layers_20_encoder_attn_q_proj_bias5) + reshape1562: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1425, R.shape([1, 1, 20, 64])) + reshape1563: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1562, R.shape([1, 20, 64])) + lv306 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), reshape1563), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1564: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv306, R.shape([1, 1, 20, 64])) + reshape1565: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1564, R.shape([1, 1, 1280])) + lv165 = R.call_tir(cls.NT_matmul, (reshape1565, model_decoder_layers_20_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1426: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv165, model_decoder_layers_20_encoder_attn_out_proj_bias5) + add1427: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1424, add1426) + layer_norm418: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1427, model_decoder_layers_20_final_layer_norm_weight5, model_decoder_layers_20_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv166 = R.call_tir(cls.NT_matmul1, (layer_norm418, model_decoder_layers_20_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + add1428: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv166, model_decoder_layers_20_fc1_bias5) + gelu150: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1428) + lv167 = R.call_tir(cls.NT_matmul2, (gelu150, model_decoder_layers_20_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1429: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv167, model_decoder_layers_20_fc2_bias5) + add1430: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1427, add1429) + layer_norm419: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1430, model_decoder_layers_21_self_attn_layer_norm_weight5, model_decoder_layers_21_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv168 = R.call_tir(cls.NT_matmul, (layer_norm419, model_decoder_layers_21_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1431: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv168, model_decoder_layers_21_self_attn_q_proj_bias5) + reshape1566: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1431, R.shape([1, 1, 20, 64])) + lv169 = R.call_tir(cls.NT_matmul, (layer_norm419, model_decoder_layers_21_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + reshape1567: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv169, R.shape([1, 1, 20, 64])) + lv170 = R.call_tir(cls.NT_matmul, (layer_norm419, model_decoder_layers_21_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1432: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv170, model_decoder_layers_21_self_attn_v_proj_bias5) + reshape1568: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1432, R.shape([1, 1, 20, 64])) + concat117: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1566, reshape1567, reshape1568), axis=2) + reshape1569: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat117, R.shape([1, 60, 64])) + lv307 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), reshape1569), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1570: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv307, R.shape([1, 1, 20, 64])) + reshape1571: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1570, R.shape([1, 1, 1280])) + lv171 = R.call_tir(cls.NT_matmul, (reshape1571, model_decoder_layers_21_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1433: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv171, model_decoder_layers_21_self_attn_out_proj_bias5) + add1434: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1430, add1433) + layer_norm420: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1434, model_decoder_layers_21_encoder_attn_layer_norm_weight5, model_decoder_layers_21_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv172 = R.call_tir(cls.NT_matmul, (layer_norm420, model_decoder_layers_21_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1435: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv172, model_decoder_layers_21_encoder_attn_q_proj_bias5) + reshape1572: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1435, R.shape([1, 1, 20, 64])) + reshape1573: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1572, R.shape([1, 20, 64])) + lv308 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), reshape1573), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1574: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv308, R.shape([1, 1, 20, 64])) + reshape1575: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1574, R.shape([1, 1, 1280])) + lv173 = R.call_tir(cls.NT_matmul, (reshape1575, model_decoder_layers_21_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1436: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv173, model_decoder_layers_21_encoder_attn_out_proj_bias5) + add1437: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1434, add1436) + layer_norm421: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1437, model_decoder_layers_21_final_layer_norm_weight5, model_decoder_layers_21_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv174 = R.call_tir(cls.NT_matmul1, (layer_norm421, model_decoder_layers_21_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + add1438: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv174, model_decoder_layers_21_fc1_bias5) + gelu151: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1438) + lv175 = R.call_tir(cls.NT_matmul2, (gelu151, model_decoder_layers_21_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1439: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv175, model_decoder_layers_21_fc2_bias5) + add1440: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1437, add1439) + layer_norm422: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1440, model_decoder_layers_22_self_attn_layer_norm_weight5, model_decoder_layers_22_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv176 = R.call_tir(cls.NT_matmul, (layer_norm422, model_decoder_layers_22_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1441: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv176, model_decoder_layers_22_self_attn_q_proj_bias5) + reshape1576: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1441, R.shape([1, 1, 20, 64])) + lv177 = R.call_tir(cls.NT_matmul, (layer_norm422, model_decoder_layers_22_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + reshape1577: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv177, R.shape([1, 1, 20, 64])) + lv178 = R.call_tir(cls.NT_matmul, (layer_norm422, model_decoder_layers_22_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1442: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv178, model_decoder_layers_22_self_attn_v_proj_bias5) + reshape1578: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1442, R.shape([1, 1, 20, 64])) + concat118: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1576, reshape1577, reshape1578), axis=2) + reshape1579: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat118, R.shape([1, 60, 64])) + lv309 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), reshape1579), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1580: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv309, R.shape([1, 1, 20, 64])) + reshape1581: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1580, R.shape([1, 1, 1280])) + lv179 = R.call_tir(cls.NT_matmul, (reshape1581, model_decoder_layers_22_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1443: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv179, model_decoder_layers_22_self_attn_out_proj_bias5) + add1444: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1440, add1443) + layer_norm423: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1444, model_decoder_layers_22_encoder_attn_layer_norm_weight5, model_decoder_layers_22_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv180 = R.call_tir(cls.NT_matmul, (layer_norm423, model_decoder_layers_22_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1445: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv180, model_decoder_layers_22_encoder_attn_q_proj_bias5) + reshape1582: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1445, R.shape([1, 1, 20, 64])) + reshape1583: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1582, R.shape([1, 20, 64])) + lv310 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), reshape1583), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1584: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv310, R.shape([1, 1, 20, 64])) + reshape1585: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1584, R.shape([1, 1, 1280])) + lv181 = R.call_tir(cls.NT_matmul, (reshape1585, model_decoder_layers_22_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1446: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv181, model_decoder_layers_22_encoder_attn_out_proj_bias5) + add1447: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1444, add1446) + layer_norm424: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1447, model_decoder_layers_22_final_layer_norm_weight5, model_decoder_layers_22_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv182 = R.call_tir(cls.NT_matmul1, (layer_norm424, model_decoder_layers_22_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + add1448: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv182, model_decoder_layers_22_fc1_bias5) + gelu152: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1448) + lv183 = R.call_tir(cls.NT_matmul2, (gelu152, model_decoder_layers_22_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1449: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv183, model_decoder_layers_22_fc2_bias5) + add1450: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1447, add1449) + layer_norm425: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1450, model_decoder_layers_23_self_attn_layer_norm_weight5, model_decoder_layers_23_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv184 = R.call_tir(cls.NT_matmul, (layer_norm425, model_decoder_layers_23_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1451: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv184, model_decoder_layers_23_self_attn_q_proj_bias5) + reshape1586: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1451, R.shape([1, 1, 20, 64])) + lv185 = R.call_tir(cls.NT_matmul, (layer_norm425, model_decoder_layers_23_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + reshape1587: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv185, R.shape([1, 1, 20, 64])) + lv186 = R.call_tir(cls.NT_matmul, (layer_norm425, model_decoder_layers_23_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1452: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv186, model_decoder_layers_23_self_attn_v_proj_bias5) + reshape1588: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1452, R.shape([1, 1, 20, 64])) + concat119: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1586, reshape1587, reshape1588), axis=2) + reshape1589: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat119, R.shape([1, 60, 64])) + lv311 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), reshape1589), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1590: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv311, R.shape([1, 1, 20, 64])) + reshape1591: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1590, R.shape([1, 1, 1280])) + lv187 = R.call_tir(cls.NT_matmul, (reshape1591, model_decoder_layers_23_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1453: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv187, model_decoder_layers_23_self_attn_out_proj_bias5) + add1454: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1450, add1453) + layer_norm426: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1454, model_decoder_layers_23_encoder_attn_layer_norm_weight5, model_decoder_layers_23_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv188 = R.call_tir(cls.NT_matmul, (layer_norm426, model_decoder_layers_23_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1455: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv188, model_decoder_layers_23_encoder_attn_q_proj_bias5) + reshape1592: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1455, R.shape([1, 1, 20, 64])) + reshape1593: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1592, R.shape([1, 20, 64])) + lv312 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), reshape1593), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1594: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv312, R.shape([1, 1, 20, 64])) + reshape1595: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1594, R.shape([1, 1, 1280])) + lv189 = R.call_tir(cls.NT_matmul, (reshape1595, model_decoder_layers_23_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1456: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv189, model_decoder_layers_23_encoder_attn_out_proj_bias5) + add1457: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1454, add1456) + layer_norm427: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1457, model_decoder_layers_23_final_layer_norm_weight5, model_decoder_layers_23_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv190 = R.call_tir(cls.NT_matmul1, (layer_norm427, model_decoder_layers_23_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + add1458: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv190, model_decoder_layers_23_fc1_bias5) + gelu153: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1458) + lv191 = R.call_tir(cls.NT_matmul2, (gelu153, model_decoder_layers_23_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1459: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv191, model_decoder_layers_23_fc2_bias5) + add1460: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1457, add1459) + layer_norm428: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1460, model_decoder_layers_24_self_attn_layer_norm_weight5, model_decoder_layers_24_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv192 = R.call_tir(cls.NT_matmul, (layer_norm428, model_decoder_layers_24_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1461: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv192, model_decoder_layers_24_self_attn_q_proj_bias5) + reshape1596: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1461, R.shape([1, 1, 20, 64])) + lv193 = R.call_tir(cls.NT_matmul, (layer_norm428, model_decoder_layers_24_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + reshape1597: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv193, R.shape([1, 1, 20, 64])) + lv194 = R.call_tir(cls.NT_matmul, (layer_norm428, model_decoder_layers_24_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1462: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv194, model_decoder_layers_24_self_attn_v_proj_bias5) + reshape1598: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1462, R.shape([1, 1, 20, 64])) + concat120: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1596, reshape1597, reshape1598), axis=2) + reshape1599: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat120, R.shape([1, 60, 64])) + lv313 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), reshape1599), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1600: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv313, R.shape([1, 1, 20, 64])) + reshape1601: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1600, R.shape([1, 1, 1280])) + lv195 = R.call_tir(cls.NT_matmul, (reshape1601, model_decoder_layers_24_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1463: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv195, model_decoder_layers_24_self_attn_out_proj_bias5) + add1464: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1460, add1463) + layer_norm429: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1464, model_decoder_layers_24_encoder_attn_layer_norm_weight5, model_decoder_layers_24_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv196 = R.call_tir(cls.NT_matmul, (layer_norm429, model_decoder_layers_24_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1465: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv196, model_decoder_layers_24_encoder_attn_q_proj_bias5) + reshape1602: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1465, R.shape([1, 1, 20, 64])) + reshape1603: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1602, R.shape([1, 20, 64])) + lv314 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), reshape1603), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1604: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv314, R.shape([1, 1, 20, 64])) + reshape1605: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1604, R.shape([1, 1, 1280])) + lv197 = R.call_tir(cls.NT_matmul, (reshape1605, model_decoder_layers_24_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1466: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv197, model_decoder_layers_24_encoder_attn_out_proj_bias5) + add1467: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1464, add1466) + layer_norm430: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1467, model_decoder_layers_24_final_layer_norm_weight5, model_decoder_layers_24_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv198 = R.call_tir(cls.NT_matmul1, (layer_norm430, model_decoder_layers_24_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + add1468: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv198, model_decoder_layers_24_fc1_bias5) + gelu154: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1468) + lv199 = R.call_tir(cls.NT_matmul2, (gelu154, model_decoder_layers_24_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1469: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv199, model_decoder_layers_24_fc2_bias5) + add1470: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1467, add1469) + layer_norm431: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1470, model_decoder_layers_25_self_attn_layer_norm_weight5, model_decoder_layers_25_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv200 = R.call_tir(cls.NT_matmul, (layer_norm431, model_decoder_layers_25_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1471: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv200, model_decoder_layers_25_self_attn_q_proj_bias5) + reshape1606: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1471, R.shape([1, 1, 20, 64])) + lv201 = R.call_tir(cls.NT_matmul, (layer_norm431, model_decoder_layers_25_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + reshape1607: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv201, R.shape([1, 1, 20, 64])) + lv202 = R.call_tir(cls.NT_matmul, (layer_norm431, model_decoder_layers_25_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1472: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv202, model_decoder_layers_25_self_attn_v_proj_bias5) + reshape1608: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1472, R.shape([1, 1, 20, 64])) + concat121: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1606, reshape1607, reshape1608), axis=2) + reshape1609: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat121, R.shape([1, 60, 64])) + lv315 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), reshape1609), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1610: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv315, R.shape([1, 1, 20, 64])) + reshape1611: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1610, R.shape([1, 1, 1280])) + lv203 = R.call_tir(cls.NT_matmul, (reshape1611, model_decoder_layers_25_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1473: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv203, model_decoder_layers_25_self_attn_out_proj_bias5) + add1474: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1470, add1473) + layer_norm432: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1474, model_decoder_layers_25_encoder_attn_layer_norm_weight5, model_decoder_layers_25_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv204 = R.call_tir(cls.NT_matmul, (layer_norm432, model_decoder_layers_25_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1475: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv204, model_decoder_layers_25_encoder_attn_q_proj_bias5) + reshape1612: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1475, R.shape([1, 1, 20, 64])) + reshape1613: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1612, R.shape([1, 20, 64])) + lv316 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), reshape1613), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1614: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv316, R.shape([1, 1, 20, 64])) + reshape1615: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1614, R.shape([1, 1, 1280])) + lv205 = R.call_tir(cls.NT_matmul, (reshape1615, model_decoder_layers_25_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1476: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv205, model_decoder_layers_25_encoder_attn_out_proj_bias5) + add1477: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1474, add1476) + layer_norm433: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1477, model_decoder_layers_25_final_layer_norm_weight5, model_decoder_layers_25_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv206 = R.call_tir(cls.NT_matmul1, (layer_norm433, model_decoder_layers_25_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + add1478: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv206, model_decoder_layers_25_fc1_bias5) + gelu155: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1478) + lv207 = R.call_tir(cls.NT_matmul2, (gelu155, model_decoder_layers_25_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1479: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv207, model_decoder_layers_25_fc2_bias5) + add1480: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1477, add1479) + layer_norm434: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1480, model_decoder_layers_26_self_attn_layer_norm_weight5, model_decoder_layers_26_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv208 = R.call_tir(cls.NT_matmul, (layer_norm434, model_decoder_layers_26_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1481: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv208, model_decoder_layers_26_self_attn_q_proj_bias5) + reshape1616: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1481, R.shape([1, 1, 20, 64])) + lv209 = R.call_tir(cls.NT_matmul, (layer_norm434, model_decoder_layers_26_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + reshape1617: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv209, R.shape([1, 1, 20, 64])) + lv210 = R.call_tir(cls.NT_matmul, (layer_norm434, model_decoder_layers_26_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1482: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv210, model_decoder_layers_26_self_attn_v_proj_bias5) + reshape1618: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1482, R.shape([1, 1, 20, 64])) + concat122: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1616, reshape1617, reshape1618), axis=2) + reshape1619: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat122, R.shape([1, 60, 64])) + lv317 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), reshape1619), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1620: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv317, R.shape([1, 1, 20, 64])) + reshape1621: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1620, R.shape([1, 1, 1280])) + lv211 = R.call_tir(cls.NT_matmul, (reshape1621, model_decoder_layers_26_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1483: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv211, model_decoder_layers_26_self_attn_out_proj_bias5) + add1484: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1480, add1483) + layer_norm435: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1484, model_decoder_layers_26_encoder_attn_layer_norm_weight5, model_decoder_layers_26_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv212 = R.call_tir(cls.NT_matmul, (layer_norm435, model_decoder_layers_26_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1485: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv212, model_decoder_layers_26_encoder_attn_q_proj_bias5) + reshape1622: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1485, R.shape([1, 1, 20, 64])) + reshape1623: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1622, R.shape([1, 20, 64])) + lv318 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), reshape1623), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1624: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv318, R.shape([1, 1, 20, 64])) + reshape1625: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1624, R.shape([1, 1, 1280])) + lv213 = R.call_tir(cls.NT_matmul, (reshape1625, model_decoder_layers_26_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1486: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv213, model_decoder_layers_26_encoder_attn_out_proj_bias5) + add1487: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1484, add1486) + layer_norm436: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1487, model_decoder_layers_26_final_layer_norm_weight5, model_decoder_layers_26_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv214 = R.call_tir(cls.NT_matmul1, (layer_norm436, model_decoder_layers_26_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + add1488: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv214, model_decoder_layers_26_fc1_bias5) + gelu156: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1488) + lv215 = R.call_tir(cls.NT_matmul2, (gelu156, model_decoder_layers_26_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1489: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv215, model_decoder_layers_26_fc2_bias5) + add1490: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1487, add1489) + layer_norm437: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1490, model_decoder_layers_27_self_attn_layer_norm_weight5, model_decoder_layers_27_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv216 = R.call_tir(cls.NT_matmul, (layer_norm437, model_decoder_layers_27_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1491: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv216, model_decoder_layers_27_self_attn_q_proj_bias5) + reshape1626: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1491, R.shape([1, 1, 20, 64])) + lv217 = R.call_tir(cls.NT_matmul, (layer_norm437, model_decoder_layers_27_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + reshape1627: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv217, R.shape([1, 1, 20, 64])) + lv218 = R.call_tir(cls.NT_matmul, (layer_norm437, model_decoder_layers_27_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1492: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv218, model_decoder_layers_27_self_attn_v_proj_bias5) + reshape1628: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1492, R.shape([1, 1, 20, 64])) + concat123: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1626, reshape1627, reshape1628), axis=2) + reshape1629: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat123, R.shape([1, 60, 64])) + lv319 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), reshape1629), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1630: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv319, R.shape([1, 1, 20, 64])) + reshape1631: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1630, R.shape([1, 1, 1280])) + lv219 = R.call_tir(cls.NT_matmul, (reshape1631, model_decoder_layers_27_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1493: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv219, model_decoder_layers_27_self_attn_out_proj_bias5) + add1494: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1490, add1493) + layer_norm438: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1494, model_decoder_layers_27_encoder_attn_layer_norm_weight5, model_decoder_layers_27_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv220 = R.call_tir(cls.NT_matmul, (layer_norm438, model_decoder_layers_27_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1495: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv220, model_decoder_layers_27_encoder_attn_q_proj_bias5) + reshape1632: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1495, R.shape([1, 1, 20, 64])) + reshape1633: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1632, R.shape([1, 20, 64])) + lv320 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), reshape1633), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1634: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv320, R.shape([1, 1, 20, 64])) + reshape1635: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1634, R.shape([1, 1, 1280])) + lv221 = R.call_tir(cls.NT_matmul, (reshape1635, model_decoder_layers_27_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1496: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv221, model_decoder_layers_27_encoder_attn_out_proj_bias5) + add1497: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1494, add1496) + layer_norm439: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1497, model_decoder_layers_27_final_layer_norm_weight5, model_decoder_layers_27_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv222 = R.call_tir(cls.NT_matmul1, (layer_norm439, model_decoder_layers_27_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + add1498: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv222, model_decoder_layers_27_fc1_bias5) + gelu157: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1498) + lv223 = R.call_tir(cls.NT_matmul2, (gelu157, model_decoder_layers_27_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1499: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv223, model_decoder_layers_27_fc2_bias5) + add1500: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1497, add1499) + layer_norm440: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1500, model_decoder_layers_28_self_attn_layer_norm_weight5, model_decoder_layers_28_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv224 = R.call_tir(cls.NT_matmul, (layer_norm440, model_decoder_layers_28_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1501: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv224, model_decoder_layers_28_self_attn_q_proj_bias5) + reshape1636: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1501, R.shape([1, 1, 20, 64])) + lv225 = R.call_tir(cls.NT_matmul, (layer_norm440, model_decoder_layers_28_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + reshape1637: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv225, R.shape([1, 1, 20, 64])) + lv226 = R.call_tir(cls.NT_matmul, (layer_norm440, model_decoder_layers_28_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1502: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv226, model_decoder_layers_28_self_attn_v_proj_bias5) + reshape1638: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1502, R.shape([1, 1, 20, 64])) + concat124: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1636, reshape1637, reshape1638), axis=2) + reshape1639: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat124, R.shape([1, 60, 64])) + lv321 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), reshape1639), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1640: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv321, R.shape([1, 1, 20, 64])) + reshape1641: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1640, R.shape([1, 1, 1280])) + lv227 = R.call_tir(cls.NT_matmul, (reshape1641, model_decoder_layers_28_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1503: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv227, model_decoder_layers_28_self_attn_out_proj_bias5) + add1504: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1500, add1503) + layer_norm441: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1504, model_decoder_layers_28_encoder_attn_layer_norm_weight5, model_decoder_layers_28_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv228 = R.call_tir(cls.NT_matmul, (layer_norm441, model_decoder_layers_28_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1505: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv228, model_decoder_layers_28_encoder_attn_q_proj_bias5) + reshape1642: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1505, R.shape([1, 1, 20, 64])) + reshape1643: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1642, R.shape([1, 20, 64])) + lv322 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), reshape1643), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1644: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv322, R.shape([1, 1, 20, 64])) + reshape1645: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1644, R.shape([1, 1, 1280])) + lv229 = R.call_tir(cls.NT_matmul, (reshape1645, model_decoder_layers_28_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1506: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv229, model_decoder_layers_28_encoder_attn_out_proj_bias5) + add1507: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1504, add1506) + layer_norm442: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1507, model_decoder_layers_28_final_layer_norm_weight5, model_decoder_layers_28_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv230 = R.call_tir(cls.NT_matmul1, (layer_norm442, model_decoder_layers_28_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + add1508: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv230, model_decoder_layers_28_fc1_bias5) + gelu158: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1508) + lv231 = R.call_tir(cls.NT_matmul2, (gelu158, model_decoder_layers_28_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1509: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv231, model_decoder_layers_28_fc2_bias5) + add1510: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1507, add1509) + layer_norm443: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1510, model_decoder_layers_29_self_attn_layer_norm_weight5, model_decoder_layers_29_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv232 = R.call_tir(cls.NT_matmul, (layer_norm443, model_decoder_layers_29_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1511: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv232, model_decoder_layers_29_self_attn_q_proj_bias5) + reshape1646: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1511, R.shape([1, 1, 20, 64])) + lv233 = R.call_tir(cls.NT_matmul, (layer_norm443, model_decoder_layers_29_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + reshape1647: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv233, R.shape([1, 1, 20, 64])) + lv234 = R.call_tir(cls.NT_matmul, (layer_norm443, model_decoder_layers_29_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1512: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv234, model_decoder_layers_29_self_attn_v_proj_bias5) + reshape1648: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1512, R.shape([1, 1, 20, 64])) + concat125: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1646, reshape1647, reshape1648), axis=2) + reshape1649: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat125, R.shape([1, 60, 64])) + lv323 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), reshape1649), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1650: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv323, R.shape([1, 1, 20, 64])) + reshape1651: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1650, R.shape([1, 1, 1280])) + lv235 = R.call_tir(cls.NT_matmul, (reshape1651, model_decoder_layers_29_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1513: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv235, model_decoder_layers_29_self_attn_out_proj_bias5) + add1514: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1510, add1513) + layer_norm444: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1514, model_decoder_layers_29_encoder_attn_layer_norm_weight5, model_decoder_layers_29_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv236 = R.call_tir(cls.NT_matmul, (layer_norm444, model_decoder_layers_29_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1515: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv236, model_decoder_layers_29_encoder_attn_q_proj_bias5) + reshape1652: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1515, R.shape([1, 1, 20, 64])) + reshape1653: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1652, R.shape([1, 20, 64])) + lv324 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), reshape1653), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1654: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv324, R.shape([1, 1, 20, 64])) + reshape1655: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1654, R.shape([1, 1, 1280])) + lv237 = R.call_tir(cls.NT_matmul, (reshape1655, model_decoder_layers_29_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1516: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv237, model_decoder_layers_29_encoder_attn_out_proj_bias5) + add1517: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1514, add1516) + layer_norm445: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1517, model_decoder_layers_29_final_layer_norm_weight5, model_decoder_layers_29_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv238 = R.call_tir(cls.NT_matmul1, (layer_norm445, model_decoder_layers_29_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + add1518: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv238, model_decoder_layers_29_fc1_bias5) + gelu159: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1518) + lv239 = R.call_tir(cls.NT_matmul2, (gelu159, model_decoder_layers_29_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1519: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv239, model_decoder_layers_29_fc2_bias5) + add1520: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1517, add1519) + layer_norm446: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1520, model_decoder_layers_30_self_attn_layer_norm_weight5, model_decoder_layers_30_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv240 = R.call_tir(cls.NT_matmul, (layer_norm446, model_decoder_layers_30_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1521: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv240, model_decoder_layers_30_self_attn_q_proj_bias5) + reshape1656: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1521, R.shape([1, 1, 20, 64])) + lv241 = R.call_tir(cls.NT_matmul, (layer_norm446, model_decoder_layers_30_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + reshape1657: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv241, R.shape([1, 1, 20, 64])) + lv242 = R.call_tir(cls.NT_matmul, (layer_norm446, model_decoder_layers_30_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1522: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv242, model_decoder_layers_30_self_attn_v_proj_bias5) + reshape1658: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1522, R.shape([1, 1, 20, 64])) + concat126: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1656, reshape1657, reshape1658), axis=2) + reshape1659: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat126, R.shape([1, 60, 64])) + lv325 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), reshape1659), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1660: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv325, R.shape([1, 1, 20, 64])) + reshape1661: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1660, R.shape([1, 1, 1280])) + lv243 = R.call_tir(cls.NT_matmul, (reshape1661, model_decoder_layers_30_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1523: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv243, model_decoder_layers_30_self_attn_out_proj_bias5) + add1524: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1520, add1523) + layer_norm447: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1524, model_decoder_layers_30_encoder_attn_layer_norm_weight5, model_decoder_layers_30_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv244 = R.call_tir(cls.NT_matmul, (layer_norm447, model_decoder_layers_30_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1525: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv244, model_decoder_layers_30_encoder_attn_q_proj_bias5) + reshape1662: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1525, R.shape([1, 1, 20, 64])) + reshape1663: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1662, R.shape([1, 20, 64])) + lv326 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), reshape1663), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1664: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv326, R.shape([1, 1, 20, 64])) + reshape1665: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1664, R.shape([1, 1, 1280])) + lv245 = R.call_tir(cls.NT_matmul, (reshape1665, model_decoder_layers_30_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1526: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv245, model_decoder_layers_30_encoder_attn_out_proj_bias5) + add1527: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1524, add1526) + layer_norm448: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1527, model_decoder_layers_30_final_layer_norm_weight5, model_decoder_layers_30_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv246 = R.call_tir(cls.NT_matmul1, (layer_norm448, model_decoder_layers_30_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + add1528: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv246, model_decoder_layers_30_fc1_bias5) + gelu160: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1528) + lv247 = R.call_tir(cls.NT_matmul2, (gelu160, model_decoder_layers_30_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1529: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv247, model_decoder_layers_30_fc2_bias5) + add1530: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1527, add1529) + layer_norm449: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1530, model_decoder_layers_31_self_attn_layer_norm_weight5, model_decoder_layers_31_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv248 = R.call_tir(cls.NT_matmul, (layer_norm449, model_decoder_layers_31_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1531: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv248, model_decoder_layers_31_self_attn_q_proj_bias5) + reshape1666: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1531, R.shape([1, 1, 20, 64])) + lv249 = R.call_tir(cls.NT_matmul, (layer_norm449, model_decoder_layers_31_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + reshape1667: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv249, R.shape([1, 1, 20, 64])) + lv250 = R.call_tir(cls.NT_matmul, (layer_norm449, model_decoder_layers_31_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1532: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv250, model_decoder_layers_31_self_attn_v_proj_bias5) + reshape1668: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1532, R.shape([1, 1, 20, 64])) + concat127: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1666, reshape1667, reshape1668), axis=2) + reshape1669: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat127, R.shape([1, 60, 64])) + lv327 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), reshape1669), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1670: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv327, R.shape([1, 1, 20, 64])) + reshape1671: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1670, R.shape([1, 1, 1280])) + lv251 = R.call_tir(cls.NT_matmul, (reshape1671, model_decoder_layers_31_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1533: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv251, model_decoder_layers_31_self_attn_out_proj_bias5) + add1534: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1530, add1533) + layer_norm450: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1534, model_decoder_layers_31_encoder_attn_layer_norm_weight5, model_decoder_layers_31_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv252 = R.call_tir(cls.NT_matmul, (layer_norm450, model_decoder_layers_31_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1535: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv252, model_decoder_layers_31_encoder_attn_q_proj_bias5) + reshape1672: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1535, R.shape([1, 1, 20, 64])) + reshape1673: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1672, R.shape([1, 20, 64])) + lv328 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), reshape1673), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + reshape1674: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv328, R.shape([1, 1, 20, 64])) + reshape1675: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1674, R.shape([1, 1, 1280])) + lv253 = R.call_tir(cls.NT_matmul, (reshape1675, model_decoder_layers_31_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1536: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv253, model_decoder_layers_31_encoder_attn_out_proj_bias5) + add1537: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1534, add1536) + layer_norm451: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1537, model_decoder_layers_31_final_layer_norm_weight5, model_decoder_layers_31_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv254 = R.call_tir(cls.NT_matmul1, (layer_norm451, model_decoder_layers_31_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + add1538: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv254, model_decoder_layers_31_fc1_bias5) + gelu161: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1538) + lv255 = R.call_tir(cls.NT_matmul2, (gelu161, model_decoder_layers_31_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + add1539: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv255, model_decoder_layers_31_fc2_bias5) + add1540: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1537, add1539) + layer_norm452: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1540, model_decoder_layer_norm_weight5, model_decoder_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv256 = R.call_tir(cls.NT_matmul3, (layer_norm452, model_decoder_embed_tokens_weight5), out_sinfo=R.Tensor((1, 1, 51866), dtype="float32")) + gv5: R.Tensor((1, 1, 51866), dtype="float32") = lv256 + R.output(gv5) + return gv5 + + @R.function(private=True) + def fused_relax_permute_dims_relax_matmul(model_decoder_layers_0_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16"), layer_norm356: R.Tensor((1, 1, 1280), dtype="float16")) -> R.Tensor((1, 1, 1280), dtype="float16"): + R.func_attr({"Composite": "transpose_matmul_fuse", "Primitive": 1}) + with R.dataflow(): + permute_dims1028: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_self_attn_q_proj_weight5, axes=None) + gv: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm356, permute_dims1028, out_dtype="void") + R.output(gv) + return gv + + @R.function(private=True) + def fused_relax_permute_dims_relax_matmul1(model_decoder_layers_0_fc1_weight5: R.Tensor((5120, 1280), dtype="float16"), layer_norm358: R.Tensor((1, 1, 1280), dtype="float16")) -> R.Tensor((1, 1, 5120), dtype="float16"): + R.func_attr({"Composite": "transpose_matmul_fuse", "Primitive": 1}) + with R.dataflow(): + permute_dims1034: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_0_fc1_weight5, axes=None) + gv: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm358, permute_dims1034, out_dtype="void") + R.output(gv) + return gv + + @R.function(private=True) + def fused_relax_permute_dims_relax_matmul2(model_decoder_layers_0_fc2_weight5: R.Tensor((1280, 5120), dtype="float16"), gelu130: R.Tensor((1, 1, 5120), dtype="float16")) -> R.Tensor((1, 1, 1280), dtype="float16"): + R.func_attr({"Composite": "transpose_matmul_fuse", "Primitive": 1}) + with R.dataflow(): + permute_dims1035: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_fc2_weight5, axes=None) + gv: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu130, permute_dims1035, out_dtype="void") + R.output(gv) + return gv + + @R.function(private=True) + def fused_relax_permute_dims_relax_matmul3(model_decoder_embed_tokens_weight5: R.Tensor((51866, 1280), dtype="float16"), layer_norm452: R.Tensor((1, 1, 1280), dtype="float16")) -> R.Tensor((1, 1, 51866), dtype="float32"): + R.func_attr({"Composite": "transpose_matmul_fuse", "Primitive": 1}) + with R.dataflow(): + permute_dims1284: R.Tensor((1280, 51866), dtype="float16") = R.permute_dims(model_decoder_embed_tokens_weight5, axes=None) + gv: R.Tensor((1, 1, 51866), dtype="float32") = R.matmul(layer_norm452, permute_dims1284, out_dtype="float32") + R.output(gv) + return gv + + @R.function + def multinomial_from_uniform(probs: R.Tensor(("batch_size", "vocab_size"), dtype="float32"), uniform_samples: R.Tensor(("num_samples",), dtype="float32"), sample_indices: R.Tensor(("num_samples",), dtype="int32")) -> R.Tensor(("num_samples",), dtype="int32"): + num_samples = T.int64() + batch_size = T.int64() + vocab_size = T.int64() + R.func_attr({"relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "num_positions": 48, "num_samples": 8}}) + with R.dataflow(): + probs_1: R.Tensor((batch_size, vocab_size), dtype="float32") = probs + uniform_samples_1: R.Tensor((num_samples, 1), dtype="float32") = R.call_pure_packed("vm.builtin.reshape", uniform_samples, R.shape([num_samples, 1]), sinfo_args=(R.Tensor((num_samples, 1), dtype="float32"),)) + sample_indices_1: R.Tensor((num_samples, 1), dtype="int32") = R.call_pure_packed("vm.builtin.reshape", sample_indices, R.shape([num_samples, 1]), sinfo_args=(R.Tensor((num_samples, 1), dtype="int32"),)) + nn_multinomial_from_uniform: R.Tensor((num_samples, 1), dtype="int32") = R.multinomial_from_uniform(probs_1, uniform_samples_1, sample_indices_1, dtype="int32") + lv: R.Tensor((num_samples,), dtype="int32") = R.call_pure_packed("vm.builtin.reshape", nn_multinomial_from_uniform, R.shape([num_samples]), sinfo_args=(R.Tensor((num_samples,), dtype="int32"),)) + gv: R.Tensor((num_samples,), dtype="int32") = lv + R.output(gv) + return gv + + @R.function + def prefill(input_ids: R.Tensor((1, "seq_len"), dtype="int32"), paged_kv_cache: R.Object, packed_params: R.Tuple(R.Tensor((1280, 128, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1500, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), 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R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"))) -> R.Tensor((1, 1, 51866), dtype="float32"): + seq_len = T.int64() + R.func_attr({"num_input": 2, "relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "seq_len": 15000, "total_seq_len": 1500}}) + cls = Module + with R.dataflow(): + model_decoder_embed_tokens_weight4: R.Tensor((51866, 1280), dtype="float16") = packed_params[487] + model_decoder_embed_positions_weight4: R.Tensor((448, 1280), dtype="float16") = packed_params[488] + model_decoder_layers_0_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[489] + model_decoder_layers_0_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[490] + model_decoder_layers_0_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[491] + model_decoder_layers_0_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[492] + model_decoder_layers_0_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[493] + model_decoder_layers_0_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[494] + model_decoder_layers_0_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[495] + model_decoder_layers_0_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[496] + model_decoder_layers_0_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[497] + model_decoder_layers_0_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[501] + model_decoder_layers_0_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[502] + model_decoder_layers_0_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[503] + model_decoder_layers_0_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[504] + model_decoder_layers_0_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[505] + model_decoder_layers_0_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[506] + model_decoder_layers_0_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[507] + model_decoder_layers_0_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[508] + model_decoder_layers_0_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[509] + model_decoder_layers_0_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[510] + model_decoder_layers_0_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[511] + model_decoder_layers_0_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[512] + model_decoder_layers_1_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[513] + model_decoder_layers_1_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[514] + model_decoder_layers_1_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[515] + model_decoder_layers_1_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[516] + model_decoder_layers_1_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[517] + model_decoder_layers_1_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[518] + model_decoder_layers_1_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[519] + model_decoder_layers_1_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[520] + model_decoder_layers_1_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[521] + model_decoder_layers_1_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[525] + model_decoder_layers_1_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[526] + model_decoder_layers_1_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[527] + model_decoder_layers_1_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[528] + model_decoder_layers_1_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[529] + model_decoder_layers_1_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[530] + model_decoder_layers_1_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[531] + model_decoder_layers_1_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[532] + model_decoder_layers_1_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[533] + model_decoder_layers_1_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[534] + model_decoder_layers_1_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[535] + model_decoder_layers_1_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[536] + model_decoder_layers_2_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[537] + model_decoder_layers_2_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[538] + model_decoder_layers_2_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[539] + model_decoder_layers_2_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[540] + model_decoder_layers_2_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[541] + model_decoder_layers_2_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[542] + model_decoder_layers_2_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[543] + model_decoder_layers_2_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[544] + model_decoder_layers_2_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[545] + model_decoder_layers_2_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[549] + model_decoder_layers_2_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[550] + model_decoder_layers_2_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[551] + model_decoder_layers_2_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[552] + model_decoder_layers_2_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[553] + model_decoder_layers_2_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[554] + model_decoder_layers_2_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[555] + model_decoder_layers_2_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[556] + model_decoder_layers_2_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[557] + model_decoder_layers_2_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[558] + model_decoder_layers_2_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[559] + model_decoder_layers_2_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[560] + model_decoder_layers_3_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[561] + model_decoder_layers_3_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[562] + model_decoder_layers_3_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[563] + model_decoder_layers_3_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[564] + model_decoder_layers_3_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[565] + model_decoder_layers_3_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[566] + model_decoder_layers_3_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[567] + model_decoder_layers_3_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[568] + model_decoder_layers_3_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[569] + model_decoder_layers_3_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[573] + model_decoder_layers_3_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[574] + model_decoder_layers_3_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[575] + model_decoder_layers_3_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[576] + model_decoder_layers_3_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[577] + model_decoder_layers_3_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[578] + model_decoder_layers_3_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[579] + model_decoder_layers_3_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[580] + model_decoder_layers_3_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[581] + model_decoder_layers_3_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[582] + model_decoder_layers_3_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[583] + model_decoder_layers_3_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[584] + model_decoder_layers_4_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[585] + model_decoder_layers_4_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[586] + model_decoder_layers_4_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[587] + model_decoder_layers_4_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[588] + model_decoder_layers_4_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[589] + model_decoder_layers_4_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[590] + model_decoder_layers_4_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[591] + model_decoder_layers_4_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[592] + model_decoder_layers_4_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[593] + model_decoder_layers_4_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[597] + model_decoder_layers_4_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[598] + model_decoder_layers_4_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[599] + model_decoder_layers_4_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[600] + model_decoder_layers_4_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[601] + model_decoder_layers_4_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[602] + model_decoder_layers_4_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[603] + model_decoder_layers_4_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[604] + model_decoder_layers_4_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[605] + model_decoder_layers_4_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[606] + model_decoder_layers_4_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[607] + model_decoder_layers_4_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[608] + model_decoder_layers_5_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[609] + model_decoder_layers_5_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[610] + model_decoder_layers_5_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[611] + model_decoder_layers_5_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[612] + model_decoder_layers_5_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[613] + model_decoder_layers_5_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[614] + model_decoder_layers_5_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[615] + model_decoder_layers_5_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[616] + model_decoder_layers_5_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[617] + model_decoder_layers_5_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[621] + model_decoder_layers_5_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[622] + model_decoder_layers_5_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[623] + model_decoder_layers_5_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[624] + model_decoder_layers_5_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[625] + model_decoder_layers_5_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[626] + model_decoder_layers_5_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[627] + model_decoder_layers_5_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[628] + model_decoder_layers_5_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[629] + model_decoder_layers_5_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[630] + model_decoder_layers_5_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[631] + model_decoder_layers_5_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[632] + model_decoder_layers_6_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[633] + model_decoder_layers_6_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[634] + model_decoder_layers_6_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[635] + model_decoder_layers_6_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[636] + model_decoder_layers_6_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[637] + model_decoder_layers_6_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[638] + model_decoder_layers_6_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[639] + model_decoder_layers_6_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[640] + model_decoder_layers_6_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[641] + model_decoder_layers_6_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[645] + model_decoder_layers_6_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[646] + model_decoder_layers_6_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[647] + model_decoder_layers_6_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[648] + model_decoder_layers_6_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[649] + model_decoder_layers_6_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[650] + model_decoder_layers_6_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[651] + model_decoder_layers_6_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[652] + model_decoder_layers_6_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[653] + model_decoder_layers_6_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[654] + model_decoder_layers_6_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[655] + model_decoder_layers_6_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[656] + model_decoder_layers_7_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[657] + model_decoder_layers_7_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[658] + model_decoder_layers_7_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[659] + model_decoder_layers_7_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[660] + model_decoder_layers_7_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[661] + model_decoder_layers_7_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[662] + model_decoder_layers_7_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[663] + model_decoder_layers_7_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[664] + model_decoder_layers_7_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[665] + model_decoder_layers_7_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[669] + model_decoder_layers_7_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[670] + model_decoder_layers_7_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[671] + model_decoder_layers_7_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[672] + model_decoder_layers_7_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[673] + model_decoder_layers_7_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[674] + model_decoder_layers_7_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[675] + model_decoder_layers_7_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[676] + model_decoder_layers_7_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[677] + model_decoder_layers_7_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[678] + model_decoder_layers_7_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[679] + model_decoder_layers_7_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[680] + model_decoder_layers_8_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[681] + model_decoder_layers_8_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[682] + model_decoder_layers_8_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[683] + model_decoder_layers_8_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[684] + model_decoder_layers_8_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[685] + model_decoder_layers_8_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[686] + model_decoder_layers_8_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[687] + model_decoder_layers_8_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[688] + model_decoder_layers_8_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[689] + model_decoder_layers_8_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[693] + model_decoder_layers_8_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[694] + model_decoder_layers_8_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[695] + model_decoder_layers_8_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[696] + model_decoder_layers_8_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[697] + model_decoder_layers_8_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[698] + model_decoder_layers_8_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[699] + model_decoder_layers_8_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[700] + model_decoder_layers_8_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[701] + model_decoder_layers_8_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[702] + model_decoder_layers_8_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[703] + model_decoder_layers_8_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[704] + model_decoder_layers_9_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[705] + model_decoder_layers_9_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[706] + model_decoder_layers_9_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[707] + model_decoder_layers_9_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[708] + model_decoder_layers_9_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[709] + model_decoder_layers_9_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[710] + model_decoder_layers_9_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[711] + model_decoder_layers_9_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[712] + model_decoder_layers_9_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[713] + model_decoder_layers_9_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[717] + model_decoder_layers_9_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[718] + model_decoder_layers_9_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[719] + model_decoder_layers_9_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[720] + model_decoder_layers_9_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[721] + model_decoder_layers_9_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[722] + model_decoder_layers_9_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[723] + model_decoder_layers_9_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[724] + model_decoder_layers_9_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[725] + model_decoder_layers_9_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[726] + model_decoder_layers_9_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[727] + model_decoder_layers_9_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[728] + model_decoder_layers_10_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[729] + model_decoder_layers_10_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[730] + model_decoder_layers_10_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[731] + model_decoder_layers_10_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[732] + model_decoder_layers_10_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[733] + model_decoder_layers_10_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[734] + model_decoder_layers_10_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[735] + model_decoder_layers_10_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[736] + model_decoder_layers_10_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[737] + model_decoder_layers_10_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[741] + model_decoder_layers_10_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[742] + model_decoder_layers_10_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[743] + model_decoder_layers_10_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[744] + model_decoder_layers_10_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[745] + model_decoder_layers_10_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[746] + model_decoder_layers_10_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[747] + model_decoder_layers_10_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[748] + model_decoder_layers_10_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[749] + model_decoder_layers_10_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[750] + model_decoder_layers_10_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[751] + model_decoder_layers_10_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[752] + model_decoder_layers_11_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[753] + model_decoder_layers_11_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[754] + model_decoder_layers_11_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[755] + model_decoder_layers_11_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[756] + model_decoder_layers_11_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[757] + model_decoder_layers_11_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[758] + model_decoder_layers_11_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[759] + model_decoder_layers_11_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[760] + model_decoder_layers_11_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[761] + model_decoder_layers_11_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[765] + model_decoder_layers_11_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[766] + model_decoder_layers_11_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[767] + model_decoder_layers_11_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[768] + model_decoder_layers_11_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[769] + model_decoder_layers_11_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[770] + model_decoder_layers_11_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[771] + model_decoder_layers_11_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[772] + model_decoder_layers_11_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[773] + model_decoder_layers_11_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[774] + model_decoder_layers_11_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[775] + model_decoder_layers_11_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[776] + model_decoder_layers_12_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[777] + model_decoder_layers_12_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[778] + model_decoder_layers_12_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[779] + model_decoder_layers_12_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[780] + model_decoder_layers_12_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[781] + model_decoder_layers_12_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[782] + model_decoder_layers_12_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[783] + model_decoder_layers_12_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[784] + model_decoder_layers_12_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[785] + model_decoder_layers_12_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[789] + model_decoder_layers_12_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[790] + model_decoder_layers_12_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[791] + model_decoder_layers_12_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[792] + model_decoder_layers_12_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[793] + model_decoder_layers_12_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[794] + model_decoder_layers_12_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[795] + model_decoder_layers_12_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[796] + model_decoder_layers_12_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[797] + model_decoder_layers_12_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[798] + model_decoder_layers_12_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[799] + model_decoder_layers_12_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[800] + model_decoder_layers_13_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[801] + model_decoder_layers_13_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[802] + model_decoder_layers_13_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[803] + model_decoder_layers_13_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[804] + model_decoder_layers_13_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[805] + model_decoder_layers_13_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[806] + model_decoder_layers_13_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[807] + model_decoder_layers_13_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[808] + model_decoder_layers_13_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[809] + model_decoder_layers_13_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[813] + model_decoder_layers_13_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[814] + model_decoder_layers_13_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[815] + model_decoder_layers_13_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[816] + model_decoder_layers_13_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[817] + model_decoder_layers_13_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[818] + model_decoder_layers_13_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[819] + model_decoder_layers_13_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[820] + model_decoder_layers_13_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[821] + model_decoder_layers_13_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[822] + model_decoder_layers_13_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[823] + model_decoder_layers_13_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[824] + model_decoder_layers_14_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[825] + model_decoder_layers_14_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[826] + model_decoder_layers_14_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[827] + model_decoder_layers_14_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[828] + model_decoder_layers_14_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[829] + model_decoder_layers_14_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[830] + model_decoder_layers_14_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[831] + model_decoder_layers_14_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[832] + model_decoder_layers_14_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[833] + model_decoder_layers_14_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[837] + model_decoder_layers_14_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[838] + model_decoder_layers_14_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[839] + model_decoder_layers_14_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[840] + model_decoder_layers_14_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[841] + model_decoder_layers_14_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[842] + model_decoder_layers_14_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[843] + model_decoder_layers_14_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[844] + model_decoder_layers_14_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[845] + model_decoder_layers_14_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[846] + model_decoder_layers_14_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[847] + model_decoder_layers_14_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[848] + model_decoder_layers_15_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[849] + model_decoder_layers_15_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[850] + model_decoder_layers_15_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[851] + model_decoder_layers_15_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[852] + model_decoder_layers_15_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[853] + model_decoder_layers_15_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[854] + model_decoder_layers_15_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[855] + model_decoder_layers_15_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[856] + model_decoder_layers_15_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[857] + model_decoder_layers_15_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[861] + model_decoder_layers_15_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[862] + model_decoder_layers_15_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[863] + model_decoder_layers_15_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[864] + model_decoder_layers_15_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[865] + model_decoder_layers_15_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[866] + model_decoder_layers_15_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[867] + model_decoder_layers_15_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[868] + model_decoder_layers_15_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[869] + model_decoder_layers_15_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[870] + model_decoder_layers_15_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[871] + model_decoder_layers_15_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[872] + model_decoder_layers_16_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[873] + model_decoder_layers_16_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[874] + model_decoder_layers_16_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[875] + model_decoder_layers_16_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[876] + model_decoder_layers_16_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[877] + model_decoder_layers_16_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[878] + model_decoder_layers_16_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[879] + model_decoder_layers_16_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[880] + model_decoder_layers_16_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[881] + model_decoder_layers_16_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[885] + model_decoder_layers_16_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[886] + model_decoder_layers_16_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[887] + model_decoder_layers_16_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[888] + model_decoder_layers_16_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[889] + model_decoder_layers_16_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[890] + model_decoder_layers_16_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[891] + model_decoder_layers_16_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[892] + model_decoder_layers_16_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[893] + model_decoder_layers_16_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[894] + model_decoder_layers_16_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[895] + model_decoder_layers_16_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[896] + model_decoder_layers_17_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[897] + model_decoder_layers_17_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[898] + model_decoder_layers_17_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[899] + model_decoder_layers_17_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[900] + model_decoder_layers_17_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[901] + model_decoder_layers_17_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[902] + model_decoder_layers_17_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[903] + model_decoder_layers_17_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[904] + model_decoder_layers_17_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[905] + model_decoder_layers_17_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[909] + model_decoder_layers_17_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[910] + model_decoder_layers_17_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[911] + model_decoder_layers_17_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[912] + model_decoder_layers_17_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[913] + model_decoder_layers_17_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[914] + model_decoder_layers_17_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[915] + model_decoder_layers_17_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[916] + model_decoder_layers_17_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[917] + model_decoder_layers_17_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[918] + model_decoder_layers_17_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[919] + model_decoder_layers_17_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[920] + model_decoder_layers_18_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[921] + model_decoder_layers_18_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[922] + model_decoder_layers_18_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[923] + model_decoder_layers_18_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[924] + model_decoder_layers_18_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[925] + model_decoder_layers_18_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[926] + model_decoder_layers_18_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[927] + model_decoder_layers_18_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[928] + model_decoder_layers_18_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[929] + model_decoder_layers_18_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[933] + model_decoder_layers_18_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[934] + model_decoder_layers_18_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[935] + model_decoder_layers_18_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[936] + model_decoder_layers_18_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[937] + model_decoder_layers_18_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[938] + model_decoder_layers_18_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[939] + model_decoder_layers_18_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[940] + model_decoder_layers_18_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[941] + model_decoder_layers_18_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[942] + model_decoder_layers_18_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[943] + model_decoder_layers_18_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[944] + model_decoder_layers_19_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[945] + model_decoder_layers_19_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[946] + model_decoder_layers_19_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[947] + model_decoder_layers_19_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[948] + model_decoder_layers_19_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[949] + model_decoder_layers_19_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[950] + model_decoder_layers_19_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[951] + model_decoder_layers_19_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[952] + model_decoder_layers_19_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[953] + model_decoder_layers_19_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[957] + model_decoder_layers_19_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[958] + model_decoder_layers_19_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[959] + model_decoder_layers_19_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[960] + model_decoder_layers_19_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[961] + model_decoder_layers_19_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[962] + model_decoder_layers_19_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[963] + model_decoder_layers_19_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[964] + model_decoder_layers_19_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[965] + model_decoder_layers_19_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[966] + model_decoder_layers_19_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[967] + model_decoder_layers_19_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[968] + model_decoder_layers_20_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[969] + model_decoder_layers_20_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[970] + model_decoder_layers_20_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[971] + model_decoder_layers_20_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[972] + model_decoder_layers_20_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[973] + model_decoder_layers_20_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[974] + model_decoder_layers_20_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[975] + model_decoder_layers_20_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[976] + model_decoder_layers_20_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[977] + model_decoder_layers_20_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[981] + model_decoder_layers_20_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[982] + model_decoder_layers_20_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[983] + model_decoder_layers_20_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[984] + model_decoder_layers_20_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[985] + model_decoder_layers_20_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[986] + model_decoder_layers_20_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[987] + model_decoder_layers_20_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[988] + model_decoder_layers_20_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[989] + model_decoder_layers_20_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[990] + model_decoder_layers_20_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[991] + model_decoder_layers_20_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[992] + model_decoder_layers_21_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[993] + model_decoder_layers_21_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[994] + model_decoder_layers_21_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[995] + model_decoder_layers_21_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[996] + model_decoder_layers_21_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[997] + model_decoder_layers_21_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[998] + model_decoder_layers_21_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[999] + model_decoder_layers_21_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1000] + model_decoder_layers_21_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1001] + model_decoder_layers_21_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1005] + model_decoder_layers_21_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1006] + model_decoder_layers_21_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1007] + model_decoder_layers_21_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1008] + model_decoder_layers_21_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1009] + model_decoder_layers_21_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1010] + model_decoder_layers_21_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1011] + model_decoder_layers_21_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1012] + model_decoder_layers_21_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1013] + model_decoder_layers_21_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1014] + model_decoder_layers_21_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1015] + model_decoder_layers_21_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1016] + model_decoder_layers_22_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1017] + model_decoder_layers_22_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1018] + model_decoder_layers_22_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1019] + model_decoder_layers_22_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1020] + model_decoder_layers_22_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1021] + model_decoder_layers_22_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1022] + model_decoder_layers_22_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1023] + model_decoder_layers_22_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1024] + model_decoder_layers_22_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1025] + model_decoder_layers_22_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1029] + model_decoder_layers_22_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1030] + model_decoder_layers_22_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1031] + model_decoder_layers_22_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1032] + model_decoder_layers_22_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1033] + model_decoder_layers_22_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1034] + model_decoder_layers_22_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1035] + model_decoder_layers_22_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1036] + model_decoder_layers_22_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1037] + model_decoder_layers_22_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1038] + model_decoder_layers_22_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1039] + model_decoder_layers_22_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1040] + model_decoder_layers_23_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1041] + model_decoder_layers_23_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1042] + model_decoder_layers_23_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1043] + model_decoder_layers_23_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1044] + model_decoder_layers_23_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1045] + model_decoder_layers_23_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1046] + model_decoder_layers_23_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1047] + model_decoder_layers_23_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1048] + model_decoder_layers_23_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1049] + model_decoder_layers_23_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1053] + model_decoder_layers_23_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1054] + model_decoder_layers_23_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1055] + model_decoder_layers_23_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1056] + model_decoder_layers_23_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1057] + model_decoder_layers_23_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1058] + model_decoder_layers_23_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1059] + model_decoder_layers_23_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1060] + model_decoder_layers_23_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1061] + model_decoder_layers_23_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1062] + model_decoder_layers_23_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1063] + model_decoder_layers_23_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1064] + model_decoder_layers_24_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1065] + model_decoder_layers_24_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1066] + model_decoder_layers_24_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1067] + model_decoder_layers_24_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1068] + model_decoder_layers_24_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1069] + model_decoder_layers_24_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1070] + model_decoder_layers_24_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1071] + model_decoder_layers_24_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1072] + model_decoder_layers_24_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1073] + model_decoder_layers_24_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1077] + model_decoder_layers_24_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1078] + model_decoder_layers_24_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1079] + model_decoder_layers_24_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1080] + model_decoder_layers_24_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1081] + model_decoder_layers_24_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1082] + model_decoder_layers_24_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1083] + model_decoder_layers_24_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1084] + model_decoder_layers_24_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1085] + model_decoder_layers_24_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1086] + model_decoder_layers_24_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1087] + model_decoder_layers_24_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1088] + model_decoder_layers_25_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1089] + model_decoder_layers_25_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1090] + model_decoder_layers_25_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1091] + model_decoder_layers_25_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1092] + model_decoder_layers_25_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1093] + model_decoder_layers_25_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1094] + model_decoder_layers_25_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1095] + model_decoder_layers_25_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1096] + model_decoder_layers_25_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1097] + model_decoder_layers_25_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1101] + model_decoder_layers_25_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1102] + model_decoder_layers_25_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1103] + model_decoder_layers_25_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1104] + model_decoder_layers_25_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1105] + model_decoder_layers_25_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1106] + model_decoder_layers_25_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1107] + model_decoder_layers_25_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1108] + model_decoder_layers_25_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1109] + model_decoder_layers_25_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1110] + model_decoder_layers_25_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1111] + model_decoder_layers_25_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1112] + model_decoder_layers_26_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1113] + model_decoder_layers_26_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1114] + model_decoder_layers_26_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1115] + model_decoder_layers_26_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1116] + model_decoder_layers_26_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1117] + model_decoder_layers_26_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1118] + model_decoder_layers_26_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1119] + model_decoder_layers_26_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1120] + model_decoder_layers_26_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1121] + model_decoder_layers_26_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1125] + model_decoder_layers_26_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1126] + model_decoder_layers_26_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1127] + model_decoder_layers_26_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1128] + model_decoder_layers_26_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1129] + model_decoder_layers_26_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1130] + model_decoder_layers_26_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1131] + model_decoder_layers_26_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1132] + model_decoder_layers_26_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1133] + model_decoder_layers_26_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1134] + model_decoder_layers_26_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1135] + model_decoder_layers_26_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1136] + model_decoder_layers_27_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1137] + model_decoder_layers_27_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1138] + model_decoder_layers_27_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1139] + model_decoder_layers_27_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1140] + model_decoder_layers_27_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1141] + model_decoder_layers_27_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1142] + model_decoder_layers_27_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1143] + model_decoder_layers_27_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1144] + model_decoder_layers_27_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1145] + model_decoder_layers_27_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1149] + model_decoder_layers_27_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1150] + model_decoder_layers_27_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1151] + model_decoder_layers_27_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1152] + model_decoder_layers_27_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1153] + model_decoder_layers_27_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1154] + model_decoder_layers_27_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1155] + model_decoder_layers_27_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1156] + model_decoder_layers_27_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1157] + model_decoder_layers_27_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1158] + model_decoder_layers_27_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1159] + model_decoder_layers_27_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1160] + model_decoder_layers_28_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1161] + model_decoder_layers_28_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1162] + model_decoder_layers_28_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1163] + model_decoder_layers_28_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1164] + model_decoder_layers_28_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1165] + model_decoder_layers_28_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1166] + model_decoder_layers_28_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1167] + model_decoder_layers_28_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1168] + model_decoder_layers_28_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1169] + model_decoder_layers_28_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1173] + model_decoder_layers_28_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1174] + model_decoder_layers_28_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1175] + model_decoder_layers_28_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1176] + model_decoder_layers_28_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1177] + model_decoder_layers_28_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1178] + model_decoder_layers_28_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1179] + model_decoder_layers_28_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1180] + model_decoder_layers_28_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1181] + model_decoder_layers_28_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1182] + model_decoder_layers_28_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1183] + model_decoder_layers_28_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1184] + model_decoder_layers_29_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1185] + model_decoder_layers_29_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1186] + model_decoder_layers_29_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1187] + model_decoder_layers_29_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1188] + model_decoder_layers_29_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1189] + model_decoder_layers_29_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1190] + model_decoder_layers_29_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1191] + model_decoder_layers_29_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1192] + model_decoder_layers_29_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1193] + model_decoder_layers_29_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1197] + model_decoder_layers_29_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1198] + model_decoder_layers_29_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1199] + model_decoder_layers_29_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1200] + model_decoder_layers_29_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1201] + model_decoder_layers_29_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1202] + model_decoder_layers_29_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1203] + model_decoder_layers_29_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1204] + model_decoder_layers_29_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1205] + model_decoder_layers_29_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1206] + model_decoder_layers_29_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1207] + model_decoder_layers_29_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1208] + model_decoder_layers_30_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1209] + model_decoder_layers_30_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1210] + model_decoder_layers_30_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1211] + model_decoder_layers_30_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1212] + model_decoder_layers_30_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1213] + model_decoder_layers_30_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1214] + model_decoder_layers_30_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1215] + model_decoder_layers_30_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1216] + model_decoder_layers_30_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1217] + model_decoder_layers_30_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1221] + model_decoder_layers_30_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1222] + model_decoder_layers_30_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1223] + model_decoder_layers_30_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1224] + model_decoder_layers_30_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1225] + model_decoder_layers_30_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1226] + model_decoder_layers_30_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1227] + model_decoder_layers_30_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1228] + model_decoder_layers_30_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1229] + model_decoder_layers_30_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1230] + model_decoder_layers_30_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1231] + model_decoder_layers_30_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1232] + model_decoder_layers_31_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1233] + model_decoder_layers_31_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1234] + model_decoder_layers_31_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1235] + model_decoder_layers_31_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1236] + model_decoder_layers_31_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1237] + model_decoder_layers_31_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1238] + model_decoder_layers_31_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1239] + model_decoder_layers_31_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1240] + model_decoder_layers_31_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1241] + model_decoder_layers_31_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1245] + model_decoder_layers_31_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1246] + model_decoder_layers_31_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1247] + model_decoder_layers_31_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1248] + model_decoder_layers_31_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1249] + model_decoder_layers_31_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1250] + model_decoder_layers_31_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1251] + model_decoder_layers_31_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1252] + model_decoder_layers_31_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1253] + model_decoder_layers_31_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1254] + model_decoder_layers_31_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1255] + model_decoder_layers_31_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1256] + model_decoder_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1257] + model_decoder_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1258] + reshape1030: R.Tensor((seq_len,), dtype="int32") = R.reshape(input_ids, R.shape([seq_len])) + take5: R.Tensor((seq_len, 1280), dtype="float16") = R.take(model_decoder_embed_tokens_weight4, reshape1030, axis=0) + reshape1031: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(take5, R.shape([1, seq_len, 1280])) + lv198: R.Tensor((seq_len,), dtype="int32") = R.call_pure_packed("vm.builtin.attention_kv_cache_get_query_positions", paged_kv_cache, sinfo_args=(R.Tensor((seq_len,), dtype="int32"),)) + take6: R.Tensor((seq_len, 1280), dtype="float16") = R.take(model_decoder_embed_positions_weight4, lv198, axis=0) + reshape1032: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(take6, R.shape([1, seq_len, 1280])) + add899: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(reshape1031, reshape1032) + layer_norm259: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add899, model_decoder_layers_0_self_attn_layer_norm_weight4, model_decoder_layers_0_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv32 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_0_self_attn_q_proj_weight4, layer_norm259, model_decoder_layers_0_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1033: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv32, R.shape([1, seq_len, 20, 64])) + lv32_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_0_self_attn_k_proj_weight4, layer_norm259), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1034: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv32_1, R.shape([1, seq_len, 20, 64])) + lv33 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_0_self_attn_v_proj_weight4, layer_norm259, model_decoder_layers_0_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1035: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv33, R.shape([1, seq_len, 20, 64])) + concat64: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1033, reshape1034, reshape1035), axis=2) + reshape1036: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat64, R.shape([seq_len, 60, 64])) + lv199 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), reshape1036), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1037: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv199, R.shape([1, seq_len, 20, 64])) + reshape1038: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1037, R.shape([1, seq_len, 1280])) + lv34 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_0_self_attn_out_proj_weight4, reshape1038, model_decoder_layers_0_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add903: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add899, lv34) + layer_norm260: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add903, model_decoder_layers_0_encoder_attn_layer_norm_weight4, model_decoder_layers_0_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv35 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_0_encoder_attn_q_proj_weight4, layer_norm260, model_decoder_layers_0_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1039: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv35, R.shape([1, seq_len, 20, 64])) + reshape1040: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1039, R.shape([seq_len, 20, 64])) + lv200 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), reshape1040), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1041: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv200, R.shape([1, seq_len, 20, 64])) + reshape1042: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1041, R.shape([1, seq_len, 1280])) + lv36 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_0_encoder_attn_out_proj_weight4, reshape1042, model_decoder_layers_0_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add906: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add903, lv36) + layer_norm261: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add906, model_decoder_layers_0_final_layer_norm_weight4, model_decoder_layers_0_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_0_fc1_weight4, layer_norm261, model_decoder_layers_0_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv37 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_0_fc2_weight4, lv, model_decoder_layers_0_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add909: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add906, lv37) + layer_norm262: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add909, model_decoder_layers_1_self_attn_layer_norm_weight4, model_decoder_layers_1_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv38 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_1_self_attn_q_proj_weight4, layer_norm262, model_decoder_layers_1_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1043: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv38, R.shape([1, seq_len, 20, 64])) + lv33_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_1_self_attn_k_proj_weight4, layer_norm262), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1044: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv33_1, R.shape([1, seq_len, 20, 64])) + lv39 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_1_self_attn_v_proj_weight4, layer_norm262, model_decoder_layers_1_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1045: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv39, R.shape([1, seq_len, 20, 64])) + concat65: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1043, reshape1044, reshape1045), axis=2) + reshape1046: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat65, R.shape([seq_len, 60, 64])) + lv201 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), reshape1046), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1047: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv201, R.shape([1, seq_len, 20, 64])) + reshape1048: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1047, R.shape([1, seq_len, 1280])) + lv40 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_1_self_attn_out_proj_weight4, reshape1048, model_decoder_layers_1_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add913: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add909, lv40) + layer_norm263: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add913, model_decoder_layers_1_encoder_attn_layer_norm_weight4, model_decoder_layers_1_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv41 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_1_encoder_attn_q_proj_weight4, layer_norm263, model_decoder_layers_1_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1049: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv41, R.shape([1, seq_len, 20, 64])) + reshape1050: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1049, R.shape([seq_len, 20, 64])) + lv202 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), reshape1050), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1051: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv202, R.shape([1, seq_len, 20, 64])) + reshape1052: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1051, R.shape([1, seq_len, 1280])) + lv42 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_1_encoder_attn_out_proj_weight4, reshape1052, model_decoder_layers_1_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add916: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add913, lv42) + layer_norm264: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add916, model_decoder_layers_1_final_layer_norm_weight4, model_decoder_layers_1_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_1_fc1_weight4, layer_norm264, model_decoder_layers_1_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv43 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_1_fc2_weight4, lv1, model_decoder_layers_1_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add919: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add916, lv43) + layer_norm265: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add919, model_decoder_layers_2_self_attn_layer_norm_weight4, model_decoder_layers_2_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv44 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_2_self_attn_q_proj_weight4, layer_norm265, model_decoder_layers_2_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1053: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv44, R.shape([1, seq_len, 20, 64])) + lv34_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_2_self_attn_k_proj_weight4, layer_norm265), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1054: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv34_1, R.shape([1, seq_len, 20, 64])) + lv45 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_2_self_attn_v_proj_weight4, layer_norm265, model_decoder_layers_2_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1055: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv45, R.shape([1, seq_len, 20, 64])) + concat66: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1053, reshape1054, reshape1055), axis=2) + reshape1056: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat66, R.shape([seq_len, 60, 64])) + lv203 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), reshape1056), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1057: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv203, R.shape([1, seq_len, 20, 64])) + reshape1058: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1057, R.shape([1, seq_len, 1280])) + lv46 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_2_self_attn_out_proj_weight4, reshape1058, model_decoder_layers_2_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add923: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add919, lv46) + layer_norm266: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add923, model_decoder_layers_2_encoder_attn_layer_norm_weight4, model_decoder_layers_2_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv47 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_2_encoder_attn_q_proj_weight4, layer_norm266, model_decoder_layers_2_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1059: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv47, R.shape([1, seq_len, 20, 64])) + reshape1060: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1059, R.shape([seq_len, 20, 64])) + lv204 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), reshape1060), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1061: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv204, R.shape([1, seq_len, 20, 64])) + reshape1062: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1061, R.shape([1, seq_len, 1280])) + lv48 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_2_encoder_attn_out_proj_weight4, reshape1062, model_decoder_layers_2_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add926: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add923, lv48) + layer_norm267: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add926, model_decoder_layers_2_final_layer_norm_weight4, model_decoder_layers_2_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv2 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_2_fc1_weight4, layer_norm267, model_decoder_layers_2_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv49 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_2_fc2_weight4, lv2, model_decoder_layers_2_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add929: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add926, lv49) + layer_norm268: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add929, model_decoder_layers_3_self_attn_layer_norm_weight4, model_decoder_layers_3_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv50 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_3_self_attn_q_proj_weight4, layer_norm268, model_decoder_layers_3_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1063: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv50, R.shape([1, seq_len, 20, 64])) + lv35_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_3_self_attn_k_proj_weight4, layer_norm268), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1064: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv35_1, R.shape([1, seq_len, 20, 64])) + lv51 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_3_self_attn_v_proj_weight4, layer_norm268, model_decoder_layers_3_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1065: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv51, R.shape([1, seq_len, 20, 64])) + concat67: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1063, reshape1064, reshape1065), axis=2) + reshape1066: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat67, R.shape([seq_len, 60, 64])) + lv205 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), reshape1066), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1067: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv205, R.shape([1, seq_len, 20, 64])) + reshape1068: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1067, R.shape([1, seq_len, 1280])) + lv52 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_3_self_attn_out_proj_weight4, reshape1068, model_decoder_layers_3_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add933: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add929, lv52) + layer_norm269: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add933, model_decoder_layers_3_encoder_attn_layer_norm_weight4, model_decoder_layers_3_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv53 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_3_encoder_attn_q_proj_weight4, layer_norm269, model_decoder_layers_3_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1069: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv53, R.shape([1, seq_len, 20, 64])) + reshape1070: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1069, R.shape([seq_len, 20, 64])) + lv206 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), reshape1070), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1071: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv206, R.shape([1, seq_len, 20, 64])) + reshape1072: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1071, R.shape([1, seq_len, 1280])) + lv54 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_3_encoder_attn_out_proj_weight4, reshape1072, model_decoder_layers_3_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add936: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add933, lv54) + layer_norm270: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add936, model_decoder_layers_3_final_layer_norm_weight4, model_decoder_layers_3_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv3 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_3_fc1_weight4, layer_norm270, model_decoder_layers_3_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv55 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_3_fc2_weight4, lv3, model_decoder_layers_3_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add939: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add936, lv55) + layer_norm271: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add939, model_decoder_layers_4_self_attn_layer_norm_weight4, model_decoder_layers_4_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv56 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_4_self_attn_q_proj_weight4, layer_norm271, model_decoder_layers_4_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1073: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv56, R.shape([1, seq_len, 20, 64])) + lv36_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_4_self_attn_k_proj_weight4, layer_norm271), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1074: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv36_1, R.shape([1, seq_len, 20, 64])) + lv57 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_4_self_attn_v_proj_weight4, layer_norm271, model_decoder_layers_4_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1075: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv57, R.shape([1, seq_len, 20, 64])) + concat68: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1073, reshape1074, reshape1075), axis=2) + reshape1076: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat68, R.shape([seq_len, 60, 64])) + lv207 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), reshape1076), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1077: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv207, R.shape([1, seq_len, 20, 64])) + reshape1078: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1077, R.shape([1, seq_len, 1280])) + lv58 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_4_self_attn_out_proj_weight4, reshape1078, model_decoder_layers_4_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add943: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add939, lv58) + layer_norm272: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add943, model_decoder_layers_4_encoder_attn_layer_norm_weight4, model_decoder_layers_4_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv59 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_4_encoder_attn_q_proj_weight4, layer_norm272, model_decoder_layers_4_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1079: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv59, R.shape([1, seq_len, 20, 64])) + reshape1080: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1079, R.shape([seq_len, 20, 64])) + lv208 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), reshape1080), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1081: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv208, R.shape([1, seq_len, 20, 64])) + reshape1082: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1081, R.shape([1, seq_len, 1280])) + lv60 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_4_encoder_attn_out_proj_weight4, reshape1082, model_decoder_layers_4_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add946: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add943, lv60) + layer_norm273: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add946, model_decoder_layers_4_final_layer_norm_weight4, model_decoder_layers_4_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv4 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_4_fc1_weight4, layer_norm273, model_decoder_layers_4_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv61 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_4_fc2_weight4, lv4, model_decoder_layers_4_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add949: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add946, lv61) + layer_norm274: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add949, model_decoder_layers_5_self_attn_layer_norm_weight4, model_decoder_layers_5_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv62 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_5_self_attn_q_proj_weight4, layer_norm274, model_decoder_layers_5_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1083: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv62, R.shape([1, seq_len, 20, 64])) + lv37_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_5_self_attn_k_proj_weight4, layer_norm274), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1084: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv37_1, R.shape([1, seq_len, 20, 64])) + lv63 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_5_self_attn_v_proj_weight4, layer_norm274, model_decoder_layers_5_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1085: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv63, R.shape([1, seq_len, 20, 64])) + concat69: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1083, reshape1084, reshape1085), axis=2) + reshape1086: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat69, R.shape([seq_len, 60, 64])) + lv209 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), reshape1086), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1087: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv209, R.shape([1, seq_len, 20, 64])) + reshape1088: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1087, R.shape([1, seq_len, 1280])) + lv64 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_5_self_attn_out_proj_weight4, reshape1088, model_decoder_layers_5_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add953: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add949, lv64) + layer_norm275: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add953, model_decoder_layers_5_encoder_attn_layer_norm_weight4, model_decoder_layers_5_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv65 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_5_encoder_attn_q_proj_weight4, layer_norm275, model_decoder_layers_5_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1089: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv65, R.shape([1, seq_len, 20, 64])) + reshape1090: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1089, R.shape([seq_len, 20, 64])) + lv210 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), reshape1090), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1091: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv210, R.shape([1, seq_len, 20, 64])) + reshape1092: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1091, R.shape([1, seq_len, 1280])) + lv66 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_5_encoder_attn_out_proj_weight4, reshape1092, model_decoder_layers_5_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add956: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add953, lv66) + layer_norm276: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add956, model_decoder_layers_5_final_layer_norm_weight4, model_decoder_layers_5_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv5 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_5_fc1_weight4, layer_norm276, model_decoder_layers_5_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv67 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_5_fc2_weight4, lv5, model_decoder_layers_5_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add959: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add956, lv67) + layer_norm277: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add959, model_decoder_layers_6_self_attn_layer_norm_weight4, model_decoder_layers_6_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv68 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_6_self_attn_q_proj_weight4, layer_norm277, model_decoder_layers_6_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1093: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv68, R.shape([1, seq_len, 20, 64])) + lv38_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_6_self_attn_k_proj_weight4, layer_norm277), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1094: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv38_1, R.shape([1, seq_len, 20, 64])) + lv69 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_6_self_attn_v_proj_weight4, layer_norm277, model_decoder_layers_6_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1095: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv69, R.shape([1, seq_len, 20, 64])) + concat70: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1093, reshape1094, reshape1095), axis=2) + reshape1096: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat70, R.shape([seq_len, 60, 64])) + lv211 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), reshape1096), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1097: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv211, R.shape([1, seq_len, 20, 64])) + reshape1098: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1097, R.shape([1, seq_len, 1280])) + lv70 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_6_self_attn_out_proj_weight4, reshape1098, model_decoder_layers_6_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add963: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add959, lv70) + layer_norm278: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add963, model_decoder_layers_6_encoder_attn_layer_norm_weight4, model_decoder_layers_6_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv71 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_6_encoder_attn_q_proj_weight4, layer_norm278, model_decoder_layers_6_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1099: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv71, R.shape([1, seq_len, 20, 64])) + reshape1100: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1099, R.shape([seq_len, 20, 64])) + lv212 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), reshape1100), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1101: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv212, R.shape([1, seq_len, 20, 64])) + reshape1102: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1101, R.shape([1, seq_len, 1280])) + lv72 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_6_encoder_attn_out_proj_weight4, reshape1102, model_decoder_layers_6_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add966: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add963, lv72) + layer_norm279: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add966, model_decoder_layers_6_final_layer_norm_weight4, model_decoder_layers_6_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv6 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_6_fc1_weight4, layer_norm279, model_decoder_layers_6_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv73 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_6_fc2_weight4, lv6, model_decoder_layers_6_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add969: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add966, lv73) + layer_norm280: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add969, model_decoder_layers_7_self_attn_layer_norm_weight4, model_decoder_layers_7_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv74 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_7_self_attn_q_proj_weight4, layer_norm280, model_decoder_layers_7_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1103: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv74, R.shape([1, seq_len, 20, 64])) + lv39_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_7_self_attn_k_proj_weight4, layer_norm280), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1104: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv39_1, R.shape([1, seq_len, 20, 64])) + lv75 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_7_self_attn_v_proj_weight4, layer_norm280, model_decoder_layers_7_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1105: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv75, R.shape([1, seq_len, 20, 64])) + concat71: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1103, reshape1104, reshape1105), axis=2) + reshape1106: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat71, R.shape([seq_len, 60, 64])) + lv213 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), reshape1106), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1107: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv213, R.shape([1, seq_len, 20, 64])) + reshape1108: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1107, R.shape([1, seq_len, 1280])) + lv76 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_7_self_attn_out_proj_weight4, reshape1108, model_decoder_layers_7_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add973: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add969, lv76) + layer_norm281: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add973, model_decoder_layers_7_encoder_attn_layer_norm_weight4, model_decoder_layers_7_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv77 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_7_encoder_attn_q_proj_weight4, layer_norm281, model_decoder_layers_7_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1109: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv77, R.shape([1, seq_len, 20, 64])) + reshape1110: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1109, R.shape([seq_len, 20, 64])) + lv214 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), reshape1110), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1111: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv214, R.shape([1, seq_len, 20, 64])) + reshape1112: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1111, R.shape([1, seq_len, 1280])) + lv78 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_7_encoder_attn_out_proj_weight4, reshape1112, model_decoder_layers_7_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add976: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add973, lv78) + layer_norm282: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add976, model_decoder_layers_7_final_layer_norm_weight4, model_decoder_layers_7_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv7 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_7_fc1_weight4, layer_norm282, model_decoder_layers_7_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv79 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_7_fc2_weight4, lv7, model_decoder_layers_7_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add979: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add976, lv79) + layer_norm283: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add979, model_decoder_layers_8_self_attn_layer_norm_weight4, model_decoder_layers_8_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv80 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_8_self_attn_q_proj_weight4, layer_norm283, model_decoder_layers_8_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1113: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv80, R.shape([1, seq_len, 20, 64])) + lv40_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_8_self_attn_k_proj_weight4, layer_norm283), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1114: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv40_1, R.shape([1, seq_len, 20, 64])) + lv81 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_8_self_attn_v_proj_weight4, layer_norm283, model_decoder_layers_8_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1115: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv81, R.shape([1, seq_len, 20, 64])) + concat72: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1113, reshape1114, reshape1115), axis=2) + reshape1116: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat72, R.shape([seq_len, 60, 64])) + lv215 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), reshape1116), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1117: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv215, R.shape([1, seq_len, 20, 64])) + reshape1118: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1117, R.shape([1, seq_len, 1280])) + lv82 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_8_self_attn_out_proj_weight4, reshape1118, model_decoder_layers_8_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add983: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add979, lv82) + layer_norm284: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add983, model_decoder_layers_8_encoder_attn_layer_norm_weight4, model_decoder_layers_8_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv83 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_8_encoder_attn_q_proj_weight4, layer_norm284, model_decoder_layers_8_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1119: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv83, R.shape([1, seq_len, 20, 64])) + reshape1120: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1119, R.shape([seq_len, 20, 64])) + lv216 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), reshape1120), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1121: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv216, R.shape([1, seq_len, 20, 64])) + reshape1122: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1121, R.shape([1, seq_len, 1280])) + lv84 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_8_encoder_attn_out_proj_weight4, reshape1122, model_decoder_layers_8_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add986: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add983, lv84) + layer_norm285: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add986, model_decoder_layers_8_final_layer_norm_weight4, model_decoder_layers_8_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv8 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_8_fc1_weight4, layer_norm285, model_decoder_layers_8_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv85 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_8_fc2_weight4, lv8, model_decoder_layers_8_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add989: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add986, lv85) + layer_norm286: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add989, model_decoder_layers_9_self_attn_layer_norm_weight4, model_decoder_layers_9_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv86 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_9_self_attn_q_proj_weight4, layer_norm286, model_decoder_layers_9_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1123: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv86, R.shape([1, seq_len, 20, 64])) + lv41_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_9_self_attn_k_proj_weight4, layer_norm286), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1124: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv41_1, R.shape([1, seq_len, 20, 64])) + lv87 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_9_self_attn_v_proj_weight4, layer_norm286, model_decoder_layers_9_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1125: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv87, R.shape([1, seq_len, 20, 64])) + concat73: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1123, reshape1124, reshape1125), axis=2) + reshape1126: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat73, R.shape([seq_len, 60, 64])) + lv217 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), reshape1126), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1127: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv217, R.shape([1, seq_len, 20, 64])) + reshape1128: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1127, R.shape([1, seq_len, 1280])) + lv88 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_9_self_attn_out_proj_weight4, reshape1128, model_decoder_layers_9_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add993: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add989, lv88) + layer_norm287: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add993, model_decoder_layers_9_encoder_attn_layer_norm_weight4, model_decoder_layers_9_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv89 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_9_encoder_attn_q_proj_weight4, layer_norm287, model_decoder_layers_9_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1129: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv89, R.shape([1, seq_len, 20, 64])) + reshape1130: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1129, R.shape([seq_len, 20, 64])) + lv218 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), reshape1130), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1131: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv218, R.shape([1, seq_len, 20, 64])) + reshape1132: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1131, R.shape([1, seq_len, 1280])) + lv90 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_9_encoder_attn_out_proj_weight4, reshape1132, model_decoder_layers_9_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add996: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add993, lv90) + layer_norm288: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add996, model_decoder_layers_9_final_layer_norm_weight4, model_decoder_layers_9_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv9 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_9_fc1_weight4, layer_norm288, model_decoder_layers_9_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv91 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_9_fc2_weight4, lv9, model_decoder_layers_9_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add999: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add996, lv91) + layer_norm289: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add999, model_decoder_layers_10_self_attn_layer_norm_weight4, model_decoder_layers_10_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv92 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_10_self_attn_q_proj_weight4, layer_norm289, model_decoder_layers_10_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1133: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv92, R.shape([1, seq_len, 20, 64])) + lv42_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_10_self_attn_k_proj_weight4, layer_norm289), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1134: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv42_1, R.shape([1, seq_len, 20, 64])) + lv93 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_10_self_attn_v_proj_weight4, layer_norm289, model_decoder_layers_10_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1135: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv93, R.shape([1, seq_len, 20, 64])) + concat74: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1133, reshape1134, reshape1135), axis=2) + reshape1136: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat74, R.shape([seq_len, 60, 64])) + lv219 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), reshape1136), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1137: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv219, R.shape([1, seq_len, 20, 64])) + reshape1138: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1137, R.shape([1, seq_len, 1280])) + lv94 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_10_self_attn_out_proj_weight4, reshape1138, model_decoder_layers_10_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1003: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add999, lv94) + layer_norm290: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1003, model_decoder_layers_10_encoder_attn_layer_norm_weight4, model_decoder_layers_10_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv95 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_10_encoder_attn_q_proj_weight4, layer_norm290, model_decoder_layers_10_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1139: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv95, R.shape([1, seq_len, 20, 64])) + reshape1140: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1139, R.shape([seq_len, 20, 64])) + lv220 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), reshape1140), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1141: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv220, R.shape([1, seq_len, 20, 64])) + reshape1142: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1141, R.shape([1, seq_len, 1280])) + lv96 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_10_encoder_attn_out_proj_weight4, reshape1142, model_decoder_layers_10_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1006: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1003, lv96) + layer_norm291: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1006, model_decoder_layers_10_final_layer_norm_weight4, model_decoder_layers_10_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv10 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_10_fc1_weight4, layer_norm291, model_decoder_layers_10_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv97 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_10_fc2_weight4, lv10, model_decoder_layers_10_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1009: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1006, lv97) + layer_norm292: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1009, model_decoder_layers_11_self_attn_layer_norm_weight4, model_decoder_layers_11_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv98 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_11_self_attn_q_proj_weight4, layer_norm292, model_decoder_layers_11_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1143: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv98, R.shape([1, seq_len, 20, 64])) + lv43_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_11_self_attn_k_proj_weight4, layer_norm292), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1144: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv43_1, R.shape([1, seq_len, 20, 64])) + lv99 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_11_self_attn_v_proj_weight4, layer_norm292, model_decoder_layers_11_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1145: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv99, R.shape([1, seq_len, 20, 64])) + concat75: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1143, reshape1144, reshape1145), axis=2) + reshape1146: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat75, R.shape([seq_len, 60, 64])) + lv221 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), reshape1146), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1147: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv221, R.shape([1, seq_len, 20, 64])) + reshape1148: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1147, R.shape([1, seq_len, 1280])) + lv100 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_11_self_attn_out_proj_weight4, reshape1148, model_decoder_layers_11_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1013: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1009, lv100) + layer_norm293: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1013, model_decoder_layers_11_encoder_attn_layer_norm_weight4, model_decoder_layers_11_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv101 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_11_encoder_attn_q_proj_weight4, layer_norm293, model_decoder_layers_11_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1149: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv101, R.shape([1, seq_len, 20, 64])) + reshape1150: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1149, R.shape([seq_len, 20, 64])) + lv222 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), reshape1150), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1151: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv222, R.shape([1, seq_len, 20, 64])) + reshape1152: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1151, R.shape([1, seq_len, 1280])) + lv102 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_11_encoder_attn_out_proj_weight4, reshape1152, model_decoder_layers_11_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1016: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1013, lv102) + layer_norm294: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1016, model_decoder_layers_11_final_layer_norm_weight4, model_decoder_layers_11_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv11 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_11_fc1_weight4, layer_norm294, model_decoder_layers_11_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv103 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_11_fc2_weight4, lv11, model_decoder_layers_11_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1019: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1016, lv103) + layer_norm295: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1019, model_decoder_layers_12_self_attn_layer_norm_weight4, model_decoder_layers_12_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv104 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_12_self_attn_q_proj_weight4, layer_norm295, model_decoder_layers_12_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1153: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv104, R.shape([1, seq_len, 20, 64])) + lv44_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_12_self_attn_k_proj_weight4, layer_norm295), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1154: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv44_1, R.shape([1, seq_len, 20, 64])) + lv105 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_12_self_attn_v_proj_weight4, layer_norm295, model_decoder_layers_12_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1155: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv105, R.shape([1, seq_len, 20, 64])) + concat76: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1153, reshape1154, reshape1155), axis=2) + reshape1156: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat76, R.shape([seq_len, 60, 64])) + lv223 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), reshape1156), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1157: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv223, R.shape([1, seq_len, 20, 64])) + reshape1158: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1157, R.shape([1, seq_len, 1280])) + lv106 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_12_self_attn_out_proj_weight4, reshape1158, model_decoder_layers_12_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1023: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1019, lv106) + layer_norm296: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1023, model_decoder_layers_12_encoder_attn_layer_norm_weight4, model_decoder_layers_12_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv107 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_12_encoder_attn_q_proj_weight4, layer_norm296, model_decoder_layers_12_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1159: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv107, R.shape([1, seq_len, 20, 64])) + reshape1160: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1159, R.shape([seq_len, 20, 64])) + lv224 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), reshape1160), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1161: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv224, R.shape([1, seq_len, 20, 64])) + reshape1162: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1161, R.shape([1, seq_len, 1280])) + lv108 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_12_encoder_attn_out_proj_weight4, reshape1162, model_decoder_layers_12_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1026: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1023, lv108) + layer_norm297: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1026, model_decoder_layers_12_final_layer_norm_weight4, model_decoder_layers_12_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv12 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_12_fc1_weight4, layer_norm297, model_decoder_layers_12_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv109 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_12_fc2_weight4, lv12, model_decoder_layers_12_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1029: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1026, lv109) + layer_norm298: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1029, model_decoder_layers_13_self_attn_layer_norm_weight4, model_decoder_layers_13_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv110 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_13_self_attn_q_proj_weight4, layer_norm298, model_decoder_layers_13_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1163: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv110, R.shape([1, seq_len, 20, 64])) + lv45_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_13_self_attn_k_proj_weight4, layer_norm298), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1164: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv45_1, R.shape([1, seq_len, 20, 64])) + lv111 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_13_self_attn_v_proj_weight4, layer_norm298, model_decoder_layers_13_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1165: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv111, R.shape([1, seq_len, 20, 64])) + concat77: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1163, reshape1164, reshape1165), axis=2) + reshape1166: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat77, R.shape([seq_len, 60, 64])) + lv225 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), reshape1166), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1167: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv225, R.shape([1, seq_len, 20, 64])) + reshape1168: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1167, R.shape([1, seq_len, 1280])) + lv112 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_13_self_attn_out_proj_weight4, reshape1168, model_decoder_layers_13_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1033: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1029, lv112) + layer_norm299: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1033, model_decoder_layers_13_encoder_attn_layer_norm_weight4, model_decoder_layers_13_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv113 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_13_encoder_attn_q_proj_weight4, layer_norm299, model_decoder_layers_13_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1169: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv113, R.shape([1, seq_len, 20, 64])) + reshape1170: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1169, R.shape([seq_len, 20, 64])) + lv226 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), reshape1170), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1171: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv226, R.shape([1, seq_len, 20, 64])) + reshape1172: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1171, R.shape([1, seq_len, 1280])) + lv114 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_13_encoder_attn_out_proj_weight4, reshape1172, model_decoder_layers_13_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1036: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1033, lv114) + layer_norm300: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1036, model_decoder_layers_13_final_layer_norm_weight4, model_decoder_layers_13_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv13 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_13_fc1_weight4, layer_norm300, model_decoder_layers_13_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv115 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_13_fc2_weight4, lv13, model_decoder_layers_13_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1039: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1036, lv115) + layer_norm301: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1039, model_decoder_layers_14_self_attn_layer_norm_weight4, model_decoder_layers_14_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv116 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_14_self_attn_q_proj_weight4, layer_norm301, model_decoder_layers_14_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1173: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv116, R.shape([1, seq_len, 20, 64])) + lv46_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_14_self_attn_k_proj_weight4, layer_norm301), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1174: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv46_1, R.shape([1, seq_len, 20, 64])) + lv117 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_14_self_attn_v_proj_weight4, layer_norm301, model_decoder_layers_14_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1175: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv117, R.shape([1, seq_len, 20, 64])) + concat78: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1173, reshape1174, reshape1175), axis=2) + reshape1176: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat78, R.shape([seq_len, 60, 64])) + lv227 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), reshape1176), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1177: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv227, R.shape([1, seq_len, 20, 64])) + reshape1178: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1177, R.shape([1, seq_len, 1280])) + lv118 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_14_self_attn_out_proj_weight4, reshape1178, model_decoder_layers_14_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1043: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1039, lv118) + layer_norm302: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1043, model_decoder_layers_14_encoder_attn_layer_norm_weight4, model_decoder_layers_14_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv119 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_14_encoder_attn_q_proj_weight4, layer_norm302, model_decoder_layers_14_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1179: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv119, R.shape([1, seq_len, 20, 64])) + reshape1180: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1179, R.shape([seq_len, 20, 64])) + lv228 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), reshape1180), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1181: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv228, R.shape([1, seq_len, 20, 64])) + reshape1182: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1181, R.shape([1, seq_len, 1280])) + lv120 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_14_encoder_attn_out_proj_weight4, reshape1182, model_decoder_layers_14_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1046: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1043, lv120) + layer_norm303: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1046, model_decoder_layers_14_final_layer_norm_weight4, model_decoder_layers_14_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv14 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_14_fc1_weight4, layer_norm303, model_decoder_layers_14_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv121 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_14_fc2_weight4, lv14, model_decoder_layers_14_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1049: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1046, lv121) + layer_norm304: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1049, model_decoder_layers_15_self_attn_layer_norm_weight4, model_decoder_layers_15_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv122 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_15_self_attn_q_proj_weight4, layer_norm304, model_decoder_layers_15_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1183: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv122, R.shape([1, seq_len, 20, 64])) + lv47_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_15_self_attn_k_proj_weight4, layer_norm304), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1184: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv47_1, R.shape([1, seq_len, 20, 64])) + lv123 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_15_self_attn_v_proj_weight4, layer_norm304, model_decoder_layers_15_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1185: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv123, R.shape([1, seq_len, 20, 64])) + concat79: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1183, reshape1184, reshape1185), axis=2) + reshape1186: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat79, R.shape([seq_len, 60, 64])) + lv229 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), reshape1186), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1187: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv229, R.shape([1, seq_len, 20, 64])) + reshape1188: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1187, R.shape([1, seq_len, 1280])) + lv124 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_15_self_attn_out_proj_weight4, reshape1188, model_decoder_layers_15_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1053: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1049, lv124) + layer_norm305: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1053, model_decoder_layers_15_encoder_attn_layer_norm_weight4, model_decoder_layers_15_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv125 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_15_encoder_attn_q_proj_weight4, layer_norm305, model_decoder_layers_15_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1189: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv125, R.shape([1, seq_len, 20, 64])) + reshape1190: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1189, R.shape([seq_len, 20, 64])) + lv230 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), reshape1190), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1191: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv230, R.shape([1, seq_len, 20, 64])) + reshape1192: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1191, R.shape([1, seq_len, 1280])) + lv126 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_15_encoder_attn_out_proj_weight4, reshape1192, model_decoder_layers_15_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1056: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1053, lv126) + layer_norm306: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1056, model_decoder_layers_15_final_layer_norm_weight4, model_decoder_layers_15_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv15 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_15_fc1_weight4, layer_norm306, model_decoder_layers_15_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv127 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_15_fc2_weight4, lv15, model_decoder_layers_15_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1059: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1056, lv127) + layer_norm307: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1059, model_decoder_layers_16_self_attn_layer_norm_weight4, model_decoder_layers_16_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv128 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_16_self_attn_q_proj_weight4, layer_norm307, model_decoder_layers_16_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1193: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv128, R.shape([1, seq_len, 20, 64])) + lv48_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_16_self_attn_k_proj_weight4, layer_norm307), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1194: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv48_1, R.shape([1, seq_len, 20, 64])) + lv129 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_16_self_attn_v_proj_weight4, layer_norm307, model_decoder_layers_16_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1195: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv129, R.shape([1, seq_len, 20, 64])) + concat80: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1193, reshape1194, reshape1195), axis=2) + reshape1196: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat80, R.shape([seq_len, 60, 64])) + lv231 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), reshape1196), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1197: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv231, R.shape([1, seq_len, 20, 64])) + reshape1198: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1197, R.shape([1, seq_len, 1280])) + lv130 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_16_self_attn_out_proj_weight4, reshape1198, model_decoder_layers_16_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1063: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1059, lv130) + layer_norm308: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1063, model_decoder_layers_16_encoder_attn_layer_norm_weight4, model_decoder_layers_16_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv131 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_16_encoder_attn_q_proj_weight4, layer_norm308, model_decoder_layers_16_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1199: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv131, R.shape([1, seq_len, 20, 64])) + reshape1200: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1199, R.shape([seq_len, 20, 64])) + lv232 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), reshape1200), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1201: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv232, R.shape([1, seq_len, 20, 64])) + reshape1202: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1201, R.shape([1, seq_len, 1280])) + lv132 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_16_encoder_attn_out_proj_weight4, reshape1202, model_decoder_layers_16_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1066: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1063, lv132) + layer_norm309: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1066, model_decoder_layers_16_final_layer_norm_weight4, model_decoder_layers_16_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv16 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_16_fc1_weight4, layer_norm309, model_decoder_layers_16_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv133 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_16_fc2_weight4, lv16, model_decoder_layers_16_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1069: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1066, lv133) + layer_norm310: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1069, model_decoder_layers_17_self_attn_layer_norm_weight4, model_decoder_layers_17_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv134 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_17_self_attn_q_proj_weight4, layer_norm310, model_decoder_layers_17_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1203: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv134, R.shape([1, seq_len, 20, 64])) + lv49_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_17_self_attn_k_proj_weight4, layer_norm310), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1204: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv49_1, R.shape([1, seq_len, 20, 64])) + lv135 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_17_self_attn_v_proj_weight4, layer_norm310, model_decoder_layers_17_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1205: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv135, R.shape([1, seq_len, 20, 64])) + concat81: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1203, reshape1204, reshape1205), axis=2) + reshape1206: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat81, R.shape([seq_len, 60, 64])) + lv233 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), reshape1206), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1207: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv233, R.shape([1, seq_len, 20, 64])) + reshape1208: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1207, R.shape([1, seq_len, 1280])) + lv136 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_17_self_attn_out_proj_weight4, reshape1208, model_decoder_layers_17_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1073: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1069, lv136) + layer_norm311: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1073, model_decoder_layers_17_encoder_attn_layer_norm_weight4, model_decoder_layers_17_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv137 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_17_encoder_attn_q_proj_weight4, layer_norm311, model_decoder_layers_17_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1209: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv137, R.shape([1, seq_len, 20, 64])) + reshape1210: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1209, R.shape([seq_len, 20, 64])) + lv234 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), reshape1210), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1211: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv234, R.shape([1, seq_len, 20, 64])) + reshape1212: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1211, R.shape([1, seq_len, 1280])) + lv138 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_17_encoder_attn_out_proj_weight4, reshape1212, model_decoder_layers_17_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1076: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1073, lv138) + layer_norm312: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1076, model_decoder_layers_17_final_layer_norm_weight4, model_decoder_layers_17_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv17 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_17_fc1_weight4, layer_norm312, model_decoder_layers_17_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv139 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_17_fc2_weight4, lv17, model_decoder_layers_17_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1079: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1076, lv139) + layer_norm313: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1079, model_decoder_layers_18_self_attn_layer_norm_weight4, model_decoder_layers_18_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv140 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_18_self_attn_q_proj_weight4, layer_norm313, model_decoder_layers_18_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1213: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv140, R.shape([1, seq_len, 20, 64])) + lv50_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_18_self_attn_k_proj_weight4, layer_norm313), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1214: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv50_1, R.shape([1, seq_len, 20, 64])) + lv141 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_18_self_attn_v_proj_weight4, layer_norm313, model_decoder_layers_18_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1215: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv141, R.shape([1, seq_len, 20, 64])) + concat82: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1213, reshape1214, reshape1215), axis=2) + reshape1216: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat82, R.shape([seq_len, 60, 64])) + lv235 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), reshape1216), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1217: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv235, R.shape([1, seq_len, 20, 64])) + reshape1218: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1217, R.shape([1, seq_len, 1280])) + lv142 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_18_self_attn_out_proj_weight4, reshape1218, model_decoder_layers_18_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1083: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1079, lv142) + layer_norm314: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1083, model_decoder_layers_18_encoder_attn_layer_norm_weight4, model_decoder_layers_18_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv143 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_18_encoder_attn_q_proj_weight4, layer_norm314, model_decoder_layers_18_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1219: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv143, R.shape([1, seq_len, 20, 64])) + reshape1220: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1219, R.shape([seq_len, 20, 64])) + lv236 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), reshape1220), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1221: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv236, R.shape([1, seq_len, 20, 64])) + reshape1222: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1221, R.shape([1, seq_len, 1280])) + lv144 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_18_encoder_attn_out_proj_weight4, reshape1222, model_decoder_layers_18_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1086: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1083, lv144) + layer_norm315: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1086, model_decoder_layers_18_final_layer_norm_weight4, model_decoder_layers_18_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv18 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_18_fc1_weight4, layer_norm315, model_decoder_layers_18_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv145 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_18_fc2_weight4, lv18, model_decoder_layers_18_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1089: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1086, lv145) + layer_norm316: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1089, model_decoder_layers_19_self_attn_layer_norm_weight4, model_decoder_layers_19_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv146 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_19_self_attn_q_proj_weight4, layer_norm316, model_decoder_layers_19_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1223: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv146, R.shape([1, seq_len, 20, 64])) + lv51_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_19_self_attn_k_proj_weight4, layer_norm316), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1224: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv51_1, R.shape([1, seq_len, 20, 64])) + lv147 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_19_self_attn_v_proj_weight4, layer_norm316, model_decoder_layers_19_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1225: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv147, R.shape([1, seq_len, 20, 64])) + concat83: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1223, reshape1224, reshape1225), axis=2) + reshape1226: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat83, R.shape([seq_len, 60, 64])) + lv237 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), reshape1226), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1227: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv237, R.shape([1, seq_len, 20, 64])) + reshape1228: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1227, R.shape([1, seq_len, 1280])) + lv148 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_19_self_attn_out_proj_weight4, reshape1228, model_decoder_layers_19_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1093: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1089, lv148) + layer_norm317: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1093, model_decoder_layers_19_encoder_attn_layer_norm_weight4, model_decoder_layers_19_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv149 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_19_encoder_attn_q_proj_weight4, layer_norm317, model_decoder_layers_19_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1229: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv149, R.shape([1, seq_len, 20, 64])) + reshape1230: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1229, R.shape([seq_len, 20, 64])) + lv238 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), reshape1230), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1231: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv238, R.shape([1, seq_len, 20, 64])) + reshape1232: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1231, R.shape([1, seq_len, 1280])) + lv150 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_19_encoder_attn_out_proj_weight4, reshape1232, model_decoder_layers_19_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1096: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1093, lv150) + layer_norm318: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1096, model_decoder_layers_19_final_layer_norm_weight4, model_decoder_layers_19_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv19 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_19_fc1_weight4, layer_norm318, model_decoder_layers_19_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv151 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_19_fc2_weight4, lv19, model_decoder_layers_19_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1099: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1096, lv151) + layer_norm319: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1099, model_decoder_layers_20_self_attn_layer_norm_weight4, model_decoder_layers_20_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv152 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_20_self_attn_q_proj_weight4, layer_norm319, model_decoder_layers_20_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1233: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv152, R.shape([1, seq_len, 20, 64])) + lv52_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_20_self_attn_k_proj_weight4, layer_norm319), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1234: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv52_1, R.shape([1, seq_len, 20, 64])) + lv153 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_20_self_attn_v_proj_weight4, layer_norm319, model_decoder_layers_20_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1235: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv153, R.shape([1, seq_len, 20, 64])) + concat84: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1233, reshape1234, reshape1235), axis=2) + reshape1236: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat84, R.shape([seq_len, 60, 64])) + lv239 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), reshape1236), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1237: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv239, R.shape([1, seq_len, 20, 64])) + reshape1238: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1237, R.shape([1, seq_len, 1280])) + lv154 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_20_self_attn_out_proj_weight4, reshape1238, model_decoder_layers_20_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1103: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1099, lv154) + layer_norm320: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1103, model_decoder_layers_20_encoder_attn_layer_norm_weight4, model_decoder_layers_20_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv155 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_20_encoder_attn_q_proj_weight4, layer_norm320, model_decoder_layers_20_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1239: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv155, R.shape([1, seq_len, 20, 64])) + reshape1240: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1239, R.shape([seq_len, 20, 64])) + lv240 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), reshape1240), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1241: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv240, R.shape([1, seq_len, 20, 64])) + reshape1242: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1241, R.shape([1, seq_len, 1280])) + lv156 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_20_encoder_attn_out_proj_weight4, reshape1242, model_decoder_layers_20_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1106: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1103, lv156) + layer_norm321: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1106, model_decoder_layers_20_final_layer_norm_weight4, model_decoder_layers_20_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv20 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_20_fc1_weight4, layer_norm321, model_decoder_layers_20_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv157 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_20_fc2_weight4, lv20, model_decoder_layers_20_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1109: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1106, lv157) + layer_norm322: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1109, model_decoder_layers_21_self_attn_layer_norm_weight4, model_decoder_layers_21_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv158 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_21_self_attn_q_proj_weight4, layer_norm322, model_decoder_layers_21_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1243: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv158, R.shape([1, seq_len, 20, 64])) + lv53_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_21_self_attn_k_proj_weight4, layer_norm322), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1244: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv53_1, R.shape([1, seq_len, 20, 64])) + lv159 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_21_self_attn_v_proj_weight4, layer_norm322, model_decoder_layers_21_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1245: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv159, R.shape([1, seq_len, 20, 64])) + concat85: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1243, reshape1244, reshape1245), axis=2) + reshape1246: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat85, R.shape([seq_len, 60, 64])) + lv241 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), reshape1246), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1247: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv241, R.shape([1, seq_len, 20, 64])) + reshape1248: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1247, R.shape([1, seq_len, 1280])) + lv160 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_21_self_attn_out_proj_weight4, reshape1248, model_decoder_layers_21_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1113: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1109, lv160) + layer_norm323: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1113, model_decoder_layers_21_encoder_attn_layer_norm_weight4, model_decoder_layers_21_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv161 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_21_encoder_attn_q_proj_weight4, layer_norm323, model_decoder_layers_21_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1249: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv161, R.shape([1, seq_len, 20, 64])) + reshape1250: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1249, R.shape([seq_len, 20, 64])) + lv242 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), reshape1250), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1251: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv242, R.shape([1, seq_len, 20, 64])) + reshape1252: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1251, R.shape([1, seq_len, 1280])) + lv162 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_21_encoder_attn_out_proj_weight4, reshape1252, model_decoder_layers_21_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1116: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1113, lv162) + layer_norm324: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1116, model_decoder_layers_21_final_layer_norm_weight4, model_decoder_layers_21_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv21 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_21_fc1_weight4, layer_norm324, model_decoder_layers_21_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv163 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_21_fc2_weight4, lv21, model_decoder_layers_21_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1119: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1116, lv163) + layer_norm325: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1119, model_decoder_layers_22_self_attn_layer_norm_weight4, model_decoder_layers_22_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv164 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_22_self_attn_q_proj_weight4, layer_norm325, model_decoder_layers_22_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1253: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv164, R.shape([1, seq_len, 20, 64])) + lv54_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_22_self_attn_k_proj_weight4, layer_norm325), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1254: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv54_1, R.shape([1, seq_len, 20, 64])) + lv165 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_22_self_attn_v_proj_weight4, layer_norm325, model_decoder_layers_22_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1255: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv165, R.shape([1, seq_len, 20, 64])) + concat86: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1253, reshape1254, reshape1255), axis=2) + reshape1256: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat86, R.shape([seq_len, 60, 64])) + lv243 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), reshape1256), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1257: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv243, R.shape([1, seq_len, 20, 64])) + reshape1258: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1257, R.shape([1, seq_len, 1280])) + lv166 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_22_self_attn_out_proj_weight4, reshape1258, model_decoder_layers_22_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1123: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1119, lv166) + layer_norm326: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1123, model_decoder_layers_22_encoder_attn_layer_norm_weight4, model_decoder_layers_22_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv167 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_22_encoder_attn_q_proj_weight4, layer_norm326, model_decoder_layers_22_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1259: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv167, R.shape([1, seq_len, 20, 64])) + reshape1260: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1259, R.shape([seq_len, 20, 64])) + lv244 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), reshape1260), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1261: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv244, R.shape([1, seq_len, 20, 64])) + reshape1262: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1261, R.shape([1, seq_len, 1280])) + lv168 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_22_encoder_attn_out_proj_weight4, reshape1262, model_decoder_layers_22_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1126: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1123, lv168) + layer_norm327: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1126, model_decoder_layers_22_final_layer_norm_weight4, model_decoder_layers_22_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv22 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_22_fc1_weight4, layer_norm327, model_decoder_layers_22_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv169 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_22_fc2_weight4, lv22, model_decoder_layers_22_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1129: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1126, lv169) + layer_norm328: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1129, model_decoder_layers_23_self_attn_layer_norm_weight4, model_decoder_layers_23_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv170 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_23_self_attn_q_proj_weight4, layer_norm328, model_decoder_layers_23_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1263: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv170, R.shape([1, seq_len, 20, 64])) + lv55_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_23_self_attn_k_proj_weight4, layer_norm328), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1264: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv55_1, R.shape([1, seq_len, 20, 64])) + lv171 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_23_self_attn_v_proj_weight4, layer_norm328, model_decoder_layers_23_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1265: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv171, R.shape([1, seq_len, 20, 64])) + concat87: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1263, reshape1264, reshape1265), axis=2) + reshape1266: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat87, R.shape([seq_len, 60, 64])) + lv245 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), reshape1266), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1267: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv245, R.shape([1, seq_len, 20, 64])) + reshape1268: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1267, R.shape([1, seq_len, 1280])) + lv172 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_23_self_attn_out_proj_weight4, reshape1268, model_decoder_layers_23_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1133: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1129, lv172) + layer_norm329: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1133, model_decoder_layers_23_encoder_attn_layer_norm_weight4, model_decoder_layers_23_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv173 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_23_encoder_attn_q_proj_weight4, layer_norm329, model_decoder_layers_23_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1269: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv173, R.shape([1, seq_len, 20, 64])) + reshape1270: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1269, R.shape([seq_len, 20, 64])) + lv246 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), reshape1270), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1271: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv246, R.shape([1, seq_len, 20, 64])) + reshape1272: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1271, R.shape([1, seq_len, 1280])) + lv174 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_23_encoder_attn_out_proj_weight4, reshape1272, model_decoder_layers_23_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1136: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1133, lv174) + layer_norm330: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1136, model_decoder_layers_23_final_layer_norm_weight4, model_decoder_layers_23_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv23 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_23_fc1_weight4, layer_norm330, model_decoder_layers_23_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv175 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_23_fc2_weight4, lv23, model_decoder_layers_23_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1139: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1136, lv175) + layer_norm331: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1139, model_decoder_layers_24_self_attn_layer_norm_weight4, model_decoder_layers_24_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv176 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_24_self_attn_q_proj_weight4, layer_norm331, model_decoder_layers_24_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1273: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv176, R.shape([1, seq_len, 20, 64])) + lv56_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_24_self_attn_k_proj_weight4, layer_norm331), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1274: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv56_1, R.shape([1, seq_len, 20, 64])) + lv177 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_24_self_attn_v_proj_weight4, layer_norm331, model_decoder_layers_24_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1275: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv177, R.shape([1, seq_len, 20, 64])) + concat88: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1273, reshape1274, reshape1275), axis=2) + reshape1276: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat88, R.shape([seq_len, 60, 64])) + lv247 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), reshape1276), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1277: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv247, R.shape([1, seq_len, 20, 64])) + reshape1278: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1277, R.shape([1, seq_len, 1280])) + lv178 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_24_self_attn_out_proj_weight4, reshape1278, model_decoder_layers_24_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1143: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1139, lv178) + layer_norm332: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1143, model_decoder_layers_24_encoder_attn_layer_norm_weight4, model_decoder_layers_24_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv179 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_24_encoder_attn_q_proj_weight4, layer_norm332, model_decoder_layers_24_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1279: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv179, R.shape([1, seq_len, 20, 64])) + reshape1280: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1279, R.shape([seq_len, 20, 64])) + lv248 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), reshape1280), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1281: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv248, R.shape([1, seq_len, 20, 64])) + reshape1282: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1281, R.shape([1, seq_len, 1280])) + lv180 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_24_encoder_attn_out_proj_weight4, reshape1282, model_decoder_layers_24_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1146: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1143, lv180) + layer_norm333: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1146, model_decoder_layers_24_final_layer_norm_weight4, model_decoder_layers_24_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv24 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_24_fc1_weight4, layer_norm333, model_decoder_layers_24_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv181 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_24_fc2_weight4, lv24, model_decoder_layers_24_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1149: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1146, lv181) + layer_norm334: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1149, model_decoder_layers_25_self_attn_layer_norm_weight4, model_decoder_layers_25_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv182 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_25_self_attn_q_proj_weight4, layer_norm334, model_decoder_layers_25_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1283: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv182, R.shape([1, seq_len, 20, 64])) + lv57_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_25_self_attn_k_proj_weight4, layer_norm334), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1284: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv57_1, R.shape([1, seq_len, 20, 64])) + lv183 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_25_self_attn_v_proj_weight4, layer_norm334, model_decoder_layers_25_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1285: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv183, R.shape([1, seq_len, 20, 64])) + concat89: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1283, reshape1284, reshape1285), axis=2) + reshape1286: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat89, R.shape([seq_len, 60, 64])) + lv249 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), reshape1286), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1287: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv249, R.shape([1, seq_len, 20, 64])) + reshape1288: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1287, R.shape([1, seq_len, 1280])) + lv184 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_25_self_attn_out_proj_weight4, reshape1288, model_decoder_layers_25_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1153: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1149, lv184) + layer_norm335: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1153, model_decoder_layers_25_encoder_attn_layer_norm_weight4, model_decoder_layers_25_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv185 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_25_encoder_attn_q_proj_weight4, layer_norm335, model_decoder_layers_25_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1289: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv185, R.shape([1, seq_len, 20, 64])) + reshape1290: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1289, R.shape([seq_len, 20, 64])) + lv250 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), reshape1290), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1291: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv250, R.shape([1, seq_len, 20, 64])) + reshape1292: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1291, R.shape([1, seq_len, 1280])) + lv186 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_25_encoder_attn_out_proj_weight4, reshape1292, model_decoder_layers_25_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1156: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1153, lv186) + layer_norm336: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1156, model_decoder_layers_25_final_layer_norm_weight4, model_decoder_layers_25_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv25 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_25_fc1_weight4, layer_norm336, model_decoder_layers_25_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv187 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_25_fc2_weight4, lv25, model_decoder_layers_25_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1159: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1156, lv187) + layer_norm337: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1159, model_decoder_layers_26_self_attn_layer_norm_weight4, model_decoder_layers_26_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv188 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_26_self_attn_q_proj_weight4, layer_norm337, model_decoder_layers_26_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1293: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv188, R.shape([1, seq_len, 20, 64])) + lv58_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_26_self_attn_k_proj_weight4, layer_norm337), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1294: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv58_1, R.shape([1, seq_len, 20, 64])) + lv189 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_26_self_attn_v_proj_weight4, layer_norm337, model_decoder_layers_26_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1295: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv189, R.shape([1, seq_len, 20, 64])) + concat90: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1293, reshape1294, reshape1295), axis=2) + reshape1296: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat90, R.shape([seq_len, 60, 64])) + lv251 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), reshape1296), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1297: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv251, R.shape([1, seq_len, 20, 64])) + reshape1298: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1297, R.shape([1, seq_len, 1280])) + lv190 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_26_self_attn_out_proj_weight4, reshape1298, model_decoder_layers_26_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1163: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1159, lv190) + layer_norm338: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1163, model_decoder_layers_26_encoder_attn_layer_norm_weight4, model_decoder_layers_26_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv191 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_26_encoder_attn_q_proj_weight4, layer_norm338, model_decoder_layers_26_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1299: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv191, R.shape([1, seq_len, 20, 64])) + reshape1300: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1299, R.shape([seq_len, 20, 64])) + lv252 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), reshape1300), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1301: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv252, R.shape([1, seq_len, 20, 64])) + reshape1302: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1301, R.shape([1, seq_len, 1280])) + lv192 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_26_encoder_attn_out_proj_weight4, reshape1302, model_decoder_layers_26_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1166: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1163, lv192) + layer_norm339: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1166, model_decoder_layers_26_final_layer_norm_weight4, model_decoder_layers_26_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv26 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_26_fc1_weight4, layer_norm339, model_decoder_layers_26_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv193 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_26_fc2_weight4, lv26, model_decoder_layers_26_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1169: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1166, lv193) + layer_norm340: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1169, model_decoder_layers_27_self_attn_layer_norm_weight4, model_decoder_layers_27_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv194 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_27_self_attn_q_proj_weight4, layer_norm340, model_decoder_layers_27_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1303: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv194, R.shape([1, seq_len, 20, 64])) + lv59_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_27_self_attn_k_proj_weight4, layer_norm340), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1304: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv59_1, R.shape([1, seq_len, 20, 64])) + lv195 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_27_self_attn_v_proj_weight4, layer_norm340, model_decoder_layers_27_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1305: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv195, R.shape([1, seq_len, 20, 64])) + concat91: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1303, reshape1304, reshape1305), axis=2) + reshape1306: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat91, R.shape([seq_len, 60, 64])) + lv253 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), reshape1306), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1307: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv253, R.shape([1, seq_len, 20, 64])) + reshape1308: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1307, R.shape([1, seq_len, 1280])) + lv196 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_27_self_attn_out_proj_weight4, reshape1308, model_decoder_layers_27_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1173: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1169, lv196) + layer_norm341: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1173, model_decoder_layers_27_encoder_attn_layer_norm_weight4, model_decoder_layers_27_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv197 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_27_encoder_attn_q_proj_weight4, layer_norm341, model_decoder_layers_27_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1309: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv197, R.shape([1, seq_len, 20, 64])) + reshape1310: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1309, R.shape([seq_len, 20, 64])) + lv254 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), reshape1310), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1311: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv254, R.shape([1, seq_len, 20, 64])) + reshape1312: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1311, R.shape([1, seq_len, 1280])) + lv198_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_27_encoder_attn_out_proj_weight4, reshape1312, model_decoder_layers_27_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1176: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1173, lv198_1) + layer_norm342: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1176, model_decoder_layers_27_final_layer_norm_weight4, model_decoder_layers_27_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv27 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_27_fc1_weight4, layer_norm342, model_decoder_layers_27_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv199_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_27_fc2_weight4, lv27, model_decoder_layers_27_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1179: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1176, lv199_1) + layer_norm343: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1179, model_decoder_layers_28_self_attn_layer_norm_weight4, model_decoder_layers_28_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv200_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_28_self_attn_q_proj_weight4, layer_norm343, model_decoder_layers_28_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1313: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv200_1, R.shape([1, seq_len, 20, 64])) + lv60_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_28_self_attn_k_proj_weight4, layer_norm343), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1314: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv60_1, R.shape([1, seq_len, 20, 64])) + lv201_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_28_self_attn_v_proj_weight4, layer_norm343, model_decoder_layers_28_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1315: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv201_1, R.shape([1, seq_len, 20, 64])) + concat92: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1313, reshape1314, reshape1315), axis=2) + reshape1316: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat92, R.shape([seq_len, 60, 64])) + lv255 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), reshape1316), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1317: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv255, R.shape([1, seq_len, 20, 64])) + reshape1318: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1317, R.shape([1, seq_len, 1280])) + lv202_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_28_self_attn_out_proj_weight4, reshape1318, model_decoder_layers_28_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1183: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1179, lv202_1) + layer_norm344: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1183, model_decoder_layers_28_encoder_attn_layer_norm_weight4, model_decoder_layers_28_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv203_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_28_encoder_attn_q_proj_weight4, layer_norm344, model_decoder_layers_28_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1319: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv203_1, R.shape([1, seq_len, 20, 64])) + reshape1320: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1319, R.shape([seq_len, 20, 64])) + lv256 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), reshape1320), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1321: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv256, R.shape([1, seq_len, 20, 64])) + reshape1322: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1321, R.shape([1, seq_len, 1280])) + lv204_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_28_encoder_attn_out_proj_weight4, reshape1322, model_decoder_layers_28_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1186: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1183, lv204_1) + layer_norm345: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1186, model_decoder_layers_28_final_layer_norm_weight4, model_decoder_layers_28_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv28 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_28_fc1_weight4, layer_norm345, model_decoder_layers_28_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv205_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_28_fc2_weight4, lv28, model_decoder_layers_28_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1189: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1186, lv205_1) + layer_norm346: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1189, model_decoder_layers_29_self_attn_layer_norm_weight4, model_decoder_layers_29_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv206_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_29_self_attn_q_proj_weight4, layer_norm346, model_decoder_layers_29_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1323: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv206_1, R.shape([1, seq_len, 20, 64])) + lv61_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_29_self_attn_k_proj_weight4, layer_norm346), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1324: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv61_1, R.shape([1, seq_len, 20, 64])) + lv207_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_29_self_attn_v_proj_weight4, layer_norm346, model_decoder_layers_29_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1325: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv207_1, R.shape([1, seq_len, 20, 64])) + concat93: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1323, reshape1324, reshape1325), axis=2) + reshape1326: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat93, R.shape([seq_len, 60, 64])) + lv257 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), reshape1326), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1327: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv257, R.shape([1, seq_len, 20, 64])) + reshape1328: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1327, R.shape([1, seq_len, 1280])) + lv208_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_29_self_attn_out_proj_weight4, reshape1328, model_decoder_layers_29_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1193: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1189, lv208_1) + layer_norm347: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1193, model_decoder_layers_29_encoder_attn_layer_norm_weight4, model_decoder_layers_29_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv209_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_29_encoder_attn_q_proj_weight4, layer_norm347, model_decoder_layers_29_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1329: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv209_1, R.shape([1, seq_len, 20, 64])) + reshape1330: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1329, R.shape([seq_len, 20, 64])) + lv258 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), reshape1330), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1331: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv258, R.shape([1, seq_len, 20, 64])) + reshape1332: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1331, R.shape([1, seq_len, 1280])) + lv210_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_29_encoder_attn_out_proj_weight4, reshape1332, model_decoder_layers_29_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1196: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1193, lv210_1) + layer_norm348: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1196, model_decoder_layers_29_final_layer_norm_weight4, model_decoder_layers_29_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv29 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_29_fc1_weight4, layer_norm348, model_decoder_layers_29_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv211_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_29_fc2_weight4, lv29, model_decoder_layers_29_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1199: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1196, lv211_1) + layer_norm349: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1199, model_decoder_layers_30_self_attn_layer_norm_weight4, model_decoder_layers_30_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv212_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_30_self_attn_q_proj_weight4, layer_norm349, model_decoder_layers_30_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1333: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv212_1, R.shape([1, seq_len, 20, 64])) + lv62_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_30_self_attn_k_proj_weight4, layer_norm349), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1334: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv62_1, R.shape([1, seq_len, 20, 64])) + lv213_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_30_self_attn_v_proj_weight4, layer_norm349, model_decoder_layers_30_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1335: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv213_1, R.shape([1, seq_len, 20, 64])) + concat94: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1333, reshape1334, reshape1335), axis=2) + reshape1336: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat94, R.shape([seq_len, 60, 64])) + lv259 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), reshape1336), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1337: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv259, R.shape([1, seq_len, 20, 64])) + reshape1338: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1337, R.shape([1, seq_len, 1280])) + lv214_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_30_self_attn_out_proj_weight4, reshape1338, model_decoder_layers_30_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1203: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1199, lv214_1) + layer_norm350: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1203, model_decoder_layers_30_encoder_attn_layer_norm_weight4, model_decoder_layers_30_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv215_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_30_encoder_attn_q_proj_weight4, layer_norm350, model_decoder_layers_30_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1339: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv215_1, R.shape([1, seq_len, 20, 64])) + reshape1340: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1339, R.shape([seq_len, 20, 64])) + lv260 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), reshape1340), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1341: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv260, R.shape([1, seq_len, 20, 64])) + reshape1342: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1341, R.shape([1, seq_len, 1280])) + lv216_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_30_encoder_attn_out_proj_weight4, reshape1342, model_decoder_layers_30_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1206: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1203, lv216_1) + layer_norm351: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1206, model_decoder_layers_30_final_layer_norm_weight4, model_decoder_layers_30_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv30 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_30_fc1_weight4, layer_norm351, model_decoder_layers_30_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv217_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_30_fc2_weight4, lv30, model_decoder_layers_30_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1209: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1206, lv217_1) + layer_norm352: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1209, model_decoder_layers_31_self_attn_layer_norm_weight4, model_decoder_layers_31_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv218_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_31_self_attn_q_proj_weight4, layer_norm352, model_decoder_layers_31_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1343: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv218_1, R.shape([1, seq_len, 20, 64])) + lv63_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_31_self_attn_k_proj_weight4, layer_norm352), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1344: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv63_1, R.shape([1, seq_len, 20, 64])) + lv219_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_31_self_attn_v_proj_weight4, layer_norm352, model_decoder_layers_31_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1345: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv219_1, R.shape([1, seq_len, 20, 64])) + concat95: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1343, reshape1344, reshape1345), axis=2) + reshape1346: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat95, R.shape([seq_len, 60, 64])) + lv261 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), reshape1346), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1347: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv261, R.shape([1, seq_len, 20, 64])) + reshape1348: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1347, R.shape([1, seq_len, 1280])) + lv220_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_31_self_attn_out_proj_weight4, reshape1348, model_decoder_layers_31_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1213: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1209, lv220_1) + layer_norm353: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1213, model_decoder_layers_31_encoder_attn_layer_norm_weight4, model_decoder_layers_31_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv221_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_31_encoder_attn_q_proj_weight4, layer_norm353, model_decoder_layers_31_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1349: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv221_1, R.shape([1, seq_len, 20, 64])) + reshape1350: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1349, R.shape([seq_len, 20, 64])) + lv262 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), reshape1350), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1351: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv262, R.shape([1, seq_len, 20, 64])) + reshape1352: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1351, R.shape([1, seq_len, 1280])) + lv222_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_31_encoder_attn_out_proj_weight4, reshape1352, model_decoder_layers_31_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1216: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1213, lv222_1) + layer_norm354: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1216, model_decoder_layers_31_final_layer_norm_weight4, model_decoder_layers_31_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv31 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_31_fc1_weight4, layer_norm354, model_decoder_layers_31_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv223_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_31_fc2_weight4, lv31, model_decoder_layers_31_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1219: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1216, lv223_1) + layer_norm355: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1219, model_decoder_layer_norm_weight4, model_decoder_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) + lv263 = R.call_tir(cls.index, (layer_norm355,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv64_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul2_cublas", (model_decoder_embed_tokens_weight4, lv263), out_sinfo=R.Tensor((1, 1, 51866), dtype="float32")) + gv4: R.Tensor((1, 1, 51866), dtype="float32") = lv64_1 + R.output(gv4) + return gv4 + + @R.function + def renormalize_by_top_p(probs: R.Tensor(("batch_size", "vocab_size"), dtype="float32"), top_p: R.Tensor(("batch_size",), dtype="float32"), init_pivots: R.Tensor(("batch_size", 3), dtype="float32")) -> R.Tensor(("batch_size", "vocab_size"), dtype="float32"): + batch_size = T.int64() + vocab_size = T.int64() + R.func_attr({"relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "num_positions": 48, "num_samples": 8}}) + cls = Module + with R.dataflow(): + lv6 = R.call_tir(cls.top_p_pivot_cutoff, (probs, top_p, init_pivots), out_sinfo=[R.Tensor((batch_size,), dtype="float32"), R.Tensor((batch_size,), dtype="float32")]) + lv7: R.Tensor((batch_size,), dtype="float32") = lv6[0] + lv8: R.Tensor((batch_size,), dtype="float32") = lv6[1] + gv5 = R.call_tir(cls.top_p_renorm_after_cutoff, (probs, lv7, lv8), out_sinfo=R.Tensor((batch_size, vocab_size), dtype="float32")) + R.output(gv5) + return gv5 + + @R.function + def sample_with_top_p(sorted_probs: R.Tensor(("batch_size", "vocab_size"), dtype="float32"), sorted_indices: R.Tensor(("batch_size", "vocab_size"), dtype="int32"), uniform_samples: R.Tensor(("num_samples",), dtype="float32"), sample_indices: R.Tensor(("num_samples",), dtype="int32"), top_p: R.Tensor(("batch_size",), dtype="float32")) -> R.Tensor(("num_samples",), dtype="int32"): + num_samples = T.int64() + batch_size = T.int64() + vocab_size = T.int64() + R.func_attr({"relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "num_positions": 48, "num_samples": 8}}) + cls = Module + with R.dataflow(): + sorted_probs_1: R.Tensor((batch_size, vocab_size), dtype="float32") = sorted_probs + sorted_indices_1: R.Tensor((batch_size, vocab_size), dtype="int32") = sorted_indices + uniform_samples1: R.Tensor((num_samples, 1), dtype="float32") = R.call_pure_packed("vm.builtin.reshape", uniform_samples, R.shape([num_samples, 1]), sinfo_args=(R.Tensor((num_samples, 1), dtype="float32"),)) + sample_indices1: R.Tensor((num_samples, 1), dtype="int32") = R.call_pure_packed("vm.builtin.reshape", sample_indices, R.shape([num_samples, 1]), sinfo_args=(R.Tensor((num_samples, 1), dtype="int32"),)) + sample_indices2: R.Tensor((batch_size, 1), dtype="float32") = R.call_pure_packed("vm.builtin.reshape", top_p, R.shape([batch_size, 1]), sinfo_args=(R.Tensor((batch_size, 1), dtype="float32"),)) + lv3 = R.call_tir(cls.full, R.tuple(), out_sinfo=R.Tensor((batch_size, 1), dtype="int32"), tir_vars=R.shape([vocab_size])) + cumsum: R.Tensor((batch_size, vocab_size), dtype="float32") = R.cumsum(sorted_probs_1, axis=1, dtype="void", exclusive=None) + lv4 = R.call_tir(cls.get_renorm_prob, (cumsum, sample_indices2, lv3), out_sinfo=R.Tensor((batch_size, 1), dtype="float32")) + lv5 = R.call_tir(cls.get_index_from_sorted, (cumsum, sorted_indices_1, lv4, uniform_samples1, sample_indices1), out_sinfo=R.Tensor((num_samples, 1), dtype="int32")) + gv2: R.Tensor((num_samples,), dtype="int32") = R.call_pure_packed("vm.builtin.reshape", lv5, R.shape([num_samples]), sinfo_args=(R.Tensor((num_samples,), dtype="int32"),)) + R.output(gv2) + return gv2 + + @R.function + def sampler_take_probs(unsorted_probs: R.Tensor(("batch_size", "vocab_size"), dtype="float32"), sorted_indices: R.Tensor(("batch_size", "vocab_size"), dtype="int32"), sample_indices: R.Tensor(("num_samples",), dtype="int32"), sampling_result: R.Tensor(("num_samples",), dtype="int32"), lobprob_offsets: R.Tensor(("num_positions",), dtype="int32")) -> R.Tuple(R.Tensor(("num_samples",), dtype="float32"), R.Tensor(("num_positions",), dtype="float32"), R.Tensor(("num_positions",), dtype="int32")): + num_samples = T.int64() + num_positions = T.int64() + batch_size = T.int64() + vocab_size = T.int64() + R.func_attr({"relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "num_positions": 48, "num_samples": 8}}) + cls = Module + with R.dataflow(): + gv3 = R.call_tir(cls.sampler_take_probs_tir, (unsorted_probs, sorted_indices, sample_indices, sampling_result, lobprob_offsets), out_sinfo=[R.Tensor((num_samples,), dtype="float32"), R.Tensor((num_positions,), dtype="float32"), R.Tensor((num_positions,), dtype="int32")]) + R.output(gv3) + return gv3 + + @R.function + def sampler_verify_draft_tokens(draft_probs: R.Tensor(("num_nodes", "vocab_size"), dtype="float32"), draft_tokens: R.Tensor(("num_nodes",), dtype="int32"), model_probs: R.Tensor(("num_nodes", "vocab_size"), dtype="float32"), token_tree_first_child: R.Tensor(("num_nodes",), dtype="int32"), token_tree_next_sibling: R.Tensor(("num_nodes",), dtype="int32"), uniform_samples: R.Tensor(("num_nodes",), dtype="float32"), token_tree_parent_ptr: R.Tensor(("nbatch",), dtype="int32")) -> R.Tuple(R.Tensor(("num_nodes", "vocab_size"), dtype="float32"), R.Tensor(("nbatch",), dtype="int32")): + num_nodes = T.int64() + vocab_size = T.int64() + nbatch = T.int64() + R.func_attr({"relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "num_positions": 48, "num_samples": 8}}) + cls = Module + with R.dataflow(): + gv4: R.Tuple(R.Tensor((num_nodes, vocab_size), dtype="float32"), R.Tensor((nbatch,), dtype="int32")) = R.call_tir_inplace(cls.batch_verify_on_gpu_single_kernel, (draft_probs, draft_tokens, model_probs, token_tree_first_child, token_tree_next_sibling, uniform_samples, token_tree_parent_ptr), out_sinfo=[R.Tensor((num_nodes, vocab_size), dtype="float32"), R.Tensor((nbatch,), dtype="int32")], inplace_indices=[2, 6]) + R.output(gv4) + return gv4 + + @R.function + def softmax_with_temperature(logits: R.Tensor(("batch_size", 1, "vocab_size"), dtype="float32"), temperature: R.Tensor(("batch_size",), dtype="float32")) -> R.Tensor(("batch_size", 1, "vocab_size"), dtype="float32"): + batch_size = T.int64() + vocab_size = T.int64() + R.func_attr({"relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "seq_len": 15000, "total_seq_len": 1500}}) + cls = Module + with R.dataflow(): + lv: R.Tensor((batch_size, vocab_size), dtype="float32") = R.call_pure_packed("vm.builtin.reshape", logits, R.shape([batch_size, vocab_size]), sinfo_args=(R.Tensor((batch_size, vocab_size), dtype="float32"),)) + lv1 = R.call_tir(cls.chunk_lse, (lv, temperature), out_sinfo=[R.Tensor((batch_size, (vocab_size + 4096 - 1) // 4096), dtype="float32"), R.Tensor((batch_size, (vocab_size + 4096 - 1) // 4096), dtype="float32")]) + lv2: R.Tensor((batch_size, (vocab_size + 4096 - 1) // 4096), dtype="float32") = lv1[0] + lv3: R.Tensor((batch_size, (vocab_size + 4096 - 1) // 4096), dtype="float32") = lv1[1] + lv4 = R.call_tir(cls.softmax_with_chunked_sum, (lv, temperature, lv2, lv3), out_sinfo=R.Tensor((batch_size, vocab_size), dtype="float32")) + gv: R.Tensor((batch_size, 1, vocab_size), dtype="float32") = R.call_pure_packed("vm.builtin.reshape", lv4, R.shape([batch_size, 1, vocab_size]), sinfo_args=(R.Tensor((batch_size, 1, vocab_size), dtype="float32"),)) + R.output(gv) + return gv + +# Metadata omitted. Use show_meta=True in script() method to show it. \ No newline at end of file diff --git a/debug/debug-phase2.py b/debug/debug-phase2.py new file mode 100644 index 0000000000000000000000000000000000000000..4e4dd89652a26b11e69fc4048d63a734441b5ee8 --- /dev/null +++ b/debug/debug-phase2.py @@ -0,0 +1,10541 @@ +# from tvm.script import ir as I +# from tvm.script import tir as T +# from tvm.script import relax as R + +@I.ir_module +class Module: + I.module_attrs({"external_mods": [metadata["runtime.Module"][0], metadata["runtime.Module"][1], metadata["runtime.Module"][2], metadata["runtime.Module"][3], metadata["runtime.Module"][4], metadata["runtime.Module"][5], metadata["runtime.Module"][6], metadata["runtime.Module"][7], metadata["runtime.Module"][8], metadata["runtime.Module"][9], metadata["runtime.Module"][10], metadata["runtime.Module"][11], metadata["runtime.Module"][12], metadata["runtime.Module"][13], metadata["runtime.Module"][14]]}) + @T.prim_func(private=True) + def NT_matmul(layer_norm356: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16"), model_decoder_layers_0_self_attn_q_proj_weight5: T.Buffer((T.int64(1280), T.int64(1280)), "float16"), NT_matmul: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16")): + T.func_attr({"op_pattern": 4, "tir.noalias": T.bool(True)}) + # with T.block("root"): + for i0, i1, i2, k in T.grid(T.int64(1), T.int64(1), T.int64(1280), T.int64(1280)): + with T.block("NT_matmul"): + v_i0, v_i1, v_i2, v_k = T.axis.remap("SSSR", [i0, i1, i2, k]) + T.reads(layer_norm356[v_i0, v_i1, v_k], model_decoder_layers_0_self_attn_q_proj_weight5[v_i2, v_k]) + T.writes(NT_matmul[v_i0, v_i1, v_i2]) + with T.init(): + NT_matmul[v_i0, v_i1, v_i2] = T.float16(0) + NT_matmul[v_i0, v_i1, v_i2] = NT_matmul[v_i0, v_i1, v_i2] + layer_norm356[v_i0, v_i1, v_k] * model_decoder_layers_0_self_attn_q_proj_weight5[v_i2, v_k] + + @T.prim_func(private=True) + def NT_matmul3(layer_norm452: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16"), model_decoder_embed_tokens_weight5: T.Buffer((T.int64(51866), T.int64(1280)), "float16"), NT_matmul: T.Buffer((T.int64(1), T.int64(1), T.int64(51866)), "float32")): + T.func_attr({"op_pattern": 4, "tir.noalias": T.bool(True)}) + # with T.block("root"): + for i0, i1, i2, k in T.grid(T.int64(1), T.int64(1), T.int64(51866), T.int64(1280)): + with T.block("NT_matmul"): + v_i0, v_i1, v_i2, v_k = T.axis.remap("SSSR", [i0, i1, i2, k]) + T.reads(layer_norm452[v_i0, v_i1, v_k], model_decoder_embed_tokens_weight5[v_i2, v_k]) + T.writes(NT_matmul[v_i0, v_i1, v_i2]) + with T.init(): + NT_matmul[v_i0, v_i1, v_i2] = T.float32(0) + NT_matmul[v_i0, v_i1, v_i2] = NT_matmul[v_i0, v_i1, v_i2] + T.Cast("float32", layer_norm452[v_i0, v_i1, v_k]) * T.Cast("float32", model_decoder_embed_tokens_weight5[v_i2, v_k]) + + @T.prim_func(private=True) + def add(var_reshape708: T.handle, var_reshape709: T.handle, var_T_add: T.handle): + T.func_attr({"op_pattern": 0, "tir.noalias": T.bool(True)}) + batch_size = T.int64() + reshape708 = T.match_buffer(var_reshape708, (batch_size, T.int64(1), T.int64(1280)), "float16") + reshape709 = T.match_buffer(var_reshape709, (batch_size, T.int64(1), T.int64(1280)), "float16") + T_add = T.match_buffer(var_T_add, (batch_size, T.int64(1), T.int64(1280)), "float16") + # with T.block("root"): + for ax0, ax1, ax2 in T.grid(batch_size, T.int64(1), T.int64(1280)): + with T.block("T_add"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(reshape708[v_ax0, v_ax1, v_ax2], reshape709[v_ax0, v_ax1, v_ax2]) + T.writes(T_add[v_ax0, v_ax1, v_ax2]) + T_add[v_ax0, v_ax1, v_ax2] = reshape708[v_ax0, v_ax1, v_ax2] + reshape709[v_ax0, v_ax1, v_ax2] + + @T.prim_func(private=True) + def add4(var_add: T.handle, var_lv610: T.handle, var_T_add: T.handle): + T.func_attr({"op_pattern": 0, "tir.noalias": T.bool(True)}) + batch_size = T.int64() + add = T.match_buffer(var_add, (batch_size, T.int64(1500), T.int64(1280)), "float16") + lv610 = T.match_buffer(var_lv610, (batch_size, T.int64(1500), T.int64(1280)), "float16") + T_add = T.match_buffer(var_T_add, (batch_size, T.int64(1500), T.int64(1280)), "float16") + # with T.block("root"): + for ax0, ax1, ax2 in T.grid(batch_size, T.int64(1500), T.int64(1280)): + with T.block("T_add"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(add[v_ax0, v_ax1, v_ax2], lv610[v_ax0, v_ax1, v_ax2]) + T.writes(T_add[v_ax0, v_ax1, v_ax2]) + T_add[v_ax0, v_ax1, v_ax2] = add[v_ax0, v_ax1, v_ax2] + lv610[v_ax0, v_ax1, v_ax2] + + @T.prim_func(private=True) + def add5(var_reshape385: T.handle, var_reshape386: T.handle, var_T_add: T.handle): + T.func_attr({"op_pattern": 0, "tir.noalias": T.bool(True)}) + seq_len = T.int64() + reshape385 = T.match_buffer(var_reshape385, (T.int64(1), seq_len, T.int64(1280)), "float16") + reshape386 = T.match_buffer(var_reshape386, (T.int64(1), seq_len, T.int64(1280)), "float16") + T_add = T.match_buffer(var_T_add, (T.int64(1), seq_len, T.int64(1280)), "float16") + # with T.block("root"): + for ax0, ax1, ax2 in T.grid(T.int64(1), seq_len, T.int64(1280)): + with T.block("T_add"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(reshape385[v_ax0, v_ax1, v_ax2], reshape386[v_ax0, v_ax1, v_ax2]) + T.writes(T_add[v_ax0, v_ax1, v_ax2]) + T_add[v_ax0, v_ax1, v_ax2] = reshape385[v_ax0, v_ax1, v_ax2] + reshape386[v_ax0, v_ax1, v_ax2] + + @T.prim_func + def apply_bitmask_inplace(var_logits: T.handle, var_seq_ids: T.handle, var_bitmask: T.handle): + T.func_attr({"op_pattern": 8, "target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": T.bool(True), "tir.noalias": T.bool(True)}) + batch_size, vocab_size = T.int32(is_size_var=True), T.int32(is_size_var=True) + logits = T.match_buffer(var_logits, (batch_size, vocab_size)) + num_seq = T.int32(is_size_var=True) + seq_ids = T.match_buffer(var_seq_ids, (num_seq,), "int32") + bitmask = T.match_buffer(var_bitmask, (batch_size, (vocab_size + 31) // 32), "int32") + # with T.block("root"): + for fused_s_v_0 in T.thread_binding((num_seq * vocab_size + 1023) // 1024, thread="blockIdx.x"): + for fused_s_v_1 in T.thread_binding(1024, thread="threadIdx.x"): + with T.block("block"): + vs = T.axis.spatial(num_seq, (fused_s_v_0 * 1024 + fused_s_v_1) // vocab_size) + vv = T.axis.spatial(vocab_size, (fused_s_v_0 * 1024 + fused_s_v_1) % vocab_size) + T.where(fused_s_v_0 * 1024 + fused_s_v_1 < num_seq * vocab_size) + T.reads(bitmask[seq_ids[vs], vv // 32], seq_ids[vs], logits[seq_ids[vs], vv]) + T.writes(logits[seq_ids[vs], vv]) + logits[seq_ids[vs], vv] = T.if_then_else(T.bitwise_and(T.shift_right(bitmask[seq_ids[vs], vv // 32], vv % 32), 1) == 1, logits[seq_ids[vs], vv], T.float32(-3.4028234663852886e+38)) + + @T.prim_func + def apply_logit_bias_inplace(var_logits: T.handle, var_pos2seq_id: T.handle, var_token_ids: T.handle, var_logit_bias: T.handle): + T.func_attr({"op_pattern": 8, "target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": T.bool(True), "tir.noalias": T.bool(True)}) + batch_size, vocab_size = T.int32(is_size_var=True), T.int32(is_size_var=True) + logits = T.match_buffer(var_logits, (batch_size, vocab_size)) + num_token = T.int32(is_size_var=True) + pos2seq_id = T.match_buffer(var_pos2seq_id, (num_token,), "int32") + token_ids = T.match_buffer(var_token_ids, (num_token,), "int32") + logit_bias = T.match_buffer(var_logit_bias, (num_token,)) + # with T.block("root"): + for p0 in T.thread_binding((num_token + 1023) // 1024, thread="blockIdx.x"): + for p1 in T.thread_binding(1024, thread="threadIdx.x"): + with T.block("block"): + vp = T.axis.spatial(num_token, p0 * 1024 + p1) + T.where(p0 * 1024 + p1 < num_token) + T.reads(logits[pos2seq_id[vp], token_ids[vp]], pos2seq_id[vp], token_ids[vp], logit_bias[vp]) + T.writes(logits[pos2seq_id[vp], token_ids[vp]]) + logits[pos2seq_id[vp], token_ids[vp]] = logits[pos2seq_id[vp], token_ids[vp]] + logit_bias[vp] + + @T.prim_func + def apply_penalty_inplace(var_logits: T.handle, var_seq_ids: T.handle, var_pos2seq_id: T.handle, var_token_ids: T.handle, var_token_cnt: T.handle, var_penalties: T.handle): + T.func_attr({"op_pattern": 8, "target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": T.bool(True), "tir.noalias": T.bool(True)}) + batch_size, vocab_size = T.int32(is_size_var=True), T.int32(is_size_var=True) + logits = T.match_buffer(var_logits, (batch_size, vocab_size)) + num_seq = T.int32(is_size_var=True) + seq_ids = T.match_buffer(var_seq_ids, (num_seq,), "int32") + num_token = T.int32(is_size_var=True) + pos2seq_id = T.match_buffer(var_pos2seq_id, (num_token,), "int32") + token_ids = T.match_buffer(var_token_ids, (num_token,), "int32") + token_cnt = T.match_buffer(var_token_cnt, (num_token,), "int32") + penalties = T.match_buffer(var_penalties, (num_seq, 3)) + # with T.block("root"): + for p0 in T.thread_binding((num_token + 1023) // 1024, thread="blockIdx.x"): + for p1 in T.thread_binding(1024, thread="threadIdx.x"): + with T.block("block"): + vp = T.axis.spatial(num_token, p0 * 1024 + p1) + T.where(p0 * 1024 + p1 < num_token) + T.reads(logits[seq_ids[pos2seq_id[vp]], token_ids[vp]], seq_ids[pos2seq_id[vp]], pos2seq_id[vp], token_ids[vp], penalties[pos2seq_id[vp], 0:3], token_cnt[vp]) + T.writes(logits[seq_ids[pos2seq_id[vp]], token_ids[vp]]) + logits[seq_ids[pos2seq_id[vp]], token_ids[vp]] = logits[seq_ids[pos2seq_id[vp]], token_ids[vp]] - (penalties[pos2seq_id[vp], 0] + T.Cast("float32", token_cnt[vp]) * penalties[pos2seq_id[vp], 1]) + logits[seq_ids[pos2seq_id[vp]], token_ids[vp]] = T.if_then_else(logits[seq_ids[pos2seq_id[vp]], token_ids[vp]] > T.float32(0), logits[seq_ids[pos2seq_id[vp]], token_ids[vp]] * penalties[pos2seq_id[vp], 2], logits[seq_ids[pos2seq_id[vp]], token_ids[vp]] / penalties[pos2seq_id[vp], 2]) + + @T.prim_func(private=True) + def argsort_thrust(var_probs: T.handle, var_lv: T.handle, var_topk_gpu_v1: T.handle): + T.func_attr({"op_pattern": 8, "tir.noalias": T.bool(True)}) + batch_size, vocab_size = T.int64(), T.int64() + data_buf = T.match_buffer(var_probs, (batch_size, vocab_size), align=8) + workspace_buf = T.match_buffer(var_lv, (T.int64(8) * (batch_size * vocab_size * T.int64(4)) + T.int64(8388608) + batch_size * vocab_size * T.int64(12),), "uint8", align=8) + indices_buf = T.match_buffer(var_topk_gpu_v1, (batch_size, vocab_size), "int32", align=8) + # with T.block("root"): + value_buf = T.alloc_buffer((batch_size, vocab_size), align=8) + with T.block("topk_gpu"): + T.reads() + T.writes() + T.call_packed("tvm.contrib.thrust.sort", T.tvm_stack_make_array(data_buf.data, T.tvm_stack_make_shape(batch_size, vocab_size), 0, 2, T.float32(0), T.int64(0)), T.tvm_stack_make_array(value_buf.data, T.tvm_stack_make_shape(batch_size, vocab_size), 0, 2, T.float32(0), T.int64(0)), T.tvm_stack_make_array(indices_buf.data, T.tvm_stack_make_shape(batch_size, vocab_size), 0, 2, 0, T.int64(0)), 0, T.tvm_stack_make_array(workspace_buf.data, T.tvm_stack_make_shape(T.int64(8) * (batch_size * vocab_size * T.int64(4)) + T.int64(8388608) + batch_size * vocab_size * T.int64(12)), 0, 1, T.uint8(0), T.int64(0))) + + @T.prim_func + def batch_decode_paged_kv(_0: T.int32, Q_handle: T.handle, pages_handle: T.handle, page_table_indptr_handle: T.handle, page_table_values_handle: T.handle, var_length_info: T.handle, k_rope_pos_offset_handle: T.handle, q_rope_position_handle: T.handle, output_handle: T.handle, lse_handle: T.handle, rotary_mode: T.int32, rope_scale: T.float32, rope_theta: T.float32, attn_score_scaling_factor: T.float32): + T.func_attr({"op_pattern": 8, "target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) + B = T.int32(is_size_var=True) + Q = T.match_buffer(Q_handle, (B, 20, 64), "float16") + max_num_pages = T.int32(is_size_var=True) + pages = T.match_buffer(pages_handle, (max_num_pages, 2, 20, 16, 64), "float16") + page_table_indptr = T.match_buffer(page_table_indptr_handle, (B + 1,), "int32", offset_factor=1) + nnz_pages = T.int32(is_size_var=True) + page_table_values = T.match_buffer(page_table_values_handle, (nnz_pages,), "int32", offset_factor=1) + length_info = T.match_buffer(var_length_info, (B,), "int32", offset_factor=1) + k_rope_pos_offset = T.match_buffer(k_rope_pos_offset_handle, (B,), "int32", offset_factor=1) + q_rope_position = T.match_buffer(q_rope_position_handle, (B,), "int32", offset_factor=1) + output = T.match_buffer(output_handle, (B, 20, 64), "float16") + lse = T.match_buffer(lse_handle, (B, 20)) + # with T.block("root"): + sm_scale: T.float32 = T.float32(0.18033688011112042) + for bx in T.thread_binding(B, thread="blockIdx.x"): + for fused_by_bz in T.thread_binding(20, thread="blockIdx.y"): + for ty in T.thread_binding(1, thread="threadIdx.y"): + for tx in T.thread_binding(16, thread="threadIdx.x"): + for tz in T.thread_binding(32, thread="threadIdx.z"): + with T.block("attn"): + T.reads(page_table_indptr[bx:bx + 2], length_info[bx], q_rope_position[bx], Q[bx, fused_by_bz // 20 + ty + fused_by_bz % 20, tx * 4 - 32:tx * 4 - 32 + 68]) + T.writes(output[bx, fused_by_bz % 20 + fused_by_bz // 20 + ty, tx * 4:tx * 4 + 4], lse[bx, fused_by_bz % 20 + fused_by_bz // 20 + ty]) + Q_local = T.alloc_buffer((4,), "float16", scope="local") + kv_chunk_len = T.alloc_buffer((1,), "int32", scope="local") + K_smem = T.alloc_buffer((64, 64), "float16", scope="shared") + V_smem = T.alloc_buffer((64, 64), "float16", scope="shared") + O_allreduce = T.alloc_buffer((32, 1, 64), scope="shared") + md_allreduce = T.alloc_buffer((32, 1, 2), scope="shared") + S_reduce_local = T.alloc_buffer((1,), scope="local") + t0 = T.alloc_buffer((1,), scope="local") + S_local = T.alloc_buffer((2,), scope="local") + QK_local = T.alloc_buffer((4,), scope="local") + V_local = T.alloc_buffer((4,), "float16", scope="local") + m_prev = T.alloc_buffer((1,), scope="local") + d_prev = T.alloc_buffer((1,), scope="local") + other_m = T.alloc_buffer((1,), scope="local") + other_d = T.alloc_buffer((1,), scope="local") + exp_mprev = T.alloc_buffer((1,), scope="local") + exp_otherm = T.alloc_buffer((1,), scope="local") + other_o = T.alloc_buffer((4,), scope="local") + st_m = T.alloc_buffer((1,), scope="local") + st_d = T.alloc_buffer((1,), scope="local") + O_local = T.alloc_buffer((4,), scope="local") + by: T.int32 = fused_by_bz % 20 + bz: T.int32 = fused_by_bz // 20 + batch_idx: T.int32 = bx + cur_page_indptr_begin: T.int32 = page_table_indptr[batch_idx] + cur_page_indptr_end: T.int32 = page_table_indptr[batch_idx + 1] + kv_chunk_len[0] = T.if_then_else(cur_page_indptr_begin != cur_page_indptr_end, (cur_page_indptr_end - cur_page_indptr_begin - 1) * 16 + length_info[batch_idx], 0) + st_m[0] = T.float32(-50000) + st_d[0] = T.float32(1) + for vec in T.vectorized(4): + O_local[vec] = T.float32(0) + for vec in T.vectorized(4): + Q_local[vec] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", q_rope_position[batch_idx]) * rope_scale / T.pow(rope_theta, T.Cast("float32", (tx * 4 + vec) * 2 % 64) / T.float32(64))) * T.Cast("float32", Q[bx, by + bz + ty, tx * 4 + vec]) + T.sin(T.Cast("float32", q_rope_position[batch_idx]) * rope_scale / T.pow(rope_theta, T.Cast("float32", (tx * 4 + vec) * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(tx * 4 + vec < 32, Q[bx, by + bz + ty, tx * 4 + vec + 32] * T.float16(-1), Q[bx, by + bz + ty, tx * 4 + vec - 32]))), Q[bx, by + bz + ty, tx * 4 + vec]) + for iterator in range((kv_chunk_len[0] + 63) // 64): + tile_start_s: T.int32 = (tz + ty) * 2 + tile_start_g: T.int32 = (iterator * 32 + tz + ty) * 2 + for j in range(2): + with T.block("KV_load"): + T.reads() + T.writes() + row_g: T.int32 = tile_start_g + j + if row_g < kv_chunk_len[0]: + seq_offset: T.int32 = row_g + page_no: T.int32 = page_table_values[cur_page_indptr_begin + seq_offset // 16] + page_offset: T.int32 = seq_offset % 16 + for vec in T.vectorized(4): + K_smem[tile_start_s + j, tx * 4 + vec] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", k_rope_pos_offset[batch_idx] + row_g) * rope_scale / T.pow(rope_theta, T.Cast("float32", (tx * 4 + vec) * 2 % 64) / T.float32(64))) * T.Cast("float32", pages[page_no, 0, by, page_offset, tx * 4 + vec]) + T.sin(T.Cast("float32", k_rope_pos_offset[batch_idx] + row_g) * rope_scale / T.pow(rope_theta, T.Cast("float32", (tx * 4 + vec) * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(tx * 4 + vec < 32, pages[page_no, 0, by, page_offset, tx * 4 + vec + 32] * T.float16(-1), pages[page_no, 0, by, page_offset, tx * 4 + vec - 32]))), pages[page_no, 0, by, page_offset, tx * 4 + vec]) + V_smem[tile_start_s + j, tx * 4 + vec] = pages[page_no, 1, by, page_offset, tx * 4 + vec] + else: + for vec in T.vectorized(4): + K_smem[tile_start_s + j, tx * 4 + vec] = T.float16(0) + V_smem[tile_start_s + j, tx * 4 + vec] = T.float16(0) + T.tvm_storage_sync("shared") + m_prev[0] = st_m[0] + for j in range(2): + for vec in T.vectorized(4): + QK_local[vec] = T.Cast("float32", Q_local[vec]) * T.Cast("float32", K_smem[tz * 2 + j, tx * 4 + vec]) * attn_score_scaling_factor * sm_scale + S_reduce_local[0] = T.float32(0) + for vec in T.unroll(4): + S_reduce_local[0] = S_reduce_local[0] + QK_local[vec] + with T.block("block_cross_thread"): + T.reads(S_reduce_local[0]) + T.writes(t0[0]) + T.attr(T.comm_reducer(lambda x0, y0: x0 + y0, [T.float32(0)]), "reduce_scope", T.reinterpret("handle", T.uint64(0))) + T.tvm_thread_allreduce(T.uint32(1), S_reduce_local[0], T.bool(True), t0[0], tx) + S_local[j] = T.float32(-50000) + if (iterator * 32 + tz) * 2 + j < kv_chunk_len[0]: + S_local[j] = t0[0] + st_m[0] = T.max(st_m[0], S_local[j]) + o_scale: T.float32 = T.exp2(m_prev[0] - st_m[0]) + st_d[0] = st_d[0] * o_scale + for j in range(2): + S_local[j] = T.exp2(S_local[j] - st_m[0]) + st_d[0] = st_d[0] + S_local[j] + for j in T.vectorized(4): + O_local[j] = O_local[j] * o_scale + for j in range(2): + for vec in T.vectorized(4): + V_local[vec] = V_smem[tz * 2 + j, tx * 4 + vec] + for vec in T.vectorized(4): + O_local[vec] = O_local[vec] + T.Cast("float32", V_local[vec]) * S_local[j] + for vec in T.vectorized(4): + O_allreduce[tz, ty, tx * 4 + vec] = O_local[vec] + md_allreduce[tz, ty, 0] = st_m[0] + md_allreduce[tz, ty, 1] = st_d[0] + T.tvm_storage_sync("shared") + st_m[0] = T.float32(-50000) + st_d[0] = T.float32(1) + for vec in T.vectorized(4): + O_local[vec] = T.float32(0) + for j in range(32): + m_prev[0] = st_m[0] + d_prev[0] = st_d[0] + other_m[0] = md_allreduce[j, ty, 0] + other_d[0] = md_allreduce[j, ty, 1] + for vec in T.vectorized(4): + other_o[vec] = O_allreduce[j, ty, tx * 4 + vec] + st_m[0] = T.max(st_m[0], other_m[0]) + st_d[0] = d_prev[0] * T.exp2(m_prev[0] - st_m[0]) + other_d[0] * T.exp2(other_m[0] - st_m[0]) + exp_mprev[0] = T.exp2(m_prev[0] - st_m[0]) + exp_otherm[0] = T.exp2(other_m[0] - st_m[0]) + for vec in T.vectorized(4): + O_local[vec] = O_local[vec] * exp_mprev[0] + other_o[vec] * exp_otherm[0] + for vec in T.vectorized(4): + O_local[vec] = O_local[vec] / st_d[0] + for vec in T.vectorized(4): + output[batch_idx, by + bz + ty, tx * 4 + vec] = T.Cast("float16", O_local[vec]) + lse[batch_idx, by + bz + ty] = st_m[0] + T.log2(st_d[0]) + + @T.prim_func + def batch_decode_paged_kv_sliding_window(_0: T.int32, Q_handle: T.handle, pages_handle: T.handle, page_table_indptr_handle: T.handle, page_table_values_handle: T.handle, var_length_info: T.handle, k_rope_pos_offset_handle: T.handle, q_rope_position_handle: T.handle, output_handle: T.handle, lse_handle: T.handle, rotary_mode: T.int32, rope_scale: T.float32, rope_theta: T.float32, attn_score_scaling_factor: T.float32): + T.func_attr({"op_pattern": 8, "target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) + B = T.int32(is_size_var=True) + Q = T.match_buffer(Q_handle, (B, 20, 64), "float16") + max_num_pages = T.int32(is_size_var=True) + pages = T.match_buffer(pages_handle, (max_num_pages, 2, 20, 16, 64), "float16") + page_table_indptr = T.match_buffer(page_table_indptr_handle, (B + 1,), "int32", offset_factor=1) + nnz_pages = T.int32(is_size_var=True) + page_table_values = T.match_buffer(page_table_values_handle, (nnz_pages,), "int32", offset_factor=1) + length_info = T.match_buffer(var_length_info, (3, B), "int32", offset_factor=1) + k_rope_pos_offset = T.match_buffer(k_rope_pos_offset_handle, (B,), "int32", offset_factor=1) + q_rope_position = T.match_buffer(q_rope_position_handle, (B,), "int32", offset_factor=1) + output = T.match_buffer(output_handle, (B, 20, 64), "float16") + lse = T.match_buffer(lse_handle, (B, 20)) + # with T.block("root"): + sm_scale: T.float32 = T.float32(0.18033688011112042) + for bx in T.thread_binding(B, thread="blockIdx.x"): + for fused_by_bz in T.thread_binding(20, thread="blockIdx.y"): + for ty in T.thread_binding(1, thread="threadIdx.y"): + for tx in T.thread_binding(16, thread="threadIdx.x"): + for tz in T.thread_binding(32, thread="threadIdx.z"): + with T.block("attn"): + T.reads(page_table_indptr[bx:bx + 2], length_info[0:3, bx], q_rope_position[bx], Q[bx, fused_by_bz // 20 + ty + fused_by_bz % 20, tx * 4 - 32:tx * 4 - 32 + 68]) + T.writes(output[bx, fused_by_bz % 20 + fused_by_bz // 20 + ty, tx * 4:tx * 4 + 4], lse[bx, fused_by_bz % 20 + fused_by_bz // 20 + ty]) + Q_local = T.alloc_buffer((4,), "float16", scope="local") + kv_chunk_len = T.alloc_buffer((1,), "int32", scope="local") + K_smem = T.alloc_buffer((64, 64), "float16", scope="shared") + V_smem = T.alloc_buffer((64, 64), "float16", scope="shared") + O_allreduce = T.alloc_buffer((32, 1, 64), scope="shared") + md_allreduce = T.alloc_buffer((32, 1, 2), scope="shared") + S_reduce_local = T.alloc_buffer((1,), scope="local") + t0 = T.alloc_buffer((1,), scope="local") + S_local = T.alloc_buffer((2,), scope="local") + QK_local = T.alloc_buffer((4,), scope="local") + V_local = T.alloc_buffer((4,), "float16", scope="local") + m_prev = T.alloc_buffer((1,), scope="local") + d_prev = T.alloc_buffer((1,), scope="local") + other_m = T.alloc_buffer((1,), scope="local") + other_d = T.alloc_buffer((1,), scope="local") + exp_mprev = T.alloc_buffer((1,), scope="local") + exp_otherm = T.alloc_buffer((1,), scope="local") + other_o = T.alloc_buffer((4,), scope="local") + st_m = T.alloc_buffer((1,), scope="local") + st_d = T.alloc_buffer((1,), scope="local") + O_local = T.alloc_buffer((4,), scope="local") + by: T.int32 = fused_by_bz % 20 + bz: T.int32 = fused_by_bz // 20 + batch_idx: T.int32 = bx + cur_page_indptr_begin: T.int32 = page_table_indptr[batch_idx] + cur_page_indptr_end: T.int32 = page_table_indptr[batch_idx + 1] + kv_chunk_len[0] = T.if_then_else(cur_page_indptr_begin != cur_page_indptr_end, (cur_page_indptr_end - cur_page_indptr_begin - 1) * 16 + length_info[0, batch_idx] - length_info[1, batch_idx] + length_info[2, batch_idx], 0) + st_m[0] = T.float32(-50000) + st_d[0] = T.float32(1) + for vec in T.vectorized(4): + O_local[vec] = T.float32(0) + for vec in T.vectorized(4): + Q_local[vec] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", q_rope_position[batch_idx]) * rope_scale / T.pow(rope_theta, T.Cast("float32", (tx * 4 + vec) * 2 % 64) / T.float32(64))) * T.Cast("float32", Q[bx, by + bz + ty, tx * 4 + vec]) + T.sin(T.Cast("float32", q_rope_position[batch_idx]) * rope_scale / T.pow(rope_theta, T.Cast("float32", (tx * 4 + vec) * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(tx * 4 + vec < 32, Q[bx, by + bz + ty, tx * 4 + vec + 32] * T.float16(-1), Q[bx, by + bz + ty, tx * 4 + vec - 32]))), Q[bx, by + bz + ty, tx * 4 + vec]) + for iterator in range((kv_chunk_len[0] + 63) // 64): + tile_start_s: T.int32 = (tz + ty) * 2 + tile_start_g: T.int32 = (iterator * 32 + tz + ty) * 2 + for j in range(2): + with T.block("KV_load"): + T.reads() + T.writes() + row_g: T.int32 = tile_start_g + j + if row_g < kv_chunk_len[0]: + seq_offset: T.int32 = T.if_then_else(row_g < length_info[2, batch_idx], row_g, row_g - length_info[2, batch_idx] + length_info[1, batch_idx]) + page_no: T.int32 = page_table_values[cur_page_indptr_begin + seq_offset // 16] + page_offset: T.int32 = seq_offset % 16 + for vec in T.vectorized(4): + K_smem[tile_start_s + j, tx * 4 + vec] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", k_rope_pos_offset[batch_idx] + row_g) * rope_scale / T.pow(rope_theta, T.Cast("float32", (tx * 4 + vec) * 2 % 64) / T.float32(64))) * T.Cast("float32", pages[page_no, 0, by, page_offset, tx * 4 + vec]) + T.sin(T.Cast("float32", k_rope_pos_offset[batch_idx] + row_g) * rope_scale / T.pow(rope_theta, T.Cast("float32", (tx * 4 + vec) * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(tx * 4 + vec < 32, pages[page_no, 0, by, page_offset, tx * 4 + vec + 32] * T.float16(-1), pages[page_no, 0, by, page_offset, tx * 4 + vec - 32]))), pages[page_no, 0, by, page_offset, tx * 4 + vec]) + V_smem[tile_start_s + j, tx * 4 + vec] = pages[page_no, 1, by, page_offset, tx * 4 + vec] + else: + for vec in T.vectorized(4): + K_smem[tile_start_s + j, tx * 4 + vec] = T.float16(0) + V_smem[tile_start_s + j, tx * 4 + vec] = T.float16(0) + T.tvm_storage_sync("shared") + m_prev[0] = st_m[0] + for j in range(2): + for vec in T.vectorized(4): + QK_local[vec] = T.Cast("float32", Q_local[vec]) * T.Cast("float32", K_smem[tz * 2 + j, tx * 4 + vec]) * attn_score_scaling_factor * sm_scale + S_reduce_local[0] = T.float32(0) + for vec in T.unroll(4): + S_reduce_local[0] = S_reduce_local[0] + QK_local[vec] + with T.block("block_cross_thread"): + T.reads(S_reduce_local[0]) + T.writes(t0[0]) + T.attr(T.comm_reducer(lambda x0, y0: x0 + y0, [T.float32(0)]), "reduce_scope", T.reinterpret("handle", T.uint64(0))) + T.tvm_thread_allreduce(T.uint32(1), S_reduce_local[0], T.bool(True), t0[0], tx) + S_local[j] = T.float32(-50000) + if (iterator * 32 + tz) * 2 + j < kv_chunk_len[0]: + S_local[j] = t0[0] + st_m[0] = T.max(st_m[0], S_local[j]) + o_scale: T.float32 = T.exp2(m_prev[0] - st_m[0]) + st_d[0] = st_d[0] * o_scale + for j in range(2): + S_local[j] = T.exp2(S_local[j] - st_m[0]) + st_d[0] = st_d[0] + S_local[j] + for j in T.vectorized(4): + O_local[j] = O_local[j] * o_scale + for j in range(2): + for vec in T.vectorized(4): + V_local[vec] = V_smem[tz * 2 + j, tx * 4 + vec] + for vec in T.vectorized(4): + O_local[vec] = O_local[vec] + T.Cast("float32", V_local[vec]) * S_local[j] + for vec in T.vectorized(4): + O_allreduce[tz, ty, tx * 4 + vec] = O_local[vec] + md_allreduce[tz, ty, 0] = st_m[0] + md_allreduce[tz, ty, 1] = st_d[0] + T.tvm_storage_sync("shared") + st_m[0] = T.float32(-50000) + st_d[0] = T.float32(1) + for vec in T.vectorized(4): + O_local[vec] = T.float32(0) + for j in range(32): + m_prev[0] = st_m[0] + d_prev[0] = st_d[0] + other_m[0] = md_allreduce[j, ty, 0] + other_d[0] = md_allreduce[j, ty, 1] + for vec in T.vectorized(4): + other_o[vec] = O_allreduce[j, ty, tx * 4 + vec] + st_m[0] = T.max(st_m[0], other_m[0]) + st_d[0] = d_prev[0] * T.exp2(m_prev[0] - st_m[0]) + other_d[0] * T.exp2(other_m[0] - st_m[0]) + exp_mprev[0] = T.exp2(m_prev[0] - st_m[0]) + exp_otherm[0] = T.exp2(other_m[0] - st_m[0]) + for vec in T.vectorized(4): + O_local[vec] = O_local[vec] * exp_mprev[0] + other_o[vec] * exp_otherm[0] + for vec in T.vectorized(4): + O_local[vec] = O_local[vec] / st_d[0] + for vec in T.vectorized(4): + output[batch_idx, by + bz + ty, tx * 4 + vec] = T.Cast("float16", O_local[vec]) + lse[batch_idx, by + bz + ty] = st_m[0] + T.log2(st_d[0]) + + @T.prim_func + def batch_prefill_paged_kv(_0: T.int32, var_q: T.handle, var_q_indptr: T.handle, var_pages: T.handle, var_page_indptr: T.handle, var_page_values: T.handle, var_length_info: T.handle, var_k_rope_pos_offset: T.handle, var_q_rope_position: T.handle, var_output: T.handle, var_lse: T.handle, causal: T.int32, rotary_mode: T.int32, rope_scale: T.float32, rope_theta: T.float32, attn_score_scaling_factor: T.float32): + T.func_attr({"op_pattern": 8, "target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) + total_len = T.int32(is_size_var=True) + q = T.match_buffer(var_q, (total_len, 20, 64), "float16") + batch_size = T.int32(is_size_var=True) + q_indptr = T.match_buffer(var_q_indptr, (batch_size + 1,), "int32", offset_factor=1) + max_num_pages = T.int32(is_size_var=True) + pages = T.match_buffer(var_pages, (max_num_pages, 2, 20, 16, 64), "float16") + page_indptr = T.match_buffer(var_page_indptr, (batch_size + 1,), "int32", offset_factor=1) + nnz_pages = T.int32(is_size_var=True) + page_values = T.match_buffer(var_page_values, (nnz_pages,), "int32", offset_factor=1) + length_info = T.match_buffer(var_length_info, (batch_size,), "int32", offset_factor=1) + k_rope_pos_offset = T.match_buffer(var_k_rope_pos_offset, (batch_size,), "int32", offset_factor=1) + q_rope_position = T.match_buffer(var_q_rope_position, (total_len,), "int32", offset_factor=1) + output = T.match_buffer(var_output, (total_len, 20, 64), "float16") + lse = T.match_buffer(var_lse, (total_len, 20)) + # with T.block("root"): + for lbx in T.thread_binding(16, thread="blockIdx.x"): + for lby in T.thread_binding(20, thread="blockIdx.y"): + for lty in T.thread_binding(4, thread="threadIdx.y"): + for ltx in T.thread_binding(32, thread="threadIdx.x"): + with T.block("attn"): + bx, by, ty, tx = T.axis.remap("SSSS", [lbx, lby, lty, ltx]) + T.reads() + T.writes() + tile_id = T.alloc_buffer((1,), "int32", scope="local") + batch_idx = T.alloc_buffer((1,), "int32", scope="local") + batch_tiles = T.alloc_buffer((1,), "int32", scope="local") + batch_rows = T.alloc_buffer((1,), "int32", scope="local") + iterator = T.alloc_buffer((1,), "int32", scope="local") + kv_chunk_len = T.alloc_buffer((1,), "int32", scope="local") + Q_smem = T.alloc_buffer((32, 64), "float16", scope="shared") + K_smem = T.alloc_buffer((16, 64), "float16", scope="shared") + V_smem = T.alloc_buffer((16, 64), "float16", scope="shared") + S_smem = T.alloc_buffer((32, 16), scope="shared") + S_local = T.alloc_buffer((32, 16), scope="local") + O_local = T.alloc_buffer((32, 64), scope="local") + m_smem = T.alloc_buffer((32,), scope="shared") + m_prev_smem = T.alloc_buffer((32,), scope="shared") + d_smem = T.alloc_buffer((32,), scope="shared") + m_new = T.alloc_buffer((1,), scope="local") + m_prev = T.alloc_buffer((1,), scope="local") + d_new = T.alloc_buffer((1,), scope="local") + tile_id[0] = bx + batch_idx[0] = 0 + batch_rows[0] = q_indptr[1] - q_indptr[0] + batch_tiles[0] = (batch_rows[0] + 32 - 1) // 32 + while T.tvm_thread_invariant(batch_idx[0] < batch_size): + while tile_id[0] >= batch_tiles[0] and batch_idx[0] < batch_size: + tile_id[0] = tile_id[0] - batch_tiles[0] + batch_idx[0] = batch_idx[0] + 1 + if batch_idx[0] < batch_size: + b_idx: T.int32 = batch_idx[0] + batch_rows[0] = q_indptr[b_idx + 1] - q_indptr[b_idx] + batch_tiles[0] = (batch_rows[0] + 32 - 1) // 32 + if T.tvm_thread_invariant(batch_idx[0] < batch_size): + b_idx: T.int32 = batch_idx[0] + LH_start: T.int32 = tile_id[0] * 32 + q_indptr_val: T.int32 = q_indptr[b_idx] + cur_page_indptr_begin: T.int32 = page_indptr[b_idx] + cur_page_indptr_end: T.int32 = page_indptr[b_idx + 1] + kv_chunk_len[0] = T.if_then_else(cur_page_indptr_begin != cur_page_indptr_end, (cur_page_indptr_end - cur_page_indptr_begin - 1) * 16 + length_info[b_idx], 0) + T.tvm_storage_sync("shared") + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + m_smem[row] = T.float32(-50000) + d_smem[row] = T.float32(1) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(4, 4): + with T.block("O_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + T.reads() + T.writes(O_local[i, j]) + O_local[i, j] = T.float32(0) + T.tvm_storage_sync("shared") + for li_lj_fused_0 in range(4): + for li_lj_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for li_lj_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for li_lj_fused_3 in T.vectorized(4): + with T.block("Q_load"): + i = T.axis.spatial(32, (li_lj_fused_0 * 512 + li_lj_fused_1 * 128 + li_lj_fused_2 * 4 + li_lj_fused_3) // 64) + j = T.axis.spatial(64, (li_lj_fused_0 * 512 + li_lj_fused_1 * 128 + li_lj_fused_2 * 4 + li_lj_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = q_indptr_val + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + Q_smem[i, j] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", q[cur_L, cur_H_qo, j]) + T.sin(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(j < 32, q[cur_L, cur_H_qo, j + 32] * T.float16(-1), q[cur_L, cur_H_qo, j - 32]))), q[cur_L, cur_H_qo, j]) + else: + Q_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + for iterator_1 in range((kv_chunk_len[0] + 15) // 16): + L_kv_start: T.int32 = iterator_1 * 16 + for lz_ly_fused_0 in range(2): + for lz_ly_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for lz_ly_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for lz_ly_fused_3 in T.vectorized(4): + with T.block("K_load"): + i = T.axis.spatial(16, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) // 64) + j = T.axis.spatial(64, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = L_kv_start + i + if cur_L < kv_chunk_len[0]: + seq_offset: T.int32 = cur_L + page_no: T.int32 = page_values[cur_page_indptr_begin + seq_offset // 16] + page_offset: T.int32 = seq_offset % 16 + K_smem[i, j] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", k_rope_pos_offset[b_idx] + cur_L) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", pages[page_no, 0, by, page_offset, j]) + T.sin(T.Cast("float32", k_rope_pos_offset[b_idx] + cur_L) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(j < 32, pages[page_no, 0, by, page_offset, j + 32] * T.float16(-1), pages[page_no, 0, by, page_offset, j - 32]))), pages[page_no, 0, by, page_offset, j]) + else: + K_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + for lz_ly_fused_0 in range(2): + for lz_ly_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for lz_ly_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for lz_ly_fused_3 in T.vectorized(4): + with T.block("V_load"): + i = T.axis.spatial(16, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) // 64) + j = T.axis.spatial(64, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = L_kv_start + i + if cur_L < kv_chunk_len[0]: + seq_offset: T.int32 = cur_L + page_no: T.int32 = page_values[cur_page_indptr_begin + seq_offset // 16] + page_offset: T.int32 = seq_offset % 16 + V_smem[i, j] = pages[page_no, 1, by, page_offset, j] + else: + V_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + with T.block(""): + T.reads(Q_smem[0:32, 0:64], K_smem[0:16, 0:64]) + T.writes(S_local[0:32, 0:16]) + for li_0_lj_0_fused_0_init in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1_init in T.thread_binding(32, thread="threadIdx.x"): + for li_1_init, lj_1_init in T.grid(2, 2): + with T.block("S_gemm_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) // 8 * 2 + li_1_init) + j = T.axis.spatial(16, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) % 8 * 2 + lj_1_init) + T.reads() + T.writes(S_local[i, j]) + S_local[i, j] = T.float32(0) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for lk_0, li_1, lj_1, lk_1 in T.grid(8, 2, 2, 8): + with T.block("S_gemm_update"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 8 * 2 + li_1) + j = T.axis.spatial(16, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 8 * 2 + lj_1) + k = T.axis.reduce(64, lk_0 * 8 + lk_1) + T.reads(S_local[i, j], Q_smem[i, k], K_smem[j, k]) + T.writes(S_local[i, j]) + S_local[i, j] = S_local[i, j] + T.Cast("float32", Q_smem[i, k]) * T.Cast("float32", K_smem[j, k]) * attn_score_scaling_factor * T.float32(0.18033688011112042) + T.tvm_storage_sync("shared") + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(2, 2): + with T.block("S_store"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 8 * 2 + li_1) + j = T.axis.spatial(16, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 8 * 2 + lj_1) + T.reads(S_local[i, j]) + T.writes(S_smem[i, j]) + S_smem[i, j] = S_local[i, j] + T.tvm_storage_sync("shared") + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + with T.block("update1"): + T.reads(m_smem[row], kv_chunk_len[0], q_indptr[b_idx:b_idx + 2], m_new[i], S_smem[row, 0:16], d_smem[row], m_prev[i]) + T.writes(m_prev[i], m_new[i], d_new[i]) + m_prev[i] = m_smem[row] + m_new[i] = m_smem[row] + row_: T.int32 = LH_start + row + for j in range(16): + if T.if_then_else(causal > 0, L_kv_start + j < kv_chunk_len[0] - (q_indptr[b_idx + 1] - q_indptr[b_idx]) + row_ + 1, L_kv_start + j < kv_chunk_len[0]): + m_new[i] = T.max(m_new[i], S_smem[row, j]) + d_new[i] = d_smem[row] * T.exp2(m_prev[i] - m_new[i]) + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + with T.block("update"): + T.reads(kv_chunk_len[0], q_indptr[b_idx:b_idx + 2], S_smem[row, 0:16], m_new[i]) + T.writes(S_smem[row, 0:16]) + for j in range(16): + if row < 32: + row_: T.int32 = LH_start + row + if T.if_then_else(causal > 0, L_kv_start + j < kv_chunk_len[0] - (q_indptr[b_idx + 1] - q_indptr[b_idx]) + row_ + 1, L_kv_start + j < kv_chunk_len[0]): + S_smem[row, j] = T.exp2(S_smem[row, j] - m_new[i]) + else: + S_smem[row, j] = T.exp2(T.float32(-50000) - m_new[i]) + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + with T.block("update"): + T.reads(d_new[i], S_smem[row, 0:16], m_new[i], m_prev[i]) + T.writes(d_new[i], m_smem[row], d_smem[row], m_prev_smem[row]) + for j in range(16): + d_new[i] = d_new[i] + S_smem[row, j] + m_smem[row] = m_new[i] + d_smem[row] = d_new[i] + m_prev_smem[row] = m_prev[i] + T.tvm_storage_sync("shared") + with T.block(""): + T.reads(m_prev_smem[0:32], m_smem[0:32], S_smem[0:32, 0:16], V_smem[0:16, 0:64]) + T.writes(O_local[0:32, 0:64]) + for li_0_lj_0_fused_0_init in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1_init in T.thread_binding(32, thread="threadIdx.x"): + for li_1_init, lj_1_init in T.grid(4, 4): + with T.block("O_gemm_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) // 16 * 4 + li_1_init) + j = T.axis.spatial(64, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) % 16 * 4 + lj_1_init) + T.reads() + T.writes(O_local[i, j]) + O_local[i, j] = O_local[i, j] * T.exp2(m_prev_smem[i] - m_smem[i]) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for lk_0, lk_1, li_1, lj_1 in T.grid(2, 8, 4, 4): + with T.block("O_gemm_update"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + k = T.axis.reduce(16, lk_0 * 8 + lk_1) + T.reads(O_local[i, j], m_prev_smem[i], m_smem[i], S_smem[i, k], V_smem[k, j]) + T.writes(O_local[i, j]) + O_local[i, j] = O_local[i, j] + S_smem[i, k] * T.Cast("float32", V_smem[k, j]) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(4, 4): + with T.block("O_store"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + T.reads(q_indptr[b_idx:b_idx + 2], O_local[i, j], d_smem[i]) + T.writes(output[q_indptr[b_idx] + (LH_start + i), by, j]) + cur_L: T.int32 = q_indptr[b_idx] + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + output[cur_L, cur_H_qo, j] = T.Cast("float16", O_local[i, j] / d_smem[i]) + for li_0 in range(1): + for li_1 in T.thread_binding(4, thread="threadIdx.y"): + for li_2 in T.thread_binding(32, thread="threadIdx.x"): + with T.block("lse_store"): + i = T.axis.spatial(32, li_0 * 128 + li_1 * 32 + li_2) + T.where((li_0 * 4 + li_1) * 32 + li_2 < 32) + T.reads(q_indptr[b_idx:b_idx + 2], m_smem[i], d_smem[i]) + T.writes(lse[q_indptr[b_idx] + (LH_start + i), by]) + cur_L: T.int32 = q_indptr[b_idx] + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + lse[cur_L, cur_H_qo] = m_smem[i] + T.log2(d_smem[i]) + tile_id[0] = tile_id[0] + 16 + + @T.prim_func + def batch_prefill_paged_kv_sliding_window(_0: T.int32, var_q: T.handle, var_q_indptr: T.handle, var_pages: T.handle, var_page_indptr: T.handle, var_page_values: T.handle, var_length_info: T.handle, var_k_rope_pos_offset: T.handle, var_q_rope_position: T.handle, var_output: T.handle, var_lse: T.handle, causal: T.int32, rotary_mode: T.int32, rope_scale: T.float32, rope_theta: T.float32, attn_score_scaling_factor: T.float32): + T.func_attr({"op_pattern": 8, "target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) + total_len = T.int32(is_size_var=True) + q = T.match_buffer(var_q, (total_len, 20, 64), "float16") + batch_size = T.int32(is_size_var=True) + q_indptr = T.match_buffer(var_q_indptr, (batch_size + 1,), "int32", offset_factor=1) + max_num_pages = T.int32(is_size_var=True) + pages = T.match_buffer(var_pages, (max_num_pages, 2, 20, 16, 64), "float16") + page_indptr = T.match_buffer(var_page_indptr, (batch_size + 1,), "int32", offset_factor=1) + nnz_pages = T.int32(is_size_var=True) + page_values = T.match_buffer(var_page_values, (nnz_pages,), "int32", offset_factor=1) + length_info = T.match_buffer(var_length_info, (3, batch_size), "int32", offset_factor=1) + k_rope_pos_offset = T.match_buffer(var_k_rope_pos_offset, (batch_size,), "int32", offset_factor=1) + q_rope_position = T.match_buffer(var_q_rope_position, (total_len,), "int32", offset_factor=1) + output = T.match_buffer(var_output, (total_len, 20, 64), "float16") + lse = T.match_buffer(var_lse, (total_len, 20)) + # with T.block("root"): + for lbx in T.thread_binding(16, thread="blockIdx.x"): + for lby in T.thread_binding(20, thread="blockIdx.y"): + for lty in T.thread_binding(4, thread="threadIdx.y"): + for ltx in T.thread_binding(32, thread="threadIdx.x"): + with T.block("attn"): + bx, by, ty, tx = T.axis.remap("SSSS", [lbx, lby, lty, ltx]) + T.reads() + T.writes() + tile_id = T.alloc_buffer((1,), "int32", scope="local") + batch_idx = T.alloc_buffer((1,), "int32", scope="local") + batch_tiles = T.alloc_buffer((1,), "int32", scope="local") + batch_rows = T.alloc_buffer((1,), "int32", scope="local") + iterator = T.alloc_buffer((1,), "int32", scope="local") + kv_chunk_len = T.alloc_buffer((1,), "int32", scope="local") + Q_smem = T.alloc_buffer((32, 64), "float16", scope="shared") + K_smem = T.alloc_buffer((16, 64), "float16", scope="shared") + V_smem = T.alloc_buffer((16, 64), "float16", scope="shared") + S_smem = T.alloc_buffer((32, 16), scope="shared") + S_local = T.alloc_buffer((32, 16), scope="local") + O_local = T.alloc_buffer((32, 64), scope="local") + m_smem = T.alloc_buffer((32,), scope="shared") + m_prev_smem = T.alloc_buffer((32,), scope="shared") + d_smem = T.alloc_buffer((32,), scope="shared") + m_new = T.alloc_buffer((1,), scope="local") + m_prev = T.alloc_buffer((1,), scope="local") + d_new = T.alloc_buffer((1,), scope="local") + tile_id[0] = bx + batch_idx[0] = 0 + batch_rows[0] = q_indptr[1] - q_indptr[0] + batch_tiles[0] = (batch_rows[0] + 32 - 1) // 32 + while T.tvm_thread_invariant(batch_idx[0] < batch_size): + while tile_id[0] >= batch_tiles[0] and batch_idx[0] < batch_size: + tile_id[0] = tile_id[0] - batch_tiles[0] + batch_idx[0] = batch_idx[0] + 1 + if batch_idx[0] < batch_size: + b_idx: T.int32 = batch_idx[0] + batch_rows[0] = q_indptr[b_idx + 1] - q_indptr[b_idx] + batch_tiles[0] = (batch_rows[0] + 32 - 1) // 32 + if T.tvm_thread_invariant(batch_idx[0] < batch_size): + b_idx: T.int32 = batch_idx[0] + LH_start: T.int32 = tile_id[0] * 32 + q_indptr_val: T.int32 = q_indptr[b_idx] + cur_page_indptr_begin: T.int32 = page_indptr[b_idx] + cur_page_indptr_end: T.int32 = page_indptr[b_idx + 1] + kv_chunk_len[0] = T.if_then_else(cur_page_indptr_begin != cur_page_indptr_end, (cur_page_indptr_end - cur_page_indptr_begin - 1) * 16 + length_info[0, b_idx] - length_info[1, b_idx] + length_info[2, b_idx], 0) + T.tvm_storage_sync("shared") + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + m_smem[row] = T.float32(-50000) + d_smem[row] = T.float32(1) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(4, 4): + with T.block("O_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + T.reads() + T.writes(O_local[i, j]) + O_local[i, j] = T.float32(0) + T.tvm_storage_sync("shared") + for li_lj_fused_0 in range(4): + for li_lj_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for li_lj_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for li_lj_fused_3 in T.vectorized(4): + with T.block("Q_load"): + i = T.axis.spatial(32, (li_lj_fused_0 * 512 + li_lj_fused_1 * 128 + li_lj_fused_2 * 4 + li_lj_fused_3) // 64) + j = T.axis.spatial(64, (li_lj_fused_0 * 512 + li_lj_fused_1 * 128 + li_lj_fused_2 * 4 + li_lj_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = q_indptr_val + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + Q_smem[i, j] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", q[cur_L, cur_H_qo, j]) + T.sin(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(j < 32, q[cur_L, cur_H_qo, j + 32] * T.float16(-1), q[cur_L, cur_H_qo, j - 32]))), q[cur_L, cur_H_qo, j]) + else: + Q_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + for iterator_1 in range((kv_chunk_len[0] + 15) // 16): + L_kv_start: T.int32 = iterator_1 * 16 + for lz_ly_fused_0 in range(2): + for lz_ly_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for lz_ly_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for lz_ly_fused_3 in T.vectorized(4): + with T.block("K_load"): + i = T.axis.spatial(16, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) // 64) + j = T.axis.spatial(64, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = L_kv_start + i + if cur_L < kv_chunk_len[0]: + seq_offset: T.int32 = T.if_then_else(cur_L < length_info[2, b_idx], cur_L, cur_L - length_info[2, b_idx] + length_info[1, b_idx]) + page_no: T.int32 = page_values[cur_page_indptr_begin + seq_offset // 16] + page_offset: T.int32 = seq_offset % 16 + K_smem[i, j] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", k_rope_pos_offset[b_idx] + cur_L) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", pages[page_no, 0, by, page_offset, j]) + T.sin(T.Cast("float32", k_rope_pos_offset[b_idx] + cur_L) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(j < 32, pages[page_no, 0, by, page_offset, j + 32] * T.float16(-1), pages[page_no, 0, by, page_offset, j - 32]))), pages[page_no, 0, by, page_offset, j]) + else: + K_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + for lz_ly_fused_0 in range(2): + for lz_ly_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for lz_ly_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for lz_ly_fused_3 in T.vectorized(4): + with T.block("V_load"): + i = T.axis.spatial(16, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) // 64) + j = T.axis.spatial(64, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = L_kv_start + i + if cur_L < kv_chunk_len[0]: + seq_offset: T.int32 = T.if_then_else(cur_L < length_info[2, b_idx], cur_L, cur_L - length_info[2, b_idx] + length_info[1, b_idx]) + page_no: T.int32 = page_values[cur_page_indptr_begin + seq_offset // 16] + page_offset: T.int32 = seq_offset % 16 + V_smem[i, j] = pages[page_no, 1, by, page_offset, j] + else: + V_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + with T.block(""): + T.reads(Q_smem[0:32, 0:64], K_smem[0:16, 0:64]) + T.writes(S_local[0:32, 0:16]) + for li_0_lj_0_fused_0_init in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1_init in T.thread_binding(32, thread="threadIdx.x"): + for li_1_init, lj_1_init in T.grid(2, 2): + with T.block("S_gemm_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) // 8 * 2 + li_1_init) + j = T.axis.spatial(16, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) % 8 * 2 + lj_1_init) + T.reads() + T.writes(S_local[i, j]) + S_local[i, j] = T.float32(0) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for lk_0, li_1, lj_1, lk_1 in T.grid(8, 2, 2, 8): + with T.block("S_gemm_update"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 8 * 2 + li_1) + j = T.axis.spatial(16, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 8 * 2 + lj_1) + k = T.axis.reduce(64, lk_0 * 8 + lk_1) + T.reads(S_local[i, j], Q_smem[i, k], K_smem[j, k]) + T.writes(S_local[i, j]) + S_local[i, j] = S_local[i, j] + T.Cast("float32", Q_smem[i, k]) * T.Cast("float32", K_smem[j, k]) * attn_score_scaling_factor * T.float32(0.18033688011112042) + T.tvm_storage_sync("shared") + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(2, 2): + with T.block("S_store"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 8 * 2 + li_1) + j = T.axis.spatial(16, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 8 * 2 + lj_1) + T.reads(S_local[i, j]) + T.writes(S_smem[i, j]) + S_smem[i, j] = S_local[i, j] + T.tvm_storage_sync("shared") + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + with T.block("update1"): + T.reads(m_smem[row], kv_chunk_len[0], q_indptr[b_idx:b_idx + 2], m_new[i], S_smem[row, 0:16], d_smem[row], m_prev[i]) + T.writes(m_prev[i], m_new[i], d_new[i]) + m_prev[i] = m_smem[row] + m_new[i] = m_smem[row] + row_: T.int32 = LH_start + row + for j in range(16): + if T.if_then_else(causal > 0, L_kv_start + j < kv_chunk_len[0] - (q_indptr[b_idx + 1] - q_indptr[b_idx]) + row_ + 1, L_kv_start + j < kv_chunk_len[0]): + m_new[i] = T.max(m_new[i], S_smem[row, j]) + d_new[i] = d_smem[row] * T.exp2(m_prev[i] - m_new[i]) + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + with T.block("update"): + T.reads(kv_chunk_len[0], q_indptr[b_idx:b_idx + 2], S_smem[row, 0:16], m_new[i]) + T.writes(S_smem[row, 0:16]) + for j in range(16): + if row < 32: + row_: T.int32 = LH_start + row + if T.if_then_else(causal > 0, L_kv_start + j < kv_chunk_len[0] - (q_indptr[b_idx + 1] - q_indptr[b_idx]) + row_ + 1, L_kv_start + j < kv_chunk_len[0]): + S_smem[row, j] = T.exp2(S_smem[row, j] - m_new[i]) + else: + S_smem[row, j] = T.exp2(T.float32(-50000) - m_new[i]) + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + with T.block("update"): + T.reads(d_new[i], S_smem[row, 0:16], m_new[i], m_prev[i]) + T.writes(d_new[i], m_smem[row], d_smem[row], m_prev_smem[row]) + for j in range(16): + d_new[i] = d_new[i] + S_smem[row, j] + m_smem[row] = m_new[i] + d_smem[row] = d_new[i] + m_prev_smem[row] = m_prev[i] + T.tvm_storage_sync("shared") + with T.block(""): + T.reads(m_prev_smem[0:32], m_smem[0:32], S_smem[0:32, 0:16], V_smem[0:16, 0:64]) + T.writes(O_local[0:32, 0:64]) + for li_0_lj_0_fused_0_init in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1_init in T.thread_binding(32, thread="threadIdx.x"): + for li_1_init, lj_1_init in T.grid(4, 4): + with T.block("O_gemm_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) // 16 * 4 + li_1_init) + j = T.axis.spatial(64, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) % 16 * 4 + lj_1_init) + T.reads() + T.writes(O_local[i, j]) + O_local[i, j] = O_local[i, j] * T.exp2(m_prev_smem[i] - m_smem[i]) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for lk_0, lk_1, li_1, lj_1 in T.grid(2, 8, 4, 4): + with T.block("O_gemm_update"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + k = T.axis.reduce(16, lk_0 * 8 + lk_1) + T.reads(O_local[i, j], m_prev_smem[i], m_smem[i], S_smem[i, k], V_smem[k, j]) + T.writes(O_local[i, j]) + O_local[i, j] = O_local[i, j] + S_smem[i, k] * T.Cast("float32", V_smem[k, j]) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(4, 4): + with T.block("O_store"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + T.reads(q_indptr[b_idx:b_idx + 2], O_local[i, j], d_smem[i]) + T.writes(output[q_indptr[b_idx] + (LH_start + i), by, j]) + cur_L: T.int32 = q_indptr[b_idx] + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + output[cur_L, cur_H_qo, j] = T.Cast("float16", O_local[i, j] / d_smem[i]) + for li_0 in range(1): + for li_1 in T.thread_binding(4, thread="threadIdx.y"): + for li_2 in T.thread_binding(32, thread="threadIdx.x"): + with T.block("lse_store"): + i = T.axis.spatial(32, li_0 * 128 + li_1 * 32 + li_2) + T.where((li_0 * 4 + li_1) * 32 + li_2 < 32) + T.reads(q_indptr[b_idx:b_idx + 2], m_smem[i], d_smem[i]) + T.writes(lse[q_indptr[b_idx] + (LH_start + i), by]) + cur_L: T.int32 = q_indptr[b_idx] + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + lse[cur_L, cur_H_qo] = m_smem[i] + T.log2(d_smem[i]) + tile_id[0] = tile_id[0] + 16 + + @T.prim_func + def batch_prefill_ragged_kv(var_q: T.handle, var_q_indptr: T.handle, var_k: T.handle, var_v: T.handle, var_kv_indptr: T.handle, var_q_rope_position: T.handle, var_k_rope_pos_offset: T.handle, var_output: T.handle, var_lse: T.handle, causal: T.int32, rotary_mode: T.int32, rope_scale: T.float32, rope_theta: T.float32, attn_score_scaling_factor: T.float32): + T.func_attr({"op_pattern": 8, "target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) + qo_len = T.int32(is_size_var=True) + q = T.match_buffer(var_q, (qo_len, 20, 64), "float16") + batch_size = T.int32(is_size_var=True) + q_indptr = T.match_buffer(var_q_indptr, (batch_size + 1,), "int32", offset_factor=1) + kv_len = T.int32(is_size_var=True) + k = T.match_buffer(var_k, (kv_len, 20, 64), "float16") + v = T.match_buffer(var_v, (kv_len, 20, 64), "float16") + kv_indptr = T.match_buffer(var_kv_indptr, (batch_size + 1,), "int32", offset_factor=1) + q_rope_position = T.match_buffer(var_q_rope_position, (qo_len,), "int32", offset_factor=1) + k_rope_pos_offset = T.match_buffer(var_k_rope_pos_offset, (batch_size,), "int32", offset_factor=1) + output = T.match_buffer(var_output, (qo_len, 20, 64), "float16") + lse = T.match_buffer(var_lse, (qo_len, 20)) + # with T.block("root"): + for lbx in T.thread_binding(16, thread="blockIdx.x"): + for lby in T.thread_binding(20, thread="blockIdx.y"): + for lty in T.thread_binding(4, thread="threadIdx.y"): + for ltx in T.thread_binding(32, thread="threadIdx.x"): + with T.block("attn"): + bx, by, ty, tx = T.axis.remap("SSSS", [lbx, lby, lty, ltx]) + T.reads() + T.writes() + tile_id = T.alloc_buffer((1,), "int32", scope="local") + batch_idx = T.alloc_buffer((1,), "int32", scope="local") + batch_tiles = T.alloc_buffer((1,), "int32", scope="local") + batch_rows = T.alloc_buffer((1,), "int32", scope="local") + iterator = T.alloc_buffer((1,), "int32", scope="local") + kv_chunk_len = T.alloc_buffer((1,), "int32", scope="local") + Q_smem = T.alloc_buffer((32, 64), "float16", scope="shared") + K_smem = T.alloc_buffer((16, 64), "float16", scope="shared") + V_smem = T.alloc_buffer((16, 64), "float16", scope="shared") + S_smem = T.alloc_buffer((32, 16), scope="shared") + S_local = T.alloc_buffer((32, 16), scope="local") + O_local = T.alloc_buffer((32, 64), scope="local") + m_smem = T.alloc_buffer((32,), scope="shared") + m_prev_smem = T.alloc_buffer((32,), scope="shared") + d_smem = T.alloc_buffer((32,), scope="shared") + m_new = T.alloc_buffer((1,), scope="local") + m_prev = T.alloc_buffer((1,), scope="local") + d_new = T.alloc_buffer((1,), scope="local") + tile_id[0] = bx + batch_idx[0] = 0 + batch_rows[0] = q_indptr[1] - q_indptr[0] + batch_tiles[0] = (batch_rows[0] + 32 - 1) // 32 + while T.tvm_thread_invariant(batch_idx[0] < batch_size): + while tile_id[0] >= batch_tiles[0] and batch_idx[0] < batch_size: + tile_id[0] = tile_id[0] - batch_tiles[0] + batch_idx[0] = batch_idx[0] + 1 + if batch_idx[0] < batch_size: + b_idx: T.int32 = batch_idx[0] + batch_rows[0] = q_indptr[b_idx + 1] - q_indptr[b_idx] + batch_tiles[0] = (batch_rows[0] + 32 - 1) // 32 + if T.tvm_thread_invariant(batch_idx[0] < batch_size): + b_idx: T.int32 = batch_idx[0] + q_indptr_val: T.int32 = q_indptr[b_idx] + LH_start: T.int32 = tile_id[0] * 32 + kv_chunk_len[0] = kv_indptr[b_idx + 1] - kv_indptr[b_idx] + T.tvm_storage_sync("shared") + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + m_smem[row] = T.float32(-50000) + d_smem[row] = T.float32(1) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(4, 4): + with T.block("O_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + T.reads() + T.writes(O_local[i, j]) + O_local[i, j] = T.float32(0) + T.tvm_storage_sync("shared") + for li_lj_fused_0 in range(4): + for li_lj_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for li_lj_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for li_lj_fused_3 in T.vectorized(4): + with T.block("Q_load"): + i = T.axis.spatial(32, (li_lj_fused_0 * 512 + li_lj_fused_1 * 128 + li_lj_fused_2 * 4 + li_lj_fused_3) // 64) + j = T.axis.spatial(64, (li_lj_fused_0 * 512 + li_lj_fused_1 * 128 + li_lj_fused_2 * 4 + li_lj_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = q_indptr_val + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + Q_smem[i, j] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", q[cur_L, cur_H_qo, j]) + T.sin(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(j < 32, q[cur_L, cur_H_qo, j + 32] * T.float16(-1), q[cur_L, cur_H_qo, j - 32]))), q[cur_L, cur_H_qo, j]) + else: + Q_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + for iterator_1 in range((kv_chunk_len[0] + 15) // 16): + L_kv_start: T.int32 = iterator_1 * 16 + L_kv_base: T.int32 = kv_indptr[b_idx] + for lz_ly_fused_0 in range(2): + for lz_ly_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for lz_ly_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for lz_ly_fused_3 in T.vectorized(4): + with T.block("K_load"): + i = T.axis.spatial(16, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) // 64) + j = T.axis.spatial(64, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = L_kv_start + i + if cur_L < kv_chunk_len[0]: + K_smem[i, j] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", k_rope_pos_offset[b_idx] + cur_L) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", k[L_kv_base + cur_L, by, j]) + T.sin(T.Cast("float32", k_rope_pos_offset[b_idx] + cur_L) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(j < 32, k[L_kv_base + cur_L, by, j + 32] * T.float16(-1), k[L_kv_base + cur_L, by, j - 32]))), k[L_kv_base + cur_L, by, j]) + else: + K_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + for lz_ly_fused_0 in range(2): + for lz_ly_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for lz_ly_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for lz_ly_fused_3 in T.vectorized(4): + with T.block("V_load"): + i = T.axis.spatial(16, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) // 64) + j = T.axis.spatial(64, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = L_kv_start + i + if cur_L < kv_chunk_len[0]: + V_smem[i, j] = v[L_kv_base + cur_L, by, j] + else: + V_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + with T.block(""): + T.reads(Q_smem[0:32, 0:64], K_smem[0:16, 0:64]) + T.writes(S_local[0:32, 0:16]) + for li_0_lj_0_fused_0_init in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1_init in T.thread_binding(32, thread="threadIdx.x"): + for li_1_init, lj_1_init in T.grid(2, 2): + with T.block("S_gemm_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) // 8 * 2 + li_1_init) + j = T.axis.spatial(16, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) % 8 * 2 + lj_1_init) + T.reads() + T.writes(S_local[i, j]) + S_local[i, j] = T.float32(0) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for lk_0, li_1, lj_1, lk_1 in T.grid(8, 2, 2, 8): + with T.block("S_gemm_update"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 8 * 2 + li_1) + j = T.axis.spatial(16, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 8 * 2 + lj_1) + k_1 = T.axis.reduce(64, lk_0 * 8 + lk_1) + T.reads(S_local[i, j], Q_smem[i, k_1], K_smem[j, k_1]) + T.writes(S_local[i, j]) + S_local[i, j] = S_local[i, j] + T.Cast("float32", Q_smem[i, k_1]) * T.Cast("float32", K_smem[j, k_1]) * attn_score_scaling_factor * T.float32(0.18033688011112042) + T.tvm_storage_sync("shared") + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(2, 2): + with T.block("S_store"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 8 * 2 + li_1) + j = T.axis.spatial(16, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 8 * 2 + lj_1) + T.reads(S_local[i, j]) + T.writes(S_smem[i, j]) + S_smem[i, j] = S_local[i, j] + T.tvm_storage_sync("shared") + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + with T.block("update1"): + T.reads(m_smem[row], kv_chunk_len[0], q_indptr[b_idx:b_idx + 2], m_new[i], S_smem[row, 0:16], d_smem[row], m_prev[i]) + T.writes(m_prev[i], m_new[i], d_new[i]) + m_prev[i] = m_smem[row] + m_new[i] = m_smem[row] + row_: T.int32 = LH_start + row + for j in range(16): + if T.if_then_else(causal > 0, L_kv_start + j < kv_chunk_len[0] - (q_indptr[b_idx + 1] - q_indptr[b_idx]) + row_ + 1, L_kv_start + j < kv_chunk_len[0]): + m_new[i] = T.max(m_new[i], S_smem[row, j]) + d_new[i] = d_smem[row] * T.exp2(m_prev[i] - m_new[i]) + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + with T.block("update"): + T.reads(kv_chunk_len[0], q_indptr[b_idx:b_idx + 2], S_smem[row, 0:16], m_new[i]) + T.writes(S_smem[row, 0:16]) + for j in range(16): + if row < 32: + row_: T.int32 = LH_start + row + if T.if_then_else(causal > 0, L_kv_start + j < kv_chunk_len[0] - (q_indptr[b_idx + 1] - q_indptr[b_idx]) + row_ + 1, L_kv_start + j < kv_chunk_len[0]): + S_smem[row, j] = T.exp2(S_smem[row, j] - m_new[i]) + else: + S_smem[row, j] = T.exp2(T.float32(-50000) - m_new[i]) + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + with T.block("update"): + T.reads(d_new[i], S_smem[row, 0:16], m_new[i], m_prev[i]) + T.writes(d_new[i], m_smem[row], d_smem[row], m_prev_smem[row]) + for j in range(16): + d_new[i] = d_new[i] + S_smem[row, j] + m_smem[row] = m_new[i] + d_smem[row] = d_new[i] + m_prev_smem[row] = m_prev[i] + T.tvm_storage_sync("shared") + with T.block(""): + T.reads(m_prev_smem[0:32], m_smem[0:32], S_smem[0:32, 0:16], V_smem[0:16, 0:64]) + T.writes(O_local[0:32, 0:64]) + for li_0_lj_0_fused_0_init in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1_init in T.thread_binding(32, thread="threadIdx.x"): + for li_1_init, lj_1_init in T.grid(4, 4): + with T.block("O_gemm_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) // 16 * 4 + li_1_init) + j = T.axis.spatial(64, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) % 16 * 4 + lj_1_init) + T.reads() + T.writes(O_local[i, j]) + O_local[i, j] = O_local[i, j] * T.exp2(m_prev_smem[i] - m_smem[i]) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for lk_0, lk_1, li_1, lj_1 in T.grid(2, 8, 4, 4): + with T.block("O_gemm_update"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + k_1 = T.axis.reduce(16, lk_0 * 8 + lk_1) + T.reads(O_local[i, j], m_prev_smem[i], m_smem[i], S_smem[i, k_1], V_smem[k_1, j]) + T.writes(O_local[i, j]) + O_local[i, j] = O_local[i, j] + S_smem[i, k_1] * T.Cast("float32", V_smem[k_1, j]) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(4, 4): + with T.block("O_store"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + T.reads(q_indptr[b_idx:b_idx + 2], O_local[i, j], d_smem[i]) + T.writes(output[q_indptr[b_idx] + (LH_start + i), by, j]) + cur_L: T.int32 = q_indptr[b_idx] + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + output[cur_L, cur_H_qo, j] = T.Cast("float16", O_local[i, j] / d_smem[i]) + for li_0 in range(1): + for li_1 in T.thread_binding(4, thread="threadIdx.y"): + for li_2 in T.thread_binding(32, thread="threadIdx.x"): + with T.block("lse_store"): + i = T.axis.spatial(32, li_0 * 128 + li_1 * 32 + li_2) + T.where((li_0 * 4 + li_1) * 32 + li_2 < 32) + T.reads(q_indptr[b_idx:b_idx + 2], m_smem[i], d_smem[i]) + T.writes(lse[q_indptr[b_idx] + (LH_start + i), by]) + cur_L: T.int32 = q_indptr[b_idx] + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + lse[cur_L, cur_H_qo] = m_smem[i] + T.log2(d_smem[i]) + tile_id[0] = tile_id[0] + 16 + + @T.prim_func + def batch_tree_attn(var_q: T.handle, var_q_indptr: T.handle, var_k: T.handle, var_v: T.handle, var_kv_indptr: T.handle, var_q_rope_position: T.handle, var_mn_indptr: T.handle, var_mask: T.handle, var_output: T.handle, var_lse: T.handle, rotary_mode: T.int32, rope_scale: T.float32, rope_theta: T.float32, attn_score_scaling_factor: T.float32, batch_size: T.int32): + T.func_attr({"op_pattern": 8, "target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) + qo_len = T.int32(is_size_var=True) + q = T.match_buffer(var_q, (qo_len, 20, 64), "float16") + q_indptr = T.match_buffer(var_q_indptr, (batch_size + 1,), "int32", offset_factor=1) + kv_len = T.int32(is_size_var=True) + k = T.match_buffer(var_k, (kv_len, 20, 64), "float16") + v = T.match_buffer(var_v, (kv_len, 20, 64), "float16") + kv_indptr = T.match_buffer(var_kv_indptr, (batch_size + 1,), "int32", offset_factor=1) + q_rope_position = T.match_buffer(var_q_rope_position, (qo_len,), "int32", offset_factor=1) + mn_indptr = T.match_buffer(var_mn_indptr, (batch_size + 1,), "int32", offset_factor=1) + tree_size = T.int32(is_size_var=True) + mask = T.match_buffer(var_mask, (tree_size,), "int32", offset_factor=1) + output = T.match_buffer(var_output, (qo_len, 20, 64), "float16") + lse = T.match_buffer(var_lse, (qo_len, 20)) + # with T.block("root"): + for lbx in T.thread_binding(16, thread="blockIdx.x"): + for lby in T.thread_binding(20, thread="blockIdx.y"): + for lty in T.thread_binding(4, thread="threadIdx.y"): + for ltx in T.thread_binding(32, thread="threadIdx.x"): + with T.block("attn"): + bx, by, ty, tx = T.axis.remap("SSSS", [lbx, lby, lty, ltx]) + T.reads() + T.writes() + tile_id = T.alloc_buffer((1,), "int32", scope="local") + batch_idx = T.alloc_buffer((1,), "int32", scope="local") + batch_tiles = T.alloc_buffer((1,), "int32", scope="local") + batch_rows = T.alloc_buffer((1,), "int32", scope="local") + iterator = T.alloc_buffer((1,), "int32", scope="local") + kv_chunk_len = T.alloc_buffer((1,), "int32", scope="local") + Q_smem = T.alloc_buffer((32, 64), "float16", scope="shared") + K_smem = T.alloc_buffer((16, 64), "float16", scope="shared") + V_smem = T.alloc_buffer((16, 64), "float16", scope="shared") + S_smem = T.alloc_buffer((32, 16), scope="shared") + S_local = T.alloc_buffer((32, 16), scope="local") + O_local = T.alloc_buffer((32, 64), scope="local") + m_smem = T.alloc_buffer((32,), scope="shared") + m_prev_smem = T.alloc_buffer((32,), scope="shared") + d_smem = T.alloc_buffer((32,), scope="shared") + m_new = T.alloc_buffer((1,), scope="local") + m_prev = T.alloc_buffer((1,), scope="local") + d_new = T.alloc_buffer((1,), scope="local") + tile_id[0] = bx + batch_idx[0] = 0 + batch_rows[0] = q_indptr[1] - q_indptr[0] + batch_tiles[0] = (batch_rows[0] + 32 - 1) // 32 + while T.tvm_thread_invariant(batch_idx[0] < batch_size): + while tile_id[0] >= batch_tiles[0] and batch_idx[0] < batch_size: + tile_id[0] = tile_id[0] - batch_tiles[0] + batch_idx[0] = batch_idx[0] + 1 + if batch_idx[0] < batch_size: + b_idx: T.int32 = batch_idx[0] + batch_rows[0] = q_indptr[b_idx + 1] - q_indptr[b_idx] + batch_tiles[0] = (batch_rows[0] + 32 - 1) // 32 + if T.tvm_thread_invariant(batch_idx[0] < batch_size): + b_idx: T.int32 = batch_idx[0] + LH_start: T.int32 = tile_id[0] * 32 + q_indptr_val: T.int32 = q_indptr[b_idx] + kv_chunk_len[0] = kv_indptr[b_idx + 1] - kv_indptr[b_idx] + T.tvm_storage_sync("shared") + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + m_smem[row] = T.float32(-50000) + d_smem[row] = T.float32(1) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(4, 4): + with T.block("O_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + T.reads() + T.writes(O_local[i, j]) + O_local[i, j] = T.float32(0) + T.tvm_storage_sync("shared") + for li_lj_fused_0 in range(4): + for li_lj_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for li_lj_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for li_lj_fused_3 in T.vectorized(4): + with T.block("Q_load"): + i = T.axis.spatial(32, (li_lj_fused_0 * 512 + li_lj_fused_1 * 128 + li_lj_fused_2 * 4 + li_lj_fused_3) // 64) + j = T.axis.spatial(64, (li_lj_fused_0 * 512 + li_lj_fused_1 * 128 + li_lj_fused_2 * 4 + li_lj_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = q_indptr_val + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + Q_smem[i, j] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64)))) * q[cur_L, cur_H_qo, j] + T.Cast("float16", T.sin(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64)))) * T.if_then_else(j < 32, q[cur_L, cur_H_qo, j + 32] * T.float16(-1), q[cur_L, cur_H_qo, j - 32]), q[cur_L, cur_H_qo, j]) + else: + Q_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + for iterator_1 in range((kv_chunk_len[0] + 15) // 16): + L_kv_start: T.int32 = iterator_1 * 16 + L_kv_base: T.int32 = kv_indptr[b_idx] + for lz_ly_fused_0 in range(2): + for lz_ly_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for lz_ly_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for lz_ly_fused_3 in T.vectorized(4): + with T.block("KV_load"): + i = T.axis.spatial(16, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) // 64) + j = T.axis.spatial(64, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = L_kv_base + L_kv_start + i + if L_kv_start + i < kv_chunk_len[0]: + K_smem[i, j] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64)))) * k[cur_L, by, j] + T.Cast("float16", T.sin(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64)))) * T.if_then_else(j < 32, k[cur_L, by, j + 32] * T.float16(-1), k[cur_L, by, j - 32]), k[cur_L, by, j]) + V_smem[i, j] = v[cur_L, by, j] + else: + K_smem[i, j] = T.float16(0) + V_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + with T.block(""): + T.reads(Q_smem[0:32, 0:64], K_smem[0:16, 0:64]) + T.writes(S_local[0:32, 0:16]) + for li_0_lj_0_fused_0_init in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1_init in T.thread_binding(32, thread="threadIdx.x"): + for li_1_init, lj_1_init in T.grid(2, 2): + with T.block("S_gemm_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) // 8 * 2 + li_1_init) + j = T.axis.spatial(16, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) % 8 * 2 + lj_1_init) + T.reads() + T.writes(S_local[i, j]) + S_local[i, j] = T.float32(0) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for lk_0, li_1, lj_1, lk_1 in T.grid(8, 2, 2, 8): + with T.block("S_gemm_update"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 8 * 2 + li_1) + j = T.axis.spatial(16, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 8 * 2 + lj_1) + k_1 = T.axis.reduce(64, lk_0 * 8 + lk_1) + T.reads(S_local[i, j], Q_smem[i, k_1], K_smem[j, k_1]) + T.writes(S_local[i, j]) + S_local[i, j] = S_local[i, j] + T.Cast("float32", Q_smem[i, k_1]) * T.Cast("float32", K_smem[j, k_1]) * attn_score_scaling_factor * T.float32(0.18033688011112042) + T.tvm_storage_sync("shared") + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(2, 2): + with T.block("S_store"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 8 * 2 + li_1) + j = T.axis.spatial(16, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 8 * 2 + lj_1) + T.reads(S_local[i, j]) + T.writes(S_smem[i, j]) + S_smem[i, j] = S_local[i, j] + T.tvm_storage_sync("shared") + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + with T.block("update1"): + T.reads(m_smem[row], kv_chunk_len[0], mask[mn_indptr[b_idx] + (LH_start + row) * (q_indptr[b_idx + 1] - q_indptr[b_idx]) + L_kv_start:mn_indptr[b_idx] + (LH_start + row) * (q_indptr[b_idx + 1] - q_indptr[b_idx]) + L_kv_start + 16], mn_indptr[b_idx], q_indptr[b_idx:b_idx + 2], m_new[i], S_smem[row, 0:16], d_smem[row], m_prev[i]) + T.writes(m_prev[i], m_new[i], d_new[i]) + m_prev[i] = m_smem[row] + m_new[i] = m_smem[row] + row_: T.int32 = LH_start + row + for j in range(16): + if L_kv_start + j < kv_chunk_len[0] and mask[mn_indptr[b_idx] + row_ * (q_indptr[b_idx + 1] - q_indptr[b_idx]) + (L_kv_start + j)] == 1: + m_new[i] = T.max(m_new[i], S_smem[row, j]) + d_new[i] = d_smem[row] * T.exp2(m_prev[i] - m_new[i]) + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + with T.block("update"): + T.reads(kv_chunk_len[0], mask[mn_indptr[b_idx] + (LH_start + row) * (q_indptr[b_idx + 1] - q_indptr[b_idx]) + L_kv_start:mn_indptr[b_idx] + (LH_start + row) * (q_indptr[b_idx + 1] - q_indptr[b_idx]) + L_kv_start + 16], mn_indptr[b_idx], q_indptr[b_idx:b_idx + 2], S_smem[row, 0:16], m_new[i]) + T.writes(S_smem[row, 0:16]) + for j in range(16): + if row < 32: + row_: T.int32 = LH_start + row + if L_kv_start + j < kv_chunk_len[0] and mask[mn_indptr[b_idx] + row_ * (q_indptr[b_idx + 1] - q_indptr[b_idx]) + (L_kv_start + j)] == 1: + S_smem[row, j] = T.exp2(S_smem[row, j] - m_new[i]) + else: + S_smem[row, j] = T.exp2(T.float32(-50000) - m_new[i]) + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + with T.block("update"): + T.reads(d_new[i], S_smem[row, 0:16], m_new[i], m_prev[i]) + T.writes(d_new[i], m_smem[row], d_smem[row], m_prev_smem[row]) + for j in range(16): + d_new[i] = d_new[i] + S_smem[row, j] + m_smem[row] = m_new[i] + d_smem[row] = d_new[i] + m_prev_smem[row] = m_prev[i] + T.tvm_storage_sync("shared") + with T.block(""): + T.reads(m_prev_smem[0:32], m_smem[0:32], S_smem[0:32, 0:16], V_smem[0:16, 0:64]) + T.writes(O_local[0:32, 0:64]) + for li_0_lj_0_fused_0_init in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1_init in T.thread_binding(32, thread="threadIdx.x"): + for li_1_init, lj_1_init in T.grid(4, 4): + with T.block("O_gemm_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) // 16 * 4 + li_1_init) + j = T.axis.spatial(64, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) % 16 * 4 + lj_1_init) + T.reads() + T.writes(O_local[i, j]) + O_local[i, j] = O_local[i, j] * T.exp2(m_prev_smem[i] - m_smem[i]) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for lk_0, lk_1, li_1, lj_1 in T.grid(2, 8, 4, 4): + with T.block("O_gemm_update"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + k_1 = T.axis.reduce(16, lk_0 * 8 + lk_1) + T.reads(O_local[i, j], m_prev_smem[i], m_smem[i], S_smem[i, k_1], V_smem[k_1, j]) + T.writes(O_local[i, j]) + O_local[i, j] = O_local[i, j] + S_smem[i, k_1] * T.Cast("float32", V_smem[k_1, j]) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(4, 4): + with T.block("O_store"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + T.reads(q_indptr[b_idx:b_idx + 2], O_local[i, j], d_smem[i]) + T.writes(output[q_indptr[b_idx] + (LH_start + i), by, j]) + cur_L: T.int32 = q_indptr[b_idx] + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + output[cur_L, cur_H_qo, j] = T.Cast("float16", O_local[i, j] / d_smem[i]) + for li_0 in range(1): + for li_1 in T.thread_binding(4, thread="threadIdx.y"): + for li_2 in T.thread_binding(32, thread="threadIdx.x"): + with T.block("lse_store"): + i = T.axis.spatial(32, li_0 * 128 + li_1 * 32 + li_2) + T.where((li_0 * 4 + li_1) * 32 + li_2 < 32) + T.reads(q_indptr[b_idx:b_idx + 2], m_smem[i], d_smem[i]) + T.writes(lse[q_indptr[b_idx] + (LH_start + i), by]) + cur_L: T.int32 = q_indptr[b_idx] + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + lse[cur_L, cur_H_qo] = m_smem[i] + T.log2(d_smem[i]) + tile_id[0] = tile_id[0] + 16 + + @T.prim_func(private=True) + def batch_verify_on_gpu_single_kernel(var_draft_probs: T.handle, var_draft_tokens: T.handle, var_model_probs: T.handle, var_token_tree_first_child: T.handle, var_token_tree_next_sibling: T.handle, var_uniform_samples: T.handle, var_token_tree_parent_ptr: T.handle): + T.func_attr({"op_pattern": 8, "target": T.target({"arch": "sm_89", "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + num_nodes, vocab_size = T.int32(is_size_var=True), T.int64() + draft_probs = T.match_buffer(var_draft_probs, (num_nodes, vocab_size)) + draft_tokens = T.match_buffer(var_draft_tokens, (num_nodes,), "int32") + model_probs = T.match_buffer(var_model_probs, (num_nodes, vocab_size)) + token_tree_first_child = T.match_buffer(var_token_tree_first_child, (num_nodes,), "int32") + token_tree_next_sibling = T.match_buffer(var_token_tree_next_sibling, (num_nodes,), "int32") + uniform_samples = T.match_buffer(var_uniform_samples, (num_nodes,)) + nbatch = T.int32(is_size_var=True) + token_tree_parent_ptr = T.match_buffer(var_token_tree_parent_ptr, (nbatch,), "int32") + # with T.block("root"): + child_ptr = T.alloc_buffer((1,), "int32", scope="local") + parent_ptr = T.alloc_buffer((1,), "int32", scope="local") + child_token = T.alloc_buffer((1,), "int32", scope="local") + done = T.alloc_buffer((1,), "bool", scope="local") + psum = T.alloc_buffer((1,), scope="local") + t0 = T.alloc_buffer((1,), scope="local") + model_prob_local = T.alloc_buffer((1,), scope="local") + draft_prob_local = T.alloc_buffer((1,), scope="local") + p_child = T.alloc_buffer((1,), scope="local") + q_child = T.alloc_buffer((1,), scope="local") + uniform_sample = T.alloc_buffer((1,), scope="local") + pred_shared = T.alloc_buffer((1,), "bool", scope="shared") + pred_local = T.alloc_buffer((1,), "bool", scope="local") + for _bx in T.thread_binding(nbatch, thread="blockIdx.x"): + for _tx in T.thread_binding(1024, thread="threadIdx.x"): + with T.block("CTA"): + b, tx = T.axis.remap("SS", [_bx, _tx]) + T.reads(token_tree_parent_ptr[b], token_tree_first_child[T.min(parent_ptr[0], child_ptr[0]):T.min(parent_ptr[0], child_ptr[0]) + (T.max(parent_ptr[0], child_ptr[0]) + 1 - T.min(parent_ptr[0], child_ptr[0]))], parent_ptr[0], done[0], child_ptr[0], draft_tokens[child_ptr[0]], model_probs[parent_ptr[0], T.min(T.Cast("int64", child_token[0]), T.Cast("int64", tx)):T.min(T.Cast("int64", child_token[0]), T.Cast("int64", tx)) + (T.max(T.Cast("int64", child_token[0]), (vocab_size + T.int64(1023)) // T.int64(1024) * T.int64(1024) + T.Cast("int64", tx) - T.int64(1024)) + T.int64(1) - T.min(T.Cast("int64", child_token[0]), T.Cast("int64", tx)))], child_token[0], draft_probs[child_ptr[0], T.min(T.Cast("int64", child_token[0]), T.Cast("int64", tx)):T.min(T.Cast("int64", child_token[0]), T.Cast("int64", tx)) + (T.max(T.Cast("int64", child_token[0]), (vocab_size + T.int64(1023)) // T.int64(1024) * T.int64(1024) + T.Cast("int64", tx) - T.int64(1024)) + T.int64(1) - T.min(T.Cast("int64", child_token[0]), T.Cast("int64", tx)))], uniform_samples[child_ptr[0]], p_child[0], uniform_sample[0], q_child[0], pred_shared[0], pred_local[0], model_prob_local[0], draft_prob_local[0], psum[0], t0[0], token_tree_next_sibling[child_ptr[0]]) + T.writes(parent_ptr[0], child_ptr[0], done[0], child_token[0], p_child[0], q_child[0], uniform_sample[0], pred_shared[0], pred_local[0], psum[0], model_prob_local[0], draft_prob_local[0], t0[0], model_probs[parent_ptr[0], T.Cast("int64", tx):T.Cast("int64", tx) + ((vocab_size + T.int64(1023)) // T.int64(1024) * T.int64(1024) - T.int64(1023))], token_tree_parent_ptr[b]) + parent_ptr[0] = token_tree_parent_ptr[b] + child_ptr[0] = token_tree_first_child[parent_ptr[0]] + done[0] = T.bool(False) + while not done[0]: + T.tvm_storage_sync("shared") + if child_ptr[0] == -1: + done[0] = T.bool(True) + T.tvm_storage_sync("shared") + else: + if tx == 0: + child_token[0] = draft_tokens[child_ptr[0]] + p_child[0] = model_probs[parent_ptr[0], child_token[0]] + q_child[0] = draft_probs[child_ptr[0], child_token[0]] + uniform_sample[0] = uniform_samples[child_ptr[0]] + pred_shared[0] = p_child[0] >= uniform_sample[0] * q_child[0] + T.tvm_storage_sync("shared") + pred_local[0] = pred_shared[0] + if pred_local[0]: + parent_ptr[0] = child_ptr[0] + child_ptr[0] = token_tree_first_child[child_ptr[0]] + else: + psum[0] = T.float32(0) + for i in range((vocab_size + T.int64(1023)) // T.int64(1024)): + if i * T.int64(1024) + T.Cast("int64", tx) < vocab_size: + model_prob_local[0] = model_probs[parent_ptr[0], i * T.int64(1024) + T.Cast("int64", tx)] + draft_prob_local[0] = draft_probs[child_ptr[0], i * T.int64(1024) + T.Cast("int64", tx)] + model_prob_local[0] = T.max(model_prob_local[0] - draft_prob_local[0], T.float32(0)) + psum[0] = psum[0] + model_prob_local[0] + with T.block("block_cross_thread"): + T.reads(psum[0]) + T.writes(t0[0]) + T.attr(T.comm_reducer(lambda x0, y0: x0 + y0, [T.float32(0)]), "reduce_scope", T.reinterpret("handle", T.uint64(0))) + T.tvm_thread_allreduce(T.uint32(1), psum[0], T.bool(True), t0[0], tx) + if t0[0] < T.float32(9.9999999999999995e-08): + parent_ptr[0] = child_ptr[0] + child_ptr[0] = token_tree_first_child[child_ptr[0]] + else: + for i in range((vocab_size + T.int64(1023)) // T.int64(1024)): + if i * T.int64(1024) + T.Cast("int64", tx) < vocab_size: + model_prob_local[0] = model_probs[parent_ptr[0], i * T.int64(1024) + T.Cast("int64", tx)] + draft_prob_local[0] = draft_probs[child_ptr[0], i * T.int64(1024) + T.Cast("int64", tx)] + model_prob_local[0] = T.max(model_prob_local[0] - draft_prob_local[0], T.float32(0)) + model_probs[parent_ptr[0], i * T.int64(1024) + T.Cast("int64", tx)] = model_prob_local[0] / t0[0] + child_ptr[0] = token_tree_next_sibling[child_ptr[0]] + if tx == 0: + token_tree_parent_ptr[b] = parent_ptr[0] + + @T.prim_func + def chunk_lse(var_A: T.handle, var_temperature: T.handle, var_chunked_sum: T.handle, var_chunked_max: T.handle): + T.func_attr({"op_pattern": 4, "target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.noalias": T.bool(True)}) + batch_size, vocab_size = T.int64(is_size_var=True), T.int64(is_size_var=True) + A = T.match_buffer(var_A, (batch_size, vocab_size)) + temperature = T.match_buffer(var_temperature, (batch_size,)) + num_chunks = T.int64(is_size_var=True) + chunked_sum = T.match_buffer(var_chunked_sum, (batch_size, num_chunks)) + chunked_max = T.match_buffer(var_chunked_max, (batch_size, num_chunks)) + # with T.block("root"): + A_pad = T.alloc_buffer((batch_size, num_chunks, T.int64(4096))) + temp_max = T.alloc_buffer((batch_size, num_chunks)) + temp_sum = T.alloc_buffer((batch_size, num_chunks)) + for l0, l1, l2 in T.grid(batch_size, num_chunks, T.int64(4096)): + with T.block("pad"): + v0, v1, v2 = T.axis.remap("SSS", [l0, l1, l2]) + T.reads(temperature[v0], A[v0, v1 * T.int64(4096) + v2]) + T.writes(A_pad[v0, v1, v2]) + A_pad[v0, v1, v2] = T.if_then_else(v1 * T.int64(4096) + v2 < vocab_size, T.if_then_else(temperature[v0] > T.float32(1.0000000000000001e-05), A[v0, v1 * T.int64(4096) + v2] / temperature[v0], A[v0, v1 * T.int64(4096) + v2]), T.float32(-3.4028234663852886e+38)) + for l0, l1, l2 in T.grid(batch_size, num_chunks, T.int64(4096)): + with T.block("max"): + v0, v1, v2 = T.axis.remap("SSR", [l0, l1, l2]) + T.reads(A_pad[v0, v1, v2]) + T.writes(temp_max[v0, v1]) + with T.init(): + temp_max[v0, v1] = T.float32(-3.4028234663852886e+38) + temp_max[v0, v1] = T.max(temp_max[v0, v1], A_pad[v0, v1, v2]) + for l0, l1, l2 in T.grid(batch_size, num_chunks, T.int64(4096)): + with T.block("sum_exp"): + v0, v1, v2 = T.axis.remap("SSR", [l0, l1, l2]) + T.reads(temperature[v0], A_pad[v0, v1, v2], temp_max[v0, v1]) + T.writes(temp_sum[v0, v1]) + with T.init(): + temp_sum[v0, v1] = T.float32(0) + temp_sum[v0, v1] = temp_sum[v0, v1] + T.if_then_else(v1 * T.int64(4096) + v2 < vocab_size, T.Select(temperature[v0] > T.float32(1.0000000000000001e-05), T.exp(A_pad[v0, v1, v2] - temp_max[v0, v1]), T.Cast("float32", A_pad[v0, v1, v2] == temp_max[v0, v1])), T.float32(0)) + for l0, l1, l2 in T.grid(batch_size, num_chunks, T.int64(1)): + with T.block("log"): + v0, v1, v2 = T.axis.remap("SSS", [l0, l1, l2]) + T.reads(temperature[v0], temp_sum[v0, v1], temp_max[v0, v1]) + T.writes(chunked_sum[v0, v1], chunked_max[v0, v1]) + chunked_sum[v0, v1] = T.Select(temperature[v0] > T.float32(1.0000000000000001e-05), T.log(temp_sum[v0, v1]), temp_sum[v0, v1]) + chunked_max[v0, v1] = temp_max[v0, v1] + + @T.prim_func + def compact_kv_copy(var_pages: T.handle, var_copy_length_indptr: T.handle, var_copy_src_dst_pos: T.handle, batch_size: T.int32): + T.func_attr({"op_pattern": 8, "target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) + num_pages = T.int32() + pages = T.match_buffer(var_pages, (num_pages, 2, 20, 16, 64), "float16") + copy_length_indptr = T.match_buffer(var_copy_length_indptr, (batch_size + 1,), "int32", offset_factor=1) + total_copy_length = T.int32() + copy_src_dst_pos = T.match_buffer(var_copy_src_dst_pos, (2, total_copy_length), "int32", offset_factor=1) + with T.block("root"): + T.reads() + T.writes() + for bhd_o in T.thread_binding((batch_size * 1280 + 1023) // 1024, thread="blockIdx.x"): + for bhd_i in T.thread_binding(1024, thread="threadIdx.x"): + b: T.int32 = (bhd_o * 1024 + bhd_i) // 1280 + h: T.int32 = (bhd_o * 1024 + bhd_i) // 64 % 20 + d: T.int32 = (bhd_o * 1024 + bhd_i) % 64 + if bhd_o * 1024 + bhd_i < batch_size * 20 * 64: + for i in range(copy_length_indptr[b + 1] - copy_length_indptr[b]): + src_pos: T.int32 = copy_src_dst_pos[0, copy_length_indptr[b] + i] + dst_pos: T.int32 = copy_src_dst_pos[1, copy_length_indptr[b] + i] + pages[dst_pos // 16, 0, h, dst_pos % 16, d] = pages[src_pos // 16, 0, h, src_pos % 16, d] + pages[dst_pos // 16, 1, h, dst_pos % 16, d] = pages[src_pos // 16, 1, h, src_pos % 16, d] + + @T.prim_func(private=True) + def concatenate(var_reshape710: T.handle, var_reshape711: T.handle, var_reshape712: T.handle, var_T_concat: T.handle): + T.func_attr({"op_pattern": 2, "tir.noalias": T.bool(True)}) + batch_size = T.int64() + reshape710 = T.match_buffer(var_reshape710, (batch_size, T.int64(1), T.int64(20), T.int64(64)), "float16") + reshape711 = T.match_buffer(var_reshape711, (batch_size, T.int64(1), T.int64(20), T.int64(64)), "float16") + reshape712 = T.match_buffer(var_reshape712, (batch_size, T.int64(1), T.int64(20), T.int64(64)), "float16") + T_concat = T.match_buffer(var_T_concat, (batch_size, T.int64(1), T.int64(60), T.int64(64)), "float16") + # with T.block("root"): + for ax0, ax1, ax2, ax3 in T.grid(batch_size, T.int64(1), T.int64(60), T.int64(64)): + with T.block("T_concat"): + v_ax0, v_ax1, v_ax2, v_ax3 = T.axis.remap("SSSS", [ax0, ax1, ax2, ax3]) + T.reads(reshape712[v_ax0, v_ax1, v_ax2 - T.int64(40), v_ax3], reshape711[v_ax0, v_ax1, v_ax2 - T.int64(20), v_ax3], reshape710[v_ax0, v_ax1, v_ax2, v_ax3]) + T.writes(T_concat[v_ax0, v_ax1, v_ax2, v_ax3]) + T_concat[v_ax0, v_ax1, v_ax2, v_ax3] = T.if_then_else(T.int64(40) <= v_ax2, reshape712[v_ax0, v_ax1, v_ax2 - T.int64(40), v_ax3], T.if_then_else(T.int64(20) <= v_ax2, reshape711[v_ax0, v_ax1, v_ax2 - T.int64(20), v_ax3], reshape710[v_ax0, v_ax1, v_ax2, v_ax3])) + + @T.prim_func(private=True) + def concatenate1(var_reshape387: T.handle, var_reshape388: T.handle, var_reshape389: T.handle, var_T_concat: T.handle): + T.func_attr({"op_pattern": 2, "tir.noalias": T.bool(True)}) + seq_len = T.int64() + reshape387 = T.match_buffer(var_reshape387, (T.int64(1), seq_len, T.int64(20), T.int64(64)), "float16") + reshape388 = T.match_buffer(var_reshape388, (T.int64(1), seq_len, T.int64(20), T.int64(64)), "float16") + reshape389 = T.match_buffer(var_reshape389, (T.int64(1), seq_len, T.int64(20), T.int64(64)), "float16") + T_concat = T.match_buffer(var_T_concat, (T.int64(1), seq_len, T.int64(60), T.int64(64)), "float16") + # with T.block("root"): + for ax0, ax1, ax2, ax3 in T.grid(T.int64(1), seq_len, T.int64(60), T.int64(64)): + with T.block("T_concat"): + v_ax0, v_ax1, v_ax2, v_ax3 = T.axis.remap("SSSS", [ax0, ax1, ax2, ax3]) + T.reads(reshape389[v_ax0, v_ax1, v_ax2 - T.int64(40), v_ax3], reshape388[v_ax0, v_ax1, v_ax2 - T.int64(20), v_ax3], reshape387[v_ax0, v_ax1, v_ax2, v_ax3]) + T.writes(T_concat[v_ax0, v_ax1, v_ax2, v_ax3]) + T_concat[v_ax0, v_ax1, v_ax2, v_ax3] = T.if_then_else(T.int64(40) <= v_ax2, reshape389[v_ax0, v_ax1, v_ax2 - T.int64(40), v_ax3], T.if_then_else(T.int64(20) <= v_ax2, reshape388[v_ax0, v_ax1, v_ax2 - T.int64(20), v_ax3], reshape387[v_ax0, v_ax1, v_ax2, v_ax3])) + + @T.prim_func + def copy_single_page(var_pages: T.handle, src_page_id: T.int64, tgt_page_id: T.int64, copy_length: T.int64): + T.func_attr({"op_pattern": 8, "target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) + num_pages, page_size = T.int32(), T.int64() + pages = T.match_buffer(var_pages, (num_pages, 2, 20, page_size, 64), "float16") + # with T.block("root"): + for b in T.thread_binding((copy_length * T.int64(1280) + T.int64(1023)) // T.int64(1024), thread="blockIdx.x"): + for t in T.thread_binding(1024, thread="threadIdx.x"): + with T.block("copy"): + vh = T.axis.spatial(20, T.Cast("int32", (b * T.int64(1024) + T.Cast("int64", t)) // (copy_length * T.int64(64)))) + vp = T.axis.spatial(copy_length, (b * T.int64(1024) + T.Cast("int64", t)) % (copy_length * T.int64(64)) // T.int64(64)) + vd = T.axis.spatial(64, T.Cast("int32", (b * T.int64(1024) + T.Cast("int64", t)) % T.int64(64))) + T.reads(pages[src_page_id, 0:2, vh, vp, vd]) + T.writes(pages[tgt_page_id, 0:2, vh, vp, vd]) + pages[tgt_page_id, 0, vh, vp, vd] = pages[src_page_id, 0, vh, vp, vd] + pages[tgt_page_id, 1, vh, vp, vd] = pages[src_page_id, 1, vh, vp, vd] + + @T.prim_func(private=True) + def cumsum(var_sorted_probs: T.handle, var_lv1: T.handle, var_exclusive_scan_thrust: T.handle): + T.func_attr({"op_pattern": 8, "tir.noalias": T.bool(True)}) + batch_size, vocab_size = T.int64(), T.int64() + data_buf = T.match_buffer(var_sorted_probs, (batch_size, vocab_size), align=8) + workspace_buf = T.match_buffer(var_lv1, (T.int64(8) * (batch_size * vocab_size * T.int64(4)) + T.int64(8388608) + batch_size * vocab_size * T.int64(12),), "uint8", align=8) + output_buf = T.match_buffer(var_exclusive_scan_thrust, (batch_size, vocab_size), align=8) + with T.block("exclusive_scan_thrust"): + T.reads() + T.writes() + T.call_packed("tvm.contrib.thrust.sum_scan", T.tvm_stack_make_array(data_buf.data, T.tvm_stack_make_shape(batch_size, vocab_size), 0, 2, T.float32(0), T.int64(0)), T.tvm_stack_make_array(output_buf.data, T.tvm_stack_make_shape(batch_size, vocab_size), 0, 2, T.float32(0), T.int64(0)), T.bool(False), T.tvm_stack_make_array(workspace_buf.data, T.tvm_stack_make_shape(T.int64(8) * (batch_size * vocab_size * T.int64(4)) + T.int64(8388608) + batch_size * vocab_size * T.int64(12)), 0, 1, T.uint8(0), T.int64(0))) + + @T.prim_func + def full(var_result: T.handle, value: T.int32): + T.func_attr({"op_pattern": 0, "target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32})}) + batch_size = T.int32(is_size_var=True) + result = T.match_buffer(var_result, (batch_size, 1), "int32") + # with T.block("root"): + for i in range(batch_size): + with T.block("block"): + vi = T.axis.spatial(batch_size, i) + T.reads() + T.writes(result[vi, 0]) + result[vi, 0] = value + + @T.prim_func(private=True) + def fused_NT_matmul1_add8_gelu2(layer_norm358: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16"), model_decoder_layers_0_fc1_weight5: T.Buffer((T.int64(5120), T.int64(1280)), "float16"), model_decoder_layers_0_fc1_bias5: T.Buffer((T.int64(5120),), "float16"), T_multiply_intermediate: T.Buffer((T.int64(1), T.int64(1), T.int64(5120)), "float16")): + T.func_attr({"tir.noalias": T.bool(True)}) + # with T.block("root"): + NT_matmul_intermediate = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(5120)), "float16") + T_add_intermediate = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(5120)), "float16") + T_multiply = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(5120)), "float16") + compute = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(5120))) + compute_1 = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(5120))) + compute_2 = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(5120)), "float16") + T_multiply_1 = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(5120)), "float16") + T_add = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(5120)), "float16") + for i0, i1, i2, k in T.grid(T.int64(1), T.int64(1), T.int64(5120), T.int64(1280)): + with T.block("NT_matmul"): + v_i0, v_i1, v_i2, v_k = T.axis.remap("SSSR", [i0, i1, i2, k]) + T.reads(layer_norm358[v_i0, v_i1, v_k], model_decoder_layers_0_fc1_weight5[v_i2, v_k]) + T.writes(NT_matmul_intermediate[v_i0, v_i1, v_i2]) + with T.init(): + NT_matmul_intermediate[v_i0, v_i1, v_i2] = T.float16(0) + NT_matmul_intermediate[v_i0, v_i1, v_i2] = NT_matmul_intermediate[v_i0, v_i1, v_i2] + layer_norm358[v_i0, v_i1, v_k] * model_decoder_layers_0_fc1_weight5[v_i2, v_k] + for ax0, ax1, ax2 in T.grid(T.int64(1), T.int64(1), T.int64(5120)): + with T.block("T_add"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(NT_matmul_intermediate[v_ax0, v_ax1, v_ax2], model_decoder_layers_0_fc1_bias5[v_ax2]) + T.writes(T_add_intermediate[v_ax0, v_ax1, v_ax2]) + T_add_intermediate[v_ax0, v_ax1, v_ax2] = NT_matmul_intermediate[v_ax0, v_ax1, v_ax2] + model_decoder_layers_0_fc1_bias5[v_ax2] + for ax0, ax1, ax2 in T.grid(T.int64(1), T.int64(1), T.int64(5120)): + with T.block("T_multiply"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(T_add_intermediate[v_ax0, v_ax1, v_ax2]) + T.writes(T_multiply[v_ax0, v_ax1, v_ax2]) + T_multiply[v_ax0, v_ax1, v_ax2] = T_add_intermediate[v_ax0, v_ax1, v_ax2] * T.float16(0.70710678118654757) + for i0, i1, i2 in T.grid(T.int64(1), T.int64(1), T.int64(5120)): + with T.block("compute"): + v_i0, v_i1, v_i2 = T.axis.remap("SSS", [i0, i1, i2]) + T.reads(T_multiply[v_i0, v_i1, v_i2]) + T.writes(compute[v_i0, v_i1, v_i2]) + compute[v_i0, v_i1, v_i2] = T.Cast("float32", T_multiply[v_i0, v_i1, v_i2]) + for i0, i1, i2 in T.grid(T.int64(1), T.int64(1), T.int64(5120)): + with T.block("compute_1"): + v_i0, v_i1, v_i2 = T.axis.remap("SSS", [i0, i1, i2]) + T.reads(compute[v_i0, v_i1, v_i2]) + T.writes(compute_1[v_i0, v_i1, v_i2]) + compute_1[v_i0, v_i1, v_i2] = T.erf(compute[v_i0, v_i1, v_i2]) + for i0, i1, i2 in T.grid(T.int64(1), T.int64(1), T.int64(5120)): + with T.block("compute_2"): + v_i0, v_i1, v_i2 = T.axis.remap("SSS", [i0, i1, i2]) + T.reads(compute_1[v_i0, v_i1, v_i2]) + T.writes(compute_2[v_i0, v_i1, v_i2]) + compute_2[v_i0, v_i1, v_i2] = T.Cast("float16", compute_1[v_i0, v_i1, v_i2]) + for ax0, ax1, ax2 in T.grid(T.int64(1), T.int64(1), T.int64(5120)): + with T.block("T_multiply_1"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(compute_2[v_ax0, v_ax1, v_ax2]) + T.writes(T_multiply_1[v_ax0, v_ax1, v_ax2]) + T_multiply_1[v_ax0, v_ax1, v_ax2] = compute_2[v_ax0, v_ax1, v_ax2] * T.float16(0.5) + for ax0, ax1, ax2 in T.grid(T.int64(1), T.int64(1), T.int64(5120)): + with T.block("T_add_1"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(T_multiply_1[v_ax0, v_ax1, v_ax2]) + T.writes(T_add[v_ax0, v_ax1, v_ax2]) + T_add[v_ax0, v_ax1, v_ax2] = T.float16(0.5) + T_multiply_1[v_ax0, v_ax1, v_ax2] + for ax0, ax1, ax2 in T.grid(T.int64(1), T.int64(1), T.int64(5120)): + with T.block("T_multiply_2"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(T_add_intermediate[v_ax0, v_ax1, v_ax2], T_add[v_ax0, v_ax1, v_ax2]) + T.writes(T_multiply_intermediate[v_ax0, v_ax1, v_ax2]) + T_multiply_intermediate[v_ax0, v_ax1, v_ax2] = T_add_intermediate[v_ax0, v_ax1, v_ax2] * T_add[v_ax0, v_ax1, v_ax2] + + @T.prim_func(private=True) + def fused_NT_matmul2_add7_add6(gelu130: T.Buffer((T.int64(1), T.int64(1), T.int64(5120)), "float16"), model_decoder_layers_0_fc2_weight5: T.Buffer((T.int64(1280), T.int64(5120)), "float16"), model_decoder_layers_0_fc2_bias5: T.Buffer((T.int64(1280),), "float16"), add1227: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16"), T_add_intermediate_1: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16")): + T.func_attr({"tir.noalias": T.bool(True)}) + # with T.block("root"): + NT_matmul_intermediate = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16") + T_add_intermediate = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16") + for i0, i1, i2, k in T.grid(T.int64(1), T.int64(1), T.int64(1280), T.int64(5120)): + with T.block("NT_matmul"): + v_i0, v_i1, v_i2, v_k = T.axis.remap("SSSR", [i0, i1, i2, k]) + T.reads(gelu130[v_i0, v_i1, v_k], model_decoder_layers_0_fc2_weight5[v_i2, v_k]) + T.writes(NT_matmul_intermediate[v_i0, v_i1, v_i2]) + with T.init(): + NT_matmul_intermediate[v_i0, v_i1, v_i2] = T.float16(0) + NT_matmul_intermediate[v_i0, v_i1, v_i2] = NT_matmul_intermediate[v_i0, v_i1, v_i2] + gelu130[v_i0, v_i1, v_k] * model_decoder_layers_0_fc2_weight5[v_i2, v_k] + for ax0, ax1, ax2 in T.grid(T.int64(1), T.int64(1), T.int64(1280)): + with T.block("T_add"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(NT_matmul_intermediate[v_ax0, v_ax1, v_ax2], model_decoder_layers_0_fc2_bias5[v_ax2]) + T.writes(T_add_intermediate[v_ax0, v_ax1, v_ax2]) + T_add_intermediate[v_ax0, v_ax1, v_ax2] = NT_matmul_intermediate[v_ax0, v_ax1, v_ax2] + model_decoder_layers_0_fc2_bias5[v_ax2] + for ax0, ax1, ax2 in T.grid(T.int64(1), T.int64(1), T.int64(1280)): + with T.block("T_add_1"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(add1227[v_ax0, v_ax1, v_ax2], T_add_intermediate[v_ax0, v_ax1, v_ax2]) + T.writes(T_add_intermediate_1[v_ax0, v_ax1, v_ax2]) + T_add_intermediate_1[v_ax0, v_ax1, v_ax2] = add1227[v_ax0, v_ax1, v_ax2] + T_add_intermediate[v_ax0, v_ax1, v_ax2] + + @T.prim_func(private=True) + def fused_NT_matmul_add7(layer_norm356: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16"), model_decoder_layers_0_self_attn_q_proj_weight5: T.Buffer((T.int64(1280), T.int64(1280)), "float16"), model_decoder_layers_0_self_attn_q_proj_bias5: T.Buffer((T.int64(1280),), "float16"), T_add_intermediate: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16")): + T.func_attr({"tir.noalias": T.bool(True)}) + # with T.block("root"): + NT_matmul_intermediate = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16") + for i0, i1, i2, k in T.grid(T.int64(1), T.int64(1), T.int64(1280), T.int64(1280)): + with T.block("NT_matmul"): + v_i0, v_i1, v_i2, v_k = T.axis.remap("SSSR", [i0, i1, i2, k]) + T.reads(layer_norm356[v_i0, v_i1, v_k], model_decoder_layers_0_self_attn_q_proj_weight5[v_i2, v_k]) + T.writes(NT_matmul_intermediate[v_i0, v_i1, v_i2]) + with T.init(): + NT_matmul_intermediate[v_i0, v_i1, v_i2] = T.float16(0) + NT_matmul_intermediate[v_i0, v_i1, v_i2] = NT_matmul_intermediate[v_i0, v_i1, v_i2] + layer_norm356[v_i0, v_i1, v_k] * model_decoder_layers_0_self_attn_q_proj_weight5[v_i2, v_k] + for ax0, ax1, ax2 in T.grid(T.int64(1), T.int64(1), T.int64(1280)): + with T.block("T_add"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(NT_matmul_intermediate[v_ax0, v_ax1, v_ax2], model_decoder_layers_0_self_attn_q_proj_bias5[v_ax2]) + T.writes(T_add_intermediate[v_ax0, v_ax1, v_ax2]) + T_add_intermediate[v_ax0, v_ax1, v_ax2] = NT_matmul_intermediate[v_ax0, v_ax1, v_ax2] + model_decoder_layers_0_self_attn_q_proj_bias5[v_ax2] + + @T.prim_func(private=True) + def fused_NT_matmul_add7_add6(reshape1361: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16"), model_decoder_layers_0_self_attn_out_proj_weight5: T.Buffer((T.int64(1280), T.int64(1280)), "float16"), model_decoder_layers_0_self_attn_out_proj_bias5: T.Buffer((T.int64(1280),), "float16"), add1220: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16"), T_add_intermediate_1: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16")): + T.func_attr({"tir.noalias": T.bool(True)}) + # with T.block("root"): + NT_matmul_intermediate = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16") + T_add_intermediate = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16") + for i0, i1, i2, k in T.grid(T.int64(1), T.int64(1), T.int64(1280), T.int64(1280)): + with T.block("NT_matmul"): + v_i0, v_i1, v_i2, v_k = T.axis.remap("SSSR", [i0, i1, i2, k]) + T.reads(reshape1361[v_i0, v_i1, v_k], model_decoder_layers_0_self_attn_out_proj_weight5[v_i2, v_k]) + T.writes(NT_matmul_intermediate[v_i0, v_i1, v_i2]) + with T.init(): + NT_matmul_intermediate[v_i0, v_i1, v_i2] = T.float16(0) + NT_matmul_intermediate[v_i0, v_i1, v_i2] = NT_matmul_intermediate[v_i0, v_i1, v_i2] + reshape1361[v_i0, v_i1, v_k] * model_decoder_layers_0_self_attn_out_proj_weight5[v_i2, v_k] + for ax0, ax1, ax2 in T.grid(T.int64(1), T.int64(1), T.int64(1280)): + with T.block("T_add"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(NT_matmul_intermediate[v_ax0, v_ax1, v_ax2], model_decoder_layers_0_self_attn_out_proj_bias5[v_ax2]) + T.writes(T_add_intermediate[v_ax0, v_ax1, v_ax2]) + T_add_intermediate[v_ax0, v_ax1, v_ax2] = NT_matmul_intermediate[v_ax0, v_ax1, v_ax2] + model_decoder_layers_0_self_attn_out_proj_bias5[v_ax2] + for ax0, ax1, ax2 in T.grid(T.int64(1), T.int64(1), T.int64(1280)): + with T.block("T_add_1"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(add1220[v_ax0, v_ax1, v_ax2], T_add_intermediate[v_ax0, v_ax1, v_ax2]) + T.writes(T_add_intermediate_1[v_ax0, v_ax1, v_ax2]) + T_add_intermediate_1[v_ax0, v_ax1, v_ax2] = add1220[v_ax0, v_ax1, v_ax2] + T_add_intermediate[v_ax0, v_ax1, v_ax2] + + @T.prim_func(private=True) + def fused_add4_maximum_minimum(p_add4: T.handle, p_lv611: T.handle, p_output0: T.handle): + T.func_attr({"tir.noalias": T.bool(True)}) + batch_size = T.int64() + add4 = T.match_buffer(p_add4, (batch_size, T.int64(1500), T.int64(1280)), "float16") + lv611 = T.match_buffer(p_lv611, (batch_size, T.int64(1500), T.int64(1280)), "float16") + T_minimum_intermediate = T.match_buffer(p_output0, (batch_size, T.int64(1500), T.int64(1280)), "float16") + # with T.block("root"): + T_add_intermediate = T.alloc_buffer((batch_size, T.int64(1500), T.int64(1280)), "float16") + T_maximum_intermediate = T.alloc_buffer((batch_size, T.int64(1500), T.int64(1280)), "float16") + for ax0, ax1, ax2 in T.grid(batch_size, T.int64(1500), T.int64(1280)): + with T.block("T_add"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(add4[v_ax0, v_ax1, v_ax2], lv611[v_ax0, v_ax1, v_ax2]) + T.writes(T_add_intermediate[v_ax0, v_ax1, v_ax2]) + T_add_intermediate[v_ax0, v_ax1, v_ax2] = add4[v_ax0, v_ax1, v_ax2] + lv611[v_ax0, v_ax1, v_ax2] + for ax0, ax1, ax2 in T.grid(batch_size, T.int64(1500), T.int64(1280)): + with T.block("T_maximum"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(T_add_intermediate[v_ax0, v_ax1, v_ax2]) + T.writes(T_maximum_intermediate[v_ax0, v_ax1, v_ax2]) + T_maximum_intermediate[v_ax0, v_ax1, v_ax2] = T.max(T_add_intermediate[v_ax0, v_ax1, v_ax2], T.float16(-65504)) + for ax0, ax1, ax2 in T.grid(batch_size, T.int64(1500), T.int64(1280)): + with T.block("T_minimum"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(T_maximum_intermediate[v_ax0, v_ax1, v_ax2]) + T.writes(T_minimum_intermediate[v_ax0, v_ax1, v_ax2]) + T_minimum_intermediate[v_ax0, v_ax1, v_ax2] = T.min(T_maximum_intermediate[v_ax0, v_ax1, v_ax2], T.float16(65504)) + + @T.prim_func(private=True) + def fused_conv1d1_add2_gelu1(p_gelu: T.handle, model_encoder_conv2_weight: T.Buffer((T.int64(1280), T.int64(1280), T.int64(3)), "float16"), lv3: T.Buffer((T.int64(1), T.int64(1280), T.int64(1)), "float16"), p_output0: T.handle): + T.func_attr({"tir.noalias": T.bool(True)}) + batch_size = T.int64() + gelu = T.match_buffer(p_gelu, (batch_size, T.int64(1280), T.int64(3000)), "float16") + T_multiply_intermediate = T.match_buffer(p_output0, (batch_size, T.int64(1280), T.int64(1500)), "float16") + # with T.block("root"): + pad_temp = T.alloc_buffer((batch_size, T.int64(1280), T.int64(3002)), "float16") + conv1d_ncw_intermediate = T.alloc_buffer((batch_size, T.int64(1280), T.int64(1500)), "float16") + T_add_intermediate = T.alloc_buffer((batch_size, T.int64(1280), T.int64(1500)), "float16") + T_multiply = T.alloc_buffer((batch_size, T.int64(1280), T.int64(1500)), "float16") + compute = T.alloc_buffer((batch_size, T.int64(1280), T.int64(1500))) + compute_1 = T.alloc_buffer((batch_size, T.int64(1280), T.int64(1500))) + compute_2 = T.alloc_buffer((batch_size, T.int64(1280), T.int64(1500)), "float16") + T_multiply_1 = T.alloc_buffer((batch_size, T.int64(1280), T.int64(1500)), "float16") + T_add = T.alloc_buffer((batch_size, T.int64(1280), T.int64(1500)), "float16") + for i0, i1, i2 in T.grid(batch_size, T.int64(1280), T.int64(3002)): + with T.block("pad_temp"): + v_i0, v_i1, v_i2 = T.axis.remap("SSS", [i0, i1, i2]) + T.reads(gelu[v_i0, v_i1, v_i2 - T.int64(1)]) + T.writes(pad_temp[v_i0, v_i1, v_i2]) + pad_temp[v_i0, v_i1, v_i2] = T.if_then_else(T.int64(1) <= v_i2 and v_i2 < T.int64(3001), gelu[v_i0, v_i1, v_i2 - T.int64(1)], T.float16(0)) + for nn, ff, yy, rc, ry in T.grid(batch_size, T.int64(1280), T.int64(1500), T.int64(1280), T.int64(3)): + with T.block("conv1d_ncw"): + v_nn, v_ff, v_yy, v_rc, v_ry = T.axis.remap("SSSRR", [nn, ff, yy, rc, ry]) + T.reads(pad_temp[v_nn, v_rc, v_yy * T.int64(2) + v_ry], model_encoder_conv2_weight[v_ff, v_rc, v_ry]) + T.writes(conv1d_ncw_intermediate[v_nn, v_ff, v_yy]) + with T.init(): + conv1d_ncw_intermediate[v_nn, v_ff, v_yy] = T.float16(0) + conv1d_ncw_intermediate[v_nn, v_ff, v_yy] = conv1d_ncw_intermediate[v_nn, v_ff, v_yy] + pad_temp[v_nn, v_rc, v_yy * T.int64(2) + v_ry] * model_encoder_conv2_weight[v_ff, v_rc, v_ry] + for ax0, ax1, ax2 in T.grid(batch_size, T.int64(1280), T.int64(1500)): + with T.block("T_add"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(conv1d_ncw_intermediate[v_ax0, v_ax1, v_ax2], lv3[T.int64(0), v_ax1, T.int64(0)]) + T.writes(T_add_intermediate[v_ax0, v_ax1, v_ax2]) + T_add_intermediate[v_ax0, v_ax1, v_ax2] = conv1d_ncw_intermediate[v_ax0, v_ax1, v_ax2] + lv3[T.int64(0), v_ax1, T.int64(0)] + for ax0, ax1, ax2 in T.grid(batch_size, T.int64(1280), T.int64(1500)): + with T.block("T_multiply"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(T_add_intermediate[v_ax0, v_ax1, v_ax2]) + T.writes(T_multiply[v_ax0, v_ax1, v_ax2]) + T_multiply[v_ax0, v_ax1, v_ax2] = T_add_intermediate[v_ax0, v_ax1, v_ax2] * T.float16(0.70710678118654757) + for i0, i1, i2 in T.grid(batch_size, T.int64(1280), T.int64(1500)): + with T.block("compute"): + v_i0, v_i1, v_i2 = T.axis.remap("SSS", [i0, i1, i2]) + T.reads(T_multiply[v_i0, v_i1, v_i2]) + T.writes(compute[v_i0, v_i1, v_i2]) + compute[v_i0, v_i1, v_i2] = T.Cast("float32", T_multiply[v_i0, v_i1, v_i2]) + for i0, i1, i2 in T.grid(batch_size, T.int64(1280), T.int64(1500)): + with T.block("compute_1"): + v_i0, v_i1, v_i2 = T.axis.remap("SSS", [i0, i1, i2]) + T.reads(compute[v_i0, v_i1, v_i2]) + T.writes(compute_1[v_i0, v_i1, v_i2]) + compute_1[v_i0, v_i1, v_i2] = T.erf(compute[v_i0, v_i1, v_i2]) + for i0, i1, i2 in T.grid(batch_size, T.int64(1280), T.int64(1500)): + with T.block("compute_2"): + v_i0, v_i1, v_i2 = T.axis.remap("SSS", [i0, i1, i2]) + T.reads(compute_1[v_i0, v_i1, v_i2]) + T.writes(compute_2[v_i0, v_i1, v_i2]) + compute_2[v_i0, v_i1, v_i2] = T.Cast("float16", compute_1[v_i0, v_i1, v_i2]) + for ax0, ax1, ax2 in T.grid(batch_size, T.int64(1280), T.int64(1500)): + with T.block("T_multiply_1"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(compute_2[v_ax0, v_ax1, v_ax2]) + T.writes(T_multiply_1[v_ax0, v_ax1, v_ax2]) + T_multiply_1[v_ax0, v_ax1, v_ax2] = compute_2[v_ax0, v_ax1, v_ax2] * T.float16(0.5) + for ax0, ax1, ax2 in T.grid(batch_size, T.int64(1280), T.int64(1500)): + with T.block("T_add_1"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(T_multiply_1[v_ax0, v_ax1, v_ax2]) + T.writes(T_add[v_ax0, v_ax1, v_ax2]) + T_add[v_ax0, v_ax1, v_ax2] = T.float16(0.5) + T_multiply_1[v_ax0, v_ax1, v_ax2] + for ax0, ax1, ax2 in T.grid(batch_size, T.int64(1280), T.int64(1500)): + with T.block("T_multiply_2"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(T_add_intermediate[v_ax0, v_ax1, v_ax2], T_add[v_ax0, v_ax1, v_ax2]) + T.writes(T_multiply_intermediate[v_ax0, v_ax1, v_ax2]) + T_multiply_intermediate[v_ax0, v_ax1, v_ax2] = T_add_intermediate[v_ax0, v_ax1, v_ax2] * T_add[v_ax0, v_ax1, v_ax2] + + @T.prim_func(private=True) + def fused_conv1d_add1_gelu(p_input_features: T.handle, model_encoder_conv1_weight: T.Buffer((T.int64(1280), T.int64(128), T.int64(3)), "float16"), lv1: T.Buffer((T.int64(1), T.int64(1280), T.int64(1)), "float16"), p_output0: T.handle): + T.func_attr({"tir.noalias": T.bool(True)}) + batch_size = T.int64() + input_features = T.match_buffer(p_input_features, (batch_size, T.int64(128), T.int64(3000)), "float16") + T_multiply_intermediate = T.match_buffer(p_output0, (batch_size, T.int64(1280), T.int64(3000)), "float16") + # with T.block("root"): + pad_temp = T.alloc_buffer((batch_size, T.int64(128), T.int64(3002)), "float16") + conv1d_ncw_intermediate = T.alloc_buffer((batch_size, T.int64(1280), T.int64(3000)), "float16") + T_add_intermediate = T.alloc_buffer((batch_size, T.int64(1280), T.int64(3000)), "float16") + T_multiply = T.alloc_buffer((batch_size, T.int64(1280), T.int64(3000)), "float16") + compute = T.alloc_buffer((batch_size, T.int64(1280), T.int64(3000))) + compute_1 = T.alloc_buffer((batch_size, T.int64(1280), T.int64(3000))) + compute_2 = T.alloc_buffer((batch_size, T.int64(1280), T.int64(3000)), "float16") + T_multiply_1 = T.alloc_buffer((batch_size, T.int64(1280), T.int64(3000)), "float16") + T_add = T.alloc_buffer((batch_size, T.int64(1280), T.int64(3000)), "float16") + for i0, i1, i2 in T.grid(batch_size, T.int64(128), T.int64(3002)): + with T.block("pad_temp"): + v_i0, v_i1, v_i2 = T.axis.remap("SSS", [i0, i1, i2]) + T.reads(input_features[v_i0, v_i1, v_i2 - T.int64(1)]) + T.writes(pad_temp[v_i0, v_i1, v_i2]) + pad_temp[v_i0, v_i1, v_i2] = T.if_then_else(T.int64(1) <= v_i2 and v_i2 < T.int64(3001), input_features[v_i0, v_i1, v_i2 - T.int64(1)], T.float16(0)) + for nn, ff, yy, rc, ry in T.grid(batch_size, T.int64(1280), T.int64(3000), T.int64(128), T.int64(3)): + with T.block("conv1d_ncw"): + v_nn, v_ff, v_yy, v_rc, v_ry = T.axis.remap("SSSRR", [nn, ff, yy, rc, ry]) + T.reads(pad_temp[v_nn, v_rc, v_yy + v_ry], model_encoder_conv1_weight[v_ff, v_rc, v_ry]) + T.writes(conv1d_ncw_intermediate[v_nn, v_ff, v_yy]) + with T.init(): + conv1d_ncw_intermediate[v_nn, v_ff, v_yy] = T.float16(0) + conv1d_ncw_intermediate[v_nn, v_ff, v_yy] = conv1d_ncw_intermediate[v_nn, v_ff, v_yy] + pad_temp[v_nn, v_rc, v_yy + v_ry] * model_encoder_conv1_weight[v_ff, v_rc, v_ry] + for ax0, ax1, ax2 in T.grid(batch_size, T.int64(1280), T.int64(3000)): + with T.block("T_add"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(conv1d_ncw_intermediate[v_ax0, v_ax1, v_ax2], lv1[T.int64(0), v_ax1, T.int64(0)]) + T.writes(T_add_intermediate[v_ax0, v_ax1, v_ax2]) + T_add_intermediate[v_ax0, v_ax1, v_ax2] = conv1d_ncw_intermediate[v_ax0, v_ax1, v_ax2] + lv1[T.int64(0), v_ax1, T.int64(0)] + for ax0, ax1, ax2 in T.grid(batch_size, T.int64(1280), T.int64(3000)): + with T.block("T_multiply"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(T_add_intermediate[v_ax0, v_ax1, v_ax2]) + T.writes(T_multiply[v_ax0, v_ax1, v_ax2]) + T_multiply[v_ax0, v_ax1, v_ax2] = T_add_intermediate[v_ax0, v_ax1, v_ax2] * T.float16(0.70710678118654757) + for i0, i1, i2 in T.grid(batch_size, T.int64(1280), T.int64(3000)): + with T.block("compute"): + v_i0, v_i1, v_i2 = T.axis.remap("SSS", [i0, i1, i2]) + T.reads(T_multiply[v_i0, v_i1, v_i2]) + T.writes(compute[v_i0, v_i1, v_i2]) + compute[v_i0, v_i1, v_i2] = T.Cast("float32", T_multiply[v_i0, v_i1, v_i2]) + for i0, i1, i2 in T.grid(batch_size, T.int64(1280), T.int64(3000)): + with T.block("compute_1"): + v_i0, v_i1, v_i2 = T.axis.remap("SSS", [i0, i1, i2]) + T.reads(compute[v_i0, v_i1, v_i2]) + T.writes(compute_1[v_i0, v_i1, v_i2]) + compute_1[v_i0, v_i1, v_i2] = T.erf(compute[v_i0, v_i1, v_i2]) + for i0, i1, i2 in T.grid(batch_size, T.int64(1280), T.int64(3000)): + with T.block("compute_2"): + v_i0, v_i1, v_i2 = T.axis.remap("SSS", [i0, i1, i2]) + T.reads(compute_1[v_i0, v_i1, v_i2]) + T.writes(compute_2[v_i0, v_i1, v_i2]) + compute_2[v_i0, v_i1, v_i2] = T.Cast("float16", compute_1[v_i0, v_i1, v_i2]) + for ax0, ax1, ax2 in T.grid(batch_size, T.int64(1280), T.int64(3000)): + with T.block("T_multiply_1"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(compute_2[v_ax0, v_ax1, v_ax2]) + T.writes(T_multiply_1[v_ax0, v_ax1, v_ax2]) + T_multiply_1[v_ax0, v_ax1, v_ax2] = compute_2[v_ax0, v_ax1, v_ax2] * T.float16(0.5) + for ax0, ax1, ax2 in T.grid(batch_size, T.int64(1280), T.int64(3000)): + with T.block("T_add_1"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(T_multiply_1[v_ax0, v_ax1, v_ax2]) + T.writes(T_add[v_ax0, v_ax1, v_ax2]) + T_add[v_ax0, v_ax1, v_ax2] = T.float16(0.5) + T_multiply_1[v_ax0, v_ax1, v_ax2] + for ax0, ax1, ax2 in T.grid(batch_size, T.int64(1280), T.int64(3000)): + with T.block("T_multiply_2"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(T_add_intermediate[v_ax0, v_ax1, v_ax2], T_add[v_ax0, v_ax1, v_ax2]) + T.writes(T_multiply_intermediate[v_ax0, v_ax1, v_ax2]) + T_multiply_intermediate[v_ax0, v_ax1, v_ax2] = T_add_intermediate[v_ax0, v_ax1, v_ax2] * T_add[v_ax0, v_ax1, v_ax2] + + @T.prim_func(private=True) + def fused_reshape20_reshape20_add6(take7: T.Buffer((T.int64(1), T.int64(1280)), "float16"), take8: T.Buffer((T.int64(1), T.int64(1280)), "float16"), T_add_intermediate: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16")): + T.func_attr({"tir.noalias": T.bool(True)}) + # with T.block("root"): + T_reshape_intermediate = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16") + T_reshape_intermediate_1 = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16") + for ax0, ax1, ax2 in T.grid(T.int64(1), T.int64(1), T.int64(1280)): + with T.block("T_reshape"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(take7[T.int64(0), v_ax2 % T.int64(1280)]) + T.writes(T_reshape_intermediate[v_ax0, v_ax1, v_ax2]) + T_reshape_intermediate[v_ax0, v_ax1, v_ax2] = take7[T.int64(0), v_ax2 % T.int64(1280)] + for ax0, ax1, ax2 in T.grid(T.int64(1), T.int64(1), T.int64(1280)): + with T.block("T_reshape_1"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(take8[T.int64(0), v_ax2 % T.int64(1280)]) + T.writes(T_reshape_intermediate_1[v_ax0, v_ax1, v_ax2]) + T_reshape_intermediate_1[v_ax0, v_ax1, v_ax2] = take8[T.int64(0), v_ax2 % T.int64(1280)] + for ax0, ax1, ax2 in T.grid(T.int64(1), T.int64(1), T.int64(1280)): + with T.block("T_add"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(T_reshape_intermediate[v_ax0, v_ax1, v_ax2], T_reshape_intermediate_1[v_ax0, v_ax1, v_ax2]) + T.writes(T_add_intermediate[v_ax0, v_ax1, v_ax2]) + T_add_intermediate[v_ax0, v_ax1, v_ax2] = T_reshape_intermediate[v_ax0, v_ax1, v_ax2] + T_reshape_intermediate_1[v_ax0, v_ax1, v_ax2] + + @T.prim_func(private=True) + def fused_reshape21_reshape21_reshape21_concatenate2_reshape22(add1221: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16"), lv1: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16"), add1222: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16"), T_reshape_intermediate_1_2_3: T.Buffer((T.int64(1), T.int64(60), T.int64(64)), "float16")): + T.func_attr({"tir.noalias": T.bool(True)}) + # with T.block("root"): + T_reshape_intermediate = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(20), T.int64(64)), "float16") + T_reshape_intermediate_1 = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(20), T.int64(64)), "float16") + T_reshape_intermediate_1_2 = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(20), T.int64(64)), "float16") + T_concat_intermediate = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(60), T.int64(64)), "float16") + for ax0, ax1, ax2, ax3 in T.grid(T.int64(1), T.int64(1), T.int64(20), T.int64(64)): + with T.block("T_reshape"): + v_ax0, v_ax1, v_ax2, v_ax3 = T.axis.remap("SSSS", [ax0, ax1, ax2, ax3]) + T.reads(add1221[T.int64(0), T.int64(0), (v_ax2 * T.int64(64) + v_ax3) % T.int64(1280)]) + T.writes(T_reshape_intermediate[v_ax0, v_ax1, v_ax2, v_ax3]) + T_reshape_intermediate[v_ax0, v_ax1, v_ax2, v_ax3] = add1221[T.int64(0), T.int64(0), (v_ax2 * T.int64(64) + v_ax3) % T.int64(1280)] + for ax0, ax1, ax2, ax3 in T.grid(T.int64(1), T.int64(1), T.int64(20), T.int64(64)): + with T.block("T_reshape_1"): + v_ax0, v_ax1, v_ax2, v_ax3 = T.axis.remap("SSSS", [ax0, ax1, ax2, ax3]) + T.reads(lv1[T.int64(0), T.int64(0), (v_ax2 * T.int64(64) + v_ax3) % T.int64(1280)]) + T.writes(T_reshape_intermediate_1[v_ax0, v_ax1, v_ax2, v_ax3]) + T_reshape_intermediate_1[v_ax0, v_ax1, v_ax2, v_ax3] = lv1[T.int64(0), T.int64(0), (v_ax2 * T.int64(64) + v_ax3) % T.int64(1280)] + for ax0, ax1, ax2, ax3 in T.grid(T.int64(1), T.int64(1), T.int64(20), T.int64(64)): + with T.block("T_reshape_2"): + v_ax0, v_ax1, v_ax2, v_ax3 = T.axis.remap("SSSS", [ax0, ax1, ax2, ax3]) + T.reads(add1222[T.int64(0), T.int64(0), (v_ax2 * T.int64(64) + v_ax3) % T.int64(1280)]) + T.writes(T_reshape_intermediate_1_2[v_ax0, v_ax1, v_ax2, v_ax3]) + T_reshape_intermediate_1_2[v_ax0, v_ax1, v_ax2, v_ax3] = add1222[T.int64(0), T.int64(0), (v_ax2 * T.int64(64) + v_ax3) % T.int64(1280)] + for ax0, ax1, ax2, ax3 in T.grid(T.int64(1), T.int64(1), T.int64(60), T.int64(64)): + with T.block("T_concat"): + v_ax0, v_ax1, v_ax2, v_ax3 = T.axis.remap("SSSS", [ax0, ax1, ax2, ax3]) + T.reads(T_reshape_intermediate_1_2[v_ax0, v_ax1, v_ax2 - T.int64(40), v_ax3], T_reshape_intermediate_1[v_ax0, v_ax1, v_ax2 - T.int64(20), v_ax3], T_reshape_intermediate[v_ax0, v_ax1, v_ax2, v_ax3]) + T.writes(T_concat_intermediate[v_ax0, v_ax1, v_ax2, v_ax3]) + T_concat_intermediate[v_ax0, v_ax1, v_ax2, v_ax3] = T.if_then_else(T.int64(40) <= v_ax2, T_reshape_intermediate_1_2[v_ax0, v_ax1, v_ax2 - T.int64(40), v_ax3], T.if_then_else(T.int64(20) <= v_ax2, T_reshape_intermediate_1[v_ax0, v_ax1, v_ax2 - T.int64(20), v_ax3], T_reshape_intermediate[v_ax0, v_ax1, v_ax2, v_ax3])) + for ax0, ax1, ax2 in T.grid(T.int64(1), T.int64(60), T.int64(64)): + with T.block("T_reshape_3"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(T_concat_intermediate[T.int64(0), T.int64(0), (v_ax2 // T.int64(64) + v_ax1) % T.int64(60), v_ax2 % T.int64(64)]) + T.writes(T_reshape_intermediate_1_2_3[v_ax0, v_ax1, v_ax2]) + T_reshape_intermediate_1_2_3[v_ax0, v_ax1, v_ax2] = T_concat_intermediate[T.int64(0), T.int64(0), (v_ax2 // T.int64(64) + v_ax1) % T.int64(60), v_ax2 % T.int64(64)] + + @T.prim_func(private=True) + def fused_reshape21_reshape25(add1225: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16"), T_reshape_intermediate_1: T.Buffer((T.int64(1), T.int64(20), T.int64(64)), "float16")): + T.func_attr({"tir.noalias": T.bool(True)}) + # with T.block("root"): + T_reshape_intermediate = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(20), T.int64(64)), "float16") + for ax0, ax1, ax2, ax3 in T.grid(T.int64(1), T.int64(1), T.int64(20), T.int64(64)): + with T.block("T_reshape"): + v_ax0, v_ax1, v_ax2, v_ax3 = T.axis.remap("SSSS", [ax0, ax1, ax2, ax3]) + T.reads(add1225[T.int64(0), T.int64(0), (v_ax2 * T.int64(64) + v_ax3) % T.int64(1280)]) + T.writes(T_reshape_intermediate[v_ax0, v_ax1, v_ax2, v_ax3]) + T_reshape_intermediate[v_ax0, v_ax1, v_ax2, v_ax3] = add1225[T.int64(0), T.int64(0), (v_ax2 * T.int64(64) + v_ax3) % T.int64(1280)] + for ax0, ax1, ax2 in T.grid(T.int64(1), T.int64(20), T.int64(64)): + with T.block("T_reshape_1"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(T_reshape_intermediate[T.int64(0), T.int64(0), (v_ax2 // T.int64(64) + v_ax1) % T.int64(20), v_ax2 % T.int64(64)]) + T.writes(T_reshape_intermediate_1[v_ax0, v_ax1, v_ax2]) + T_reshape_intermediate_1[v_ax0, v_ax1, v_ax2] = T_reshape_intermediate[T.int64(0), T.int64(0), (v_ax2 // T.int64(64) + v_ax1) % T.int64(20), v_ax2 % T.int64(64)] + + @T.prim_func(private=True) + def fused_reshape23_reshape24(lv265: T.Buffer((T.int64(1), T.int64(20), T.int64(64)), "float16"), T_reshape_intermediate_1: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16")): + T.func_attr({"tir.noalias": T.bool(True)}) + # with T.block("root"): + T_reshape_intermediate = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(20), T.int64(64)), "float16") + for ax0, ax1, ax2, ax3 in T.grid(T.int64(1), T.int64(1), T.int64(20), T.int64(64)): + with T.block("T_reshape"): + v_ax0, v_ax1, v_ax2, v_ax3 = T.axis.remap("SSSS", [ax0, ax1, ax2, ax3]) + T.reads(lv265[T.int64(0), (v_ax3 // T.int64(64) + v_ax2) % T.int64(20), v_ax3 % T.int64(64)]) + T.writes(T_reshape_intermediate[v_ax0, v_ax1, v_ax2, v_ax3]) + T_reshape_intermediate[v_ax0, v_ax1, v_ax2, v_ax3] = lv265[T.int64(0), (v_ax3 // T.int64(64) + v_ax2) % T.int64(20), v_ax3 % T.int64(64)] + for ax0, ax1, ax2 in T.grid(T.int64(1), T.int64(1), T.int64(1280)): + with T.block("T_reshape_1"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(T_reshape_intermediate[T.int64(0), T.int64(0), v_ax2 % T.int64(1280) // T.int64(64), v_ax2 % T.int64(64)]) + T.writes(T_reshape_intermediate_1[v_ax0, v_ax1, v_ax2]) + T_reshape_intermediate_1[v_ax0, v_ax1, v_ax2] = T_reshape_intermediate[T.int64(0), T.int64(0), v_ax2 % T.int64(1280) // T.int64(64), v_ax2 % T.int64(64)] + + @T.prim_func(private=True) + def fused_reshape9(packed_params_1: T.Buffer((T.int64(1280),), "float16"), T_reshape_intermediate: T.Buffer((T.int64(1), T.int64(1280), T.int64(1)), "float16")): + T.func_attr({"tir.noalias": T.bool(True)}) + # with T.block("root"): + for ax0, ax1, ax2 in T.grid(T.int64(1), T.int64(1280), T.int64(1)): + with T.block("T_reshape"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(packed_params_1[(v_ax1 + v_ax2) % T.int64(1280)]) + T.writes(T_reshape_intermediate[v_ax0, v_ax1, v_ax2]) + T_reshape_intermediate[v_ax0, v_ax1, v_ax2] = packed_params_1[(v_ax1 + v_ax2) % T.int64(1280)] + + @T.prim_func + def fused_rope(var_qkv: T.handle, var_position_map: T.handle, var_q: T.handle, var_k: T.handle, var_v: T.handle, apply_rope: T.int32): + T.func_attr({"op_pattern": 8, "target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.noalias": T.bool(True)}) + seq_len = T.int64() + qkv = T.match_buffer(var_qkv, (seq_len, 60, 64), "float16") + position_map = T.match_buffer(var_position_map, (seq_len,), "int32", offset_factor=1) + q = T.match_buffer(var_q, (seq_len, 20, 64), "float16") + k = T.match_buffer(var_k, (seq_len, 20, 64), "float16") + v = T.match_buffer(var_v, (seq_len, 20, 64), "float16") + # with T.block("root"): + for iters_0, iters_1, iters_2 in T.grid(seq_len, 60, 64): + with T.block("llama_fused_rope"): + s, h, d = T.axis.remap("SSS", [iters_0, iters_1, iters_2]) + T.reads(position_map[s], qkv[s, h, d - 32:d - 32 + 65]) + T.writes(q[s, h, d], k[s, h - 20, d], v[s, h - 40, d]) + if h < 20: + q[s, h, d] = T.if_then_else(apply_rope > 0 and d < 64, T.Cast("float16", T.cos(T.Cast("float32", position_map[s]) / T.pow(T.float32(1), T.Cast("float32", d * 2 % 64) / T.float32(64))) * T.Cast("float32", qkv[s, h, d]) + T.sin(T.Cast("float32", position_map[s]) / T.pow(T.float32(1), T.Cast("float32", d * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(d < 32, qkv[s, h, d + 32] * T.float16(-1), qkv[s, h, d - 32]))), qkv[s, h, d]) + else: + if h < 40: + k[s, h - 20, d] = T.if_then_else(apply_rope > 0 and d < 64, T.Cast("float16", T.cos(T.Cast("float32", position_map[s]) / T.pow(T.float32(1), T.Cast("float32", d * 2 % 64) / T.float32(64))) * T.Cast("float32", qkv[s, h, d]) + T.sin(T.Cast("float32", position_map[s]) / T.pow(T.float32(1), T.Cast("float32", d * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(d < 32, qkv[s, h, d + 32] * T.float16(-1), qkv[s, h, d - 32]))), qkv[s, h, d]) + else: + v[s, h - 40, d] = qkv[s, h, d] + + @T.prim_func(private=True) + def fused_transpose_add3(packed_params_4: T.Buffer((T.int64(1500), T.int64(1280)), "float16"), p_gelu1: T.handle, p_output0: T.handle): + T.func_attr({"tir.noalias": T.bool(True)}) + batch_size = T.int64() + gelu1 = T.match_buffer(p_gelu1, (batch_size, T.int64(1280), T.int64(1500)), "float16") + T_add_intermediate = T.match_buffer(p_output0, (batch_size, T.int64(1500), T.int64(1280)), "float16") + # with T.block("root"): + T_transpose_intermediate = T.alloc_buffer((batch_size, T.int64(1500), T.int64(1280)), "float16") + for ax0, ax1, ax2 in T.grid(batch_size, T.int64(1500), T.int64(1280)): + with T.block("T_transpose"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(gelu1[v_ax0, v_ax2, v_ax1]) + T.writes(T_transpose_intermediate[v_ax0, v_ax1, v_ax2]) + T_transpose_intermediate[v_ax0, v_ax1, v_ax2] = gelu1[v_ax0, v_ax2, v_ax1] + for ax0, ax1, ax2 in T.grid(batch_size, T.int64(1500), T.int64(1280)): + with T.block("T_add"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(T_transpose_intermediate[v_ax0, v_ax1, v_ax2], packed_params_4[v_ax1, v_ax2]) + T.writes(T_add_intermediate[v_ax0, v_ax1, v_ax2]) + T_add_intermediate[v_ax0, v_ax1, v_ax2] = T_transpose_intermediate[v_ax0, v_ax1, v_ax2] + packed_params_4[v_ax1, v_ax2] + + @T.prim_func + def gather_probs(var_src: T.handle, var_indices: T.handle, var_dst: T.handle): + T.func_attr({"op_pattern": 8, "target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.noalias": T.bool(True)}) + m, n = T.int32(is_size_var=True), T.int32(is_size_var=True) + src = T.match_buffer(var_src, (m, n)) + batch_size = T.int32(is_size_var=True) + indices = T.match_buffer(var_indices, (batch_size,), "int32") + dst = T.match_buffer(var_dst, (batch_size, n)) + # with T.block("root"): + for b, j in T.grid(batch_size, n): + with T.block("gather_2d"): + vb, vj = T.axis.remap("SS", [b, j]) + T.reads(src[indices[vb], vj], indices[vb]) + T.writes(dst[vb, vj]) + dst[vb, vj] = src[indices[vb], vj] + + @T.prim_func(private=True) + def get_index_from_sorted(A: T.handle, B: T.handle, C: T.handle, D: T.handle, E: T.handle, F: T.handle): + T.func_attr({"op_pattern": 8, "target": T.target({"arch": "sm_89", "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32})}) + batch, vocab_size = T.int64(), T.int64() + cumsum_sorted = T.match_buffer(A, (batch, vocab_size)) + indices = T.match_buffer(B, (batch, vocab_size), "int32") + renorm_prob = T.match_buffer(C, (batch, 1)) + out_batch = T.int64() + usample = T.match_buffer(D, (out_batch, 1)) + sample_indices = T.match_buffer(E, (out_batch, 1), "int32") + output_index = T.match_buffer(F, (out_batch, 1), "int32") + # with T.block("root"): + for ax0, ax1 in T.grid(out_batch, vocab_size): + with T.block("T_get_index_from_sorted"): + v_ax0, v_ax1 = T.axis.remap("SS", [ax0, ax1]) + T.reads(usample[v_ax0, T.int64(0)], cumsum_sorted[sample_indices[v_ax0, T.int64(0)], v_ax1 - T.int64(1):v_ax1 - T.int64(1) + T.int64(2)], sample_indices[v_ax0, T.int64(0)], renorm_prob[sample_indices[v_ax0, T.int64(0)], 0], indices[sample_indices[v_ax0, T.int64(0)], T.min(T.int64(0), v_ax1):T.min(T.int64(0), v_ax1) + (T.max(T.int64(0), v_ax1) + T.int64(1) - T.min(T.int64(0), v_ax1))]) + T.writes(output_index[v_ax0, 0]) + if usample[v_ax0, T.int64(0)] < cumsum_sorted[sample_indices[v_ax0, T.int64(0)], v_ax1] / renorm_prob[sample_indices[v_ax0, T.int64(0)], 0] or v_ax1 + T.int64(1) == vocab_size: + if v_ax1 == T.int64(0): + output_index[v_ax0, 0] = indices[sample_indices[v_ax0, T.int64(0)], 0] + else: + if usample[v_ax0, T.int64(0)] >= cumsum_sorted[sample_indices[v_ax0, T.int64(0)], v_ax1 - T.int64(1)] / renorm_prob[sample_indices[v_ax0, T.int64(0)], 0]: + output_index[v_ax0, 0] = indices[sample_indices[v_ax0, T.int64(0)], v_ax1] + + @T.prim_func(private=True) + def get_renorm_prob(A: T.handle, B: T.handle, C: T.handle, D: T.handle): + T.func_attr({"op_pattern": 8, "target": T.target({"arch": "sm_89", "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32})}) + batch, vocab_size = T.int64(), T.int64() + cumsum_sorted = T.match_buffer(A, (batch, vocab_size)) + top_p = T.match_buffer(B, (batch, 1)) + top_k = T.match_buffer(C, (batch, 1), "int32") + renorm_prob = T.match_buffer(D, (batch, 1)) + # with T.block("root"): + for ax0, ax1 in T.grid(batch, vocab_size): + with T.block("T_get_renorm_prob"): + v_ax0, v_ax1 = T.axis.remap("SS", [ax0, ax1]) + T.reads(cumsum_sorted[v_ax0, T.min(T.min(T.int64(0), v_ax1), v_ax1 + T.int64(1)):T.min(T.min(T.int64(0), v_ax1), v_ax1 + T.int64(1)) + (T.max(T.max(T.int64(0), v_ax1), v_ax1 + T.int64(1)) + T.int64(1) - T.min(T.min(T.int64(0), v_ax1), v_ax1 + T.int64(1)))], top_p[v_ax0, 0], top_k[v_ax0, 0]) + T.writes(renorm_prob[v_ax0, 0]) + if not (cumsum_sorted[v_ax0, 0] < top_p[v_ax0, 0] and top_k[v_ax0, 0] > 1): + renorm_prob[v_ax0, 0] = cumsum_sorted[v_ax0, 0] + else: + if cumsum_sorted[v_ax0, v_ax1] < top_p[v_ax0, 0] and v_ax1 + T.int64(1) < T.Cast("int64", top_k[v_ax0, 0]): + if v_ax1 + T.int64(1) == vocab_size: + renorm_prob[v_ax0, 0] = cumsum_sorted[v_ax0, v_ax1] + else: + if not (cumsum_sorted[v_ax0, v_ax1 + T.int64(1)] < top_p[v_ax0, 0] and v_ax1 + T.int64(1) + T.int64(1) < T.Cast("int64", top_k[v_ax0, 0])): + renorm_prob[v_ax0, 0] = cumsum_sorted[v_ax0, v_ax1 + T.int64(1)] + + @T.prim_func(private=True) + def index(var_layer_norm355: T.handle, index: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16")): + T.func_attr({"op_pattern": 8, "target": T.target({"arch": "sm_89", "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.noalias": T.bool(True)}) + seq_len = T.int64() + layer_norm355 = T.match_buffer(var_layer_norm355, (T.int64(1), seq_len, T.int64(1280)), "float16") + # with T.block("root"): + for i, _, k in T.grid(T.int64(1), T.int64(1), T.int64(1280)): + with T.block("index"): + v_i, v__, v_k = T.axis.remap("SSS", [i, _, k]) + T.reads(layer_norm355[v_i, seq_len - T.int64(1), v_k]) + T.writes(index[v_i, v__, v_k]) + index[v_i, v__, v_k] = layer_norm355[v_i, seq_len - T.int64(1), v_k] + + @T.prim_func(private=True) + def layer_norm(var_add578: T.handle, model_decoder_layers_0_self_attn_layer_norm_weight3: T.Buffer((T.int64(1280),), "float16"), model_decoder_layers_0_self_attn_layer_norm_bias3: T.Buffer((T.int64(1280),), "float16"), var_T_layer_norm: T.handle): + T.func_attr({"op_pattern": 4, "tir.noalias": T.bool(True)}) + batch_size = T.int64() + add578 = T.match_buffer(var_add578, (batch_size, T.int64(1), T.int64(1280)), "float16") + T_layer_norm = T.match_buffer(var_T_layer_norm, (batch_size, T.int64(1), T.int64(1280)), "float16") + # with T.block("root"): + add578_red_temp_v0 = T.alloc_buffer((batch_size, T.int64(1))) + add578_red_temp_v1 = T.alloc_buffer((batch_size, T.int64(1))) + for ax0, ax1, k2 in T.grid(batch_size, T.int64(1), T.int64(1280)): + with T.block("add578_red_temp"): + v_ax0, v_ax1, v_k2 = T.axis.remap("SSR", [ax0, ax1, k2]) + T.reads(add578[v_ax0, v_ax1, v_k2]) + T.writes(add578_red_temp_v0[v_ax0, v_ax1], add578_red_temp_v1[v_ax0, v_ax1]) + with T.init(): + add578_red_temp_v0[v_ax0, v_ax1] = T.float32(0) + add578_red_temp_v1[v_ax0, v_ax1] = T.float32(0) + v_add578_red_temp_v0: T.float32 = add578_red_temp_v0[v_ax0, v_ax1] + T.Cast("float32", add578[v_ax0, v_ax1, v_k2]) + v_add578_red_temp_v1: T.float32 = add578_red_temp_v1[v_ax0, v_ax1] + T.Cast("float32", add578[v_ax0, v_ax1, v_k2]) * T.Cast("float32", add578[v_ax0, v_ax1, v_k2]) + add578_red_temp_v0[v_ax0, v_ax1] = v_add578_red_temp_v0 + add578_red_temp_v1[v_ax0, v_ax1] = v_add578_red_temp_v1 + for ax0, ax1, ax2 in T.grid(batch_size, T.int64(1), T.int64(1280)): + with T.block("T_layer_norm"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(add578[v_ax0, v_ax1, v_ax2], add578_red_temp_v0[v_ax0, v_ax1], add578_red_temp_v1[v_ax0, v_ax1], model_decoder_layers_0_self_attn_layer_norm_weight3[v_ax2], model_decoder_layers_0_self_attn_layer_norm_bias3[v_ax2]) + T.writes(T_layer_norm[v_ax0, v_ax1, v_ax2]) + T_layer_norm[v_ax0, v_ax1, v_ax2] = T.Cast("float16", (T.Cast("float32", add578[v_ax0, v_ax1, v_ax2]) - add578_red_temp_v0[v_ax0, v_ax1] * T.float32(0.00078125000000000004)) * T.rsqrt(add578_red_temp_v1[v_ax0, v_ax1] * T.float32(0.00078125000000000004) - add578_red_temp_v0[v_ax0, v_ax1] * T.float32(0.00078125000000000004) * (add578_red_temp_v0[v_ax0, v_ax1] * T.float32(0.00078125000000000004)) + T.float32(1.0000000000000001e-05))) * model_decoder_layers_0_self_attn_layer_norm_weight3[v_ax2] + model_decoder_layers_0_self_attn_layer_norm_bias3[v_ax2] + + @T.prim_func(private=True) + def layer_norm1(var_add: T.handle, model_encoder_layers_0_self_attn_layer_norm_weight: T.Buffer((T.int64(1280),), "float16"), model_encoder_layers_0_self_attn_layer_norm_bias: T.Buffer((T.int64(1280),), "float16"), var_T_layer_norm: T.handle): + T.func_attr({"op_pattern": 4, "tir.noalias": T.bool(True)}) + batch_size = T.int64() + add = T.match_buffer(var_add, (batch_size, T.int64(1500), T.int64(1280)), "float16") + T_layer_norm = T.match_buffer(var_T_layer_norm, (batch_size, T.int64(1500), T.int64(1280)), "float16") + # with T.block("root"): + add_red_temp_v0 = T.alloc_buffer((batch_size, T.int64(1500))) + add_red_temp_v1 = T.alloc_buffer((batch_size, T.int64(1500))) + for ax0, ax1, k2 in T.grid(batch_size, T.int64(1500), T.int64(1280)): + with T.block("add_red_temp"): + v_ax0, v_ax1, v_k2 = T.axis.remap("SSR", [ax0, ax1, k2]) + T.reads(add[v_ax0, v_ax1, v_k2]) + T.writes(add_red_temp_v0[v_ax0, v_ax1], add_red_temp_v1[v_ax0, v_ax1]) + with T.init(): + add_red_temp_v0[v_ax0, v_ax1] = T.float32(0) + add_red_temp_v1[v_ax0, v_ax1] = T.float32(0) + v_add_red_temp_v0: T.float32 = add_red_temp_v0[v_ax0, v_ax1] + T.Cast("float32", add[v_ax0, v_ax1, v_k2]) + v_add_red_temp_v1: T.float32 = add_red_temp_v1[v_ax0, v_ax1] + T.Cast("float32", add[v_ax0, v_ax1, v_k2]) * T.Cast("float32", add[v_ax0, v_ax1, v_k2]) + add_red_temp_v0[v_ax0, v_ax1] = v_add_red_temp_v0 + add_red_temp_v1[v_ax0, v_ax1] = v_add_red_temp_v1 + for ax0, ax1, ax2 in T.grid(batch_size, T.int64(1500), T.int64(1280)): + with T.block("T_layer_norm"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(add[v_ax0, v_ax1, v_ax2], add_red_temp_v0[v_ax0, v_ax1], add_red_temp_v1[v_ax0, v_ax1], model_encoder_layers_0_self_attn_layer_norm_weight[v_ax2], model_encoder_layers_0_self_attn_layer_norm_bias[v_ax2]) + T.writes(T_layer_norm[v_ax0, v_ax1, v_ax2]) + T_layer_norm[v_ax0, v_ax1, v_ax2] = T.Cast("float16", (T.Cast("float32", add[v_ax0, v_ax1, v_ax2]) - add_red_temp_v0[v_ax0, v_ax1] * T.float32(0.00078125000000000004)) * T.rsqrt(add_red_temp_v1[v_ax0, v_ax1] * T.float32(0.00078125000000000004) - add_red_temp_v0[v_ax0, v_ax1] * T.float32(0.00078125000000000004) * (add_red_temp_v0[v_ax0, v_ax1] * T.float32(0.00078125000000000004)) + T.float32(1.0000000000000001e-05))) * model_encoder_layers_0_self_attn_layer_norm_weight[v_ax2] + model_encoder_layers_0_self_attn_layer_norm_bias[v_ax2] + + @T.prim_func(private=True) + def layer_norm2(var_add257: T.handle, model_decoder_layers_0_self_attn_layer_norm_weight2: T.Buffer((T.int64(1280),), "float16"), model_decoder_layers_0_self_attn_layer_norm_bias2: T.Buffer((T.int64(1280),), "float16"), var_T_layer_norm: T.handle): + T.func_attr({"op_pattern": 4, "tir.noalias": T.bool(True)}) + seq_len = T.int64() + add257 = T.match_buffer(var_add257, (T.int64(1), seq_len, T.int64(1280)), "float16") + T_layer_norm = T.match_buffer(var_T_layer_norm, (T.int64(1), seq_len, T.int64(1280)), "float16") + # with T.block("root"): + add257_red_temp_v0 = T.alloc_buffer((T.int64(1), seq_len)) + add257_red_temp_v1 = T.alloc_buffer((T.int64(1), seq_len)) + for ax0, ax1, k2 in T.grid(T.int64(1), seq_len, T.int64(1280)): + with T.block("add257_red_temp"): + v_ax0, v_ax1, v_k2 = T.axis.remap("SSR", [ax0, ax1, k2]) + T.reads(add257[v_ax0, v_ax1, v_k2]) + T.writes(add257_red_temp_v0[v_ax0, v_ax1], add257_red_temp_v1[v_ax0, v_ax1]) + with T.init(): + add257_red_temp_v0[v_ax0, v_ax1] = T.float32(0) + add257_red_temp_v1[v_ax0, v_ax1] = T.float32(0) + v_add257_red_temp_v0: T.float32 = add257_red_temp_v0[v_ax0, v_ax1] + T.Cast("float32", add257[v_ax0, v_ax1, v_k2]) + v_add257_red_temp_v1: T.float32 = add257_red_temp_v1[v_ax0, v_ax1] + T.Cast("float32", add257[v_ax0, v_ax1, v_k2]) * T.Cast("float32", add257[v_ax0, v_ax1, v_k2]) + add257_red_temp_v0[v_ax0, v_ax1] = v_add257_red_temp_v0 + add257_red_temp_v1[v_ax0, v_ax1] = v_add257_red_temp_v1 + for ax0, ax1, ax2 in T.grid(T.int64(1), seq_len, T.int64(1280)): + with T.block("T_layer_norm"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(add257[v_ax0, v_ax1, v_ax2], add257_red_temp_v0[v_ax0, v_ax1], add257_red_temp_v1[v_ax0, v_ax1], model_decoder_layers_0_self_attn_layer_norm_weight2[v_ax2], model_decoder_layers_0_self_attn_layer_norm_bias2[v_ax2]) + T.writes(T_layer_norm[v_ax0, v_ax1, v_ax2]) + T_layer_norm[v_ax0, v_ax1, v_ax2] = T.Cast("float16", (T.Cast("float32", add257[v_ax0, v_ax1, v_ax2]) - add257_red_temp_v0[v_ax0, v_ax1] * T.float32(0.00078125000000000004)) * T.rsqrt(add257_red_temp_v1[v_ax0, v_ax1] * T.float32(0.00078125000000000004) - add257_red_temp_v0[v_ax0, v_ax1] * T.float32(0.00078125000000000004) * (add257_red_temp_v0[v_ax0, v_ax1] * T.float32(0.00078125000000000004)) + T.float32(1.0000000000000001e-05))) * model_decoder_layers_0_self_attn_layer_norm_weight2[v_ax2] + model_decoder_layers_0_self_attn_layer_norm_bias2[v_ax2] + + @T.prim_func(private=True) + def layer_norm3(add1220: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16"), model_decoder_layers_0_self_attn_layer_norm_weight5: T.Buffer((T.int64(1280),), "float16"), model_decoder_layers_0_self_attn_layer_norm_bias5: T.Buffer((T.int64(1280),), "float16"), T_layer_norm: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16")): + T.func_attr({"op_pattern": 4, "tir.noalias": T.bool(True)}) + # with T.block("root"): + add1220_red_temp_v0 = T.alloc_buffer((T.int64(1), T.int64(1))) + add1220_red_temp_v1 = T.alloc_buffer((T.int64(1), T.int64(1))) + for ax0, ax1, k2 in T.grid(T.int64(1), T.int64(1), T.int64(1280)): + with T.block("add1220_red_temp"): + v_ax0, v_ax1, v_k2 = T.axis.remap("SSR", [ax0, ax1, k2]) + T.reads(add1220[v_ax0, v_ax1, v_k2]) + T.writes(add1220_red_temp_v0[v_ax0, v_ax1], add1220_red_temp_v1[v_ax0, v_ax1]) + with T.init(): + add1220_red_temp_v0[v_ax0, v_ax1] = T.float32(0) + add1220_red_temp_v1[v_ax0, v_ax1] = T.float32(0) + v_add1220_red_temp_v0: T.float32 = add1220_red_temp_v0[v_ax0, v_ax1] + T.Cast("float32", add1220[v_ax0, v_ax1, v_k2]) + v_add1220_red_temp_v1: T.float32 = add1220_red_temp_v1[v_ax0, v_ax1] + T.Cast("float32", add1220[v_ax0, v_ax1, v_k2]) * T.Cast("float32", add1220[v_ax0, v_ax1, v_k2]) + add1220_red_temp_v0[v_ax0, v_ax1] = v_add1220_red_temp_v0 + add1220_red_temp_v1[v_ax0, v_ax1] = v_add1220_red_temp_v1 + for ax0, ax1, ax2 in T.grid(T.int64(1), T.int64(1), T.int64(1280)): + with T.block("T_layer_norm"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(add1220[v_ax0, v_ax1, v_ax2], add1220_red_temp_v0[v_ax0, v_ax1], add1220_red_temp_v1[v_ax0, v_ax1], model_decoder_layers_0_self_attn_layer_norm_weight5[v_ax2], model_decoder_layers_0_self_attn_layer_norm_bias5[v_ax2]) + T.writes(T_layer_norm[v_ax0, v_ax1, v_ax2]) + T_layer_norm[v_ax0, v_ax1, v_ax2] = T.Cast("float16", (T.Cast("float32", add1220[v_ax0, v_ax1, v_ax2]) - add1220_red_temp_v0[v_ax0, v_ax1] * T.float32(0.00078125000000000004)) * T.rsqrt(add1220_red_temp_v1[v_ax0, v_ax1] * T.float32(0.00078125000000000004) - add1220_red_temp_v0[v_ax0, v_ax1] * T.float32(0.00078125000000000004) * (add1220_red_temp_v0[v_ax0, v_ax1] * T.float32(0.00078125000000000004)) + T.float32(1.0000000000000001e-05))) * model_decoder_layers_0_self_attn_layer_norm_weight5[v_ax2] + model_decoder_layers_0_self_attn_layer_norm_bias5[v_ax2] + + @T.prim_func + def merge_state_inplace(v: T.handle, s: T.handle, v_other: T.handle, s_other: T.handle): + T.func_attr({"op_pattern": 8, "target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) + N, H, D = T.int32(is_size_var=True), T.int32(is_size_var=True), T.int32(is_size_var=True) + V = T.match_buffer(v, (N, H, D), "float16") + S = T.match_buffer(s, (N, H)) + V_other = T.match_buffer(v_other, (N, H, D), "float16") + S_other = T.match_buffer(s_other, (N, H)) + # with T.block("root"): + for bx in T.thread_binding(N, thread="blockIdx.x"): + for by in T.thread_binding(1, thread="blockIdx.y"): + for ty in T.thread_binding(20, thread="threadIdx.y"): + for tx in T.thread_binding(16, thread="threadIdx.x"): + with T.block("merge"): + T.reads(S[bx, ty + by * 20], S_other[bx, ty + by * 20], V[bx, ty + by * 20, tx * 4:tx * 4 + 4], V_other[bx, ty + by * 20, tx * 4:tx * 4 + 4]) + T.writes(V[bx, ty + by * 20, tx * 4:tx * 4 + 4], S[bx, ty + by * 20]) + s_val = T.alloc_buffer((1,), scope="local") + s_other_val = T.alloc_buffer((1,), scope="local") + s_max = T.alloc_buffer((1,), scope="local") + scale = T.alloc_buffer((1,), scope="local") + other_scale = T.alloc_buffer((1,), scope="local") + v_vec = T.alloc_buffer((4,), "float16", scope="local") + v_other_vec = T.alloc_buffer((4,), "float16", scope="local") + s_val[0] = S[bx, ty + by * 20] + s_other_val[0] = S_other[bx, ty + by * 20] + s_max[0] = T.max(s_val[0], s_other_val[0]) + s_val[0] = T.exp2(s_val[0] - s_max[0]) + s_other_val[0] = T.exp2(s_other_val[0] - s_max[0]) + scale[0] = s_val[0] / (s_val[0] + s_other_val[0]) + other_scale[0] = s_other_val[0] / (s_val[0] + s_other_val[0]) + for vec in T.vectorized(4): + v_vec[vec] = V[bx, ty + by * 20, tx * 4 + vec] + for vec in T.vectorized(4): + v_other_vec[vec] = V_other[bx, ty + by * 20, tx * 4 + vec] + for vec in range(4): + v_vec[vec] = T.Cast("float16", T.Cast("float32", v_vec[vec]) * scale[0] + T.Cast("float32", v_other_vec[vec]) * other_scale[0]) + for vec in T.vectorized(4): + V[bx, ty + by * 20, tx * 4 + vec] = v_vec[vec] + S[bx, ty + by * 20] = T.log2(s_val[0] + s_other_val[0]) + s_max[0] + + @T.prim_func + def parallel_sampling_from_prob(var_prob: T.handle, var_uniform_samples: T.handle, var_row_indices: T.handle, var_sampled_token_ids: T.handle): + T.func_attr({"op_pattern": 8, "tir.is_scheduled": 1}) + n, vocab_size = T.int64(), T.int64() + prob = T.match_buffer(var_prob, (n, vocab_size)) + batch_size = T.int64() + uniform_samples = T.match_buffer(var_uniform_samples, (batch_size, 1)) + row_indices = T.match_buffer(var_row_indices, (batch_size, 1), "int32") + token_ids = T.match_buffer(var_sampled_token_ids, (batch_size, 1), "int32") + # with T.block("root"): + aggregate = T.alloc_buffer((), scope="local") + sample_id_local = T.alloc_buffer((), "int32", scope="local") + step_iter = T.alloc_buffer((), "int32", scope="local") + for bx in T.thread_binding(batch_size, thread="blockIdx.x"): + row_idx: T.int32 = row_indices[bx, 0] + for ty in T.thread_binding(T.int64(4), thread="threadIdx.y"): + for tx in T.thread_binding(T.int64(32), thread="threadIdx.x"): + u: T.float32 = uniform_samples[bx, 0] + aggregate[()] = T.Cast("float32", 0) + step_iter[()] = 0 + while T.tvm_thread_invariant((step_iter[()] == 0 or aggregate[()] < u - T.float32(9.9999999999999995e-07)) and T.Cast("int64", step_iter[()]) < (vocab_size + T.int64(512) - T.int64(1)) // T.int64(512)): + with T.block(""): + T.reads(step_iter[()], prob[row_idx, T.Cast("int64", step_iter[()]) * T.int64(512) + ty * T.int64(128) + tx * T.int64(4):T.Cast("int64", step_iter[()]) * T.int64(512) + ty * T.int64(128) + tx * T.int64(4) + T.int64(4)], aggregate[()]) + T.writes(sample_id_local[()], aggregate[()]) + prob_gt_threshold = T.alloc_buffer((T.int64(4),), scope="local") + cumsum = T.alloc_buffer((T.int64(512),), scope="shared") + greater_than_u = T.alloc_buffer((T.int64(4),), "bool", scope="local") + mask = T.alloc_buffer((T.int64(4),), "bool", scope="local") + valid = T.alloc_buffer((T.int64(4),), "bool", scope="local") + indices = T.alloc_buffer((T.int64(4),), "int32", scope="local") + step_aggregate = T.alloc_buffer((), scope="local") + for v in T.unroll(T.int64(4)): + idx: T.int64 = T.Cast("int64", step_iter[()]) * T.int64(512) + ty * T.int64(128) + tx * T.int64(4) + v + prob_local: T.float32 = T.if_then_else(idx < vocab_size, prob[row_idx, idx], T.Cast("float32", 0)) + prob_gt_threshold[v] = T.if_then_else(prob_local > T.float32(0), prob_local, T.Cast("float32", 0)) + valid[v] = prob_local > T.float32(0) and idx < vocab_size + with T.block(""): + T.reads(prob_gt_threshold[T.int64(0):T.int64(4)]) + T.writes(step_aggregate[()]) + local_sum = T.alloc_buffer((), scope="local") + shared_buf = T.alloc_buffer((T.int64(128),), scope="shared") + idx: T.int64 = ty * T.int64(32) + tx + local_sum[()] = T.Cast("float32", 0) + for i in T.unroll(T.int64(4)): + local_sum[()] = local_sum[()] + prob_gt_threshold[i] + shared_buf[idx] = local_sum[()] + for i in T.unroll(T.int64(7)): + if idx % T.shift_left(T.int64(1), i + T.int64(1)) == T.int64(0): + shared_buf[idx] = shared_buf[idx] + shared_buf[idx + T.shift_left(T.int64(1), i)] + step_aggregate[()] = shared_buf[0] + if T.tvm_thread_invariant(aggregate[()] + step_aggregate[()] >= u - T.float32(9.9999999999999995e-07)): + for i in T.unroll(T.int64(1), T.int64(4)): + prob_gt_threshold[i] = prob_gt_threshold[i] + prob_gt_threshold[i - T.int64(1)] + for i in T.vectorized(T.int64(4)): + cumsum[ty * T.int64(128) + tx * T.int64(4) + i] = prob_gt_threshold[i] + for i in T.unroll(T.int64(5)): + for j in T.vectorized(T.int64(4)): + idx: T.int64 = ty * T.int64(128) + tx * T.int64(4) + if tx >= T.shift_left(T.int64(1), i): + cumsum[idx + j] = cumsum[idx + j] + cumsum[idx - T.shift_left(T.int64(1), i) * T.int64(4) + T.int64(4) - T.int64(1)] + for i in T.unroll(T.int64(1), T.int64(4)): + for j in T.vectorized(T.int64(4)): + if ty == T.int64(0): + idx: T.int64 = i * T.int64(128) + tx * T.int64(4) + cumsum[idx + j] = cumsum[idx + j] + cumsum[i * T.int64(128) - T.int64(1)] + for v in T.unroll(T.int64(4)): + greater_than_u[v] = cumsum[ty * T.int64(128) + tx * T.int64(4) + v] + aggregate[()] >= u - T.float32(9.9999999999999995e-07) + with T.block(""): + T.reads(greater_than_u[T.int64(0):T.int64(4)]) + T.writes(mask[T.int64(0):T.int64(4)]) + shared_buf = T.alloc_buffer((T.int64(128),), "bool", scope="shared") + tx_idx: T.int64 = ty * T.int64(32) + tx + shared_buf[tx_idx] = greater_than_u[T.int64(3)] + mask[0] = T.if_then_else(tx_idx != T.int64(0), T.Cast("int8", greater_than_u[0]) != T.Cast("int8", shared_buf[tx_idx - T.int64(1)]), greater_than_u[0]) + for i in T.unroll(T.int64(1), T.int64(4)): + mask[i] = T.Cast("int8", greater_than_u[i]) != T.Cast("int8", greater_than_u[i - T.int64(1)]) + for v in T.unroll(T.int64(4)): + mask[v] = mask[v] and valid[v] + indices[v] = T.Cast("int32", T.Cast("int64", step_iter[()]) * T.int64(512) + ty * T.int64(128) + tx * T.int64(4) + v) + with T.block(""): + T.reads(mask[T.int64(0):T.int64(4)], indices[T.int64(0):T.int64(4)]) + T.writes(sample_id_local[()]) + local_sum = T.alloc_buffer((), "int32", scope="local") + shared_buf = T.alloc_buffer((T.int64(128),), "int32", scope="shared") + idx: T.int64 = ty * T.int64(32) + tx + local_sum[()] = T.Cast("int32", vocab_size - T.int64(1)) + for i in T.unroll(T.int64(4)): + if mask[i]: + local_sum[()] = T.min(local_sum[()], indices[i]) + shared_buf[idx] = local_sum[()] + for i in T.unroll(T.int64(7)): + if idx % T.shift_left(T.int64(1), i + T.int64(1)) == T.int64(0): + shared_buf[idx] = T.min(shared_buf[idx], shared_buf[idx + T.shift_left(T.int64(1), i)]) + sample_id_local[()] = shared_buf[0] + aggregate[()] = aggregate[()] + step_aggregate[()] + step_iter[()] = step_iter[()] + 1 + if tx == T.int64(0) and ty == T.int64(0): + token_ids[bx, 0] = sample_id_local[()] + + @T.prim_func(private=True) + def reshape(var_lv: T.handle, var_T_reshape: T.handle): + T.func_attr({"op_pattern": 8, "tir.noalias": T.bool(True)}) + batch_size = T.int64() + lv = T.match_buffer(var_lv, (batch_size, T.int64(1500), T.int64(1280)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (batch_size, T.int64(1500), T.int64(20), T.int64(64)), "float16") + # with T.block("root"): + for ax0, ax1, ax2, ax3 in T.grid(batch_size, T.int64(1500), T.int64(20), T.int64(64)): + with T.block("T_reshape"): + v_ax0, v_ax1, v_ax2, v_ax3 = T.axis.remap("SSSS", [ax0, ax1, ax2, ax3]) + T.reads(lv[(((v_ax2 * T.int64(64) + v_ax3) // T.int64(1280) + v_ax1) // T.int64(1500) + v_ax0) % batch_size, ((v_ax2 * T.int64(64) + v_ax3) // T.int64(1280) + v_ax1) % T.int64(1500), (v_ax2 * T.int64(64) + v_ax3) % T.int64(1280)]) + T.writes(T_reshape[v_ax0, v_ax1, v_ax2, v_ax3]) + T_reshape[v_ax0, v_ax1, v_ax2, v_ax3] = lv[(((v_ax2 * T.int64(64) + v_ax3) // T.int64(1280) + v_ax1) // T.int64(1500) + v_ax0) % batch_size, ((v_ax2 * T.int64(64) + v_ax3) // T.int64(1280) + v_ax1) % T.int64(1500), (v_ax2 * T.int64(64) + v_ax3) % T.int64(1280)] + + @T.prim_func(private=True) + def reshape1(var_reshape256: T.handle, var_T_reshape: T.handle): + T.func_attr({"op_pattern": 8, "tir.noalias": T.bool(True)}) + batch_size = T.int64() + reshape256 = T.match_buffer(var_reshape256, (batch_size, T.int64(1500), T.int64(20), T.int64(64)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (batch_size * T.int64(1500), T.int64(20), T.int64(64)), "float16") + # with T.block("root"): + for ax0, ax1, ax2 in T.grid(batch_size * T.int64(1500), T.int64(20), T.int64(64)): + with T.block("T_reshape"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(reshape256[((v_ax2 // T.int64(64) + v_ax1) // T.int64(20) + v_ax0) // T.int64(1500) % batch_size, ((v_ax2 // T.int64(64) + v_ax1) // T.int64(20) + v_ax0) % T.int64(1500), (v_ax2 // T.int64(64) + v_ax1) % T.int64(20), v_ax2 % T.int64(64)]) + T.writes(T_reshape[v_ax0, v_ax1, v_ax2]) + T_reshape[v_ax0, v_ax1, v_ax2] = reshape256[((v_ax2 // T.int64(64) + v_ax1) // T.int64(20) + v_ax0) // T.int64(1500) % batch_size, ((v_ax2 // T.int64(64) + v_ax1) // T.int64(20) + v_ax0) % T.int64(1500), (v_ax2 // T.int64(64) + v_ax1) % T.int64(20), v_ax2 % T.int64(64)] + + @T.prim_func(private=True) + def reshape10(var_lv4: T.handle, var_T_reshape: T.handle): + T.func_attr({"op_pattern": 8, "tir.noalias": T.bool(True)}) + batch_size = T.int64() + lv4 = T.match_buffer(var_lv4, (batch_size * T.int64(1500), T.int64(20), T.int64(64)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (batch_size, T.int64(1500), T.int64(20), T.int64(64)), "float16") + # with T.block("root"): + for ax0, ax1, ax2, ax3 in T.grid(batch_size, T.int64(1500), T.int64(20), T.int64(64)): + with T.block("T_reshape"): + v_ax0, v_ax1, v_ax2, v_ax3 = T.axis.remap("SSSS", [ax0, ax1, ax2, ax3]) + T.reads(lv4[(v_ax0 * T.int64(1500) + (v_ax3 // T.int64(64) + v_ax2) // T.int64(20) + v_ax1) % (batch_size * T.int64(1500)), (v_ax3 // T.int64(64) + v_ax2) % T.int64(20), v_ax3 % T.int64(64)]) + T.writes(T_reshape[v_ax0, v_ax1, v_ax2, v_ax3]) + T_reshape[v_ax0, v_ax1, v_ax2, v_ax3] = lv4[(v_ax0 * T.int64(1500) + (v_ax3 // T.int64(64) + v_ax2) // T.int64(20) + v_ax1) % (batch_size * T.int64(1500)), (v_ax3 // T.int64(64) + v_ax2) % T.int64(20), v_ax3 % T.int64(64)] + + @T.prim_func(private=True) + def reshape11(var_reshape6: T.handle, var_T_reshape: T.handle): + T.func_attr({"op_pattern": 8, "tir.noalias": T.bool(True)}) + batch_size = T.int64() + reshape6 = T.match_buffer(var_reshape6, (batch_size, T.int64(1500), T.int64(20), T.int64(64)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (batch_size, T.int64(1500), T.int64(1280)), "float16") + # with T.block("root"): + for ax0, ax1, ax2 in T.grid(batch_size, T.int64(1500), T.int64(1280)): + with T.block("T_reshape"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(reshape6[((v_ax2 // T.int64(1280) + v_ax1) // T.int64(1500) + v_ax0) % batch_size, (v_ax2 // T.int64(1280) + v_ax1) % T.int64(1500), v_ax2 % T.int64(1280) // T.int64(64), v_ax2 % T.int64(64)]) + T.writes(T_reshape[v_ax0, v_ax1, v_ax2]) + T_reshape[v_ax0, v_ax1, v_ax2] = reshape6[((v_ax2 // T.int64(1280) + v_ax1) // T.int64(1500) + v_ax0) % batch_size, (v_ax2 // T.int64(1280) + v_ax1) % T.int64(1500), v_ax2 % T.int64(1280) // T.int64(64), v_ax2 % T.int64(64)] + + @T.prim_func(private=True) + def reshape12(var_input_ids: T.handle, var_T_reshape: T.handle): + T.func_attr({"op_pattern": 8, "tir.noalias": T.bool(True)}) + seq_len = T.int64() + input_ids = T.match_buffer(var_input_ids, (T.int64(1), seq_len), "int32") + T_reshape = T.match_buffer(var_T_reshape, (seq_len,), "int32") + # with T.block("root"): + for ax0 in range(seq_len): + with T.block("T_reshape"): + v_ax0 = T.axis.spatial(seq_len, ax0) + T.reads(input_ids[T.int64(0), v_ax0 % seq_len]) + T.writes(T_reshape[v_ax0]) + T_reshape[v_ax0] = input_ids[T.int64(0), v_ax0 % seq_len] + + @T.prim_func(private=True) + def reshape13(var_take: T.handle, var_T_reshape: T.handle): + T.func_attr({"op_pattern": 8, "tir.noalias": T.bool(True)}) + seq_len = T.int64() + take = T.match_buffer(var_take, (seq_len, T.int64(1280)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (T.int64(1), seq_len, T.int64(1280)), "float16") + # with T.block("root"): + for ax0, ax1, ax2 in T.grid(T.int64(1), seq_len, T.int64(1280)): + with T.block("T_reshape"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(take[(v_ax2 // T.int64(1280) + v_ax0 * seq_len + v_ax1) % seq_len, v_ax2 % T.int64(1280)]) + T.writes(T_reshape[v_ax0, v_ax1, v_ax2]) + T_reshape[v_ax0, v_ax1, v_ax2] = take[(v_ax2 // T.int64(1280) + v_ax0 * seq_len + v_ax1) % seq_len, v_ax2 % T.int64(1280)] + + @T.prim_func(private=True) + def reshape14(var_lv416: T.handle, var_T_reshape: T.handle): + T.func_attr({"op_pattern": 8, "tir.noalias": T.bool(True)}) + seq_len = T.int64() + lv416 = T.match_buffer(var_lv416, (T.int64(1), seq_len, T.int64(1280)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (T.int64(1), seq_len, T.int64(20), T.int64(64)), "float16") + # with T.block("root"): + for ax0, ax1, ax2, ax3 in T.grid(T.int64(1), seq_len, T.int64(20), T.int64(64)): + with T.block("T_reshape"): + v_ax0, v_ax1, v_ax2, v_ax3 = T.axis.remap("SSSS", [ax0, ax1, ax2, ax3]) + T.reads(lv416[T.int64(0), ((v_ax2 * T.int64(64) + v_ax3) // T.int64(1280) + v_ax0 * seq_len + v_ax1) % seq_len, (v_ax2 * T.int64(64) + v_ax3) % T.int64(1280)]) + T.writes(T_reshape[v_ax0, v_ax1, v_ax2, v_ax3]) + T_reshape[v_ax0, v_ax1, v_ax2, v_ax3] = lv416[T.int64(0), ((v_ax2 * T.int64(64) + v_ax3) // T.int64(1280) + v_ax0 * seq_len + v_ax1) % seq_len, (v_ax2 * T.int64(64) + v_ax3) % T.int64(1280)] + + @T.prim_func(private=True) + def reshape15(var_concat: T.handle, var_T_reshape: T.handle): + T.func_attr({"op_pattern": 8, "tir.noalias": T.bool(True)}) + seq_len = T.int64() + concat = T.match_buffer(var_concat, (T.int64(1), seq_len, T.int64(60), T.int64(64)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (seq_len, T.int64(60), T.int64(64)), "float16") + # with T.block("root"): + for ax0, ax1, ax2 in T.grid(seq_len, T.int64(60), T.int64(64)): + with T.block("T_reshape"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(concat[T.int64(0), ((v_ax2 // T.int64(64) + v_ax1) // T.int64(60) + v_ax0) % seq_len, (v_ax2 // T.int64(64) + v_ax1) % T.int64(60), v_ax2 % T.int64(64)]) + T.writes(T_reshape[v_ax0, v_ax1, v_ax2]) + T_reshape[v_ax0, v_ax1, v_ax2] = concat[T.int64(0), ((v_ax2 // T.int64(64) + v_ax1) // T.int64(60) + v_ax0) % seq_len, (v_ax2 // T.int64(64) + v_ax1) % T.int64(60), v_ax2 % T.int64(64)] + + @T.prim_func(private=True) + def reshape16(var_lv69: T.handle, var_T_reshape: T.handle): + T.func_attr({"op_pattern": 8, "tir.noalias": T.bool(True)}) + seq_len = T.int64() + lv69 = T.match_buffer(var_lv69, (seq_len, T.int64(20), T.int64(64)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (T.int64(1), seq_len, T.int64(20), T.int64(64)), "float16") + # with T.block("root"): + for ax0, ax1, ax2, ax3 in T.grid(T.int64(1), seq_len, T.int64(20), T.int64(64)): + with T.block("T_reshape"): + v_ax0, v_ax1, v_ax2, v_ax3 = T.axis.remap("SSSS", [ax0, ax1, ax2, ax3]) + T.reads(lv69[((v_ax3 // T.int64(64) + v_ax2) // T.int64(20) + v_ax0 * seq_len + v_ax1) % seq_len, (v_ax3 // T.int64(64) + v_ax2) % T.int64(20), v_ax3 % T.int64(64)]) + T.writes(T_reshape[v_ax0, v_ax1, v_ax2, v_ax3]) + T_reshape[v_ax0, v_ax1, v_ax2, v_ax3] = lv69[((v_ax3 // T.int64(64) + v_ax2) // T.int64(20) + v_ax0 * seq_len + v_ax1) % seq_len, (v_ax3 // T.int64(64) + v_ax2) % T.int64(20), v_ax3 % T.int64(64)] + + @T.prim_func(private=True) + def reshape17(var_reshape391: T.handle, var_T_reshape: T.handle): + T.func_attr({"op_pattern": 8, "tir.noalias": T.bool(True)}) + seq_len = T.int64() + reshape391 = T.match_buffer(var_reshape391, (T.int64(1), seq_len, T.int64(20), T.int64(64)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (T.int64(1), seq_len, T.int64(1280)), "float16") + # with T.block("root"): + for ax0, ax1, ax2 in T.grid(T.int64(1), seq_len, T.int64(1280)): + with T.block("T_reshape"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(reshape391[T.int64(0), (v_ax2 // T.int64(1280) + v_ax0 * seq_len + v_ax1) % seq_len, v_ax2 % T.int64(1280) // T.int64(64), v_ax2 % T.int64(64)]) + T.writes(T_reshape[v_ax0, v_ax1, v_ax2]) + T_reshape[v_ax0, v_ax1, v_ax2] = reshape391[T.int64(0), (v_ax2 // T.int64(1280) + v_ax0 * seq_len + v_ax1) % seq_len, v_ax2 % T.int64(1280) // T.int64(64), v_ax2 % T.int64(64)] + + @T.prim_func(private=True) + def reshape18(var_reshape393: T.handle, var_T_reshape: T.handle): + T.func_attr({"op_pattern": 8, "tir.noalias": T.bool(True)}) + seq_len = T.int64() + reshape393 = T.match_buffer(var_reshape393, (T.int64(1), seq_len, T.int64(20), T.int64(64)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (seq_len, T.int64(20), T.int64(64)), "float16") + # with T.block("root"): + for ax0, ax1, ax2 in T.grid(seq_len, T.int64(20), T.int64(64)): + with T.block("T_reshape"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(reshape393[T.int64(0), ((v_ax2 // T.int64(64) + v_ax1) // T.int64(20) + v_ax0) % seq_len, (v_ax2 // T.int64(64) + v_ax1) % T.int64(20), v_ax2 % T.int64(64)]) + T.writes(T_reshape[v_ax0, v_ax1, v_ax2]) + T_reshape[v_ax0, v_ax1, v_ax2] = reshape393[T.int64(0), ((v_ax2 // T.int64(64) + v_ax1) // T.int64(20) + v_ax0) % seq_len, (v_ax2 // T.int64(64) + v_ax1) % T.int64(20), v_ax2 % T.int64(64)] + + @T.prim_func(private=True) + def reshape19(input_ids: T.Buffer((T.int64(1), T.int64(1)), "int32"), T_reshape: T.Buffer((T.int64(1),), "int32")): + T.func_attr({"op_pattern": 1, "tir.noalias": T.bool(True)}) + # with T.block("root"): + for ax0 in range(T.int64(1)): + with T.block("T_reshape"): + v_ax0 = T.axis.spatial(T.int64(1), ax0) + T.reads(input_ids[T.int64(0), T.int64(0)]) + T.writes(T_reshape[v_ax0]) + T_reshape[v_ax0] = input_ids[T.int64(0), T.int64(0)] + + @T.prim_func(private=True) + def reshape2(var_input_ids: T.handle, var_T_reshape: T.handle): + T.func_attr({"op_pattern": 8, "tir.noalias": T.bool(True)}) + batch_size = T.int64() + input_ids = T.match_buffer(var_input_ids, (batch_size, T.int64(1)), "int32") + T_reshape = T.match_buffer(var_T_reshape, (batch_size,), "int32") + # with T.block("root"): + for ax0 in range(batch_size): + with T.block("T_reshape"): + v_ax0 = T.axis.spatial(batch_size, ax0) + T.reads(input_ids[v_ax0 % batch_size, T.int64(0)]) + T.writes(T_reshape[v_ax0]) + T_reshape[v_ax0] = input_ids[v_ax0 % batch_size, T.int64(0)] + + @T.prim_func(private=True) + def reshape3(var_take3: T.handle, var_T_reshape: T.handle): + T.func_attr({"op_pattern": 8, "tir.noalias": T.bool(True)}) + batch_size = T.int64() + take3 = T.match_buffer(var_take3, (batch_size, T.int64(1280)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (batch_size, T.int64(1), T.int64(1280)), "float16") + # with T.block("root"): + for ax0, ax1, ax2 in T.grid(batch_size, T.int64(1), T.int64(1280)): + with T.block("T_reshape"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(take3[(v_ax2 // T.int64(1280) + v_ax0 + v_ax1) % batch_size, v_ax2 % T.int64(1280)]) + T.writes(T_reshape[v_ax0, v_ax1, v_ax2]) + T_reshape[v_ax0, v_ax1, v_ax2] = take3[(v_ax2 // T.int64(1280) + v_ax0 + v_ax1) % batch_size, v_ax2 % T.int64(1280)] + + @T.prim_func(private=True) + def reshape4(var_lv224: T.handle, var_T_reshape: T.handle): + T.func_attr({"op_pattern": 8, "tir.noalias": T.bool(True)}) + batch_size = T.int64() + lv224 = T.match_buffer(var_lv224, (batch_size, T.int64(1), T.int64(1280)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (batch_size, T.int64(1), T.int64(20), T.int64(64)), "float16") + # with T.block("root"): + for ax0, ax1, ax2, ax3 in T.grid(batch_size, T.int64(1), T.int64(20), T.int64(64)): + with T.block("T_reshape"): + v_ax0, v_ax1, v_ax2, v_ax3 = T.axis.remap("SSSS", [ax0, ax1, ax2, ax3]) + T.reads(lv224[((v_ax2 * T.int64(64) + v_ax3) // T.int64(1280) + v_ax0 + v_ax1) % batch_size, T.int64(0), (v_ax2 * T.int64(64) + v_ax3) % T.int64(1280)]) + T.writes(T_reshape[v_ax0, v_ax1, v_ax2, v_ax3]) + T_reshape[v_ax0, v_ax1, v_ax2, v_ax3] = lv224[((v_ax2 * T.int64(64) + v_ax3) // T.int64(1280) + v_ax0 + v_ax1) % batch_size, T.int64(0), (v_ax2 * T.int64(64) + v_ax3) % T.int64(1280)] + + @T.prim_func(private=True) + def reshape5(var_concat32: T.handle, var_T_reshape: T.handle): + T.func_attr({"op_pattern": 8, "tir.noalias": T.bool(True)}) + batch_size = T.int64() + concat32 = T.match_buffer(var_concat32, (batch_size, T.int64(1), T.int64(60), T.int64(64)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (batch_size, T.int64(60), T.int64(64)), "float16") + # with T.block("root"): + for ax0, ax1, ax2 in T.grid(batch_size, T.int64(60), T.int64(64)): + with T.block("T_reshape"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(concat32[((v_ax2 // T.int64(64) + v_ax1) // T.int64(60) + v_ax0) % batch_size, T.int64(0), (v_ax2 // T.int64(64) + v_ax1) % T.int64(60), v_ax2 % T.int64(64)]) + T.writes(T_reshape[v_ax0, v_ax1, v_ax2]) + T_reshape[v_ax0, v_ax1, v_ax2] = concat32[((v_ax2 // T.int64(64) + v_ax1) // T.int64(60) + v_ax0) % batch_size, T.int64(0), (v_ax2 // T.int64(64) + v_ax1) % T.int64(60), v_ax2 % T.int64(64)] + + @T.prim_func(private=True) + def reshape6(var_lv134: T.handle, var_T_reshape: T.handle): + T.func_attr({"op_pattern": 8, "tir.noalias": T.bool(True)}) + batch_size = T.int64() + lv134 = T.match_buffer(var_lv134, (batch_size, T.int64(20), T.int64(64)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (batch_size, T.int64(1), T.int64(20), T.int64(64)), "float16") + # with T.block("root"): + for ax0, ax1, ax2, ax3 in T.grid(batch_size, T.int64(1), T.int64(20), T.int64(64)): + with T.block("T_reshape"): + v_ax0, v_ax1, v_ax2, v_ax3 = T.axis.remap("SSSS", [ax0, ax1, ax2, ax3]) + T.reads(lv134[((v_ax3 // T.int64(64) + v_ax2) // T.int64(20) + v_ax0 + v_ax1) % batch_size, (v_ax3 // T.int64(64) + v_ax2) % T.int64(20), v_ax3 % T.int64(64)]) + T.writes(T_reshape[v_ax0, v_ax1, v_ax2, v_ax3]) + T_reshape[v_ax0, v_ax1, v_ax2, v_ax3] = lv134[((v_ax3 // T.int64(64) + v_ax2) // T.int64(20) + v_ax0 + v_ax1) % batch_size, (v_ax3 // T.int64(64) + v_ax2) % T.int64(20), v_ax3 % T.int64(64)] + + @T.prim_func(private=True) + def reshape7(var_reshape714: T.handle, var_T_reshape: T.handle): + T.func_attr({"op_pattern": 8, "tir.noalias": T.bool(True)}) + batch_size = T.int64() + reshape714 = T.match_buffer(var_reshape714, (batch_size, T.int64(1), T.int64(20), T.int64(64)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (batch_size, T.int64(1), T.int64(1280)), "float16") + # with T.block("root"): + for ax0, ax1, ax2 in T.grid(batch_size, T.int64(1), T.int64(1280)): + with T.block("T_reshape"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(reshape714[(v_ax2 // T.int64(1280) + v_ax0 + v_ax1) % batch_size, T.int64(0), v_ax2 % T.int64(1280) // T.int64(64), v_ax2 % T.int64(64)]) + T.writes(T_reshape[v_ax0, v_ax1, v_ax2]) + T_reshape[v_ax0, v_ax1, v_ax2] = reshape714[(v_ax2 // T.int64(1280) + v_ax0 + v_ax1) % batch_size, T.int64(0), v_ax2 % T.int64(1280) // T.int64(64), v_ax2 % T.int64(64)] + + @T.prim_func(private=True) + def reshape8(var_reshape716: T.handle, var_T_reshape: T.handle): + T.func_attr({"op_pattern": 8, "tir.noalias": T.bool(True)}) + batch_size = T.int64() + reshape716 = T.match_buffer(var_reshape716, (batch_size, T.int64(1), T.int64(20), T.int64(64)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (batch_size, T.int64(20), T.int64(64)), "float16") + # with T.block("root"): + for ax0, ax1, ax2 in T.grid(batch_size, T.int64(20), T.int64(64)): + with T.block("T_reshape"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(reshape716[((v_ax2 // T.int64(64) + v_ax1) // T.int64(20) + v_ax0) % batch_size, T.int64(0), (v_ax2 // T.int64(64) + v_ax1) % T.int64(20), v_ax2 % T.int64(64)]) + T.writes(T_reshape[v_ax0, v_ax1, v_ax2]) + T_reshape[v_ax0, v_ax1, v_ax2] = reshape716[((v_ax2 // T.int64(64) + v_ax1) // T.int64(20) + v_ax0) % batch_size, T.int64(0), (v_ax2 // T.int64(64) + v_ax1) % T.int64(20), v_ax2 % T.int64(64)] + + @T.prim_func + def sampler_take_probs_tir(var_unsorted_probs: T.handle, var_sorted_indices: T.handle, var_sample_indices: T.handle, var_sampling_results: T.handle, var_top_prob_offsets: T.handle, var_sampled_values: T.handle, var_top_prob_probs: T.handle, var_top_prob_indices: T.handle): + T.func_attr({"op_pattern": 8, "target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32})}) + batch_size, vocab_size = T.int32(is_size_var=True), T.int32(is_size_var=True) + unsorted_probs = T.match_buffer(var_unsorted_probs, (batch_size, vocab_size)) + sorted_indices = T.match_buffer(var_sorted_indices, (batch_size, vocab_size), "int32") + num_samples = T.int32(is_size_var=True) + sample_indices = T.match_buffer(var_sample_indices, (num_samples,), "int32") + sampling_results = T.match_buffer(var_sampling_results, (num_samples,), "int32") + num_positions = T.int32(is_size_var=True) + top_prob_offsets = T.match_buffer(var_top_prob_offsets, (num_positions,), "int32") + sampled_values = T.match_buffer(var_sampled_values, (num_samples,)) + top_prob_probs = T.match_buffer(var_top_prob_probs, (num_positions,)) + top_prob_indices = T.match_buffer(var_top_prob_indices, (num_positions,), "int32") + # with T.block("root"): + for i in range(num_positions + num_samples): + with T.block("block"): + vi = T.axis.spatial(num_positions + num_samples, i) + T.reads(top_prob_offsets[vi], sorted_indices[top_prob_offsets[vi] // vocab_size, top_prob_offsets[vi] % vocab_size], unsorted_probs[T.min(top_prob_offsets[vi] // vocab_size, sample_indices[vi - num_positions]):T.min(top_prob_offsets[vi] // vocab_size, sample_indices[vi - num_positions]) + (T.max(top_prob_offsets[vi] // vocab_size, sample_indices[vi - num_positions]) + 1 - T.min(top_prob_offsets[vi] // vocab_size, sample_indices[vi - num_positions])), T.min(sorted_indices[top_prob_offsets[vi] // vocab_size, top_prob_offsets[vi] % vocab_size], sampling_results[vi - num_positions]):T.min(sorted_indices[top_prob_offsets[vi] // vocab_size, top_prob_offsets[vi] % vocab_size], sampling_results[vi - num_positions]) + (T.max(sorted_indices[top_prob_offsets[vi] // vocab_size, top_prob_offsets[vi] % vocab_size], sampling_results[vi - num_positions]) + 1 - T.min(sorted_indices[top_prob_offsets[vi] // vocab_size, top_prob_offsets[vi] % vocab_size], sampling_results[vi - num_positions]))], sample_indices[vi - num_positions], sampling_results[vi - num_positions]) + T.writes(top_prob_indices[vi], top_prob_probs[vi], sampled_values[vi - num_positions]) + if vi < num_positions: + row: T.int32 = top_prob_offsets[vi] // vocab_size + col: T.int32 = top_prob_offsets[vi] % vocab_size + top_prob_indices[vi] = sorted_indices[row, col] + top_prob_probs[vi] = unsorted_probs[row, sorted_indices[row, col]] + else: + vj: T.int32 = vi - num_positions + sampled_values[vj] = unsorted_probs[sample_indices[vj], sampling_results[vj]] + + @T.prim_func + def scatter_probs(var_src: T.handle, var_indices: T.handle, var_dst: T.handle): + T.func_attr({"op_pattern": 8, "target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.noalias": T.bool(True)}) + batch_size, n = T.int32(is_size_var=True), T.int32(is_size_var=True) + src = T.match_buffer(var_src, (batch_size, n)) + indices = T.match_buffer(var_indices, (batch_size,), "int32") + m = T.int32(is_size_var=True) + dst = T.match_buffer(var_dst, (m, n)) + # with T.block("root"): + for b, j in T.grid(batch_size, n): + with T.block("scatter_2d"): + vb, vj = T.axis.remap("SS", [b, j]) + T.reads(src[vb, vj], indices[vb]) + T.writes(dst[indices[vb], vj]) + dst[indices[vb], vj] = src[vb, vj] + + @T.prim_func + def softmax_with_chunked_sum(var_A: T.handle, var_temperature: T.handle, var_chunked_sum: T.handle, var_chunked_max: T.handle, var_softmax: T.handle): + T.func_attr({"op_pattern": 8, "target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + batch_size, vocab_size = T.int64(is_size_var=True), T.int64(is_size_var=True) + A = T.match_buffer(var_A, (batch_size, vocab_size)) + temperature = T.match_buffer(var_temperature, (batch_size,)) + num_chunks = T.int64(is_size_var=True) + chunked_sum = T.match_buffer(var_chunked_sum, (batch_size, num_chunks)) + chunked_max = T.match_buffer(var_chunked_max, (batch_size, num_chunks)) + softmax = T.match_buffer(var_softmax, (batch_size, vocab_size)) + # with T.block("root"): + temp_max_shared = T.alloc_buffer((batch_size,), scope="shared") + temp_sum_shared = T.alloc_buffer((batch_size,), scope="shared") + for l0_l1_fused in T.thread_binding(batch_size * num_chunks, thread="blockIdx.x"): + for ax0_1 in T.thread_binding(T.int64(32), thread="threadIdx.x"): + for ax0_0 in T.serial((num_chunks + T.int64(31)) // T.int64(32), annotations={"pragma_auto_unroll_max_step": 64, "pragma_unroll_explicit": 1}): + with T.block("max"): + v0 = T.axis.spatial(batch_size, l0_l1_fused % (num_chunks * batch_size) // num_chunks) + v1 = T.axis.reduce(num_chunks, ax0_0 * T.int64(32) + ax0_1) + T.where(ax0_0 * T.int64(32) + ax0_1 < num_chunks) + T.reads(chunked_max[v0, v1]) + T.writes(temp_max_shared[v0]) + with T.init(): + temp_max_shared[v0] = T.float32(-3.4028234663852886e+38) + temp_max_shared[v0] = T.max(temp_max_shared[v0], chunked_max[v0, v1]) + for ax0_1 in T.thread_binding(T.int64(32), thread="threadIdx.x"): + for ax0_0 in T.serial((num_chunks + T.int64(31)) // T.int64(32), annotations={"pragma_auto_unroll_max_step": 64, "pragma_unroll_explicit": 1}): + with T.block("sum_exp"): + v0 = T.axis.spatial(batch_size, l0_l1_fused % (num_chunks * batch_size) // num_chunks) + v1 = T.axis.reduce(num_chunks, ax0_0 * T.int64(32) + ax0_1) + T.where(ax0_0 * T.int64(32) + ax0_1 < num_chunks) + T.reads(temperature[v0], chunked_sum[v0, v1], chunked_max[v0, v1], temp_max_shared[v0]) + T.writes(temp_sum_shared[v0]) + with T.init(): + temp_sum_shared[v0] = T.float32(0) + temp_sum_shared[v0] = temp_sum_shared[v0] + T.Select(temperature[v0] > T.float32(1.0000000000000001e-05), T.exp(chunked_sum[v0, v1] + chunked_max[v0, v1] - temp_max_shared[v0]), T.Cast("float32", chunked_max[v0, v1] == temp_max_shared[v0]) * chunked_sum[v0, v1]) + for l2_0 in T.serial(T.int64(4), annotations={"pragma_auto_unroll_max_step": 64, "pragma_unroll_explicit": 1}): + for l2_1 in T.thread_binding(T.int64(32), thread="threadIdx.y"): + for l2_2 in T.thread_binding(T.int64(32), thread="threadIdx.x"): + with T.block("log_pad"): + v0 = T.axis.spatial(batch_size, l0_l1_fused % (num_chunks * batch_size) // num_chunks) + v1 = T.axis.spatial(num_chunks, l0_l1_fused % num_chunks) + v2 = T.axis.spatial(T.int64(4096), l2_0 * T.int64(1024) + l2_1 * T.int64(32) + l2_2) + T.reads(temperature[v0], A[v0, v1 * T.int64(4096) + v2], temp_sum_shared[v0], temp_max_shared[v0]) + T.writes(softmax[v0, v1 * T.int64(4096) + v2]) + if v1 * T.int64(4096) + v2 < vocab_size: + softmax[v0, v1 * T.int64(4096) + v2] = T.if_then_else(temperature[v0] > T.float32(1.0000000000000001e-05), T.exp(A[v0, v1 * T.int64(4096) + v2] / temperature[v0] - (T.log(temp_sum_shared[v0]) + temp_max_shared[v0])), T.Cast("float32", A[v0, v1 * T.int64(4096) + v2] == temp_max_shared[v0]) / temp_sum_shared[v0]) + + @T.prim_func(private=True) + def take(model_decoder_embed_tokens_weight3: T.Buffer((T.int64(51866), T.int64(1280)), "float16"), var_reshape707: T.handle, var_T_take: T.handle): + T.func_attr({"op_pattern": 8, "tir.noalias": T.bool(True)}) + batch_size = T.int64() + reshape707 = T.match_buffer(var_reshape707, (batch_size,), "int32") + T_take = T.match_buffer(var_T_take, (batch_size, T.int64(1280)), "float16") + # with T.block("root"): + for ax0, ax1 in T.grid(batch_size, T.int64(1280)): + with T.block("T_take"): + v_ax0, v_ax1 = T.axis.remap("SS", [ax0, ax1]) + T.reads(model_decoder_embed_tokens_weight3[reshape707[v_ax0], v_ax1], reshape707[v_ax0]) + T.writes(T_take[v_ax0, v_ax1]) + T_take[v_ax0, v_ax1] = model_decoder_embed_tokens_weight3[reshape707[v_ax0], v_ax1] + + @T.prim_func(private=True) + def take1(model_decoder_embed_positions_weight3: T.Buffer((T.int64(448), T.int64(1280)), "float16"), var_lv133: T.handle, var_T_take: T.handle): + T.func_attr({"op_pattern": 8, "tir.noalias": T.bool(True)}) + batch_size = T.int64() + lv133 = T.match_buffer(var_lv133, (batch_size,), "int32") + T_take = T.match_buffer(var_T_take, (batch_size, T.int64(1280)), "float16") + # with T.block("root"): + for ax0, ax1 in T.grid(batch_size, T.int64(1280)): + with T.block("T_take"): + v_ax0, v_ax1 = T.axis.remap("SS", [ax0, ax1]) + T.reads(model_decoder_embed_positions_weight3[lv133[v_ax0], v_ax1], lv133[v_ax0]) + T.writes(T_take[v_ax0, v_ax1]) + T_take[v_ax0, v_ax1] = model_decoder_embed_positions_weight3[lv133[v_ax0], v_ax1] + + @T.prim_func(private=True) + def take2(var_layer_norm161: T.handle, var_logit_positions: T.handle, var_T_take: T.handle): + T.func_attr({"op_pattern": 8, "tir.noalias": T.bool(True)}) + seq_len = T.int64() + layer_norm161 = T.match_buffer(var_layer_norm161, (T.int64(1), seq_len, T.int64(1280)), "float16") + batch_size = T.int64() + logit_positions = T.match_buffer(var_logit_positions, (batch_size,), "int32") + T_take = T.match_buffer(var_T_take, (T.int64(1), batch_size, T.int64(1280)), "float16") + # with T.block("root"): + for ax0, ax1, ax2 in T.grid(T.int64(1), batch_size, T.int64(1280)): + with T.block("T_take"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(layer_norm161[v_ax0, logit_positions[v_ax1], v_ax2], logit_positions[v_ax1]) + T.writes(T_take[v_ax0, v_ax1, v_ax2]) + T_take[v_ax0, v_ax1, v_ax2] = layer_norm161[v_ax0, logit_positions[v_ax1], v_ax2] + + @T.prim_func(private=True) + def take3(model_decoder_embed_tokens_weight5: T.Buffer((T.int64(51866), T.int64(1280)), "float16"), reshape1353: T.Buffer((T.int64(1),), "int32"), T_take: T.Buffer((T.int64(1), T.int64(1280)), "float16")): + T.func_attr({"op_pattern": 8, "tir.noalias": T.bool(True)}) + # with T.block("root"): + for ax0, ax1 in T.grid(T.int64(1), T.int64(1280)): + with T.block("T_take"): + v_ax0, v_ax1 = T.axis.remap("SS", [ax0, ax1]) + T.reads(model_decoder_embed_tokens_weight5[reshape1353[v_ax0], v_ax1], reshape1353[v_ax0]) + T.writes(T_take[v_ax0, v_ax1]) + T_take[v_ax0, v_ax1] = model_decoder_embed_tokens_weight5[reshape1353[v_ax0], v_ax1] + + @T.prim_func(private=True) + def take4(model_decoder_embed_positions_weight5: T.Buffer((T.int64(448), T.int64(1280)), "float16"), lv264: T.Buffer((T.int64(1),), "int32"), T_take: T.Buffer((T.int64(1), T.int64(1280)), "float16")): + T.func_attr({"op_pattern": 8, "tir.noalias": T.bool(True)}) + # with T.block("root"): + for ax0, ax1 in T.grid(T.int64(1), T.int64(1280)): + with T.block("T_take"): + v_ax0, v_ax1 = T.axis.remap("SS", [ax0, ax1]) + T.reads(model_decoder_embed_positions_weight5[lv264[v_ax0], v_ax1], lv264[v_ax0]) + T.writes(T_take[v_ax0, v_ax1]) + T_take[v_ax0, v_ax1] = model_decoder_embed_positions_weight5[lv264[v_ax0], v_ax1] + + @T.prim_func(private=True) + def take_sorted_probs(var_probs: T.handle, var_lv1: T.handle, var_take_sorted_probs: T.handle): + T.func_attr({"op_pattern": 8, "target": T.target({"arch": "sm_89", "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.noalias": T.bool(True)}) + batch_size, vocab_size = T.int64(), T.int64() + probs = T.match_buffer(var_probs, (batch_size, vocab_size)) + lv1 = T.match_buffer(var_lv1, (batch_size, vocab_size), "int32") + batch_size_1, vocab_size_1 = T.int64(), T.int64() + take_sorted_probs = T.match_buffer(var_take_sorted_probs, (batch_size_1, vocab_size_1)) + # with T.block("root"): + for i, j in T.grid(batch_size_1, vocab_size_1): + with T.block("take_sorted_probs"): + v_i, v_j = T.axis.remap("SS", [i, j]) + T.reads(probs[v_i, lv1[v_i, v_j]], lv1[v_i, v_j]) + T.writes(take_sorted_probs[v_i, v_j]) + take_sorted_probs[v_i, v_j] = probs[v_i, lv1[v_i, v_j]] + + @T.prim_func + def tir_kv_cache_debug_get_kv(var_pages: T.handle, var_position_map: T.handle, var_k_data: T.handle, var_v_data: T.handle, layer_id: T.int64): + T.func_attr({"op_pattern": 8, "target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.noalias": T.bool(True)}) + num_pages, page_size = T.int64(), T.int64(is_size_var=True) + pages = T.match_buffer(var_pages, (num_pages, 2, 20, page_size, 64), "float16") + seqlen = T.int64(is_size_var=True) + position_map = T.match_buffer(var_position_map, (seqlen,), "int32", offset_factor=1) + k_data = T.match_buffer(var_k_data, (32, seqlen, 20, 64), "float16") + v_data = T.match_buffer(var_v_data, (32, seqlen, 20, 64), "float16") + # with T.block("root"): + for p, h, d in T.grid(seqlen, 20, 64): + with T.block("copy0"): + vp, vh, vd = T.axis.remap("SSS", [p, h, d]) + T.reads(position_map[vp], pages[T.Cast("int64", position_map[vp]) // page_size, 0:2, vh, T.Cast("int64", position_map[vp]) % page_size, vd]) + T.writes(k_data[layer_id, vp, vh, vd], v_data[layer_id, vp, vh, vd]) + position: T.int32 = position_map[vp] + k_data[layer_id, vp, vh, vd] = pages[T.Cast("int64", position) // page_size, 0, vh, T.Cast("int64", position) % page_size, vd] + v_data[layer_id, vp, vh, vd] = pages[T.Cast("int64", position) // page_size, 1, vh, T.Cast("int64", position) % page_size, vd] + + @T.prim_func + def tir_kv_cache_transpose_append(var_pages: T.handle, var_k_data: T.handle, var_v_data: T.handle, var_position_map: T.handle): + T.func_attr({"op_pattern": 8, "target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.noalias": T.bool(True)}) + num_pages = T.int64() + pages = T.match_buffer(var_pages, (num_pages, 2, 20, 16, 64), "float16") + ntoken = T.int64(is_size_var=True) + k_data = T.match_buffer(var_k_data, (ntoken, 20, 64), "float16") + v_data = T.match_buffer(var_v_data, (ntoken, 20, 64), "float16") + position_map = T.match_buffer(var_position_map, (ntoken,), "int32", offset_factor=1) + # with T.block("root"): + for global_pos, h, f in T.grid(ntoken, 20, 64): + if position_map[global_pos] != -1: + with T.block("k_transpose_append"): + vgpos, vh, vf = T.axis.remap("SSS", [global_pos, h, f]) + T.reads(position_map[vgpos], k_data[vgpos, vh, vf]) + T.writes(pages[position_map[vgpos] // 16, 0, vh, position_map[vgpos] % 16, vf]) + position: T.int32 = position_map[vgpos] + pages[position // 16, 0, vh, position % 16, vf] = k_data[vgpos, vh, vf] + with T.block("v_transpose_append"): + vgpos, vh, vf = T.axis.remap("SSS", [global_pos, h, f]) + T.reads(position_map[vgpos], v_data[vgpos, vh, vf]) + T.writes(pages[position_map[vgpos] // 16, 1, vh, position_map[vgpos] % 16, vf]) + position: T.int32 = position_map[vgpos] + pages[position // 16, 1, vh, position % 16, vf] = v_data[vgpos, vh, vf] + + @T.prim_func(private=True) + def top_p_pivot_cutoff(var_prob: T.handle, var_top_p_arr: T.handle, var_init_pivots: T.handle, var_final_pivot: T.handle, var_final_lsum: T.handle): + T.func_attr({"op_pattern": 8, "target": T.target({"arch": "sm_89", "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + B, N = T.int32(), T.int32() + prob = T.match_buffer(var_prob, (B, N)) + top_p_arr = T.match_buffer(var_top_p_arr, (B,)) + init_pivots = T.match_buffer(var_init_pivots, (B, 3)) + final_pivot = T.match_buffer(var_final_pivot, (B,)) + final_lsum = T.match_buffer(var_final_lsum, (B,)) + # with T.block("root"): + pivot = T.alloc_buffer((3,), scope="local") + top_p = T.alloc_buffer((1,), scope="local") + L = T.alloc_buffer((1,), scope="shared") + R_1 = T.alloc_buffer((1,), scope="shared") + L_local = T.alloc_buffer((1,), scope="local") + R_local = T.alloc_buffer((1,), scope="local") + q = T.alloc_buffer((1,), scope="local") + lsum = T.alloc_buffer((3,), scope="local") + lmin_broadcast = T.alloc_buffer((1,), scope="shared") + lmin_broadcast_local = T.alloc_buffer((1,), scope="local") + lmin = T.alloc_buffer((3,), scope="local") + cmin = T.alloc_buffer((3,), "int32", scope="local") + total_sum = T.alloc_buffer((1,), scope="local") + it = T.alloc_buffer((1,), "int32", scope="local") + es_local = T.alloc_buffer((1,), "bool", scope="local") + es = T.alloc_buffer((1,), "bool", scope="shared") + find_pivot_local = T.alloc_buffer((1,), "bool", scope="local") + find_pivot = T.alloc_buffer((1,), "bool", scope="shared") + total_sum_reduce = T.alloc_buffer((1,), scope="local") + lsum_reduce = T.alloc_buffer((1,), scope="local") + lmin_reduce = T.alloc_buffer((1,), scope="local") + cmin_reduce = T.alloc_buffer((1,), "int32", scope="local") + for _bx in T.thread_binding(B, thread="blockIdx.x"): + for _tx in T.thread_binding(1024, thread="threadIdx.x"): + with T.block("CTA"): + b, tx = T.axis.remap("SS", [_bx, _tx]) + T.reads(top_p_arr[b], top_p[0], L[0], R_1[0], init_pivots[b, 0:3], L_local[0], R_local[0], find_pivot_local[0], it[0], es_local[0], prob[b, it[0] * 1024 + tx], total_sum[0], q[0], pivot[T.min(0, it[0]):T.min(0, it[0]) + (T.max(2, it[0]) + 1 - T.min(0, it[0]))], lsum[T.min(0, it[0]):T.min(0, it[0]) + (T.max(2, it[0]) + 1 - T.min(0, it[0]))], lmin[T.min(0, it[0]):T.min(0, it[0]) + (T.max(2, it[0]) + 1 - T.min(0, it[0]))], cmin[T.min(0, it[0]):T.min(0, it[0]) + (T.max(2, it[0]) + 1 - T.min(0, it[0]))], total_sum_reduce[0], es[0], lmin_reduce[0], lmin_broadcast[0], lmin_broadcast_local[0], lsum_reduce[0], cmin_reduce[0], find_pivot[0]) + T.writes(top_p[0], L[0], R_1[0], find_pivot[0], L_local[0], R_local[0], pivot[0:3], find_pivot_local[0], final_lsum[b], final_pivot[b], lsum[0:3], lmin[0:3], cmin[0:3], total_sum[0], it[0], es_local[0], q[0], total_sum_reduce[0], es[0], lsum_reduce[0], lmin_reduce[0], lmin_broadcast[0], lmin_broadcast_local[0], cmin_reduce[0]) + top_p[0] = top_p_arr[b] + if tx == 0: + L[0] = T.float32(1) - top_p[0] + R_1[0] = T.float32(9.9999999999999995e-08) + find_pivot[0] = T.bool(False) + T.tvm_storage_sync("shared") + L_local[0] = L[0] + R_local[0] = R_1[0] + for i in T.unroll(3): + pivot[i] = init_pivots[b, i] + find_pivot_local[0] = T.bool(False) + if L_local[0] - R_local[0] <= T.float32(9.9999999999999995e-08): + if tx == 0: + final_lsum[b] = T.float32(1) + final_pivot[b] = T.float32(0) + find_pivot_local[0] = T.bool(True) + while T.tvm_thread_invariant(L_local[0] - R_local[0] > T.float32(9.9999999999999995e-08) and not find_pivot_local[0]): + T.tvm_storage_sync("shared") + for pidx in T.unroll(3): + lsum[pidx] = T.float32(0) + lmin[pidx] = T.float32(3.4028234663852886e+38) + cmin[pidx] = 0 + total_sum[0] = T.float32(0) + it[0] = 0 + es_local[0] = T.bool(False) + while it[0] < (N + 1024 - 1) // 1024 and not es_local[0]: + q[0] = T.if_then_else(it[0] * 1024 + tx < N, prob[b, it[0] * 1024 + tx], T.float32(0)) + total_sum[0] = total_sum[0] + q[0] + for pidx in T.unroll(3): + if q[0] >= pivot[pidx]: + lsum[pidx] = lsum[pidx] + q[0] + if lmin[pidx] > q[0]: + lmin[pidx] = q[0] + cmin[pidx] = 1 + else: + if lmin[pidx] == q[0]: + cmin[pidx] = cmin[pidx] + 1 + it[0] = it[0] + 1 + if it[0] % 32 == 0: + with T.block("block_cross_thread"): + T.reads(total_sum[0]) + T.writes(total_sum_reduce[0]) + T.attr(T.comm_reducer(lambda x0, y0: x0 + y0, [T.float32(0)]), "reduce_scope", T.reinterpret("handle", T.uint64(0))) + T.tvm_thread_allreduce(T.uint32(1), total_sum[0], T.bool(True), total_sum_reduce[0], tx) + if tx == 0: + es[0] = T.float32(1) - total_sum_reduce[0] < pivot[2] + T.tvm_storage_sync("shared") + es_local[0] = es[0] + T.tvm_storage_sync("shared") + for pidx in range(3): + with T.block("block_cross_thread"): + T.reads(lsum[pidx]) + T.writes(lsum_reduce[0]) + T.attr(T.comm_reducer(lambda x0, y0: x0 + y0, [T.float32(0)]), "reduce_scope", T.reinterpret("handle", T.uint64(0))) + T.tvm_thread_allreduce(T.uint32(1), lsum[pidx], T.bool(True), lsum_reduce[0], tx) + with T.block("block_cross_thread"): + T.reads(lmin[pidx]) + T.writes(lmin_reduce[0]) + T.attr(T.comm_reducer(lambda x0, y0: T.min(x0, y0), [T.float32(0)]), "reduce_scope", T.reinterpret("handle", T.uint64(0))) + T.tvm_thread_allreduce(T.uint32(1), lmin[pidx], T.bool(True), lmin_reduce[0], tx) + if tx == 0: + lmin_broadcast[0] = lmin_reduce[0] + T.tvm_storage_sync("shared") + lmin_broadcast_local[0] = lmin_broadcast[0] + if lmin[pidx] > lmin_broadcast_local[0]: + cmin[pidx] = 0 + if tx == 0: + lsum[pidx] = lsum_reduce[0] + lmin[pidx] = lmin_reduce[0] + with T.block("block_cross_thread"): + T.reads(cmin[pidx]) + T.writes(cmin_reduce[0]) + T.attr(T.comm_reducer(lambda x0, y0: x0 + y0, [0]), "reduce_scope", T.reinterpret("handle", T.uint64(0))) + T.tvm_thread_allreduce(T.uint32(1), cmin[pidx], T.bool(True), cmin_reduce[0], tx) + if tx == 0: + cmin[pidx] = cmin_reduce[0] + T.tvm_storage_sync("shared") + if tx == 0: + it[0] = 0 + while it[0] < 3 and not find_pivot_local[0]: + if lsum[it[0]] >= top_p[0] and top_p[0] > lsum[it[0]] - T.Cast("float32", cmin[it[0]]) * lmin[it[0]]: + find_pivot[0] = T.bool(True) + find_pivot_local[0] = T.bool(True) + final_pivot[b] = pivot[it[0]] + final_lsum[b] = lsum[it[0]] + else: + if lsum[it[0]] - lmin[it[0]] * T.Cast("float32", cmin[it[0]]) >= top_p[0]: + R_1[0] = pivot[it[0]] + final_lsum[b] = lsum[it[0]] + else: + if lsum[it[0]] < top_p[0]: + L[0] = pivot[it[0]] + it[0] = it[0] + 1 + T.tvm_storage_sync("shared") + L_local[0] = L[0] + R_local[0] = R_1[0] + find_pivot_local[0] = find_pivot[0] + for pidx in T.unroll(3): + pivot[pidx] = L[0] - T.Cast("float32", pidx + 1) * (L_local[0] - R_local[0]) / T.float32(4) + if tx == 0: + if not find_pivot_local[0]: + final_pivot[b] = R_local[0] + if R_local[0] == T.float32(9.9999999999999995e-08): + final_lsum[b] = lsum[2] + + @T.prim_func(private=True) + def top_p_renorm_after_cutoff(var_prob: T.handle, var_final_pivot: T.handle, var_final_lsum: T.handle, var_renorm_prob: T.handle): + T.func_attr({"op_pattern": 8, "target": T.target({"arch": "sm_89", "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + B, N = T.int32(), T.int32() + prob = T.match_buffer(var_prob, (B, N)) + final_pivot = T.match_buffer(var_final_pivot, (B,)) + final_lsum = T.match_buffer(var_final_lsum, (B,)) + renorm_prob = T.match_buffer(var_renorm_prob, (B, N)) + # with T.block("root"): + pivot = T.alloc_buffer((1,), scope="local") + lsum = T.alloc_buffer((1,), scope="local") + for _by in T.thread_binding(B, thread="blockIdx.y"): + for _bx in T.thread_binding((B + 511) // B, thread="blockIdx.x"): + for _tx in T.thread_binding(1024, thread="threadIdx.x"): + with T.block("CTA"): + by, bx, tx = T.axis.remap("SSS", [_by, _bx, _tx]) + T.reads(final_pivot[by], final_lsum[by], prob[by, T.Select(0 <= (B + 511) // B, 0, (((B + 511) // B * 1024 + N - 1) // ((B + 511) // B * 1024) - 1) * ((B + 511) // B)) * 1024 + bx * 1024 + tx:T.Select(0 <= (B + 511) // B, 0, (((B + 511) // B * 1024 + N - 1) // ((B + 511) // B * 1024) - 1) * ((B + 511) // B)) * 1024 + bx * 1024 + tx + (T.Select(0 <= (B + 511) // B, (N - 1) // ((B + 511) // B * 1024) * ((B + 511) // B), 0 - (((B + 511) // B * 1024 + N - 1) // ((B + 511) // B * 1024) - 1) * ((B + 511) // B)) * 1024 + 1)], pivot[0], lsum[0]) + T.writes(pivot[0], lsum[0], renorm_prob[by, T.Select(0 <= (B + 511) // B, 0, (((B + 511) // B * 1024 + N - 1) // ((B + 511) // B * 1024) - 1) * ((B + 511) // B)) * 1024 + bx * 1024 + tx:T.Select(0 <= (B + 511) // B, 0, (((B + 511) // B * 1024 + N - 1) // ((B + 511) // B * 1024) - 1) * ((B + 511) // B)) * 1024 + bx * 1024 + tx + (T.Select(0 <= (B + 511) // B, (N - 1) // ((B + 511) // B * 1024) * ((B + 511) // B), 0 - (((B + 511) // B * 1024 + N - 1) // ((B + 511) // B * 1024) - 1) * ((B + 511) // B)) * 1024 + 1)]) + pivot[0] = final_pivot[by] + lsum[0] = final_lsum[by] + for i in range(((B + 511) // B * 1024 + N - 1) // ((B + 511) // B * 1024)): + if i * ((512 + B - 1) // B) * 1024 + bx * 1024 + tx < N: + renorm_prob[by, i * ((512 + B - 1) // B) * 1024 + bx * 1024 + tx] = T.if_then_else(prob[by, i * ((512 + B - 1) // B) * 1024 + bx * 1024 + tx] >= pivot[0], prob[by, i * ((512 + B - 1) // B) * 1024 + bx * 1024 + tx] / lsum[0], T.float32(0)) + + @R.function + def argsort_probs(probs: R.Tensor(("batch_size", "vocab_size"), dtype="float32")) -> R.Tuple(R.Tensor(("batch_size", "vocab_size"), dtype="float32"), R.Tensor(("batch_size", "vocab_size"), dtype="int32")): + batch_size = T.int64() + vocab_size = T.int64() + R.func_attr({"relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "num_positions": 48, "num_samples": 8}}) + cls = Module + with R.dataflow(): + lv: R.Tensor((8 * (batch_size * vocab_size * 4) + 8388608 + batch_size * vocab_size * 12,), dtype="uint8") = R.builtin.alloc_tensor(R.shape([8 * (batch_size * vocab_size * 4) + 8388608 + batch_size * vocab_size * 12]), R.dtype("uint8"), R.prim_value(0), R.str("global")) + lv1 = R.call_tir(cls.argsort_thrust, (probs, lv), out_sinfo=R.Tensor((batch_size, vocab_size), dtype="int32")) + lv2 = R.call_tir(cls.take_sorted_probs, (probs, lv1), out_sinfo=R.Tensor((batch_size, vocab_size), dtype="float32")) + gv1: R.Tuple(R.Tensor((batch_size, vocab_size), dtype="float32"), R.Tensor((batch_size, vocab_size), dtype="int32")) = lv2, lv1 + R.output(gv1) + return gv1 + + @R.function + def batch_compute_cross_attn_kv(encoder_hidden_states: R.Tensor(("batch_size", 1500, 1280), dtype="float16"), paged_kv_cache: R.Object, packed_params: R.Tuple(R.Tensor((1280, 128, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1500, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), 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R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"))) -> R.Object: + batch_size = T.int64() + R.func_attr({"num_input": 2, "relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "seq_len": 15000, "total_seq_len": 1500}}) + cls = Module + with R.dataflow(): + model_decoder_layers_0_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[498] + model_decoder_layers_0_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[499] + model_decoder_layers_0_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[500] + model_decoder_layers_1_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[522] + model_decoder_layers_1_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[523] + model_decoder_layers_1_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[524] + model_decoder_layers_2_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[546] + model_decoder_layers_2_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[547] + model_decoder_layers_2_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[548] + model_decoder_layers_3_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[570] + model_decoder_layers_3_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[571] + model_decoder_layers_3_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[572] + model_decoder_layers_4_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[594] + model_decoder_layers_4_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[595] + model_decoder_layers_4_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[596] + model_decoder_layers_5_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[618] + model_decoder_layers_5_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[619] + model_decoder_layers_5_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[620] + model_decoder_layers_6_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[642] + model_decoder_layers_6_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[643] + model_decoder_layers_6_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[644] + model_decoder_layers_7_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[666] + model_decoder_layers_7_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[667] + model_decoder_layers_7_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[668] + model_decoder_layers_8_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[690] + model_decoder_layers_8_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[691] + model_decoder_layers_8_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[692] + model_decoder_layers_9_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[714] + model_decoder_layers_9_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[715] + model_decoder_layers_9_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[716] + model_decoder_layers_10_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[738] + model_decoder_layers_10_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[739] + model_decoder_layers_10_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[740] + model_decoder_layers_11_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[762] + model_decoder_layers_11_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[763] + model_decoder_layers_11_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[764] + model_decoder_layers_12_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[786] + model_decoder_layers_12_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[787] + model_decoder_layers_12_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[788] + model_decoder_layers_13_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[810] + model_decoder_layers_13_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[811] + model_decoder_layers_13_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[812] + model_decoder_layers_14_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[834] + model_decoder_layers_14_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[835] + model_decoder_layers_14_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[836] + model_decoder_layers_15_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[858] + model_decoder_layers_15_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[859] + model_decoder_layers_15_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[860] + model_decoder_layers_16_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[882] + model_decoder_layers_16_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[883] + model_decoder_layers_16_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[884] + model_decoder_layers_17_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[906] + model_decoder_layers_17_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[907] + model_decoder_layers_17_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[908] + model_decoder_layers_18_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[930] + model_decoder_layers_18_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[931] + model_decoder_layers_18_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[932] + model_decoder_layers_19_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[954] + model_decoder_layers_19_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[955] + model_decoder_layers_19_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[956] + model_decoder_layers_20_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[978] + model_decoder_layers_20_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[979] + model_decoder_layers_20_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[980] + model_decoder_layers_21_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1002] + model_decoder_layers_21_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1003] + model_decoder_layers_21_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1004] + model_decoder_layers_22_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1026] + model_decoder_layers_22_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1027] + model_decoder_layers_22_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1028] + model_decoder_layers_23_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1050] + model_decoder_layers_23_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1051] + model_decoder_layers_23_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1052] + model_decoder_layers_24_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1074] + model_decoder_layers_24_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1075] + model_decoder_layers_24_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1076] + model_decoder_layers_25_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1098] + model_decoder_layers_25_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1099] + model_decoder_layers_25_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1100] + model_decoder_layers_26_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1122] + model_decoder_layers_26_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1123] + model_decoder_layers_26_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1124] + model_decoder_layers_27_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1146] + model_decoder_layers_27_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1147] + model_decoder_layers_27_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1148] + model_decoder_layers_28_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1170] + model_decoder_layers_28_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1171] + model_decoder_layers_28_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1172] + model_decoder_layers_29_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1194] + model_decoder_layers_29_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1195] + model_decoder_layers_29_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1196] + model_decoder_layers_30_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1218] + model_decoder_layers_30_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1219] + model_decoder_layers_30_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1220] + model_decoder_layers_31_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1242] + model_decoder_layers_31_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1243] + model_decoder_layers_31_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1244] + lv = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_0_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape256 = R.call_tir(cls.reshape, (lv,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_0_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_0_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape257 = R.call_tir(cls.reshape, (lv_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape258 = R.call_tir(cls.reshape1, (reshape256,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape259 = R.call_tir(cls.reshape1, (reshape257,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv36: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", paged_kv_cache, R.prim_value(0), reshape258, reshape259, sinfo_args=(R.Object,)) + lv1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_1_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape260 = R.call_tir(cls.reshape, (lv1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv1_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_1_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_1_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape261 = R.call_tir(cls.reshape, (lv1_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape262 = R.call_tir(cls.reshape1, (reshape260,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape263 = R.call_tir(cls.reshape1, (reshape261,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv37: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv36, R.prim_value(1), reshape262, reshape263, sinfo_args=(R.Object,)) + lv2 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_2_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape264 = R.call_tir(cls.reshape, (lv2,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv2_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_2_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_2_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape265 = R.call_tir(cls.reshape, (lv2_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape266 = R.call_tir(cls.reshape1, (reshape264,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape267 = R.call_tir(cls.reshape1, (reshape265,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv38: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv37, R.prim_value(2), reshape266, reshape267, sinfo_args=(R.Object,)) + lv3 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_3_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape268 = R.call_tir(cls.reshape, (lv3,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv3_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_3_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_3_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape269 = R.call_tir(cls.reshape, (lv3_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape270 = R.call_tir(cls.reshape1, (reshape268,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape271 = R.call_tir(cls.reshape1, (reshape269,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv39: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv38, R.prim_value(3), reshape270, reshape271, sinfo_args=(R.Object,)) + lv4 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_4_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape272 = R.call_tir(cls.reshape, (lv4,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv4_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_4_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_4_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape273 = R.call_tir(cls.reshape, (lv4_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape274 = R.call_tir(cls.reshape1, (reshape272,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape275 = R.call_tir(cls.reshape1, (reshape273,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv40: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv39, R.prim_value(4), reshape274, reshape275, sinfo_args=(R.Object,)) + lv5 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_5_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape276 = R.call_tir(cls.reshape, (lv5,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv5_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_5_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_5_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape277 = R.call_tir(cls.reshape, (lv5_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape278 = R.call_tir(cls.reshape1, (reshape276,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape279 = R.call_tir(cls.reshape1, (reshape277,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv41: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv40, R.prim_value(5), reshape278, reshape279, sinfo_args=(R.Object,)) + lv6 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_6_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape280 = R.call_tir(cls.reshape, (lv6,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv6_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_6_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_6_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape281 = R.call_tir(cls.reshape, (lv6_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape282 = R.call_tir(cls.reshape1, (reshape280,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape283 = R.call_tir(cls.reshape1, (reshape281,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv42: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv41, R.prim_value(6), reshape282, reshape283, sinfo_args=(R.Object,)) + lv7 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_7_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape284 = R.call_tir(cls.reshape, (lv7,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv7_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_7_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_7_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape285 = R.call_tir(cls.reshape, (lv7_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape286 = R.call_tir(cls.reshape1, (reshape284,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape287 = R.call_tir(cls.reshape1, (reshape285,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv43: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv42, R.prim_value(7), reshape286, reshape287, sinfo_args=(R.Object,)) + lv8 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_8_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape288 = R.call_tir(cls.reshape, (lv8,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv8_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_8_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_8_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape289 = R.call_tir(cls.reshape, (lv8_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape290 = R.call_tir(cls.reshape1, (reshape288,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape291 = R.call_tir(cls.reshape1, (reshape289,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv44: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv43, R.prim_value(8), reshape290, reshape291, sinfo_args=(R.Object,)) + lv9 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_9_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape292 = R.call_tir(cls.reshape, (lv9,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv9_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_9_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_9_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape293 = R.call_tir(cls.reshape, (lv9_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape294 = R.call_tir(cls.reshape1, (reshape292,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape295 = R.call_tir(cls.reshape1, (reshape293,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv45: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv44, R.prim_value(9), reshape294, reshape295, sinfo_args=(R.Object,)) + lv10 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_10_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape296 = R.call_tir(cls.reshape, (lv10,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv10_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_10_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_10_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape297 = R.call_tir(cls.reshape, (lv10_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape298 = R.call_tir(cls.reshape1, (reshape296,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape299 = R.call_tir(cls.reshape1, (reshape297,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv46: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv45, R.prim_value(10), reshape298, reshape299, sinfo_args=(R.Object,)) + lv11 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_11_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape300 = R.call_tir(cls.reshape, (lv11,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv11_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_11_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_11_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape301 = R.call_tir(cls.reshape, (lv11_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape302 = R.call_tir(cls.reshape1, (reshape300,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape303 = R.call_tir(cls.reshape1, (reshape301,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv47: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv46, R.prim_value(11), reshape302, reshape303, sinfo_args=(R.Object,)) + lv12 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_12_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape304 = R.call_tir(cls.reshape, (lv12,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv12_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_12_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_12_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape305 = R.call_tir(cls.reshape, (lv12_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape306 = R.call_tir(cls.reshape1, (reshape304,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape307 = R.call_tir(cls.reshape1, (reshape305,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv48: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv47, R.prim_value(12), reshape306, reshape307, sinfo_args=(R.Object,)) + lv13 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_13_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape308 = R.call_tir(cls.reshape, (lv13,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv13_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_13_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_13_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape309 = R.call_tir(cls.reshape, (lv13_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape310 = R.call_tir(cls.reshape1, (reshape308,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape311 = R.call_tir(cls.reshape1, (reshape309,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv49: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv48, R.prim_value(13), reshape310, reshape311, sinfo_args=(R.Object,)) + lv14 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_14_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape312 = R.call_tir(cls.reshape, (lv14,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv14_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_14_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_14_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape313 = R.call_tir(cls.reshape, (lv14_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape314 = R.call_tir(cls.reshape1, (reshape312,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape315 = R.call_tir(cls.reshape1, (reshape313,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv50: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv49, R.prim_value(14), reshape314, reshape315, sinfo_args=(R.Object,)) + lv15 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_15_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape316 = R.call_tir(cls.reshape, (lv15,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv15_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_15_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_15_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape317 = R.call_tir(cls.reshape, (lv15_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape318 = R.call_tir(cls.reshape1, (reshape316,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape319 = R.call_tir(cls.reshape1, (reshape317,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv51: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv50, R.prim_value(15), reshape318, reshape319, sinfo_args=(R.Object,)) + lv16 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_16_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape320 = R.call_tir(cls.reshape, (lv16,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv16_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_16_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_16_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape321 = R.call_tir(cls.reshape, (lv16_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape322 = R.call_tir(cls.reshape1, (reshape320,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape323 = R.call_tir(cls.reshape1, (reshape321,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv52: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv51, R.prim_value(16), reshape322, reshape323, sinfo_args=(R.Object,)) + lv17 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_17_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape324 = R.call_tir(cls.reshape, (lv17,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv17_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_17_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_17_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape325 = R.call_tir(cls.reshape, (lv17_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape326 = R.call_tir(cls.reshape1, (reshape324,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape327 = R.call_tir(cls.reshape1, (reshape325,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv53: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv52, R.prim_value(17), reshape326, reshape327, sinfo_args=(R.Object,)) + lv18 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_18_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape328 = R.call_tir(cls.reshape, (lv18,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv18_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_18_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_18_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape329 = R.call_tir(cls.reshape, (lv18_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape330 = R.call_tir(cls.reshape1, (reshape328,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape331 = R.call_tir(cls.reshape1, (reshape329,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv54: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv53, R.prim_value(18), reshape330, reshape331, sinfo_args=(R.Object,)) + lv19 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_19_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape332 = R.call_tir(cls.reshape, (lv19,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv19_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_19_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_19_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape333 = R.call_tir(cls.reshape, (lv19_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape334 = R.call_tir(cls.reshape1, (reshape332,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape335 = R.call_tir(cls.reshape1, (reshape333,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv55: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv54, R.prim_value(19), reshape334, reshape335, sinfo_args=(R.Object,)) + lv20 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_20_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape336 = R.call_tir(cls.reshape, (lv20,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv20_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_20_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_20_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape337 = R.call_tir(cls.reshape, (lv20_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape338 = R.call_tir(cls.reshape1, (reshape336,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape339 = R.call_tir(cls.reshape1, (reshape337,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv56: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv55, R.prim_value(20), reshape338, reshape339, sinfo_args=(R.Object,)) + lv21 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_21_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape340 = R.call_tir(cls.reshape, (lv21,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv21_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_21_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_21_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape341 = R.call_tir(cls.reshape, (lv21_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape342 = R.call_tir(cls.reshape1, (reshape340,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape343 = R.call_tir(cls.reshape1, (reshape341,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv57: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv56, R.prim_value(21), reshape342, reshape343, sinfo_args=(R.Object,)) + lv22 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_22_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape344 = R.call_tir(cls.reshape, (lv22,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv22_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_22_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_22_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape345 = R.call_tir(cls.reshape, (lv22_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape346 = R.call_tir(cls.reshape1, (reshape344,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape347 = R.call_tir(cls.reshape1, (reshape345,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv58: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv57, R.prim_value(22), reshape346, reshape347, sinfo_args=(R.Object,)) + lv23 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_23_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape348 = R.call_tir(cls.reshape, (lv23,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv23_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_23_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_23_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape349 = R.call_tir(cls.reshape, (lv23_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape350 = R.call_tir(cls.reshape1, (reshape348,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape351 = R.call_tir(cls.reshape1, (reshape349,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv59: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv58, R.prim_value(23), reshape350, reshape351, sinfo_args=(R.Object,)) + lv24 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_24_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape352 = R.call_tir(cls.reshape, (lv24,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv24_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_24_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_24_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape353 = R.call_tir(cls.reshape, (lv24_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape354 = R.call_tir(cls.reshape1, (reshape352,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape355 = R.call_tir(cls.reshape1, (reshape353,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv60: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv59, R.prim_value(24), reshape354, reshape355, sinfo_args=(R.Object,)) + lv25 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_25_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape356 = R.call_tir(cls.reshape, (lv25,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv25_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_25_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_25_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape357 = R.call_tir(cls.reshape, (lv25_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape358 = R.call_tir(cls.reshape1, (reshape356,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape359 = R.call_tir(cls.reshape1, (reshape357,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv61: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv60, R.prim_value(25), reshape358, reshape359, sinfo_args=(R.Object,)) + lv26 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_26_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape360 = R.call_tir(cls.reshape, (lv26,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv26_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_26_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_26_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape361 = R.call_tir(cls.reshape, (lv26_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape362 = R.call_tir(cls.reshape1, (reshape360,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape363 = R.call_tir(cls.reshape1, (reshape361,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv62: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv61, R.prim_value(26), reshape362, reshape363, sinfo_args=(R.Object,)) + lv27 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_27_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape364 = R.call_tir(cls.reshape, (lv27,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv27_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_27_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_27_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape365 = R.call_tir(cls.reshape, (lv27_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape366 = R.call_tir(cls.reshape1, (reshape364,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape367 = R.call_tir(cls.reshape1, (reshape365,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv63: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv62, R.prim_value(27), reshape366, reshape367, sinfo_args=(R.Object,)) + lv28 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_28_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape368 = R.call_tir(cls.reshape, (lv28,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv28_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_28_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_28_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape369 = R.call_tir(cls.reshape, (lv28_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape370 = R.call_tir(cls.reshape1, (reshape368,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape371 = R.call_tir(cls.reshape1, (reshape369,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv64: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv63, R.prim_value(28), reshape370, reshape371, sinfo_args=(R.Object,)) + lv29 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_29_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape372 = R.call_tir(cls.reshape, (lv29,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv29_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_29_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_29_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape373 = R.call_tir(cls.reshape, (lv29_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape374 = R.call_tir(cls.reshape1, (reshape372,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape375 = R.call_tir(cls.reshape1, (reshape373,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv65: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv64, R.prim_value(29), reshape374, reshape375, sinfo_args=(R.Object,)) + lv30 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_30_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape376 = R.call_tir(cls.reshape, (lv30,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv30_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_30_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_30_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape377 = R.call_tir(cls.reshape, (lv30_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape378 = R.call_tir(cls.reshape1, (reshape376,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape379 = R.call_tir(cls.reshape1, (reshape377,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv66: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv65, R.prim_value(30), reshape378, reshape379, sinfo_args=(R.Object,)) + lv31 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_31_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape380 = R.call_tir(cls.reshape, (lv31,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv31_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_31_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_31_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape381 = R.call_tir(cls.reshape, (lv31_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape382 = R.call_tir(cls.reshape1, (reshape380,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape383 = R.call_tir(cls.reshape1, (reshape381,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + gv1: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv66, R.prim_value(31), reshape382, reshape383, sinfo_args=(R.Object,)) + R.output(gv1) + return gv1 + + @R.function + def batch_decode(input_ids: R.Tensor(("batch_size", 1), dtype="int32"), paged_kv_cache: R.Object, packed_params: R.Tuple(R.Tensor((1280, 128, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1500, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), 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R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"))) -> R.Tensor(("batch_size", 1, 51866), dtype="float32"): + batch_size = T.int64() + R.func_attr({"num_input": 2, "relax.memory_plan_dynamic_func_output": 1, "relax.rewrite_cuda_graph.capture_symbolic_vars": ["batch_size"], "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "seq_len": 15000, "total_seq_len": 1500}}) + cls = Module + with R.dataflow(): + model_decoder_embed_tokens_weight3: R.Tensor((51866, 1280), dtype="float16") = packed_params[487] + model_decoder_embed_positions_weight3: R.Tensor((448, 1280), dtype="float16") = packed_params[488] + model_decoder_layers_0_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[489] + model_decoder_layers_0_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[490] + model_decoder_layers_0_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[491] + model_decoder_layers_0_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[492] + model_decoder_layers_0_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[493] + model_decoder_layers_0_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[494] + model_decoder_layers_0_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[495] + model_decoder_layers_0_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[496] + model_decoder_layers_0_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[497] + model_decoder_layers_0_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[501] + model_decoder_layers_0_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[502] + model_decoder_layers_0_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[503] + model_decoder_layers_0_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[504] + model_decoder_layers_0_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[505] + model_decoder_layers_0_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[506] + model_decoder_layers_0_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[507] + model_decoder_layers_0_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[508] + model_decoder_layers_0_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[509] + model_decoder_layers_0_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[510] + model_decoder_layers_0_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[511] + model_decoder_layers_0_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[512] + model_decoder_layers_1_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[513] + model_decoder_layers_1_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[514] + model_decoder_layers_1_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[515] + model_decoder_layers_1_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[516] + model_decoder_layers_1_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[517] + model_decoder_layers_1_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[518] + model_decoder_layers_1_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[519] + model_decoder_layers_1_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[520] + model_decoder_layers_1_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[521] + model_decoder_layers_1_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[525] + model_decoder_layers_1_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[526] + model_decoder_layers_1_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[527] + model_decoder_layers_1_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[528] + model_decoder_layers_1_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[529] + model_decoder_layers_1_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[530] + model_decoder_layers_1_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[531] + model_decoder_layers_1_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[532] + model_decoder_layers_1_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[533] + model_decoder_layers_1_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[534] + model_decoder_layers_1_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[535] + model_decoder_layers_1_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[536] + model_decoder_layers_2_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[537] + model_decoder_layers_2_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[538] + model_decoder_layers_2_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[539] + model_decoder_layers_2_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[540] + model_decoder_layers_2_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[541] + model_decoder_layers_2_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[542] + model_decoder_layers_2_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[543] + model_decoder_layers_2_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[544] + model_decoder_layers_2_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[545] + model_decoder_layers_2_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[549] + model_decoder_layers_2_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[550] + model_decoder_layers_2_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[551] + model_decoder_layers_2_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[552] + model_decoder_layers_2_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[553] + model_decoder_layers_2_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[554] + model_decoder_layers_2_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[555] + model_decoder_layers_2_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[556] + model_decoder_layers_2_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[557] + model_decoder_layers_2_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[558] + model_decoder_layers_2_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[559] + model_decoder_layers_2_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[560] + model_decoder_layers_3_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[561] + model_decoder_layers_3_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[562] + model_decoder_layers_3_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[563] + model_decoder_layers_3_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[564] + model_decoder_layers_3_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[565] + model_decoder_layers_3_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[566] + model_decoder_layers_3_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[567] + model_decoder_layers_3_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[568] + model_decoder_layers_3_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[569] + model_decoder_layers_3_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[573] + model_decoder_layers_3_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[574] + model_decoder_layers_3_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[575] + model_decoder_layers_3_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[576] + model_decoder_layers_3_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[577] + model_decoder_layers_3_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[578] + model_decoder_layers_3_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[579] + model_decoder_layers_3_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[580] + model_decoder_layers_3_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[581] + model_decoder_layers_3_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[582] + model_decoder_layers_3_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[583] + model_decoder_layers_3_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[584] + model_decoder_layers_4_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[585] + model_decoder_layers_4_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[586] + model_decoder_layers_4_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[587] + model_decoder_layers_4_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[588] + model_decoder_layers_4_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[589] + model_decoder_layers_4_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[590] + model_decoder_layers_4_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[591] + model_decoder_layers_4_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[592] + model_decoder_layers_4_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[593] + model_decoder_layers_4_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[597] + model_decoder_layers_4_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[598] + model_decoder_layers_4_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[599] + model_decoder_layers_4_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[600] + model_decoder_layers_4_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[601] + model_decoder_layers_4_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[602] + model_decoder_layers_4_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[603] + model_decoder_layers_4_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[604] + model_decoder_layers_4_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[605] + model_decoder_layers_4_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[606] + model_decoder_layers_4_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[607] + model_decoder_layers_4_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[608] + model_decoder_layers_5_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[609] + model_decoder_layers_5_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[610] + model_decoder_layers_5_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[611] + model_decoder_layers_5_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[612] + model_decoder_layers_5_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[613] + model_decoder_layers_5_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[614] + model_decoder_layers_5_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[615] + model_decoder_layers_5_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[616] + model_decoder_layers_5_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[617] + model_decoder_layers_5_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[621] + model_decoder_layers_5_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[622] + model_decoder_layers_5_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[623] + model_decoder_layers_5_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[624] + model_decoder_layers_5_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[625] + model_decoder_layers_5_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[626] + model_decoder_layers_5_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[627] + model_decoder_layers_5_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[628] + model_decoder_layers_5_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[629] + model_decoder_layers_5_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[630] + model_decoder_layers_5_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[631] + model_decoder_layers_5_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[632] + model_decoder_layers_6_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[633] + model_decoder_layers_6_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[634] + model_decoder_layers_6_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[635] + model_decoder_layers_6_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[636] + model_decoder_layers_6_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[637] + model_decoder_layers_6_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[638] + model_decoder_layers_6_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[639] + model_decoder_layers_6_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[640] + model_decoder_layers_6_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[641] + model_decoder_layers_6_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[645] + model_decoder_layers_6_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[646] + model_decoder_layers_6_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[647] + model_decoder_layers_6_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[648] + model_decoder_layers_6_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[649] + model_decoder_layers_6_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[650] + model_decoder_layers_6_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[651] + model_decoder_layers_6_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[652] + model_decoder_layers_6_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[653] + model_decoder_layers_6_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[654] + model_decoder_layers_6_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[655] + model_decoder_layers_6_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[656] + model_decoder_layers_7_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[657] + model_decoder_layers_7_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[658] + model_decoder_layers_7_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[659] + model_decoder_layers_7_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[660] + model_decoder_layers_7_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[661] + model_decoder_layers_7_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[662] + model_decoder_layers_7_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[663] + model_decoder_layers_7_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[664] + model_decoder_layers_7_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[665] + model_decoder_layers_7_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[669] + model_decoder_layers_7_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[670] + model_decoder_layers_7_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[671] + model_decoder_layers_7_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[672] + model_decoder_layers_7_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[673] + model_decoder_layers_7_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[674] + model_decoder_layers_7_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[675] + model_decoder_layers_7_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[676] + model_decoder_layers_7_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[677] + model_decoder_layers_7_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[678] + model_decoder_layers_7_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[679] + model_decoder_layers_7_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[680] + model_decoder_layers_8_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[681] + model_decoder_layers_8_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[682] + model_decoder_layers_8_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[683] + model_decoder_layers_8_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[684] + model_decoder_layers_8_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[685] + model_decoder_layers_8_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[686] + model_decoder_layers_8_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[687] + model_decoder_layers_8_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[688] + model_decoder_layers_8_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[689] + model_decoder_layers_8_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[693] + model_decoder_layers_8_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[694] + model_decoder_layers_8_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[695] + model_decoder_layers_8_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[696] + model_decoder_layers_8_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[697] + model_decoder_layers_8_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[698] + model_decoder_layers_8_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[699] + model_decoder_layers_8_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[700] + model_decoder_layers_8_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[701] + model_decoder_layers_8_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[702] + model_decoder_layers_8_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[703] + model_decoder_layers_8_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[704] + model_decoder_layers_9_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[705] + model_decoder_layers_9_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[706] + model_decoder_layers_9_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[707] + model_decoder_layers_9_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[708] + model_decoder_layers_9_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[709] + model_decoder_layers_9_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[710] + model_decoder_layers_9_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[711] + model_decoder_layers_9_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[712] + model_decoder_layers_9_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[713] + model_decoder_layers_9_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[717] + model_decoder_layers_9_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[718] + model_decoder_layers_9_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[719] + model_decoder_layers_9_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[720] + model_decoder_layers_9_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[721] + model_decoder_layers_9_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[722] + model_decoder_layers_9_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[723] + model_decoder_layers_9_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[724] + model_decoder_layers_9_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[725] + model_decoder_layers_9_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[726] + model_decoder_layers_9_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[727] + model_decoder_layers_9_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[728] + model_decoder_layers_10_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[729] + model_decoder_layers_10_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[730] + model_decoder_layers_10_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[731] + model_decoder_layers_10_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[732] + model_decoder_layers_10_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[733] + model_decoder_layers_10_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[734] + model_decoder_layers_10_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[735] + model_decoder_layers_10_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[736] + model_decoder_layers_10_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[737] + model_decoder_layers_10_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[741] + model_decoder_layers_10_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[742] + model_decoder_layers_10_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[743] + model_decoder_layers_10_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[744] + model_decoder_layers_10_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[745] + model_decoder_layers_10_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[746] + model_decoder_layers_10_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[747] + model_decoder_layers_10_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[748] + model_decoder_layers_10_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[749] + model_decoder_layers_10_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[750] + model_decoder_layers_10_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[751] + model_decoder_layers_10_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[752] + model_decoder_layers_11_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[753] + model_decoder_layers_11_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[754] + model_decoder_layers_11_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[755] + model_decoder_layers_11_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[756] + model_decoder_layers_11_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[757] + model_decoder_layers_11_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[758] + model_decoder_layers_11_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[759] + model_decoder_layers_11_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[760] + model_decoder_layers_11_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[761] + model_decoder_layers_11_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[765] + model_decoder_layers_11_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[766] + model_decoder_layers_11_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[767] + model_decoder_layers_11_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[768] + model_decoder_layers_11_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[769] + model_decoder_layers_11_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[770] + model_decoder_layers_11_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[771] + model_decoder_layers_11_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[772] + model_decoder_layers_11_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[773] + model_decoder_layers_11_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[774] + model_decoder_layers_11_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[775] + model_decoder_layers_11_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[776] + model_decoder_layers_12_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[777] + model_decoder_layers_12_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[778] + model_decoder_layers_12_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[779] + model_decoder_layers_12_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[780] + model_decoder_layers_12_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[781] + model_decoder_layers_12_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[782] + model_decoder_layers_12_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[783] + model_decoder_layers_12_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[784] + model_decoder_layers_12_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[785] + model_decoder_layers_12_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[789] + model_decoder_layers_12_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[790] + model_decoder_layers_12_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[791] + model_decoder_layers_12_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[792] + model_decoder_layers_12_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[793] + model_decoder_layers_12_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[794] + model_decoder_layers_12_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[795] + model_decoder_layers_12_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[796] + model_decoder_layers_12_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[797] + model_decoder_layers_12_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[798] + model_decoder_layers_12_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[799] + model_decoder_layers_12_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[800] + model_decoder_layers_13_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[801] + model_decoder_layers_13_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[802] + model_decoder_layers_13_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[803] + model_decoder_layers_13_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[804] + model_decoder_layers_13_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[805] + model_decoder_layers_13_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[806] + model_decoder_layers_13_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[807] + model_decoder_layers_13_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[808] + model_decoder_layers_13_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[809] + model_decoder_layers_13_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[813] + model_decoder_layers_13_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[814] + model_decoder_layers_13_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[815] + model_decoder_layers_13_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[816] + model_decoder_layers_13_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[817] + model_decoder_layers_13_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[818] + model_decoder_layers_13_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[819] + model_decoder_layers_13_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[820] + model_decoder_layers_13_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[821] + model_decoder_layers_13_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[822] + model_decoder_layers_13_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[823] + model_decoder_layers_13_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[824] + model_decoder_layers_14_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[825] + model_decoder_layers_14_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[826] + model_decoder_layers_14_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[827] + model_decoder_layers_14_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[828] + model_decoder_layers_14_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[829] + model_decoder_layers_14_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[830] + model_decoder_layers_14_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[831] + model_decoder_layers_14_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[832] + model_decoder_layers_14_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[833] + model_decoder_layers_14_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[837] + model_decoder_layers_14_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[838] + model_decoder_layers_14_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[839] + model_decoder_layers_14_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[840] + model_decoder_layers_14_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[841] + model_decoder_layers_14_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[842] + model_decoder_layers_14_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[843] + model_decoder_layers_14_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[844] + model_decoder_layers_14_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[845] + model_decoder_layers_14_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[846] + model_decoder_layers_14_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[847] + model_decoder_layers_14_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[848] + model_decoder_layers_15_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[849] + model_decoder_layers_15_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[850] + model_decoder_layers_15_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[851] + model_decoder_layers_15_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[852] + model_decoder_layers_15_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[853] + model_decoder_layers_15_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[854] + model_decoder_layers_15_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[855] + model_decoder_layers_15_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[856] + model_decoder_layers_15_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[857] + model_decoder_layers_15_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[861] + model_decoder_layers_15_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[862] + model_decoder_layers_15_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[863] + model_decoder_layers_15_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[864] + model_decoder_layers_15_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[865] + model_decoder_layers_15_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[866] + model_decoder_layers_15_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[867] + model_decoder_layers_15_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[868] + model_decoder_layers_15_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[869] + model_decoder_layers_15_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[870] + model_decoder_layers_15_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[871] + model_decoder_layers_15_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[872] + model_decoder_layers_16_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[873] + model_decoder_layers_16_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[874] + model_decoder_layers_16_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[875] + model_decoder_layers_16_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[876] + model_decoder_layers_16_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[877] + model_decoder_layers_16_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[878] + model_decoder_layers_16_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[879] + model_decoder_layers_16_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[880] + model_decoder_layers_16_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[881] + model_decoder_layers_16_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[885] + model_decoder_layers_16_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[886] + model_decoder_layers_16_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[887] + model_decoder_layers_16_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[888] + model_decoder_layers_16_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[889] + model_decoder_layers_16_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[890] + model_decoder_layers_16_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[891] + model_decoder_layers_16_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[892] + model_decoder_layers_16_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[893] + model_decoder_layers_16_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[894] + model_decoder_layers_16_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[895] + model_decoder_layers_16_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[896] + model_decoder_layers_17_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[897] + model_decoder_layers_17_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[898] + model_decoder_layers_17_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[899] + model_decoder_layers_17_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[900] + model_decoder_layers_17_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[901] + model_decoder_layers_17_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[902] + model_decoder_layers_17_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[903] + model_decoder_layers_17_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[904] + model_decoder_layers_17_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[905] + model_decoder_layers_17_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[909] + model_decoder_layers_17_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[910] + model_decoder_layers_17_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[911] + model_decoder_layers_17_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[912] + model_decoder_layers_17_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[913] + model_decoder_layers_17_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[914] + model_decoder_layers_17_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[915] + model_decoder_layers_17_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[916] + model_decoder_layers_17_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[917] + model_decoder_layers_17_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[918] + model_decoder_layers_17_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[919] + model_decoder_layers_17_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[920] + model_decoder_layers_18_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[921] + model_decoder_layers_18_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[922] + model_decoder_layers_18_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[923] + model_decoder_layers_18_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[924] + model_decoder_layers_18_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[925] + model_decoder_layers_18_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[926] + model_decoder_layers_18_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[927] + model_decoder_layers_18_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[928] + model_decoder_layers_18_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[929] + model_decoder_layers_18_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[933] + model_decoder_layers_18_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[934] + model_decoder_layers_18_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[935] + model_decoder_layers_18_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[936] + model_decoder_layers_18_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[937] + model_decoder_layers_18_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[938] + model_decoder_layers_18_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[939] + model_decoder_layers_18_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[940] + model_decoder_layers_18_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[941] + model_decoder_layers_18_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[942] + model_decoder_layers_18_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[943] + model_decoder_layers_18_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[944] + model_decoder_layers_19_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[945] + model_decoder_layers_19_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[946] + model_decoder_layers_19_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[947] + model_decoder_layers_19_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[948] + model_decoder_layers_19_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[949] + model_decoder_layers_19_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[950] + model_decoder_layers_19_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[951] + model_decoder_layers_19_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[952] + model_decoder_layers_19_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[953] + model_decoder_layers_19_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[957] + model_decoder_layers_19_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[958] + model_decoder_layers_19_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[959] + model_decoder_layers_19_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[960] + model_decoder_layers_19_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[961] + model_decoder_layers_19_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[962] + model_decoder_layers_19_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[963] + model_decoder_layers_19_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[964] + model_decoder_layers_19_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[965] + model_decoder_layers_19_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[966] + model_decoder_layers_19_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[967] + model_decoder_layers_19_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[968] + model_decoder_layers_20_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[969] + model_decoder_layers_20_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[970] + model_decoder_layers_20_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[971] + model_decoder_layers_20_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[972] + model_decoder_layers_20_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[973] + model_decoder_layers_20_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[974] + model_decoder_layers_20_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[975] + model_decoder_layers_20_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[976] + model_decoder_layers_20_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[977] + model_decoder_layers_20_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[981] + model_decoder_layers_20_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[982] + model_decoder_layers_20_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[983] + model_decoder_layers_20_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[984] + model_decoder_layers_20_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[985] + model_decoder_layers_20_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[986] + model_decoder_layers_20_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[987] + model_decoder_layers_20_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[988] + model_decoder_layers_20_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[989] + model_decoder_layers_20_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[990] + model_decoder_layers_20_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[991] + model_decoder_layers_20_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[992] + model_decoder_layers_21_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[993] + model_decoder_layers_21_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[994] + model_decoder_layers_21_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[995] + model_decoder_layers_21_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[996] + model_decoder_layers_21_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[997] + model_decoder_layers_21_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[998] + model_decoder_layers_21_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[999] + model_decoder_layers_21_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1000] + model_decoder_layers_21_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1001] + model_decoder_layers_21_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1005] + model_decoder_layers_21_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1006] + model_decoder_layers_21_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1007] + model_decoder_layers_21_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1008] + model_decoder_layers_21_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1009] + model_decoder_layers_21_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1010] + model_decoder_layers_21_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1011] + model_decoder_layers_21_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1012] + model_decoder_layers_21_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1013] + model_decoder_layers_21_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1014] + model_decoder_layers_21_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1015] + model_decoder_layers_21_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1016] + model_decoder_layers_22_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1017] + model_decoder_layers_22_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1018] + model_decoder_layers_22_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1019] + model_decoder_layers_22_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1020] + model_decoder_layers_22_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1021] + model_decoder_layers_22_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1022] + model_decoder_layers_22_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1023] + model_decoder_layers_22_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1024] + model_decoder_layers_22_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1025] + model_decoder_layers_22_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1029] + model_decoder_layers_22_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1030] + model_decoder_layers_22_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1031] + model_decoder_layers_22_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1032] + model_decoder_layers_22_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1033] + model_decoder_layers_22_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1034] + model_decoder_layers_22_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1035] + model_decoder_layers_22_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1036] + model_decoder_layers_22_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1037] + model_decoder_layers_22_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1038] + model_decoder_layers_22_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1039] + model_decoder_layers_22_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1040] + model_decoder_layers_23_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1041] + model_decoder_layers_23_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1042] + model_decoder_layers_23_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1043] + model_decoder_layers_23_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1044] + model_decoder_layers_23_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1045] + model_decoder_layers_23_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1046] + model_decoder_layers_23_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1047] + model_decoder_layers_23_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1048] + model_decoder_layers_23_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1049] + model_decoder_layers_23_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1053] + model_decoder_layers_23_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1054] + model_decoder_layers_23_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1055] + model_decoder_layers_23_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1056] + model_decoder_layers_23_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1057] + model_decoder_layers_23_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1058] + model_decoder_layers_23_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1059] + model_decoder_layers_23_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1060] + model_decoder_layers_23_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1061] + model_decoder_layers_23_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1062] + model_decoder_layers_23_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1063] + model_decoder_layers_23_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1064] + model_decoder_layers_24_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1065] + model_decoder_layers_24_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1066] + model_decoder_layers_24_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1067] + model_decoder_layers_24_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1068] + model_decoder_layers_24_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1069] + model_decoder_layers_24_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1070] + model_decoder_layers_24_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1071] + model_decoder_layers_24_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1072] + model_decoder_layers_24_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1073] + model_decoder_layers_24_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1077] + model_decoder_layers_24_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1078] + model_decoder_layers_24_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1079] + model_decoder_layers_24_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1080] + model_decoder_layers_24_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1081] + model_decoder_layers_24_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1082] + model_decoder_layers_24_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1083] + model_decoder_layers_24_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1084] + model_decoder_layers_24_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1085] + model_decoder_layers_24_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1086] + model_decoder_layers_24_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1087] + model_decoder_layers_24_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1088] + model_decoder_layers_25_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1089] + model_decoder_layers_25_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1090] + model_decoder_layers_25_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1091] + model_decoder_layers_25_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1092] + model_decoder_layers_25_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1093] + model_decoder_layers_25_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1094] + model_decoder_layers_25_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1095] + model_decoder_layers_25_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1096] + model_decoder_layers_25_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1097] + model_decoder_layers_25_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1101] + model_decoder_layers_25_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1102] + model_decoder_layers_25_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1103] + model_decoder_layers_25_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1104] + model_decoder_layers_25_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1105] + model_decoder_layers_25_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1106] + model_decoder_layers_25_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1107] + model_decoder_layers_25_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1108] + model_decoder_layers_25_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1109] + model_decoder_layers_25_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1110] + model_decoder_layers_25_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1111] + model_decoder_layers_25_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1112] + model_decoder_layers_26_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1113] + model_decoder_layers_26_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1114] + model_decoder_layers_26_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1115] + model_decoder_layers_26_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1116] + model_decoder_layers_26_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1117] + model_decoder_layers_26_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1118] + model_decoder_layers_26_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1119] + model_decoder_layers_26_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1120] + model_decoder_layers_26_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1121] + model_decoder_layers_26_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1125] + model_decoder_layers_26_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1126] + model_decoder_layers_26_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1127] + model_decoder_layers_26_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1128] + model_decoder_layers_26_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1129] + model_decoder_layers_26_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1130] + model_decoder_layers_26_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1131] + model_decoder_layers_26_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1132] + model_decoder_layers_26_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1133] + model_decoder_layers_26_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1134] + model_decoder_layers_26_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1135] + model_decoder_layers_26_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1136] + model_decoder_layers_27_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1137] + model_decoder_layers_27_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1138] + model_decoder_layers_27_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1139] + model_decoder_layers_27_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1140] + model_decoder_layers_27_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1141] + model_decoder_layers_27_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1142] + model_decoder_layers_27_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1143] + model_decoder_layers_27_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1144] + model_decoder_layers_27_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1145] + model_decoder_layers_27_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1149] + model_decoder_layers_27_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1150] + model_decoder_layers_27_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1151] + model_decoder_layers_27_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1152] + model_decoder_layers_27_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1153] + model_decoder_layers_27_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1154] + model_decoder_layers_27_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1155] + model_decoder_layers_27_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1156] + model_decoder_layers_27_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1157] + model_decoder_layers_27_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1158] + model_decoder_layers_27_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1159] + model_decoder_layers_27_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1160] + model_decoder_layers_28_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1161] + model_decoder_layers_28_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1162] + model_decoder_layers_28_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1163] + model_decoder_layers_28_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1164] + model_decoder_layers_28_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1165] + model_decoder_layers_28_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1166] + model_decoder_layers_28_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1167] + model_decoder_layers_28_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1168] + model_decoder_layers_28_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1169] + model_decoder_layers_28_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1173] + model_decoder_layers_28_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1174] + model_decoder_layers_28_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1175] + model_decoder_layers_28_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1176] + model_decoder_layers_28_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1177] + model_decoder_layers_28_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1178] + model_decoder_layers_28_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1179] + model_decoder_layers_28_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1180] + model_decoder_layers_28_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1181] + model_decoder_layers_28_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1182] + model_decoder_layers_28_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1183] + model_decoder_layers_28_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1184] + model_decoder_layers_29_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1185] + model_decoder_layers_29_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1186] + model_decoder_layers_29_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1187] + model_decoder_layers_29_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1188] + model_decoder_layers_29_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1189] + model_decoder_layers_29_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1190] + model_decoder_layers_29_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1191] + model_decoder_layers_29_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1192] + model_decoder_layers_29_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1193] + model_decoder_layers_29_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1197] + model_decoder_layers_29_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1198] + model_decoder_layers_29_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1199] + model_decoder_layers_29_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1200] + model_decoder_layers_29_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1201] + model_decoder_layers_29_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1202] + model_decoder_layers_29_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1203] + model_decoder_layers_29_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1204] + model_decoder_layers_29_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1205] + model_decoder_layers_29_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1206] + model_decoder_layers_29_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1207] + model_decoder_layers_29_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1208] + model_decoder_layers_30_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1209] + model_decoder_layers_30_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1210] + model_decoder_layers_30_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1211] + model_decoder_layers_30_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1212] + model_decoder_layers_30_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1213] + model_decoder_layers_30_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1214] + model_decoder_layers_30_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1215] + model_decoder_layers_30_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1216] + model_decoder_layers_30_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1217] + model_decoder_layers_30_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1221] + model_decoder_layers_30_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1222] + model_decoder_layers_30_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1223] + model_decoder_layers_30_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1224] + model_decoder_layers_30_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1225] + model_decoder_layers_30_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1226] + model_decoder_layers_30_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1227] + model_decoder_layers_30_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1228] + model_decoder_layers_30_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1229] + model_decoder_layers_30_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1230] + model_decoder_layers_30_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1231] + model_decoder_layers_30_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1232] + model_decoder_layers_31_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1233] + model_decoder_layers_31_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1234] + model_decoder_layers_31_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1235] + model_decoder_layers_31_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1236] + model_decoder_layers_31_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1237] + model_decoder_layers_31_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1238] + model_decoder_layers_31_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1239] + model_decoder_layers_31_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1240] + model_decoder_layers_31_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1241] + model_decoder_layers_31_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1245] + model_decoder_layers_31_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1246] + model_decoder_layers_31_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1247] + model_decoder_layers_31_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1248] + model_decoder_layers_31_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1249] + model_decoder_layers_31_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1250] + model_decoder_layers_31_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1251] + model_decoder_layers_31_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1252] + model_decoder_layers_31_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1253] + model_decoder_layers_31_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1254] + model_decoder_layers_31_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1255] + model_decoder_layers_31_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1256] + model_decoder_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1257] + model_decoder_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1258] + reshape707 = R.call_tir(cls.reshape2, (input_ids,), out_sinfo=R.Tensor((batch_size,), dtype="int32")) + take3 = R.call_tir(cls.take, (model_decoder_embed_tokens_weight3, reshape707), out_sinfo=R.Tensor((batch_size, 1280), dtype="float16")) + reshape708 = R.call_tir(cls.reshape3, (take3,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv133: R.Tensor((batch_size,), dtype="int32") = R.call_pure_packed("vm.builtin.attention_kv_cache_get_query_positions", paged_kv_cache, sinfo_args=(R.Tensor((batch_size,), dtype="int32"),)) + take4 = R.call_tir(cls.take1, (model_decoder_embed_positions_weight3, lv133), out_sinfo=R.Tensor((batch_size, 1280), dtype="float16")) + reshape709 = R.call_tir(cls.reshape3, (take4,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add578 = R.call_tir(cls.add, (reshape708, reshape709), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm162 = R.call_tir(cls.layer_norm, (add578, model_decoder_layers_0_self_attn_layer_norm_weight3, model_decoder_layers_0_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv224 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_0_self_attn_q_proj_weight3, layer_norm162, model_decoder_layers_0_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape710 = R.call_tir(cls.reshape4, (lv224,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv65 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_0_self_attn_k_proj_weight3, layer_norm162), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape711 = R.call_tir(cls.reshape4, (lv65,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv225 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_0_self_attn_v_proj_weight3, layer_norm162, model_decoder_layers_0_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape712 = R.call_tir(cls.reshape4, (lv225,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat32 = R.call_tir(cls.concatenate, (reshape710, reshape711, reshape712), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape713 = R.call_tir(cls.reshape5, (concat32,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv134 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), reshape713), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape714 = R.call_tir(cls.reshape6, (lv134,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape715 = R.call_tir(cls.reshape7, (reshape714,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv226 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_0_self_attn_out_proj_weight3, reshape715, model_decoder_layers_0_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add582 = R.call_tir(cls.add, (add578, lv226), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm163 = R.call_tir(cls.layer_norm, (add582, model_decoder_layers_0_encoder_attn_layer_norm_weight3, model_decoder_layers_0_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv227 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_0_encoder_attn_q_proj_weight3, layer_norm163, model_decoder_layers_0_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape716 = R.call_tir(cls.reshape4, (lv227,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape717 = R.call_tir(cls.reshape8, (reshape716,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv135 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), reshape717), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape718 = R.call_tir(cls.reshape6, (lv135,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape719 = R.call_tir(cls.reshape7, (reshape718,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv228 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_0_encoder_attn_out_proj_weight3, reshape719, model_decoder_layers_0_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add585 = R.call_tir(cls.add, (add582, lv228), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm164 = R.call_tir(cls.layer_norm, (add585, model_decoder_layers_0_final_layer_norm_weight3, model_decoder_layers_0_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv32 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_0_fc1_weight3, layer_norm164, model_decoder_layers_0_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv229 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_0_fc2_weight3, lv32, model_decoder_layers_0_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add588 = R.call_tir(cls.add, (add585, lv229), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm165 = R.call_tir(cls.layer_norm, (add588, model_decoder_layers_1_self_attn_layer_norm_weight3, model_decoder_layers_1_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv230 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_1_self_attn_q_proj_weight3, layer_norm165, model_decoder_layers_1_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape720 = R.call_tir(cls.reshape4, (lv230,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv66 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_1_self_attn_k_proj_weight3, layer_norm165), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape721 = R.call_tir(cls.reshape4, (lv66,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv231 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_1_self_attn_v_proj_weight3, layer_norm165, model_decoder_layers_1_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape722 = R.call_tir(cls.reshape4, (lv231,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat33 = R.call_tir(cls.concatenate, (reshape720, reshape721, reshape722), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape723 = R.call_tir(cls.reshape5, (concat33,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv136 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), reshape723), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape724 = R.call_tir(cls.reshape6, (lv136,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape725 = R.call_tir(cls.reshape7, (reshape724,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv232 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_1_self_attn_out_proj_weight3, reshape725, model_decoder_layers_1_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add592 = R.call_tir(cls.add, (add588, lv232), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm166 = R.call_tir(cls.layer_norm, (add592, model_decoder_layers_1_encoder_attn_layer_norm_weight3, model_decoder_layers_1_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv233 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_1_encoder_attn_q_proj_weight3, layer_norm166, model_decoder_layers_1_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape726 = R.call_tir(cls.reshape4, (lv233,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape727 = R.call_tir(cls.reshape8, (reshape726,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv137 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), reshape727), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape728 = R.call_tir(cls.reshape6, (lv137,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape729 = R.call_tir(cls.reshape7, (reshape728,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv234 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_1_encoder_attn_out_proj_weight3, reshape729, model_decoder_layers_1_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add595 = R.call_tir(cls.add, (add592, lv234), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm167 = R.call_tir(cls.layer_norm, (add595, model_decoder_layers_1_final_layer_norm_weight3, model_decoder_layers_1_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv33 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_1_fc1_weight3, layer_norm167, model_decoder_layers_1_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv235 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_1_fc2_weight3, lv33, model_decoder_layers_1_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add598 = R.call_tir(cls.add, (add595, lv235), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm168 = R.call_tir(cls.layer_norm, (add598, model_decoder_layers_2_self_attn_layer_norm_weight3, model_decoder_layers_2_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv236 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_2_self_attn_q_proj_weight3, layer_norm168, model_decoder_layers_2_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape730 = R.call_tir(cls.reshape4, (lv236,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv67 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_2_self_attn_k_proj_weight3, layer_norm168), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape731 = R.call_tir(cls.reshape4, (lv67,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv237 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_2_self_attn_v_proj_weight3, layer_norm168, model_decoder_layers_2_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape732 = R.call_tir(cls.reshape4, (lv237,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat34 = R.call_tir(cls.concatenate, (reshape730, reshape731, reshape732), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape733 = R.call_tir(cls.reshape5, (concat34,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv138 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), reshape733), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape734 = R.call_tir(cls.reshape6, (lv138,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape735 = R.call_tir(cls.reshape7, (reshape734,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv238 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_2_self_attn_out_proj_weight3, reshape735, model_decoder_layers_2_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add602 = R.call_tir(cls.add, (add598, lv238), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm169 = R.call_tir(cls.layer_norm, (add602, model_decoder_layers_2_encoder_attn_layer_norm_weight3, model_decoder_layers_2_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv239 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_2_encoder_attn_q_proj_weight3, layer_norm169, model_decoder_layers_2_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape736 = R.call_tir(cls.reshape4, (lv239,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape737 = R.call_tir(cls.reshape8, (reshape736,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv139 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), reshape737), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape738 = R.call_tir(cls.reshape6, (lv139,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape739 = R.call_tir(cls.reshape7, (reshape738,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv240 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_2_encoder_attn_out_proj_weight3, reshape739, model_decoder_layers_2_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add605 = R.call_tir(cls.add, (add602, lv240), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm170 = R.call_tir(cls.layer_norm, (add605, model_decoder_layers_2_final_layer_norm_weight3, model_decoder_layers_2_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv34 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_2_fc1_weight3, layer_norm170, model_decoder_layers_2_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv241 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_2_fc2_weight3, lv34, model_decoder_layers_2_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add608 = R.call_tir(cls.add, (add605, lv241), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm171 = R.call_tir(cls.layer_norm, (add608, model_decoder_layers_3_self_attn_layer_norm_weight3, model_decoder_layers_3_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv242 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_3_self_attn_q_proj_weight3, layer_norm171, model_decoder_layers_3_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape740 = R.call_tir(cls.reshape4, (lv242,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv68 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_3_self_attn_k_proj_weight3, layer_norm171), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape741 = R.call_tir(cls.reshape4, (lv68,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv243 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_3_self_attn_v_proj_weight3, layer_norm171, model_decoder_layers_3_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape742 = R.call_tir(cls.reshape4, (lv243,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat35 = R.call_tir(cls.concatenate, (reshape740, reshape741, reshape742), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape743 = R.call_tir(cls.reshape5, (concat35,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv140 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), reshape743), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape744 = R.call_tir(cls.reshape6, (lv140,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape745 = R.call_tir(cls.reshape7, (reshape744,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv244 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_3_self_attn_out_proj_weight3, reshape745, model_decoder_layers_3_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add612 = R.call_tir(cls.add, (add608, lv244), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm172 = R.call_tir(cls.layer_norm, (add612, model_decoder_layers_3_encoder_attn_layer_norm_weight3, model_decoder_layers_3_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv245 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_3_encoder_attn_q_proj_weight3, layer_norm172, model_decoder_layers_3_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape746 = R.call_tir(cls.reshape4, (lv245,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape747 = R.call_tir(cls.reshape8, (reshape746,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv141 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), reshape747), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape748 = R.call_tir(cls.reshape6, (lv141,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape749 = R.call_tir(cls.reshape7, (reshape748,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv246 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_3_encoder_attn_out_proj_weight3, reshape749, model_decoder_layers_3_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add615 = R.call_tir(cls.add, (add612, lv246), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm173 = R.call_tir(cls.layer_norm, (add615, model_decoder_layers_3_final_layer_norm_weight3, model_decoder_layers_3_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv35 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_3_fc1_weight3, layer_norm173, model_decoder_layers_3_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv247 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_3_fc2_weight3, lv35, model_decoder_layers_3_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add618 = R.call_tir(cls.add, (add615, lv247), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm174 = R.call_tir(cls.layer_norm, (add618, model_decoder_layers_4_self_attn_layer_norm_weight3, model_decoder_layers_4_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv248 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_4_self_attn_q_proj_weight3, layer_norm174, model_decoder_layers_4_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape750 = R.call_tir(cls.reshape4, (lv248,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv69 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_4_self_attn_k_proj_weight3, layer_norm174), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape751 = R.call_tir(cls.reshape4, (lv69,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv249 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_4_self_attn_v_proj_weight3, layer_norm174, model_decoder_layers_4_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape752 = R.call_tir(cls.reshape4, (lv249,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat36 = R.call_tir(cls.concatenate, (reshape750, reshape751, reshape752), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape753 = R.call_tir(cls.reshape5, (concat36,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv142 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), reshape753), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape754 = R.call_tir(cls.reshape6, (lv142,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape755 = R.call_tir(cls.reshape7, (reshape754,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv250 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_4_self_attn_out_proj_weight3, reshape755, model_decoder_layers_4_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add622 = R.call_tir(cls.add, (add618, lv250), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm175 = R.call_tir(cls.layer_norm, (add622, model_decoder_layers_4_encoder_attn_layer_norm_weight3, model_decoder_layers_4_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv251 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_4_encoder_attn_q_proj_weight3, layer_norm175, model_decoder_layers_4_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape756 = R.call_tir(cls.reshape4, (lv251,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape757 = R.call_tir(cls.reshape8, (reshape756,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv143 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), reshape757), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape758 = R.call_tir(cls.reshape6, (lv143,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape759 = R.call_tir(cls.reshape7, (reshape758,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv252 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_4_encoder_attn_out_proj_weight3, reshape759, model_decoder_layers_4_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add625 = R.call_tir(cls.add, (add622, lv252), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm176 = R.call_tir(cls.layer_norm, (add625, model_decoder_layers_4_final_layer_norm_weight3, model_decoder_layers_4_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv36 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_4_fc1_weight3, layer_norm176, model_decoder_layers_4_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv253 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_4_fc2_weight3, lv36, model_decoder_layers_4_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add628 = R.call_tir(cls.add, (add625, lv253), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm177 = R.call_tir(cls.layer_norm, (add628, model_decoder_layers_5_self_attn_layer_norm_weight3, model_decoder_layers_5_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv254 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_5_self_attn_q_proj_weight3, layer_norm177, model_decoder_layers_5_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape760 = R.call_tir(cls.reshape4, (lv254,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv70 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_5_self_attn_k_proj_weight3, layer_norm177), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape761 = R.call_tir(cls.reshape4, (lv70,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv255 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_5_self_attn_v_proj_weight3, layer_norm177, model_decoder_layers_5_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape762 = R.call_tir(cls.reshape4, (lv255,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat37 = R.call_tir(cls.concatenate, (reshape760, reshape761, reshape762), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape763 = R.call_tir(cls.reshape5, (concat37,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv144 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), reshape763), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape764 = R.call_tir(cls.reshape6, (lv144,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape765 = R.call_tir(cls.reshape7, (reshape764,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv256 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_5_self_attn_out_proj_weight3, reshape765, model_decoder_layers_5_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add632 = R.call_tir(cls.add, (add628, lv256), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm178 = R.call_tir(cls.layer_norm, (add632, model_decoder_layers_5_encoder_attn_layer_norm_weight3, model_decoder_layers_5_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv257 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_5_encoder_attn_q_proj_weight3, layer_norm178, model_decoder_layers_5_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape766 = R.call_tir(cls.reshape4, (lv257,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape767 = R.call_tir(cls.reshape8, (reshape766,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv145 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), reshape767), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape768 = R.call_tir(cls.reshape6, (lv145,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape769 = R.call_tir(cls.reshape7, (reshape768,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv258 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_5_encoder_attn_out_proj_weight3, reshape769, model_decoder_layers_5_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add635 = R.call_tir(cls.add, (add632, lv258), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm179 = R.call_tir(cls.layer_norm, (add635, model_decoder_layers_5_final_layer_norm_weight3, model_decoder_layers_5_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv37 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_5_fc1_weight3, layer_norm179, model_decoder_layers_5_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv259 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_5_fc2_weight3, lv37, model_decoder_layers_5_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add638 = R.call_tir(cls.add, (add635, lv259), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm180 = R.call_tir(cls.layer_norm, (add638, model_decoder_layers_6_self_attn_layer_norm_weight3, model_decoder_layers_6_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv260 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_6_self_attn_q_proj_weight3, layer_norm180, model_decoder_layers_6_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape770 = R.call_tir(cls.reshape4, (lv260,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv71 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_6_self_attn_k_proj_weight3, layer_norm180), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape771 = R.call_tir(cls.reshape4, (lv71,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv261 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_6_self_attn_v_proj_weight3, layer_norm180, model_decoder_layers_6_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape772 = R.call_tir(cls.reshape4, (lv261,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat38 = R.call_tir(cls.concatenate, (reshape770, reshape771, reshape772), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape773 = R.call_tir(cls.reshape5, (concat38,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv146 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), reshape773), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape774 = R.call_tir(cls.reshape6, (lv146,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape775 = R.call_tir(cls.reshape7, (reshape774,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv262 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_6_self_attn_out_proj_weight3, reshape775, model_decoder_layers_6_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add642 = R.call_tir(cls.add, (add638, lv262), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm181 = R.call_tir(cls.layer_norm, (add642, model_decoder_layers_6_encoder_attn_layer_norm_weight3, model_decoder_layers_6_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv263 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_6_encoder_attn_q_proj_weight3, layer_norm181, model_decoder_layers_6_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape776 = R.call_tir(cls.reshape4, (lv263,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape777 = R.call_tir(cls.reshape8, (reshape776,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv147 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), reshape777), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape778 = R.call_tir(cls.reshape6, (lv147,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape779 = R.call_tir(cls.reshape7, (reshape778,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv264 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_6_encoder_attn_out_proj_weight3, reshape779, model_decoder_layers_6_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add645 = R.call_tir(cls.add, (add642, lv264), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm182 = R.call_tir(cls.layer_norm, (add645, model_decoder_layers_6_final_layer_norm_weight3, model_decoder_layers_6_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv38 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_6_fc1_weight3, layer_norm182, model_decoder_layers_6_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv265 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_6_fc2_weight3, lv38, model_decoder_layers_6_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add648 = R.call_tir(cls.add, (add645, lv265), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm183 = R.call_tir(cls.layer_norm, (add648, model_decoder_layers_7_self_attn_layer_norm_weight3, model_decoder_layers_7_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv266 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_7_self_attn_q_proj_weight3, layer_norm183, model_decoder_layers_7_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape780 = R.call_tir(cls.reshape4, (lv266,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv72 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_7_self_attn_k_proj_weight3, layer_norm183), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape781 = R.call_tir(cls.reshape4, (lv72,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv267 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_7_self_attn_v_proj_weight3, layer_norm183, model_decoder_layers_7_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape782 = R.call_tir(cls.reshape4, (lv267,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat39 = R.call_tir(cls.concatenate, (reshape780, reshape781, reshape782), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape783 = R.call_tir(cls.reshape5, (concat39,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv148 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), reshape783), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape784 = R.call_tir(cls.reshape6, (lv148,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape785 = R.call_tir(cls.reshape7, (reshape784,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv268 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_7_self_attn_out_proj_weight3, reshape785, model_decoder_layers_7_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add652 = R.call_tir(cls.add, (add648, lv268), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm184 = R.call_tir(cls.layer_norm, (add652, model_decoder_layers_7_encoder_attn_layer_norm_weight3, model_decoder_layers_7_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv269 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_7_encoder_attn_q_proj_weight3, layer_norm184, model_decoder_layers_7_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape786 = R.call_tir(cls.reshape4, (lv269,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape787 = R.call_tir(cls.reshape8, (reshape786,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv149 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), reshape787), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape788 = R.call_tir(cls.reshape6, (lv149,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape789 = R.call_tir(cls.reshape7, (reshape788,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv270 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_7_encoder_attn_out_proj_weight3, reshape789, model_decoder_layers_7_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add655 = R.call_tir(cls.add, (add652, lv270), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm185 = R.call_tir(cls.layer_norm, (add655, model_decoder_layers_7_final_layer_norm_weight3, model_decoder_layers_7_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv39 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_7_fc1_weight3, layer_norm185, model_decoder_layers_7_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv271 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_7_fc2_weight3, lv39, model_decoder_layers_7_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add658 = R.call_tir(cls.add, (add655, lv271), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm186 = R.call_tir(cls.layer_norm, (add658, model_decoder_layers_8_self_attn_layer_norm_weight3, model_decoder_layers_8_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv272 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_8_self_attn_q_proj_weight3, layer_norm186, model_decoder_layers_8_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape790 = R.call_tir(cls.reshape4, (lv272,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv73 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_8_self_attn_k_proj_weight3, layer_norm186), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape791 = R.call_tir(cls.reshape4, (lv73,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv273 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_8_self_attn_v_proj_weight3, layer_norm186, model_decoder_layers_8_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape792 = R.call_tir(cls.reshape4, (lv273,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat40 = R.call_tir(cls.concatenate, (reshape790, reshape791, reshape792), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape793 = R.call_tir(cls.reshape5, (concat40,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv150 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), reshape793), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape794 = R.call_tir(cls.reshape6, (lv150,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape795 = R.call_tir(cls.reshape7, (reshape794,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv274 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_8_self_attn_out_proj_weight3, reshape795, model_decoder_layers_8_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add662 = R.call_tir(cls.add, (add658, lv274), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm187 = R.call_tir(cls.layer_norm, (add662, model_decoder_layers_8_encoder_attn_layer_norm_weight3, model_decoder_layers_8_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv275 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_8_encoder_attn_q_proj_weight3, layer_norm187, model_decoder_layers_8_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape796 = R.call_tir(cls.reshape4, (lv275,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape797 = R.call_tir(cls.reshape8, (reshape796,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv151 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), reshape797), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape798 = R.call_tir(cls.reshape6, (lv151,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape799 = R.call_tir(cls.reshape7, (reshape798,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv276 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_8_encoder_attn_out_proj_weight3, reshape799, model_decoder_layers_8_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add665 = R.call_tir(cls.add, (add662, lv276), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm188 = R.call_tir(cls.layer_norm, (add665, model_decoder_layers_8_final_layer_norm_weight3, model_decoder_layers_8_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv40 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_8_fc1_weight3, layer_norm188, model_decoder_layers_8_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv277 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_8_fc2_weight3, lv40, model_decoder_layers_8_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add668 = R.call_tir(cls.add, (add665, lv277), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm189 = R.call_tir(cls.layer_norm, (add668, model_decoder_layers_9_self_attn_layer_norm_weight3, model_decoder_layers_9_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv278 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_9_self_attn_q_proj_weight3, layer_norm189, model_decoder_layers_9_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape800 = R.call_tir(cls.reshape4, (lv278,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv74 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_9_self_attn_k_proj_weight3, layer_norm189), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape801 = R.call_tir(cls.reshape4, (lv74,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv279 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_9_self_attn_v_proj_weight3, layer_norm189, model_decoder_layers_9_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape802 = R.call_tir(cls.reshape4, (lv279,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat41 = R.call_tir(cls.concatenate, (reshape800, reshape801, reshape802), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape803 = R.call_tir(cls.reshape5, (concat41,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv152 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), reshape803), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape804 = R.call_tir(cls.reshape6, (lv152,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape805 = R.call_tir(cls.reshape7, (reshape804,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv280 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_9_self_attn_out_proj_weight3, reshape805, model_decoder_layers_9_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add672 = R.call_tir(cls.add, (add668, lv280), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm190 = R.call_tir(cls.layer_norm, (add672, model_decoder_layers_9_encoder_attn_layer_norm_weight3, model_decoder_layers_9_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv281 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_9_encoder_attn_q_proj_weight3, layer_norm190, model_decoder_layers_9_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape806 = R.call_tir(cls.reshape4, (lv281,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape807 = R.call_tir(cls.reshape8, (reshape806,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv153 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), reshape807), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape808 = R.call_tir(cls.reshape6, (lv153,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape809 = R.call_tir(cls.reshape7, (reshape808,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv282 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_9_encoder_attn_out_proj_weight3, reshape809, model_decoder_layers_9_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add675 = R.call_tir(cls.add, (add672, lv282), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm191 = R.call_tir(cls.layer_norm, (add675, model_decoder_layers_9_final_layer_norm_weight3, model_decoder_layers_9_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv41 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_9_fc1_weight3, layer_norm191, model_decoder_layers_9_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv283 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_9_fc2_weight3, lv41, model_decoder_layers_9_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add678 = R.call_tir(cls.add, (add675, lv283), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm192 = R.call_tir(cls.layer_norm, (add678, model_decoder_layers_10_self_attn_layer_norm_weight3, model_decoder_layers_10_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv284 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_10_self_attn_q_proj_weight3, layer_norm192, model_decoder_layers_10_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape810 = R.call_tir(cls.reshape4, (lv284,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv75 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_10_self_attn_k_proj_weight3, layer_norm192), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape811 = R.call_tir(cls.reshape4, (lv75,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv285 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_10_self_attn_v_proj_weight3, layer_norm192, model_decoder_layers_10_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape812 = R.call_tir(cls.reshape4, (lv285,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat42 = R.call_tir(cls.concatenate, (reshape810, reshape811, reshape812), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape813 = R.call_tir(cls.reshape5, (concat42,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv154 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), reshape813), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape814 = R.call_tir(cls.reshape6, (lv154,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape815 = R.call_tir(cls.reshape7, (reshape814,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv286 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_10_self_attn_out_proj_weight3, reshape815, model_decoder_layers_10_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add682 = R.call_tir(cls.add, (add678, lv286), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm193 = R.call_tir(cls.layer_norm, (add682, model_decoder_layers_10_encoder_attn_layer_norm_weight3, model_decoder_layers_10_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv287 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_10_encoder_attn_q_proj_weight3, layer_norm193, model_decoder_layers_10_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape816 = R.call_tir(cls.reshape4, (lv287,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape817 = R.call_tir(cls.reshape8, (reshape816,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv155 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), reshape817), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape818 = R.call_tir(cls.reshape6, (lv155,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape819 = R.call_tir(cls.reshape7, (reshape818,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv288 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_10_encoder_attn_out_proj_weight3, reshape819, model_decoder_layers_10_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add685 = R.call_tir(cls.add, (add682, lv288), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm194 = R.call_tir(cls.layer_norm, (add685, model_decoder_layers_10_final_layer_norm_weight3, model_decoder_layers_10_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv42 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_10_fc1_weight3, layer_norm194, model_decoder_layers_10_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv289 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_10_fc2_weight3, lv42, model_decoder_layers_10_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add688 = R.call_tir(cls.add, (add685, lv289), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm195 = R.call_tir(cls.layer_norm, (add688, model_decoder_layers_11_self_attn_layer_norm_weight3, model_decoder_layers_11_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv290 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_11_self_attn_q_proj_weight3, layer_norm195, model_decoder_layers_11_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape820 = R.call_tir(cls.reshape4, (lv290,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv76 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_11_self_attn_k_proj_weight3, layer_norm195), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape821 = R.call_tir(cls.reshape4, (lv76,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv291 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_11_self_attn_v_proj_weight3, layer_norm195, model_decoder_layers_11_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape822 = R.call_tir(cls.reshape4, (lv291,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat43 = R.call_tir(cls.concatenate, (reshape820, reshape821, reshape822), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape823 = R.call_tir(cls.reshape5, (concat43,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv156 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), reshape823), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape824 = R.call_tir(cls.reshape6, (lv156,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape825 = R.call_tir(cls.reshape7, (reshape824,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv292 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_11_self_attn_out_proj_weight3, reshape825, model_decoder_layers_11_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add692 = R.call_tir(cls.add, (add688, lv292), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm196 = R.call_tir(cls.layer_norm, (add692, model_decoder_layers_11_encoder_attn_layer_norm_weight3, model_decoder_layers_11_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv293 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_11_encoder_attn_q_proj_weight3, layer_norm196, model_decoder_layers_11_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape826 = R.call_tir(cls.reshape4, (lv293,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape827 = R.call_tir(cls.reshape8, (reshape826,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv157 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), reshape827), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape828 = R.call_tir(cls.reshape6, (lv157,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape829 = R.call_tir(cls.reshape7, (reshape828,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv294 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_11_encoder_attn_out_proj_weight3, reshape829, model_decoder_layers_11_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add695 = R.call_tir(cls.add, (add692, lv294), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm197 = R.call_tir(cls.layer_norm, (add695, model_decoder_layers_11_final_layer_norm_weight3, model_decoder_layers_11_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv43 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_11_fc1_weight3, layer_norm197, model_decoder_layers_11_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv295 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_11_fc2_weight3, lv43, model_decoder_layers_11_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add698 = R.call_tir(cls.add, (add695, lv295), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm198 = R.call_tir(cls.layer_norm, (add698, model_decoder_layers_12_self_attn_layer_norm_weight3, model_decoder_layers_12_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv296 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_12_self_attn_q_proj_weight3, layer_norm198, model_decoder_layers_12_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape830 = R.call_tir(cls.reshape4, (lv296,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv77 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_12_self_attn_k_proj_weight3, layer_norm198), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape831 = R.call_tir(cls.reshape4, (lv77,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv297 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_12_self_attn_v_proj_weight3, layer_norm198, model_decoder_layers_12_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape832 = R.call_tir(cls.reshape4, (lv297,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat44 = R.call_tir(cls.concatenate, (reshape830, reshape831, reshape832), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape833 = R.call_tir(cls.reshape5, (concat44,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv158 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), reshape833), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape834 = R.call_tir(cls.reshape6, (lv158,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape835 = R.call_tir(cls.reshape7, (reshape834,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv298 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_12_self_attn_out_proj_weight3, reshape835, model_decoder_layers_12_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add702 = R.call_tir(cls.add, (add698, lv298), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm199 = R.call_tir(cls.layer_norm, (add702, model_decoder_layers_12_encoder_attn_layer_norm_weight3, model_decoder_layers_12_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv299 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_12_encoder_attn_q_proj_weight3, layer_norm199, model_decoder_layers_12_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape836 = R.call_tir(cls.reshape4, (lv299,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape837 = R.call_tir(cls.reshape8, (reshape836,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv159 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), reshape837), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape838 = R.call_tir(cls.reshape6, (lv159,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape839 = R.call_tir(cls.reshape7, (reshape838,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv300 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_12_encoder_attn_out_proj_weight3, reshape839, model_decoder_layers_12_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add705 = R.call_tir(cls.add, (add702, lv300), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm200 = R.call_tir(cls.layer_norm, (add705, model_decoder_layers_12_final_layer_norm_weight3, model_decoder_layers_12_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv44 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_12_fc1_weight3, layer_norm200, model_decoder_layers_12_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv301 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_12_fc2_weight3, lv44, model_decoder_layers_12_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add708 = R.call_tir(cls.add, (add705, lv301), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm201 = R.call_tir(cls.layer_norm, (add708, model_decoder_layers_13_self_attn_layer_norm_weight3, model_decoder_layers_13_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv302 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_13_self_attn_q_proj_weight3, layer_norm201, model_decoder_layers_13_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape840 = R.call_tir(cls.reshape4, (lv302,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv78 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_13_self_attn_k_proj_weight3, layer_norm201), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape841 = R.call_tir(cls.reshape4, (lv78,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv303 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_13_self_attn_v_proj_weight3, layer_norm201, model_decoder_layers_13_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape842 = R.call_tir(cls.reshape4, (lv303,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat45 = R.call_tir(cls.concatenate, (reshape840, reshape841, reshape842), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape843 = R.call_tir(cls.reshape5, (concat45,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv160 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), reshape843), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape844 = R.call_tir(cls.reshape6, (lv160,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape845 = R.call_tir(cls.reshape7, (reshape844,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv304 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_13_self_attn_out_proj_weight3, reshape845, model_decoder_layers_13_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add712 = R.call_tir(cls.add, (add708, lv304), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm202 = R.call_tir(cls.layer_norm, (add712, model_decoder_layers_13_encoder_attn_layer_norm_weight3, model_decoder_layers_13_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv305 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_13_encoder_attn_q_proj_weight3, layer_norm202, model_decoder_layers_13_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape846 = R.call_tir(cls.reshape4, (lv305,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape847 = R.call_tir(cls.reshape8, (reshape846,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv161 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), reshape847), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape848 = R.call_tir(cls.reshape6, (lv161,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape849 = R.call_tir(cls.reshape7, (reshape848,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv306 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_13_encoder_attn_out_proj_weight3, reshape849, model_decoder_layers_13_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add715 = R.call_tir(cls.add, (add712, lv306), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm203 = R.call_tir(cls.layer_norm, (add715, model_decoder_layers_13_final_layer_norm_weight3, model_decoder_layers_13_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv45 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_13_fc1_weight3, layer_norm203, model_decoder_layers_13_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv307 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_13_fc2_weight3, lv45, model_decoder_layers_13_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add718 = R.call_tir(cls.add, (add715, lv307), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm204 = R.call_tir(cls.layer_norm, (add718, model_decoder_layers_14_self_attn_layer_norm_weight3, model_decoder_layers_14_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv308 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_14_self_attn_q_proj_weight3, layer_norm204, model_decoder_layers_14_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape850 = R.call_tir(cls.reshape4, (lv308,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv79 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_14_self_attn_k_proj_weight3, layer_norm204), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape851 = R.call_tir(cls.reshape4, (lv79,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv309 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_14_self_attn_v_proj_weight3, layer_norm204, model_decoder_layers_14_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape852 = R.call_tir(cls.reshape4, (lv309,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat46 = R.call_tir(cls.concatenate, (reshape850, reshape851, reshape852), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape853 = R.call_tir(cls.reshape5, (concat46,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv162 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), reshape853), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape854 = R.call_tir(cls.reshape6, (lv162,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape855 = R.call_tir(cls.reshape7, (reshape854,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv310 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_14_self_attn_out_proj_weight3, reshape855, model_decoder_layers_14_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add722 = R.call_tir(cls.add, (add718, lv310), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm205 = R.call_tir(cls.layer_norm, (add722, model_decoder_layers_14_encoder_attn_layer_norm_weight3, model_decoder_layers_14_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv311 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_14_encoder_attn_q_proj_weight3, layer_norm205, model_decoder_layers_14_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape856 = R.call_tir(cls.reshape4, (lv311,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape857 = R.call_tir(cls.reshape8, (reshape856,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv163 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), reshape857), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape858 = R.call_tir(cls.reshape6, (lv163,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape859 = R.call_tir(cls.reshape7, (reshape858,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv312 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_14_encoder_attn_out_proj_weight3, reshape859, model_decoder_layers_14_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add725 = R.call_tir(cls.add, (add722, lv312), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm206 = R.call_tir(cls.layer_norm, (add725, model_decoder_layers_14_final_layer_norm_weight3, model_decoder_layers_14_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv46 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_14_fc1_weight3, layer_norm206, model_decoder_layers_14_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv313 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_14_fc2_weight3, lv46, model_decoder_layers_14_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add728 = R.call_tir(cls.add, (add725, lv313), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm207 = R.call_tir(cls.layer_norm, (add728, model_decoder_layers_15_self_attn_layer_norm_weight3, model_decoder_layers_15_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv314 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_15_self_attn_q_proj_weight3, layer_norm207, model_decoder_layers_15_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape860 = R.call_tir(cls.reshape4, (lv314,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv80 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_15_self_attn_k_proj_weight3, layer_norm207), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape861 = R.call_tir(cls.reshape4, (lv80,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv315 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_15_self_attn_v_proj_weight3, layer_norm207, model_decoder_layers_15_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape862 = R.call_tir(cls.reshape4, (lv315,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat47 = R.call_tir(cls.concatenate, (reshape860, reshape861, reshape862), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape863 = R.call_tir(cls.reshape5, (concat47,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv164 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), reshape863), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape864 = R.call_tir(cls.reshape6, (lv164,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape865 = R.call_tir(cls.reshape7, (reshape864,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv316 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_15_self_attn_out_proj_weight3, reshape865, model_decoder_layers_15_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add732 = R.call_tir(cls.add, (add728, lv316), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm208 = R.call_tir(cls.layer_norm, (add732, model_decoder_layers_15_encoder_attn_layer_norm_weight3, model_decoder_layers_15_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv317 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_15_encoder_attn_q_proj_weight3, layer_norm208, model_decoder_layers_15_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape866 = R.call_tir(cls.reshape4, (lv317,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape867 = R.call_tir(cls.reshape8, (reshape866,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv165 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), reshape867), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape868 = R.call_tir(cls.reshape6, (lv165,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape869 = R.call_tir(cls.reshape7, (reshape868,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv318 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_15_encoder_attn_out_proj_weight3, reshape869, model_decoder_layers_15_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add735 = R.call_tir(cls.add, (add732, lv318), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm209 = R.call_tir(cls.layer_norm, (add735, model_decoder_layers_15_final_layer_norm_weight3, model_decoder_layers_15_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv47 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_15_fc1_weight3, layer_norm209, model_decoder_layers_15_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv319 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_15_fc2_weight3, lv47, model_decoder_layers_15_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add738 = R.call_tir(cls.add, (add735, lv319), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm210 = R.call_tir(cls.layer_norm, (add738, model_decoder_layers_16_self_attn_layer_norm_weight3, model_decoder_layers_16_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv320 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_16_self_attn_q_proj_weight3, layer_norm210, model_decoder_layers_16_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape870 = R.call_tir(cls.reshape4, (lv320,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv81 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_16_self_attn_k_proj_weight3, layer_norm210), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape871 = R.call_tir(cls.reshape4, (lv81,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv321 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_16_self_attn_v_proj_weight3, layer_norm210, model_decoder_layers_16_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape872 = R.call_tir(cls.reshape4, (lv321,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat48 = R.call_tir(cls.concatenate, (reshape870, reshape871, reshape872), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape873 = R.call_tir(cls.reshape5, (concat48,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv166 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), reshape873), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape874 = R.call_tir(cls.reshape6, (lv166,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape875 = R.call_tir(cls.reshape7, (reshape874,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv322 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_16_self_attn_out_proj_weight3, reshape875, model_decoder_layers_16_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add742 = R.call_tir(cls.add, (add738, lv322), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm211 = R.call_tir(cls.layer_norm, (add742, model_decoder_layers_16_encoder_attn_layer_norm_weight3, model_decoder_layers_16_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv323 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_16_encoder_attn_q_proj_weight3, layer_norm211, model_decoder_layers_16_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape876 = R.call_tir(cls.reshape4, (lv323,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape877 = R.call_tir(cls.reshape8, (reshape876,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv167 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), reshape877), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape878 = R.call_tir(cls.reshape6, (lv167,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape879 = R.call_tir(cls.reshape7, (reshape878,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv324 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_16_encoder_attn_out_proj_weight3, reshape879, model_decoder_layers_16_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add745 = R.call_tir(cls.add, (add742, lv324), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm212 = R.call_tir(cls.layer_norm, (add745, model_decoder_layers_16_final_layer_norm_weight3, model_decoder_layers_16_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv48 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_16_fc1_weight3, layer_norm212, model_decoder_layers_16_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv325 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_16_fc2_weight3, lv48, model_decoder_layers_16_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add748 = R.call_tir(cls.add, (add745, lv325), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm213 = R.call_tir(cls.layer_norm, (add748, model_decoder_layers_17_self_attn_layer_norm_weight3, model_decoder_layers_17_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv326 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_17_self_attn_q_proj_weight3, layer_norm213, model_decoder_layers_17_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape880 = R.call_tir(cls.reshape4, (lv326,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv82 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_17_self_attn_k_proj_weight3, layer_norm213), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape881 = R.call_tir(cls.reshape4, (lv82,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv327 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_17_self_attn_v_proj_weight3, layer_norm213, model_decoder_layers_17_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape882 = R.call_tir(cls.reshape4, (lv327,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat49 = R.call_tir(cls.concatenate, (reshape880, reshape881, reshape882), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape883 = R.call_tir(cls.reshape5, (concat49,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv168 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), reshape883), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape884 = R.call_tir(cls.reshape6, (lv168,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape885 = R.call_tir(cls.reshape7, (reshape884,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv328 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_17_self_attn_out_proj_weight3, reshape885, model_decoder_layers_17_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add752 = R.call_tir(cls.add, (add748, lv328), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm214 = R.call_tir(cls.layer_norm, (add752, model_decoder_layers_17_encoder_attn_layer_norm_weight3, model_decoder_layers_17_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv329 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_17_encoder_attn_q_proj_weight3, layer_norm214, model_decoder_layers_17_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape886 = R.call_tir(cls.reshape4, (lv329,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape887 = R.call_tir(cls.reshape8, (reshape886,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv169 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), reshape887), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape888 = R.call_tir(cls.reshape6, (lv169,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape889 = R.call_tir(cls.reshape7, (reshape888,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv330 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_17_encoder_attn_out_proj_weight3, reshape889, model_decoder_layers_17_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add755 = R.call_tir(cls.add, (add752, lv330), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm215 = R.call_tir(cls.layer_norm, (add755, model_decoder_layers_17_final_layer_norm_weight3, model_decoder_layers_17_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv49 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_17_fc1_weight3, layer_norm215, model_decoder_layers_17_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv331 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_17_fc2_weight3, lv49, model_decoder_layers_17_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add758 = R.call_tir(cls.add, (add755, lv331), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm216 = R.call_tir(cls.layer_norm, (add758, model_decoder_layers_18_self_attn_layer_norm_weight3, model_decoder_layers_18_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv332 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_18_self_attn_q_proj_weight3, layer_norm216, model_decoder_layers_18_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape890 = R.call_tir(cls.reshape4, (lv332,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv83 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_18_self_attn_k_proj_weight3, layer_norm216), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape891 = R.call_tir(cls.reshape4, (lv83,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv333 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_18_self_attn_v_proj_weight3, layer_norm216, model_decoder_layers_18_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape892 = R.call_tir(cls.reshape4, (lv333,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat50 = R.call_tir(cls.concatenate, (reshape890, reshape891, reshape892), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape893 = R.call_tir(cls.reshape5, (concat50,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv170 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), reshape893), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape894 = R.call_tir(cls.reshape6, (lv170,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape895 = R.call_tir(cls.reshape7, (reshape894,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv334 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_18_self_attn_out_proj_weight3, reshape895, model_decoder_layers_18_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add762 = R.call_tir(cls.add, (add758, lv334), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm217 = R.call_tir(cls.layer_norm, (add762, model_decoder_layers_18_encoder_attn_layer_norm_weight3, model_decoder_layers_18_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv335 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_18_encoder_attn_q_proj_weight3, layer_norm217, model_decoder_layers_18_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape896 = R.call_tir(cls.reshape4, (lv335,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape897 = R.call_tir(cls.reshape8, (reshape896,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv171 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), reshape897), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape898 = R.call_tir(cls.reshape6, (lv171,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape899 = R.call_tir(cls.reshape7, (reshape898,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv336 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_18_encoder_attn_out_proj_weight3, reshape899, model_decoder_layers_18_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add765 = R.call_tir(cls.add, (add762, lv336), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm218 = R.call_tir(cls.layer_norm, (add765, model_decoder_layers_18_final_layer_norm_weight3, model_decoder_layers_18_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv50 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_18_fc1_weight3, layer_norm218, model_decoder_layers_18_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv337 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_18_fc2_weight3, lv50, model_decoder_layers_18_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add768 = R.call_tir(cls.add, (add765, lv337), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm219 = R.call_tir(cls.layer_norm, (add768, model_decoder_layers_19_self_attn_layer_norm_weight3, model_decoder_layers_19_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv338 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_19_self_attn_q_proj_weight3, layer_norm219, model_decoder_layers_19_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape900 = R.call_tir(cls.reshape4, (lv338,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv84 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_19_self_attn_k_proj_weight3, layer_norm219), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape901 = R.call_tir(cls.reshape4, (lv84,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv339 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_19_self_attn_v_proj_weight3, layer_norm219, model_decoder_layers_19_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape902 = R.call_tir(cls.reshape4, (lv339,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat51 = R.call_tir(cls.concatenate, (reshape900, reshape901, reshape902), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape903 = R.call_tir(cls.reshape5, (concat51,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv172 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), reshape903), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape904 = R.call_tir(cls.reshape6, (lv172,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape905 = R.call_tir(cls.reshape7, (reshape904,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv340 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_19_self_attn_out_proj_weight3, reshape905, model_decoder_layers_19_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add772 = R.call_tir(cls.add, (add768, lv340), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm220 = R.call_tir(cls.layer_norm, (add772, model_decoder_layers_19_encoder_attn_layer_norm_weight3, model_decoder_layers_19_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv341 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_19_encoder_attn_q_proj_weight3, layer_norm220, model_decoder_layers_19_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape906 = R.call_tir(cls.reshape4, (lv341,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape907 = R.call_tir(cls.reshape8, (reshape906,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv173 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), reshape907), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape908 = R.call_tir(cls.reshape6, (lv173,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape909 = R.call_tir(cls.reshape7, (reshape908,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv342 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_19_encoder_attn_out_proj_weight3, reshape909, model_decoder_layers_19_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add775 = R.call_tir(cls.add, (add772, lv342), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm221 = R.call_tir(cls.layer_norm, (add775, model_decoder_layers_19_final_layer_norm_weight3, model_decoder_layers_19_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv51 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_19_fc1_weight3, layer_norm221, model_decoder_layers_19_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv343 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_19_fc2_weight3, lv51, model_decoder_layers_19_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add778 = R.call_tir(cls.add, (add775, lv343), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm222 = R.call_tir(cls.layer_norm, (add778, model_decoder_layers_20_self_attn_layer_norm_weight3, model_decoder_layers_20_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv344 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_20_self_attn_q_proj_weight3, layer_norm222, model_decoder_layers_20_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape910 = R.call_tir(cls.reshape4, (lv344,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv85 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_20_self_attn_k_proj_weight3, layer_norm222), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape911 = R.call_tir(cls.reshape4, (lv85,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv345 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_20_self_attn_v_proj_weight3, layer_norm222, model_decoder_layers_20_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape912 = R.call_tir(cls.reshape4, (lv345,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat52 = R.call_tir(cls.concatenate, (reshape910, reshape911, reshape912), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape913 = R.call_tir(cls.reshape5, (concat52,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv174 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), reshape913), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape914 = R.call_tir(cls.reshape6, (lv174,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape915 = R.call_tir(cls.reshape7, (reshape914,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv346 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_20_self_attn_out_proj_weight3, reshape915, model_decoder_layers_20_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add782 = R.call_tir(cls.add, (add778, lv346), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm223 = R.call_tir(cls.layer_norm, (add782, model_decoder_layers_20_encoder_attn_layer_norm_weight3, model_decoder_layers_20_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv347 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_20_encoder_attn_q_proj_weight3, layer_norm223, model_decoder_layers_20_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape916 = R.call_tir(cls.reshape4, (lv347,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape917 = R.call_tir(cls.reshape8, (reshape916,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv175 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), reshape917), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape918 = R.call_tir(cls.reshape6, (lv175,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape919 = R.call_tir(cls.reshape7, (reshape918,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv348 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_20_encoder_attn_out_proj_weight3, reshape919, model_decoder_layers_20_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add785 = R.call_tir(cls.add, (add782, lv348), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm224 = R.call_tir(cls.layer_norm, (add785, model_decoder_layers_20_final_layer_norm_weight3, model_decoder_layers_20_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv52 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_20_fc1_weight3, layer_norm224, model_decoder_layers_20_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv349 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_20_fc2_weight3, lv52, model_decoder_layers_20_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add788 = R.call_tir(cls.add, (add785, lv349), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm225 = R.call_tir(cls.layer_norm, (add788, model_decoder_layers_21_self_attn_layer_norm_weight3, model_decoder_layers_21_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv350 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_21_self_attn_q_proj_weight3, layer_norm225, model_decoder_layers_21_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape920 = R.call_tir(cls.reshape4, (lv350,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv86 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_21_self_attn_k_proj_weight3, layer_norm225), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape921 = R.call_tir(cls.reshape4, (lv86,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv351 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_21_self_attn_v_proj_weight3, layer_norm225, model_decoder_layers_21_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape922 = R.call_tir(cls.reshape4, (lv351,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat53 = R.call_tir(cls.concatenate, (reshape920, reshape921, reshape922), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape923 = R.call_tir(cls.reshape5, (concat53,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv176 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), reshape923), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape924 = R.call_tir(cls.reshape6, (lv176,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape925 = R.call_tir(cls.reshape7, (reshape924,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv352 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_21_self_attn_out_proj_weight3, reshape925, model_decoder_layers_21_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add792 = R.call_tir(cls.add, (add788, lv352), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm226 = R.call_tir(cls.layer_norm, (add792, model_decoder_layers_21_encoder_attn_layer_norm_weight3, model_decoder_layers_21_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv353 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_21_encoder_attn_q_proj_weight3, layer_norm226, model_decoder_layers_21_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape926 = R.call_tir(cls.reshape4, (lv353,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape927 = R.call_tir(cls.reshape8, (reshape926,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv177 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), reshape927), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape928 = R.call_tir(cls.reshape6, (lv177,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape929 = R.call_tir(cls.reshape7, (reshape928,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv354 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_21_encoder_attn_out_proj_weight3, reshape929, model_decoder_layers_21_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add795 = R.call_tir(cls.add, (add792, lv354), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm227 = R.call_tir(cls.layer_norm, (add795, model_decoder_layers_21_final_layer_norm_weight3, model_decoder_layers_21_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv53 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_21_fc1_weight3, layer_norm227, model_decoder_layers_21_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv355 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_21_fc2_weight3, lv53, model_decoder_layers_21_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add798 = R.call_tir(cls.add, (add795, lv355), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm228 = R.call_tir(cls.layer_norm, (add798, model_decoder_layers_22_self_attn_layer_norm_weight3, model_decoder_layers_22_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv356 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_22_self_attn_q_proj_weight3, layer_norm228, model_decoder_layers_22_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape930 = R.call_tir(cls.reshape4, (lv356,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv87 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_22_self_attn_k_proj_weight3, layer_norm228), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape931 = R.call_tir(cls.reshape4, (lv87,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv357 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_22_self_attn_v_proj_weight3, layer_norm228, model_decoder_layers_22_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape932 = R.call_tir(cls.reshape4, (lv357,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat54 = R.call_tir(cls.concatenate, (reshape930, reshape931, reshape932), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape933 = R.call_tir(cls.reshape5, (concat54,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv178 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), reshape933), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape934 = R.call_tir(cls.reshape6, (lv178,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape935 = R.call_tir(cls.reshape7, (reshape934,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv358 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_22_self_attn_out_proj_weight3, reshape935, model_decoder_layers_22_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add802 = R.call_tir(cls.add, (add798, lv358), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm229 = R.call_tir(cls.layer_norm, (add802, model_decoder_layers_22_encoder_attn_layer_norm_weight3, model_decoder_layers_22_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv359 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_22_encoder_attn_q_proj_weight3, layer_norm229, model_decoder_layers_22_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape936 = R.call_tir(cls.reshape4, (lv359,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape937 = R.call_tir(cls.reshape8, (reshape936,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv179 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), reshape937), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape938 = R.call_tir(cls.reshape6, (lv179,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape939 = R.call_tir(cls.reshape7, (reshape938,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv360 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_22_encoder_attn_out_proj_weight3, reshape939, model_decoder_layers_22_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add805 = R.call_tir(cls.add, (add802, lv360), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm230 = R.call_tir(cls.layer_norm, (add805, model_decoder_layers_22_final_layer_norm_weight3, model_decoder_layers_22_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv54 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_22_fc1_weight3, layer_norm230, model_decoder_layers_22_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv361 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_22_fc2_weight3, lv54, model_decoder_layers_22_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add808 = R.call_tir(cls.add, (add805, lv361), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm231 = R.call_tir(cls.layer_norm, (add808, model_decoder_layers_23_self_attn_layer_norm_weight3, model_decoder_layers_23_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv362 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_23_self_attn_q_proj_weight3, layer_norm231, model_decoder_layers_23_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape940 = R.call_tir(cls.reshape4, (lv362,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv88 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_23_self_attn_k_proj_weight3, layer_norm231), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape941 = R.call_tir(cls.reshape4, (lv88,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv363 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_23_self_attn_v_proj_weight3, layer_norm231, model_decoder_layers_23_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape942 = R.call_tir(cls.reshape4, (lv363,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat55 = R.call_tir(cls.concatenate, (reshape940, reshape941, reshape942), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape943 = R.call_tir(cls.reshape5, (concat55,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv180 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), reshape943), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape944 = R.call_tir(cls.reshape6, (lv180,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape945 = R.call_tir(cls.reshape7, (reshape944,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv364 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_23_self_attn_out_proj_weight3, reshape945, model_decoder_layers_23_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add812 = R.call_tir(cls.add, (add808, lv364), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm232 = R.call_tir(cls.layer_norm, (add812, model_decoder_layers_23_encoder_attn_layer_norm_weight3, model_decoder_layers_23_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv365 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_23_encoder_attn_q_proj_weight3, layer_norm232, model_decoder_layers_23_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape946 = R.call_tir(cls.reshape4, (lv365,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape947 = R.call_tir(cls.reshape8, (reshape946,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv181 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), reshape947), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape948 = R.call_tir(cls.reshape6, (lv181,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape949 = R.call_tir(cls.reshape7, (reshape948,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv366 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_23_encoder_attn_out_proj_weight3, reshape949, model_decoder_layers_23_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add815 = R.call_tir(cls.add, (add812, lv366), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm233 = R.call_tir(cls.layer_norm, (add815, model_decoder_layers_23_final_layer_norm_weight3, model_decoder_layers_23_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv55 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_23_fc1_weight3, layer_norm233, model_decoder_layers_23_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv367 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_23_fc2_weight3, lv55, model_decoder_layers_23_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add818 = R.call_tir(cls.add, (add815, lv367), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm234 = R.call_tir(cls.layer_norm, (add818, model_decoder_layers_24_self_attn_layer_norm_weight3, model_decoder_layers_24_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv368 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_24_self_attn_q_proj_weight3, layer_norm234, model_decoder_layers_24_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape950 = R.call_tir(cls.reshape4, (lv368,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv89 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_24_self_attn_k_proj_weight3, layer_norm234), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape951 = R.call_tir(cls.reshape4, (lv89,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv369 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_24_self_attn_v_proj_weight3, layer_norm234, model_decoder_layers_24_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape952 = R.call_tir(cls.reshape4, (lv369,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat56 = R.call_tir(cls.concatenate, (reshape950, reshape951, reshape952), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape953 = R.call_tir(cls.reshape5, (concat56,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv182 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), reshape953), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape954 = R.call_tir(cls.reshape6, (lv182,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape955 = R.call_tir(cls.reshape7, (reshape954,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv370 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_24_self_attn_out_proj_weight3, reshape955, model_decoder_layers_24_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add822 = R.call_tir(cls.add, (add818, lv370), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm235 = R.call_tir(cls.layer_norm, (add822, model_decoder_layers_24_encoder_attn_layer_norm_weight3, model_decoder_layers_24_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv371 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_24_encoder_attn_q_proj_weight3, layer_norm235, model_decoder_layers_24_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape956 = R.call_tir(cls.reshape4, (lv371,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape957 = R.call_tir(cls.reshape8, (reshape956,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv183 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), reshape957), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape958 = R.call_tir(cls.reshape6, (lv183,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape959 = R.call_tir(cls.reshape7, (reshape958,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv372 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_24_encoder_attn_out_proj_weight3, reshape959, model_decoder_layers_24_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add825 = R.call_tir(cls.add, (add822, lv372), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm236 = R.call_tir(cls.layer_norm, (add825, model_decoder_layers_24_final_layer_norm_weight3, model_decoder_layers_24_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv56 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_24_fc1_weight3, layer_norm236, model_decoder_layers_24_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv373 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_24_fc2_weight3, lv56, model_decoder_layers_24_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add828 = R.call_tir(cls.add, (add825, lv373), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm237 = R.call_tir(cls.layer_norm, (add828, model_decoder_layers_25_self_attn_layer_norm_weight3, model_decoder_layers_25_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv374 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_25_self_attn_q_proj_weight3, layer_norm237, model_decoder_layers_25_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape960 = R.call_tir(cls.reshape4, (lv374,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv90 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_25_self_attn_k_proj_weight3, layer_norm237), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape961 = R.call_tir(cls.reshape4, (lv90,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv375 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_25_self_attn_v_proj_weight3, layer_norm237, model_decoder_layers_25_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape962 = R.call_tir(cls.reshape4, (lv375,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat57 = R.call_tir(cls.concatenate, (reshape960, reshape961, reshape962), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape963 = R.call_tir(cls.reshape5, (concat57,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv184 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), reshape963), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape964 = R.call_tir(cls.reshape6, (lv184,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape965 = R.call_tir(cls.reshape7, (reshape964,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv376 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_25_self_attn_out_proj_weight3, reshape965, model_decoder_layers_25_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add832 = R.call_tir(cls.add, (add828, lv376), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm238 = R.call_tir(cls.layer_norm, (add832, model_decoder_layers_25_encoder_attn_layer_norm_weight3, model_decoder_layers_25_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv377 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_25_encoder_attn_q_proj_weight3, layer_norm238, model_decoder_layers_25_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape966 = R.call_tir(cls.reshape4, (lv377,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape967 = R.call_tir(cls.reshape8, (reshape966,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv185 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), reshape967), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape968 = R.call_tir(cls.reshape6, (lv185,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape969 = R.call_tir(cls.reshape7, (reshape968,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv378 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_25_encoder_attn_out_proj_weight3, reshape969, model_decoder_layers_25_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add835 = R.call_tir(cls.add, (add832, lv378), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm239 = R.call_tir(cls.layer_norm, (add835, model_decoder_layers_25_final_layer_norm_weight3, model_decoder_layers_25_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv57 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_25_fc1_weight3, layer_norm239, model_decoder_layers_25_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv379 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_25_fc2_weight3, lv57, model_decoder_layers_25_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add838 = R.call_tir(cls.add, (add835, lv379), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm240 = R.call_tir(cls.layer_norm, (add838, model_decoder_layers_26_self_attn_layer_norm_weight3, model_decoder_layers_26_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv380 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_26_self_attn_q_proj_weight3, layer_norm240, model_decoder_layers_26_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape970 = R.call_tir(cls.reshape4, (lv380,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv91 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_26_self_attn_k_proj_weight3, layer_norm240), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape971 = R.call_tir(cls.reshape4, (lv91,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv381 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_26_self_attn_v_proj_weight3, layer_norm240, model_decoder_layers_26_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape972 = R.call_tir(cls.reshape4, (lv381,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat58 = R.call_tir(cls.concatenate, (reshape970, reshape971, reshape972), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape973 = R.call_tir(cls.reshape5, (concat58,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv186 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), reshape973), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape974 = R.call_tir(cls.reshape6, (lv186,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape975 = R.call_tir(cls.reshape7, (reshape974,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv382 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_26_self_attn_out_proj_weight3, reshape975, model_decoder_layers_26_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add842 = R.call_tir(cls.add, (add838, lv382), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm241 = R.call_tir(cls.layer_norm, (add842, model_decoder_layers_26_encoder_attn_layer_norm_weight3, model_decoder_layers_26_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv383 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_26_encoder_attn_q_proj_weight3, layer_norm241, model_decoder_layers_26_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape976 = R.call_tir(cls.reshape4, (lv383,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape977 = R.call_tir(cls.reshape8, (reshape976,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv187 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), reshape977), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape978 = R.call_tir(cls.reshape6, (lv187,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape979 = R.call_tir(cls.reshape7, (reshape978,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv384 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_26_encoder_attn_out_proj_weight3, reshape979, model_decoder_layers_26_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add845 = R.call_tir(cls.add, (add842, lv384), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm242 = R.call_tir(cls.layer_norm, (add845, model_decoder_layers_26_final_layer_norm_weight3, model_decoder_layers_26_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv58 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_26_fc1_weight3, layer_norm242, model_decoder_layers_26_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv385 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_26_fc2_weight3, lv58, model_decoder_layers_26_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add848 = R.call_tir(cls.add, (add845, lv385), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm243 = R.call_tir(cls.layer_norm, (add848, model_decoder_layers_27_self_attn_layer_norm_weight3, model_decoder_layers_27_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv386 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_27_self_attn_q_proj_weight3, layer_norm243, model_decoder_layers_27_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape980 = R.call_tir(cls.reshape4, (lv386,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv92 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_27_self_attn_k_proj_weight3, layer_norm243), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape981 = R.call_tir(cls.reshape4, (lv92,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv387 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_27_self_attn_v_proj_weight3, layer_norm243, model_decoder_layers_27_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape982 = R.call_tir(cls.reshape4, (lv387,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat59 = R.call_tir(cls.concatenate, (reshape980, reshape981, reshape982), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape983 = R.call_tir(cls.reshape5, (concat59,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv188 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), reshape983), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape984 = R.call_tir(cls.reshape6, (lv188,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape985 = R.call_tir(cls.reshape7, (reshape984,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv388 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_27_self_attn_out_proj_weight3, reshape985, model_decoder_layers_27_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add852 = R.call_tir(cls.add, (add848, lv388), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm244 = R.call_tir(cls.layer_norm, (add852, model_decoder_layers_27_encoder_attn_layer_norm_weight3, model_decoder_layers_27_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv389 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_27_encoder_attn_q_proj_weight3, layer_norm244, model_decoder_layers_27_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape986 = R.call_tir(cls.reshape4, (lv389,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape987 = R.call_tir(cls.reshape8, (reshape986,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv189 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), reshape987), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape988 = R.call_tir(cls.reshape6, (lv189,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape989 = R.call_tir(cls.reshape7, (reshape988,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv390 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_27_encoder_attn_out_proj_weight3, reshape989, model_decoder_layers_27_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add855 = R.call_tir(cls.add, (add852, lv390), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm245 = R.call_tir(cls.layer_norm, (add855, model_decoder_layers_27_final_layer_norm_weight3, model_decoder_layers_27_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv59 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_27_fc1_weight3, layer_norm245, model_decoder_layers_27_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv391 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_27_fc2_weight3, lv59, model_decoder_layers_27_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add858 = R.call_tir(cls.add, (add855, lv391), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm246 = R.call_tir(cls.layer_norm, (add858, model_decoder_layers_28_self_attn_layer_norm_weight3, model_decoder_layers_28_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv392 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_28_self_attn_q_proj_weight3, layer_norm246, model_decoder_layers_28_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape990 = R.call_tir(cls.reshape4, (lv392,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv93 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_28_self_attn_k_proj_weight3, layer_norm246), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape991 = R.call_tir(cls.reshape4, (lv93,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv393 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_28_self_attn_v_proj_weight3, layer_norm246, model_decoder_layers_28_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape992 = R.call_tir(cls.reshape4, (lv393,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat60 = R.call_tir(cls.concatenate, (reshape990, reshape991, reshape992), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape993 = R.call_tir(cls.reshape5, (concat60,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv190 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), reshape993), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape994 = R.call_tir(cls.reshape6, (lv190,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape995 = R.call_tir(cls.reshape7, (reshape994,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv394 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_28_self_attn_out_proj_weight3, reshape995, model_decoder_layers_28_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add862 = R.call_tir(cls.add, (add858, lv394), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm247 = R.call_tir(cls.layer_norm, (add862, model_decoder_layers_28_encoder_attn_layer_norm_weight3, model_decoder_layers_28_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv395 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_28_encoder_attn_q_proj_weight3, layer_norm247, model_decoder_layers_28_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape996 = R.call_tir(cls.reshape4, (lv395,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape997 = R.call_tir(cls.reshape8, (reshape996,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv191 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), reshape997), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape998 = R.call_tir(cls.reshape6, (lv191,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape999 = R.call_tir(cls.reshape7, (reshape998,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv396 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_28_encoder_attn_out_proj_weight3, reshape999, model_decoder_layers_28_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add865 = R.call_tir(cls.add, (add862, lv396), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm248 = R.call_tir(cls.layer_norm, (add865, model_decoder_layers_28_final_layer_norm_weight3, model_decoder_layers_28_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv60 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_28_fc1_weight3, layer_norm248, model_decoder_layers_28_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv397 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_28_fc2_weight3, lv60, model_decoder_layers_28_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add868 = R.call_tir(cls.add, (add865, lv397), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm249 = R.call_tir(cls.layer_norm, (add868, model_decoder_layers_29_self_attn_layer_norm_weight3, model_decoder_layers_29_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv398 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_29_self_attn_q_proj_weight3, layer_norm249, model_decoder_layers_29_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape1000 = R.call_tir(cls.reshape4, (lv398,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv94 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_29_self_attn_k_proj_weight3, layer_norm249), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape1001 = R.call_tir(cls.reshape4, (lv94,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv399 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_29_self_attn_v_proj_weight3, layer_norm249, model_decoder_layers_29_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape1002 = R.call_tir(cls.reshape4, (lv399,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat61 = R.call_tir(cls.concatenate, (reshape1000, reshape1001, reshape1002), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape1003 = R.call_tir(cls.reshape5, (concat61,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv192 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), reshape1003), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape1004 = R.call_tir(cls.reshape6, (lv192,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape1005 = R.call_tir(cls.reshape7, (reshape1004,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv400 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_29_self_attn_out_proj_weight3, reshape1005, model_decoder_layers_29_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add872 = R.call_tir(cls.add, (add868, lv400), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm250 = R.call_tir(cls.layer_norm, (add872, model_decoder_layers_29_encoder_attn_layer_norm_weight3, model_decoder_layers_29_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv401 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_29_encoder_attn_q_proj_weight3, layer_norm250, model_decoder_layers_29_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape1006 = R.call_tir(cls.reshape4, (lv401,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape1007 = R.call_tir(cls.reshape8, (reshape1006,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv193 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), reshape1007), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape1008 = R.call_tir(cls.reshape6, (lv193,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape1009 = R.call_tir(cls.reshape7, (reshape1008,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv402 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_29_encoder_attn_out_proj_weight3, reshape1009, model_decoder_layers_29_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add875 = R.call_tir(cls.add, (add872, lv402), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm251 = R.call_tir(cls.layer_norm, (add875, model_decoder_layers_29_final_layer_norm_weight3, model_decoder_layers_29_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv61 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_29_fc1_weight3, layer_norm251, model_decoder_layers_29_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv403 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_29_fc2_weight3, lv61, model_decoder_layers_29_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add878 = R.call_tir(cls.add, (add875, lv403), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm252 = R.call_tir(cls.layer_norm, (add878, model_decoder_layers_30_self_attn_layer_norm_weight3, model_decoder_layers_30_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv404 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_30_self_attn_q_proj_weight3, layer_norm252, model_decoder_layers_30_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape1010 = R.call_tir(cls.reshape4, (lv404,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv95 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_30_self_attn_k_proj_weight3, layer_norm252), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape1011 = R.call_tir(cls.reshape4, (lv95,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv405 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_30_self_attn_v_proj_weight3, layer_norm252, model_decoder_layers_30_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape1012 = R.call_tir(cls.reshape4, (lv405,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat62 = R.call_tir(cls.concatenate, (reshape1010, reshape1011, reshape1012), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape1013 = R.call_tir(cls.reshape5, (concat62,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv194 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), reshape1013), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape1014 = R.call_tir(cls.reshape6, (lv194,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape1015 = R.call_tir(cls.reshape7, (reshape1014,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv406 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_30_self_attn_out_proj_weight3, reshape1015, model_decoder_layers_30_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add882 = R.call_tir(cls.add, (add878, lv406), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm253 = R.call_tir(cls.layer_norm, (add882, model_decoder_layers_30_encoder_attn_layer_norm_weight3, model_decoder_layers_30_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv407 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_30_encoder_attn_q_proj_weight3, layer_norm253, model_decoder_layers_30_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape1016 = R.call_tir(cls.reshape4, (lv407,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape1017 = R.call_tir(cls.reshape8, (reshape1016,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv195 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), reshape1017), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape1018 = R.call_tir(cls.reshape6, (lv195,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape1019 = R.call_tir(cls.reshape7, (reshape1018,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv408 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_30_encoder_attn_out_proj_weight3, reshape1019, model_decoder_layers_30_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add885 = R.call_tir(cls.add, (add882, lv408), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm254 = R.call_tir(cls.layer_norm, (add885, model_decoder_layers_30_final_layer_norm_weight3, model_decoder_layers_30_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv62 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_30_fc1_weight3, layer_norm254, model_decoder_layers_30_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv409 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_30_fc2_weight3, lv62, model_decoder_layers_30_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add888 = R.call_tir(cls.add, (add885, lv409), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm255 = R.call_tir(cls.layer_norm, (add888, model_decoder_layers_31_self_attn_layer_norm_weight3, model_decoder_layers_31_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv410 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_31_self_attn_q_proj_weight3, layer_norm255, model_decoder_layers_31_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape1020 = R.call_tir(cls.reshape4, (lv410,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv96 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_31_self_attn_k_proj_weight3, layer_norm255), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape1021 = R.call_tir(cls.reshape4, (lv96,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv411 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_31_self_attn_v_proj_weight3, layer_norm255, model_decoder_layers_31_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape1022 = R.call_tir(cls.reshape4, (lv411,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat63 = R.call_tir(cls.concatenate, (reshape1020, reshape1021, reshape1022), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape1023 = R.call_tir(cls.reshape5, (concat63,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv196 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), reshape1023), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape1024 = R.call_tir(cls.reshape6, (lv196,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape1025 = R.call_tir(cls.reshape7, (reshape1024,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv412 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_31_self_attn_out_proj_weight3, reshape1025, model_decoder_layers_31_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add892 = R.call_tir(cls.add, (add888, lv412), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm256 = R.call_tir(cls.layer_norm, (add892, model_decoder_layers_31_encoder_attn_layer_norm_weight3, model_decoder_layers_31_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv413 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_31_encoder_attn_q_proj_weight3, layer_norm256, model_decoder_layers_31_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape1026 = R.call_tir(cls.reshape4, (lv413,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape1027 = R.call_tir(cls.reshape8, (reshape1026,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv197 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), reshape1027), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape1028 = R.call_tir(cls.reshape6, (lv197,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape1029 = R.call_tir(cls.reshape7, (reshape1028,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv414 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_31_encoder_attn_out_proj_weight3, reshape1029, model_decoder_layers_31_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add895 = R.call_tir(cls.add, (add892, lv414), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm257 = R.call_tir(cls.layer_norm, (add895, model_decoder_layers_31_final_layer_norm_weight3, model_decoder_layers_31_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv63 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_31_fc1_weight3, layer_norm257, model_decoder_layers_31_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv415 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_31_fc2_weight3, lv63, model_decoder_layers_31_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add898 = R.call_tir(cls.add, (add895, lv415), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm258 = R.call_tir(cls.layer_norm, (add898, model_decoder_layer_norm_weight3, model_decoder_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + gv3 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul4_cublas", (model_decoder_embed_tokens_weight3, layer_norm258), out_sinfo=R.Tensor((batch_size, 1, 51866), dtype="float32")) + R.output(gv3) + return gv3 + + @R.function + def batch_encode(input_features: R.Tensor(("batch_size", 128, 3000), dtype="float16"), paged_kv_cache: R.Object, packed_params: R.Tuple(R.Tensor((1280, 128, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1500, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), 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R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), 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R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"))) -> R.Tensor(("batch_size", 1500, 1280), dtype="float16"): + batch_size = T.int64() + R.func_attr({"num_input": 2, "relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "seq_len": 15000, "total_seq_len": 1500}}) + cls = Module + with R.dataflow(): + model_encoder_conv1_weight: R.Tensor((1280, 128, 3), dtype="float16") = packed_params[0] + lv: R.Tensor((1280,), dtype="float16") = packed_params[1] + lv1 = R.call_tir(cls.fused_reshape9, (lv,), out_sinfo=R.Tensor((1, 1280, 1), dtype="float16")) + model_encoder_conv2_weight: R.Tensor((1280, 1280, 3), dtype="float16") = packed_params[2] + lv2: R.Tensor((1280,), dtype="float16") = packed_params[3] + lv3 = R.call_tir(cls.fused_reshape9, (lv2,), out_sinfo=R.Tensor((1, 1280, 1), dtype="float16")) + lv4 = R.call_tir(cls.fused_conv1d_add1_gelu, (input_features, model_encoder_conv1_weight, lv1), out_sinfo=R.Tensor((batch_size, 1280, 3000), dtype="float16")) + lv5 = R.call_tir(cls.fused_conv1d1_add2_gelu1, (lv4, model_encoder_conv2_weight, lv3), out_sinfo=R.Tensor((batch_size, 1280, 1500), dtype="float16")) + lv6: R.Tensor((1500, 1280), dtype="float16") = packed_params[4] + lv7 = R.call_tir(cls.fused_transpose_add3, (lv6, lv5), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + model_encoder_layers_0_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[5] + model_encoder_layers_0_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[6] + model_encoder_layers_0_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[7] + model_encoder_layers_0_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[8] + model_encoder_layers_0_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[9] + model_encoder_layers_0_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[10] + model_encoder_layers_0_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[11] + model_encoder_layers_0_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[12] + model_encoder_layers_0_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[13] + model_encoder_layers_0_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[14] + model_encoder_layers_0_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[15] + model_encoder_layers_0_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[16] + model_encoder_layers_0_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[17] + model_encoder_layers_0_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[18] + model_encoder_layers_0_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[19] + model_encoder_layers_1_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[20] + model_encoder_layers_1_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[21] + model_encoder_layers_1_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[22] + model_encoder_layers_1_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[23] + model_encoder_layers_1_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[24] + model_encoder_layers_1_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[25] + model_encoder_layers_1_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[26] + model_encoder_layers_1_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[27] + model_encoder_layers_1_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[28] + model_encoder_layers_1_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[29] + model_encoder_layers_1_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[30] + model_encoder_layers_1_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[31] + model_encoder_layers_1_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[32] + model_encoder_layers_1_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[33] + model_encoder_layers_1_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[34] + model_encoder_layers_2_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[35] + model_encoder_layers_2_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[36] + model_encoder_layers_2_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[37] + model_encoder_layers_2_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[38] + model_encoder_layers_2_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[39] + model_encoder_layers_2_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[40] + model_encoder_layers_2_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[41] + model_encoder_layers_2_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[42] + model_encoder_layers_2_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[43] + model_encoder_layers_2_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[44] + model_encoder_layers_2_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[45] + model_encoder_layers_2_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[46] + model_encoder_layers_2_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[47] + model_encoder_layers_2_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[48] + model_encoder_layers_2_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[49] + model_encoder_layers_3_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[50] + model_encoder_layers_3_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[51] + model_encoder_layers_3_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[52] + model_encoder_layers_3_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[53] + model_encoder_layers_3_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[54] + model_encoder_layers_3_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[55] + model_encoder_layers_3_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[56] + model_encoder_layers_3_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[57] + model_encoder_layers_3_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[58] + model_encoder_layers_3_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[59] + model_encoder_layers_3_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[60] + model_encoder_layers_3_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[61] + model_encoder_layers_3_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[62] + model_encoder_layers_3_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[63] + model_encoder_layers_3_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[64] + model_encoder_layers_4_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[65] + model_encoder_layers_4_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[66] + model_encoder_layers_4_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[67] + model_encoder_layers_4_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[68] + model_encoder_layers_4_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[69] + model_encoder_layers_4_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[70] + model_encoder_layers_4_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[71] + model_encoder_layers_4_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[72] + model_encoder_layers_4_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[73] + model_encoder_layers_4_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[74] + model_encoder_layers_4_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[75] + model_encoder_layers_4_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[76] + model_encoder_layers_4_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[77] + model_encoder_layers_4_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[78] + model_encoder_layers_4_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[79] + model_encoder_layers_5_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[80] + model_encoder_layers_5_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[81] + model_encoder_layers_5_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[82] + model_encoder_layers_5_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[83] + model_encoder_layers_5_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[84] + model_encoder_layers_5_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[85] + model_encoder_layers_5_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[86] + model_encoder_layers_5_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[87] + model_encoder_layers_5_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[88] + model_encoder_layers_5_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[89] + model_encoder_layers_5_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[90] + model_encoder_layers_5_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[91] + model_encoder_layers_5_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[92] + model_encoder_layers_5_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[93] + model_encoder_layers_5_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[94] + model_encoder_layers_6_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[95] + model_encoder_layers_6_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[96] + model_encoder_layers_6_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[97] + model_encoder_layers_6_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[98] + model_encoder_layers_6_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[99] + model_encoder_layers_6_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[100] + model_encoder_layers_6_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[101] + model_encoder_layers_6_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[102] + model_encoder_layers_6_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[103] + model_encoder_layers_6_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[104] + model_encoder_layers_6_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[105] + model_encoder_layers_6_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[106] + model_encoder_layers_6_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[107] + model_encoder_layers_6_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[108] + model_encoder_layers_6_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[109] + model_encoder_layers_7_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[110] + model_encoder_layers_7_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[111] + model_encoder_layers_7_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[112] + model_encoder_layers_7_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[113] + model_encoder_layers_7_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[114] + model_encoder_layers_7_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[115] + model_encoder_layers_7_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[116] + model_encoder_layers_7_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[117] + model_encoder_layers_7_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[118] + model_encoder_layers_7_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[119] + model_encoder_layers_7_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[120] + model_encoder_layers_7_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[121] + model_encoder_layers_7_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[122] + model_encoder_layers_7_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[123] + model_encoder_layers_7_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[124] + model_encoder_layers_8_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[125] + model_encoder_layers_8_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[126] + model_encoder_layers_8_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[127] + model_encoder_layers_8_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[128] + model_encoder_layers_8_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[129] + model_encoder_layers_8_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[130] + model_encoder_layers_8_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[131] + model_encoder_layers_8_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[132] + model_encoder_layers_8_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[133] + model_encoder_layers_8_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[134] + model_encoder_layers_8_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[135] + model_encoder_layers_8_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[136] + model_encoder_layers_8_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[137] + model_encoder_layers_8_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[138] + model_encoder_layers_8_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[139] + model_encoder_layers_9_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[140] + model_encoder_layers_9_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[141] + model_encoder_layers_9_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[142] + model_encoder_layers_9_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[143] + model_encoder_layers_9_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[144] + model_encoder_layers_9_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[145] + model_encoder_layers_9_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[146] + model_encoder_layers_9_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[147] + model_encoder_layers_9_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[148] + model_encoder_layers_9_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[149] + model_encoder_layers_9_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[150] + model_encoder_layers_9_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[151] + model_encoder_layers_9_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[152] + model_encoder_layers_9_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[153] + model_encoder_layers_9_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[154] + model_encoder_layers_10_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[155] + model_encoder_layers_10_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[156] + model_encoder_layers_10_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[157] + model_encoder_layers_10_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[158] + model_encoder_layers_10_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[159] + model_encoder_layers_10_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[160] + model_encoder_layers_10_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[161] + model_encoder_layers_10_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[162] + model_encoder_layers_10_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[163] + model_encoder_layers_10_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[164] + model_encoder_layers_10_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[165] + model_encoder_layers_10_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[166] + model_encoder_layers_10_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[167] + model_encoder_layers_10_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[168] + model_encoder_layers_10_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[169] + model_encoder_layers_11_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[170] + model_encoder_layers_11_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[171] + model_encoder_layers_11_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[172] + model_encoder_layers_11_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[173] + model_encoder_layers_11_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[174] + model_encoder_layers_11_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[175] + model_encoder_layers_11_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[176] + model_encoder_layers_11_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[177] + model_encoder_layers_11_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[178] + model_encoder_layers_11_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[179] + model_encoder_layers_11_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[180] + model_encoder_layers_11_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[181] + model_encoder_layers_11_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[182] + model_encoder_layers_11_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[183] + model_encoder_layers_11_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[184] + model_encoder_layers_12_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[185] + model_encoder_layers_12_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[186] + model_encoder_layers_12_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[187] + model_encoder_layers_12_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[188] + model_encoder_layers_12_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[189] + model_encoder_layers_12_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[190] + model_encoder_layers_12_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[191] + model_encoder_layers_12_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[192] + model_encoder_layers_12_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[193] + model_encoder_layers_12_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[194] + model_encoder_layers_12_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[195] + model_encoder_layers_12_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[196] + model_encoder_layers_12_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[197] + model_encoder_layers_12_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[198] + model_encoder_layers_12_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[199] + model_encoder_layers_13_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[200] + model_encoder_layers_13_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[201] + model_encoder_layers_13_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[202] + model_encoder_layers_13_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[203] + model_encoder_layers_13_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[204] + model_encoder_layers_13_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[205] + model_encoder_layers_13_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[206] + model_encoder_layers_13_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[207] + model_encoder_layers_13_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[208] + model_encoder_layers_13_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[209] + model_encoder_layers_13_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[210] + model_encoder_layers_13_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[211] + model_encoder_layers_13_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[212] + model_encoder_layers_13_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[213] + model_encoder_layers_13_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[214] + model_encoder_layers_14_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[215] + model_encoder_layers_14_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[216] + model_encoder_layers_14_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[217] + model_encoder_layers_14_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[218] + model_encoder_layers_14_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[219] + model_encoder_layers_14_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[220] + model_encoder_layers_14_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[221] + model_encoder_layers_14_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[222] + model_encoder_layers_14_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[223] + model_encoder_layers_14_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[224] + model_encoder_layers_14_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[225] + model_encoder_layers_14_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[226] + model_encoder_layers_14_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[227] + model_encoder_layers_14_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[228] + model_encoder_layers_14_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[229] + model_encoder_layers_15_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[230] + model_encoder_layers_15_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[231] + model_encoder_layers_15_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[232] + model_encoder_layers_15_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[233] + model_encoder_layers_15_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[234] + model_encoder_layers_15_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[235] + model_encoder_layers_15_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[236] + model_encoder_layers_15_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[237] + model_encoder_layers_15_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[238] + model_encoder_layers_15_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[239] + model_encoder_layers_15_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[240] + model_encoder_layers_15_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[241] + model_encoder_layers_15_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[242] + model_encoder_layers_15_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[243] + model_encoder_layers_15_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[244] + model_encoder_layers_16_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[245] + model_encoder_layers_16_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[246] + model_encoder_layers_16_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[247] + model_encoder_layers_16_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[248] + model_encoder_layers_16_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[249] + model_encoder_layers_16_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[250] + model_encoder_layers_16_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[251] + model_encoder_layers_16_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[252] + model_encoder_layers_16_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[253] + model_encoder_layers_16_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[254] + model_encoder_layers_16_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[255] + model_encoder_layers_16_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[256] + model_encoder_layers_16_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[257] + model_encoder_layers_16_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[258] + model_encoder_layers_16_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[259] + model_encoder_layers_17_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[260] + model_encoder_layers_17_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[261] + model_encoder_layers_17_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[262] + model_encoder_layers_17_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[263] + model_encoder_layers_17_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[264] + model_encoder_layers_17_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[265] + model_encoder_layers_17_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[266] + model_encoder_layers_17_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[267] + model_encoder_layers_17_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[268] + model_encoder_layers_17_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[269] + model_encoder_layers_17_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[270] + model_encoder_layers_17_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[271] + model_encoder_layers_17_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[272] + model_encoder_layers_17_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[273] + model_encoder_layers_17_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[274] + model_encoder_layers_18_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[275] + model_encoder_layers_18_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[276] + model_encoder_layers_18_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[277] + model_encoder_layers_18_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[278] + model_encoder_layers_18_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[279] + model_encoder_layers_18_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[280] + model_encoder_layers_18_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[281] + model_encoder_layers_18_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[282] + model_encoder_layers_18_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[283] + model_encoder_layers_18_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[284] + model_encoder_layers_18_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[285] + model_encoder_layers_18_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[286] + model_encoder_layers_18_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[287] + model_encoder_layers_18_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[288] + model_encoder_layers_18_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[289] + model_encoder_layers_19_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[290] + model_encoder_layers_19_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[291] + model_encoder_layers_19_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[292] + model_encoder_layers_19_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[293] + model_encoder_layers_19_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[294] + model_encoder_layers_19_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[295] + model_encoder_layers_19_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[296] + model_encoder_layers_19_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[297] + model_encoder_layers_19_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[298] + model_encoder_layers_19_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[299] + model_encoder_layers_19_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[300] + model_encoder_layers_19_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[301] + model_encoder_layers_19_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[302] + model_encoder_layers_19_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[303] + model_encoder_layers_19_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[304] + model_encoder_layers_20_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[305] + model_encoder_layers_20_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[306] + model_encoder_layers_20_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[307] + model_encoder_layers_20_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[308] + model_encoder_layers_20_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[309] + model_encoder_layers_20_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[310] + model_encoder_layers_20_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[311] + model_encoder_layers_20_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[312] + model_encoder_layers_20_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[313] + model_encoder_layers_20_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[314] + model_encoder_layers_20_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[315] + model_encoder_layers_20_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[316] + model_encoder_layers_20_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[317] + model_encoder_layers_20_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[318] + model_encoder_layers_20_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[319] + model_encoder_layers_21_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[320] + model_encoder_layers_21_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[321] + model_encoder_layers_21_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[322] + model_encoder_layers_21_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[323] + model_encoder_layers_21_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[324] + model_encoder_layers_21_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[325] + model_encoder_layers_21_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[326] + model_encoder_layers_21_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[327] + model_encoder_layers_21_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[328] + model_encoder_layers_21_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[329] + model_encoder_layers_21_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[330] + model_encoder_layers_21_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[331] + model_encoder_layers_21_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[332] + model_encoder_layers_21_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[333] + model_encoder_layers_21_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[334] + model_encoder_layers_22_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[335] + model_encoder_layers_22_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[336] + model_encoder_layers_22_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[337] + model_encoder_layers_22_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[338] + model_encoder_layers_22_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[339] + model_encoder_layers_22_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[340] + model_encoder_layers_22_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[341] + model_encoder_layers_22_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[342] + model_encoder_layers_22_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[343] + model_encoder_layers_22_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[344] + model_encoder_layers_22_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[345] + model_encoder_layers_22_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[346] + model_encoder_layers_22_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[347] + model_encoder_layers_22_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[348] + model_encoder_layers_22_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[349] + model_encoder_layers_23_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[350] + model_encoder_layers_23_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[351] + model_encoder_layers_23_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[352] + model_encoder_layers_23_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[353] + model_encoder_layers_23_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[354] + model_encoder_layers_23_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[355] + model_encoder_layers_23_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[356] + model_encoder_layers_23_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[357] + model_encoder_layers_23_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[358] + model_encoder_layers_23_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[359] + model_encoder_layers_23_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[360] + model_encoder_layers_23_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[361] + model_encoder_layers_23_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[362] + model_encoder_layers_23_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[363] + model_encoder_layers_23_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[364] + model_encoder_layers_24_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[365] + model_encoder_layers_24_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[366] + model_encoder_layers_24_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[367] + model_encoder_layers_24_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[368] + model_encoder_layers_24_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[369] + model_encoder_layers_24_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[370] + model_encoder_layers_24_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[371] + model_encoder_layers_24_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[372] + model_encoder_layers_24_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[373] + model_encoder_layers_24_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[374] + model_encoder_layers_24_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[375] + model_encoder_layers_24_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[376] + model_encoder_layers_24_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[377] + model_encoder_layers_24_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[378] + model_encoder_layers_24_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[379] + model_encoder_layers_25_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[380] + model_encoder_layers_25_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[381] + model_encoder_layers_25_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[382] + model_encoder_layers_25_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[383] + model_encoder_layers_25_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[384] + model_encoder_layers_25_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[385] + model_encoder_layers_25_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[386] + model_encoder_layers_25_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[387] + model_encoder_layers_25_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[388] + model_encoder_layers_25_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[389] + model_encoder_layers_25_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[390] + model_encoder_layers_25_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[391] + model_encoder_layers_25_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[392] + model_encoder_layers_25_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[393] + model_encoder_layers_25_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[394] + model_encoder_layers_26_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[395] + model_encoder_layers_26_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[396] + model_encoder_layers_26_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[397] + model_encoder_layers_26_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[398] + model_encoder_layers_26_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[399] + model_encoder_layers_26_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[400] + model_encoder_layers_26_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[401] + model_encoder_layers_26_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[402] + model_encoder_layers_26_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[403] + model_encoder_layers_26_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[404] + model_encoder_layers_26_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[405] + model_encoder_layers_26_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[406] + model_encoder_layers_26_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[407] + model_encoder_layers_26_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[408] + model_encoder_layers_26_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[409] + model_encoder_layers_27_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[410] + model_encoder_layers_27_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[411] + model_encoder_layers_27_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[412] + model_encoder_layers_27_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[413] + model_encoder_layers_27_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[414] + model_encoder_layers_27_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[415] + model_encoder_layers_27_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[416] + model_encoder_layers_27_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[417] + model_encoder_layers_27_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[418] + model_encoder_layers_27_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[419] + model_encoder_layers_27_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[420] + model_encoder_layers_27_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[421] + model_encoder_layers_27_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[422] + model_encoder_layers_27_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[423] + model_encoder_layers_27_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[424] + model_encoder_layers_28_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[425] + model_encoder_layers_28_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[426] + model_encoder_layers_28_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[427] + model_encoder_layers_28_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[428] + model_encoder_layers_28_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[429] + model_encoder_layers_28_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[430] + model_encoder_layers_28_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[431] + model_encoder_layers_28_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[432] + model_encoder_layers_28_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[433] + model_encoder_layers_28_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[434] + model_encoder_layers_28_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[435] + model_encoder_layers_28_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[436] + model_encoder_layers_28_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[437] + model_encoder_layers_28_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[438] + model_encoder_layers_28_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[439] + model_encoder_layers_29_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[440] + model_encoder_layers_29_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[441] + model_encoder_layers_29_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[442] + model_encoder_layers_29_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[443] + model_encoder_layers_29_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[444] + model_encoder_layers_29_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[445] + model_encoder_layers_29_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[446] + model_encoder_layers_29_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[447] + model_encoder_layers_29_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[448] + model_encoder_layers_29_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[449] + model_encoder_layers_29_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[450] + model_encoder_layers_29_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[451] + model_encoder_layers_29_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[452] + model_encoder_layers_29_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[453] + model_encoder_layers_29_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[454] + model_encoder_layers_30_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[455] + model_encoder_layers_30_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[456] + model_encoder_layers_30_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[457] + model_encoder_layers_30_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[458] + model_encoder_layers_30_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[459] + model_encoder_layers_30_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[460] + model_encoder_layers_30_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[461] + model_encoder_layers_30_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[462] + model_encoder_layers_30_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[463] + model_encoder_layers_30_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[464] + model_encoder_layers_30_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[465] + model_encoder_layers_30_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[466] + model_encoder_layers_30_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[467] + model_encoder_layers_30_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[468] + model_encoder_layers_30_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[469] + model_encoder_layers_31_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[470] + model_encoder_layers_31_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[471] + model_encoder_layers_31_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[472] + model_encoder_layers_31_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[473] + model_encoder_layers_31_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[474] + model_encoder_layers_31_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[475] + model_encoder_layers_31_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[476] + model_encoder_layers_31_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[477] + model_encoder_layers_31_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[478] + model_encoder_layers_31_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[479] + model_encoder_layers_31_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[480] + model_encoder_layers_31_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[481] + model_encoder_layers_31_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[482] + model_encoder_layers_31_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[483] + model_encoder_layers_31_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[484] + model_encoder_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[485] + model_encoder_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[486] + layer_norm = R.call_tir(cls.layer_norm1, (lv7, model_encoder_layers_0_self_attn_layer_norm_weight, model_encoder_layers_0_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv608 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_0_self_attn_q_proj_weight, layer_norm, model_encoder_layers_0_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape = R.call_tir(cls.reshape, (lv608,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv131 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_0_self_attn_k_proj_weight, layer_norm), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape1 = R.call_tir(cls.reshape, (lv131,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv609 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_0_self_attn_v_proj_weight, layer_norm, model_encoder_layers_0_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape2 = R.call_tir(cls.reshape, (lv609,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape3 = R.call_tir(cls.reshape1, (reshape,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape4 = R.call_tir(cls.reshape1, (reshape1,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape5 = R.call_tir(cls.reshape1, (reshape2,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv4_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), reshape3, reshape4, reshape5), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape6 = R.call_tir(cls.reshape10, (lv4_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape7 = R.call_tir(cls.reshape11, (reshape6,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv610 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_0_self_attn_out_proj_weight, reshape7, model_encoder_layers_0_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add4 = R.call_tir(cls.add4, (lv7, lv610), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm1 = R.call_tir(cls.layer_norm1, (add4, model_encoder_layers_0_final_layer_norm_weight, model_encoder_layers_0_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv96 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_0_fc1_weight, layer_norm1, model_encoder_layers_0_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv611 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_0_fc2_weight, lv96, model_encoder_layers_0_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv8 = R.call_tir(cls.fused_add4_maximum_minimum, (add4, lv611), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm2 = R.call_tir(cls.layer_norm1, (lv8, model_encoder_layers_1_self_attn_layer_norm_weight, model_encoder_layers_1_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv612 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_1_self_attn_q_proj_weight, layer_norm2, model_encoder_layers_1_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape8 = R.call_tir(cls.reshape, (lv612,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv132 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_1_self_attn_k_proj_weight, layer_norm2), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape9 = R.call_tir(cls.reshape, (lv132,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv613 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_1_self_attn_v_proj_weight, layer_norm2, model_encoder_layers_1_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape10 = R.call_tir(cls.reshape, (lv613,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape11 = R.call_tir(cls.reshape1, (reshape8,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape12 = R.call_tir(cls.reshape1, (reshape9,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape13 = R.call_tir(cls.reshape1, (reshape10,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv5_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), reshape11, reshape12, reshape13), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape14 = R.call_tir(cls.reshape10, (lv5_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape15 = R.call_tir(cls.reshape11, (reshape14,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv614 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_1_self_attn_out_proj_weight, reshape15, model_encoder_layers_1_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add11 = R.call_tir(cls.add4, (lv8, lv614), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm3 = R.call_tir(cls.layer_norm1, (add11, model_encoder_layers_1_final_layer_norm_weight, model_encoder_layers_1_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv97 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_1_fc1_weight, layer_norm3, model_encoder_layers_1_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv615 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_1_fc2_weight, lv97, model_encoder_layers_1_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv9 = R.call_tir(cls.fused_add4_maximum_minimum, (add11, lv615), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm4 = R.call_tir(cls.layer_norm1, (lv9, model_encoder_layers_2_self_attn_layer_norm_weight, model_encoder_layers_2_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv616 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_2_self_attn_q_proj_weight, layer_norm4, model_encoder_layers_2_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape16 = R.call_tir(cls.reshape, (lv616,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv133 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_2_self_attn_k_proj_weight, layer_norm4), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape17 = R.call_tir(cls.reshape, (lv133,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv617 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_2_self_attn_v_proj_weight, layer_norm4, model_encoder_layers_2_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape18 = R.call_tir(cls.reshape, (lv617,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape19 = R.call_tir(cls.reshape1, (reshape16,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape20 = R.call_tir(cls.reshape1, (reshape17,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape21 = R.call_tir(cls.reshape1, (reshape18,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv6_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), reshape19, reshape20, reshape21), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape22 = R.call_tir(cls.reshape10, (lv6_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape23 = R.call_tir(cls.reshape11, (reshape22,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv618 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_2_self_attn_out_proj_weight, reshape23, model_encoder_layers_2_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add18 = R.call_tir(cls.add4, (lv9, lv618), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm5 = R.call_tir(cls.layer_norm1, (add18, model_encoder_layers_2_final_layer_norm_weight, model_encoder_layers_2_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv98 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_2_fc1_weight, layer_norm5, model_encoder_layers_2_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv619 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_2_fc2_weight, lv98, model_encoder_layers_2_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv10 = R.call_tir(cls.fused_add4_maximum_minimum, (add18, lv619), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm6 = R.call_tir(cls.layer_norm1, (lv10, model_encoder_layers_3_self_attn_layer_norm_weight, model_encoder_layers_3_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv620 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_3_self_attn_q_proj_weight, layer_norm6, model_encoder_layers_3_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape24 = R.call_tir(cls.reshape, (lv620,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv134 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_3_self_attn_k_proj_weight, layer_norm6), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape25 = R.call_tir(cls.reshape, (lv134,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv621 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_3_self_attn_v_proj_weight, layer_norm6, model_encoder_layers_3_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape26 = R.call_tir(cls.reshape, (lv621,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape27 = R.call_tir(cls.reshape1, (reshape24,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape28 = R.call_tir(cls.reshape1, (reshape25,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape29 = R.call_tir(cls.reshape1, (reshape26,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv7_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), reshape27, reshape28, reshape29), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape30 = R.call_tir(cls.reshape10, (lv7_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape31 = R.call_tir(cls.reshape11, (reshape30,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv622 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_3_self_attn_out_proj_weight, reshape31, model_encoder_layers_3_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add25 = R.call_tir(cls.add4, (lv10, lv622), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm7 = R.call_tir(cls.layer_norm1, (add25, model_encoder_layers_3_final_layer_norm_weight, model_encoder_layers_3_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv99 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_3_fc1_weight, layer_norm7, model_encoder_layers_3_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv623 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_3_fc2_weight, lv99, model_encoder_layers_3_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv11 = R.call_tir(cls.fused_add4_maximum_minimum, (add25, lv623), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm8 = R.call_tir(cls.layer_norm1, (lv11, model_encoder_layers_4_self_attn_layer_norm_weight, model_encoder_layers_4_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv624 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_4_self_attn_q_proj_weight, layer_norm8, model_encoder_layers_4_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape32 = R.call_tir(cls.reshape, (lv624,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv135 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_4_self_attn_k_proj_weight, layer_norm8), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape33 = R.call_tir(cls.reshape, (lv135,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv625 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_4_self_attn_v_proj_weight, layer_norm8, model_encoder_layers_4_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape34 = R.call_tir(cls.reshape, (lv625,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape35 = R.call_tir(cls.reshape1, (reshape32,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape36 = R.call_tir(cls.reshape1, (reshape33,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape37 = R.call_tir(cls.reshape1, (reshape34,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv8_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), reshape35, reshape36, reshape37), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape38 = R.call_tir(cls.reshape10, (lv8_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape39 = R.call_tir(cls.reshape11, (reshape38,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv626 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_4_self_attn_out_proj_weight, reshape39, model_encoder_layers_4_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add32 = R.call_tir(cls.add4, (lv11, lv626), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm9 = R.call_tir(cls.layer_norm1, (add32, model_encoder_layers_4_final_layer_norm_weight, model_encoder_layers_4_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv100 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_4_fc1_weight, layer_norm9, model_encoder_layers_4_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv627 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_4_fc2_weight, lv100, model_encoder_layers_4_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv12 = R.call_tir(cls.fused_add4_maximum_minimum, (add32, lv627), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm10 = R.call_tir(cls.layer_norm1, (lv12, model_encoder_layers_5_self_attn_layer_norm_weight, model_encoder_layers_5_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv628 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_5_self_attn_q_proj_weight, layer_norm10, model_encoder_layers_5_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape40 = R.call_tir(cls.reshape, (lv628,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv136 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_5_self_attn_k_proj_weight, layer_norm10), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape41 = R.call_tir(cls.reshape, (lv136,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv629 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_5_self_attn_v_proj_weight, layer_norm10, model_encoder_layers_5_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape42 = R.call_tir(cls.reshape, (lv629,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape43 = R.call_tir(cls.reshape1, (reshape40,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape44 = R.call_tir(cls.reshape1, (reshape41,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape45 = R.call_tir(cls.reshape1, (reshape42,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv9_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), reshape43, reshape44, reshape45), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape46 = R.call_tir(cls.reshape10, (lv9_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape47 = R.call_tir(cls.reshape11, (reshape46,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv630 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_5_self_attn_out_proj_weight, reshape47, model_encoder_layers_5_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add39 = R.call_tir(cls.add4, (lv12, lv630), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm11 = R.call_tir(cls.layer_norm1, (add39, model_encoder_layers_5_final_layer_norm_weight, model_encoder_layers_5_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv101 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_5_fc1_weight, layer_norm11, model_encoder_layers_5_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv631 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_5_fc2_weight, lv101, model_encoder_layers_5_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv13 = R.call_tir(cls.fused_add4_maximum_minimum, (add39, lv631), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm12 = R.call_tir(cls.layer_norm1, (lv13, model_encoder_layers_6_self_attn_layer_norm_weight, model_encoder_layers_6_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv632 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_6_self_attn_q_proj_weight, layer_norm12, model_encoder_layers_6_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape48 = R.call_tir(cls.reshape, (lv632,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv137 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_6_self_attn_k_proj_weight, layer_norm12), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape49 = R.call_tir(cls.reshape, (lv137,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv633 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_6_self_attn_v_proj_weight, layer_norm12, model_encoder_layers_6_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape50 = R.call_tir(cls.reshape, (lv633,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape51 = R.call_tir(cls.reshape1, (reshape48,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape52 = R.call_tir(cls.reshape1, (reshape49,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape53 = R.call_tir(cls.reshape1, (reshape50,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv10_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), reshape51, reshape52, reshape53), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape54 = R.call_tir(cls.reshape10, (lv10_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape55 = R.call_tir(cls.reshape11, (reshape54,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv634 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_6_self_attn_out_proj_weight, reshape55, model_encoder_layers_6_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add46 = R.call_tir(cls.add4, (lv13, lv634), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm13 = R.call_tir(cls.layer_norm1, (add46, model_encoder_layers_6_final_layer_norm_weight, model_encoder_layers_6_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv102 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_6_fc1_weight, layer_norm13, model_encoder_layers_6_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv635 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_6_fc2_weight, lv102, model_encoder_layers_6_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv14 = R.call_tir(cls.fused_add4_maximum_minimum, (add46, lv635), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm14 = R.call_tir(cls.layer_norm1, (lv14, model_encoder_layers_7_self_attn_layer_norm_weight, model_encoder_layers_7_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv636 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_7_self_attn_q_proj_weight, layer_norm14, model_encoder_layers_7_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape56 = R.call_tir(cls.reshape, (lv636,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv138 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_7_self_attn_k_proj_weight, layer_norm14), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape57 = R.call_tir(cls.reshape, (lv138,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv637 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_7_self_attn_v_proj_weight, layer_norm14, model_encoder_layers_7_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape58 = R.call_tir(cls.reshape, (lv637,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape59 = R.call_tir(cls.reshape1, (reshape56,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape60 = R.call_tir(cls.reshape1, (reshape57,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape61 = R.call_tir(cls.reshape1, (reshape58,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv11_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), reshape59, reshape60, reshape61), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape62 = R.call_tir(cls.reshape10, (lv11_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape63 = R.call_tir(cls.reshape11, (reshape62,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv638 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_7_self_attn_out_proj_weight, reshape63, model_encoder_layers_7_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add53 = R.call_tir(cls.add4, (lv14, lv638), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm15 = R.call_tir(cls.layer_norm1, (add53, model_encoder_layers_7_final_layer_norm_weight, model_encoder_layers_7_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv103 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_7_fc1_weight, layer_norm15, model_encoder_layers_7_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv639 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_7_fc2_weight, lv103, model_encoder_layers_7_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv15 = R.call_tir(cls.fused_add4_maximum_minimum, (add53, lv639), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm16 = R.call_tir(cls.layer_norm1, (lv15, model_encoder_layers_8_self_attn_layer_norm_weight, model_encoder_layers_8_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv640 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_8_self_attn_q_proj_weight, layer_norm16, model_encoder_layers_8_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape64 = R.call_tir(cls.reshape, (lv640,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv139 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_8_self_attn_k_proj_weight, layer_norm16), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape65 = R.call_tir(cls.reshape, (lv139,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv641 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_8_self_attn_v_proj_weight, layer_norm16, model_encoder_layers_8_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape66 = R.call_tir(cls.reshape, (lv641,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape67 = R.call_tir(cls.reshape1, (reshape64,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape68 = R.call_tir(cls.reshape1, (reshape65,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape69 = R.call_tir(cls.reshape1, (reshape66,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv12_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), reshape67, reshape68, reshape69), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape70 = R.call_tir(cls.reshape10, (lv12_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape71 = R.call_tir(cls.reshape11, (reshape70,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv642 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_8_self_attn_out_proj_weight, reshape71, model_encoder_layers_8_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add60 = R.call_tir(cls.add4, (lv15, lv642), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm17 = R.call_tir(cls.layer_norm1, (add60, model_encoder_layers_8_final_layer_norm_weight, model_encoder_layers_8_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv104 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_8_fc1_weight, layer_norm17, model_encoder_layers_8_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv643 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_8_fc2_weight, lv104, model_encoder_layers_8_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv16 = R.call_tir(cls.fused_add4_maximum_minimum, (add60, lv643), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm18 = R.call_tir(cls.layer_norm1, (lv16, model_encoder_layers_9_self_attn_layer_norm_weight, model_encoder_layers_9_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv644 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_9_self_attn_q_proj_weight, layer_norm18, model_encoder_layers_9_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape72 = R.call_tir(cls.reshape, (lv644,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv140 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_9_self_attn_k_proj_weight, layer_norm18), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape73 = R.call_tir(cls.reshape, (lv140,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv645 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_9_self_attn_v_proj_weight, layer_norm18, model_encoder_layers_9_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape74 = R.call_tir(cls.reshape, (lv645,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape75 = R.call_tir(cls.reshape1, (reshape72,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape76 = R.call_tir(cls.reshape1, (reshape73,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape77 = R.call_tir(cls.reshape1, (reshape74,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv13_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), reshape75, reshape76, reshape77), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape78 = R.call_tir(cls.reshape10, (lv13_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape79 = R.call_tir(cls.reshape11, (reshape78,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv646 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_9_self_attn_out_proj_weight, reshape79, model_encoder_layers_9_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add67 = R.call_tir(cls.add4, (lv16, lv646), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm19 = R.call_tir(cls.layer_norm1, (add67, model_encoder_layers_9_final_layer_norm_weight, model_encoder_layers_9_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv105 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_9_fc1_weight, layer_norm19, model_encoder_layers_9_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv647 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_9_fc2_weight, lv105, model_encoder_layers_9_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv17 = R.call_tir(cls.fused_add4_maximum_minimum, (add67, lv647), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm20 = R.call_tir(cls.layer_norm1, (lv17, model_encoder_layers_10_self_attn_layer_norm_weight, model_encoder_layers_10_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv648 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_10_self_attn_q_proj_weight, layer_norm20, model_encoder_layers_10_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape80 = R.call_tir(cls.reshape, (lv648,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv141 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_10_self_attn_k_proj_weight, layer_norm20), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape81 = R.call_tir(cls.reshape, (lv141,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv649 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_10_self_attn_v_proj_weight, layer_norm20, model_encoder_layers_10_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape82 = R.call_tir(cls.reshape, (lv649,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape83 = R.call_tir(cls.reshape1, (reshape80,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape84 = R.call_tir(cls.reshape1, (reshape81,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape85 = R.call_tir(cls.reshape1, (reshape82,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv14_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), reshape83, reshape84, reshape85), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape86 = R.call_tir(cls.reshape10, (lv14_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape87 = R.call_tir(cls.reshape11, (reshape86,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv650 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_10_self_attn_out_proj_weight, reshape87, model_encoder_layers_10_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add74 = R.call_tir(cls.add4, (lv17, lv650), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm21 = R.call_tir(cls.layer_norm1, (add74, model_encoder_layers_10_final_layer_norm_weight, model_encoder_layers_10_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv106 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_10_fc1_weight, layer_norm21, model_encoder_layers_10_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv651 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_10_fc2_weight, lv106, model_encoder_layers_10_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv18 = R.call_tir(cls.fused_add4_maximum_minimum, (add74, lv651), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm22 = R.call_tir(cls.layer_norm1, (lv18, model_encoder_layers_11_self_attn_layer_norm_weight, model_encoder_layers_11_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv652 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_11_self_attn_q_proj_weight, layer_norm22, model_encoder_layers_11_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape88 = R.call_tir(cls.reshape, (lv652,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv142 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_11_self_attn_k_proj_weight, layer_norm22), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape89 = R.call_tir(cls.reshape, (lv142,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv653 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_11_self_attn_v_proj_weight, layer_norm22, model_encoder_layers_11_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape90 = R.call_tir(cls.reshape, (lv653,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape91 = R.call_tir(cls.reshape1, (reshape88,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape92 = R.call_tir(cls.reshape1, (reshape89,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape93 = R.call_tir(cls.reshape1, (reshape90,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv15_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), reshape91, reshape92, reshape93), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape94 = R.call_tir(cls.reshape10, (lv15_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape95 = R.call_tir(cls.reshape11, (reshape94,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv654 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_11_self_attn_out_proj_weight, reshape95, model_encoder_layers_11_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add81 = R.call_tir(cls.add4, (lv18, lv654), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm23 = R.call_tir(cls.layer_norm1, (add81, model_encoder_layers_11_final_layer_norm_weight, model_encoder_layers_11_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv107 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_11_fc1_weight, layer_norm23, model_encoder_layers_11_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv655 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_11_fc2_weight, lv107, model_encoder_layers_11_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv19 = R.call_tir(cls.fused_add4_maximum_minimum, (add81, lv655), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm24 = R.call_tir(cls.layer_norm1, (lv19, model_encoder_layers_12_self_attn_layer_norm_weight, model_encoder_layers_12_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv656 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_12_self_attn_q_proj_weight, layer_norm24, model_encoder_layers_12_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape96 = R.call_tir(cls.reshape, (lv656,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv143 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_12_self_attn_k_proj_weight, layer_norm24), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape97 = R.call_tir(cls.reshape, (lv143,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv657 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_12_self_attn_v_proj_weight, layer_norm24, model_encoder_layers_12_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape98 = R.call_tir(cls.reshape, (lv657,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape99 = R.call_tir(cls.reshape1, (reshape96,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape100 = R.call_tir(cls.reshape1, (reshape97,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape101 = R.call_tir(cls.reshape1, (reshape98,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv16_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), reshape99, reshape100, reshape101), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape102 = R.call_tir(cls.reshape10, (lv16_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape103 = R.call_tir(cls.reshape11, (reshape102,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv658 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_12_self_attn_out_proj_weight, reshape103, model_encoder_layers_12_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add88 = R.call_tir(cls.add4, (lv19, lv658), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm25 = R.call_tir(cls.layer_norm1, (add88, model_encoder_layers_12_final_layer_norm_weight, model_encoder_layers_12_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv108 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_12_fc1_weight, layer_norm25, model_encoder_layers_12_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv659 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_12_fc2_weight, lv108, model_encoder_layers_12_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv20 = R.call_tir(cls.fused_add4_maximum_minimum, (add88, lv659), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm26 = R.call_tir(cls.layer_norm1, (lv20, model_encoder_layers_13_self_attn_layer_norm_weight, model_encoder_layers_13_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv660 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_13_self_attn_q_proj_weight, layer_norm26, model_encoder_layers_13_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape104 = R.call_tir(cls.reshape, (lv660,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv144 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_13_self_attn_k_proj_weight, layer_norm26), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape105 = R.call_tir(cls.reshape, (lv144,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv661 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_13_self_attn_v_proj_weight, layer_norm26, model_encoder_layers_13_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape106 = R.call_tir(cls.reshape, (lv661,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape107 = R.call_tir(cls.reshape1, (reshape104,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape108 = R.call_tir(cls.reshape1, (reshape105,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape109 = R.call_tir(cls.reshape1, (reshape106,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv17_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), reshape107, reshape108, reshape109), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape110 = R.call_tir(cls.reshape10, (lv17_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape111 = R.call_tir(cls.reshape11, (reshape110,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv662 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_13_self_attn_out_proj_weight, reshape111, model_encoder_layers_13_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add95 = R.call_tir(cls.add4, (lv20, lv662), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm27 = R.call_tir(cls.layer_norm1, (add95, model_encoder_layers_13_final_layer_norm_weight, model_encoder_layers_13_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv109 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_13_fc1_weight, layer_norm27, model_encoder_layers_13_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv663 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_13_fc2_weight, lv109, model_encoder_layers_13_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv21 = R.call_tir(cls.fused_add4_maximum_minimum, (add95, lv663), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm28 = R.call_tir(cls.layer_norm1, (lv21, model_encoder_layers_14_self_attn_layer_norm_weight, model_encoder_layers_14_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv664 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_14_self_attn_q_proj_weight, layer_norm28, model_encoder_layers_14_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape112 = R.call_tir(cls.reshape, (lv664,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv145 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_14_self_attn_k_proj_weight, layer_norm28), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape113 = R.call_tir(cls.reshape, (lv145,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv665 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_14_self_attn_v_proj_weight, layer_norm28, model_encoder_layers_14_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape114 = R.call_tir(cls.reshape, (lv665,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape115 = R.call_tir(cls.reshape1, (reshape112,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape116 = R.call_tir(cls.reshape1, (reshape113,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape117 = R.call_tir(cls.reshape1, (reshape114,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv18_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), reshape115, reshape116, reshape117), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape118 = R.call_tir(cls.reshape10, (lv18_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape119 = R.call_tir(cls.reshape11, (reshape118,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv666 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_14_self_attn_out_proj_weight, reshape119, model_encoder_layers_14_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add102 = R.call_tir(cls.add4, (lv21, lv666), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm29 = R.call_tir(cls.layer_norm1, (add102, model_encoder_layers_14_final_layer_norm_weight, model_encoder_layers_14_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv110 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_14_fc1_weight, layer_norm29, model_encoder_layers_14_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv667 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_14_fc2_weight, lv110, model_encoder_layers_14_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv22 = R.call_tir(cls.fused_add4_maximum_minimum, (add102, lv667), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm30 = R.call_tir(cls.layer_norm1, (lv22, model_encoder_layers_15_self_attn_layer_norm_weight, model_encoder_layers_15_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv668 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_15_self_attn_q_proj_weight, layer_norm30, model_encoder_layers_15_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape120 = R.call_tir(cls.reshape, (lv668,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv146 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_15_self_attn_k_proj_weight, layer_norm30), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape121 = R.call_tir(cls.reshape, (lv146,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv669 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_15_self_attn_v_proj_weight, layer_norm30, model_encoder_layers_15_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape122 = R.call_tir(cls.reshape, (lv669,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape123 = R.call_tir(cls.reshape1, (reshape120,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape124 = R.call_tir(cls.reshape1, (reshape121,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape125 = R.call_tir(cls.reshape1, (reshape122,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv19_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), reshape123, reshape124, reshape125), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape126 = R.call_tir(cls.reshape10, (lv19_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape127 = R.call_tir(cls.reshape11, (reshape126,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv670 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_15_self_attn_out_proj_weight, reshape127, model_encoder_layers_15_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add109 = R.call_tir(cls.add4, (lv22, lv670), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm31 = R.call_tir(cls.layer_norm1, (add109, model_encoder_layers_15_final_layer_norm_weight, model_encoder_layers_15_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv111 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_15_fc1_weight, layer_norm31, model_encoder_layers_15_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv671 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_15_fc2_weight, lv111, model_encoder_layers_15_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv23 = R.call_tir(cls.fused_add4_maximum_minimum, (add109, lv671), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm32 = R.call_tir(cls.layer_norm1, (lv23, model_encoder_layers_16_self_attn_layer_norm_weight, model_encoder_layers_16_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv672 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_16_self_attn_q_proj_weight, layer_norm32, model_encoder_layers_16_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape128 = R.call_tir(cls.reshape, (lv672,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv147 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_16_self_attn_k_proj_weight, layer_norm32), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape129 = R.call_tir(cls.reshape, (lv147,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv673 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_16_self_attn_v_proj_weight, layer_norm32, model_encoder_layers_16_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape130 = R.call_tir(cls.reshape, (lv673,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape131 = R.call_tir(cls.reshape1, (reshape128,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape132 = R.call_tir(cls.reshape1, (reshape129,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape133 = R.call_tir(cls.reshape1, (reshape130,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv20_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), reshape131, reshape132, reshape133), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape134 = R.call_tir(cls.reshape10, (lv20_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape135 = R.call_tir(cls.reshape11, (reshape134,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv674 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_16_self_attn_out_proj_weight, reshape135, model_encoder_layers_16_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add116 = R.call_tir(cls.add4, (lv23, lv674), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm33 = R.call_tir(cls.layer_norm1, (add116, model_encoder_layers_16_final_layer_norm_weight, model_encoder_layers_16_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv112 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_16_fc1_weight, layer_norm33, model_encoder_layers_16_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv675 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_16_fc2_weight, lv112, model_encoder_layers_16_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv24 = R.call_tir(cls.fused_add4_maximum_minimum, (add116, lv675), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm34 = R.call_tir(cls.layer_norm1, (lv24, model_encoder_layers_17_self_attn_layer_norm_weight, model_encoder_layers_17_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv676 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_17_self_attn_q_proj_weight, layer_norm34, model_encoder_layers_17_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape136 = R.call_tir(cls.reshape, (lv676,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv148 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_17_self_attn_k_proj_weight, layer_norm34), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape137 = R.call_tir(cls.reshape, (lv148,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv677 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_17_self_attn_v_proj_weight, layer_norm34, model_encoder_layers_17_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape138 = R.call_tir(cls.reshape, (lv677,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape139 = R.call_tir(cls.reshape1, (reshape136,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape140 = R.call_tir(cls.reshape1, (reshape137,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape141 = R.call_tir(cls.reshape1, (reshape138,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv21_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), reshape139, reshape140, reshape141), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape142 = R.call_tir(cls.reshape10, (lv21_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape143 = R.call_tir(cls.reshape11, (reshape142,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv678 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_17_self_attn_out_proj_weight, reshape143, model_encoder_layers_17_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add123 = R.call_tir(cls.add4, (lv24, lv678), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm35 = R.call_tir(cls.layer_norm1, (add123, model_encoder_layers_17_final_layer_norm_weight, model_encoder_layers_17_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv113 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_17_fc1_weight, layer_norm35, model_encoder_layers_17_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv679 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_17_fc2_weight, lv113, model_encoder_layers_17_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv25 = R.call_tir(cls.fused_add4_maximum_minimum, (add123, lv679), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm36 = R.call_tir(cls.layer_norm1, (lv25, model_encoder_layers_18_self_attn_layer_norm_weight, model_encoder_layers_18_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv680 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_18_self_attn_q_proj_weight, layer_norm36, model_encoder_layers_18_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape144 = R.call_tir(cls.reshape, (lv680,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv149 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_18_self_attn_k_proj_weight, layer_norm36), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape145 = R.call_tir(cls.reshape, (lv149,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv681 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_18_self_attn_v_proj_weight, layer_norm36, model_encoder_layers_18_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape146 = R.call_tir(cls.reshape, (lv681,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape147 = R.call_tir(cls.reshape1, (reshape144,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape148 = R.call_tir(cls.reshape1, (reshape145,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape149 = R.call_tir(cls.reshape1, (reshape146,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv22_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), reshape147, reshape148, reshape149), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape150 = R.call_tir(cls.reshape10, (lv22_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape151 = R.call_tir(cls.reshape11, (reshape150,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv682 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_18_self_attn_out_proj_weight, reshape151, model_encoder_layers_18_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add130 = R.call_tir(cls.add4, (lv25, lv682), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm37 = R.call_tir(cls.layer_norm1, (add130, model_encoder_layers_18_final_layer_norm_weight, model_encoder_layers_18_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv114 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_18_fc1_weight, layer_norm37, model_encoder_layers_18_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv683 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_18_fc2_weight, lv114, model_encoder_layers_18_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv26 = R.call_tir(cls.fused_add4_maximum_minimum, (add130, lv683), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm38 = R.call_tir(cls.layer_norm1, (lv26, model_encoder_layers_19_self_attn_layer_norm_weight, model_encoder_layers_19_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv684 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_19_self_attn_q_proj_weight, layer_norm38, model_encoder_layers_19_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape152 = R.call_tir(cls.reshape, (lv684,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv150 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_19_self_attn_k_proj_weight, layer_norm38), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape153 = R.call_tir(cls.reshape, (lv150,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv685 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_19_self_attn_v_proj_weight, layer_norm38, model_encoder_layers_19_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape154 = R.call_tir(cls.reshape, (lv685,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape155 = R.call_tir(cls.reshape1, (reshape152,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape156 = R.call_tir(cls.reshape1, (reshape153,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape157 = R.call_tir(cls.reshape1, (reshape154,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv23_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), reshape155, reshape156, reshape157), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape158 = R.call_tir(cls.reshape10, (lv23_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape159 = R.call_tir(cls.reshape11, (reshape158,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv686 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_19_self_attn_out_proj_weight, reshape159, model_encoder_layers_19_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add137 = R.call_tir(cls.add4, (lv26, lv686), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm39 = R.call_tir(cls.layer_norm1, (add137, model_encoder_layers_19_final_layer_norm_weight, model_encoder_layers_19_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv115 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_19_fc1_weight, layer_norm39, model_encoder_layers_19_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv687 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_19_fc2_weight, lv115, model_encoder_layers_19_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv27 = R.call_tir(cls.fused_add4_maximum_minimum, (add137, lv687), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm40 = R.call_tir(cls.layer_norm1, (lv27, model_encoder_layers_20_self_attn_layer_norm_weight, model_encoder_layers_20_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv688 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_20_self_attn_q_proj_weight, layer_norm40, model_encoder_layers_20_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape160 = R.call_tir(cls.reshape, (lv688,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv151 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_20_self_attn_k_proj_weight, layer_norm40), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape161 = R.call_tir(cls.reshape, (lv151,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv689 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_20_self_attn_v_proj_weight, layer_norm40, model_encoder_layers_20_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape162 = R.call_tir(cls.reshape, (lv689,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape163 = R.call_tir(cls.reshape1, (reshape160,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape164 = R.call_tir(cls.reshape1, (reshape161,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape165 = R.call_tir(cls.reshape1, (reshape162,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv24_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), reshape163, reshape164, reshape165), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape166 = R.call_tir(cls.reshape10, (lv24_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape167 = R.call_tir(cls.reshape11, (reshape166,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv690 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_20_self_attn_out_proj_weight, reshape167, model_encoder_layers_20_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add144 = R.call_tir(cls.add4, (lv27, lv690), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm41 = R.call_tir(cls.layer_norm1, (add144, model_encoder_layers_20_final_layer_norm_weight, model_encoder_layers_20_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv116 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_20_fc1_weight, layer_norm41, model_encoder_layers_20_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv691 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_20_fc2_weight, lv116, model_encoder_layers_20_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv28 = R.call_tir(cls.fused_add4_maximum_minimum, (add144, lv691), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm42 = R.call_tir(cls.layer_norm1, (lv28, model_encoder_layers_21_self_attn_layer_norm_weight, model_encoder_layers_21_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv692 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_21_self_attn_q_proj_weight, layer_norm42, model_encoder_layers_21_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape168 = R.call_tir(cls.reshape, (lv692,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv152 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_21_self_attn_k_proj_weight, layer_norm42), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape169 = R.call_tir(cls.reshape, (lv152,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv693 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_21_self_attn_v_proj_weight, layer_norm42, model_encoder_layers_21_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape170 = R.call_tir(cls.reshape, (lv693,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape171 = R.call_tir(cls.reshape1, (reshape168,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape172 = R.call_tir(cls.reshape1, (reshape169,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape173 = R.call_tir(cls.reshape1, (reshape170,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv25_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), reshape171, reshape172, reshape173), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape174 = R.call_tir(cls.reshape10, (lv25_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape175 = R.call_tir(cls.reshape11, (reshape174,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv694 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_21_self_attn_out_proj_weight, reshape175, model_encoder_layers_21_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add151 = R.call_tir(cls.add4, (lv28, lv694), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm43 = R.call_tir(cls.layer_norm1, (add151, model_encoder_layers_21_final_layer_norm_weight, model_encoder_layers_21_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv117 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_21_fc1_weight, layer_norm43, model_encoder_layers_21_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv695 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_21_fc2_weight, lv117, model_encoder_layers_21_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv29 = R.call_tir(cls.fused_add4_maximum_minimum, (add151, lv695), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm44 = R.call_tir(cls.layer_norm1, (lv29, model_encoder_layers_22_self_attn_layer_norm_weight, model_encoder_layers_22_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv696 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_22_self_attn_q_proj_weight, layer_norm44, model_encoder_layers_22_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape176 = R.call_tir(cls.reshape, (lv696,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv153 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_22_self_attn_k_proj_weight, layer_norm44), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape177 = R.call_tir(cls.reshape, (lv153,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv697 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_22_self_attn_v_proj_weight, layer_norm44, model_encoder_layers_22_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape178 = R.call_tir(cls.reshape, (lv697,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape179 = R.call_tir(cls.reshape1, (reshape176,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape180 = R.call_tir(cls.reshape1, (reshape177,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape181 = R.call_tir(cls.reshape1, (reshape178,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv26_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), reshape179, reshape180, reshape181), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape182 = R.call_tir(cls.reshape10, (lv26_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape183 = R.call_tir(cls.reshape11, (reshape182,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv698 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_22_self_attn_out_proj_weight, reshape183, model_encoder_layers_22_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add158 = R.call_tir(cls.add4, (lv29, lv698), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm45 = R.call_tir(cls.layer_norm1, (add158, model_encoder_layers_22_final_layer_norm_weight, model_encoder_layers_22_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv118 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_22_fc1_weight, layer_norm45, model_encoder_layers_22_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv699 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_22_fc2_weight, lv118, model_encoder_layers_22_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv30 = R.call_tir(cls.fused_add4_maximum_minimum, (add158, lv699), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm46 = R.call_tir(cls.layer_norm1, (lv30, model_encoder_layers_23_self_attn_layer_norm_weight, model_encoder_layers_23_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv700 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_23_self_attn_q_proj_weight, layer_norm46, model_encoder_layers_23_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape184 = R.call_tir(cls.reshape, (lv700,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv154 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_23_self_attn_k_proj_weight, layer_norm46), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape185 = R.call_tir(cls.reshape, (lv154,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv701 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_23_self_attn_v_proj_weight, layer_norm46, model_encoder_layers_23_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape186 = R.call_tir(cls.reshape, (lv701,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape187 = R.call_tir(cls.reshape1, (reshape184,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape188 = R.call_tir(cls.reshape1, (reshape185,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape189 = R.call_tir(cls.reshape1, (reshape186,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv27_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), reshape187, reshape188, reshape189), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape190 = R.call_tir(cls.reshape10, (lv27_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape191 = R.call_tir(cls.reshape11, (reshape190,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv702 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_23_self_attn_out_proj_weight, reshape191, model_encoder_layers_23_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add165 = R.call_tir(cls.add4, (lv30, lv702), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm47 = R.call_tir(cls.layer_norm1, (add165, model_encoder_layers_23_final_layer_norm_weight, model_encoder_layers_23_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv119 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_23_fc1_weight, layer_norm47, model_encoder_layers_23_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv703 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_23_fc2_weight, lv119, model_encoder_layers_23_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv31 = R.call_tir(cls.fused_add4_maximum_minimum, (add165, lv703), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm48 = R.call_tir(cls.layer_norm1, (lv31, model_encoder_layers_24_self_attn_layer_norm_weight, model_encoder_layers_24_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv704 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_24_self_attn_q_proj_weight, layer_norm48, model_encoder_layers_24_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape192 = R.call_tir(cls.reshape, (lv704,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv155 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_24_self_attn_k_proj_weight, layer_norm48), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape193 = R.call_tir(cls.reshape, (lv155,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv705 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_24_self_attn_v_proj_weight, layer_norm48, model_encoder_layers_24_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape194 = R.call_tir(cls.reshape, (lv705,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape195 = R.call_tir(cls.reshape1, (reshape192,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape196 = R.call_tir(cls.reshape1, (reshape193,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape197 = R.call_tir(cls.reshape1, (reshape194,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv28_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), reshape195, reshape196, reshape197), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape198 = R.call_tir(cls.reshape10, (lv28_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape199 = R.call_tir(cls.reshape11, (reshape198,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv706 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_24_self_attn_out_proj_weight, reshape199, model_encoder_layers_24_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add172 = R.call_tir(cls.add4, (lv31, lv706), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm49 = R.call_tir(cls.layer_norm1, (add172, model_encoder_layers_24_final_layer_norm_weight, model_encoder_layers_24_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv120 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_24_fc1_weight, layer_norm49, model_encoder_layers_24_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv707 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_24_fc2_weight, lv120, model_encoder_layers_24_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv32 = R.call_tir(cls.fused_add4_maximum_minimum, (add172, lv707), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm50 = R.call_tir(cls.layer_norm1, (lv32, model_encoder_layers_25_self_attn_layer_norm_weight, model_encoder_layers_25_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv708 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_25_self_attn_q_proj_weight, layer_norm50, model_encoder_layers_25_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape200 = R.call_tir(cls.reshape, (lv708,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv156 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_25_self_attn_k_proj_weight, layer_norm50), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape201 = R.call_tir(cls.reshape, (lv156,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv709 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_25_self_attn_v_proj_weight, layer_norm50, model_encoder_layers_25_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape202 = R.call_tir(cls.reshape, (lv709,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape203 = R.call_tir(cls.reshape1, (reshape200,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape204 = R.call_tir(cls.reshape1, (reshape201,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape205 = R.call_tir(cls.reshape1, (reshape202,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv29_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), reshape203, reshape204, reshape205), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape206 = R.call_tir(cls.reshape10, (lv29_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape207 = R.call_tir(cls.reshape11, (reshape206,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv710 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_25_self_attn_out_proj_weight, reshape207, model_encoder_layers_25_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add179 = R.call_tir(cls.add4, (lv32, lv710), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm51 = R.call_tir(cls.layer_norm1, (add179, model_encoder_layers_25_final_layer_norm_weight, model_encoder_layers_25_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv121 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_25_fc1_weight, layer_norm51, model_encoder_layers_25_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv711 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_25_fc2_weight, lv121, model_encoder_layers_25_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv33 = R.call_tir(cls.fused_add4_maximum_minimum, (add179, lv711), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm52 = R.call_tir(cls.layer_norm1, (lv33, model_encoder_layers_26_self_attn_layer_norm_weight, model_encoder_layers_26_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv712 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_26_self_attn_q_proj_weight, layer_norm52, model_encoder_layers_26_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape208 = R.call_tir(cls.reshape, (lv712,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv157 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_26_self_attn_k_proj_weight, layer_norm52), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape209 = R.call_tir(cls.reshape, (lv157,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv713 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_26_self_attn_v_proj_weight, layer_norm52, model_encoder_layers_26_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape210 = R.call_tir(cls.reshape, (lv713,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape211 = R.call_tir(cls.reshape1, (reshape208,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape212 = R.call_tir(cls.reshape1, (reshape209,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape213 = R.call_tir(cls.reshape1, (reshape210,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv30_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), reshape211, reshape212, reshape213), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape214 = R.call_tir(cls.reshape10, (lv30_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape215 = R.call_tir(cls.reshape11, (reshape214,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv714 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_26_self_attn_out_proj_weight, reshape215, model_encoder_layers_26_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add186 = R.call_tir(cls.add4, (lv33, lv714), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm53 = R.call_tir(cls.layer_norm1, (add186, model_encoder_layers_26_final_layer_norm_weight, model_encoder_layers_26_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv122 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_26_fc1_weight, layer_norm53, model_encoder_layers_26_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv715 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_26_fc2_weight, lv122, model_encoder_layers_26_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv34 = R.call_tir(cls.fused_add4_maximum_minimum, (add186, lv715), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm54 = R.call_tir(cls.layer_norm1, (lv34, model_encoder_layers_27_self_attn_layer_norm_weight, model_encoder_layers_27_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv716 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_27_self_attn_q_proj_weight, layer_norm54, model_encoder_layers_27_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape216 = R.call_tir(cls.reshape, (lv716,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv158 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_27_self_attn_k_proj_weight, layer_norm54), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape217 = R.call_tir(cls.reshape, (lv158,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv717 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_27_self_attn_v_proj_weight, layer_norm54, model_encoder_layers_27_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape218 = R.call_tir(cls.reshape, (lv717,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape219 = R.call_tir(cls.reshape1, (reshape216,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape220 = R.call_tir(cls.reshape1, (reshape217,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape221 = R.call_tir(cls.reshape1, (reshape218,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv31_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), reshape219, reshape220, reshape221), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape222 = R.call_tir(cls.reshape10, (lv31_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape223 = R.call_tir(cls.reshape11, (reshape222,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv718 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_27_self_attn_out_proj_weight, reshape223, model_encoder_layers_27_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add193 = R.call_tir(cls.add4, (lv34, lv718), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm55 = R.call_tir(cls.layer_norm1, (add193, model_encoder_layers_27_final_layer_norm_weight, model_encoder_layers_27_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv123 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_27_fc1_weight, layer_norm55, model_encoder_layers_27_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv719 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_27_fc2_weight, lv123, model_encoder_layers_27_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv35 = R.call_tir(cls.fused_add4_maximum_minimum, (add193, lv719), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm56 = R.call_tir(cls.layer_norm1, (lv35, model_encoder_layers_28_self_attn_layer_norm_weight, model_encoder_layers_28_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv720 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_28_self_attn_q_proj_weight, layer_norm56, model_encoder_layers_28_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape224 = R.call_tir(cls.reshape, (lv720,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv159 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_28_self_attn_k_proj_weight, layer_norm56), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape225 = R.call_tir(cls.reshape, (lv159,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv721 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_28_self_attn_v_proj_weight, layer_norm56, model_encoder_layers_28_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape226 = R.call_tir(cls.reshape, (lv721,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape227 = R.call_tir(cls.reshape1, (reshape224,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape228 = R.call_tir(cls.reshape1, (reshape225,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape229 = R.call_tir(cls.reshape1, (reshape226,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv32_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), reshape227, reshape228, reshape229), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape230 = R.call_tir(cls.reshape10, (lv32_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape231 = R.call_tir(cls.reshape11, (reshape230,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv722 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_28_self_attn_out_proj_weight, reshape231, model_encoder_layers_28_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add200 = R.call_tir(cls.add4, (lv35, lv722), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm57 = R.call_tir(cls.layer_norm1, (add200, model_encoder_layers_28_final_layer_norm_weight, model_encoder_layers_28_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv124 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_28_fc1_weight, layer_norm57, model_encoder_layers_28_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv723 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_28_fc2_weight, lv124, model_encoder_layers_28_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv36 = R.call_tir(cls.fused_add4_maximum_minimum, (add200, lv723), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm58 = R.call_tir(cls.layer_norm1, (lv36, model_encoder_layers_29_self_attn_layer_norm_weight, model_encoder_layers_29_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv724 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_29_self_attn_q_proj_weight, layer_norm58, model_encoder_layers_29_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape232 = R.call_tir(cls.reshape, (lv724,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv160 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_29_self_attn_k_proj_weight, layer_norm58), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape233 = R.call_tir(cls.reshape, (lv160,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv725 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_29_self_attn_v_proj_weight, layer_norm58, model_encoder_layers_29_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape234 = R.call_tir(cls.reshape, (lv725,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape235 = R.call_tir(cls.reshape1, (reshape232,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape236 = R.call_tir(cls.reshape1, (reshape233,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape237 = R.call_tir(cls.reshape1, (reshape234,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv33_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), reshape235, reshape236, reshape237), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape238 = R.call_tir(cls.reshape10, (lv33_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape239 = R.call_tir(cls.reshape11, (reshape238,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv726 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_29_self_attn_out_proj_weight, reshape239, model_encoder_layers_29_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add207 = R.call_tir(cls.add4, (lv36, lv726), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm59 = R.call_tir(cls.layer_norm1, (add207, model_encoder_layers_29_final_layer_norm_weight, model_encoder_layers_29_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv125 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_29_fc1_weight, layer_norm59, model_encoder_layers_29_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv727 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_29_fc2_weight, lv125, model_encoder_layers_29_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv37 = R.call_tir(cls.fused_add4_maximum_minimum, (add207, lv727), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm60 = R.call_tir(cls.layer_norm1, (lv37, model_encoder_layers_30_self_attn_layer_norm_weight, model_encoder_layers_30_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv728 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_30_self_attn_q_proj_weight, layer_norm60, model_encoder_layers_30_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape240 = R.call_tir(cls.reshape, (lv728,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv161 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_30_self_attn_k_proj_weight, layer_norm60), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape241 = R.call_tir(cls.reshape, (lv161,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv729 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_30_self_attn_v_proj_weight, layer_norm60, model_encoder_layers_30_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape242 = R.call_tir(cls.reshape, (lv729,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape243 = R.call_tir(cls.reshape1, (reshape240,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape244 = R.call_tir(cls.reshape1, (reshape241,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape245 = R.call_tir(cls.reshape1, (reshape242,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv34_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), reshape243, reshape244, reshape245), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape246 = R.call_tir(cls.reshape10, (lv34_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape247 = R.call_tir(cls.reshape11, (reshape246,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv730 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_30_self_attn_out_proj_weight, reshape247, model_encoder_layers_30_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add214 = R.call_tir(cls.add4, (lv37, lv730), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm61 = R.call_tir(cls.layer_norm1, (add214, model_encoder_layers_30_final_layer_norm_weight, model_encoder_layers_30_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv126 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_30_fc1_weight, layer_norm61, model_encoder_layers_30_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv731 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_30_fc2_weight, lv126, model_encoder_layers_30_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv38 = R.call_tir(cls.fused_add4_maximum_minimum, (add214, lv731), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm62 = R.call_tir(cls.layer_norm1, (lv38, model_encoder_layers_31_self_attn_layer_norm_weight, model_encoder_layers_31_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv732 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_31_self_attn_q_proj_weight, layer_norm62, model_encoder_layers_31_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape248 = R.call_tir(cls.reshape, (lv732,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv162 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_31_self_attn_k_proj_weight, layer_norm62), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape249 = R.call_tir(cls.reshape, (lv162,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv733 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_31_self_attn_v_proj_weight, layer_norm62, model_encoder_layers_31_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape250 = R.call_tir(cls.reshape, (lv733,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape251 = R.call_tir(cls.reshape1, (reshape248,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape252 = R.call_tir(cls.reshape1, (reshape249,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape253 = R.call_tir(cls.reshape1, (reshape250,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv35_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), reshape251, reshape252, reshape253), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape254 = R.call_tir(cls.reshape10, (lv35_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape255 = R.call_tir(cls.reshape11, (reshape254,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv734 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_31_self_attn_out_proj_weight, reshape255, model_encoder_layers_31_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add221 = R.call_tir(cls.add4, (lv38, lv734), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm63 = R.call_tir(cls.layer_norm1, (add221, model_encoder_layers_31_final_layer_norm_weight, model_encoder_layers_31_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv127 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_31_fc1_weight, layer_norm63, model_encoder_layers_31_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv735 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_31_fc2_weight, lv127, model_encoder_layers_31_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv39 = R.call_tir(cls.fused_add4_maximum_minimum, (add221, lv735), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + gv = R.call_tir(cls.layer_norm1, (lv39, model_encoder_layer_norm_weight, model_encoder_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + R.output(gv) + return gv + + @R.function + def batch_prefill(input_ids: R.Tensor((1, "seq_len"), dtype="int32"), logit_positions: R.Tensor(("batch_size",), dtype="int32"), paged_kv_cache: R.Object, packed_params: R.Tuple(R.Tensor((1280, 128, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1500, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), 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dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), 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dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"))) -> R.Tensor((1, "batch_size", 51866), dtype="float32"): + batch_size = T.int64() + seq_len = T.int64() + R.func_attr({"num_input": 3, "relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "seq_len": 15000, "total_seq_len": 1500}}) + cls = Module + with R.dataflow(): + model_decoder_embed_tokens_weight2: R.Tensor((51866, 1280), dtype="float16") = packed_params[487] + model_decoder_embed_positions_weight2: R.Tensor((448, 1280), dtype="float16") = packed_params[488] + model_decoder_layers_0_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[489] + model_decoder_layers_0_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[490] + model_decoder_layers_0_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[491] + model_decoder_layers_0_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[492] + model_decoder_layers_0_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[493] + model_decoder_layers_0_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[494] + model_decoder_layers_0_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[495] + model_decoder_layers_0_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[496] + model_decoder_layers_0_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[497] + model_decoder_layers_0_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[501] + model_decoder_layers_0_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[502] + model_decoder_layers_0_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[503] + model_decoder_layers_0_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[504] + model_decoder_layers_0_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[505] + model_decoder_layers_0_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[506] + model_decoder_layers_0_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[507] + model_decoder_layers_0_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[508] + model_decoder_layers_0_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[509] + model_decoder_layers_0_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[510] + model_decoder_layers_0_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[511] + model_decoder_layers_0_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[512] + model_decoder_layers_1_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[513] + model_decoder_layers_1_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[514] + model_decoder_layers_1_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[515] + model_decoder_layers_1_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[516] + model_decoder_layers_1_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[517] + model_decoder_layers_1_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[518] + model_decoder_layers_1_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[519] + model_decoder_layers_1_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[520] + model_decoder_layers_1_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[521] + model_decoder_layers_1_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[525] + model_decoder_layers_1_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[526] + model_decoder_layers_1_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[527] + model_decoder_layers_1_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[528] + model_decoder_layers_1_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[529] + model_decoder_layers_1_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[530] + model_decoder_layers_1_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[531] + model_decoder_layers_1_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[532] + model_decoder_layers_1_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[533] + model_decoder_layers_1_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[534] + model_decoder_layers_1_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[535] + model_decoder_layers_1_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[536] + model_decoder_layers_2_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[537] + model_decoder_layers_2_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[538] + model_decoder_layers_2_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[539] + model_decoder_layers_2_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[540] + model_decoder_layers_2_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[541] + model_decoder_layers_2_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[542] + model_decoder_layers_2_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[543] + model_decoder_layers_2_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[544] + model_decoder_layers_2_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[545] + model_decoder_layers_2_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[549] + model_decoder_layers_2_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[550] + model_decoder_layers_2_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[551] + model_decoder_layers_2_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[552] + model_decoder_layers_2_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[553] + model_decoder_layers_2_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[554] + model_decoder_layers_2_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[555] + model_decoder_layers_2_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[556] + model_decoder_layers_2_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[557] + model_decoder_layers_2_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[558] + model_decoder_layers_2_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[559] + model_decoder_layers_2_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[560] + model_decoder_layers_3_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[561] + model_decoder_layers_3_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[562] + model_decoder_layers_3_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[563] + model_decoder_layers_3_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[564] + model_decoder_layers_3_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[565] + model_decoder_layers_3_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[566] + model_decoder_layers_3_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[567] + model_decoder_layers_3_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[568] + model_decoder_layers_3_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[569] + model_decoder_layers_3_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[573] + model_decoder_layers_3_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[574] + model_decoder_layers_3_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[575] + model_decoder_layers_3_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[576] + model_decoder_layers_3_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[577] + model_decoder_layers_3_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[578] + model_decoder_layers_3_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[579] + model_decoder_layers_3_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[580] + model_decoder_layers_3_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[581] + model_decoder_layers_3_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[582] + model_decoder_layers_3_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[583] + model_decoder_layers_3_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[584] + model_decoder_layers_4_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[585] + model_decoder_layers_4_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[586] + model_decoder_layers_4_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[587] + model_decoder_layers_4_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[588] + model_decoder_layers_4_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[589] + model_decoder_layers_4_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[590] + model_decoder_layers_4_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[591] + model_decoder_layers_4_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[592] + model_decoder_layers_4_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[593] + model_decoder_layers_4_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[597] + model_decoder_layers_4_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[598] + model_decoder_layers_4_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[599] + model_decoder_layers_4_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[600] + model_decoder_layers_4_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[601] + model_decoder_layers_4_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[602] + model_decoder_layers_4_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[603] + model_decoder_layers_4_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[604] + model_decoder_layers_4_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[605] + model_decoder_layers_4_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[606] + model_decoder_layers_4_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[607] + model_decoder_layers_4_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[608] + model_decoder_layers_5_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[609] + model_decoder_layers_5_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[610] + model_decoder_layers_5_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[611] + model_decoder_layers_5_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[612] + model_decoder_layers_5_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[613] + model_decoder_layers_5_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[614] + model_decoder_layers_5_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[615] + model_decoder_layers_5_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[616] + model_decoder_layers_5_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[617] + model_decoder_layers_5_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[621] + model_decoder_layers_5_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[622] + model_decoder_layers_5_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[623] + model_decoder_layers_5_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[624] + model_decoder_layers_5_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[625] + model_decoder_layers_5_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[626] + model_decoder_layers_5_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[627] + model_decoder_layers_5_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[628] + model_decoder_layers_5_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[629] + model_decoder_layers_5_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[630] + model_decoder_layers_5_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[631] + model_decoder_layers_5_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[632] + model_decoder_layers_6_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[633] + model_decoder_layers_6_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[634] + model_decoder_layers_6_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[635] + model_decoder_layers_6_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[636] + model_decoder_layers_6_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[637] + model_decoder_layers_6_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[638] + model_decoder_layers_6_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[639] + model_decoder_layers_6_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[640] + model_decoder_layers_6_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[641] + model_decoder_layers_6_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[645] + model_decoder_layers_6_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[646] + model_decoder_layers_6_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[647] + model_decoder_layers_6_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[648] + model_decoder_layers_6_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[649] + model_decoder_layers_6_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[650] + model_decoder_layers_6_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[651] + model_decoder_layers_6_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[652] + model_decoder_layers_6_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[653] + model_decoder_layers_6_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[654] + model_decoder_layers_6_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[655] + model_decoder_layers_6_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[656] + model_decoder_layers_7_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[657] + model_decoder_layers_7_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[658] + model_decoder_layers_7_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[659] + model_decoder_layers_7_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[660] + model_decoder_layers_7_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[661] + model_decoder_layers_7_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[662] + model_decoder_layers_7_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[663] + model_decoder_layers_7_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[664] + model_decoder_layers_7_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[665] + model_decoder_layers_7_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[669] + model_decoder_layers_7_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[670] + model_decoder_layers_7_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[671] + model_decoder_layers_7_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[672] + model_decoder_layers_7_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[673] + model_decoder_layers_7_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[674] + model_decoder_layers_7_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[675] + model_decoder_layers_7_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[676] + model_decoder_layers_7_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[677] + model_decoder_layers_7_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[678] + model_decoder_layers_7_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[679] + model_decoder_layers_7_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[680] + model_decoder_layers_8_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[681] + model_decoder_layers_8_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[682] + model_decoder_layers_8_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[683] + model_decoder_layers_8_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[684] + model_decoder_layers_8_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[685] + model_decoder_layers_8_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[686] + model_decoder_layers_8_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[687] + model_decoder_layers_8_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[688] + model_decoder_layers_8_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[689] + model_decoder_layers_8_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[693] + model_decoder_layers_8_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[694] + model_decoder_layers_8_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[695] + model_decoder_layers_8_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[696] + model_decoder_layers_8_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[697] + model_decoder_layers_8_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[698] + model_decoder_layers_8_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[699] + model_decoder_layers_8_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[700] + model_decoder_layers_8_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[701] + model_decoder_layers_8_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[702] + model_decoder_layers_8_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[703] + model_decoder_layers_8_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[704] + model_decoder_layers_9_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[705] + model_decoder_layers_9_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[706] + model_decoder_layers_9_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[707] + model_decoder_layers_9_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[708] + model_decoder_layers_9_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[709] + model_decoder_layers_9_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[710] + model_decoder_layers_9_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[711] + model_decoder_layers_9_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[712] + model_decoder_layers_9_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[713] + model_decoder_layers_9_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[717] + model_decoder_layers_9_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[718] + model_decoder_layers_9_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[719] + model_decoder_layers_9_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[720] + model_decoder_layers_9_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[721] + model_decoder_layers_9_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[722] + model_decoder_layers_9_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[723] + model_decoder_layers_9_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[724] + model_decoder_layers_9_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[725] + model_decoder_layers_9_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[726] + model_decoder_layers_9_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[727] + model_decoder_layers_9_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[728] + model_decoder_layers_10_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[729] + model_decoder_layers_10_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[730] + model_decoder_layers_10_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[731] + model_decoder_layers_10_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[732] + model_decoder_layers_10_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[733] + model_decoder_layers_10_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[734] + model_decoder_layers_10_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[735] + model_decoder_layers_10_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[736] + model_decoder_layers_10_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[737] + model_decoder_layers_10_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[741] + model_decoder_layers_10_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[742] + model_decoder_layers_10_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[743] + model_decoder_layers_10_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[744] + model_decoder_layers_10_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[745] + model_decoder_layers_10_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[746] + model_decoder_layers_10_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[747] + model_decoder_layers_10_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[748] + model_decoder_layers_10_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[749] + model_decoder_layers_10_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[750] + model_decoder_layers_10_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[751] + model_decoder_layers_10_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[752] + model_decoder_layers_11_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[753] + model_decoder_layers_11_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[754] + model_decoder_layers_11_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[755] + model_decoder_layers_11_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[756] + model_decoder_layers_11_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[757] + model_decoder_layers_11_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[758] + model_decoder_layers_11_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[759] + model_decoder_layers_11_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[760] + model_decoder_layers_11_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[761] + model_decoder_layers_11_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[765] + model_decoder_layers_11_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[766] + model_decoder_layers_11_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[767] + model_decoder_layers_11_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[768] + model_decoder_layers_11_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[769] + model_decoder_layers_11_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[770] + model_decoder_layers_11_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[771] + model_decoder_layers_11_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[772] + model_decoder_layers_11_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[773] + model_decoder_layers_11_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[774] + model_decoder_layers_11_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[775] + model_decoder_layers_11_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[776] + model_decoder_layers_12_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[777] + model_decoder_layers_12_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[778] + model_decoder_layers_12_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[779] + model_decoder_layers_12_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[780] + model_decoder_layers_12_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[781] + model_decoder_layers_12_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[782] + model_decoder_layers_12_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[783] + model_decoder_layers_12_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[784] + model_decoder_layers_12_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[785] + model_decoder_layers_12_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[789] + model_decoder_layers_12_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[790] + model_decoder_layers_12_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[791] + model_decoder_layers_12_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[792] + model_decoder_layers_12_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[793] + model_decoder_layers_12_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[794] + model_decoder_layers_12_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[795] + model_decoder_layers_12_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[796] + model_decoder_layers_12_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[797] + model_decoder_layers_12_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[798] + model_decoder_layers_12_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[799] + model_decoder_layers_12_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[800] + model_decoder_layers_13_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[801] + model_decoder_layers_13_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[802] + model_decoder_layers_13_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[803] + model_decoder_layers_13_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[804] + model_decoder_layers_13_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[805] + model_decoder_layers_13_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[806] + model_decoder_layers_13_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[807] + model_decoder_layers_13_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[808] + model_decoder_layers_13_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[809] + model_decoder_layers_13_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[813] + model_decoder_layers_13_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[814] + model_decoder_layers_13_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[815] + model_decoder_layers_13_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[816] + model_decoder_layers_13_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[817] + model_decoder_layers_13_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[818] + model_decoder_layers_13_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[819] + model_decoder_layers_13_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[820] + model_decoder_layers_13_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[821] + model_decoder_layers_13_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[822] + model_decoder_layers_13_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[823] + model_decoder_layers_13_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[824] + model_decoder_layers_14_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[825] + model_decoder_layers_14_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[826] + model_decoder_layers_14_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[827] + model_decoder_layers_14_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[828] + model_decoder_layers_14_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[829] + model_decoder_layers_14_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[830] + model_decoder_layers_14_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[831] + model_decoder_layers_14_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[832] + model_decoder_layers_14_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[833] + model_decoder_layers_14_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[837] + model_decoder_layers_14_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[838] + model_decoder_layers_14_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[839] + model_decoder_layers_14_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[840] + model_decoder_layers_14_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[841] + model_decoder_layers_14_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[842] + model_decoder_layers_14_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[843] + model_decoder_layers_14_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[844] + model_decoder_layers_14_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[845] + model_decoder_layers_14_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[846] + model_decoder_layers_14_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[847] + model_decoder_layers_14_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[848] + model_decoder_layers_15_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[849] + model_decoder_layers_15_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[850] + model_decoder_layers_15_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[851] + model_decoder_layers_15_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[852] + model_decoder_layers_15_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[853] + model_decoder_layers_15_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[854] + model_decoder_layers_15_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[855] + model_decoder_layers_15_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[856] + model_decoder_layers_15_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[857] + model_decoder_layers_15_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[861] + model_decoder_layers_15_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[862] + model_decoder_layers_15_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[863] + model_decoder_layers_15_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[864] + model_decoder_layers_15_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[865] + model_decoder_layers_15_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[866] + model_decoder_layers_15_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[867] + model_decoder_layers_15_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[868] + model_decoder_layers_15_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[869] + model_decoder_layers_15_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[870] + model_decoder_layers_15_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[871] + model_decoder_layers_15_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[872] + model_decoder_layers_16_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[873] + model_decoder_layers_16_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[874] + model_decoder_layers_16_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[875] + model_decoder_layers_16_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[876] + model_decoder_layers_16_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[877] + model_decoder_layers_16_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[878] + model_decoder_layers_16_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[879] + model_decoder_layers_16_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[880] + model_decoder_layers_16_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[881] + model_decoder_layers_16_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[885] + model_decoder_layers_16_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[886] + model_decoder_layers_16_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[887] + model_decoder_layers_16_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[888] + model_decoder_layers_16_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[889] + model_decoder_layers_16_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[890] + model_decoder_layers_16_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[891] + model_decoder_layers_16_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[892] + model_decoder_layers_16_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[893] + model_decoder_layers_16_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[894] + model_decoder_layers_16_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[895] + model_decoder_layers_16_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[896] + model_decoder_layers_17_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[897] + model_decoder_layers_17_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[898] + model_decoder_layers_17_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[899] + model_decoder_layers_17_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[900] + model_decoder_layers_17_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[901] + model_decoder_layers_17_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[902] + model_decoder_layers_17_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[903] + model_decoder_layers_17_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[904] + model_decoder_layers_17_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[905] + model_decoder_layers_17_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[909] + model_decoder_layers_17_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[910] + model_decoder_layers_17_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[911] + model_decoder_layers_17_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[912] + model_decoder_layers_17_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[913] + model_decoder_layers_17_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[914] + model_decoder_layers_17_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[915] + model_decoder_layers_17_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[916] + model_decoder_layers_17_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[917] + model_decoder_layers_17_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[918] + model_decoder_layers_17_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[919] + model_decoder_layers_17_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[920] + model_decoder_layers_18_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[921] + model_decoder_layers_18_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[922] + model_decoder_layers_18_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[923] + model_decoder_layers_18_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[924] + model_decoder_layers_18_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[925] + model_decoder_layers_18_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[926] + model_decoder_layers_18_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[927] + model_decoder_layers_18_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[928] + model_decoder_layers_18_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[929] + model_decoder_layers_18_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[933] + model_decoder_layers_18_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[934] + model_decoder_layers_18_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[935] + model_decoder_layers_18_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[936] + model_decoder_layers_18_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[937] + model_decoder_layers_18_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[938] + model_decoder_layers_18_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[939] + model_decoder_layers_18_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[940] + model_decoder_layers_18_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[941] + model_decoder_layers_18_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[942] + model_decoder_layers_18_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[943] + model_decoder_layers_18_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[944] + model_decoder_layers_19_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[945] + model_decoder_layers_19_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[946] + model_decoder_layers_19_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[947] + model_decoder_layers_19_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[948] + model_decoder_layers_19_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[949] + model_decoder_layers_19_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[950] + model_decoder_layers_19_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[951] + model_decoder_layers_19_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[952] + model_decoder_layers_19_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[953] + model_decoder_layers_19_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[957] + model_decoder_layers_19_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[958] + model_decoder_layers_19_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[959] + model_decoder_layers_19_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[960] + model_decoder_layers_19_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[961] + model_decoder_layers_19_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[962] + model_decoder_layers_19_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[963] + model_decoder_layers_19_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[964] + model_decoder_layers_19_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[965] + model_decoder_layers_19_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[966] + model_decoder_layers_19_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[967] + model_decoder_layers_19_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[968] + model_decoder_layers_20_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[969] + model_decoder_layers_20_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[970] + model_decoder_layers_20_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[971] + model_decoder_layers_20_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[972] + model_decoder_layers_20_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[973] + model_decoder_layers_20_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[974] + model_decoder_layers_20_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[975] + model_decoder_layers_20_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[976] + model_decoder_layers_20_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[977] + model_decoder_layers_20_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[981] + model_decoder_layers_20_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[982] + model_decoder_layers_20_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[983] + model_decoder_layers_20_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[984] + model_decoder_layers_20_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[985] + model_decoder_layers_20_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[986] + model_decoder_layers_20_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[987] + model_decoder_layers_20_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[988] + model_decoder_layers_20_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[989] + model_decoder_layers_20_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[990] + model_decoder_layers_20_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[991] + model_decoder_layers_20_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[992] + model_decoder_layers_21_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[993] + model_decoder_layers_21_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[994] + model_decoder_layers_21_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[995] + model_decoder_layers_21_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[996] + model_decoder_layers_21_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[997] + model_decoder_layers_21_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[998] + model_decoder_layers_21_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[999] + model_decoder_layers_21_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1000] + model_decoder_layers_21_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1001] + model_decoder_layers_21_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1005] + model_decoder_layers_21_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1006] + model_decoder_layers_21_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1007] + model_decoder_layers_21_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1008] + model_decoder_layers_21_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1009] + model_decoder_layers_21_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1010] + model_decoder_layers_21_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1011] + model_decoder_layers_21_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1012] + model_decoder_layers_21_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1013] + model_decoder_layers_21_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1014] + model_decoder_layers_21_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1015] + model_decoder_layers_21_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1016] + model_decoder_layers_22_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1017] + model_decoder_layers_22_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1018] + model_decoder_layers_22_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1019] + model_decoder_layers_22_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1020] + model_decoder_layers_22_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1021] + model_decoder_layers_22_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1022] + model_decoder_layers_22_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1023] + model_decoder_layers_22_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1024] + model_decoder_layers_22_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1025] + model_decoder_layers_22_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1029] + model_decoder_layers_22_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1030] + model_decoder_layers_22_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1031] + model_decoder_layers_22_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1032] + model_decoder_layers_22_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1033] + model_decoder_layers_22_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1034] + model_decoder_layers_22_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1035] + model_decoder_layers_22_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1036] + model_decoder_layers_22_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1037] + model_decoder_layers_22_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1038] + model_decoder_layers_22_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1039] + model_decoder_layers_22_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1040] + model_decoder_layers_23_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1041] + model_decoder_layers_23_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1042] + model_decoder_layers_23_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1043] + model_decoder_layers_23_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1044] + model_decoder_layers_23_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1045] + model_decoder_layers_23_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1046] + model_decoder_layers_23_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1047] + model_decoder_layers_23_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1048] + model_decoder_layers_23_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1049] + model_decoder_layers_23_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1053] + model_decoder_layers_23_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1054] + model_decoder_layers_23_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1055] + model_decoder_layers_23_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1056] + model_decoder_layers_23_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1057] + model_decoder_layers_23_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1058] + model_decoder_layers_23_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1059] + model_decoder_layers_23_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1060] + model_decoder_layers_23_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1061] + model_decoder_layers_23_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1062] + model_decoder_layers_23_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1063] + model_decoder_layers_23_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1064] + model_decoder_layers_24_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1065] + model_decoder_layers_24_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1066] + model_decoder_layers_24_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1067] + model_decoder_layers_24_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1068] + model_decoder_layers_24_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1069] + model_decoder_layers_24_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1070] + model_decoder_layers_24_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1071] + model_decoder_layers_24_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1072] + model_decoder_layers_24_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1073] + model_decoder_layers_24_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1077] + model_decoder_layers_24_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1078] + model_decoder_layers_24_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1079] + model_decoder_layers_24_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1080] + model_decoder_layers_24_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1081] + model_decoder_layers_24_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1082] + model_decoder_layers_24_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1083] + model_decoder_layers_24_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1084] + model_decoder_layers_24_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1085] + model_decoder_layers_24_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1086] + model_decoder_layers_24_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1087] + model_decoder_layers_24_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1088] + model_decoder_layers_25_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1089] + model_decoder_layers_25_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1090] + model_decoder_layers_25_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1091] + model_decoder_layers_25_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1092] + model_decoder_layers_25_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1093] + model_decoder_layers_25_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1094] + model_decoder_layers_25_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1095] + model_decoder_layers_25_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1096] + model_decoder_layers_25_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1097] + model_decoder_layers_25_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1101] + model_decoder_layers_25_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1102] + model_decoder_layers_25_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1103] + model_decoder_layers_25_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1104] + model_decoder_layers_25_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1105] + model_decoder_layers_25_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1106] + model_decoder_layers_25_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1107] + model_decoder_layers_25_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1108] + model_decoder_layers_25_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1109] + model_decoder_layers_25_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1110] + model_decoder_layers_25_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1111] + model_decoder_layers_25_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1112] + model_decoder_layers_26_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1113] + model_decoder_layers_26_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1114] + model_decoder_layers_26_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1115] + model_decoder_layers_26_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1116] + model_decoder_layers_26_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1117] + model_decoder_layers_26_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1118] + model_decoder_layers_26_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1119] + model_decoder_layers_26_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1120] + model_decoder_layers_26_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1121] + model_decoder_layers_26_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1125] + model_decoder_layers_26_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1126] + model_decoder_layers_26_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1127] + model_decoder_layers_26_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1128] + model_decoder_layers_26_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1129] + model_decoder_layers_26_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1130] + model_decoder_layers_26_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1131] + model_decoder_layers_26_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1132] + model_decoder_layers_26_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1133] + model_decoder_layers_26_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1134] + model_decoder_layers_26_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1135] + model_decoder_layers_26_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1136] + model_decoder_layers_27_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1137] + model_decoder_layers_27_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1138] + model_decoder_layers_27_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1139] + model_decoder_layers_27_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1140] + model_decoder_layers_27_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1141] + model_decoder_layers_27_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1142] + model_decoder_layers_27_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1143] + model_decoder_layers_27_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1144] + model_decoder_layers_27_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1145] + model_decoder_layers_27_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1149] + model_decoder_layers_27_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1150] + model_decoder_layers_27_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1151] + model_decoder_layers_27_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1152] + model_decoder_layers_27_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1153] + model_decoder_layers_27_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1154] + model_decoder_layers_27_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1155] + model_decoder_layers_27_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1156] + model_decoder_layers_27_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1157] + model_decoder_layers_27_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1158] + model_decoder_layers_27_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1159] + model_decoder_layers_27_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1160] + model_decoder_layers_28_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1161] + model_decoder_layers_28_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1162] + model_decoder_layers_28_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1163] + model_decoder_layers_28_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1164] + model_decoder_layers_28_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1165] + model_decoder_layers_28_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1166] + model_decoder_layers_28_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1167] + model_decoder_layers_28_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1168] + model_decoder_layers_28_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1169] + model_decoder_layers_28_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1173] + model_decoder_layers_28_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1174] + model_decoder_layers_28_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1175] + model_decoder_layers_28_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1176] + model_decoder_layers_28_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1177] + model_decoder_layers_28_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1178] + model_decoder_layers_28_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1179] + model_decoder_layers_28_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1180] + model_decoder_layers_28_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1181] + model_decoder_layers_28_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1182] + model_decoder_layers_28_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1183] + model_decoder_layers_28_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1184] + model_decoder_layers_29_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1185] + model_decoder_layers_29_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1186] + model_decoder_layers_29_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1187] + model_decoder_layers_29_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1188] + model_decoder_layers_29_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1189] + model_decoder_layers_29_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1190] + model_decoder_layers_29_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1191] + model_decoder_layers_29_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1192] + model_decoder_layers_29_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1193] + model_decoder_layers_29_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1197] + model_decoder_layers_29_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1198] + model_decoder_layers_29_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1199] + model_decoder_layers_29_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1200] + model_decoder_layers_29_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1201] + model_decoder_layers_29_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1202] + model_decoder_layers_29_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1203] + model_decoder_layers_29_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1204] + model_decoder_layers_29_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1205] + model_decoder_layers_29_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1206] + model_decoder_layers_29_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1207] + model_decoder_layers_29_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1208] + model_decoder_layers_30_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1209] + model_decoder_layers_30_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1210] + model_decoder_layers_30_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1211] + model_decoder_layers_30_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1212] + model_decoder_layers_30_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1213] + model_decoder_layers_30_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1214] + model_decoder_layers_30_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1215] + model_decoder_layers_30_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1216] + model_decoder_layers_30_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1217] + model_decoder_layers_30_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1221] + model_decoder_layers_30_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1222] + model_decoder_layers_30_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1223] + model_decoder_layers_30_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1224] + model_decoder_layers_30_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1225] + model_decoder_layers_30_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1226] + model_decoder_layers_30_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1227] + model_decoder_layers_30_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1228] + model_decoder_layers_30_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1229] + model_decoder_layers_30_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1230] + model_decoder_layers_30_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1231] + model_decoder_layers_30_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1232] + model_decoder_layers_31_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1233] + model_decoder_layers_31_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1234] + model_decoder_layers_31_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1235] + model_decoder_layers_31_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1236] + model_decoder_layers_31_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1237] + model_decoder_layers_31_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1238] + model_decoder_layers_31_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1239] + model_decoder_layers_31_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1240] + model_decoder_layers_31_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1241] + model_decoder_layers_31_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1245] + model_decoder_layers_31_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1246] + model_decoder_layers_31_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1247] + model_decoder_layers_31_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1248] + model_decoder_layers_31_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1249] + model_decoder_layers_31_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1250] + model_decoder_layers_31_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1251] + model_decoder_layers_31_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1252] + model_decoder_layers_31_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1253] + model_decoder_layers_31_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1254] + model_decoder_layers_31_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1255] + model_decoder_layers_31_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1256] + model_decoder_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1257] + model_decoder_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1258] + reshape384 = R.call_tir(cls.reshape12, (input_ids,), out_sinfo=R.Tensor((seq_len,), dtype="int32")) + take = R.call_tir(cls.take, (model_decoder_embed_tokens_weight2, reshape384), out_sinfo=R.Tensor((seq_len, 1280), dtype="float16")) + reshape385 = R.call_tir(cls.reshape13, (take,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv68: R.Tensor((seq_len,), dtype="int32") = R.call_pure_packed("vm.builtin.attention_kv_cache_get_query_positions", paged_kv_cache, sinfo_args=(R.Tensor((seq_len,), dtype="int32"),)) + take1 = R.call_tir(cls.take1, (model_decoder_embed_positions_weight2, lv68), out_sinfo=R.Tensor((seq_len, 1280), dtype="float16")) + reshape386 = R.call_tir(cls.reshape13, (take1,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add257 = R.call_tir(cls.add5, (reshape385, reshape386), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm65 = R.call_tir(cls.layer_norm2, (add257, model_decoder_layers_0_self_attn_layer_norm_weight2, model_decoder_layers_0_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv416 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_0_self_attn_q_proj_weight2, layer_norm65, model_decoder_layers_0_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape387 = R.call_tir(cls.reshape14, (lv416,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv98 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_0_self_attn_k_proj_weight2, layer_norm65), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape388 = R.call_tir(cls.reshape14, (lv98,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv417 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_0_self_attn_v_proj_weight2, layer_norm65, model_decoder_layers_0_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape389 = R.call_tir(cls.reshape14, (lv417,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat = R.call_tir(cls.concatenate1, (reshape387, reshape388, reshape389), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape390 = R.call_tir(cls.reshape15, (concat,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv69 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), reshape390), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape391 = R.call_tir(cls.reshape16, (lv69,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape392 = R.call_tir(cls.reshape17, (reshape391,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv418 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_0_self_attn_out_proj_weight2, reshape392, model_decoder_layers_0_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add261 = R.call_tir(cls.add5, (add257, lv418), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm66 = R.call_tir(cls.layer_norm2, (add261, model_decoder_layers_0_encoder_attn_layer_norm_weight2, model_decoder_layers_0_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv419 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_0_encoder_attn_q_proj_weight2, layer_norm66, model_decoder_layers_0_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape393 = R.call_tir(cls.reshape14, (lv419,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape394 = R.call_tir(cls.reshape18, (reshape393,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv70 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), reshape394), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape395 = R.call_tir(cls.reshape16, (lv70,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape396 = R.call_tir(cls.reshape17, (reshape395,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv420 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_0_encoder_attn_out_proj_weight2, reshape396, model_decoder_layers_0_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add264 = R.call_tir(cls.add5, (add261, lv420), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm67 = R.call_tir(cls.layer_norm2, (add264, model_decoder_layers_0_final_layer_norm_weight2, model_decoder_layers_0_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv64 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_0_fc1_weight2, layer_norm67, model_decoder_layers_0_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv421 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_0_fc2_weight2, lv64, model_decoder_layers_0_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add267 = R.call_tir(cls.add5, (add264, lv421), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm68 = R.call_tir(cls.layer_norm2, (add267, model_decoder_layers_1_self_attn_layer_norm_weight2, model_decoder_layers_1_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv422 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_1_self_attn_q_proj_weight2, layer_norm68, model_decoder_layers_1_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape397 = R.call_tir(cls.reshape14, (lv422,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv99 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_1_self_attn_k_proj_weight2, layer_norm68), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape398 = R.call_tir(cls.reshape14, (lv99,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv423 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_1_self_attn_v_proj_weight2, layer_norm68, model_decoder_layers_1_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape399 = R.call_tir(cls.reshape14, (lv423,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat1 = R.call_tir(cls.concatenate1, (reshape397, reshape398, reshape399), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape400 = R.call_tir(cls.reshape15, (concat1,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv71 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), reshape400), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape401 = R.call_tir(cls.reshape16, (lv71,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape402 = R.call_tir(cls.reshape17, (reshape401,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv424 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_1_self_attn_out_proj_weight2, reshape402, model_decoder_layers_1_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add271 = R.call_tir(cls.add5, (add267, lv424), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm69 = R.call_tir(cls.layer_norm2, (add271, model_decoder_layers_1_encoder_attn_layer_norm_weight2, model_decoder_layers_1_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv425 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_1_encoder_attn_q_proj_weight2, layer_norm69, model_decoder_layers_1_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape403 = R.call_tir(cls.reshape14, (lv425,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape404 = R.call_tir(cls.reshape18, (reshape403,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv72 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), reshape404), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape405 = R.call_tir(cls.reshape16, (lv72,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape406 = R.call_tir(cls.reshape17, (reshape405,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv426 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_1_encoder_attn_out_proj_weight2, reshape406, model_decoder_layers_1_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add274 = R.call_tir(cls.add5, (add271, lv426), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm70 = R.call_tir(cls.layer_norm2, (add274, model_decoder_layers_1_final_layer_norm_weight2, model_decoder_layers_1_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv65 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_1_fc1_weight2, layer_norm70, model_decoder_layers_1_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv427 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_1_fc2_weight2, lv65, model_decoder_layers_1_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add277 = R.call_tir(cls.add5, (add274, lv427), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm71 = R.call_tir(cls.layer_norm2, (add277, model_decoder_layers_2_self_attn_layer_norm_weight2, model_decoder_layers_2_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv428 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_2_self_attn_q_proj_weight2, layer_norm71, model_decoder_layers_2_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape407 = R.call_tir(cls.reshape14, (lv428,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv100 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_2_self_attn_k_proj_weight2, layer_norm71), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape408 = R.call_tir(cls.reshape14, (lv100,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv429 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_2_self_attn_v_proj_weight2, layer_norm71, model_decoder_layers_2_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape409 = R.call_tir(cls.reshape14, (lv429,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat2 = R.call_tir(cls.concatenate1, (reshape407, reshape408, reshape409), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape410 = R.call_tir(cls.reshape15, (concat2,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv73 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), reshape410), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape411 = R.call_tir(cls.reshape16, (lv73,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape412 = R.call_tir(cls.reshape17, (reshape411,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv430 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_2_self_attn_out_proj_weight2, reshape412, model_decoder_layers_2_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add281 = R.call_tir(cls.add5, (add277, lv430), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm72 = R.call_tir(cls.layer_norm2, (add281, model_decoder_layers_2_encoder_attn_layer_norm_weight2, model_decoder_layers_2_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv431 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_2_encoder_attn_q_proj_weight2, layer_norm72, model_decoder_layers_2_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape413 = R.call_tir(cls.reshape14, (lv431,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape414 = R.call_tir(cls.reshape18, (reshape413,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv74 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), reshape414), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape415 = R.call_tir(cls.reshape16, (lv74,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape416 = R.call_tir(cls.reshape17, (reshape415,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv432 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_2_encoder_attn_out_proj_weight2, reshape416, model_decoder_layers_2_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add284 = R.call_tir(cls.add5, (add281, lv432), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm73 = R.call_tir(cls.layer_norm2, (add284, model_decoder_layers_2_final_layer_norm_weight2, model_decoder_layers_2_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv66 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_2_fc1_weight2, layer_norm73, model_decoder_layers_2_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv433 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_2_fc2_weight2, lv66, model_decoder_layers_2_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add287 = R.call_tir(cls.add5, (add284, lv433), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm74 = R.call_tir(cls.layer_norm2, (add287, model_decoder_layers_3_self_attn_layer_norm_weight2, model_decoder_layers_3_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv434 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_3_self_attn_q_proj_weight2, layer_norm74, model_decoder_layers_3_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape417 = R.call_tir(cls.reshape14, (lv434,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv101 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_3_self_attn_k_proj_weight2, layer_norm74), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape418 = R.call_tir(cls.reshape14, (lv101,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv435 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_3_self_attn_v_proj_weight2, layer_norm74, model_decoder_layers_3_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape419 = R.call_tir(cls.reshape14, (lv435,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat3 = R.call_tir(cls.concatenate1, (reshape417, reshape418, reshape419), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape420 = R.call_tir(cls.reshape15, (concat3,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv75 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), reshape420), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape421 = R.call_tir(cls.reshape16, (lv75,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape422 = R.call_tir(cls.reshape17, (reshape421,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv436 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_3_self_attn_out_proj_weight2, reshape422, model_decoder_layers_3_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add291 = R.call_tir(cls.add5, (add287, lv436), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm75 = R.call_tir(cls.layer_norm2, (add291, model_decoder_layers_3_encoder_attn_layer_norm_weight2, model_decoder_layers_3_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv437 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_3_encoder_attn_q_proj_weight2, layer_norm75, model_decoder_layers_3_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape423 = R.call_tir(cls.reshape14, (lv437,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape424 = R.call_tir(cls.reshape18, (reshape423,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv76 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), reshape424), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape425 = R.call_tir(cls.reshape16, (lv76,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape426 = R.call_tir(cls.reshape17, (reshape425,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv438 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_3_encoder_attn_out_proj_weight2, reshape426, model_decoder_layers_3_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add294 = R.call_tir(cls.add5, (add291, lv438), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm76 = R.call_tir(cls.layer_norm2, (add294, model_decoder_layers_3_final_layer_norm_weight2, model_decoder_layers_3_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv67 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_3_fc1_weight2, layer_norm76, model_decoder_layers_3_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv439 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_3_fc2_weight2, lv67, model_decoder_layers_3_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add297 = R.call_tir(cls.add5, (add294, lv439), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm77 = R.call_tir(cls.layer_norm2, (add297, model_decoder_layers_4_self_attn_layer_norm_weight2, model_decoder_layers_4_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv440 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_4_self_attn_q_proj_weight2, layer_norm77, model_decoder_layers_4_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape427 = R.call_tir(cls.reshape14, (lv440,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv102 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_4_self_attn_k_proj_weight2, layer_norm77), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape428 = R.call_tir(cls.reshape14, (lv102,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv441 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_4_self_attn_v_proj_weight2, layer_norm77, model_decoder_layers_4_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape429 = R.call_tir(cls.reshape14, (lv441,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat4 = R.call_tir(cls.concatenate1, (reshape427, reshape428, reshape429), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape430 = R.call_tir(cls.reshape15, (concat4,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv77 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), reshape430), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape431 = R.call_tir(cls.reshape16, (lv77,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape432 = R.call_tir(cls.reshape17, (reshape431,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv442 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_4_self_attn_out_proj_weight2, reshape432, model_decoder_layers_4_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add301 = R.call_tir(cls.add5, (add297, lv442), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm78 = R.call_tir(cls.layer_norm2, (add301, model_decoder_layers_4_encoder_attn_layer_norm_weight2, model_decoder_layers_4_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv443 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_4_encoder_attn_q_proj_weight2, layer_norm78, model_decoder_layers_4_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape433 = R.call_tir(cls.reshape14, (lv443,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape434 = R.call_tir(cls.reshape18, (reshape433,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv78 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), reshape434), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape435 = R.call_tir(cls.reshape16, (lv78,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape436 = R.call_tir(cls.reshape17, (reshape435,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv444 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_4_encoder_attn_out_proj_weight2, reshape436, model_decoder_layers_4_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add304 = R.call_tir(cls.add5, (add301, lv444), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm79 = R.call_tir(cls.layer_norm2, (add304, model_decoder_layers_4_final_layer_norm_weight2, model_decoder_layers_4_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv68_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_4_fc1_weight2, layer_norm79, model_decoder_layers_4_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv445 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_4_fc2_weight2, lv68_1, model_decoder_layers_4_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add307 = R.call_tir(cls.add5, (add304, lv445), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm80 = R.call_tir(cls.layer_norm2, (add307, model_decoder_layers_5_self_attn_layer_norm_weight2, model_decoder_layers_5_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv446 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_5_self_attn_q_proj_weight2, layer_norm80, model_decoder_layers_5_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape437 = R.call_tir(cls.reshape14, (lv446,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv103 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_5_self_attn_k_proj_weight2, layer_norm80), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape438 = R.call_tir(cls.reshape14, (lv103,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv447 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_5_self_attn_v_proj_weight2, layer_norm80, model_decoder_layers_5_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape439 = R.call_tir(cls.reshape14, (lv447,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat5 = R.call_tir(cls.concatenate1, (reshape437, reshape438, reshape439), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape440 = R.call_tir(cls.reshape15, (concat5,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv79 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), reshape440), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape441 = R.call_tir(cls.reshape16, (lv79,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape442 = R.call_tir(cls.reshape17, (reshape441,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv448 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_5_self_attn_out_proj_weight2, reshape442, model_decoder_layers_5_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add311 = R.call_tir(cls.add5, (add307, lv448), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm81 = R.call_tir(cls.layer_norm2, (add311, model_decoder_layers_5_encoder_attn_layer_norm_weight2, model_decoder_layers_5_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv449 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_5_encoder_attn_q_proj_weight2, layer_norm81, model_decoder_layers_5_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape443 = R.call_tir(cls.reshape14, (lv449,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape444 = R.call_tir(cls.reshape18, (reshape443,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv80 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), reshape444), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape445 = R.call_tir(cls.reshape16, (lv80,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape446 = R.call_tir(cls.reshape17, (reshape445,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv450 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_5_encoder_attn_out_proj_weight2, reshape446, model_decoder_layers_5_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add314 = R.call_tir(cls.add5, (add311, lv450), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm82 = R.call_tir(cls.layer_norm2, (add314, model_decoder_layers_5_final_layer_norm_weight2, model_decoder_layers_5_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv69_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_5_fc1_weight2, layer_norm82, model_decoder_layers_5_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv451 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_5_fc2_weight2, lv69_1, model_decoder_layers_5_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add317 = R.call_tir(cls.add5, (add314, lv451), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm83 = R.call_tir(cls.layer_norm2, (add317, model_decoder_layers_6_self_attn_layer_norm_weight2, model_decoder_layers_6_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv452 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_6_self_attn_q_proj_weight2, layer_norm83, model_decoder_layers_6_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape447 = R.call_tir(cls.reshape14, (lv452,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv104 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_6_self_attn_k_proj_weight2, layer_norm83), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape448 = R.call_tir(cls.reshape14, (lv104,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv453 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_6_self_attn_v_proj_weight2, layer_norm83, model_decoder_layers_6_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape449 = R.call_tir(cls.reshape14, (lv453,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat6 = R.call_tir(cls.concatenate1, (reshape447, reshape448, reshape449), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape450 = R.call_tir(cls.reshape15, (concat6,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv81 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), reshape450), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape451 = R.call_tir(cls.reshape16, (lv81,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape452 = R.call_tir(cls.reshape17, (reshape451,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv454 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_6_self_attn_out_proj_weight2, reshape452, model_decoder_layers_6_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add321 = R.call_tir(cls.add5, (add317, lv454), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm84 = R.call_tir(cls.layer_norm2, (add321, model_decoder_layers_6_encoder_attn_layer_norm_weight2, model_decoder_layers_6_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv455 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_6_encoder_attn_q_proj_weight2, layer_norm84, model_decoder_layers_6_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape453 = R.call_tir(cls.reshape14, (lv455,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape454 = R.call_tir(cls.reshape18, (reshape453,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv82 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), reshape454), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape455 = R.call_tir(cls.reshape16, (lv82,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape456 = R.call_tir(cls.reshape17, (reshape455,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv456 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_6_encoder_attn_out_proj_weight2, reshape456, model_decoder_layers_6_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add324 = R.call_tir(cls.add5, (add321, lv456), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm85 = R.call_tir(cls.layer_norm2, (add324, model_decoder_layers_6_final_layer_norm_weight2, model_decoder_layers_6_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv70_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_6_fc1_weight2, layer_norm85, model_decoder_layers_6_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv457 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_6_fc2_weight2, lv70_1, model_decoder_layers_6_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add327 = R.call_tir(cls.add5, (add324, lv457), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm86 = R.call_tir(cls.layer_norm2, (add327, model_decoder_layers_7_self_attn_layer_norm_weight2, model_decoder_layers_7_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv458 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_7_self_attn_q_proj_weight2, layer_norm86, model_decoder_layers_7_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape457 = R.call_tir(cls.reshape14, (lv458,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv105 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_7_self_attn_k_proj_weight2, layer_norm86), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape458 = R.call_tir(cls.reshape14, (lv105,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv459 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_7_self_attn_v_proj_weight2, layer_norm86, model_decoder_layers_7_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape459 = R.call_tir(cls.reshape14, (lv459,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat7 = R.call_tir(cls.concatenate1, (reshape457, reshape458, reshape459), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape460 = R.call_tir(cls.reshape15, (concat7,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv83 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), reshape460), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape461 = R.call_tir(cls.reshape16, (lv83,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape462 = R.call_tir(cls.reshape17, (reshape461,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv460 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_7_self_attn_out_proj_weight2, reshape462, model_decoder_layers_7_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add331 = R.call_tir(cls.add5, (add327, lv460), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm87 = R.call_tir(cls.layer_norm2, (add331, model_decoder_layers_7_encoder_attn_layer_norm_weight2, model_decoder_layers_7_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv461 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_7_encoder_attn_q_proj_weight2, layer_norm87, model_decoder_layers_7_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape463 = R.call_tir(cls.reshape14, (lv461,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape464 = R.call_tir(cls.reshape18, (reshape463,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv84 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), reshape464), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape465 = R.call_tir(cls.reshape16, (lv84,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape466 = R.call_tir(cls.reshape17, (reshape465,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv462 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_7_encoder_attn_out_proj_weight2, reshape466, model_decoder_layers_7_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add334 = R.call_tir(cls.add5, (add331, lv462), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm88 = R.call_tir(cls.layer_norm2, (add334, model_decoder_layers_7_final_layer_norm_weight2, model_decoder_layers_7_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv71_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_7_fc1_weight2, layer_norm88, model_decoder_layers_7_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv463 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_7_fc2_weight2, lv71_1, model_decoder_layers_7_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add337 = R.call_tir(cls.add5, (add334, lv463), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm89 = R.call_tir(cls.layer_norm2, (add337, model_decoder_layers_8_self_attn_layer_norm_weight2, model_decoder_layers_8_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv464 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_8_self_attn_q_proj_weight2, layer_norm89, model_decoder_layers_8_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape467 = R.call_tir(cls.reshape14, (lv464,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv106 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_8_self_attn_k_proj_weight2, layer_norm89), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape468 = R.call_tir(cls.reshape14, (lv106,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv465 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_8_self_attn_v_proj_weight2, layer_norm89, model_decoder_layers_8_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape469 = R.call_tir(cls.reshape14, (lv465,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat8 = R.call_tir(cls.concatenate1, (reshape467, reshape468, reshape469), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape470 = R.call_tir(cls.reshape15, (concat8,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv85 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), reshape470), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape471 = R.call_tir(cls.reshape16, (lv85,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape472 = R.call_tir(cls.reshape17, (reshape471,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv466 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_8_self_attn_out_proj_weight2, reshape472, model_decoder_layers_8_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add341 = R.call_tir(cls.add5, (add337, lv466), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm90 = R.call_tir(cls.layer_norm2, (add341, model_decoder_layers_8_encoder_attn_layer_norm_weight2, model_decoder_layers_8_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv467 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_8_encoder_attn_q_proj_weight2, layer_norm90, model_decoder_layers_8_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape473 = R.call_tir(cls.reshape14, (lv467,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape474 = R.call_tir(cls.reshape18, (reshape473,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv86 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), reshape474), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape475 = R.call_tir(cls.reshape16, (lv86,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape476 = R.call_tir(cls.reshape17, (reshape475,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv468 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_8_encoder_attn_out_proj_weight2, reshape476, model_decoder_layers_8_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add344 = R.call_tir(cls.add5, (add341, lv468), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm91 = R.call_tir(cls.layer_norm2, (add344, model_decoder_layers_8_final_layer_norm_weight2, model_decoder_layers_8_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv72_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_8_fc1_weight2, layer_norm91, model_decoder_layers_8_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv469 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_8_fc2_weight2, lv72_1, model_decoder_layers_8_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add347 = R.call_tir(cls.add5, (add344, lv469), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm92 = R.call_tir(cls.layer_norm2, (add347, model_decoder_layers_9_self_attn_layer_norm_weight2, model_decoder_layers_9_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv470 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_9_self_attn_q_proj_weight2, layer_norm92, model_decoder_layers_9_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape477 = R.call_tir(cls.reshape14, (lv470,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv107 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_9_self_attn_k_proj_weight2, layer_norm92), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape478 = R.call_tir(cls.reshape14, (lv107,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv471 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_9_self_attn_v_proj_weight2, layer_norm92, model_decoder_layers_9_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape479 = R.call_tir(cls.reshape14, (lv471,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat9 = R.call_tir(cls.concatenate1, (reshape477, reshape478, reshape479), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape480 = R.call_tir(cls.reshape15, (concat9,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv87 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), reshape480), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape481 = R.call_tir(cls.reshape16, (lv87,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape482 = R.call_tir(cls.reshape17, (reshape481,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv472 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_9_self_attn_out_proj_weight2, reshape482, model_decoder_layers_9_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add351 = R.call_tir(cls.add5, (add347, lv472), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm93 = R.call_tir(cls.layer_norm2, (add351, model_decoder_layers_9_encoder_attn_layer_norm_weight2, model_decoder_layers_9_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv473 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_9_encoder_attn_q_proj_weight2, layer_norm93, model_decoder_layers_9_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape483 = R.call_tir(cls.reshape14, (lv473,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape484 = R.call_tir(cls.reshape18, (reshape483,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv88 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), reshape484), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape485 = R.call_tir(cls.reshape16, (lv88,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape486 = R.call_tir(cls.reshape17, (reshape485,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv474 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_9_encoder_attn_out_proj_weight2, reshape486, model_decoder_layers_9_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add354 = R.call_tir(cls.add5, (add351, lv474), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm94 = R.call_tir(cls.layer_norm2, (add354, model_decoder_layers_9_final_layer_norm_weight2, model_decoder_layers_9_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv73_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_9_fc1_weight2, layer_norm94, model_decoder_layers_9_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv475 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_9_fc2_weight2, lv73_1, model_decoder_layers_9_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add357 = R.call_tir(cls.add5, (add354, lv475), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm95 = R.call_tir(cls.layer_norm2, (add357, model_decoder_layers_10_self_attn_layer_norm_weight2, model_decoder_layers_10_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv476 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_10_self_attn_q_proj_weight2, layer_norm95, model_decoder_layers_10_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape487 = R.call_tir(cls.reshape14, (lv476,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv108 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_10_self_attn_k_proj_weight2, layer_norm95), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape488 = R.call_tir(cls.reshape14, (lv108,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv477 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_10_self_attn_v_proj_weight2, layer_norm95, model_decoder_layers_10_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape489 = R.call_tir(cls.reshape14, (lv477,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat10 = R.call_tir(cls.concatenate1, (reshape487, reshape488, reshape489), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape490 = R.call_tir(cls.reshape15, (concat10,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv89 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), reshape490), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape491 = R.call_tir(cls.reshape16, (lv89,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape492 = R.call_tir(cls.reshape17, (reshape491,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv478 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_10_self_attn_out_proj_weight2, reshape492, model_decoder_layers_10_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add361 = R.call_tir(cls.add5, (add357, lv478), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm96 = R.call_tir(cls.layer_norm2, (add361, model_decoder_layers_10_encoder_attn_layer_norm_weight2, model_decoder_layers_10_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv479 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_10_encoder_attn_q_proj_weight2, layer_norm96, model_decoder_layers_10_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape493 = R.call_tir(cls.reshape14, (lv479,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape494 = R.call_tir(cls.reshape18, (reshape493,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv90 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), reshape494), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape495 = R.call_tir(cls.reshape16, (lv90,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape496 = R.call_tir(cls.reshape17, (reshape495,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv480 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_10_encoder_attn_out_proj_weight2, reshape496, model_decoder_layers_10_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add364 = R.call_tir(cls.add5, (add361, lv480), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm97 = R.call_tir(cls.layer_norm2, (add364, model_decoder_layers_10_final_layer_norm_weight2, model_decoder_layers_10_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv74_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_10_fc1_weight2, layer_norm97, model_decoder_layers_10_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv481 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_10_fc2_weight2, lv74_1, model_decoder_layers_10_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add367 = R.call_tir(cls.add5, (add364, lv481), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm98 = R.call_tir(cls.layer_norm2, (add367, model_decoder_layers_11_self_attn_layer_norm_weight2, model_decoder_layers_11_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv482 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_11_self_attn_q_proj_weight2, layer_norm98, model_decoder_layers_11_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape497 = R.call_tir(cls.reshape14, (lv482,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv109 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_11_self_attn_k_proj_weight2, layer_norm98), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape498 = R.call_tir(cls.reshape14, (lv109,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv483 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_11_self_attn_v_proj_weight2, layer_norm98, model_decoder_layers_11_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape499 = R.call_tir(cls.reshape14, (lv483,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat11 = R.call_tir(cls.concatenate1, (reshape497, reshape498, reshape499), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape500 = R.call_tir(cls.reshape15, (concat11,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv91 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), reshape500), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape501 = R.call_tir(cls.reshape16, (lv91,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape502 = R.call_tir(cls.reshape17, (reshape501,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv484 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_11_self_attn_out_proj_weight2, reshape502, model_decoder_layers_11_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add371 = R.call_tir(cls.add5, (add367, lv484), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm99 = R.call_tir(cls.layer_norm2, (add371, model_decoder_layers_11_encoder_attn_layer_norm_weight2, model_decoder_layers_11_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv485 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_11_encoder_attn_q_proj_weight2, layer_norm99, model_decoder_layers_11_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape503 = R.call_tir(cls.reshape14, (lv485,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape504 = R.call_tir(cls.reshape18, (reshape503,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv92 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), reshape504), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape505 = R.call_tir(cls.reshape16, (lv92,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape506 = R.call_tir(cls.reshape17, (reshape505,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv486 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_11_encoder_attn_out_proj_weight2, reshape506, model_decoder_layers_11_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add374 = R.call_tir(cls.add5, (add371, lv486), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm100 = R.call_tir(cls.layer_norm2, (add374, model_decoder_layers_11_final_layer_norm_weight2, model_decoder_layers_11_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv75_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_11_fc1_weight2, layer_norm100, model_decoder_layers_11_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv487 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_11_fc2_weight2, lv75_1, model_decoder_layers_11_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add377 = R.call_tir(cls.add5, (add374, lv487), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm101 = R.call_tir(cls.layer_norm2, (add377, model_decoder_layers_12_self_attn_layer_norm_weight2, model_decoder_layers_12_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv488 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_12_self_attn_q_proj_weight2, layer_norm101, model_decoder_layers_12_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape507 = R.call_tir(cls.reshape14, (lv488,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv110 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_12_self_attn_k_proj_weight2, layer_norm101), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape508 = R.call_tir(cls.reshape14, (lv110,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv489 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_12_self_attn_v_proj_weight2, layer_norm101, model_decoder_layers_12_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape509 = R.call_tir(cls.reshape14, (lv489,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat12 = R.call_tir(cls.concatenate1, (reshape507, reshape508, reshape509), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape510 = R.call_tir(cls.reshape15, (concat12,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv93 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), reshape510), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape511 = R.call_tir(cls.reshape16, (lv93,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape512 = R.call_tir(cls.reshape17, (reshape511,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv490 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_12_self_attn_out_proj_weight2, reshape512, model_decoder_layers_12_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add381 = R.call_tir(cls.add5, (add377, lv490), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm102 = R.call_tir(cls.layer_norm2, (add381, model_decoder_layers_12_encoder_attn_layer_norm_weight2, model_decoder_layers_12_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv491 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_12_encoder_attn_q_proj_weight2, layer_norm102, model_decoder_layers_12_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape513 = R.call_tir(cls.reshape14, (lv491,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape514 = R.call_tir(cls.reshape18, (reshape513,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv94 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), reshape514), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape515 = R.call_tir(cls.reshape16, (lv94,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape516 = R.call_tir(cls.reshape17, (reshape515,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv492 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_12_encoder_attn_out_proj_weight2, reshape516, model_decoder_layers_12_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add384 = R.call_tir(cls.add5, (add381, lv492), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm103 = R.call_tir(cls.layer_norm2, (add384, model_decoder_layers_12_final_layer_norm_weight2, model_decoder_layers_12_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv76_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_12_fc1_weight2, layer_norm103, model_decoder_layers_12_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv493 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_12_fc2_weight2, lv76_1, model_decoder_layers_12_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add387 = R.call_tir(cls.add5, (add384, lv493), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm104 = R.call_tir(cls.layer_norm2, (add387, model_decoder_layers_13_self_attn_layer_norm_weight2, model_decoder_layers_13_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv494 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_13_self_attn_q_proj_weight2, layer_norm104, model_decoder_layers_13_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape517 = R.call_tir(cls.reshape14, (lv494,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv111 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_13_self_attn_k_proj_weight2, layer_norm104), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape518 = R.call_tir(cls.reshape14, (lv111,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv495 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_13_self_attn_v_proj_weight2, layer_norm104, model_decoder_layers_13_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape519 = R.call_tir(cls.reshape14, (lv495,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat13 = R.call_tir(cls.concatenate1, (reshape517, reshape518, reshape519), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape520 = R.call_tir(cls.reshape15, (concat13,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv95 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), reshape520), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape521 = R.call_tir(cls.reshape16, (lv95,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape522 = R.call_tir(cls.reshape17, (reshape521,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv496 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_13_self_attn_out_proj_weight2, reshape522, model_decoder_layers_13_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add391 = R.call_tir(cls.add5, (add387, lv496), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm105 = R.call_tir(cls.layer_norm2, (add391, model_decoder_layers_13_encoder_attn_layer_norm_weight2, model_decoder_layers_13_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv497 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_13_encoder_attn_q_proj_weight2, layer_norm105, model_decoder_layers_13_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape523 = R.call_tir(cls.reshape14, (lv497,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape524 = R.call_tir(cls.reshape18, (reshape523,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv96 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), reshape524), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape525 = R.call_tir(cls.reshape16, (lv96,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape526 = R.call_tir(cls.reshape17, (reshape525,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv498 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_13_encoder_attn_out_proj_weight2, reshape526, model_decoder_layers_13_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add394 = R.call_tir(cls.add5, (add391, lv498), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm106 = R.call_tir(cls.layer_norm2, (add394, model_decoder_layers_13_final_layer_norm_weight2, model_decoder_layers_13_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv77_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_13_fc1_weight2, layer_norm106, model_decoder_layers_13_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv499 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_13_fc2_weight2, lv77_1, model_decoder_layers_13_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add397 = R.call_tir(cls.add5, (add394, lv499), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm107 = R.call_tir(cls.layer_norm2, (add397, model_decoder_layers_14_self_attn_layer_norm_weight2, model_decoder_layers_14_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv500 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_14_self_attn_q_proj_weight2, layer_norm107, model_decoder_layers_14_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape527 = R.call_tir(cls.reshape14, (lv500,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv112 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_14_self_attn_k_proj_weight2, layer_norm107), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape528 = R.call_tir(cls.reshape14, (lv112,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv501 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_14_self_attn_v_proj_weight2, layer_norm107, model_decoder_layers_14_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape529 = R.call_tir(cls.reshape14, (lv501,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat14 = R.call_tir(cls.concatenate1, (reshape527, reshape528, reshape529), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape530 = R.call_tir(cls.reshape15, (concat14,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv97 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), reshape530), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape531 = R.call_tir(cls.reshape16, (lv97,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape532 = R.call_tir(cls.reshape17, (reshape531,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv502 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_14_self_attn_out_proj_weight2, reshape532, model_decoder_layers_14_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add401 = R.call_tir(cls.add5, (add397, lv502), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm108 = R.call_tir(cls.layer_norm2, (add401, model_decoder_layers_14_encoder_attn_layer_norm_weight2, model_decoder_layers_14_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv503 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_14_encoder_attn_q_proj_weight2, layer_norm108, model_decoder_layers_14_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape533 = R.call_tir(cls.reshape14, (lv503,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape534 = R.call_tir(cls.reshape18, (reshape533,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv98_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), reshape534), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape535 = R.call_tir(cls.reshape16, (lv98_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape536 = R.call_tir(cls.reshape17, (reshape535,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv504 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_14_encoder_attn_out_proj_weight2, reshape536, model_decoder_layers_14_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add404 = R.call_tir(cls.add5, (add401, lv504), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm109 = R.call_tir(cls.layer_norm2, (add404, model_decoder_layers_14_final_layer_norm_weight2, model_decoder_layers_14_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv78_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_14_fc1_weight2, layer_norm109, model_decoder_layers_14_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv505 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_14_fc2_weight2, lv78_1, model_decoder_layers_14_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add407 = R.call_tir(cls.add5, (add404, lv505), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm110 = R.call_tir(cls.layer_norm2, (add407, model_decoder_layers_15_self_attn_layer_norm_weight2, model_decoder_layers_15_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv506 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_15_self_attn_q_proj_weight2, layer_norm110, model_decoder_layers_15_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape537 = R.call_tir(cls.reshape14, (lv506,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv113 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_15_self_attn_k_proj_weight2, layer_norm110), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape538 = R.call_tir(cls.reshape14, (lv113,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv507 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_15_self_attn_v_proj_weight2, layer_norm110, model_decoder_layers_15_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape539 = R.call_tir(cls.reshape14, (lv507,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat15 = R.call_tir(cls.concatenate1, (reshape537, reshape538, reshape539), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape540 = R.call_tir(cls.reshape15, (concat15,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv99_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), reshape540), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape541 = R.call_tir(cls.reshape16, (lv99_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape542 = R.call_tir(cls.reshape17, (reshape541,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv508 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_15_self_attn_out_proj_weight2, reshape542, model_decoder_layers_15_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add411 = R.call_tir(cls.add5, (add407, lv508), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm111 = R.call_tir(cls.layer_norm2, (add411, model_decoder_layers_15_encoder_attn_layer_norm_weight2, model_decoder_layers_15_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv509 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_15_encoder_attn_q_proj_weight2, layer_norm111, model_decoder_layers_15_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape543 = R.call_tir(cls.reshape14, (lv509,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape544 = R.call_tir(cls.reshape18, (reshape543,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv100_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), reshape544), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape545 = R.call_tir(cls.reshape16, (lv100_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape546 = R.call_tir(cls.reshape17, (reshape545,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv510 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_15_encoder_attn_out_proj_weight2, reshape546, model_decoder_layers_15_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add414 = R.call_tir(cls.add5, (add411, lv510), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm112 = R.call_tir(cls.layer_norm2, (add414, model_decoder_layers_15_final_layer_norm_weight2, model_decoder_layers_15_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv79_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_15_fc1_weight2, layer_norm112, model_decoder_layers_15_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv511 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_15_fc2_weight2, lv79_1, model_decoder_layers_15_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add417 = R.call_tir(cls.add5, (add414, lv511), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm113 = R.call_tir(cls.layer_norm2, (add417, model_decoder_layers_16_self_attn_layer_norm_weight2, model_decoder_layers_16_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv512 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_16_self_attn_q_proj_weight2, layer_norm113, model_decoder_layers_16_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape547 = R.call_tir(cls.reshape14, (lv512,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv114 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_16_self_attn_k_proj_weight2, layer_norm113), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape548 = R.call_tir(cls.reshape14, (lv114,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv513 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_16_self_attn_v_proj_weight2, layer_norm113, model_decoder_layers_16_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape549 = R.call_tir(cls.reshape14, (lv513,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat16 = R.call_tir(cls.concatenate1, (reshape547, reshape548, reshape549), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape550 = R.call_tir(cls.reshape15, (concat16,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv101_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), reshape550), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape551 = R.call_tir(cls.reshape16, (lv101_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape552 = R.call_tir(cls.reshape17, (reshape551,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv514 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_16_self_attn_out_proj_weight2, reshape552, model_decoder_layers_16_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add421 = R.call_tir(cls.add5, (add417, lv514), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm114 = R.call_tir(cls.layer_norm2, (add421, model_decoder_layers_16_encoder_attn_layer_norm_weight2, model_decoder_layers_16_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv515 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_16_encoder_attn_q_proj_weight2, layer_norm114, model_decoder_layers_16_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape553 = R.call_tir(cls.reshape14, (lv515,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape554 = R.call_tir(cls.reshape18, (reshape553,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv102_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), reshape554), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape555 = R.call_tir(cls.reshape16, (lv102_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape556 = R.call_tir(cls.reshape17, (reshape555,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv516 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_16_encoder_attn_out_proj_weight2, reshape556, model_decoder_layers_16_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add424 = R.call_tir(cls.add5, (add421, lv516), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm115 = R.call_tir(cls.layer_norm2, (add424, model_decoder_layers_16_final_layer_norm_weight2, model_decoder_layers_16_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv80_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_16_fc1_weight2, layer_norm115, model_decoder_layers_16_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv517 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_16_fc2_weight2, lv80_1, model_decoder_layers_16_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add427 = R.call_tir(cls.add5, (add424, lv517), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm116 = R.call_tir(cls.layer_norm2, (add427, model_decoder_layers_17_self_attn_layer_norm_weight2, model_decoder_layers_17_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv518 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_17_self_attn_q_proj_weight2, layer_norm116, model_decoder_layers_17_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape557 = R.call_tir(cls.reshape14, (lv518,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv115 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_17_self_attn_k_proj_weight2, layer_norm116), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape558 = R.call_tir(cls.reshape14, (lv115,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv519 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_17_self_attn_v_proj_weight2, layer_norm116, model_decoder_layers_17_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape559 = R.call_tir(cls.reshape14, (lv519,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat17 = R.call_tir(cls.concatenate1, (reshape557, reshape558, reshape559), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape560 = R.call_tir(cls.reshape15, (concat17,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv103_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), reshape560), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape561 = R.call_tir(cls.reshape16, (lv103_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape562 = R.call_tir(cls.reshape17, (reshape561,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv520 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_17_self_attn_out_proj_weight2, reshape562, model_decoder_layers_17_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add431 = R.call_tir(cls.add5, (add427, lv520), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm117 = R.call_tir(cls.layer_norm2, (add431, model_decoder_layers_17_encoder_attn_layer_norm_weight2, model_decoder_layers_17_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv521 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_17_encoder_attn_q_proj_weight2, layer_norm117, model_decoder_layers_17_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape563 = R.call_tir(cls.reshape14, (lv521,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape564 = R.call_tir(cls.reshape18, (reshape563,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv104_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), reshape564), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape565 = R.call_tir(cls.reshape16, (lv104_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape566 = R.call_tir(cls.reshape17, (reshape565,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv522 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_17_encoder_attn_out_proj_weight2, reshape566, model_decoder_layers_17_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add434 = R.call_tir(cls.add5, (add431, lv522), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm118 = R.call_tir(cls.layer_norm2, (add434, model_decoder_layers_17_final_layer_norm_weight2, model_decoder_layers_17_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv81_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_17_fc1_weight2, layer_norm118, model_decoder_layers_17_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv523 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_17_fc2_weight2, lv81_1, model_decoder_layers_17_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add437 = R.call_tir(cls.add5, (add434, lv523), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm119 = R.call_tir(cls.layer_norm2, (add437, model_decoder_layers_18_self_attn_layer_norm_weight2, model_decoder_layers_18_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv524 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_18_self_attn_q_proj_weight2, layer_norm119, model_decoder_layers_18_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape567 = R.call_tir(cls.reshape14, (lv524,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv116 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_18_self_attn_k_proj_weight2, layer_norm119), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape568 = R.call_tir(cls.reshape14, (lv116,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv525 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_18_self_attn_v_proj_weight2, layer_norm119, model_decoder_layers_18_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape569 = R.call_tir(cls.reshape14, (lv525,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat18 = R.call_tir(cls.concatenate1, (reshape567, reshape568, reshape569), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape570 = R.call_tir(cls.reshape15, (concat18,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv105_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), reshape570), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape571 = R.call_tir(cls.reshape16, (lv105_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape572 = R.call_tir(cls.reshape17, (reshape571,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv526 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_18_self_attn_out_proj_weight2, reshape572, model_decoder_layers_18_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add441 = R.call_tir(cls.add5, (add437, lv526), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm120 = R.call_tir(cls.layer_norm2, (add441, model_decoder_layers_18_encoder_attn_layer_norm_weight2, model_decoder_layers_18_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv527 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_18_encoder_attn_q_proj_weight2, layer_norm120, model_decoder_layers_18_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape573 = R.call_tir(cls.reshape14, (lv527,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape574 = R.call_tir(cls.reshape18, (reshape573,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv106_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), reshape574), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape575 = R.call_tir(cls.reshape16, (lv106_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape576 = R.call_tir(cls.reshape17, (reshape575,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv528 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_18_encoder_attn_out_proj_weight2, reshape576, model_decoder_layers_18_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add444 = R.call_tir(cls.add5, (add441, lv528), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm121 = R.call_tir(cls.layer_norm2, (add444, model_decoder_layers_18_final_layer_norm_weight2, model_decoder_layers_18_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv82_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_18_fc1_weight2, layer_norm121, model_decoder_layers_18_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv529 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_18_fc2_weight2, lv82_1, model_decoder_layers_18_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add447 = R.call_tir(cls.add5, (add444, lv529), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm122 = R.call_tir(cls.layer_norm2, (add447, model_decoder_layers_19_self_attn_layer_norm_weight2, model_decoder_layers_19_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv530 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_19_self_attn_q_proj_weight2, layer_norm122, model_decoder_layers_19_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape577 = R.call_tir(cls.reshape14, (lv530,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv117 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_19_self_attn_k_proj_weight2, layer_norm122), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape578 = R.call_tir(cls.reshape14, (lv117,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv531 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_19_self_attn_v_proj_weight2, layer_norm122, model_decoder_layers_19_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape579 = R.call_tir(cls.reshape14, (lv531,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat19 = R.call_tir(cls.concatenate1, (reshape577, reshape578, reshape579), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape580 = R.call_tir(cls.reshape15, (concat19,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv107_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), reshape580), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape581 = R.call_tir(cls.reshape16, (lv107_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape582 = R.call_tir(cls.reshape17, (reshape581,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv532 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_19_self_attn_out_proj_weight2, reshape582, model_decoder_layers_19_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add451 = R.call_tir(cls.add5, (add447, lv532), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm123 = R.call_tir(cls.layer_norm2, (add451, model_decoder_layers_19_encoder_attn_layer_norm_weight2, model_decoder_layers_19_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv533 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_19_encoder_attn_q_proj_weight2, layer_norm123, model_decoder_layers_19_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape583 = R.call_tir(cls.reshape14, (lv533,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape584 = R.call_tir(cls.reshape18, (reshape583,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv108_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), reshape584), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape585 = R.call_tir(cls.reshape16, (lv108_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape586 = R.call_tir(cls.reshape17, (reshape585,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv534 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_19_encoder_attn_out_proj_weight2, reshape586, model_decoder_layers_19_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add454 = R.call_tir(cls.add5, (add451, lv534), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm124 = R.call_tir(cls.layer_norm2, (add454, model_decoder_layers_19_final_layer_norm_weight2, model_decoder_layers_19_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv83_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_19_fc1_weight2, layer_norm124, model_decoder_layers_19_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv535 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_19_fc2_weight2, lv83_1, model_decoder_layers_19_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add457 = R.call_tir(cls.add5, (add454, lv535), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm125 = R.call_tir(cls.layer_norm2, (add457, model_decoder_layers_20_self_attn_layer_norm_weight2, model_decoder_layers_20_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv536 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_20_self_attn_q_proj_weight2, layer_norm125, model_decoder_layers_20_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape587 = R.call_tir(cls.reshape14, (lv536,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv118 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_20_self_attn_k_proj_weight2, layer_norm125), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape588 = R.call_tir(cls.reshape14, (lv118,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv537 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_20_self_attn_v_proj_weight2, layer_norm125, model_decoder_layers_20_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape589 = R.call_tir(cls.reshape14, (lv537,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat20 = R.call_tir(cls.concatenate1, (reshape587, reshape588, reshape589), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape590 = R.call_tir(cls.reshape15, (concat20,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv109_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), reshape590), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape591 = R.call_tir(cls.reshape16, (lv109_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape592 = R.call_tir(cls.reshape17, (reshape591,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv538 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_20_self_attn_out_proj_weight2, reshape592, model_decoder_layers_20_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add461 = R.call_tir(cls.add5, (add457, lv538), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm126 = R.call_tir(cls.layer_norm2, (add461, model_decoder_layers_20_encoder_attn_layer_norm_weight2, model_decoder_layers_20_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv539 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_20_encoder_attn_q_proj_weight2, layer_norm126, model_decoder_layers_20_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape593 = R.call_tir(cls.reshape14, (lv539,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape594 = R.call_tir(cls.reshape18, (reshape593,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv110_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), reshape594), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape595 = R.call_tir(cls.reshape16, (lv110_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape596 = R.call_tir(cls.reshape17, (reshape595,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv540 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_20_encoder_attn_out_proj_weight2, reshape596, model_decoder_layers_20_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add464 = R.call_tir(cls.add5, (add461, lv540), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm127 = R.call_tir(cls.layer_norm2, (add464, model_decoder_layers_20_final_layer_norm_weight2, model_decoder_layers_20_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv84_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_20_fc1_weight2, layer_norm127, model_decoder_layers_20_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv541 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_20_fc2_weight2, lv84_1, model_decoder_layers_20_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add467 = R.call_tir(cls.add5, (add464, lv541), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm128 = R.call_tir(cls.layer_norm2, (add467, model_decoder_layers_21_self_attn_layer_norm_weight2, model_decoder_layers_21_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv542 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_21_self_attn_q_proj_weight2, layer_norm128, model_decoder_layers_21_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape597 = R.call_tir(cls.reshape14, (lv542,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv119 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_21_self_attn_k_proj_weight2, layer_norm128), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape598 = R.call_tir(cls.reshape14, (lv119,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv543 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_21_self_attn_v_proj_weight2, layer_norm128, model_decoder_layers_21_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape599 = R.call_tir(cls.reshape14, (lv543,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat21 = R.call_tir(cls.concatenate1, (reshape597, reshape598, reshape599), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape600 = R.call_tir(cls.reshape15, (concat21,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv111_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), reshape600), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape601 = R.call_tir(cls.reshape16, (lv111_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape602 = R.call_tir(cls.reshape17, (reshape601,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv544 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_21_self_attn_out_proj_weight2, reshape602, model_decoder_layers_21_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add471 = R.call_tir(cls.add5, (add467, lv544), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm129 = R.call_tir(cls.layer_norm2, (add471, model_decoder_layers_21_encoder_attn_layer_norm_weight2, model_decoder_layers_21_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv545 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_21_encoder_attn_q_proj_weight2, layer_norm129, model_decoder_layers_21_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape603 = R.call_tir(cls.reshape14, (lv545,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape604 = R.call_tir(cls.reshape18, (reshape603,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv112_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), reshape604), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape605 = R.call_tir(cls.reshape16, (lv112_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape606 = R.call_tir(cls.reshape17, (reshape605,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv546 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_21_encoder_attn_out_proj_weight2, reshape606, model_decoder_layers_21_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add474 = R.call_tir(cls.add5, (add471, lv546), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm130 = R.call_tir(cls.layer_norm2, (add474, model_decoder_layers_21_final_layer_norm_weight2, model_decoder_layers_21_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv85_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_21_fc1_weight2, layer_norm130, model_decoder_layers_21_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv547 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_21_fc2_weight2, lv85_1, model_decoder_layers_21_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add477 = R.call_tir(cls.add5, (add474, lv547), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm131 = R.call_tir(cls.layer_norm2, (add477, model_decoder_layers_22_self_attn_layer_norm_weight2, model_decoder_layers_22_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv548 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_22_self_attn_q_proj_weight2, layer_norm131, model_decoder_layers_22_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape607 = R.call_tir(cls.reshape14, (lv548,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv120 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_22_self_attn_k_proj_weight2, layer_norm131), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape608 = R.call_tir(cls.reshape14, (lv120,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv549 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_22_self_attn_v_proj_weight2, layer_norm131, model_decoder_layers_22_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape609 = R.call_tir(cls.reshape14, (lv549,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat22 = R.call_tir(cls.concatenate1, (reshape607, reshape608, reshape609), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape610 = R.call_tir(cls.reshape15, (concat22,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv113_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), reshape610), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape611 = R.call_tir(cls.reshape16, (lv113_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape612 = R.call_tir(cls.reshape17, (reshape611,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv550 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_22_self_attn_out_proj_weight2, reshape612, model_decoder_layers_22_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add481 = R.call_tir(cls.add5, (add477, lv550), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm132 = R.call_tir(cls.layer_norm2, (add481, model_decoder_layers_22_encoder_attn_layer_norm_weight2, model_decoder_layers_22_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv551 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_22_encoder_attn_q_proj_weight2, layer_norm132, model_decoder_layers_22_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape613 = R.call_tir(cls.reshape14, (lv551,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape614 = R.call_tir(cls.reshape18, (reshape613,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv114_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), reshape614), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape615 = R.call_tir(cls.reshape16, (lv114_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape616 = R.call_tir(cls.reshape17, (reshape615,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv552 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_22_encoder_attn_out_proj_weight2, reshape616, model_decoder_layers_22_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add484 = R.call_tir(cls.add5, (add481, lv552), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm133 = R.call_tir(cls.layer_norm2, (add484, model_decoder_layers_22_final_layer_norm_weight2, model_decoder_layers_22_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv86_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_22_fc1_weight2, layer_norm133, model_decoder_layers_22_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv553 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_22_fc2_weight2, lv86_1, model_decoder_layers_22_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add487 = R.call_tir(cls.add5, (add484, lv553), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm134 = R.call_tir(cls.layer_norm2, (add487, model_decoder_layers_23_self_attn_layer_norm_weight2, model_decoder_layers_23_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv554 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_23_self_attn_q_proj_weight2, layer_norm134, model_decoder_layers_23_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape617 = R.call_tir(cls.reshape14, (lv554,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv121 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_23_self_attn_k_proj_weight2, layer_norm134), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape618 = R.call_tir(cls.reshape14, (lv121,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv555 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_23_self_attn_v_proj_weight2, layer_norm134, model_decoder_layers_23_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape619 = R.call_tir(cls.reshape14, (lv555,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat23 = R.call_tir(cls.concatenate1, (reshape617, reshape618, reshape619), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape620 = R.call_tir(cls.reshape15, (concat23,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv115_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), reshape620), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape621 = R.call_tir(cls.reshape16, (lv115_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape622 = R.call_tir(cls.reshape17, (reshape621,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv556 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_23_self_attn_out_proj_weight2, reshape622, model_decoder_layers_23_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add491 = R.call_tir(cls.add5, (add487, lv556), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm135 = R.call_tir(cls.layer_norm2, (add491, model_decoder_layers_23_encoder_attn_layer_norm_weight2, model_decoder_layers_23_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv557 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_23_encoder_attn_q_proj_weight2, layer_norm135, model_decoder_layers_23_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape623 = R.call_tir(cls.reshape14, (lv557,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape624 = R.call_tir(cls.reshape18, (reshape623,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv116_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), reshape624), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape625 = R.call_tir(cls.reshape16, (lv116_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape626 = R.call_tir(cls.reshape17, (reshape625,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv558 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_23_encoder_attn_out_proj_weight2, reshape626, model_decoder_layers_23_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add494 = R.call_tir(cls.add5, (add491, lv558), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm136 = R.call_tir(cls.layer_norm2, (add494, model_decoder_layers_23_final_layer_norm_weight2, model_decoder_layers_23_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv87_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_23_fc1_weight2, layer_norm136, model_decoder_layers_23_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv559 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_23_fc2_weight2, lv87_1, model_decoder_layers_23_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add497 = R.call_tir(cls.add5, (add494, lv559), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm137 = R.call_tir(cls.layer_norm2, (add497, model_decoder_layers_24_self_attn_layer_norm_weight2, model_decoder_layers_24_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv560 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_24_self_attn_q_proj_weight2, layer_norm137, model_decoder_layers_24_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape627 = R.call_tir(cls.reshape14, (lv560,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv122 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_24_self_attn_k_proj_weight2, layer_norm137), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape628 = R.call_tir(cls.reshape14, (lv122,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv561 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_24_self_attn_v_proj_weight2, layer_norm137, model_decoder_layers_24_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape629 = R.call_tir(cls.reshape14, (lv561,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat24 = R.call_tir(cls.concatenate1, (reshape627, reshape628, reshape629), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape630 = R.call_tir(cls.reshape15, (concat24,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv117_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), reshape630), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape631 = R.call_tir(cls.reshape16, (lv117_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape632 = R.call_tir(cls.reshape17, (reshape631,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv562 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_24_self_attn_out_proj_weight2, reshape632, model_decoder_layers_24_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add501 = R.call_tir(cls.add5, (add497, lv562), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm138 = R.call_tir(cls.layer_norm2, (add501, model_decoder_layers_24_encoder_attn_layer_norm_weight2, model_decoder_layers_24_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv563 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_24_encoder_attn_q_proj_weight2, layer_norm138, model_decoder_layers_24_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape633 = R.call_tir(cls.reshape14, (lv563,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape634 = R.call_tir(cls.reshape18, (reshape633,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv118_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), reshape634), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape635 = R.call_tir(cls.reshape16, (lv118_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape636 = R.call_tir(cls.reshape17, (reshape635,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv564 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_24_encoder_attn_out_proj_weight2, reshape636, model_decoder_layers_24_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add504 = R.call_tir(cls.add5, (add501, lv564), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm139 = R.call_tir(cls.layer_norm2, (add504, model_decoder_layers_24_final_layer_norm_weight2, model_decoder_layers_24_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv88_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_24_fc1_weight2, layer_norm139, model_decoder_layers_24_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv565 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_24_fc2_weight2, lv88_1, model_decoder_layers_24_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add507 = R.call_tir(cls.add5, (add504, lv565), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm140 = R.call_tir(cls.layer_norm2, (add507, model_decoder_layers_25_self_attn_layer_norm_weight2, model_decoder_layers_25_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv566 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_25_self_attn_q_proj_weight2, layer_norm140, model_decoder_layers_25_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape637 = R.call_tir(cls.reshape14, (lv566,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv123 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_25_self_attn_k_proj_weight2, layer_norm140), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape638 = R.call_tir(cls.reshape14, (lv123,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv567 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_25_self_attn_v_proj_weight2, layer_norm140, model_decoder_layers_25_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape639 = R.call_tir(cls.reshape14, (lv567,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat25 = R.call_tir(cls.concatenate1, (reshape637, reshape638, reshape639), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape640 = R.call_tir(cls.reshape15, (concat25,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv119_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), reshape640), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape641 = R.call_tir(cls.reshape16, (lv119_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape642 = R.call_tir(cls.reshape17, (reshape641,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv568 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_25_self_attn_out_proj_weight2, reshape642, model_decoder_layers_25_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add511 = R.call_tir(cls.add5, (add507, lv568), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm141 = R.call_tir(cls.layer_norm2, (add511, model_decoder_layers_25_encoder_attn_layer_norm_weight2, model_decoder_layers_25_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv569 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_25_encoder_attn_q_proj_weight2, layer_norm141, model_decoder_layers_25_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape643 = R.call_tir(cls.reshape14, (lv569,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape644 = R.call_tir(cls.reshape18, (reshape643,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv120_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), reshape644), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape645 = R.call_tir(cls.reshape16, (lv120_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape646 = R.call_tir(cls.reshape17, (reshape645,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv570 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_25_encoder_attn_out_proj_weight2, reshape646, model_decoder_layers_25_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add514 = R.call_tir(cls.add5, (add511, lv570), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm142 = R.call_tir(cls.layer_norm2, (add514, model_decoder_layers_25_final_layer_norm_weight2, model_decoder_layers_25_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv89_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_25_fc1_weight2, layer_norm142, model_decoder_layers_25_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv571 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_25_fc2_weight2, lv89_1, model_decoder_layers_25_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add517 = R.call_tir(cls.add5, (add514, lv571), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm143 = R.call_tir(cls.layer_norm2, (add517, model_decoder_layers_26_self_attn_layer_norm_weight2, model_decoder_layers_26_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv572 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_26_self_attn_q_proj_weight2, layer_norm143, model_decoder_layers_26_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape647 = R.call_tir(cls.reshape14, (lv572,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv124 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_26_self_attn_k_proj_weight2, layer_norm143), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape648 = R.call_tir(cls.reshape14, (lv124,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv573 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_26_self_attn_v_proj_weight2, layer_norm143, model_decoder_layers_26_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape649 = R.call_tir(cls.reshape14, (lv573,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat26 = R.call_tir(cls.concatenate1, (reshape647, reshape648, reshape649), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape650 = R.call_tir(cls.reshape15, (concat26,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv121_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), reshape650), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape651 = R.call_tir(cls.reshape16, (lv121_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape652 = R.call_tir(cls.reshape17, (reshape651,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv574 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_26_self_attn_out_proj_weight2, reshape652, model_decoder_layers_26_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add521 = R.call_tir(cls.add5, (add517, lv574), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm144 = R.call_tir(cls.layer_norm2, (add521, model_decoder_layers_26_encoder_attn_layer_norm_weight2, model_decoder_layers_26_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv575 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_26_encoder_attn_q_proj_weight2, layer_norm144, model_decoder_layers_26_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape653 = R.call_tir(cls.reshape14, (lv575,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape654 = R.call_tir(cls.reshape18, (reshape653,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv122_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), reshape654), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape655 = R.call_tir(cls.reshape16, (lv122_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape656 = R.call_tir(cls.reshape17, (reshape655,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv576 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_26_encoder_attn_out_proj_weight2, reshape656, model_decoder_layers_26_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add524 = R.call_tir(cls.add5, (add521, lv576), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm145 = R.call_tir(cls.layer_norm2, (add524, model_decoder_layers_26_final_layer_norm_weight2, model_decoder_layers_26_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv90_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_26_fc1_weight2, layer_norm145, model_decoder_layers_26_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv577 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_26_fc2_weight2, lv90_1, model_decoder_layers_26_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add527 = R.call_tir(cls.add5, (add524, lv577), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm146 = R.call_tir(cls.layer_norm2, (add527, model_decoder_layers_27_self_attn_layer_norm_weight2, model_decoder_layers_27_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv578 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_27_self_attn_q_proj_weight2, layer_norm146, model_decoder_layers_27_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape657 = R.call_tir(cls.reshape14, (lv578,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv125 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_27_self_attn_k_proj_weight2, layer_norm146), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape658 = R.call_tir(cls.reshape14, (lv125,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv579 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_27_self_attn_v_proj_weight2, layer_norm146, model_decoder_layers_27_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape659 = R.call_tir(cls.reshape14, (lv579,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat27 = R.call_tir(cls.concatenate1, (reshape657, reshape658, reshape659), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape660 = R.call_tir(cls.reshape15, (concat27,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv123_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), reshape660), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape661 = R.call_tir(cls.reshape16, (lv123_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape662 = R.call_tir(cls.reshape17, (reshape661,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv580 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_27_self_attn_out_proj_weight2, reshape662, model_decoder_layers_27_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add531 = R.call_tir(cls.add5, (add527, lv580), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm147 = R.call_tir(cls.layer_norm2, (add531, model_decoder_layers_27_encoder_attn_layer_norm_weight2, model_decoder_layers_27_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv581 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_27_encoder_attn_q_proj_weight2, layer_norm147, model_decoder_layers_27_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape663 = R.call_tir(cls.reshape14, (lv581,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape664 = R.call_tir(cls.reshape18, (reshape663,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv124_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), reshape664), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape665 = R.call_tir(cls.reshape16, (lv124_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape666 = R.call_tir(cls.reshape17, (reshape665,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv582 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_27_encoder_attn_out_proj_weight2, reshape666, model_decoder_layers_27_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add534 = R.call_tir(cls.add5, (add531, lv582), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm148 = R.call_tir(cls.layer_norm2, (add534, model_decoder_layers_27_final_layer_norm_weight2, model_decoder_layers_27_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv91_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_27_fc1_weight2, layer_norm148, model_decoder_layers_27_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv583 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_27_fc2_weight2, lv91_1, model_decoder_layers_27_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add537 = R.call_tir(cls.add5, (add534, lv583), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm149 = R.call_tir(cls.layer_norm2, (add537, model_decoder_layers_28_self_attn_layer_norm_weight2, model_decoder_layers_28_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv584 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_28_self_attn_q_proj_weight2, layer_norm149, model_decoder_layers_28_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape667 = R.call_tir(cls.reshape14, (lv584,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv126 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_28_self_attn_k_proj_weight2, layer_norm149), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape668 = R.call_tir(cls.reshape14, (lv126,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv585 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_28_self_attn_v_proj_weight2, layer_norm149, model_decoder_layers_28_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape669 = R.call_tir(cls.reshape14, (lv585,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat28 = R.call_tir(cls.concatenate1, (reshape667, reshape668, reshape669), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape670 = R.call_tir(cls.reshape15, (concat28,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv125_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), reshape670), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape671 = R.call_tir(cls.reshape16, (lv125_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape672 = R.call_tir(cls.reshape17, (reshape671,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv586 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_28_self_attn_out_proj_weight2, reshape672, model_decoder_layers_28_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add541 = R.call_tir(cls.add5, (add537, lv586), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm150 = R.call_tir(cls.layer_norm2, (add541, model_decoder_layers_28_encoder_attn_layer_norm_weight2, model_decoder_layers_28_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv587 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_28_encoder_attn_q_proj_weight2, layer_norm150, model_decoder_layers_28_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape673 = R.call_tir(cls.reshape14, (lv587,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape674 = R.call_tir(cls.reshape18, (reshape673,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv126_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), reshape674), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape675 = R.call_tir(cls.reshape16, (lv126_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape676 = R.call_tir(cls.reshape17, (reshape675,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv588 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_28_encoder_attn_out_proj_weight2, reshape676, model_decoder_layers_28_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add544 = R.call_tir(cls.add5, (add541, lv588), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm151 = R.call_tir(cls.layer_norm2, (add544, model_decoder_layers_28_final_layer_norm_weight2, model_decoder_layers_28_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv92_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_28_fc1_weight2, layer_norm151, model_decoder_layers_28_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv589 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_28_fc2_weight2, lv92_1, model_decoder_layers_28_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add547 = R.call_tir(cls.add5, (add544, lv589), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm152 = R.call_tir(cls.layer_norm2, (add547, model_decoder_layers_29_self_attn_layer_norm_weight2, model_decoder_layers_29_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv590 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_29_self_attn_q_proj_weight2, layer_norm152, model_decoder_layers_29_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape677 = R.call_tir(cls.reshape14, (lv590,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv127 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_29_self_attn_k_proj_weight2, layer_norm152), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape678 = R.call_tir(cls.reshape14, (lv127,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv591 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_29_self_attn_v_proj_weight2, layer_norm152, model_decoder_layers_29_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape679 = R.call_tir(cls.reshape14, (lv591,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat29 = R.call_tir(cls.concatenate1, (reshape677, reshape678, reshape679), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape680 = R.call_tir(cls.reshape15, (concat29,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv127_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), reshape680), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape681 = R.call_tir(cls.reshape16, (lv127_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape682 = R.call_tir(cls.reshape17, (reshape681,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv592 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_29_self_attn_out_proj_weight2, reshape682, model_decoder_layers_29_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add551 = R.call_tir(cls.add5, (add547, lv592), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm153 = R.call_tir(cls.layer_norm2, (add551, model_decoder_layers_29_encoder_attn_layer_norm_weight2, model_decoder_layers_29_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv593 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_29_encoder_attn_q_proj_weight2, layer_norm153, model_decoder_layers_29_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape683 = R.call_tir(cls.reshape14, (lv593,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape684 = R.call_tir(cls.reshape18, (reshape683,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv128 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), reshape684), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape685 = R.call_tir(cls.reshape16, (lv128,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape686 = R.call_tir(cls.reshape17, (reshape685,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv594 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_29_encoder_attn_out_proj_weight2, reshape686, model_decoder_layers_29_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add554 = R.call_tir(cls.add5, (add551, lv594), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm154 = R.call_tir(cls.layer_norm2, (add554, model_decoder_layers_29_final_layer_norm_weight2, model_decoder_layers_29_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv93_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_29_fc1_weight2, layer_norm154, model_decoder_layers_29_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv595 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_29_fc2_weight2, lv93_1, model_decoder_layers_29_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add557 = R.call_tir(cls.add5, (add554, lv595), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm155 = R.call_tir(cls.layer_norm2, (add557, model_decoder_layers_30_self_attn_layer_norm_weight2, model_decoder_layers_30_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv596 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_30_self_attn_q_proj_weight2, layer_norm155, model_decoder_layers_30_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape687 = R.call_tir(cls.reshape14, (lv596,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv128_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_30_self_attn_k_proj_weight2, layer_norm155), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape688 = R.call_tir(cls.reshape14, (lv128_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv597 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_30_self_attn_v_proj_weight2, layer_norm155, model_decoder_layers_30_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape689 = R.call_tir(cls.reshape14, (lv597,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat30 = R.call_tir(cls.concatenate1, (reshape687, reshape688, reshape689), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape690 = R.call_tir(cls.reshape15, (concat30,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv129 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), reshape690), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape691 = R.call_tir(cls.reshape16, (lv129,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape692 = R.call_tir(cls.reshape17, (reshape691,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv598 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_30_self_attn_out_proj_weight2, reshape692, model_decoder_layers_30_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add561 = R.call_tir(cls.add5, (add557, lv598), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm156 = R.call_tir(cls.layer_norm2, (add561, model_decoder_layers_30_encoder_attn_layer_norm_weight2, model_decoder_layers_30_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv599 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_30_encoder_attn_q_proj_weight2, layer_norm156, model_decoder_layers_30_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape693 = R.call_tir(cls.reshape14, (lv599,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape694 = R.call_tir(cls.reshape18, (reshape693,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv130 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), reshape694), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape695 = R.call_tir(cls.reshape16, (lv130,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape696 = R.call_tir(cls.reshape17, (reshape695,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv600 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_30_encoder_attn_out_proj_weight2, reshape696, model_decoder_layers_30_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add564 = R.call_tir(cls.add5, (add561, lv600), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm157 = R.call_tir(cls.layer_norm2, (add564, model_decoder_layers_30_final_layer_norm_weight2, model_decoder_layers_30_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv94_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_30_fc1_weight2, layer_norm157, model_decoder_layers_30_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv601 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_30_fc2_weight2, lv94_1, model_decoder_layers_30_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add567 = R.call_tir(cls.add5, (add564, lv601), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm158 = R.call_tir(cls.layer_norm2, (add567, model_decoder_layers_31_self_attn_layer_norm_weight2, model_decoder_layers_31_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv602 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_31_self_attn_q_proj_weight2, layer_norm158, model_decoder_layers_31_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape697 = R.call_tir(cls.reshape14, (lv602,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv129_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_31_self_attn_k_proj_weight2, layer_norm158), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape698 = R.call_tir(cls.reshape14, (lv129_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv603 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_31_self_attn_v_proj_weight2, layer_norm158, model_decoder_layers_31_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape699 = R.call_tir(cls.reshape14, (lv603,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat31 = R.call_tir(cls.concatenate1, (reshape697, reshape698, reshape699), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape700 = R.call_tir(cls.reshape15, (concat31,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv131 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), reshape700), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape701 = R.call_tir(cls.reshape16, (lv131,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape702 = R.call_tir(cls.reshape17, (reshape701,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv604 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_31_self_attn_out_proj_weight2, reshape702, model_decoder_layers_31_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add571 = R.call_tir(cls.add5, (add567, lv604), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm159 = R.call_tir(cls.layer_norm2, (add571, model_decoder_layers_31_encoder_attn_layer_norm_weight2, model_decoder_layers_31_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv605 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_31_encoder_attn_q_proj_weight2, layer_norm159, model_decoder_layers_31_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape703 = R.call_tir(cls.reshape14, (lv605,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape704 = R.call_tir(cls.reshape18, (reshape703,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv132 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), reshape704), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape705 = R.call_tir(cls.reshape16, (lv132,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape706 = R.call_tir(cls.reshape17, (reshape705,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv606 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_31_encoder_attn_out_proj_weight2, reshape706, model_decoder_layers_31_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add574 = R.call_tir(cls.add5, (add571, lv606), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm160 = R.call_tir(cls.layer_norm2, (add574, model_decoder_layers_31_final_layer_norm_weight2, model_decoder_layers_31_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv95_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_31_fc1_weight2, layer_norm160, model_decoder_layers_31_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv607 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_31_fc2_weight2, lv95_1, model_decoder_layers_31_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add577 = R.call_tir(cls.add5, (add574, lv607), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm161 = R.call_tir(cls.layer_norm2, (add577, model_decoder_layer_norm_weight2, model_decoder_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + take2 = R.call_tir(cls.take2, (layer_norm161, logit_positions), out_sinfo=R.Tensor((1, batch_size, 1280), dtype="float16")) + gv2 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul5_cublas", (model_decoder_embed_tokens_weight2, take2), out_sinfo=R.Tensor((1, batch_size, 51866), dtype="float32")) + R.output(gv2) + return gv2 + + @R.function + def create_tir_paged_kv_cache(max_batch_size_: R.Shape(["max_batch_size"]), max_total_seq_len_: R.Shape(["max_total_seq_len"]), prefill_chunk_size_: R.Shape(["prefill_chunk_size"]), page_size_: R.Shape(["page_size"]), support_sliding_window_: R.Shape(["support_sliding_window"])) -> R.Object: + max_batch_size = T.int64() + max_total_seq_len = T.int64() + prefill_chunk_size = T.int64() + page_size = T.int64() + support_sliding_window = T.int64() + R.func_attr({"relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "seq_len": 15000, "total_seq_len": 1500}}) + cls = Module + paged_kv_cache: R.Object = R.call_pure_packed("vm.builtin.paged_attention_kv_cache_create_reduced", R.shape([max_batch_size, max_total_seq_len, prefill_chunk_size, page_size, support_sliding_window]), R.prim_value(32), R.prim_value(20), R.prim_value(20), R.prim_value(64), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.const(0, "float16"), cls.tir_kv_cache_transpose_append, cls.batch_prefill_paged_kv, cls.batch_decode_paged_kv, cls.batch_prefill_paged_kv_sliding_window, cls.batch_decode_paged_kv_sliding_window, cls.batch_prefill_ragged_kv, cls.merge_state_inplace, cls.fused_rope, cls.copy_single_page, cls.tir_kv_cache_debug_get_kv, cls.compact_kv_copy, cls.batch_tree_attn, sinfo_args=(R.Object,)) + return paged_kv_cache + + @R.function + def decode(input_ids: R.Tensor((1, 1), dtype="int32"), paged_kv_cache: R.Object, packed_params: R.Tuple(R.Tensor((1280, 128, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1500, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), 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R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"))) -> R.Tensor((1, 1, 51866), dtype="float32"): + R.func_attr({"num_input": 2, "relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "seq_len": 15000, "total_seq_len": 1500}}) + cls = Module + with R.dataflow(): + model_decoder_embed_tokens_weight5: R.Tensor((51866, 1280), dtype="float16") = packed_params[487] + model_decoder_embed_positions_weight5: R.Tensor((448, 1280), dtype="float16") = packed_params[488] + model_decoder_layers_0_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[489] + model_decoder_layers_0_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[490] + model_decoder_layers_0_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[491] + model_decoder_layers_0_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[492] + model_decoder_layers_0_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[493] + model_decoder_layers_0_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[494] + model_decoder_layers_0_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[495] + model_decoder_layers_0_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[496] + model_decoder_layers_0_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[497] + model_decoder_layers_0_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[501] + model_decoder_layers_0_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[502] + model_decoder_layers_0_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[503] + model_decoder_layers_0_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[504] + model_decoder_layers_0_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[505] + model_decoder_layers_0_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[506] + model_decoder_layers_0_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[507] + model_decoder_layers_0_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[508] + model_decoder_layers_0_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[509] + model_decoder_layers_0_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[510] + model_decoder_layers_0_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[511] + model_decoder_layers_0_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[512] + model_decoder_layers_1_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[513] + model_decoder_layers_1_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[514] + model_decoder_layers_1_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[515] + model_decoder_layers_1_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[516] + model_decoder_layers_1_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[517] + model_decoder_layers_1_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[518] + model_decoder_layers_1_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[519] + model_decoder_layers_1_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[520] + model_decoder_layers_1_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[521] + model_decoder_layers_1_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[525] + model_decoder_layers_1_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[526] + model_decoder_layers_1_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[527] + model_decoder_layers_1_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[528] + model_decoder_layers_1_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[529] + model_decoder_layers_1_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[530] + model_decoder_layers_1_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[531] + model_decoder_layers_1_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[532] + model_decoder_layers_1_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[533] + model_decoder_layers_1_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[534] + model_decoder_layers_1_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[535] + model_decoder_layers_1_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[536] + model_decoder_layers_2_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[537] + model_decoder_layers_2_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[538] + model_decoder_layers_2_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[539] + model_decoder_layers_2_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[540] + model_decoder_layers_2_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[541] + model_decoder_layers_2_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[542] + model_decoder_layers_2_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[543] + model_decoder_layers_2_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[544] + model_decoder_layers_2_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[545] + model_decoder_layers_2_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[549] + model_decoder_layers_2_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[550] + model_decoder_layers_2_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[551] + model_decoder_layers_2_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[552] + model_decoder_layers_2_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[553] + model_decoder_layers_2_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[554] + model_decoder_layers_2_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[555] + model_decoder_layers_2_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[556] + model_decoder_layers_2_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[557] + model_decoder_layers_2_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[558] + model_decoder_layers_2_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[559] + model_decoder_layers_2_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[560] + model_decoder_layers_3_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[561] + model_decoder_layers_3_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[562] + model_decoder_layers_3_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[563] + model_decoder_layers_3_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[564] + model_decoder_layers_3_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[565] + model_decoder_layers_3_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[566] + model_decoder_layers_3_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[567] + model_decoder_layers_3_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[568] + model_decoder_layers_3_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[569] + model_decoder_layers_3_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[573] + model_decoder_layers_3_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[574] + model_decoder_layers_3_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[575] + model_decoder_layers_3_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[576] + model_decoder_layers_3_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[577] + model_decoder_layers_3_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[578] + model_decoder_layers_3_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[579] + model_decoder_layers_3_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[580] + model_decoder_layers_3_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[581] + model_decoder_layers_3_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[582] + model_decoder_layers_3_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[583] + model_decoder_layers_3_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[584] + model_decoder_layers_4_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[585] + model_decoder_layers_4_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[586] + model_decoder_layers_4_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[587] + model_decoder_layers_4_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[588] + model_decoder_layers_4_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[589] + model_decoder_layers_4_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[590] + model_decoder_layers_4_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[591] + model_decoder_layers_4_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[592] + model_decoder_layers_4_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[593] + model_decoder_layers_4_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[597] + model_decoder_layers_4_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[598] + model_decoder_layers_4_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[599] + model_decoder_layers_4_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[600] + model_decoder_layers_4_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[601] + model_decoder_layers_4_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[602] + model_decoder_layers_4_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[603] + model_decoder_layers_4_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[604] + model_decoder_layers_4_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[605] + model_decoder_layers_4_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[606] + model_decoder_layers_4_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[607] + model_decoder_layers_4_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[608] + model_decoder_layers_5_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[609] + model_decoder_layers_5_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[610] + model_decoder_layers_5_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[611] + model_decoder_layers_5_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[612] + model_decoder_layers_5_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[613] + model_decoder_layers_5_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[614] + model_decoder_layers_5_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[615] + model_decoder_layers_5_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[616] + model_decoder_layers_5_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[617] + model_decoder_layers_5_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[621] + model_decoder_layers_5_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[622] + model_decoder_layers_5_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[623] + model_decoder_layers_5_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[624] + model_decoder_layers_5_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[625] + model_decoder_layers_5_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[626] + model_decoder_layers_5_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[627] + model_decoder_layers_5_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[628] + model_decoder_layers_5_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[629] + model_decoder_layers_5_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[630] + model_decoder_layers_5_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[631] + model_decoder_layers_5_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[632] + model_decoder_layers_6_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[633] + model_decoder_layers_6_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[634] + model_decoder_layers_6_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[635] + model_decoder_layers_6_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[636] + model_decoder_layers_6_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[637] + model_decoder_layers_6_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[638] + model_decoder_layers_6_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[639] + model_decoder_layers_6_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[640] + model_decoder_layers_6_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[641] + model_decoder_layers_6_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[645] + model_decoder_layers_6_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[646] + model_decoder_layers_6_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[647] + model_decoder_layers_6_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[648] + model_decoder_layers_6_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[649] + model_decoder_layers_6_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[650] + model_decoder_layers_6_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[651] + model_decoder_layers_6_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[652] + model_decoder_layers_6_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[653] + model_decoder_layers_6_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[654] + model_decoder_layers_6_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[655] + model_decoder_layers_6_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[656] + model_decoder_layers_7_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[657] + model_decoder_layers_7_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[658] + model_decoder_layers_7_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[659] + model_decoder_layers_7_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[660] + model_decoder_layers_7_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[661] + model_decoder_layers_7_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[662] + model_decoder_layers_7_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[663] + model_decoder_layers_7_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[664] + model_decoder_layers_7_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[665] + model_decoder_layers_7_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[669] + model_decoder_layers_7_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[670] + model_decoder_layers_7_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[671] + model_decoder_layers_7_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[672] + model_decoder_layers_7_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[673] + model_decoder_layers_7_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[674] + model_decoder_layers_7_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[675] + model_decoder_layers_7_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[676] + model_decoder_layers_7_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[677] + model_decoder_layers_7_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[678] + model_decoder_layers_7_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[679] + model_decoder_layers_7_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[680] + model_decoder_layers_8_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[681] + model_decoder_layers_8_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[682] + model_decoder_layers_8_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[683] + model_decoder_layers_8_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[684] + model_decoder_layers_8_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[685] + model_decoder_layers_8_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[686] + model_decoder_layers_8_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[687] + model_decoder_layers_8_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[688] + model_decoder_layers_8_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[689] + model_decoder_layers_8_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[693] + model_decoder_layers_8_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[694] + model_decoder_layers_8_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[695] + model_decoder_layers_8_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[696] + model_decoder_layers_8_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[697] + model_decoder_layers_8_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[698] + model_decoder_layers_8_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[699] + model_decoder_layers_8_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[700] + model_decoder_layers_8_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[701] + model_decoder_layers_8_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[702] + model_decoder_layers_8_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[703] + model_decoder_layers_8_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[704] + model_decoder_layers_9_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[705] + model_decoder_layers_9_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[706] + model_decoder_layers_9_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[707] + model_decoder_layers_9_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[708] + model_decoder_layers_9_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[709] + model_decoder_layers_9_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[710] + model_decoder_layers_9_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[711] + model_decoder_layers_9_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[712] + model_decoder_layers_9_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[713] + model_decoder_layers_9_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[717] + model_decoder_layers_9_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[718] + model_decoder_layers_9_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[719] + model_decoder_layers_9_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[720] + model_decoder_layers_9_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[721] + model_decoder_layers_9_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[722] + model_decoder_layers_9_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[723] + model_decoder_layers_9_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[724] + model_decoder_layers_9_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[725] + model_decoder_layers_9_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[726] + model_decoder_layers_9_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[727] + model_decoder_layers_9_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[728] + model_decoder_layers_10_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[729] + model_decoder_layers_10_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[730] + model_decoder_layers_10_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[731] + model_decoder_layers_10_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[732] + model_decoder_layers_10_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[733] + model_decoder_layers_10_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[734] + model_decoder_layers_10_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[735] + model_decoder_layers_10_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[736] + model_decoder_layers_10_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[737] + model_decoder_layers_10_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[741] + model_decoder_layers_10_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[742] + model_decoder_layers_10_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[743] + model_decoder_layers_10_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[744] + model_decoder_layers_10_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[745] + model_decoder_layers_10_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[746] + model_decoder_layers_10_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[747] + model_decoder_layers_10_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[748] + model_decoder_layers_10_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[749] + model_decoder_layers_10_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[750] + model_decoder_layers_10_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[751] + model_decoder_layers_10_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[752] + model_decoder_layers_11_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[753] + model_decoder_layers_11_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[754] + model_decoder_layers_11_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[755] + model_decoder_layers_11_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[756] + model_decoder_layers_11_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[757] + model_decoder_layers_11_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[758] + model_decoder_layers_11_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[759] + model_decoder_layers_11_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[760] + model_decoder_layers_11_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[761] + model_decoder_layers_11_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[765] + model_decoder_layers_11_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[766] + model_decoder_layers_11_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[767] + model_decoder_layers_11_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[768] + model_decoder_layers_11_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[769] + model_decoder_layers_11_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[770] + model_decoder_layers_11_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[771] + model_decoder_layers_11_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[772] + model_decoder_layers_11_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[773] + model_decoder_layers_11_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[774] + model_decoder_layers_11_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[775] + model_decoder_layers_11_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[776] + model_decoder_layers_12_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[777] + model_decoder_layers_12_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[778] + model_decoder_layers_12_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[779] + model_decoder_layers_12_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[780] + model_decoder_layers_12_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[781] + model_decoder_layers_12_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[782] + model_decoder_layers_12_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[783] + model_decoder_layers_12_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[784] + model_decoder_layers_12_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[785] + model_decoder_layers_12_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[789] + model_decoder_layers_12_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[790] + model_decoder_layers_12_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[791] + model_decoder_layers_12_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[792] + model_decoder_layers_12_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[793] + model_decoder_layers_12_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[794] + model_decoder_layers_12_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[795] + model_decoder_layers_12_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[796] + model_decoder_layers_12_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[797] + model_decoder_layers_12_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[798] + model_decoder_layers_12_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[799] + model_decoder_layers_12_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[800] + model_decoder_layers_13_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[801] + model_decoder_layers_13_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[802] + model_decoder_layers_13_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[803] + model_decoder_layers_13_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[804] + model_decoder_layers_13_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[805] + model_decoder_layers_13_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[806] + model_decoder_layers_13_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[807] + model_decoder_layers_13_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[808] + model_decoder_layers_13_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[809] + model_decoder_layers_13_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[813] + model_decoder_layers_13_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[814] + model_decoder_layers_13_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[815] + model_decoder_layers_13_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[816] + model_decoder_layers_13_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[817] + model_decoder_layers_13_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[818] + model_decoder_layers_13_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[819] + model_decoder_layers_13_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[820] + model_decoder_layers_13_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[821] + model_decoder_layers_13_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[822] + model_decoder_layers_13_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[823] + model_decoder_layers_13_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[824] + model_decoder_layers_14_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[825] + model_decoder_layers_14_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[826] + model_decoder_layers_14_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[827] + model_decoder_layers_14_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[828] + model_decoder_layers_14_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[829] + model_decoder_layers_14_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[830] + model_decoder_layers_14_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[831] + model_decoder_layers_14_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[832] + model_decoder_layers_14_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[833] + model_decoder_layers_14_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[837] + model_decoder_layers_14_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[838] + model_decoder_layers_14_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[839] + model_decoder_layers_14_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[840] + model_decoder_layers_14_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[841] + model_decoder_layers_14_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[842] + model_decoder_layers_14_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[843] + model_decoder_layers_14_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[844] + model_decoder_layers_14_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[845] + model_decoder_layers_14_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[846] + model_decoder_layers_14_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[847] + model_decoder_layers_14_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[848] + model_decoder_layers_15_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[849] + model_decoder_layers_15_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[850] + model_decoder_layers_15_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[851] + model_decoder_layers_15_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[852] + model_decoder_layers_15_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[853] + model_decoder_layers_15_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[854] + model_decoder_layers_15_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[855] + model_decoder_layers_15_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[856] + model_decoder_layers_15_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[857] + model_decoder_layers_15_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[861] + model_decoder_layers_15_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[862] + model_decoder_layers_15_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[863] + model_decoder_layers_15_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[864] + model_decoder_layers_15_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[865] + model_decoder_layers_15_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[866] + model_decoder_layers_15_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[867] + model_decoder_layers_15_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[868] + model_decoder_layers_15_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[869] + model_decoder_layers_15_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[870] + model_decoder_layers_15_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[871] + model_decoder_layers_15_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[872] + model_decoder_layers_16_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[873] + model_decoder_layers_16_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[874] + model_decoder_layers_16_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[875] + model_decoder_layers_16_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[876] + model_decoder_layers_16_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[877] + model_decoder_layers_16_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[878] + model_decoder_layers_16_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[879] + model_decoder_layers_16_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[880] + model_decoder_layers_16_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[881] + model_decoder_layers_16_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[885] + model_decoder_layers_16_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[886] + model_decoder_layers_16_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[887] + model_decoder_layers_16_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[888] + model_decoder_layers_16_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[889] + model_decoder_layers_16_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[890] + model_decoder_layers_16_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[891] + model_decoder_layers_16_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[892] + model_decoder_layers_16_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[893] + model_decoder_layers_16_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[894] + model_decoder_layers_16_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[895] + model_decoder_layers_16_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[896] + model_decoder_layers_17_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[897] + model_decoder_layers_17_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[898] + model_decoder_layers_17_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[899] + model_decoder_layers_17_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[900] + model_decoder_layers_17_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[901] + model_decoder_layers_17_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[902] + model_decoder_layers_17_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[903] + model_decoder_layers_17_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[904] + model_decoder_layers_17_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[905] + model_decoder_layers_17_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[909] + model_decoder_layers_17_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[910] + model_decoder_layers_17_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[911] + model_decoder_layers_17_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[912] + model_decoder_layers_17_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[913] + model_decoder_layers_17_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[914] + model_decoder_layers_17_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[915] + model_decoder_layers_17_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[916] + model_decoder_layers_17_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[917] + model_decoder_layers_17_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[918] + model_decoder_layers_17_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[919] + model_decoder_layers_17_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[920] + model_decoder_layers_18_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[921] + model_decoder_layers_18_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[922] + model_decoder_layers_18_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[923] + model_decoder_layers_18_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[924] + model_decoder_layers_18_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[925] + model_decoder_layers_18_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[926] + model_decoder_layers_18_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[927] + model_decoder_layers_18_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[928] + model_decoder_layers_18_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[929] + model_decoder_layers_18_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[933] + model_decoder_layers_18_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[934] + model_decoder_layers_18_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[935] + model_decoder_layers_18_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[936] + model_decoder_layers_18_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[937] + model_decoder_layers_18_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[938] + model_decoder_layers_18_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[939] + model_decoder_layers_18_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[940] + model_decoder_layers_18_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[941] + model_decoder_layers_18_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[942] + model_decoder_layers_18_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[943] + model_decoder_layers_18_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[944] + model_decoder_layers_19_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[945] + model_decoder_layers_19_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[946] + model_decoder_layers_19_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[947] + model_decoder_layers_19_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[948] + model_decoder_layers_19_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[949] + model_decoder_layers_19_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[950] + model_decoder_layers_19_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[951] + model_decoder_layers_19_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[952] + model_decoder_layers_19_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[953] + model_decoder_layers_19_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[957] + model_decoder_layers_19_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[958] + model_decoder_layers_19_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[959] + model_decoder_layers_19_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[960] + model_decoder_layers_19_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[961] + model_decoder_layers_19_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[962] + model_decoder_layers_19_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[963] + model_decoder_layers_19_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[964] + model_decoder_layers_19_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[965] + model_decoder_layers_19_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[966] + model_decoder_layers_19_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[967] + model_decoder_layers_19_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[968] + model_decoder_layers_20_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[969] + model_decoder_layers_20_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[970] + model_decoder_layers_20_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[971] + model_decoder_layers_20_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[972] + model_decoder_layers_20_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[973] + model_decoder_layers_20_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[974] + model_decoder_layers_20_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[975] + model_decoder_layers_20_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[976] + model_decoder_layers_20_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[977] + model_decoder_layers_20_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[981] + model_decoder_layers_20_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[982] + model_decoder_layers_20_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[983] + model_decoder_layers_20_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[984] + model_decoder_layers_20_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[985] + model_decoder_layers_20_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[986] + model_decoder_layers_20_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[987] + model_decoder_layers_20_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[988] + model_decoder_layers_20_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[989] + model_decoder_layers_20_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[990] + model_decoder_layers_20_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[991] + model_decoder_layers_20_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[992] + model_decoder_layers_21_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[993] + model_decoder_layers_21_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[994] + model_decoder_layers_21_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[995] + model_decoder_layers_21_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[996] + model_decoder_layers_21_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[997] + model_decoder_layers_21_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[998] + model_decoder_layers_21_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[999] + model_decoder_layers_21_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1000] + model_decoder_layers_21_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1001] + model_decoder_layers_21_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1005] + model_decoder_layers_21_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1006] + model_decoder_layers_21_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1007] + model_decoder_layers_21_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1008] + model_decoder_layers_21_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1009] + model_decoder_layers_21_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1010] + model_decoder_layers_21_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1011] + model_decoder_layers_21_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1012] + model_decoder_layers_21_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1013] + model_decoder_layers_21_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1014] + model_decoder_layers_21_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1015] + model_decoder_layers_21_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1016] + model_decoder_layers_22_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1017] + model_decoder_layers_22_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1018] + model_decoder_layers_22_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1019] + model_decoder_layers_22_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1020] + model_decoder_layers_22_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1021] + model_decoder_layers_22_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1022] + model_decoder_layers_22_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1023] + model_decoder_layers_22_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1024] + model_decoder_layers_22_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1025] + model_decoder_layers_22_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1029] + model_decoder_layers_22_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1030] + model_decoder_layers_22_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1031] + model_decoder_layers_22_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1032] + model_decoder_layers_22_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1033] + model_decoder_layers_22_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1034] + model_decoder_layers_22_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1035] + model_decoder_layers_22_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1036] + model_decoder_layers_22_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1037] + model_decoder_layers_22_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1038] + model_decoder_layers_22_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1039] + model_decoder_layers_22_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1040] + model_decoder_layers_23_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1041] + model_decoder_layers_23_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1042] + model_decoder_layers_23_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1043] + model_decoder_layers_23_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1044] + model_decoder_layers_23_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1045] + model_decoder_layers_23_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1046] + model_decoder_layers_23_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1047] + model_decoder_layers_23_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1048] + model_decoder_layers_23_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1049] + model_decoder_layers_23_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1053] + model_decoder_layers_23_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1054] + model_decoder_layers_23_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1055] + model_decoder_layers_23_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1056] + model_decoder_layers_23_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1057] + model_decoder_layers_23_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1058] + model_decoder_layers_23_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1059] + model_decoder_layers_23_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1060] + model_decoder_layers_23_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1061] + model_decoder_layers_23_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1062] + model_decoder_layers_23_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1063] + model_decoder_layers_23_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1064] + model_decoder_layers_24_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1065] + model_decoder_layers_24_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1066] + model_decoder_layers_24_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1067] + model_decoder_layers_24_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1068] + model_decoder_layers_24_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1069] + model_decoder_layers_24_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1070] + model_decoder_layers_24_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1071] + model_decoder_layers_24_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1072] + model_decoder_layers_24_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1073] + model_decoder_layers_24_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1077] + model_decoder_layers_24_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1078] + model_decoder_layers_24_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1079] + model_decoder_layers_24_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1080] + model_decoder_layers_24_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1081] + model_decoder_layers_24_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1082] + model_decoder_layers_24_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1083] + model_decoder_layers_24_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1084] + model_decoder_layers_24_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1085] + model_decoder_layers_24_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1086] + model_decoder_layers_24_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1087] + model_decoder_layers_24_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1088] + model_decoder_layers_25_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1089] + model_decoder_layers_25_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1090] + model_decoder_layers_25_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1091] + model_decoder_layers_25_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1092] + model_decoder_layers_25_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1093] + model_decoder_layers_25_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1094] + model_decoder_layers_25_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1095] + model_decoder_layers_25_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1096] + model_decoder_layers_25_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1097] + model_decoder_layers_25_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1101] + model_decoder_layers_25_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1102] + model_decoder_layers_25_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1103] + model_decoder_layers_25_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1104] + model_decoder_layers_25_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1105] + model_decoder_layers_25_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1106] + model_decoder_layers_25_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1107] + model_decoder_layers_25_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1108] + model_decoder_layers_25_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1109] + model_decoder_layers_25_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1110] + model_decoder_layers_25_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1111] + model_decoder_layers_25_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1112] + model_decoder_layers_26_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1113] + model_decoder_layers_26_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1114] + model_decoder_layers_26_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1115] + model_decoder_layers_26_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1116] + model_decoder_layers_26_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1117] + model_decoder_layers_26_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1118] + model_decoder_layers_26_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1119] + model_decoder_layers_26_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1120] + model_decoder_layers_26_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1121] + model_decoder_layers_26_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1125] + model_decoder_layers_26_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1126] + model_decoder_layers_26_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1127] + model_decoder_layers_26_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1128] + model_decoder_layers_26_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1129] + model_decoder_layers_26_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1130] + model_decoder_layers_26_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1131] + model_decoder_layers_26_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1132] + model_decoder_layers_26_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1133] + model_decoder_layers_26_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1134] + model_decoder_layers_26_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1135] + model_decoder_layers_26_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1136] + model_decoder_layers_27_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1137] + model_decoder_layers_27_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1138] + model_decoder_layers_27_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1139] + model_decoder_layers_27_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1140] + model_decoder_layers_27_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1141] + model_decoder_layers_27_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1142] + model_decoder_layers_27_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1143] + model_decoder_layers_27_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1144] + model_decoder_layers_27_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1145] + model_decoder_layers_27_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1149] + model_decoder_layers_27_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1150] + model_decoder_layers_27_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1151] + model_decoder_layers_27_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1152] + model_decoder_layers_27_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1153] + model_decoder_layers_27_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1154] + model_decoder_layers_27_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1155] + model_decoder_layers_27_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1156] + model_decoder_layers_27_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1157] + model_decoder_layers_27_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1158] + model_decoder_layers_27_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1159] + model_decoder_layers_27_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1160] + model_decoder_layers_28_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1161] + model_decoder_layers_28_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1162] + model_decoder_layers_28_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1163] + model_decoder_layers_28_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1164] + model_decoder_layers_28_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1165] + model_decoder_layers_28_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1166] + model_decoder_layers_28_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1167] + model_decoder_layers_28_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1168] + model_decoder_layers_28_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1169] + model_decoder_layers_28_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1173] + model_decoder_layers_28_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1174] + model_decoder_layers_28_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1175] + model_decoder_layers_28_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1176] + model_decoder_layers_28_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1177] + model_decoder_layers_28_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1178] + model_decoder_layers_28_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1179] + model_decoder_layers_28_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1180] + model_decoder_layers_28_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1181] + model_decoder_layers_28_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1182] + model_decoder_layers_28_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1183] + model_decoder_layers_28_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1184] + model_decoder_layers_29_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1185] + model_decoder_layers_29_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1186] + model_decoder_layers_29_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1187] + model_decoder_layers_29_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1188] + model_decoder_layers_29_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1189] + model_decoder_layers_29_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1190] + model_decoder_layers_29_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1191] + model_decoder_layers_29_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1192] + model_decoder_layers_29_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1193] + model_decoder_layers_29_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1197] + model_decoder_layers_29_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1198] + model_decoder_layers_29_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1199] + model_decoder_layers_29_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1200] + model_decoder_layers_29_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1201] + model_decoder_layers_29_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1202] + model_decoder_layers_29_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1203] + model_decoder_layers_29_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1204] + model_decoder_layers_29_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1205] + model_decoder_layers_29_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1206] + model_decoder_layers_29_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1207] + model_decoder_layers_29_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1208] + model_decoder_layers_30_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1209] + model_decoder_layers_30_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1210] + model_decoder_layers_30_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1211] + model_decoder_layers_30_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1212] + model_decoder_layers_30_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1213] + model_decoder_layers_30_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1214] + model_decoder_layers_30_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1215] + model_decoder_layers_30_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1216] + model_decoder_layers_30_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1217] + model_decoder_layers_30_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1221] + model_decoder_layers_30_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1222] + model_decoder_layers_30_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1223] + model_decoder_layers_30_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1224] + model_decoder_layers_30_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1225] + model_decoder_layers_30_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1226] + model_decoder_layers_30_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1227] + model_decoder_layers_30_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1228] + model_decoder_layers_30_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1229] + model_decoder_layers_30_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1230] + model_decoder_layers_30_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1231] + model_decoder_layers_30_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1232] + model_decoder_layers_31_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1233] + model_decoder_layers_31_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1234] + model_decoder_layers_31_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1235] + model_decoder_layers_31_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1236] + model_decoder_layers_31_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1237] + model_decoder_layers_31_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1238] + model_decoder_layers_31_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1239] + model_decoder_layers_31_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1240] + model_decoder_layers_31_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1241] + model_decoder_layers_31_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1245] + model_decoder_layers_31_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1246] + model_decoder_layers_31_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1247] + model_decoder_layers_31_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1248] + model_decoder_layers_31_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1249] + model_decoder_layers_31_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1250] + model_decoder_layers_31_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1251] + model_decoder_layers_31_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1252] + model_decoder_layers_31_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1253] + model_decoder_layers_31_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1254] + model_decoder_layers_31_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1255] + model_decoder_layers_31_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1256] + model_decoder_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1257] + model_decoder_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1258] + reshape1353 = R.call_tir(cls.reshape19, (input_ids,), out_sinfo=R.Tensor((1,), dtype="int32")) + take7 = R.call_tir(cls.take3, (model_decoder_embed_tokens_weight5, reshape1353), out_sinfo=R.Tensor((1, 1280), dtype="float16")) + lv264: R.Tensor((1,), dtype="int32") = R.call_pure_packed("vm.builtin.attention_kv_cache_get_query_positions", paged_kv_cache, sinfo_args=(R.Tensor((1,), dtype="int32"),)) + take8 = R.call_tir(cls.take4, (model_decoder_embed_positions_weight5, lv264), out_sinfo=R.Tensor((1, 1280), dtype="float16")) + lv40 = R.call_tir(cls.fused_reshape20_reshape20_add6, (take7, take8), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm356 = R.call_tir(cls.layer_norm3, (lv40, model_decoder_layers_0_self_attn_layer_norm_weight5, model_decoder_layers_0_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv41 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm356, model_decoder_layers_0_self_attn_q_proj_weight5, model_decoder_layers_0_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv1 = R.call_tir(cls.NT_matmul, (layer_norm356, model_decoder_layers_0_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv42 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm356, model_decoder_layers_0_self_attn_v_proj_weight5, model_decoder_layers_0_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv43 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv41, lv1, lv42), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv265 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), lv43), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv44 = R.call_tir(cls.fused_reshape23_reshape24, (lv265,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv45 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv44, model_decoder_layers_0_self_attn_out_proj_weight5, model_decoder_layers_0_self_attn_out_proj_bias5, lv40), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm357 = R.call_tir(cls.layer_norm3, (lv45, model_decoder_layers_0_encoder_attn_layer_norm_weight5, model_decoder_layers_0_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv46 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm357, model_decoder_layers_0_encoder_attn_q_proj_weight5, model_decoder_layers_0_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv47 = R.call_tir(cls.fused_reshape21_reshape25, (lv46,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv266 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), lv47), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv48 = R.call_tir(cls.fused_reshape23_reshape24, (lv266,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv49 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv48, model_decoder_layers_0_encoder_attn_out_proj_weight5, model_decoder_layers_0_encoder_attn_out_proj_bias5, lv45), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm358 = R.call_tir(cls.layer_norm3, (lv49, model_decoder_layers_0_final_layer_norm_weight5, model_decoder_layers_0_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv50 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm358, model_decoder_layers_0_fc1_weight5, model_decoder_layers_0_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv51 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv50, model_decoder_layers_0_fc2_weight5, model_decoder_layers_0_fc2_bias5, lv49), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm359 = R.call_tir(cls.layer_norm3, (lv51, model_decoder_layers_1_self_attn_layer_norm_weight5, model_decoder_layers_1_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv52 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm359, model_decoder_layers_1_self_attn_q_proj_weight5, model_decoder_layers_1_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv9 = R.call_tir(cls.NT_matmul, (layer_norm359, model_decoder_layers_1_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv53 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm359, model_decoder_layers_1_self_attn_v_proj_weight5, model_decoder_layers_1_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv54 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv52, lv9, lv53), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv267 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), lv54), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv55 = R.call_tir(cls.fused_reshape23_reshape24, (lv267,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv56 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv55, model_decoder_layers_1_self_attn_out_proj_weight5, model_decoder_layers_1_self_attn_out_proj_bias5, lv51), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm360 = R.call_tir(cls.layer_norm3, (lv56, model_decoder_layers_1_encoder_attn_layer_norm_weight5, model_decoder_layers_1_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv57 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm360, model_decoder_layers_1_encoder_attn_q_proj_weight5, model_decoder_layers_1_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv58 = R.call_tir(cls.fused_reshape21_reshape25, (lv57,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv268 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), lv58), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv59 = R.call_tir(cls.fused_reshape23_reshape24, (lv268,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv60 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv59, model_decoder_layers_1_encoder_attn_out_proj_weight5, model_decoder_layers_1_encoder_attn_out_proj_bias5, lv56), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm361 = R.call_tir(cls.layer_norm3, (lv60, model_decoder_layers_1_final_layer_norm_weight5, model_decoder_layers_1_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv61 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm361, model_decoder_layers_1_fc1_weight5, model_decoder_layers_1_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv62 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv61, model_decoder_layers_1_fc2_weight5, model_decoder_layers_1_fc2_bias5, lv60), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm362 = R.call_tir(cls.layer_norm3, (lv62, model_decoder_layers_2_self_attn_layer_norm_weight5, model_decoder_layers_2_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv63 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm362, model_decoder_layers_2_self_attn_q_proj_weight5, model_decoder_layers_2_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv17 = R.call_tir(cls.NT_matmul, (layer_norm362, model_decoder_layers_2_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv64 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm362, model_decoder_layers_2_self_attn_v_proj_weight5, model_decoder_layers_2_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv65 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv63, lv17, lv64), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv269 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), lv65), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv66 = R.call_tir(cls.fused_reshape23_reshape24, (lv269,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv67 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv66, model_decoder_layers_2_self_attn_out_proj_weight5, model_decoder_layers_2_self_attn_out_proj_bias5, lv62), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm363 = R.call_tir(cls.layer_norm3, (lv67, model_decoder_layers_2_encoder_attn_layer_norm_weight5, model_decoder_layers_2_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv68 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm363, model_decoder_layers_2_encoder_attn_q_proj_weight5, model_decoder_layers_2_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv69 = R.call_tir(cls.fused_reshape21_reshape25, (lv68,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv270 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), lv69), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv70 = R.call_tir(cls.fused_reshape23_reshape24, (lv270,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv71 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv70, model_decoder_layers_2_encoder_attn_out_proj_weight5, model_decoder_layers_2_encoder_attn_out_proj_bias5, lv67), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm364 = R.call_tir(cls.layer_norm3, (lv71, model_decoder_layers_2_final_layer_norm_weight5, model_decoder_layers_2_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv72 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm364, model_decoder_layers_2_fc1_weight5, model_decoder_layers_2_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv73 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv72, model_decoder_layers_2_fc2_weight5, model_decoder_layers_2_fc2_bias5, lv71), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm365 = R.call_tir(cls.layer_norm3, (lv73, model_decoder_layers_3_self_attn_layer_norm_weight5, model_decoder_layers_3_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv74 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm365, model_decoder_layers_3_self_attn_q_proj_weight5, model_decoder_layers_3_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv25 = R.call_tir(cls.NT_matmul, (layer_norm365, model_decoder_layers_3_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv75 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm365, model_decoder_layers_3_self_attn_v_proj_weight5, model_decoder_layers_3_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv76 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv74, lv25, lv75), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv271 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), lv76), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv77 = R.call_tir(cls.fused_reshape23_reshape24, (lv271,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv78 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv77, model_decoder_layers_3_self_attn_out_proj_weight5, model_decoder_layers_3_self_attn_out_proj_bias5, lv73), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm366 = R.call_tir(cls.layer_norm3, (lv78, model_decoder_layers_3_encoder_attn_layer_norm_weight5, model_decoder_layers_3_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv79 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm366, model_decoder_layers_3_encoder_attn_q_proj_weight5, model_decoder_layers_3_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv80 = R.call_tir(cls.fused_reshape21_reshape25, (lv79,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv272 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), lv80), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv81 = R.call_tir(cls.fused_reshape23_reshape24, (lv272,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv82 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv81, model_decoder_layers_3_encoder_attn_out_proj_weight5, model_decoder_layers_3_encoder_attn_out_proj_bias5, lv78), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm367 = R.call_tir(cls.layer_norm3, (lv82, model_decoder_layers_3_final_layer_norm_weight5, model_decoder_layers_3_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv83 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm367, model_decoder_layers_3_fc1_weight5, model_decoder_layers_3_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv84 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv83, model_decoder_layers_3_fc2_weight5, model_decoder_layers_3_fc2_bias5, lv82), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm368 = R.call_tir(cls.layer_norm3, (lv84, model_decoder_layers_4_self_attn_layer_norm_weight5, model_decoder_layers_4_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv85 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm368, model_decoder_layers_4_self_attn_q_proj_weight5, model_decoder_layers_4_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv33 = R.call_tir(cls.NT_matmul, (layer_norm368, model_decoder_layers_4_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv86 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm368, model_decoder_layers_4_self_attn_v_proj_weight5, model_decoder_layers_4_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv87 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv85, lv33, lv86), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv273 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), lv87), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv88 = R.call_tir(cls.fused_reshape23_reshape24, (lv273,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv89 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv88, model_decoder_layers_4_self_attn_out_proj_weight5, model_decoder_layers_4_self_attn_out_proj_bias5, lv84), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm369 = R.call_tir(cls.layer_norm3, (lv89, model_decoder_layers_4_encoder_attn_layer_norm_weight5, model_decoder_layers_4_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv90 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm369, model_decoder_layers_4_encoder_attn_q_proj_weight5, model_decoder_layers_4_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv91 = R.call_tir(cls.fused_reshape21_reshape25, (lv90,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv274 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), lv91), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv92 = R.call_tir(cls.fused_reshape23_reshape24, (lv274,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv93 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv92, model_decoder_layers_4_encoder_attn_out_proj_weight5, model_decoder_layers_4_encoder_attn_out_proj_bias5, lv89), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm370 = R.call_tir(cls.layer_norm3, (lv93, model_decoder_layers_4_final_layer_norm_weight5, model_decoder_layers_4_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv94 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm370, model_decoder_layers_4_fc1_weight5, model_decoder_layers_4_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv95 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv94, model_decoder_layers_4_fc2_weight5, model_decoder_layers_4_fc2_bias5, lv93), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm371 = R.call_tir(cls.layer_norm3, (lv95, model_decoder_layers_5_self_attn_layer_norm_weight5, model_decoder_layers_5_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv96 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm371, model_decoder_layers_5_self_attn_q_proj_weight5, model_decoder_layers_5_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv41_1 = R.call_tir(cls.NT_matmul, (layer_norm371, model_decoder_layers_5_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv97 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm371, model_decoder_layers_5_self_attn_v_proj_weight5, model_decoder_layers_5_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv98 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv96, lv41_1, lv97), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv275 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), lv98), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv99 = R.call_tir(cls.fused_reshape23_reshape24, (lv275,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv100 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv99, model_decoder_layers_5_self_attn_out_proj_weight5, model_decoder_layers_5_self_attn_out_proj_bias5, lv95), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm372 = R.call_tir(cls.layer_norm3, (lv100, model_decoder_layers_5_encoder_attn_layer_norm_weight5, model_decoder_layers_5_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv101 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm372, model_decoder_layers_5_encoder_attn_q_proj_weight5, model_decoder_layers_5_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv102 = R.call_tir(cls.fused_reshape21_reshape25, (lv101,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv276 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), lv102), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv103 = R.call_tir(cls.fused_reshape23_reshape24, (lv276,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv104 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv103, model_decoder_layers_5_encoder_attn_out_proj_weight5, model_decoder_layers_5_encoder_attn_out_proj_bias5, lv100), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm373 = R.call_tir(cls.layer_norm3, (lv104, model_decoder_layers_5_final_layer_norm_weight5, model_decoder_layers_5_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv105 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm373, model_decoder_layers_5_fc1_weight5, model_decoder_layers_5_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv106 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv105, model_decoder_layers_5_fc2_weight5, model_decoder_layers_5_fc2_bias5, lv104), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm374 = R.call_tir(cls.layer_norm3, (lv106, model_decoder_layers_6_self_attn_layer_norm_weight5, model_decoder_layers_6_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv107 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm374, model_decoder_layers_6_self_attn_q_proj_weight5, model_decoder_layers_6_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv49_1 = R.call_tir(cls.NT_matmul, (layer_norm374, model_decoder_layers_6_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv108 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm374, model_decoder_layers_6_self_attn_v_proj_weight5, model_decoder_layers_6_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv109 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv107, lv49_1, lv108), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv277 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), lv109), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv110 = R.call_tir(cls.fused_reshape23_reshape24, (lv277,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv111 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv110, model_decoder_layers_6_self_attn_out_proj_weight5, model_decoder_layers_6_self_attn_out_proj_bias5, lv106), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm375 = R.call_tir(cls.layer_norm3, (lv111, model_decoder_layers_6_encoder_attn_layer_norm_weight5, model_decoder_layers_6_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv112 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm375, model_decoder_layers_6_encoder_attn_q_proj_weight5, model_decoder_layers_6_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv113 = R.call_tir(cls.fused_reshape21_reshape25, (lv112,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv278 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), lv113), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv114 = R.call_tir(cls.fused_reshape23_reshape24, (lv278,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv115 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv114, model_decoder_layers_6_encoder_attn_out_proj_weight5, model_decoder_layers_6_encoder_attn_out_proj_bias5, lv111), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm376 = R.call_tir(cls.layer_norm3, (lv115, model_decoder_layers_6_final_layer_norm_weight5, model_decoder_layers_6_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv116 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm376, model_decoder_layers_6_fc1_weight5, model_decoder_layers_6_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv117 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv116, model_decoder_layers_6_fc2_weight5, model_decoder_layers_6_fc2_bias5, lv115), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm377 = R.call_tir(cls.layer_norm3, (lv117, model_decoder_layers_7_self_attn_layer_norm_weight5, model_decoder_layers_7_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv118 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm377, model_decoder_layers_7_self_attn_q_proj_weight5, model_decoder_layers_7_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv57_1 = R.call_tir(cls.NT_matmul, (layer_norm377, model_decoder_layers_7_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv119 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm377, model_decoder_layers_7_self_attn_v_proj_weight5, model_decoder_layers_7_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv120 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv118, lv57_1, lv119), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv279 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), lv120), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv121 = R.call_tir(cls.fused_reshape23_reshape24, (lv279,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv122 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv121, model_decoder_layers_7_self_attn_out_proj_weight5, model_decoder_layers_7_self_attn_out_proj_bias5, lv117), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm378 = R.call_tir(cls.layer_norm3, (lv122, model_decoder_layers_7_encoder_attn_layer_norm_weight5, model_decoder_layers_7_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv123 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm378, model_decoder_layers_7_encoder_attn_q_proj_weight5, model_decoder_layers_7_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv124 = R.call_tir(cls.fused_reshape21_reshape25, (lv123,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv280 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), lv124), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv125 = R.call_tir(cls.fused_reshape23_reshape24, (lv280,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv126 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv125, model_decoder_layers_7_encoder_attn_out_proj_weight5, model_decoder_layers_7_encoder_attn_out_proj_bias5, lv122), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm379 = R.call_tir(cls.layer_norm3, (lv126, model_decoder_layers_7_final_layer_norm_weight5, model_decoder_layers_7_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv127 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm379, model_decoder_layers_7_fc1_weight5, model_decoder_layers_7_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv128 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv127, model_decoder_layers_7_fc2_weight5, model_decoder_layers_7_fc2_bias5, lv126), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm380 = R.call_tir(cls.layer_norm3, (lv128, model_decoder_layers_8_self_attn_layer_norm_weight5, model_decoder_layers_8_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv129 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm380, model_decoder_layers_8_self_attn_q_proj_weight5, model_decoder_layers_8_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv65_1 = R.call_tir(cls.NT_matmul, (layer_norm380, model_decoder_layers_8_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv130 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm380, model_decoder_layers_8_self_attn_v_proj_weight5, model_decoder_layers_8_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv131 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv129, lv65_1, lv130), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv281 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), lv131), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv132 = R.call_tir(cls.fused_reshape23_reshape24, (lv281,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv133 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv132, model_decoder_layers_8_self_attn_out_proj_weight5, model_decoder_layers_8_self_attn_out_proj_bias5, lv128), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm381 = R.call_tir(cls.layer_norm3, (lv133, model_decoder_layers_8_encoder_attn_layer_norm_weight5, model_decoder_layers_8_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv134 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm381, model_decoder_layers_8_encoder_attn_q_proj_weight5, model_decoder_layers_8_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv135 = R.call_tir(cls.fused_reshape21_reshape25, (lv134,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv282 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), lv135), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv136 = R.call_tir(cls.fused_reshape23_reshape24, (lv282,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv137 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv136, model_decoder_layers_8_encoder_attn_out_proj_weight5, model_decoder_layers_8_encoder_attn_out_proj_bias5, lv133), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm382 = R.call_tir(cls.layer_norm3, (lv137, model_decoder_layers_8_final_layer_norm_weight5, model_decoder_layers_8_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv138 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm382, model_decoder_layers_8_fc1_weight5, model_decoder_layers_8_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv139 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv138, model_decoder_layers_8_fc2_weight5, model_decoder_layers_8_fc2_bias5, lv137), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm383 = R.call_tir(cls.layer_norm3, (lv139, model_decoder_layers_9_self_attn_layer_norm_weight5, model_decoder_layers_9_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv140 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm383, model_decoder_layers_9_self_attn_q_proj_weight5, model_decoder_layers_9_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv73_1 = R.call_tir(cls.NT_matmul, (layer_norm383, model_decoder_layers_9_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv141 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm383, model_decoder_layers_9_self_attn_v_proj_weight5, model_decoder_layers_9_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv142 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv140, lv73_1, lv141), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv283 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), lv142), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv143 = R.call_tir(cls.fused_reshape23_reshape24, (lv283,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv144 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv143, model_decoder_layers_9_self_attn_out_proj_weight5, model_decoder_layers_9_self_attn_out_proj_bias5, lv139), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm384 = R.call_tir(cls.layer_norm3, (lv144, model_decoder_layers_9_encoder_attn_layer_norm_weight5, model_decoder_layers_9_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv145 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm384, model_decoder_layers_9_encoder_attn_q_proj_weight5, model_decoder_layers_9_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv146 = R.call_tir(cls.fused_reshape21_reshape25, (lv145,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv284 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), lv146), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv147 = R.call_tir(cls.fused_reshape23_reshape24, (lv284,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv148 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv147, model_decoder_layers_9_encoder_attn_out_proj_weight5, model_decoder_layers_9_encoder_attn_out_proj_bias5, lv144), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm385 = R.call_tir(cls.layer_norm3, (lv148, model_decoder_layers_9_final_layer_norm_weight5, model_decoder_layers_9_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv149 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm385, model_decoder_layers_9_fc1_weight5, model_decoder_layers_9_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv150 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv149, model_decoder_layers_9_fc2_weight5, model_decoder_layers_9_fc2_bias5, lv148), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm386 = R.call_tir(cls.layer_norm3, (lv150, model_decoder_layers_10_self_attn_layer_norm_weight5, model_decoder_layers_10_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv151 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm386, model_decoder_layers_10_self_attn_q_proj_weight5, model_decoder_layers_10_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv81_1 = R.call_tir(cls.NT_matmul, (layer_norm386, model_decoder_layers_10_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv152 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm386, model_decoder_layers_10_self_attn_v_proj_weight5, model_decoder_layers_10_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv153 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv151, lv81_1, lv152), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv285 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), lv153), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv154 = R.call_tir(cls.fused_reshape23_reshape24, (lv285,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv155 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv154, model_decoder_layers_10_self_attn_out_proj_weight5, model_decoder_layers_10_self_attn_out_proj_bias5, lv150), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm387 = R.call_tir(cls.layer_norm3, (lv155, model_decoder_layers_10_encoder_attn_layer_norm_weight5, model_decoder_layers_10_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv156 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm387, model_decoder_layers_10_encoder_attn_q_proj_weight5, model_decoder_layers_10_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv157 = R.call_tir(cls.fused_reshape21_reshape25, (lv156,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv286 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), lv157), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv158 = R.call_tir(cls.fused_reshape23_reshape24, (lv286,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv159 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv158, model_decoder_layers_10_encoder_attn_out_proj_weight5, model_decoder_layers_10_encoder_attn_out_proj_bias5, lv155), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm388 = R.call_tir(cls.layer_norm3, (lv159, model_decoder_layers_10_final_layer_norm_weight5, model_decoder_layers_10_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv160 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm388, model_decoder_layers_10_fc1_weight5, model_decoder_layers_10_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv161 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv160, model_decoder_layers_10_fc2_weight5, model_decoder_layers_10_fc2_bias5, lv159), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm389 = R.call_tir(cls.layer_norm3, (lv161, model_decoder_layers_11_self_attn_layer_norm_weight5, model_decoder_layers_11_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv162 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm389, model_decoder_layers_11_self_attn_q_proj_weight5, model_decoder_layers_11_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv89_1 = R.call_tir(cls.NT_matmul, (layer_norm389, model_decoder_layers_11_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv163 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm389, model_decoder_layers_11_self_attn_v_proj_weight5, model_decoder_layers_11_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv164 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv162, lv89_1, lv163), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv287 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), lv164), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv165 = R.call_tir(cls.fused_reshape23_reshape24, (lv287,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv166 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv165, model_decoder_layers_11_self_attn_out_proj_weight5, model_decoder_layers_11_self_attn_out_proj_bias5, lv161), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm390 = R.call_tir(cls.layer_norm3, (lv166, model_decoder_layers_11_encoder_attn_layer_norm_weight5, model_decoder_layers_11_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv167 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm390, model_decoder_layers_11_encoder_attn_q_proj_weight5, model_decoder_layers_11_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv168 = R.call_tir(cls.fused_reshape21_reshape25, (lv167,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv288 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), lv168), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv169 = R.call_tir(cls.fused_reshape23_reshape24, (lv288,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv170 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv169, model_decoder_layers_11_encoder_attn_out_proj_weight5, model_decoder_layers_11_encoder_attn_out_proj_bias5, lv166), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm391 = R.call_tir(cls.layer_norm3, (lv170, model_decoder_layers_11_final_layer_norm_weight5, model_decoder_layers_11_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv171 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm391, model_decoder_layers_11_fc1_weight5, model_decoder_layers_11_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv172 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv171, model_decoder_layers_11_fc2_weight5, model_decoder_layers_11_fc2_bias5, lv170), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm392 = R.call_tir(cls.layer_norm3, (lv172, model_decoder_layers_12_self_attn_layer_norm_weight5, model_decoder_layers_12_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv173 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm392, model_decoder_layers_12_self_attn_q_proj_weight5, model_decoder_layers_12_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv97_1 = R.call_tir(cls.NT_matmul, (layer_norm392, model_decoder_layers_12_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv174 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm392, model_decoder_layers_12_self_attn_v_proj_weight5, model_decoder_layers_12_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv175 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv173, lv97_1, lv174), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv289 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), lv175), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv176 = R.call_tir(cls.fused_reshape23_reshape24, (lv289,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv177 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv176, model_decoder_layers_12_self_attn_out_proj_weight5, model_decoder_layers_12_self_attn_out_proj_bias5, lv172), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm393 = R.call_tir(cls.layer_norm3, (lv177, model_decoder_layers_12_encoder_attn_layer_norm_weight5, model_decoder_layers_12_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv178 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm393, model_decoder_layers_12_encoder_attn_q_proj_weight5, model_decoder_layers_12_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv179 = R.call_tir(cls.fused_reshape21_reshape25, (lv178,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv290 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), lv179), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv180 = R.call_tir(cls.fused_reshape23_reshape24, (lv290,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv181 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv180, model_decoder_layers_12_encoder_attn_out_proj_weight5, model_decoder_layers_12_encoder_attn_out_proj_bias5, lv177), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm394 = R.call_tir(cls.layer_norm3, (lv181, model_decoder_layers_12_final_layer_norm_weight5, model_decoder_layers_12_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv182 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm394, model_decoder_layers_12_fc1_weight5, model_decoder_layers_12_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv183 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv182, model_decoder_layers_12_fc2_weight5, model_decoder_layers_12_fc2_bias5, lv181), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm395 = R.call_tir(cls.layer_norm3, (lv183, model_decoder_layers_13_self_attn_layer_norm_weight5, model_decoder_layers_13_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv184 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm395, model_decoder_layers_13_self_attn_q_proj_weight5, model_decoder_layers_13_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv105_1 = R.call_tir(cls.NT_matmul, (layer_norm395, model_decoder_layers_13_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv185 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm395, model_decoder_layers_13_self_attn_v_proj_weight5, model_decoder_layers_13_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv186 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv184, lv105_1, lv185), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv291 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), lv186), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv187 = R.call_tir(cls.fused_reshape23_reshape24, (lv291,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv188 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv187, model_decoder_layers_13_self_attn_out_proj_weight5, model_decoder_layers_13_self_attn_out_proj_bias5, lv183), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm396 = R.call_tir(cls.layer_norm3, (lv188, model_decoder_layers_13_encoder_attn_layer_norm_weight5, model_decoder_layers_13_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv189 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm396, model_decoder_layers_13_encoder_attn_q_proj_weight5, model_decoder_layers_13_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv190 = R.call_tir(cls.fused_reshape21_reshape25, (lv189,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv292 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), lv190), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv191 = R.call_tir(cls.fused_reshape23_reshape24, (lv292,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv192 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv191, model_decoder_layers_13_encoder_attn_out_proj_weight5, model_decoder_layers_13_encoder_attn_out_proj_bias5, lv188), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm397 = R.call_tir(cls.layer_norm3, (lv192, model_decoder_layers_13_final_layer_norm_weight5, model_decoder_layers_13_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv193 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm397, model_decoder_layers_13_fc1_weight5, model_decoder_layers_13_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv194 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv193, model_decoder_layers_13_fc2_weight5, model_decoder_layers_13_fc2_bias5, lv192), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm398 = R.call_tir(cls.layer_norm3, (lv194, model_decoder_layers_14_self_attn_layer_norm_weight5, model_decoder_layers_14_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv195 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm398, model_decoder_layers_14_self_attn_q_proj_weight5, model_decoder_layers_14_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv113_1 = R.call_tir(cls.NT_matmul, (layer_norm398, model_decoder_layers_14_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv196 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm398, model_decoder_layers_14_self_attn_v_proj_weight5, model_decoder_layers_14_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv197 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv195, lv113_1, lv196), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv293 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), lv197), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv198 = R.call_tir(cls.fused_reshape23_reshape24, (lv293,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv199 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv198, model_decoder_layers_14_self_attn_out_proj_weight5, model_decoder_layers_14_self_attn_out_proj_bias5, lv194), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm399 = R.call_tir(cls.layer_norm3, (lv199, model_decoder_layers_14_encoder_attn_layer_norm_weight5, model_decoder_layers_14_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv200 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm399, model_decoder_layers_14_encoder_attn_q_proj_weight5, model_decoder_layers_14_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv201 = R.call_tir(cls.fused_reshape21_reshape25, (lv200,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv294 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), lv201), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv202 = R.call_tir(cls.fused_reshape23_reshape24, (lv294,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv203 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv202, model_decoder_layers_14_encoder_attn_out_proj_weight5, model_decoder_layers_14_encoder_attn_out_proj_bias5, lv199), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm400 = R.call_tir(cls.layer_norm3, (lv203, model_decoder_layers_14_final_layer_norm_weight5, model_decoder_layers_14_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv204 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm400, model_decoder_layers_14_fc1_weight5, model_decoder_layers_14_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv205 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv204, model_decoder_layers_14_fc2_weight5, model_decoder_layers_14_fc2_bias5, lv203), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm401 = R.call_tir(cls.layer_norm3, (lv205, model_decoder_layers_15_self_attn_layer_norm_weight5, model_decoder_layers_15_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv206 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm401, model_decoder_layers_15_self_attn_q_proj_weight5, model_decoder_layers_15_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv121_1 = R.call_tir(cls.NT_matmul, (layer_norm401, model_decoder_layers_15_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv207 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm401, model_decoder_layers_15_self_attn_v_proj_weight5, model_decoder_layers_15_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv208 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv206, lv121_1, lv207), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv295 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), lv208), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv209 = R.call_tir(cls.fused_reshape23_reshape24, (lv295,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv210 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv209, model_decoder_layers_15_self_attn_out_proj_weight5, model_decoder_layers_15_self_attn_out_proj_bias5, lv205), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm402 = R.call_tir(cls.layer_norm3, (lv210, model_decoder_layers_15_encoder_attn_layer_norm_weight5, model_decoder_layers_15_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv211 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm402, model_decoder_layers_15_encoder_attn_q_proj_weight5, model_decoder_layers_15_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv212 = R.call_tir(cls.fused_reshape21_reshape25, (lv211,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv296 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), lv212), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv213 = R.call_tir(cls.fused_reshape23_reshape24, (lv296,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv214 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv213, model_decoder_layers_15_encoder_attn_out_proj_weight5, model_decoder_layers_15_encoder_attn_out_proj_bias5, lv210), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm403 = R.call_tir(cls.layer_norm3, (lv214, model_decoder_layers_15_final_layer_norm_weight5, model_decoder_layers_15_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv215 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm403, model_decoder_layers_15_fc1_weight5, model_decoder_layers_15_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv216 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv215, model_decoder_layers_15_fc2_weight5, model_decoder_layers_15_fc2_bias5, lv214), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm404 = R.call_tir(cls.layer_norm3, (lv216, model_decoder_layers_16_self_attn_layer_norm_weight5, model_decoder_layers_16_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv217 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm404, model_decoder_layers_16_self_attn_q_proj_weight5, model_decoder_layers_16_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv129_1 = R.call_tir(cls.NT_matmul, (layer_norm404, model_decoder_layers_16_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv218 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm404, model_decoder_layers_16_self_attn_v_proj_weight5, model_decoder_layers_16_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv219 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv217, lv129_1, lv218), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv297 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), lv219), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv220 = R.call_tir(cls.fused_reshape23_reshape24, (lv297,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv221 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv220, model_decoder_layers_16_self_attn_out_proj_weight5, model_decoder_layers_16_self_attn_out_proj_bias5, lv216), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm405 = R.call_tir(cls.layer_norm3, (lv221, model_decoder_layers_16_encoder_attn_layer_norm_weight5, model_decoder_layers_16_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv222 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm405, model_decoder_layers_16_encoder_attn_q_proj_weight5, model_decoder_layers_16_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv223 = R.call_tir(cls.fused_reshape21_reshape25, (lv222,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv298 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), lv223), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv224 = R.call_tir(cls.fused_reshape23_reshape24, (lv298,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv225 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv224, model_decoder_layers_16_encoder_attn_out_proj_weight5, model_decoder_layers_16_encoder_attn_out_proj_bias5, lv221), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm406 = R.call_tir(cls.layer_norm3, (lv225, model_decoder_layers_16_final_layer_norm_weight5, model_decoder_layers_16_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv226 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm406, model_decoder_layers_16_fc1_weight5, model_decoder_layers_16_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv227 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv226, model_decoder_layers_16_fc2_weight5, model_decoder_layers_16_fc2_bias5, lv225), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm407 = R.call_tir(cls.layer_norm3, (lv227, model_decoder_layers_17_self_attn_layer_norm_weight5, model_decoder_layers_17_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv228 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm407, model_decoder_layers_17_self_attn_q_proj_weight5, model_decoder_layers_17_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv137_1 = R.call_tir(cls.NT_matmul, (layer_norm407, model_decoder_layers_17_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv229 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm407, model_decoder_layers_17_self_attn_v_proj_weight5, model_decoder_layers_17_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv230 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv228, lv137_1, lv229), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv299 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), lv230), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv231 = R.call_tir(cls.fused_reshape23_reshape24, (lv299,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv232 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv231, model_decoder_layers_17_self_attn_out_proj_weight5, model_decoder_layers_17_self_attn_out_proj_bias5, lv227), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm408 = R.call_tir(cls.layer_norm3, (lv232, model_decoder_layers_17_encoder_attn_layer_norm_weight5, model_decoder_layers_17_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv233 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm408, model_decoder_layers_17_encoder_attn_q_proj_weight5, model_decoder_layers_17_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv234 = R.call_tir(cls.fused_reshape21_reshape25, (lv233,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv300 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), lv234), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv235 = R.call_tir(cls.fused_reshape23_reshape24, (lv300,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv236 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv235, model_decoder_layers_17_encoder_attn_out_proj_weight5, model_decoder_layers_17_encoder_attn_out_proj_bias5, lv232), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm409 = R.call_tir(cls.layer_norm3, (lv236, model_decoder_layers_17_final_layer_norm_weight5, model_decoder_layers_17_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv237 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm409, model_decoder_layers_17_fc1_weight5, model_decoder_layers_17_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv238 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv237, model_decoder_layers_17_fc2_weight5, model_decoder_layers_17_fc2_bias5, lv236), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm410 = R.call_tir(cls.layer_norm3, (lv238, model_decoder_layers_18_self_attn_layer_norm_weight5, model_decoder_layers_18_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv239 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm410, model_decoder_layers_18_self_attn_q_proj_weight5, model_decoder_layers_18_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv145_1 = R.call_tir(cls.NT_matmul, (layer_norm410, model_decoder_layers_18_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv240 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm410, model_decoder_layers_18_self_attn_v_proj_weight5, model_decoder_layers_18_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv241 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv239, lv145_1, lv240), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv301 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), lv241), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv242 = R.call_tir(cls.fused_reshape23_reshape24, (lv301,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv243 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv242, model_decoder_layers_18_self_attn_out_proj_weight5, model_decoder_layers_18_self_attn_out_proj_bias5, lv238), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm411 = R.call_tir(cls.layer_norm3, (lv243, model_decoder_layers_18_encoder_attn_layer_norm_weight5, model_decoder_layers_18_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv244 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm411, model_decoder_layers_18_encoder_attn_q_proj_weight5, model_decoder_layers_18_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv245 = R.call_tir(cls.fused_reshape21_reshape25, (lv244,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv302 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), lv245), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv246 = R.call_tir(cls.fused_reshape23_reshape24, (lv302,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv247 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv246, model_decoder_layers_18_encoder_attn_out_proj_weight5, model_decoder_layers_18_encoder_attn_out_proj_bias5, lv243), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm412 = R.call_tir(cls.layer_norm3, (lv247, model_decoder_layers_18_final_layer_norm_weight5, model_decoder_layers_18_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv248 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm412, model_decoder_layers_18_fc1_weight5, model_decoder_layers_18_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv249 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv248, model_decoder_layers_18_fc2_weight5, model_decoder_layers_18_fc2_bias5, lv247), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm413 = R.call_tir(cls.layer_norm3, (lv249, model_decoder_layers_19_self_attn_layer_norm_weight5, model_decoder_layers_19_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv250 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm413, model_decoder_layers_19_self_attn_q_proj_weight5, model_decoder_layers_19_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv153_1 = R.call_tir(cls.NT_matmul, (layer_norm413, model_decoder_layers_19_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv251 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm413, model_decoder_layers_19_self_attn_v_proj_weight5, model_decoder_layers_19_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv252 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv250, lv153_1, lv251), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv303 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), lv252), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv253 = R.call_tir(cls.fused_reshape23_reshape24, (lv303,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv254 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv253, model_decoder_layers_19_self_attn_out_proj_weight5, model_decoder_layers_19_self_attn_out_proj_bias5, lv249), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm414 = R.call_tir(cls.layer_norm3, (lv254, model_decoder_layers_19_encoder_attn_layer_norm_weight5, model_decoder_layers_19_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv255 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm414, model_decoder_layers_19_encoder_attn_q_proj_weight5, model_decoder_layers_19_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv256 = R.call_tir(cls.fused_reshape21_reshape25, (lv255,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv304 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), lv256), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv257 = R.call_tir(cls.fused_reshape23_reshape24, (lv304,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv258 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv257, model_decoder_layers_19_encoder_attn_out_proj_weight5, model_decoder_layers_19_encoder_attn_out_proj_bias5, lv254), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm415 = R.call_tir(cls.layer_norm3, (lv258, model_decoder_layers_19_final_layer_norm_weight5, model_decoder_layers_19_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv259 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm415, model_decoder_layers_19_fc1_weight5, model_decoder_layers_19_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv260 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv259, model_decoder_layers_19_fc2_weight5, model_decoder_layers_19_fc2_bias5, lv258), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm416 = R.call_tir(cls.layer_norm3, (lv260, model_decoder_layers_20_self_attn_layer_norm_weight5, model_decoder_layers_20_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv261 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm416, model_decoder_layers_20_self_attn_q_proj_weight5, model_decoder_layers_20_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv161_1 = R.call_tir(cls.NT_matmul, (layer_norm416, model_decoder_layers_20_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv262 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm416, model_decoder_layers_20_self_attn_v_proj_weight5, model_decoder_layers_20_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv263 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv261, lv161_1, lv262), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv305 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), lv263), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv264_1 = R.call_tir(cls.fused_reshape23_reshape24, (lv305,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv265_1 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv264_1, model_decoder_layers_20_self_attn_out_proj_weight5, model_decoder_layers_20_self_attn_out_proj_bias5, lv260), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm417 = R.call_tir(cls.layer_norm3, (lv265_1, model_decoder_layers_20_encoder_attn_layer_norm_weight5, model_decoder_layers_20_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv266_1 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm417, model_decoder_layers_20_encoder_attn_q_proj_weight5, model_decoder_layers_20_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv267_1 = R.call_tir(cls.fused_reshape21_reshape25, (lv266_1,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv306 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), lv267_1), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv268_1 = R.call_tir(cls.fused_reshape23_reshape24, (lv306,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv269_1 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv268_1, model_decoder_layers_20_encoder_attn_out_proj_weight5, model_decoder_layers_20_encoder_attn_out_proj_bias5, lv265_1), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm418 = R.call_tir(cls.layer_norm3, (lv269_1, model_decoder_layers_20_final_layer_norm_weight5, model_decoder_layers_20_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv270_1 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm418, model_decoder_layers_20_fc1_weight5, model_decoder_layers_20_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv271_1 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv270_1, model_decoder_layers_20_fc2_weight5, model_decoder_layers_20_fc2_bias5, lv269_1), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm419 = R.call_tir(cls.layer_norm3, (lv271_1, model_decoder_layers_21_self_attn_layer_norm_weight5, model_decoder_layers_21_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv272_1 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm419, model_decoder_layers_21_self_attn_q_proj_weight5, model_decoder_layers_21_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv169_1 = R.call_tir(cls.NT_matmul, (layer_norm419, model_decoder_layers_21_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv273_1 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm419, model_decoder_layers_21_self_attn_v_proj_weight5, model_decoder_layers_21_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv274_1 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv272_1, lv169_1, lv273_1), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv307 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), lv274_1), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv275_1 = R.call_tir(cls.fused_reshape23_reshape24, (lv307,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv276_1 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv275_1, model_decoder_layers_21_self_attn_out_proj_weight5, model_decoder_layers_21_self_attn_out_proj_bias5, lv271_1), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm420 = R.call_tir(cls.layer_norm3, (lv276_1, model_decoder_layers_21_encoder_attn_layer_norm_weight5, model_decoder_layers_21_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv277_1 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm420, model_decoder_layers_21_encoder_attn_q_proj_weight5, model_decoder_layers_21_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv278_1 = R.call_tir(cls.fused_reshape21_reshape25, (lv277_1,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv308 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), lv278_1), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv279_1 = R.call_tir(cls.fused_reshape23_reshape24, (lv308,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv280_1 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv279_1, model_decoder_layers_21_encoder_attn_out_proj_weight5, model_decoder_layers_21_encoder_attn_out_proj_bias5, lv276_1), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm421 = R.call_tir(cls.layer_norm3, (lv280_1, model_decoder_layers_21_final_layer_norm_weight5, model_decoder_layers_21_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv281_1 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm421, model_decoder_layers_21_fc1_weight5, model_decoder_layers_21_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv282_1 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv281_1, model_decoder_layers_21_fc2_weight5, model_decoder_layers_21_fc2_bias5, lv280_1), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm422 = R.call_tir(cls.layer_norm3, (lv282_1, model_decoder_layers_22_self_attn_layer_norm_weight5, model_decoder_layers_22_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv283_1 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm422, model_decoder_layers_22_self_attn_q_proj_weight5, model_decoder_layers_22_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv177_1 = R.call_tir(cls.NT_matmul, (layer_norm422, model_decoder_layers_22_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv284_1 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm422, model_decoder_layers_22_self_attn_v_proj_weight5, model_decoder_layers_22_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv285_1 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv283_1, lv177_1, lv284_1), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv309 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), lv285_1), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv286_1 = R.call_tir(cls.fused_reshape23_reshape24, (lv309,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv287_1 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv286_1, model_decoder_layers_22_self_attn_out_proj_weight5, model_decoder_layers_22_self_attn_out_proj_bias5, lv282_1), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm423 = R.call_tir(cls.layer_norm3, (lv287_1, model_decoder_layers_22_encoder_attn_layer_norm_weight5, model_decoder_layers_22_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv288_1 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm423, model_decoder_layers_22_encoder_attn_q_proj_weight5, model_decoder_layers_22_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv289_1 = R.call_tir(cls.fused_reshape21_reshape25, (lv288_1,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv310 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), lv289_1), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv290_1 = R.call_tir(cls.fused_reshape23_reshape24, (lv310,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv291_1 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv290_1, model_decoder_layers_22_encoder_attn_out_proj_weight5, model_decoder_layers_22_encoder_attn_out_proj_bias5, lv287_1), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm424 = R.call_tir(cls.layer_norm3, (lv291_1, model_decoder_layers_22_final_layer_norm_weight5, model_decoder_layers_22_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv292_1 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm424, model_decoder_layers_22_fc1_weight5, model_decoder_layers_22_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv293_1 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv292_1, model_decoder_layers_22_fc2_weight5, model_decoder_layers_22_fc2_bias5, lv291_1), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm425 = R.call_tir(cls.layer_norm3, (lv293_1, model_decoder_layers_23_self_attn_layer_norm_weight5, model_decoder_layers_23_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv294_1 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm425, model_decoder_layers_23_self_attn_q_proj_weight5, model_decoder_layers_23_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv185_1 = R.call_tir(cls.NT_matmul, (layer_norm425, model_decoder_layers_23_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv295_1 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm425, model_decoder_layers_23_self_attn_v_proj_weight5, model_decoder_layers_23_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv296_1 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv294_1, lv185_1, lv295_1), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv311 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), lv296_1), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv297_1 = R.call_tir(cls.fused_reshape23_reshape24, (lv311,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv298_1 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv297_1, model_decoder_layers_23_self_attn_out_proj_weight5, model_decoder_layers_23_self_attn_out_proj_bias5, lv293_1), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm426 = R.call_tir(cls.layer_norm3, (lv298_1, model_decoder_layers_23_encoder_attn_layer_norm_weight5, model_decoder_layers_23_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv299_1 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm426, model_decoder_layers_23_encoder_attn_q_proj_weight5, model_decoder_layers_23_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv300_1 = R.call_tir(cls.fused_reshape21_reshape25, (lv299_1,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv312 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), lv300_1), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv301_1 = R.call_tir(cls.fused_reshape23_reshape24, (lv312,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv302_1 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv301_1, model_decoder_layers_23_encoder_attn_out_proj_weight5, model_decoder_layers_23_encoder_attn_out_proj_bias5, lv298_1), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm427 = R.call_tir(cls.layer_norm3, (lv302_1, model_decoder_layers_23_final_layer_norm_weight5, model_decoder_layers_23_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv303_1 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm427, model_decoder_layers_23_fc1_weight5, model_decoder_layers_23_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv304_1 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv303_1, model_decoder_layers_23_fc2_weight5, model_decoder_layers_23_fc2_bias5, lv302_1), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm428 = R.call_tir(cls.layer_norm3, (lv304_1, model_decoder_layers_24_self_attn_layer_norm_weight5, model_decoder_layers_24_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv305_1 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm428, model_decoder_layers_24_self_attn_q_proj_weight5, model_decoder_layers_24_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv193_1 = R.call_tir(cls.NT_matmul, (layer_norm428, model_decoder_layers_24_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv306_1 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm428, model_decoder_layers_24_self_attn_v_proj_weight5, model_decoder_layers_24_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv307_1 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv305_1, lv193_1, lv306_1), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv313 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), lv307_1), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv308_1 = R.call_tir(cls.fused_reshape23_reshape24, (lv313,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv309_1 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv308_1, model_decoder_layers_24_self_attn_out_proj_weight5, model_decoder_layers_24_self_attn_out_proj_bias5, lv304_1), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm429 = R.call_tir(cls.layer_norm3, (lv309_1, model_decoder_layers_24_encoder_attn_layer_norm_weight5, model_decoder_layers_24_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv310_1 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm429, model_decoder_layers_24_encoder_attn_q_proj_weight5, model_decoder_layers_24_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv311_1 = R.call_tir(cls.fused_reshape21_reshape25, (lv310_1,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv314 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), lv311_1), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv312_1 = R.call_tir(cls.fused_reshape23_reshape24, (lv314,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv313_1 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv312_1, model_decoder_layers_24_encoder_attn_out_proj_weight5, model_decoder_layers_24_encoder_attn_out_proj_bias5, lv309_1), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm430 = R.call_tir(cls.layer_norm3, (lv313_1, model_decoder_layers_24_final_layer_norm_weight5, model_decoder_layers_24_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv314_1 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm430, model_decoder_layers_24_fc1_weight5, model_decoder_layers_24_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv315 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv314_1, model_decoder_layers_24_fc2_weight5, model_decoder_layers_24_fc2_bias5, lv313_1), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm431 = R.call_tir(cls.layer_norm3, (lv315, model_decoder_layers_25_self_attn_layer_norm_weight5, model_decoder_layers_25_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv316 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm431, model_decoder_layers_25_self_attn_q_proj_weight5, model_decoder_layers_25_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv201_1 = R.call_tir(cls.NT_matmul, (layer_norm431, model_decoder_layers_25_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv317 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm431, model_decoder_layers_25_self_attn_v_proj_weight5, model_decoder_layers_25_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv318 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv316, lv201_1, lv317), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv315_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), lv318), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv319 = R.call_tir(cls.fused_reshape23_reshape24, (lv315_1,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv320 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv319, model_decoder_layers_25_self_attn_out_proj_weight5, model_decoder_layers_25_self_attn_out_proj_bias5, lv315), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm432 = R.call_tir(cls.layer_norm3, (lv320, model_decoder_layers_25_encoder_attn_layer_norm_weight5, model_decoder_layers_25_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv321 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm432, model_decoder_layers_25_encoder_attn_q_proj_weight5, model_decoder_layers_25_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv322 = R.call_tir(cls.fused_reshape21_reshape25, (lv321,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv316_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), lv322), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv323 = R.call_tir(cls.fused_reshape23_reshape24, (lv316_1,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv324 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv323, model_decoder_layers_25_encoder_attn_out_proj_weight5, model_decoder_layers_25_encoder_attn_out_proj_bias5, lv320), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm433 = R.call_tir(cls.layer_norm3, (lv324, model_decoder_layers_25_final_layer_norm_weight5, model_decoder_layers_25_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv325 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm433, model_decoder_layers_25_fc1_weight5, model_decoder_layers_25_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv326 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv325, model_decoder_layers_25_fc2_weight5, model_decoder_layers_25_fc2_bias5, lv324), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm434 = R.call_tir(cls.layer_norm3, (lv326, model_decoder_layers_26_self_attn_layer_norm_weight5, model_decoder_layers_26_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv327 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm434, model_decoder_layers_26_self_attn_q_proj_weight5, model_decoder_layers_26_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv209_1 = R.call_tir(cls.NT_matmul, (layer_norm434, model_decoder_layers_26_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv328 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm434, model_decoder_layers_26_self_attn_v_proj_weight5, model_decoder_layers_26_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv329 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv327, lv209_1, lv328), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv317_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), lv329), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv330 = R.call_tir(cls.fused_reshape23_reshape24, (lv317_1,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv331 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv330, model_decoder_layers_26_self_attn_out_proj_weight5, model_decoder_layers_26_self_attn_out_proj_bias5, lv326), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm435 = R.call_tir(cls.layer_norm3, (lv331, model_decoder_layers_26_encoder_attn_layer_norm_weight5, model_decoder_layers_26_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv332 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm435, model_decoder_layers_26_encoder_attn_q_proj_weight5, model_decoder_layers_26_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv333 = R.call_tir(cls.fused_reshape21_reshape25, (lv332,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv318_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), lv333), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv334 = R.call_tir(cls.fused_reshape23_reshape24, (lv318_1,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv335 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv334, model_decoder_layers_26_encoder_attn_out_proj_weight5, model_decoder_layers_26_encoder_attn_out_proj_bias5, lv331), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm436 = R.call_tir(cls.layer_norm3, (lv335, model_decoder_layers_26_final_layer_norm_weight5, model_decoder_layers_26_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv336 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm436, model_decoder_layers_26_fc1_weight5, model_decoder_layers_26_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv337 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv336, model_decoder_layers_26_fc2_weight5, model_decoder_layers_26_fc2_bias5, lv335), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm437 = R.call_tir(cls.layer_norm3, (lv337, model_decoder_layers_27_self_attn_layer_norm_weight5, model_decoder_layers_27_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv338 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm437, model_decoder_layers_27_self_attn_q_proj_weight5, model_decoder_layers_27_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv217_1 = R.call_tir(cls.NT_matmul, (layer_norm437, model_decoder_layers_27_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv339 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm437, model_decoder_layers_27_self_attn_v_proj_weight5, model_decoder_layers_27_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv340 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv338, lv217_1, lv339), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv319_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), lv340), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv341 = R.call_tir(cls.fused_reshape23_reshape24, (lv319_1,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv342 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv341, model_decoder_layers_27_self_attn_out_proj_weight5, model_decoder_layers_27_self_attn_out_proj_bias5, lv337), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm438 = R.call_tir(cls.layer_norm3, (lv342, model_decoder_layers_27_encoder_attn_layer_norm_weight5, model_decoder_layers_27_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv343 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm438, model_decoder_layers_27_encoder_attn_q_proj_weight5, model_decoder_layers_27_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv344 = R.call_tir(cls.fused_reshape21_reshape25, (lv343,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv320_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), lv344), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv345 = R.call_tir(cls.fused_reshape23_reshape24, (lv320_1,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv346 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv345, model_decoder_layers_27_encoder_attn_out_proj_weight5, model_decoder_layers_27_encoder_attn_out_proj_bias5, lv342), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm439 = R.call_tir(cls.layer_norm3, (lv346, model_decoder_layers_27_final_layer_norm_weight5, model_decoder_layers_27_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv347 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm439, model_decoder_layers_27_fc1_weight5, model_decoder_layers_27_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv348 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv347, model_decoder_layers_27_fc2_weight5, model_decoder_layers_27_fc2_bias5, lv346), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm440 = R.call_tir(cls.layer_norm3, (lv348, model_decoder_layers_28_self_attn_layer_norm_weight5, model_decoder_layers_28_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv349 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm440, model_decoder_layers_28_self_attn_q_proj_weight5, model_decoder_layers_28_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv225_1 = R.call_tir(cls.NT_matmul, (layer_norm440, model_decoder_layers_28_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv350 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm440, model_decoder_layers_28_self_attn_v_proj_weight5, model_decoder_layers_28_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv351 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv349, lv225_1, lv350), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv321_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), lv351), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv352 = R.call_tir(cls.fused_reshape23_reshape24, (lv321_1,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv353 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv352, model_decoder_layers_28_self_attn_out_proj_weight5, model_decoder_layers_28_self_attn_out_proj_bias5, lv348), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm441 = R.call_tir(cls.layer_norm3, (lv353, model_decoder_layers_28_encoder_attn_layer_norm_weight5, model_decoder_layers_28_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv354 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm441, model_decoder_layers_28_encoder_attn_q_proj_weight5, model_decoder_layers_28_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv355 = R.call_tir(cls.fused_reshape21_reshape25, (lv354,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv322_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), lv355), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv356 = R.call_tir(cls.fused_reshape23_reshape24, (lv322_1,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv357 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv356, model_decoder_layers_28_encoder_attn_out_proj_weight5, model_decoder_layers_28_encoder_attn_out_proj_bias5, lv353), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm442 = R.call_tir(cls.layer_norm3, (lv357, model_decoder_layers_28_final_layer_norm_weight5, model_decoder_layers_28_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv358 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm442, model_decoder_layers_28_fc1_weight5, model_decoder_layers_28_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv359 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv358, model_decoder_layers_28_fc2_weight5, model_decoder_layers_28_fc2_bias5, lv357), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm443 = R.call_tir(cls.layer_norm3, (lv359, model_decoder_layers_29_self_attn_layer_norm_weight5, model_decoder_layers_29_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv360 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm443, model_decoder_layers_29_self_attn_q_proj_weight5, model_decoder_layers_29_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv233_1 = R.call_tir(cls.NT_matmul, (layer_norm443, model_decoder_layers_29_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv361 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm443, model_decoder_layers_29_self_attn_v_proj_weight5, model_decoder_layers_29_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv362 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv360, lv233_1, lv361), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv323_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), lv362), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv363 = R.call_tir(cls.fused_reshape23_reshape24, (lv323_1,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv364 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv363, model_decoder_layers_29_self_attn_out_proj_weight5, model_decoder_layers_29_self_attn_out_proj_bias5, lv359), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm444 = R.call_tir(cls.layer_norm3, (lv364, model_decoder_layers_29_encoder_attn_layer_norm_weight5, model_decoder_layers_29_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv365 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm444, model_decoder_layers_29_encoder_attn_q_proj_weight5, model_decoder_layers_29_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv366 = R.call_tir(cls.fused_reshape21_reshape25, (lv365,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv324_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), lv366), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv367 = R.call_tir(cls.fused_reshape23_reshape24, (lv324_1,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv368 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv367, model_decoder_layers_29_encoder_attn_out_proj_weight5, model_decoder_layers_29_encoder_attn_out_proj_bias5, lv364), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm445 = R.call_tir(cls.layer_norm3, (lv368, model_decoder_layers_29_final_layer_norm_weight5, model_decoder_layers_29_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv369 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm445, model_decoder_layers_29_fc1_weight5, model_decoder_layers_29_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv370 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv369, model_decoder_layers_29_fc2_weight5, model_decoder_layers_29_fc2_bias5, lv368), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm446 = R.call_tir(cls.layer_norm3, (lv370, model_decoder_layers_30_self_attn_layer_norm_weight5, model_decoder_layers_30_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv371 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm446, model_decoder_layers_30_self_attn_q_proj_weight5, model_decoder_layers_30_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv241_1 = R.call_tir(cls.NT_matmul, (layer_norm446, model_decoder_layers_30_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv372 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm446, model_decoder_layers_30_self_attn_v_proj_weight5, model_decoder_layers_30_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv373 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv371, lv241_1, lv372), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv325_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), lv373), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv374 = R.call_tir(cls.fused_reshape23_reshape24, (lv325_1,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv375 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv374, model_decoder_layers_30_self_attn_out_proj_weight5, model_decoder_layers_30_self_attn_out_proj_bias5, lv370), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm447 = R.call_tir(cls.layer_norm3, (lv375, model_decoder_layers_30_encoder_attn_layer_norm_weight5, model_decoder_layers_30_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv376 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm447, model_decoder_layers_30_encoder_attn_q_proj_weight5, model_decoder_layers_30_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv377 = R.call_tir(cls.fused_reshape21_reshape25, (lv376,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv326_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), lv377), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv378 = R.call_tir(cls.fused_reshape23_reshape24, (lv326_1,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv379 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv378, model_decoder_layers_30_encoder_attn_out_proj_weight5, model_decoder_layers_30_encoder_attn_out_proj_bias5, lv375), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm448 = R.call_tir(cls.layer_norm3, (lv379, model_decoder_layers_30_final_layer_norm_weight5, model_decoder_layers_30_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv380 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm448, model_decoder_layers_30_fc1_weight5, model_decoder_layers_30_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv381 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv380, model_decoder_layers_30_fc2_weight5, model_decoder_layers_30_fc2_bias5, lv379), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm449 = R.call_tir(cls.layer_norm3, (lv381, model_decoder_layers_31_self_attn_layer_norm_weight5, model_decoder_layers_31_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv382 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm449, model_decoder_layers_31_self_attn_q_proj_weight5, model_decoder_layers_31_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv249_1 = R.call_tir(cls.NT_matmul, (layer_norm449, model_decoder_layers_31_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv383 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm449, model_decoder_layers_31_self_attn_v_proj_weight5, model_decoder_layers_31_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv384 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv382, lv249_1, lv383), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv327_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), lv384), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv385 = R.call_tir(cls.fused_reshape23_reshape24, (lv327_1,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv386 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv385, model_decoder_layers_31_self_attn_out_proj_weight5, model_decoder_layers_31_self_attn_out_proj_bias5, lv381), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm450 = R.call_tir(cls.layer_norm3, (lv386, model_decoder_layers_31_encoder_attn_layer_norm_weight5, model_decoder_layers_31_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv387 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm450, model_decoder_layers_31_encoder_attn_q_proj_weight5, model_decoder_layers_31_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv388 = R.call_tir(cls.fused_reshape21_reshape25, (lv387,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv328_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), lv388), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv389 = R.call_tir(cls.fused_reshape23_reshape24, (lv328_1,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv390 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv389, model_decoder_layers_31_encoder_attn_out_proj_weight5, model_decoder_layers_31_encoder_attn_out_proj_bias5, lv386), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm451 = R.call_tir(cls.layer_norm3, (lv390, model_decoder_layers_31_final_layer_norm_weight5, model_decoder_layers_31_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv391 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm451, model_decoder_layers_31_fc1_weight5, model_decoder_layers_31_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv392 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv391, model_decoder_layers_31_fc2_weight5, model_decoder_layers_31_fc2_bias5, lv390), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm452 = R.call_tir(cls.layer_norm3, (lv392, model_decoder_layer_norm_weight5, model_decoder_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + gv5 = R.call_tir(cls.NT_matmul3, (layer_norm452, model_decoder_embed_tokens_weight5), out_sinfo=R.Tensor((1, 1, 51866), dtype="float32")) + R.output(gv5) + return gv5 + + @R.function + def multinomial_from_uniform(probs: R.Tensor(("batch_size", "vocab_size"), dtype="float32"), uniform_samples: R.Tensor(("num_samples",), dtype="float32"), sample_indices: R.Tensor(("num_samples",), dtype="int32")) -> R.Tensor(("num_samples",), dtype="int32"): + num_samples = T.int64() + batch_size = T.int64() + vocab_size = T.int64() + R.func_attr({"relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "num_positions": 48, "num_samples": 8}}) + cls = Module + with R.dataflow(): + uniform_samples_1: R.Tensor((num_samples, 1), dtype="float32") = R.call_pure_packed("vm.builtin.reshape", uniform_samples, R.shape([num_samples, 1]), sinfo_args=(R.Tensor((num_samples, 1), dtype="float32"),)) + sample_indices_1: R.Tensor((num_samples, 1), dtype="int32") = R.call_pure_packed("vm.builtin.reshape", sample_indices, R.shape([num_samples, 1]), sinfo_args=(R.Tensor((num_samples, 1), dtype="int32"),)) + nn_multinomial_from_uniform = R.call_tir(cls.parallel_sampling_from_prob, (probs, uniform_samples_1, sample_indices_1), out_sinfo=R.Tensor((num_samples, 1), dtype="int32")) + gv: R.Tensor((num_samples,), dtype="int32") = R.call_pure_packed("vm.builtin.reshape", nn_multinomial_from_uniform, R.shape([num_samples]), sinfo_args=(R.Tensor((num_samples,), dtype="int32"),)) + R.output(gv) + return gv + + @R.function + def prefill(input_ids: R.Tensor((1, "seq_len"), dtype="int32"), paged_kv_cache: R.Object, packed_params: R.Tuple(R.Tensor((1280, 128, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1500, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), 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R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"))) -> R.Tensor((1, 1, 51866), dtype="float32"): + seq_len = T.int64() + R.func_attr({"num_input": 2, "relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "seq_len": 15000, "total_seq_len": 1500}}) + cls = Module + with R.dataflow(): + model_decoder_embed_tokens_weight4: R.Tensor((51866, 1280), dtype="float16") = packed_params[487] + model_decoder_embed_positions_weight4: R.Tensor((448, 1280), dtype="float16") = packed_params[488] + model_decoder_layers_0_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[489] + model_decoder_layers_0_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[490] + model_decoder_layers_0_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[491] + model_decoder_layers_0_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[492] + model_decoder_layers_0_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[493] + model_decoder_layers_0_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[494] + model_decoder_layers_0_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[495] + model_decoder_layers_0_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[496] + model_decoder_layers_0_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[497] + model_decoder_layers_0_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[501] + model_decoder_layers_0_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[502] + model_decoder_layers_0_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[503] + model_decoder_layers_0_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[504] + model_decoder_layers_0_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[505] + model_decoder_layers_0_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[506] + model_decoder_layers_0_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[507] + model_decoder_layers_0_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[508] + model_decoder_layers_0_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[509] + model_decoder_layers_0_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[510] + model_decoder_layers_0_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[511] + model_decoder_layers_0_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[512] + model_decoder_layers_1_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[513] + model_decoder_layers_1_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[514] + model_decoder_layers_1_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[515] + model_decoder_layers_1_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[516] + model_decoder_layers_1_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[517] + model_decoder_layers_1_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[518] + model_decoder_layers_1_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[519] + model_decoder_layers_1_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[520] + model_decoder_layers_1_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[521] + model_decoder_layers_1_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[525] + model_decoder_layers_1_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[526] + model_decoder_layers_1_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[527] + model_decoder_layers_1_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[528] + model_decoder_layers_1_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[529] + model_decoder_layers_1_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[530] + model_decoder_layers_1_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[531] + model_decoder_layers_1_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[532] + model_decoder_layers_1_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[533] + model_decoder_layers_1_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[534] + model_decoder_layers_1_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[535] + model_decoder_layers_1_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[536] + model_decoder_layers_2_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[537] + model_decoder_layers_2_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[538] + model_decoder_layers_2_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[539] + model_decoder_layers_2_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[540] + model_decoder_layers_2_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[541] + model_decoder_layers_2_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[542] + model_decoder_layers_2_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[543] + model_decoder_layers_2_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[544] + model_decoder_layers_2_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[545] + model_decoder_layers_2_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[549] + model_decoder_layers_2_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[550] + model_decoder_layers_2_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[551] + model_decoder_layers_2_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[552] + model_decoder_layers_2_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[553] + model_decoder_layers_2_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[554] + model_decoder_layers_2_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[555] + model_decoder_layers_2_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[556] + model_decoder_layers_2_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[557] + model_decoder_layers_2_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[558] + model_decoder_layers_2_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[559] + model_decoder_layers_2_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[560] + model_decoder_layers_3_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[561] + model_decoder_layers_3_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[562] + model_decoder_layers_3_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[563] + model_decoder_layers_3_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[564] + model_decoder_layers_3_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[565] + model_decoder_layers_3_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[566] + model_decoder_layers_3_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[567] + model_decoder_layers_3_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[568] + model_decoder_layers_3_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[569] + model_decoder_layers_3_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[573] + model_decoder_layers_3_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[574] + model_decoder_layers_3_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[575] + model_decoder_layers_3_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[576] + model_decoder_layers_3_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[577] + model_decoder_layers_3_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[578] + model_decoder_layers_3_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[579] + model_decoder_layers_3_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[580] + model_decoder_layers_3_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[581] + model_decoder_layers_3_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[582] + model_decoder_layers_3_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[583] + model_decoder_layers_3_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[584] + model_decoder_layers_4_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[585] + model_decoder_layers_4_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[586] + model_decoder_layers_4_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[587] + model_decoder_layers_4_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[588] + model_decoder_layers_4_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[589] + model_decoder_layers_4_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[590] + model_decoder_layers_4_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[591] + model_decoder_layers_4_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[592] + model_decoder_layers_4_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[593] + model_decoder_layers_4_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[597] + model_decoder_layers_4_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[598] + model_decoder_layers_4_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[599] + model_decoder_layers_4_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[600] + model_decoder_layers_4_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[601] + model_decoder_layers_4_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[602] + model_decoder_layers_4_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[603] + model_decoder_layers_4_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[604] + model_decoder_layers_4_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[605] + model_decoder_layers_4_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[606] + model_decoder_layers_4_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[607] + model_decoder_layers_4_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[608] + model_decoder_layers_5_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[609] + model_decoder_layers_5_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[610] + model_decoder_layers_5_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[611] + model_decoder_layers_5_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[612] + model_decoder_layers_5_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[613] + model_decoder_layers_5_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[614] + model_decoder_layers_5_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[615] + model_decoder_layers_5_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[616] + model_decoder_layers_5_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[617] + model_decoder_layers_5_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[621] + model_decoder_layers_5_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[622] + model_decoder_layers_5_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[623] + model_decoder_layers_5_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[624] + model_decoder_layers_5_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[625] + model_decoder_layers_5_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[626] + model_decoder_layers_5_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[627] + model_decoder_layers_5_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[628] + model_decoder_layers_5_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[629] + model_decoder_layers_5_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[630] + model_decoder_layers_5_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[631] + model_decoder_layers_5_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[632] + model_decoder_layers_6_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[633] + model_decoder_layers_6_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[634] + model_decoder_layers_6_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[635] + model_decoder_layers_6_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[636] + model_decoder_layers_6_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[637] + model_decoder_layers_6_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[638] + model_decoder_layers_6_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[639] + model_decoder_layers_6_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[640] + model_decoder_layers_6_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[641] + model_decoder_layers_6_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[645] + model_decoder_layers_6_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[646] + model_decoder_layers_6_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[647] + model_decoder_layers_6_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[648] + model_decoder_layers_6_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[649] + model_decoder_layers_6_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[650] + model_decoder_layers_6_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[651] + model_decoder_layers_6_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[652] + model_decoder_layers_6_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[653] + model_decoder_layers_6_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[654] + model_decoder_layers_6_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[655] + model_decoder_layers_6_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[656] + model_decoder_layers_7_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[657] + model_decoder_layers_7_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[658] + model_decoder_layers_7_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[659] + model_decoder_layers_7_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[660] + model_decoder_layers_7_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[661] + model_decoder_layers_7_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[662] + model_decoder_layers_7_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[663] + model_decoder_layers_7_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[664] + model_decoder_layers_7_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[665] + model_decoder_layers_7_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[669] + model_decoder_layers_7_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[670] + model_decoder_layers_7_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[671] + model_decoder_layers_7_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[672] + model_decoder_layers_7_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[673] + model_decoder_layers_7_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[674] + model_decoder_layers_7_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[675] + model_decoder_layers_7_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[676] + model_decoder_layers_7_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[677] + model_decoder_layers_7_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[678] + model_decoder_layers_7_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[679] + model_decoder_layers_7_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[680] + model_decoder_layers_8_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[681] + model_decoder_layers_8_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[682] + model_decoder_layers_8_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[683] + model_decoder_layers_8_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[684] + model_decoder_layers_8_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[685] + model_decoder_layers_8_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[686] + model_decoder_layers_8_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[687] + model_decoder_layers_8_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[688] + model_decoder_layers_8_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[689] + model_decoder_layers_8_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[693] + model_decoder_layers_8_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[694] + model_decoder_layers_8_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[695] + model_decoder_layers_8_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[696] + model_decoder_layers_8_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[697] + model_decoder_layers_8_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[698] + model_decoder_layers_8_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[699] + model_decoder_layers_8_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[700] + model_decoder_layers_8_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[701] + model_decoder_layers_8_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[702] + model_decoder_layers_8_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[703] + model_decoder_layers_8_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[704] + model_decoder_layers_9_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[705] + model_decoder_layers_9_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[706] + model_decoder_layers_9_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[707] + model_decoder_layers_9_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[708] + model_decoder_layers_9_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[709] + model_decoder_layers_9_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[710] + model_decoder_layers_9_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[711] + model_decoder_layers_9_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[712] + model_decoder_layers_9_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[713] + model_decoder_layers_9_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[717] + model_decoder_layers_9_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[718] + model_decoder_layers_9_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[719] + model_decoder_layers_9_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[720] + model_decoder_layers_9_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[721] + model_decoder_layers_9_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[722] + model_decoder_layers_9_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[723] + model_decoder_layers_9_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[724] + model_decoder_layers_9_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[725] + model_decoder_layers_9_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[726] + model_decoder_layers_9_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[727] + model_decoder_layers_9_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[728] + model_decoder_layers_10_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[729] + model_decoder_layers_10_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[730] + model_decoder_layers_10_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[731] + model_decoder_layers_10_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[732] + model_decoder_layers_10_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[733] + model_decoder_layers_10_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[734] + model_decoder_layers_10_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[735] + model_decoder_layers_10_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[736] + model_decoder_layers_10_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[737] + model_decoder_layers_10_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[741] + model_decoder_layers_10_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[742] + model_decoder_layers_10_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[743] + model_decoder_layers_10_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[744] + model_decoder_layers_10_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[745] + model_decoder_layers_10_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[746] + model_decoder_layers_10_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[747] + model_decoder_layers_10_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[748] + model_decoder_layers_10_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[749] + model_decoder_layers_10_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[750] + model_decoder_layers_10_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[751] + model_decoder_layers_10_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[752] + model_decoder_layers_11_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[753] + model_decoder_layers_11_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[754] + model_decoder_layers_11_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[755] + model_decoder_layers_11_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[756] + model_decoder_layers_11_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[757] + model_decoder_layers_11_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[758] + model_decoder_layers_11_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[759] + model_decoder_layers_11_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[760] + model_decoder_layers_11_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[761] + model_decoder_layers_11_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[765] + model_decoder_layers_11_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[766] + model_decoder_layers_11_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[767] + model_decoder_layers_11_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[768] + model_decoder_layers_11_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[769] + model_decoder_layers_11_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[770] + model_decoder_layers_11_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[771] + model_decoder_layers_11_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[772] + model_decoder_layers_11_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[773] + model_decoder_layers_11_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[774] + model_decoder_layers_11_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[775] + model_decoder_layers_11_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[776] + model_decoder_layers_12_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[777] + model_decoder_layers_12_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[778] + model_decoder_layers_12_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[779] + model_decoder_layers_12_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[780] + model_decoder_layers_12_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[781] + model_decoder_layers_12_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[782] + model_decoder_layers_12_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[783] + model_decoder_layers_12_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[784] + model_decoder_layers_12_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[785] + model_decoder_layers_12_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[789] + model_decoder_layers_12_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[790] + model_decoder_layers_12_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[791] + model_decoder_layers_12_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[792] + model_decoder_layers_12_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[793] + model_decoder_layers_12_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[794] + model_decoder_layers_12_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[795] + model_decoder_layers_12_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[796] + model_decoder_layers_12_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[797] + model_decoder_layers_12_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[798] + model_decoder_layers_12_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[799] + model_decoder_layers_12_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[800] + model_decoder_layers_13_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[801] + model_decoder_layers_13_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[802] + model_decoder_layers_13_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[803] + model_decoder_layers_13_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[804] + model_decoder_layers_13_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[805] + model_decoder_layers_13_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[806] + model_decoder_layers_13_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[807] + model_decoder_layers_13_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[808] + model_decoder_layers_13_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[809] + model_decoder_layers_13_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[813] + model_decoder_layers_13_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[814] + model_decoder_layers_13_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[815] + model_decoder_layers_13_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[816] + model_decoder_layers_13_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[817] + model_decoder_layers_13_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[818] + model_decoder_layers_13_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[819] + model_decoder_layers_13_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[820] + model_decoder_layers_13_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[821] + model_decoder_layers_13_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[822] + model_decoder_layers_13_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[823] + model_decoder_layers_13_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[824] + model_decoder_layers_14_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[825] + model_decoder_layers_14_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[826] + model_decoder_layers_14_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[827] + model_decoder_layers_14_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[828] + model_decoder_layers_14_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[829] + model_decoder_layers_14_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[830] + model_decoder_layers_14_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[831] + model_decoder_layers_14_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[832] + model_decoder_layers_14_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[833] + model_decoder_layers_14_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[837] + model_decoder_layers_14_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[838] + model_decoder_layers_14_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[839] + model_decoder_layers_14_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[840] + model_decoder_layers_14_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[841] + model_decoder_layers_14_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[842] + model_decoder_layers_14_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[843] + model_decoder_layers_14_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[844] + model_decoder_layers_14_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[845] + model_decoder_layers_14_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[846] + model_decoder_layers_14_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[847] + model_decoder_layers_14_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[848] + model_decoder_layers_15_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[849] + model_decoder_layers_15_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[850] + model_decoder_layers_15_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[851] + model_decoder_layers_15_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[852] + model_decoder_layers_15_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[853] + model_decoder_layers_15_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[854] + model_decoder_layers_15_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[855] + model_decoder_layers_15_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[856] + model_decoder_layers_15_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[857] + model_decoder_layers_15_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[861] + model_decoder_layers_15_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[862] + model_decoder_layers_15_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[863] + model_decoder_layers_15_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[864] + model_decoder_layers_15_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[865] + model_decoder_layers_15_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[866] + model_decoder_layers_15_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[867] + model_decoder_layers_15_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[868] + model_decoder_layers_15_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[869] + model_decoder_layers_15_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[870] + model_decoder_layers_15_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[871] + model_decoder_layers_15_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[872] + model_decoder_layers_16_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[873] + model_decoder_layers_16_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[874] + model_decoder_layers_16_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[875] + model_decoder_layers_16_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[876] + model_decoder_layers_16_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[877] + model_decoder_layers_16_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[878] + model_decoder_layers_16_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[879] + model_decoder_layers_16_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[880] + model_decoder_layers_16_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[881] + model_decoder_layers_16_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[885] + model_decoder_layers_16_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[886] + model_decoder_layers_16_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[887] + model_decoder_layers_16_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[888] + model_decoder_layers_16_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[889] + model_decoder_layers_16_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[890] + model_decoder_layers_16_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[891] + model_decoder_layers_16_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[892] + model_decoder_layers_16_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[893] + model_decoder_layers_16_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[894] + model_decoder_layers_16_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[895] + model_decoder_layers_16_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[896] + model_decoder_layers_17_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[897] + model_decoder_layers_17_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[898] + model_decoder_layers_17_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[899] + model_decoder_layers_17_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[900] + model_decoder_layers_17_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[901] + model_decoder_layers_17_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[902] + model_decoder_layers_17_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[903] + model_decoder_layers_17_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[904] + model_decoder_layers_17_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[905] + model_decoder_layers_17_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[909] + model_decoder_layers_17_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[910] + model_decoder_layers_17_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[911] + model_decoder_layers_17_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[912] + model_decoder_layers_17_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[913] + model_decoder_layers_17_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[914] + model_decoder_layers_17_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[915] + model_decoder_layers_17_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[916] + model_decoder_layers_17_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[917] + model_decoder_layers_17_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[918] + model_decoder_layers_17_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[919] + model_decoder_layers_17_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[920] + model_decoder_layers_18_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[921] + model_decoder_layers_18_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[922] + model_decoder_layers_18_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[923] + model_decoder_layers_18_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[924] + model_decoder_layers_18_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[925] + model_decoder_layers_18_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[926] + model_decoder_layers_18_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[927] + model_decoder_layers_18_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[928] + model_decoder_layers_18_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[929] + model_decoder_layers_18_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[933] + model_decoder_layers_18_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[934] + model_decoder_layers_18_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[935] + model_decoder_layers_18_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[936] + model_decoder_layers_18_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[937] + model_decoder_layers_18_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[938] + model_decoder_layers_18_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[939] + model_decoder_layers_18_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[940] + model_decoder_layers_18_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[941] + model_decoder_layers_18_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[942] + model_decoder_layers_18_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[943] + model_decoder_layers_18_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[944] + model_decoder_layers_19_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[945] + model_decoder_layers_19_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[946] + model_decoder_layers_19_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[947] + model_decoder_layers_19_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[948] + model_decoder_layers_19_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[949] + model_decoder_layers_19_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[950] + model_decoder_layers_19_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[951] + model_decoder_layers_19_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[952] + model_decoder_layers_19_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[953] + model_decoder_layers_19_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[957] + model_decoder_layers_19_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[958] + model_decoder_layers_19_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[959] + model_decoder_layers_19_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[960] + model_decoder_layers_19_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[961] + model_decoder_layers_19_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[962] + model_decoder_layers_19_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[963] + model_decoder_layers_19_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[964] + model_decoder_layers_19_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[965] + model_decoder_layers_19_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[966] + model_decoder_layers_19_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[967] + model_decoder_layers_19_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[968] + model_decoder_layers_20_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[969] + model_decoder_layers_20_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[970] + model_decoder_layers_20_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[971] + model_decoder_layers_20_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[972] + model_decoder_layers_20_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[973] + model_decoder_layers_20_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[974] + model_decoder_layers_20_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[975] + model_decoder_layers_20_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[976] + model_decoder_layers_20_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[977] + model_decoder_layers_20_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[981] + model_decoder_layers_20_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[982] + model_decoder_layers_20_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[983] + model_decoder_layers_20_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[984] + model_decoder_layers_20_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[985] + model_decoder_layers_20_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[986] + model_decoder_layers_20_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[987] + model_decoder_layers_20_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[988] + model_decoder_layers_20_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[989] + model_decoder_layers_20_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[990] + model_decoder_layers_20_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[991] + model_decoder_layers_20_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[992] + model_decoder_layers_21_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[993] + model_decoder_layers_21_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[994] + model_decoder_layers_21_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[995] + model_decoder_layers_21_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[996] + model_decoder_layers_21_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[997] + model_decoder_layers_21_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[998] + model_decoder_layers_21_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[999] + model_decoder_layers_21_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1000] + model_decoder_layers_21_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1001] + model_decoder_layers_21_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1005] + model_decoder_layers_21_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1006] + model_decoder_layers_21_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1007] + model_decoder_layers_21_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1008] + model_decoder_layers_21_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1009] + model_decoder_layers_21_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1010] + model_decoder_layers_21_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1011] + model_decoder_layers_21_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1012] + model_decoder_layers_21_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1013] + model_decoder_layers_21_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1014] + model_decoder_layers_21_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1015] + model_decoder_layers_21_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1016] + model_decoder_layers_22_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1017] + model_decoder_layers_22_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1018] + model_decoder_layers_22_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1019] + model_decoder_layers_22_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1020] + model_decoder_layers_22_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1021] + model_decoder_layers_22_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1022] + model_decoder_layers_22_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1023] + model_decoder_layers_22_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1024] + model_decoder_layers_22_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1025] + model_decoder_layers_22_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1029] + model_decoder_layers_22_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1030] + model_decoder_layers_22_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1031] + model_decoder_layers_22_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1032] + model_decoder_layers_22_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1033] + model_decoder_layers_22_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1034] + model_decoder_layers_22_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1035] + model_decoder_layers_22_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1036] + model_decoder_layers_22_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1037] + model_decoder_layers_22_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1038] + model_decoder_layers_22_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1039] + model_decoder_layers_22_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1040] + model_decoder_layers_23_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1041] + model_decoder_layers_23_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1042] + model_decoder_layers_23_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1043] + model_decoder_layers_23_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1044] + model_decoder_layers_23_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1045] + model_decoder_layers_23_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1046] + model_decoder_layers_23_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1047] + model_decoder_layers_23_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1048] + model_decoder_layers_23_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1049] + model_decoder_layers_23_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1053] + model_decoder_layers_23_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1054] + model_decoder_layers_23_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1055] + model_decoder_layers_23_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1056] + model_decoder_layers_23_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1057] + model_decoder_layers_23_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1058] + model_decoder_layers_23_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1059] + model_decoder_layers_23_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1060] + model_decoder_layers_23_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1061] + model_decoder_layers_23_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1062] + model_decoder_layers_23_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1063] + model_decoder_layers_23_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1064] + model_decoder_layers_24_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1065] + model_decoder_layers_24_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1066] + model_decoder_layers_24_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1067] + model_decoder_layers_24_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1068] + model_decoder_layers_24_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1069] + model_decoder_layers_24_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1070] + model_decoder_layers_24_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1071] + model_decoder_layers_24_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1072] + model_decoder_layers_24_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1073] + model_decoder_layers_24_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1077] + model_decoder_layers_24_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1078] + model_decoder_layers_24_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1079] + model_decoder_layers_24_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1080] + model_decoder_layers_24_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1081] + model_decoder_layers_24_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1082] + model_decoder_layers_24_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1083] + model_decoder_layers_24_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1084] + model_decoder_layers_24_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1085] + model_decoder_layers_24_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1086] + model_decoder_layers_24_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1087] + model_decoder_layers_24_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1088] + model_decoder_layers_25_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1089] + model_decoder_layers_25_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1090] + model_decoder_layers_25_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1091] + model_decoder_layers_25_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1092] + model_decoder_layers_25_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1093] + model_decoder_layers_25_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1094] + model_decoder_layers_25_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1095] + model_decoder_layers_25_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1096] + model_decoder_layers_25_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1097] + model_decoder_layers_25_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1101] + model_decoder_layers_25_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1102] + model_decoder_layers_25_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1103] + model_decoder_layers_25_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1104] + model_decoder_layers_25_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1105] + model_decoder_layers_25_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1106] + model_decoder_layers_25_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1107] + model_decoder_layers_25_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1108] + model_decoder_layers_25_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1109] + model_decoder_layers_25_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1110] + model_decoder_layers_25_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1111] + model_decoder_layers_25_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1112] + model_decoder_layers_26_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1113] + model_decoder_layers_26_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1114] + model_decoder_layers_26_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1115] + model_decoder_layers_26_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1116] + model_decoder_layers_26_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1117] + model_decoder_layers_26_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1118] + model_decoder_layers_26_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1119] + model_decoder_layers_26_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1120] + model_decoder_layers_26_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1121] + model_decoder_layers_26_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1125] + model_decoder_layers_26_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1126] + model_decoder_layers_26_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1127] + model_decoder_layers_26_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1128] + model_decoder_layers_26_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1129] + model_decoder_layers_26_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1130] + model_decoder_layers_26_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1131] + model_decoder_layers_26_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1132] + model_decoder_layers_26_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1133] + model_decoder_layers_26_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1134] + model_decoder_layers_26_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1135] + model_decoder_layers_26_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1136] + model_decoder_layers_27_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1137] + model_decoder_layers_27_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1138] + model_decoder_layers_27_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1139] + model_decoder_layers_27_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1140] + model_decoder_layers_27_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1141] + model_decoder_layers_27_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1142] + model_decoder_layers_27_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1143] + model_decoder_layers_27_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1144] + model_decoder_layers_27_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1145] + model_decoder_layers_27_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1149] + model_decoder_layers_27_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1150] + model_decoder_layers_27_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1151] + model_decoder_layers_27_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1152] + model_decoder_layers_27_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1153] + model_decoder_layers_27_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1154] + model_decoder_layers_27_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1155] + model_decoder_layers_27_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1156] + model_decoder_layers_27_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1157] + model_decoder_layers_27_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1158] + model_decoder_layers_27_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1159] + model_decoder_layers_27_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1160] + model_decoder_layers_28_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1161] + model_decoder_layers_28_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1162] + model_decoder_layers_28_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1163] + model_decoder_layers_28_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1164] + model_decoder_layers_28_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1165] + model_decoder_layers_28_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1166] + model_decoder_layers_28_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1167] + model_decoder_layers_28_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1168] + model_decoder_layers_28_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1169] + model_decoder_layers_28_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1173] + model_decoder_layers_28_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1174] + model_decoder_layers_28_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1175] + model_decoder_layers_28_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1176] + model_decoder_layers_28_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1177] + model_decoder_layers_28_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1178] + model_decoder_layers_28_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1179] + model_decoder_layers_28_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1180] + model_decoder_layers_28_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1181] + model_decoder_layers_28_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1182] + model_decoder_layers_28_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1183] + model_decoder_layers_28_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1184] + model_decoder_layers_29_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1185] + model_decoder_layers_29_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1186] + model_decoder_layers_29_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1187] + model_decoder_layers_29_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1188] + model_decoder_layers_29_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1189] + model_decoder_layers_29_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1190] + model_decoder_layers_29_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1191] + model_decoder_layers_29_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1192] + model_decoder_layers_29_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1193] + model_decoder_layers_29_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1197] + model_decoder_layers_29_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1198] + model_decoder_layers_29_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1199] + model_decoder_layers_29_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1200] + model_decoder_layers_29_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1201] + model_decoder_layers_29_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1202] + model_decoder_layers_29_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1203] + model_decoder_layers_29_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1204] + model_decoder_layers_29_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1205] + model_decoder_layers_29_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1206] + model_decoder_layers_29_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1207] + model_decoder_layers_29_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1208] + model_decoder_layers_30_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1209] + model_decoder_layers_30_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1210] + model_decoder_layers_30_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1211] + model_decoder_layers_30_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1212] + model_decoder_layers_30_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1213] + model_decoder_layers_30_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1214] + model_decoder_layers_30_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1215] + model_decoder_layers_30_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1216] + model_decoder_layers_30_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1217] + model_decoder_layers_30_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1221] + model_decoder_layers_30_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1222] + model_decoder_layers_30_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1223] + model_decoder_layers_30_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1224] + model_decoder_layers_30_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1225] + model_decoder_layers_30_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1226] + model_decoder_layers_30_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1227] + model_decoder_layers_30_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1228] + model_decoder_layers_30_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1229] + model_decoder_layers_30_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1230] + model_decoder_layers_30_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1231] + model_decoder_layers_30_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1232] + model_decoder_layers_31_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1233] + model_decoder_layers_31_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1234] + model_decoder_layers_31_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1235] + model_decoder_layers_31_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1236] + model_decoder_layers_31_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1237] + model_decoder_layers_31_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1238] + model_decoder_layers_31_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1239] + model_decoder_layers_31_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1240] + model_decoder_layers_31_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1241] + model_decoder_layers_31_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1245] + model_decoder_layers_31_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1246] + model_decoder_layers_31_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1247] + model_decoder_layers_31_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1248] + model_decoder_layers_31_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1249] + model_decoder_layers_31_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1250] + model_decoder_layers_31_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1251] + model_decoder_layers_31_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1252] + model_decoder_layers_31_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1253] + model_decoder_layers_31_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1254] + model_decoder_layers_31_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1255] + model_decoder_layers_31_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1256] + model_decoder_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1257] + model_decoder_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1258] + reshape1030 = R.call_tir(cls.reshape12, (input_ids,), out_sinfo=R.Tensor((seq_len,), dtype="int32")) + take5 = R.call_tir(cls.take, (model_decoder_embed_tokens_weight4, reshape1030), out_sinfo=R.Tensor((seq_len, 1280), dtype="float16")) + reshape1031 = R.call_tir(cls.reshape13, (take5,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv198: R.Tensor((seq_len,), dtype="int32") = R.call_pure_packed("vm.builtin.attention_kv_cache_get_query_positions", paged_kv_cache, sinfo_args=(R.Tensor((seq_len,), dtype="int32"),)) + take6 = R.call_tir(cls.take1, (model_decoder_embed_positions_weight4, lv198), out_sinfo=R.Tensor((seq_len, 1280), dtype="float16")) + reshape1032 = R.call_tir(cls.reshape13, (take6,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add899 = R.call_tir(cls.add5, (reshape1031, reshape1032), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm259 = R.call_tir(cls.layer_norm2, (add899, model_decoder_layers_0_self_attn_layer_norm_weight4, model_decoder_layers_0_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv32 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_0_self_attn_q_proj_weight4, layer_norm259, model_decoder_layers_0_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1033 = R.call_tir(cls.reshape14, (lv32,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv32_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_0_self_attn_k_proj_weight4, layer_norm259), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1034 = R.call_tir(cls.reshape14, (lv32_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv33 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_0_self_attn_v_proj_weight4, layer_norm259, model_decoder_layers_0_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1035 = R.call_tir(cls.reshape14, (lv33,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat64 = R.call_tir(cls.concatenate1, (reshape1033, reshape1034, reshape1035), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1036 = R.call_tir(cls.reshape15, (concat64,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv199 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), reshape1036), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1037 = R.call_tir(cls.reshape16, (lv199,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1038 = R.call_tir(cls.reshape17, (reshape1037,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv34 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_0_self_attn_out_proj_weight4, reshape1038, model_decoder_layers_0_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add903 = R.call_tir(cls.add5, (add899, lv34), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm260 = R.call_tir(cls.layer_norm2, (add903, model_decoder_layers_0_encoder_attn_layer_norm_weight4, model_decoder_layers_0_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv35 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_0_encoder_attn_q_proj_weight4, layer_norm260, model_decoder_layers_0_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1039 = R.call_tir(cls.reshape14, (lv35,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1040 = R.call_tir(cls.reshape18, (reshape1039,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv200 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), reshape1040), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1041 = R.call_tir(cls.reshape16, (lv200,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1042 = R.call_tir(cls.reshape17, (reshape1041,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv36 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_0_encoder_attn_out_proj_weight4, reshape1042, model_decoder_layers_0_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add906 = R.call_tir(cls.add5, (add903, lv36), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm261 = R.call_tir(cls.layer_norm2, (add906, model_decoder_layers_0_final_layer_norm_weight4, model_decoder_layers_0_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_0_fc1_weight4, layer_norm261, model_decoder_layers_0_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv37 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_0_fc2_weight4, lv, model_decoder_layers_0_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add909 = R.call_tir(cls.add5, (add906, lv37), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm262 = R.call_tir(cls.layer_norm2, (add909, model_decoder_layers_1_self_attn_layer_norm_weight4, model_decoder_layers_1_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv38 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_1_self_attn_q_proj_weight4, layer_norm262, model_decoder_layers_1_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1043 = R.call_tir(cls.reshape14, (lv38,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv33_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_1_self_attn_k_proj_weight4, layer_norm262), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1044 = R.call_tir(cls.reshape14, (lv33_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv39 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_1_self_attn_v_proj_weight4, layer_norm262, model_decoder_layers_1_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1045 = R.call_tir(cls.reshape14, (lv39,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat65 = R.call_tir(cls.concatenate1, (reshape1043, reshape1044, reshape1045), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1046 = R.call_tir(cls.reshape15, (concat65,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv201 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), reshape1046), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1047 = R.call_tir(cls.reshape16, (lv201,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1048 = R.call_tir(cls.reshape17, (reshape1047,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv40 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_1_self_attn_out_proj_weight4, reshape1048, model_decoder_layers_1_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add913 = R.call_tir(cls.add5, (add909, lv40), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm263 = R.call_tir(cls.layer_norm2, (add913, model_decoder_layers_1_encoder_attn_layer_norm_weight4, model_decoder_layers_1_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv41 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_1_encoder_attn_q_proj_weight4, layer_norm263, model_decoder_layers_1_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1049 = R.call_tir(cls.reshape14, (lv41,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1050 = R.call_tir(cls.reshape18, (reshape1049,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv202 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), reshape1050), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1051 = R.call_tir(cls.reshape16, (lv202,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1052 = R.call_tir(cls.reshape17, (reshape1051,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv42 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_1_encoder_attn_out_proj_weight4, reshape1052, model_decoder_layers_1_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add916 = R.call_tir(cls.add5, (add913, lv42), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm264 = R.call_tir(cls.layer_norm2, (add916, model_decoder_layers_1_final_layer_norm_weight4, model_decoder_layers_1_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_1_fc1_weight4, layer_norm264, model_decoder_layers_1_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv43 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_1_fc2_weight4, lv1, model_decoder_layers_1_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add919 = R.call_tir(cls.add5, (add916, lv43), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm265 = R.call_tir(cls.layer_norm2, (add919, model_decoder_layers_2_self_attn_layer_norm_weight4, model_decoder_layers_2_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv44 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_2_self_attn_q_proj_weight4, layer_norm265, model_decoder_layers_2_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1053 = R.call_tir(cls.reshape14, (lv44,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv34_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_2_self_attn_k_proj_weight4, layer_norm265), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1054 = R.call_tir(cls.reshape14, (lv34_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv45 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_2_self_attn_v_proj_weight4, layer_norm265, model_decoder_layers_2_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1055 = R.call_tir(cls.reshape14, (lv45,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat66 = R.call_tir(cls.concatenate1, (reshape1053, reshape1054, reshape1055), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1056 = R.call_tir(cls.reshape15, (concat66,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv203 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), reshape1056), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1057 = R.call_tir(cls.reshape16, (lv203,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1058 = R.call_tir(cls.reshape17, (reshape1057,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv46 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_2_self_attn_out_proj_weight4, reshape1058, model_decoder_layers_2_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add923 = R.call_tir(cls.add5, (add919, lv46), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm266 = R.call_tir(cls.layer_norm2, (add923, model_decoder_layers_2_encoder_attn_layer_norm_weight4, model_decoder_layers_2_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv47 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_2_encoder_attn_q_proj_weight4, layer_norm266, model_decoder_layers_2_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1059 = R.call_tir(cls.reshape14, (lv47,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1060 = R.call_tir(cls.reshape18, (reshape1059,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv204 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), reshape1060), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1061 = R.call_tir(cls.reshape16, (lv204,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1062 = R.call_tir(cls.reshape17, (reshape1061,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv48 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_2_encoder_attn_out_proj_weight4, reshape1062, model_decoder_layers_2_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add926 = R.call_tir(cls.add5, (add923, lv48), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm267 = R.call_tir(cls.layer_norm2, (add926, model_decoder_layers_2_final_layer_norm_weight4, model_decoder_layers_2_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv2 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_2_fc1_weight4, layer_norm267, model_decoder_layers_2_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv49 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_2_fc2_weight4, lv2, model_decoder_layers_2_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add929 = R.call_tir(cls.add5, (add926, lv49), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm268 = R.call_tir(cls.layer_norm2, (add929, model_decoder_layers_3_self_attn_layer_norm_weight4, model_decoder_layers_3_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv50 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_3_self_attn_q_proj_weight4, layer_norm268, model_decoder_layers_3_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1063 = R.call_tir(cls.reshape14, (lv50,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv35_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_3_self_attn_k_proj_weight4, layer_norm268), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1064 = R.call_tir(cls.reshape14, (lv35_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv51 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_3_self_attn_v_proj_weight4, layer_norm268, model_decoder_layers_3_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1065 = R.call_tir(cls.reshape14, (lv51,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat67 = R.call_tir(cls.concatenate1, (reshape1063, reshape1064, reshape1065), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1066 = R.call_tir(cls.reshape15, (concat67,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv205 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), reshape1066), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1067 = R.call_tir(cls.reshape16, (lv205,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1068 = R.call_tir(cls.reshape17, (reshape1067,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv52 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_3_self_attn_out_proj_weight4, reshape1068, model_decoder_layers_3_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add933 = R.call_tir(cls.add5, (add929, lv52), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm269 = R.call_tir(cls.layer_norm2, (add933, model_decoder_layers_3_encoder_attn_layer_norm_weight4, model_decoder_layers_3_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv53 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_3_encoder_attn_q_proj_weight4, layer_norm269, model_decoder_layers_3_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1069 = R.call_tir(cls.reshape14, (lv53,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1070 = R.call_tir(cls.reshape18, (reshape1069,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv206 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), reshape1070), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1071 = R.call_tir(cls.reshape16, (lv206,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1072 = R.call_tir(cls.reshape17, (reshape1071,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv54 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_3_encoder_attn_out_proj_weight4, reshape1072, model_decoder_layers_3_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add936 = R.call_tir(cls.add5, (add933, lv54), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm270 = R.call_tir(cls.layer_norm2, (add936, model_decoder_layers_3_final_layer_norm_weight4, model_decoder_layers_3_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv3 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_3_fc1_weight4, layer_norm270, model_decoder_layers_3_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv55 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_3_fc2_weight4, lv3, model_decoder_layers_3_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add939 = R.call_tir(cls.add5, (add936, lv55), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm271 = R.call_tir(cls.layer_norm2, (add939, model_decoder_layers_4_self_attn_layer_norm_weight4, model_decoder_layers_4_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv56 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_4_self_attn_q_proj_weight4, layer_norm271, model_decoder_layers_4_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1073 = R.call_tir(cls.reshape14, (lv56,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv36_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_4_self_attn_k_proj_weight4, layer_norm271), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1074 = R.call_tir(cls.reshape14, (lv36_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv57 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_4_self_attn_v_proj_weight4, layer_norm271, model_decoder_layers_4_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1075 = R.call_tir(cls.reshape14, (lv57,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat68 = R.call_tir(cls.concatenate1, (reshape1073, reshape1074, reshape1075), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1076 = R.call_tir(cls.reshape15, (concat68,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv207 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), reshape1076), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1077 = R.call_tir(cls.reshape16, (lv207,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1078 = R.call_tir(cls.reshape17, (reshape1077,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv58 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_4_self_attn_out_proj_weight4, reshape1078, model_decoder_layers_4_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add943 = R.call_tir(cls.add5, (add939, lv58), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm272 = R.call_tir(cls.layer_norm2, (add943, model_decoder_layers_4_encoder_attn_layer_norm_weight4, model_decoder_layers_4_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv59 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_4_encoder_attn_q_proj_weight4, layer_norm272, model_decoder_layers_4_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1079 = R.call_tir(cls.reshape14, (lv59,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1080 = R.call_tir(cls.reshape18, (reshape1079,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv208 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), reshape1080), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1081 = R.call_tir(cls.reshape16, (lv208,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1082 = R.call_tir(cls.reshape17, (reshape1081,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv60 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_4_encoder_attn_out_proj_weight4, reshape1082, model_decoder_layers_4_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add946 = R.call_tir(cls.add5, (add943, lv60), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm273 = R.call_tir(cls.layer_norm2, (add946, model_decoder_layers_4_final_layer_norm_weight4, model_decoder_layers_4_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv4 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_4_fc1_weight4, layer_norm273, model_decoder_layers_4_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv61 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_4_fc2_weight4, lv4, model_decoder_layers_4_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add949 = R.call_tir(cls.add5, (add946, lv61), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm274 = R.call_tir(cls.layer_norm2, (add949, model_decoder_layers_5_self_attn_layer_norm_weight4, model_decoder_layers_5_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv62 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_5_self_attn_q_proj_weight4, layer_norm274, model_decoder_layers_5_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1083 = R.call_tir(cls.reshape14, (lv62,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv37_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_5_self_attn_k_proj_weight4, layer_norm274), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1084 = R.call_tir(cls.reshape14, (lv37_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv63 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_5_self_attn_v_proj_weight4, layer_norm274, model_decoder_layers_5_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1085 = R.call_tir(cls.reshape14, (lv63,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat69 = R.call_tir(cls.concatenate1, (reshape1083, reshape1084, reshape1085), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1086 = R.call_tir(cls.reshape15, (concat69,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv209 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), reshape1086), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1087 = R.call_tir(cls.reshape16, (lv209,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1088 = R.call_tir(cls.reshape17, (reshape1087,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv64 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_5_self_attn_out_proj_weight4, reshape1088, model_decoder_layers_5_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add953 = R.call_tir(cls.add5, (add949, lv64), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm275 = R.call_tir(cls.layer_norm2, (add953, model_decoder_layers_5_encoder_attn_layer_norm_weight4, model_decoder_layers_5_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv65 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_5_encoder_attn_q_proj_weight4, layer_norm275, model_decoder_layers_5_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1089 = R.call_tir(cls.reshape14, (lv65,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1090 = R.call_tir(cls.reshape18, (reshape1089,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv210 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), reshape1090), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1091 = R.call_tir(cls.reshape16, (lv210,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1092 = R.call_tir(cls.reshape17, (reshape1091,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv66 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_5_encoder_attn_out_proj_weight4, reshape1092, model_decoder_layers_5_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add956 = R.call_tir(cls.add5, (add953, lv66), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm276 = R.call_tir(cls.layer_norm2, (add956, model_decoder_layers_5_final_layer_norm_weight4, model_decoder_layers_5_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv5 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_5_fc1_weight4, layer_norm276, model_decoder_layers_5_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv67 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_5_fc2_weight4, lv5, model_decoder_layers_5_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add959 = R.call_tir(cls.add5, (add956, lv67), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm277 = R.call_tir(cls.layer_norm2, (add959, model_decoder_layers_6_self_attn_layer_norm_weight4, model_decoder_layers_6_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv68 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_6_self_attn_q_proj_weight4, layer_norm277, model_decoder_layers_6_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1093 = R.call_tir(cls.reshape14, (lv68,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv38_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_6_self_attn_k_proj_weight4, layer_norm277), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1094 = R.call_tir(cls.reshape14, (lv38_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv69 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_6_self_attn_v_proj_weight4, layer_norm277, model_decoder_layers_6_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1095 = R.call_tir(cls.reshape14, (lv69,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat70 = R.call_tir(cls.concatenate1, (reshape1093, reshape1094, reshape1095), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1096 = R.call_tir(cls.reshape15, (concat70,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv211 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), reshape1096), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1097 = R.call_tir(cls.reshape16, (lv211,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1098 = R.call_tir(cls.reshape17, (reshape1097,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv70 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_6_self_attn_out_proj_weight4, reshape1098, model_decoder_layers_6_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add963 = R.call_tir(cls.add5, (add959, lv70), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm278 = R.call_tir(cls.layer_norm2, (add963, model_decoder_layers_6_encoder_attn_layer_norm_weight4, model_decoder_layers_6_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv71 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_6_encoder_attn_q_proj_weight4, layer_norm278, model_decoder_layers_6_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1099 = R.call_tir(cls.reshape14, (lv71,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1100 = R.call_tir(cls.reshape18, (reshape1099,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv212 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), reshape1100), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1101 = R.call_tir(cls.reshape16, (lv212,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1102 = R.call_tir(cls.reshape17, (reshape1101,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv72 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_6_encoder_attn_out_proj_weight4, reshape1102, model_decoder_layers_6_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add966 = R.call_tir(cls.add5, (add963, lv72), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm279 = R.call_tir(cls.layer_norm2, (add966, model_decoder_layers_6_final_layer_norm_weight4, model_decoder_layers_6_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv6 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_6_fc1_weight4, layer_norm279, model_decoder_layers_6_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv73 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_6_fc2_weight4, lv6, model_decoder_layers_6_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add969 = R.call_tir(cls.add5, (add966, lv73), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm280 = R.call_tir(cls.layer_norm2, (add969, model_decoder_layers_7_self_attn_layer_norm_weight4, model_decoder_layers_7_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv74 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_7_self_attn_q_proj_weight4, layer_norm280, model_decoder_layers_7_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1103 = R.call_tir(cls.reshape14, (lv74,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv39_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_7_self_attn_k_proj_weight4, layer_norm280), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1104 = R.call_tir(cls.reshape14, (lv39_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv75 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_7_self_attn_v_proj_weight4, layer_norm280, model_decoder_layers_7_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1105 = R.call_tir(cls.reshape14, (lv75,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat71 = R.call_tir(cls.concatenate1, (reshape1103, reshape1104, reshape1105), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1106 = R.call_tir(cls.reshape15, (concat71,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv213 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), reshape1106), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1107 = R.call_tir(cls.reshape16, (lv213,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1108 = R.call_tir(cls.reshape17, (reshape1107,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv76 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_7_self_attn_out_proj_weight4, reshape1108, model_decoder_layers_7_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add973 = R.call_tir(cls.add5, (add969, lv76), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm281 = R.call_tir(cls.layer_norm2, (add973, model_decoder_layers_7_encoder_attn_layer_norm_weight4, model_decoder_layers_7_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv77 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_7_encoder_attn_q_proj_weight4, layer_norm281, model_decoder_layers_7_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1109 = R.call_tir(cls.reshape14, (lv77,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1110 = R.call_tir(cls.reshape18, (reshape1109,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv214 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), reshape1110), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1111 = R.call_tir(cls.reshape16, (lv214,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1112 = R.call_tir(cls.reshape17, (reshape1111,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv78 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_7_encoder_attn_out_proj_weight4, reshape1112, model_decoder_layers_7_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add976 = R.call_tir(cls.add5, (add973, lv78), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm282 = R.call_tir(cls.layer_norm2, (add976, model_decoder_layers_7_final_layer_norm_weight4, model_decoder_layers_7_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv7 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_7_fc1_weight4, layer_norm282, model_decoder_layers_7_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv79 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_7_fc2_weight4, lv7, model_decoder_layers_7_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add979 = R.call_tir(cls.add5, (add976, lv79), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm283 = R.call_tir(cls.layer_norm2, (add979, model_decoder_layers_8_self_attn_layer_norm_weight4, model_decoder_layers_8_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv80 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_8_self_attn_q_proj_weight4, layer_norm283, model_decoder_layers_8_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1113 = R.call_tir(cls.reshape14, (lv80,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv40_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_8_self_attn_k_proj_weight4, layer_norm283), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1114 = R.call_tir(cls.reshape14, (lv40_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv81 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_8_self_attn_v_proj_weight4, layer_norm283, model_decoder_layers_8_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1115 = R.call_tir(cls.reshape14, (lv81,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat72 = R.call_tir(cls.concatenate1, (reshape1113, reshape1114, reshape1115), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1116 = R.call_tir(cls.reshape15, (concat72,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv215 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), reshape1116), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1117 = R.call_tir(cls.reshape16, (lv215,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1118 = R.call_tir(cls.reshape17, (reshape1117,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv82 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_8_self_attn_out_proj_weight4, reshape1118, model_decoder_layers_8_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add983 = R.call_tir(cls.add5, (add979, lv82), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm284 = R.call_tir(cls.layer_norm2, (add983, model_decoder_layers_8_encoder_attn_layer_norm_weight4, model_decoder_layers_8_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv83 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_8_encoder_attn_q_proj_weight4, layer_norm284, model_decoder_layers_8_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1119 = R.call_tir(cls.reshape14, (lv83,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1120 = R.call_tir(cls.reshape18, (reshape1119,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv216 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), reshape1120), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1121 = R.call_tir(cls.reshape16, (lv216,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1122 = R.call_tir(cls.reshape17, (reshape1121,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv84 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_8_encoder_attn_out_proj_weight4, reshape1122, model_decoder_layers_8_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add986 = R.call_tir(cls.add5, (add983, lv84), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm285 = R.call_tir(cls.layer_norm2, (add986, model_decoder_layers_8_final_layer_norm_weight4, model_decoder_layers_8_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv8 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_8_fc1_weight4, layer_norm285, model_decoder_layers_8_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv85 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_8_fc2_weight4, lv8, model_decoder_layers_8_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add989 = R.call_tir(cls.add5, (add986, lv85), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm286 = R.call_tir(cls.layer_norm2, (add989, model_decoder_layers_9_self_attn_layer_norm_weight4, model_decoder_layers_9_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv86 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_9_self_attn_q_proj_weight4, layer_norm286, model_decoder_layers_9_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1123 = R.call_tir(cls.reshape14, (lv86,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv41_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_9_self_attn_k_proj_weight4, layer_norm286), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1124 = R.call_tir(cls.reshape14, (lv41_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv87 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_9_self_attn_v_proj_weight4, layer_norm286, model_decoder_layers_9_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1125 = R.call_tir(cls.reshape14, (lv87,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat73 = R.call_tir(cls.concatenate1, (reshape1123, reshape1124, reshape1125), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1126 = R.call_tir(cls.reshape15, (concat73,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv217 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), reshape1126), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1127 = R.call_tir(cls.reshape16, (lv217,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1128 = R.call_tir(cls.reshape17, (reshape1127,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv88 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_9_self_attn_out_proj_weight4, reshape1128, model_decoder_layers_9_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add993 = R.call_tir(cls.add5, (add989, lv88), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm287 = R.call_tir(cls.layer_norm2, (add993, model_decoder_layers_9_encoder_attn_layer_norm_weight4, model_decoder_layers_9_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv89 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_9_encoder_attn_q_proj_weight4, layer_norm287, model_decoder_layers_9_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1129 = R.call_tir(cls.reshape14, (lv89,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1130 = R.call_tir(cls.reshape18, (reshape1129,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv218 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), reshape1130), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1131 = R.call_tir(cls.reshape16, (lv218,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1132 = R.call_tir(cls.reshape17, (reshape1131,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv90 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_9_encoder_attn_out_proj_weight4, reshape1132, model_decoder_layers_9_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add996 = R.call_tir(cls.add5, (add993, lv90), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm288 = R.call_tir(cls.layer_norm2, (add996, model_decoder_layers_9_final_layer_norm_weight4, model_decoder_layers_9_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv9 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_9_fc1_weight4, layer_norm288, model_decoder_layers_9_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv91 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_9_fc2_weight4, lv9, model_decoder_layers_9_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add999 = R.call_tir(cls.add5, (add996, lv91), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm289 = R.call_tir(cls.layer_norm2, (add999, model_decoder_layers_10_self_attn_layer_norm_weight4, model_decoder_layers_10_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv92 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_10_self_attn_q_proj_weight4, layer_norm289, model_decoder_layers_10_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1133 = R.call_tir(cls.reshape14, (lv92,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv42_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_10_self_attn_k_proj_weight4, layer_norm289), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1134 = R.call_tir(cls.reshape14, (lv42_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv93 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_10_self_attn_v_proj_weight4, layer_norm289, model_decoder_layers_10_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1135 = R.call_tir(cls.reshape14, (lv93,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat74 = R.call_tir(cls.concatenate1, (reshape1133, reshape1134, reshape1135), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1136 = R.call_tir(cls.reshape15, (concat74,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv219 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), reshape1136), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1137 = R.call_tir(cls.reshape16, (lv219,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1138 = R.call_tir(cls.reshape17, (reshape1137,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv94 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_10_self_attn_out_proj_weight4, reshape1138, model_decoder_layers_10_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1003 = R.call_tir(cls.add5, (add999, lv94), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm290 = R.call_tir(cls.layer_norm2, (add1003, model_decoder_layers_10_encoder_attn_layer_norm_weight4, model_decoder_layers_10_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv95 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_10_encoder_attn_q_proj_weight4, layer_norm290, model_decoder_layers_10_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1139 = R.call_tir(cls.reshape14, (lv95,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1140 = R.call_tir(cls.reshape18, (reshape1139,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv220 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), reshape1140), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1141 = R.call_tir(cls.reshape16, (lv220,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1142 = R.call_tir(cls.reshape17, (reshape1141,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv96 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_10_encoder_attn_out_proj_weight4, reshape1142, model_decoder_layers_10_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1006 = R.call_tir(cls.add5, (add1003, lv96), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm291 = R.call_tir(cls.layer_norm2, (add1006, model_decoder_layers_10_final_layer_norm_weight4, model_decoder_layers_10_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv10 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_10_fc1_weight4, layer_norm291, model_decoder_layers_10_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv97 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_10_fc2_weight4, lv10, model_decoder_layers_10_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1009 = R.call_tir(cls.add5, (add1006, lv97), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm292 = R.call_tir(cls.layer_norm2, (add1009, model_decoder_layers_11_self_attn_layer_norm_weight4, model_decoder_layers_11_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv98 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_11_self_attn_q_proj_weight4, layer_norm292, model_decoder_layers_11_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1143 = R.call_tir(cls.reshape14, (lv98,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv43_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_11_self_attn_k_proj_weight4, layer_norm292), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1144 = R.call_tir(cls.reshape14, (lv43_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv99 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_11_self_attn_v_proj_weight4, layer_norm292, model_decoder_layers_11_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1145 = R.call_tir(cls.reshape14, (lv99,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat75 = R.call_tir(cls.concatenate1, (reshape1143, reshape1144, reshape1145), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1146 = R.call_tir(cls.reshape15, (concat75,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv221 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), reshape1146), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1147 = R.call_tir(cls.reshape16, (lv221,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1148 = R.call_tir(cls.reshape17, (reshape1147,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv100 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_11_self_attn_out_proj_weight4, reshape1148, model_decoder_layers_11_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1013 = R.call_tir(cls.add5, (add1009, lv100), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm293 = R.call_tir(cls.layer_norm2, (add1013, model_decoder_layers_11_encoder_attn_layer_norm_weight4, model_decoder_layers_11_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv101 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_11_encoder_attn_q_proj_weight4, layer_norm293, model_decoder_layers_11_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1149 = R.call_tir(cls.reshape14, (lv101,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1150 = R.call_tir(cls.reshape18, (reshape1149,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv222 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), reshape1150), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1151 = R.call_tir(cls.reshape16, (lv222,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1152 = R.call_tir(cls.reshape17, (reshape1151,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv102 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_11_encoder_attn_out_proj_weight4, reshape1152, model_decoder_layers_11_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1016 = R.call_tir(cls.add5, (add1013, lv102), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm294 = R.call_tir(cls.layer_norm2, (add1016, model_decoder_layers_11_final_layer_norm_weight4, model_decoder_layers_11_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv11 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_11_fc1_weight4, layer_norm294, model_decoder_layers_11_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv103 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_11_fc2_weight4, lv11, model_decoder_layers_11_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1019 = R.call_tir(cls.add5, (add1016, lv103), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm295 = R.call_tir(cls.layer_norm2, (add1019, model_decoder_layers_12_self_attn_layer_norm_weight4, model_decoder_layers_12_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv104 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_12_self_attn_q_proj_weight4, layer_norm295, model_decoder_layers_12_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1153 = R.call_tir(cls.reshape14, (lv104,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv44_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_12_self_attn_k_proj_weight4, layer_norm295), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1154 = R.call_tir(cls.reshape14, (lv44_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv105 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_12_self_attn_v_proj_weight4, layer_norm295, model_decoder_layers_12_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1155 = R.call_tir(cls.reshape14, (lv105,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat76 = R.call_tir(cls.concatenate1, (reshape1153, reshape1154, reshape1155), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1156 = R.call_tir(cls.reshape15, (concat76,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv223 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), reshape1156), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1157 = R.call_tir(cls.reshape16, (lv223,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1158 = R.call_tir(cls.reshape17, (reshape1157,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv106 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_12_self_attn_out_proj_weight4, reshape1158, model_decoder_layers_12_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1023 = R.call_tir(cls.add5, (add1019, lv106), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm296 = R.call_tir(cls.layer_norm2, (add1023, model_decoder_layers_12_encoder_attn_layer_norm_weight4, model_decoder_layers_12_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv107 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_12_encoder_attn_q_proj_weight4, layer_norm296, model_decoder_layers_12_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1159 = R.call_tir(cls.reshape14, (lv107,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1160 = R.call_tir(cls.reshape18, (reshape1159,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv224 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), reshape1160), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1161 = R.call_tir(cls.reshape16, (lv224,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1162 = R.call_tir(cls.reshape17, (reshape1161,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv108 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_12_encoder_attn_out_proj_weight4, reshape1162, model_decoder_layers_12_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1026 = R.call_tir(cls.add5, (add1023, lv108), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm297 = R.call_tir(cls.layer_norm2, (add1026, model_decoder_layers_12_final_layer_norm_weight4, model_decoder_layers_12_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv12 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_12_fc1_weight4, layer_norm297, model_decoder_layers_12_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv109 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_12_fc2_weight4, lv12, model_decoder_layers_12_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1029 = R.call_tir(cls.add5, (add1026, lv109), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm298 = R.call_tir(cls.layer_norm2, (add1029, model_decoder_layers_13_self_attn_layer_norm_weight4, model_decoder_layers_13_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv110 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_13_self_attn_q_proj_weight4, layer_norm298, model_decoder_layers_13_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1163 = R.call_tir(cls.reshape14, (lv110,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv45_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_13_self_attn_k_proj_weight4, layer_norm298), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1164 = R.call_tir(cls.reshape14, (lv45_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv111 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_13_self_attn_v_proj_weight4, layer_norm298, model_decoder_layers_13_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1165 = R.call_tir(cls.reshape14, (lv111,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat77 = R.call_tir(cls.concatenate1, (reshape1163, reshape1164, reshape1165), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1166 = R.call_tir(cls.reshape15, (concat77,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv225 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), reshape1166), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1167 = R.call_tir(cls.reshape16, (lv225,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1168 = R.call_tir(cls.reshape17, (reshape1167,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv112 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_13_self_attn_out_proj_weight4, reshape1168, model_decoder_layers_13_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1033 = R.call_tir(cls.add5, (add1029, lv112), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm299 = R.call_tir(cls.layer_norm2, (add1033, model_decoder_layers_13_encoder_attn_layer_norm_weight4, model_decoder_layers_13_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv113 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_13_encoder_attn_q_proj_weight4, layer_norm299, model_decoder_layers_13_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1169 = R.call_tir(cls.reshape14, (lv113,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1170 = R.call_tir(cls.reshape18, (reshape1169,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv226 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), reshape1170), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1171 = R.call_tir(cls.reshape16, (lv226,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1172 = R.call_tir(cls.reshape17, (reshape1171,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv114 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_13_encoder_attn_out_proj_weight4, reshape1172, model_decoder_layers_13_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1036 = R.call_tir(cls.add5, (add1033, lv114), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm300 = R.call_tir(cls.layer_norm2, (add1036, model_decoder_layers_13_final_layer_norm_weight4, model_decoder_layers_13_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv13 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_13_fc1_weight4, layer_norm300, model_decoder_layers_13_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv115 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_13_fc2_weight4, lv13, model_decoder_layers_13_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1039 = R.call_tir(cls.add5, (add1036, lv115), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm301 = R.call_tir(cls.layer_norm2, (add1039, model_decoder_layers_14_self_attn_layer_norm_weight4, model_decoder_layers_14_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv116 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_14_self_attn_q_proj_weight4, layer_norm301, model_decoder_layers_14_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1173 = R.call_tir(cls.reshape14, (lv116,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv46_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_14_self_attn_k_proj_weight4, layer_norm301), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1174 = R.call_tir(cls.reshape14, (lv46_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv117 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_14_self_attn_v_proj_weight4, layer_norm301, model_decoder_layers_14_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1175 = R.call_tir(cls.reshape14, (lv117,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat78 = R.call_tir(cls.concatenate1, (reshape1173, reshape1174, reshape1175), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1176 = R.call_tir(cls.reshape15, (concat78,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv227 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), reshape1176), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1177 = R.call_tir(cls.reshape16, (lv227,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1178 = R.call_tir(cls.reshape17, (reshape1177,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv118 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_14_self_attn_out_proj_weight4, reshape1178, model_decoder_layers_14_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1043 = R.call_tir(cls.add5, (add1039, lv118), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm302 = R.call_tir(cls.layer_norm2, (add1043, model_decoder_layers_14_encoder_attn_layer_norm_weight4, model_decoder_layers_14_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv119 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_14_encoder_attn_q_proj_weight4, layer_norm302, model_decoder_layers_14_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1179 = R.call_tir(cls.reshape14, (lv119,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1180 = R.call_tir(cls.reshape18, (reshape1179,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv228 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), reshape1180), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1181 = R.call_tir(cls.reshape16, (lv228,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1182 = R.call_tir(cls.reshape17, (reshape1181,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv120 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_14_encoder_attn_out_proj_weight4, reshape1182, model_decoder_layers_14_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1046 = R.call_tir(cls.add5, (add1043, lv120), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm303 = R.call_tir(cls.layer_norm2, (add1046, model_decoder_layers_14_final_layer_norm_weight4, model_decoder_layers_14_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv14 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_14_fc1_weight4, layer_norm303, model_decoder_layers_14_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv121 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_14_fc2_weight4, lv14, model_decoder_layers_14_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1049 = R.call_tir(cls.add5, (add1046, lv121), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm304 = R.call_tir(cls.layer_norm2, (add1049, model_decoder_layers_15_self_attn_layer_norm_weight4, model_decoder_layers_15_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv122 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_15_self_attn_q_proj_weight4, layer_norm304, model_decoder_layers_15_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1183 = R.call_tir(cls.reshape14, (lv122,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv47_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_15_self_attn_k_proj_weight4, layer_norm304), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1184 = R.call_tir(cls.reshape14, (lv47_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv123 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_15_self_attn_v_proj_weight4, layer_norm304, model_decoder_layers_15_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1185 = R.call_tir(cls.reshape14, (lv123,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat79 = R.call_tir(cls.concatenate1, (reshape1183, reshape1184, reshape1185), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1186 = R.call_tir(cls.reshape15, (concat79,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv229 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), reshape1186), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1187 = R.call_tir(cls.reshape16, (lv229,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1188 = R.call_tir(cls.reshape17, (reshape1187,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv124 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_15_self_attn_out_proj_weight4, reshape1188, model_decoder_layers_15_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1053 = R.call_tir(cls.add5, (add1049, lv124), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm305 = R.call_tir(cls.layer_norm2, (add1053, model_decoder_layers_15_encoder_attn_layer_norm_weight4, model_decoder_layers_15_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv125 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_15_encoder_attn_q_proj_weight4, layer_norm305, model_decoder_layers_15_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1189 = R.call_tir(cls.reshape14, (lv125,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1190 = R.call_tir(cls.reshape18, (reshape1189,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv230 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), reshape1190), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1191 = R.call_tir(cls.reshape16, (lv230,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1192 = R.call_tir(cls.reshape17, (reshape1191,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv126 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_15_encoder_attn_out_proj_weight4, reshape1192, model_decoder_layers_15_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1056 = R.call_tir(cls.add5, (add1053, lv126), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm306 = R.call_tir(cls.layer_norm2, (add1056, model_decoder_layers_15_final_layer_norm_weight4, model_decoder_layers_15_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv15 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_15_fc1_weight4, layer_norm306, model_decoder_layers_15_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv127 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_15_fc2_weight4, lv15, model_decoder_layers_15_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1059 = R.call_tir(cls.add5, (add1056, lv127), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm307 = R.call_tir(cls.layer_norm2, (add1059, model_decoder_layers_16_self_attn_layer_norm_weight4, model_decoder_layers_16_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv128 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_16_self_attn_q_proj_weight4, layer_norm307, model_decoder_layers_16_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1193 = R.call_tir(cls.reshape14, (lv128,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv48_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_16_self_attn_k_proj_weight4, layer_norm307), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1194 = R.call_tir(cls.reshape14, (lv48_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv129 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_16_self_attn_v_proj_weight4, layer_norm307, model_decoder_layers_16_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1195 = R.call_tir(cls.reshape14, (lv129,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat80 = R.call_tir(cls.concatenate1, (reshape1193, reshape1194, reshape1195), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1196 = R.call_tir(cls.reshape15, (concat80,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv231 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), reshape1196), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1197 = R.call_tir(cls.reshape16, (lv231,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1198 = R.call_tir(cls.reshape17, (reshape1197,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv130 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_16_self_attn_out_proj_weight4, reshape1198, model_decoder_layers_16_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1063 = R.call_tir(cls.add5, (add1059, lv130), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm308 = R.call_tir(cls.layer_norm2, (add1063, model_decoder_layers_16_encoder_attn_layer_norm_weight4, model_decoder_layers_16_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv131 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_16_encoder_attn_q_proj_weight4, layer_norm308, model_decoder_layers_16_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1199 = R.call_tir(cls.reshape14, (lv131,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1200 = R.call_tir(cls.reshape18, (reshape1199,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv232 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), reshape1200), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1201 = R.call_tir(cls.reshape16, (lv232,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1202 = R.call_tir(cls.reshape17, (reshape1201,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv132 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_16_encoder_attn_out_proj_weight4, reshape1202, model_decoder_layers_16_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1066 = R.call_tir(cls.add5, (add1063, lv132), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm309 = R.call_tir(cls.layer_norm2, (add1066, model_decoder_layers_16_final_layer_norm_weight4, model_decoder_layers_16_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv16 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_16_fc1_weight4, layer_norm309, model_decoder_layers_16_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv133 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_16_fc2_weight4, lv16, model_decoder_layers_16_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1069 = R.call_tir(cls.add5, (add1066, lv133), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm310 = R.call_tir(cls.layer_norm2, (add1069, model_decoder_layers_17_self_attn_layer_norm_weight4, model_decoder_layers_17_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv134 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_17_self_attn_q_proj_weight4, layer_norm310, model_decoder_layers_17_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1203 = R.call_tir(cls.reshape14, (lv134,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv49_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_17_self_attn_k_proj_weight4, layer_norm310), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1204 = R.call_tir(cls.reshape14, (lv49_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv135 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_17_self_attn_v_proj_weight4, layer_norm310, model_decoder_layers_17_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1205 = R.call_tir(cls.reshape14, (lv135,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat81 = R.call_tir(cls.concatenate1, (reshape1203, reshape1204, reshape1205), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1206 = R.call_tir(cls.reshape15, (concat81,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv233 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), reshape1206), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1207 = R.call_tir(cls.reshape16, (lv233,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1208 = R.call_tir(cls.reshape17, (reshape1207,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv136 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_17_self_attn_out_proj_weight4, reshape1208, model_decoder_layers_17_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1073 = R.call_tir(cls.add5, (add1069, lv136), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm311 = R.call_tir(cls.layer_norm2, (add1073, model_decoder_layers_17_encoder_attn_layer_norm_weight4, model_decoder_layers_17_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv137 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_17_encoder_attn_q_proj_weight4, layer_norm311, model_decoder_layers_17_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1209 = R.call_tir(cls.reshape14, (lv137,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1210 = R.call_tir(cls.reshape18, (reshape1209,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv234 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), reshape1210), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1211 = R.call_tir(cls.reshape16, (lv234,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1212 = R.call_tir(cls.reshape17, (reshape1211,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv138 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_17_encoder_attn_out_proj_weight4, reshape1212, model_decoder_layers_17_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1076 = R.call_tir(cls.add5, (add1073, lv138), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm312 = R.call_tir(cls.layer_norm2, (add1076, model_decoder_layers_17_final_layer_norm_weight4, model_decoder_layers_17_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv17 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_17_fc1_weight4, layer_norm312, model_decoder_layers_17_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv139 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_17_fc2_weight4, lv17, model_decoder_layers_17_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1079 = R.call_tir(cls.add5, (add1076, lv139), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm313 = R.call_tir(cls.layer_norm2, (add1079, model_decoder_layers_18_self_attn_layer_norm_weight4, model_decoder_layers_18_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv140 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_18_self_attn_q_proj_weight4, layer_norm313, model_decoder_layers_18_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1213 = R.call_tir(cls.reshape14, (lv140,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv50_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_18_self_attn_k_proj_weight4, layer_norm313), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1214 = R.call_tir(cls.reshape14, (lv50_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv141 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_18_self_attn_v_proj_weight4, layer_norm313, model_decoder_layers_18_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1215 = R.call_tir(cls.reshape14, (lv141,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat82 = R.call_tir(cls.concatenate1, (reshape1213, reshape1214, reshape1215), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1216 = R.call_tir(cls.reshape15, (concat82,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv235 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), reshape1216), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1217 = R.call_tir(cls.reshape16, (lv235,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1218 = R.call_tir(cls.reshape17, (reshape1217,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv142 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_18_self_attn_out_proj_weight4, reshape1218, model_decoder_layers_18_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1083 = R.call_tir(cls.add5, (add1079, lv142), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm314 = R.call_tir(cls.layer_norm2, (add1083, model_decoder_layers_18_encoder_attn_layer_norm_weight4, model_decoder_layers_18_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv143 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_18_encoder_attn_q_proj_weight4, layer_norm314, model_decoder_layers_18_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1219 = R.call_tir(cls.reshape14, (lv143,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1220 = R.call_tir(cls.reshape18, (reshape1219,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv236 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), reshape1220), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1221 = R.call_tir(cls.reshape16, (lv236,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1222 = R.call_tir(cls.reshape17, (reshape1221,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv144 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_18_encoder_attn_out_proj_weight4, reshape1222, model_decoder_layers_18_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1086 = R.call_tir(cls.add5, (add1083, lv144), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm315 = R.call_tir(cls.layer_norm2, (add1086, model_decoder_layers_18_final_layer_norm_weight4, model_decoder_layers_18_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv18 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_18_fc1_weight4, layer_norm315, model_decoder_layers_18_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv145 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_18_fc2_weight4, lv18, model_decoder_layers_18_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1089 = R.call_tir(cls.add5, (add1086, lv145), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm316 = R.call_tir(cls.layer_norm2, (add1089, model_decoder_layers_19_self_attn_layer_norm_weight4, model_decoder_layers_19_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv146 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_19_self_attn_q_proj_weight4, layer_norm316, model_decoder_layers_19_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1223 = R.call_tir(cls.reshape14, (lv146,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv51_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_19_self_attn_k_proj_weight4, layer_norm316), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1224 = R.call_tir(cls.reshape14, (lv51_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv147 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_19_self_attn_v_proj_weight4, layer_norm316, model_decoder_layers_19_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1225 = R.call_tir(cls.reshape14, (lv147,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat83 = R.call_tir(cls.concatenate1, (reshape1223, reshape1224, reshape1225), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1226 = R.call_tir(cls.reshape15, (concat83,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv237 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), reshape1226), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1227 = R.call_tir(cls.reshape16, (lv237,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1228 = R.call_tir(cls.reshape17, (reshape1227,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv148 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_19_self_attn_out_proj_weight4, reshape1228, model_decoder_layers_19_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1093 = R.call_tir(cls.add5, (add1089, lv148), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm317 = R.call_tir(cls.layer_norm2, (add1093, model_decoder_layers_19_encoder_attn_layer_norm_weight4, model_decoder_layers_19_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv149 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_19_encoder_attn_q_proj_weight4, layer_norm317, model_decoder_layers_19_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1229 = R.call_tir(cls.reshape14, (lv149,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1230 = R.call_tir(cls.reshape18, (reshape1229,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv238 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), reshape1230), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1231 = R.call_tir(cls.reshape16, (lv238,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1232 = R.call_tir(cls.reshape17, (reshape1231,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv150 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_19_encoder_attn_out_proj_weight4, reshape1232, model_decoder_layers_19_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1096 = R.call_tir(cls.add5, (add1093, lv150), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm318 = R.call_tir(cls.layer_norm2, (add1096, model_decoder_layers_19_final_layer_norm_weight4, model_decoder_layers_19_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv19 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_19_fc1_weight4, layer_norm318, model_decoder_layers_19_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv151 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_19_fc2_weight4, lv19, model_decoder_layers_19_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1099 = R.call_tir(cls.add5, (add1096, lv151), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm319 = R.call_tir(cls.layer_norm2, (add1099, model_decoder_layers_20_self_attn_layer_norm_weight4, model_decoder_layers_20_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv152 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_20_self_attn_q_proj_weight4, layer_norm319, model_decoder_layers_20_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1233 = R.call_tir(cls.reshape14, (lv152,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv52_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_20_self_attn_k_proj_weight4, layer_norm319), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1234 = R.call_tir(cls.reshape14, (lv52_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv153 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_20_self_attn_v_proj_weight4, layer_norm319, model_decoder_layers_20_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1235 = R.call_tir(cls.reshape14, (lv153,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat84 = R.call_tir(cls.concatenate1, (reshape1233, reshape1234, reshape1235), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1236 = R.call_tir(cls.reshape15, (concat84,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv239 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), reshape1236), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1237 = R.call_tir(cls.reshape16, (lv239,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1238 = R.call_tir(cls.reshape17, (reshape1237,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv154 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_20_self_attn_out_proj_weight4, reshape1238, model_decoder_layers_20_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1103 = R.call_tir(cls.add5, (add1099, lv154), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm320 = R.call_tir(cls.layer_norm2, (add1103, model_decoder_layers_20_encoder_attn_layer_norm_weight4, model_decoder_layers_20_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv155 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_20_encoder_attn_q_proj_weight4, layer_norm320, model_decoder_layers_20_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1239 = R.call_tir(cls.reshape14, (lv155,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1240 = R.call_tir(cls.reshape18, (reshape1239,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv240 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), reshape1240), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1241 = R.call_tir(cls.reshape16, (lv240,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1242 = R.call_tir(cls.reshape17, (reshape1241,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv156 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_20_encoder_attn_out_proj_weight4, reshape1242, model_decoder_layers_20_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1106 = R.call_tir(cls.add5, (add1103, lv156), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm321 = R.call_tir(cls.layer_norm2, (add1106, model_decoder_layers_20_final_layer_norm_weight4, model_decoder_layers_20_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv20 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_20_fc1_weight4, layer_norm321, model_decoder_layers_20_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv157 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_20_fc2_weight4, lv20, model_decoder_layers_20_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1109 = R.call_tir(cls.add5, (add1106, lv157), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm322 = R.call_tir(cls.layer_norm2, (add1109, model_decoder_layers_21_self_attn_layer_norm_weight4, model_decoder_layers_21_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv158 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_21_self_attn_q_proj_weight4, layer_norm322, model_decoder_layers_21_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1243 = R.call_tir(cls.reshape14, (lv158,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv53_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_21_self_attn_k_proj_weight4, layer_norm322), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1244 = R.call_tir(cls.reshape14, (lv53_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv159 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_21_self_attn_v_proj_weight4, layer_norm322, model_decoder_layers_21_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1245 = R.call_tir(cls.reshape14, (lv159,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat85 = R.call_tir(cls.concatenate1, (reshape1243, reshape1244, reshape1245), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1246 = R.call_tir(cls.reshape15, (concat85,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv241 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), reshape1246), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1247 = R.call_tir(cls.reshape16, (lv241,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1248 = R.call_tir(cls.reshape17, (reshape1247,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv160 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_21_self_attn_out_proj_weight4, reshape1248, model_decoder_layers_21_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1113 = R.call_tir(cls.add5, (add1109, lv160), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm323 = R.call_tir(cls.layer_norm2, (add1113, model_decoder_layers_21_encoder_attn_layer_norm_weight4, model_decoder_layers_21_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv161 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_21_encoder_attn_q_proj_weight4, layer_norm323, model_decoder_layers_21_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1249 = R.call_tir(cls.reshape14, (lv161,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1250 = R.call_tir(cls.reshape18, (reshape1249,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv242 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), reshape1250), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1251 = R.call_tir(cls.reshape16, (lv242,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1252 = R.call_tir(cls.reshape17, (reshape1251,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv162 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_21_encoder_attn_out_proj_weight4, reshape1252, model_decoder_layers_21_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1116 = R.call_tir(cls.add5, (add1113, lv162), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm324 = R.call_tir(cls.layer_norm2, (add1116, model_decoder_layers_21_final_layer_norm_weight4, model_decoder_layers_21_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv21 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_21_fc1_weight4, layer_norm324, model_decoder_layers_21_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv163 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_21_fc2_weight4, lv21, model_decoder_layers_21_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1119 = R.call_tir(cls.add5, (add1116, lv163), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm325 = R.call_tir(cls.layer_norm2, (add1119, model_decoder_layers_22_self_attn_layer_norm_weight4, model_decoder_layers_22_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv164 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_22_self_attn_q_proj_weight4, layer_norm325, model_decoder_layers_22_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1253 = R.call_tir(cls.reshape14, (lv164,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv54_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_22_self_attn_k_proj_weight4, layer_norm325), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1254 = R.call_tir(cls.reshape14, (lv54_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv165 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_22_self_attn_v_proj_weight4, layer_norm325, model_decoder_layers_22_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1255 = R.call_tir(cls.reshape14, (lv165,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat86 = R.call_tir(cls.concatenate1, (reshape1253, reshape1254, reshape1255), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1256 = R.call_tir(cls.reshape15, (concat86,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv243 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), reshape1256), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1257 = R.call_tir(cls.reshape16, (lv243,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1258 = R.call_tir(cls.reshape17, (reshape1257,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv166 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_22_self_attn_out_proj_weight4, reshape1258, model_decoder_layers_22_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1123 = R.call_tir(cls.add5, (add1119, lv166), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm326 = R.call_tir(cls.layer_norm2, (add1123, model_decoder_layers_22_encoder_attn_layer_norm_weight4, model_decoder_layers_22_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv167 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_22_encoder_attn_q_proj_weight4, layer_norm326, model_decoder_layers_22_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1259 = R.call_tir(cls.reshape14, (lv167,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1260 = R.call_tir(cls.reshape18, (reshape1259,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv244 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), reshape1260), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1261 = R.call_tir(cls.reshape16, (lv244,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1262 = R.call_tir(cls.reshape17, (reshape1261,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv168 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_22_encoder_attn_out_proj_weight4, reshape1262, model_decoder_layers_22_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1126 = R.call_tir(cls.add5, (add1123, lv168), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm327 = R.call_tir(cls.layer_norm2, (add1126, model_decoder_layers_22_final_layer_norm_weight4, model_decoder_layers_22_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv22 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_22_fc1_weight4, layer_norm327, model_decoder_layers_22_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv169 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_22_fc2_weight4, lv22, model_decoder_layers_22_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1129 = R.call_tir(cls.add5, (add1126, lv169), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm328 = R.call_tir(cls.layer_norm2, (add1129, model_decoder_layers_23_self_attn_layer_norm_weight4, model_decoder_layers_23_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv170 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_23_self_attn_q_proj_weight4, layer_norm328, model_decoder_layers_23_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1263 = R.call_tir(cls.reshape14, (lv170,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv55_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_23_self_attn_k_proj_weight4, layer_norm328), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1264 = R.call_tir(cls.reshape14, (lv55_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv171 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_23_self_attn_v_proj_weight4, layer_norm328, model_decoder_layers_23_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1265 = R.call_tir(cls.reshape14, (lv171,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat87 = R.call_tir(cls.concatenate1, (reshape1263, reshape1264, reshape1265), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1266 = R.call_tir(cls.reshape15, (concat87,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv245 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), reshape1266), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1267 = R.call_tir(cls.reshape16, (lv245,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1268 = R.call_tir(cls.reshape17, (reshape1267,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv172 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_23_self_attn_out_proj_weight4, reshape1268, model_decoder_layers_23_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1133 = R.call_tir(cls.add5, (add1129, lv172), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm329 = R.call_tir(cls.layer_norm2, (add1133, model_decoder_layers_23_encoder_attn_layer_norm_weight4, model_decoder_layers_23_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv173 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_23_encoder_attn_q_proj_weight4, layer_norm329, model_decoder_layers_23_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1269 = R.call_tir(cls.reshape14, (lv173,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1270 = R.call_tir(cls.reshape18, (reshape1269,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv246 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), reshape1270), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1271 = R.call_tir(cls.reshape16, (lv246,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1272 = R.call_tir(cls.reshape17, (reshape1271,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv174 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_23_encoder_attn_out_proj_weight4, reshape1272, model_decoder_layers_23_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1136 = R.call_tir(cls.add5, (add1133, lv174), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm330 = R.call_tir(cls.layer_norm2, (add1136, model_decoder_layers_23_final_layer_norm_weight4, model_decoder_layers_23_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv23 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_23_fc1_weight4, layer_norm330, model_decoder_layers_23_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv175 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_23_fc2_weight4, lv23, model_decoder_layers_23_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1139 = R.call_tir(cls.add5, (add1136, lv175), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm331 = R.call_tir(cls.layer_norm2, (add1139, model_decoder_layers_24_self_attn_layer_norm_weight4, model_decoder_layers_24_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv176 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_24_self_attn_q_proj_weight4, layer_norm331, model_decoder_layers_24_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1273 = R.call_tir(cls.reshape14, (lv176,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv56_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_24_self_attn_k_proj_weight4, layer_norm331), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1274 = R.call_tir(cls.reshape14, (lv56_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv177 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_24_self_attn_v_proj_weight4, layer_norm331, model_decoder_layers_24_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1275 = R.call_tir(cls.reshape14, (lv177,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat88 = R.call_tir(cls.concatenate1, (reshape1273, reshape1274, reshape1275), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1276 = R.call_tir(cls.reshape15, (concat88,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv247 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), reshape1276), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1277 = R.call_tir(cls.reshape16, (lv247,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1278 = R.call_tir(cls.reshape17, (reshape1277,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv178 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_24_self_attn_out_proj_weight4, reshape1278, model_decoder_layers_24_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1143 = R.call_tir(cls.add5, (add1139, lv178), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm332 = R.call_tir(cls.layer_norm2, (add1143, model_decoder_layers_24_encoder_attn_layer_norm_weight4, model_decoder_layers_24_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv179 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_24_encoder_attn_q_proj_weight4, layer_norm332, model_decoder_layers_24_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1279 = R.call_tir(cls.reshape14, (lv179,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1280 = R.call_tir(cls.reshape18, (reshape1279,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv248 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), reshape1280), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1281 = R.call_tir(cls.reshape16, (lv248,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1282 = R.call_tir(cls.reshape17, (reshape1281,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv180 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_24_encoder_attn_out_proj_weight4, reshape1282, model_decoder_layers_24_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1146 = R.call_tir(cls.add5, (add1143, lv180), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm333 = R.call_tir(cls.layer_norm2, (add1146, model_decoder_layers_24_final_layer_norm_weight4, model_decoder_layers_24_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv24 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_24_fc1_weight4, layer_norm333, model_decoder_layers_24_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv181 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_24_fc2_weight4, lv24, model_decoder_layers_24_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1149 = R.call_tir(cls.add5, (add1146, lv181), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm334 = R.call_tir(cls.layer_norm2, (add1149, model_decoder_layers_25_self_attn_layer_norm_weight4, model_decoder_layers_25_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv182 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_25_self_attn_q_proj_weight4, layer_norm334, model_decoder_layers_25_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1283 = R.call_tir(cls.reshape14, (lv182,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv57_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_25_self_attn_k_proj_weight4, layer_norm334), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1284 = R.call_tir(cls.reshape14, (lv57_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv183 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_25_self_attn_v_proj_weight4, layer_norm334, model_decoder_layers_25_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1285 = R.call_tir(cls.reshape14, (lv183,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat89 = R.call_tir(cls.concatenate1, (reshape1283, reshape1284, reshape1285), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1286 = R.call_tir(cls.reshape15, (concat89,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv249 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), reshape1286), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1287 = R.call_tir(cls.reshape16, (lv249,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1288 = R.call_tir(cls.reshape17, (reshape1287,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv184 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_25_self_attn_out_proj_weight4, reshape1288, model_decoder_layers_25_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1153 = R.call_tir(cls.add5, (add1149, lv184), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm335 = R.call_tir(cls.layer_norm2, (add1153, model_decoder_layers_25_encoder_attn_layer_norm_weight4, model_decoder_layers_25_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv185 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_25_encoder_attn_q_proj_weight4, layer_norm335, model_decoder_layers_25_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1289 = R.call_tir(cls.reshape14, (lv185,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1290 = R.call_tir(cls.reshape18, (reshape1289,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv250 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), reshape1290), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1291 = R.call_tir(cls.reshape16, (lv250,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1292 = R.call_tir(cls.reshape17, (reshape1291,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv186 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_25_encoder_attn_out_proj_weight4, reshape1292, model_decoder_layers_25_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1156 = R.call_tir(cls.add5, (add1153, lv186), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm336 = R.call_tir(cls.layer_norm2, (add1156, model_decoder_layers_25_final_layer_norm_weight4, model_decoder_layers_25_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv25 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_25_fc1_weight4, layer_norm336, model_decoder_layers_25_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv187 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_25_fc2_weight4, lv25, model_decoder_layers_25_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1159 = R.call_tir(cls.add5, (add1156, lv187), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm337 = R.call_tir(cls.layer_norm2, (add1159, model_decoder_layers_26_self_attn_layer_norm_weight4, model_decoder_layers_26_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv188 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_26_self_attn_q_proj_weight4, layer_norm337, model_decoder_layers_26_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1293 = R.call_tir(cls.reshape14, (lv188,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv58_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_26_self_attn_k_proj_weight4, layer_norm337), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1294 = R.call_tir(cls.reshape14, (lv58_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv189 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_26_self_attn_v_proj_weight4, layer_norm337, model_decoder_layers_26_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1295 = R.call_tir(cls.reshape14, (lv189,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat90 = R.call_tir(cls.concatenate1, (reshape1293, reshape1294, reshape1295), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1296 = R.call_tir(cls.reshape15, (concat90,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv251 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), reshape1296), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1297 = R.call_tir(cls.reshape16, (lv251,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1298 = R.call_tir(cls.reshape17, (reshape1297,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv190 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_26_self_attn_out_proj_weight4, reshape1298, model_decoder_layers_26_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1163 = R.call_tir(cls.add5, (add1159, lv190), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm338 = R.call_tir(cls.layer_norm2, (add1163, model_decoder_layers_26_encoder_attn_layer_norm_weight4, model_decoder_layers_26_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv191 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_26_encoder_attn_q_proj_weight4, layer_norm338, model_decoder_layers_26_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1299 = R.call_tir(cls.reshape14, (lv191,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1300 = R.call_tir(cls.reshape18, (reshape1299,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv252 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), reshape1300), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1301 = R.call_tir(cls.reshape16, (lv252,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1302 = R.call_tir(cls.reshape17, (reshape1301,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv192 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_26_encoder_attn_out_proj_weight4, reshape1302, model_decoder_layers_26_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1166 = R.call_tir(cls.add5, (add1163, lv192), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm339 = R.call_tir(cls.layer_norm2, (add1166, model_decoder_layers_26_final_layer_norm_weight4, model_decoder_layers_26_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv26 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_26_fc1_weight4, layer_norm339, model_decoder_layers_26_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv193 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_26_fc2_weight4, lv26, model_decoder_layers_26_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1169 = R.call_tir(cls.add5, (add1166, lv193), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm340 = R.call_tir(cls.layer_norm2, (add1169, model_decoder_layers_27_self_attn_layer_norm_weight4, model_decoder_layers_27_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv194 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_27_self_attn_q_proj_weight4, layer_norm340, model_decoder_layers_27_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1303 = R.call_tir(cls.reshape14, (lv194,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv59_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_27_self_attn_k_proj_weight4, layer_norm340), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1304 = R.call_tir(cls.reshape14, (lv59_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv195 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_27_self_attn_v_proj_weight4, layer_norm340, model_decoder_layers_27_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1305 = R.call_tir(cls.reshape14, (lv195,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat91 = R.call_tir(cls.concatenate1, (reshape1303, reshape1304, reshape1305), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1306 = R.call_tir(cls.reshape15, (concat91,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv253 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), reshape1306), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1307 = R.call_tir(cls.reshape16, (lv253,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1308 = R.call_tir(cls.reshape17, (reshape1307,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv196 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_27_self_attn_out_proj_weight4, reshape1308, model_decoder_layers_27_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1173 = R.call_tir(cls.add5, (add1169, lv196), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm341 = R.call_tir(cls.layer_norm2, (add1173, model_decoder_layers_27_encoder_attn_layer_norm_weight4, model_decoder_layers_27_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv197 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_27_encoder_attn_q_proj_weight4, layer_norm341, model_decoder_layers_27_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1309 = R.call_tir(cls.reshape14, (lv197,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1310 = R.call_tir(cls.reshape18, (reshape1309,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv254 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), reshape1310), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1311 = R.call_tir(cls.reshape16, (lv254,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1312 = R.call_tir(cls.reshape17, (reshape1311,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv198_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_27_encoder_attn_out_proj_weight4, reshape1312, model_decoder_layers_27_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1176 = R.call_tir(cls.add5, (add1173, lv198_1), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm342 = R.call_tir(cls.layer_norm2, (add1176, model_decoder_layers_27_final_layer_norm_weight4, model_decoder_layers_27_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv27 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_27_fc1_weight4, layer_norm342, model_decoder_layers_27_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv199_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_27_fc2_weight4, lv27, model_decoder_layers_27_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1179 = R.call_tir(cls.add5, (add1176, lv199_1), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm343 = R.call_tir(cls.layer_norm2, (add1179, model_decoder_layers_28_self_attn_layer_norm_weight4, model_decoder_layers_28_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv200_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_28_self_attn_q_proj_weight4, layer_norm343, model_decoder_layers_28_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1313 = R.call_tir(cls.reshape14, (lv200_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv60_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_28_self_attn_k_proj_weight4, layer_norm343), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1314 = R.call_tir(cls.reshape14, (lv60_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv201_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_28_self_attn_v_proj_weight4, layer_norm343, model_decoder_layers_28_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1315 = R.call_tir(cls.reshape14, (lv201_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat92 = R.call_tir(cls.concatenate1, (reshape1313, reshape1314, reshape1315), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1316 = R.call_tir(cls.reshape15, (concat92,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv255 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), reshape1316), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1317 = R.call_tir(cls.reshape16, (lv255,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1318 = R.call_tir(cls.reshape17, (reshape1317,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv202_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_28_self_attn_out_proj_weight4, reshape1318, model_decoder_layers_28_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1183 = R.call_tir(cls.add5, (add1179, lv202_1), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm344 = R.call_tir(cls.layer_norm2, (add1183, model_decoder_layers_28_encoder_attn_layer_norm_weight4, model_decoder_layers_28_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv203_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_28_encoder_attn_q_proj_weight4, layer_norm344, model_decoder_layers_28_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1319 = R.call_tir(cls.reshape14, (lv203_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1320 = R.call_tir(cls.reshape18, (reshape1319,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv256 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), reshape1320), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1321 = R.call_tir(cls.reshape16, (lv256,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1322 = R.call_tir(cls.reshape17, (reshape1321,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv204_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_28_encoder_attn_out_proj_weight4, reshape1322, model_decoder_layers_28_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1186 = R.call_tir(cls.add5, (add1183, lv204_1), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm345 = R.call_tir(cls.layer_norm2, (add1186, model_decoder_layers_28_final_layer_norm_weight4, model_decoder_layers_28_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv28 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_28_fc1_weight4, layer_norm345, model_decoder_layers_28_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv205_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_28_fc2_weight4, lv28, model_decoder_layers_28_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1189 = R.call_tir(cls.add5, (add1186, lv205_1), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm346 = R.call_tir(cls.layer_norm2, (add1189, model_decoder_layers_29_self_attn_layer_norm_weight4, model_decoder_layers_29_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv206_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_29_self_attn_q_proj_weight4, layer_norm346, model_decoder_layers_29_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1323 = R.call_tir(cls.reshape14, (lv206_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv61_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_29_self_attn_k_proj_weight4, layer_norm346), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1324 = R.call_tir(cls.reshape14, (lv61_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv207_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_29_self_attn_v_proj_weight4, layer_norm346, model_decoder_layers_29_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1325 = R.call_tir(cls.reshape14, (lv207_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat93 = R.call_tir(cls.concatenate1, (reshape1323, reshape1324, reshape1325), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1326 = R.call_tir(cls.reshape15, (concat93,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv257 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), reshape1326), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1327 = R.call_tir(cls.reshape16, (lv257,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1328 = R.call_tir(cls.reshape17, (reshape1327,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv208_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_29_self_attn_out_proj_weight4, reshape1328, model_decoder_layers_29_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1193 = R.call_tir(cls.add5, (add1189, lv208_1), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm347 = R.call_tir(cls.layer_norm2, (add1193, model_decoder_layers_29_encoder_attn_layer_norm_weight4, model_decoder_layers_29_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv209_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_29_encoder_attn_q_proj_weight4, layer_norm347, model_decoder_layers_29_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1329 = R.call_tir(cls.reshape14, (lv209_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1330 = R.call_tir(cls.reshape18, (reshape1329,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv258 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), reshape1330), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1331 = R.call_tir(cls.reshape16, (lv258,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1332 = R.call_tir(cls.reshape17, (reshape1331,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv210_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_29_encoder_attn_out_proj_weight4, reshape1332, model_decoder_layers_29_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1196 = R.call_tir(cls.add5, (add1193, lv210_1), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm348 = R.call_tir(cls.layer_norm2, (add1196, model_decoder_layers_29_final_layer_norm_weight4, model_decoder_layers_29_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv29 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_29_fc1_weight4, layer_norm348, model_decoder_layers_29_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv211_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_29_fc2_weight4, lv29, model_decoder_layers_29_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1199 = R.call_tir(cls.add5, (add1196, lv211_1), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm349 = R.call_tir(cls.layer_norm2, (add1199, model_decoder_layers_30_self_attn_layer_norm_weight4, model_decoder_layers_30_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv212_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_30_self_attn_q_proj_weight4, layer_norm349, model_decoder_layers_30_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1333 = R.call_tir(cls.reshape14, (lv212_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv62_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_30_self_attn_k_proj_weight4, layer_norm349), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1334 = R.call_tir(cls.reshape14, (lv62_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv213_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_30_self_attn_v_proj_weight4, layer_norm349, model_decoder_layers_30_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1335 = R.call_tir(cls.reshape14, (lv213_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat94 = R.call_tir(cls.concatenate1, (reshape1333, reshape1334, reshape1335), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1336 = R.call_tir(cls.reshape15, (concat94,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv259 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), reshape1336), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1337 = R.call_tir(cls.reshape16, (lv259,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1338 = R.call_tir(cls.reshape17, (reshape1337,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv214_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_30_self_attn_out_proj_weight4, reshape1338, model_decoder_layers_30_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1203 = R.call_tir(cls.add5, (add1199, lv214_1), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm350 = R.call_tir(cls.layer_norm2, (add1203, model_decoder_layers_30_encoder_attn_layer_norm_weight4, model_decoder_layers_30_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv215_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_30_encoder_attn_q_proj_weight4, layer_norm350, model_decoder_layers_30_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1339 = R.call_tir(cls.reshape14, (lv215_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1340 = R.call_tir(cls.reshape18, (reshape1339,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv260 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), reshape1340), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1341 = R.call_tir(cls.reshape16, (lv260,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1342 = R.call_tir(cls.reshape17, (reshape1341,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv216_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_30_encoder_attn_out_proj_weight4, reshape1342, model_decoder_layers_30_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1206 = R.call_tir(cls.add5, (add1203, lv216_1), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm351 = R.call_tir(cls.layer_norm2, (add1206, model_decoder_layers_30_final_layer_norm_weight4, model_decoder_layers_30_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv30 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_30_fc1_weight4, layer_norm351, model_decoder_layers_30_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv217_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_30_fc2_weight4, lv30, model_decoder_layers_30_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1209 = R.call_tir(cls.add5, (add1206, lv217_1), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm352 = R.call_tir(cls.layer_norm2, (add1209, model_decoder_layers_31_self_attn_layer_norm_weight4, model_decoder_layers_31_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv218_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_31_self_attn_q_proj_weight4, layer_norm352, model_decoder_layers_31_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1343 = R.call_tir(cls.reshape14, (lv218_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv63_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_31_self_attn_k_proj_weight4, layer_norm352), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1344 = R.call_tir(cls.reshape14, (lv63_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv219_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_31_self_attn_v_proj_weight4, layer_norm352, model_decoder_layers_31_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1345 = R.call_tir(cls.reshape14, (lv219_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat95 = R.call_tir(cls.concatenate1, (reshape1343, reshape1344, reshape1345), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1346 = R.call_tir(cls.reshape15, (concat95,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv261 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), reshape1346), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1347 = R.call_tir(cls.reshape16, (lv261,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1348 = R.call_tir(cls.reshape17, (reshape1347,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv220_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_31_self_attn_out_proj_weight4, reshape1348, model_decoder_layers_31_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1213 = R.call_tir(cls.add5, (add1209, lv220_1), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm353 = R.call_tir(cls.layer_norm2, (add1213, model_decoder_layers_31_encoder_attn_layer_norm_weight4, model_decoder_layers_31_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv221_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_31_encoder_attn_q_proj_weight4, layer_norm353, model_decoder_layers_31_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1349 = R.call_tir(cls.reshape14, (lv221_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1350 = R.call_tir(cls.reshape18, (reshape1349,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv262 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), reshape1350), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1351 = R.call_tir(cls.reshape16, (lv262,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1352 = R.call_tir(cls.reshape17, (reshape1351,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv222_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_31_encoder_attn_out_proj_weight4, reshape1352, model_decoder_layers_31_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1216 = R.call_tir(cls.add5, (add1213, lv222_1), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm354 = R.call_tir(cls.layer_norm2, (add1216, model_decoder_layers_31_final_layer_norm_weight4, model_decoder_layers_31_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv31 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_31_fc1_weight4, layer_norm354, model_decoder_layers_31_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv223_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_31_fc2_weight4, lv31, model_decoder_layers_31_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1219 = R.call_tir(cls.add5, (add1216, lv223_1), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm355 = R.call_tir(cls.layer_norm2, (add1219, model_decoder_layer_norm_weight4, model_decoder_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv263 = R.call_tir(cls.index, (layer_norm355,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + gv4 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul2_cublas", (model_decoder_embed_tokens_weight4, lv263), out_sinfo=R.Tensor((1, 1, 51866), dtype="float32")) + R.output(gv4) + return gv4 + + @R.function + def renormalize_by_top_p(probs: R.Tensor(("batch_size", "vocab_size"), dtype="float32"), top_p: R.Tensor(("batch_size",), dtype="float32"), init_pivots: R.Tensor(("batch_size", 3), dtype="float32")) -> R.Tensor(("batch_size", "vocab_size"), dtype="float32"): + batch_size = T.int64() + vocab_size = T.int64() + R.func_attr({"relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "num_positions": 48, "num_samples": 8}}) + cls = Module + with R.dataflow(): + lv6 = R.call_tir(cls.top_p_pivot_cutoff, (probs, top_p, init_pivots), out_sinfo=[R.Tensor((batch_size,), dtype="float32"), R.Tensor((batch_size,), dtype="float32")]) + lv7: R.Tensor((batch_size,), dtype="float32") = lv6[0] + lv8: R.Tensor((batch_size,), dtype="float32") = lv6[1] + gv5 = R.call_tir(cls.top_p_renorm_after_cutoff, (probs, lv7, lv8), out_sinfo=R.Tensor((batch_size, vocab_size), dtype="float32")) + R.output(gv5) + return gv5 + + @R.function + def sample_with_top_p(sorted_probs: R.Tensor(("batch_size", "vocab_size"), dtype="float32"), sorted_indices: R.Tensor(("batch_size", "vocab_size"), dtype="int32"), uniform_samples: R.Tensor(("num_samples",), dtype="float32"), sample_indices: R.Tensor(("num_samples",), dtype="int32"), top_p: R.Tensor(("batch_size",), dtype="float32")) -> R.Tensor(("num_samples",), dtype="int32"): + num_samples = T.int64() + batch_size = T.int64() + vocab_size = T.int64() + R.func_attr({"relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "num_positions": 48, "num_samples": 8}}) + cls = Module + with R.dataflow(): + uniform_samples1: R.Tensor((num_samples, 1), dtype="float32") = R.call_pure_packed("vm.builtin.reshape", uniform_samples, R.shape([num_samples, 1]), sinfo_args=(R.Tensor((num_samples, 1), dtype="float32"),)) + sample_indices1: R.Tensor((num_samples, 1), dtype="int32") = R.call_pure_packed("vm.builtin.reshape", sample_indices, R.shape([num_samples, 1]), sinfo_args=(R.Tensor((num_samples, 1), dtype="int32"),)) + sample_indices2: R.Tensor((batch_size, 1), dtype="float32") = R.call_pure_packed("vm.builtin.reshape", top_p, R.shape([batch_size, 1]), sinfo_args=(R.Tensor((batch_size, 1), dtype="float32"),)) + lv3 = R.call_tir(cls.full, R.tuple(), out_sinfo=R.Tensor((batch_size, 1), dtype="int32"), tir_vars=R.shape([vocab_size])) + lv1: R.Tensor((8 * (batch_size * vocab_size * 4) + 8388608 + batch_size * vocab_size * 12,), dtype="uint8") = R.builtin.alloc_tensor(R.shape([8 * (batch_size * vocab_size * 4) + 8388608 + batch_size * vocab_size * 12]), R.dtype("uint8"), R.prim_value(0), R.str("global")) + cumsum = R.call_tir(cls.cumsum, (sorted_probs, lv1), out_sinfo=R.Tensor((batch_size, vocab_size), dtype="float32")) + lv4 = R.call_tir(cls.get_renorm_prob, (cumsum, sample_indices2, lv3), out_sinfo=R.Tensor((batch_size, 1), dtype="float32")) + lv5 = R.call_tir(cls.get_index_from_sorted, (cumsum, sorted_indices, lv4, uniform_samples1, sample_indices1), out_sinfo=R.Tensor((num_samples, 1), dtype="int32")) + gv2: R.Tensor((num_samples,), dtype="int32") = R.call_pure_packed("vm.builtin.reshape", lv5, R.shape([num_samples]), sinfo_args=(R.Tensor((num_samples,), dtype="int32"),)) + R.output(gv2) + return gv2 + + @R.function + def sampler_take_probs(unsorted_probs: R.Tensor(("batch_size", "vocab_size"), dtype="float32"), sorted_indices: R.Tensor(("batch_size", "vocab_size"), dtype="int32"), sample_indices: R.Tensor(("num_samples",), dtype="int32"), sampling_result: R.Tensor(("num_samples",), dtype="int32"), lobprob_offsets: R.Tensor(("num_positions",), dtype="int32")) -> R.Tuple(R.Tensor(("num_samples",), dtype="float32"), R.Tensor(("num_positions",), dtype="float32"), R.Tensor(("num_positions",), dtype="int32")): + num_samples = T.int64() + num_positions = T.int64() + batch_size = T.int64() + vocab_size = T.int64() + R.func_attr({"relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "num_positions": 48, "num_samples": 8}}) + cls = Module + with R.dataflow(): + gv3 = R.call_tir(cls.sampler_take_probs_tir, (unsorted_probs, sorted_indices, sample_indices, sampling_result, lobprob_offsets), out_sinfo=[R.Tensor((num_samples,), dtype="float32"), R.Tensor((num_positions,), dtype="float32"), R.Tensor((num_positions,), dtype="int32")]) + R.output(gv3) + return gv3 + + @R.function + def sampler_verify_draft_tokens(draft_probs: R.Tensor(("num_nodes", "vocab_size"), dtype="float32"), draft_tokens: R.Tensor(("num_nodes",), dtype="int32"), model_probs: R.Tensor(("num_nodes", "vocab_size"), dtype="float32"), token_tree_first_child: R.Tensor(("num_nodes",), dtype="int32"), token_tree_next_sibling: R.Tensor(("num_nodes",), dtype="int32"), uniform_samples: R.Tensor(("num_nodes",), dtype="float32"), token_tree_parent_ptr: R.Tensor(("nbatch",), dtype="int32")) -> R.Tuple(R.Tensor(("num_nodes", "vocab_size"), dtype="float32"), R.Tensor(("nbatch",), dtype="int32")): + num_nodes = T.int64() + vocab_size = T.int64() + nbatch = T.int64() + R.func_attr({"relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "num_positions": 48, "num_samples": 8}}) + cls = Module + with R.dataflow(): + gv4: R.Tuple(R.Tensor((num_nodes, vocab_size), dtype="float32"), R.Tensor((nbatch,), dtype="int32")) = R.call_tir_inplace(cls.batch_verify_on_gpu_single_kernel, (draft_probs, draft_tokens, model_probs, token_tree_first_child, token_tree_next_sibling, uniform_samples, token_tree_parent_ptr), out_sinfo=[R.Tensor((num_nodes, vocab_size), dtype="float32"), R.Tensor((nbatch,), dtype="int32")], inplace_indices=[2, 6]) + R.output(gv4) + return gv4 + + @R.function + def softmax_with_temperature(logits: R.Tensor(("batch_size", 1, "vocab_size"), dtype="float32"), temperature: R.Tensor(("batch_size",), dtype="float32")) -> R.Tensor(("batch_size", 1, "vocab_size"), dtype="float32"): + batch_size = T.int64() + vocab_size = T.int64() + R.func_attr({"relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "seq_len": 15000, "total_seq_len": 1500}}) + cls = Module + with R.dataflow(): + lv: R.Tensor((batch_size, vocab_size), dtype="float32") = R.call_pure_packed("vm.builtin.reshape", logits, R.shape([batch_size, vocab_size]), sinfo_args=(R.Tensor((batch_size, vocab_size), dtype="float32"),)) + lv1 = R.call_tir(cls.chunk_lse, (lv, temperature), out_sinfo=[R.Tensor((batch_size, (vocab_size + 4096 - 1) // 4096), dtype="float32"), R.Tensor((batch_size, (vocab_size + 4096 - 1) // 4096), dtype="float32")]) + lv2: R.Tensor((batch_size, (vocab_size + 4096 - 1) // 4096), dtype="float32") = lv1[0] + lv3: R.Tensor((batch_size, (vocab_size + 4096 - 1) // 4096), dtype="float32") = lv1[1] + lv4 = R.call_tir(cls.softmax_with_chunked_sum, (lv, temperature, lv2, lv3), out_sinfo=R.Tensor((batch_size, vocab_size), dtype="float32")) + gv: R.Tensor((batch_size, 1, vocab_size), dtype="float32") = R.call_pure_packed("vm.builtin.reshape", lv4, R.shape([batch_size, 1, vocab_size]), sinfo_args=(R.Tensor((batch_size, 1, vocab_size), dtype="float32"),)) + R.output(gv) + return gv + +# Metadata omitted. Use show_meta=True in script() method to show it. \ No newline at end of file diff --git a/debug/debug-phase3.py b/debug/debug-phase3.py new file mode 100644 index 0000000000000000000000000000000000000000..88e92ad5c84f802e99a79c13c64b22872c65c8a3 --- /dev/null +++ b/debug/debug-phase3.py @@ -0,0 +1,10541 @@ +# from tvm.script import ir as I +# from tvm.script import tir as T +# from tvm.script import relax as R + +@I.ir_module +class Module: + I.module_attrs({"external_mods": [metadata["runtime.Module"][0], metadata["runtime.Module"][1], metadata["runtime.Module"][2], metadata["runtime.Module"][3], metadata["runtime.Module"][4], metadata["runtime.Module"][5], metadata["runtime.Module"][6], metadata["runtime.Module"][7], metadata["runtime.Module"][8], metadata["runtime.Module"][9], metadata["runtime.Module"][10], metadata["runtime.Module"][11], metadata["runtime.Module"][12], metadata["runtime.Module"][13], metadata["runtime.Module"][14]]}) + @T.prim_func(private=True) + def NT_matmul(layer_norm356: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16"), model_decoder_layers_0_self_attn_q_proj_weight5: T.Buffer((T.int64(1280), T.int64(1280)), "float16"), NT_matmul: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16")): + T.func_attr({"tir.noalias": T.bool(True)}) + # with T.block("root"): + for i0, i1, i2, k in T.grid(T.int64(1), T.int64(1), T.int64(1280), T.int64(1280)): + with T.block("NT_matmul"): + v_i0, v_i1, v_i2, v_k = T.axis.remap("SSSR", [i0, i1, i2, k]) + T.reads(layer_norm356[v_i0, v_i1, v_k], model_decoder_layers_0_self_attn_q_proj_weight5[v_i2, v_k]) + T.writes(NT_matmul[v_i0, v_i1, v_i2]) + with T.init(): + NT_matmul[v_i0, v_i1, v_i2] = T.float16(0) + NT_matmul[v_i0, v_i1, v_i2] = NT_matmul[v_i0, v_i1, v_i2] + layer_norm356[v_i0, v_i1, v_k] * model_decoder_layers_0_self_attn_q_proj_weight5[v_i2, v_k] + + @T.prim_func(private=True) + def NT_matmul3(layer_norm452: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16"), model_decoder_embed_tokens_weight5: T.Buffer((T.int64(51866), T.int64(1280)), "float16"), NT_matmul: T.Buffer((T.int64(1), T.int64(1), T.int64(51866)), "float32")): + T.func_attr({"tir.noalias": T.bool(True)}) + # with T.block("root"): + for i0, i1, i2, k in T.grid(T.int64(1), T.int64(1), T.int64(51866), T.int64(1280)): + with T.block("NT_matmul"): + v_i0, v_i1, v_i2, v_k = T.axis.remap("SSSR", [i0, i1, i2, k]) + T.reads(layer_norm452[v_i0, v_i1, v_k], model_decoder_embed_tokens_weight5[v_i2, v_k]) + T.writes(NT_matmul[v_i0, v_i1, v_i2]) + with T.init(): + NT_matmul[v_i0, v_i1, v_i2] = T.float32(0) + NT_matmul[v_i0, v_i1, v_i2] = NT_matmul[v_i0, v_i1, v_i2] + T.Cast("float32", layer_norm452[v_i0, v_i1, v_k]) * T.Cast("float32", model_decoder_embed_tokens_weight5[v_i2, v_k]) + + @T.prim_func(private=True) + def add(var_reshape708: T.handle, var_reshape709: T.handle, var_T_add: T.handle): + T.func_attr({"tir.noalias": T.bool(True)}) + batch_size = T.int64() + reshape708 = T.match_buffer(var_reshape708, (batch_size, T.int64(1), T.int64(1280)), "float16") + reshape709 = T.match_buffer(var_reshape709, (batch_size, T.int64(1), T.int64(1280)), "float16") + T_add = T.match_buffer(var_T_add, (batch_size, T.int64(1), T.int64(1280)), "float16") + # with T.block("root"): + for ax0, ax1, ax2 in T.grid(batch_size, T.int64(1), T.int64(1280)): + with T.block("T_add"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(reshape708[v_ax0, v_ax1, v_ax2], reshape709[v_ax0, v_ax1, v_ax2]) + T.writes(T_add[v_ax0, v_ax1, v_ax2]) + T_add[v_ax0, v_ax1, v_ax2] = reshape708[v_ax0, v_ax1, v_ax2] + reshape709[v_ax0, v_ax1, v_ax2] + + @T.prim_func(private=True) + def add4(var_add: T.handle, var_lv610: T.handle, var_T_add: T.handle): + T.func_attr({"tir.noalias": T.bool(True)}) + batch_size = T.int64() + add = T.match_buffer(var_add, (batch_size, T.int64(1500), T.int64(1280)), "float16") + lv610 = T.match_buffer(var_lv610, (batch_size, T.int64(1500), T.int64(1280)), "float16") + T_add = T.match_buffer(var_T_add, (batch_size, T.int64(1500), T.int64(1280)), "float16") + # with T.block("root"): + for ax0, ax1, ax2 in T.grid(batch_size, T.int64(1500), T.int64(1280)): + with T.block("T_add"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(add[v_ax0, v_ax1, v_ax2], lv610[v_ax0, v_ax1, v_ax2]) + T.writes(T_add[v_ax0, v_ax1, v_ax2]) + T_add[v_ax0, v_ax1, v_ax2] = add[v_ax0, v_ax1, v_ax2] + lv610[v_ax0, v_ax1, v_ax2] + + @T.prim_func(private=True) + def add5(var_reshape385: T.handle, var_reshape386: T.handle, var_T_add: T.handle): + T.func_attr({"tir.noalias": T.bool(True)}) + seq_len = T.int64() + reshape385 = T.match_buffer(var_reshape385, (T.int64(1), seq_len, T.int64(1280)), "float16") + reshape386 = T.match_buffer(var_reshape386, (T.int64(1), seq_len, T.int64(1280)), "float16") + T_add = T.match_buffer(var_T_add, (T.int64(1), seq_len, T.int64(1280)), "float16") + # with T.block("root"): + for ax0, ax1, ax2 in T.grid(T.int64(1), seq_len, T.int64(1280)): + with T.block("T_add"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(reshape385[v_ax0, v_ax1, v_ax2], reshape386[v_ax0, v_ax1, v_ax2]) + T.writes(T_add[v_ax0, v_ax1, v_ax2]) + T_add[v_ax0, v_ax1, v_ax2] = reshape385[v_ax0, v_ax1, v_ax2] + reshape386[v_ax0, v_ax1, v_ax2] + + @T.prim_func + def apply_bitmask_inplace(var_logits: T.handle, var_seq_ids: T.handle, var_bitmask: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": T.bool(True), "tir.noalias": T.bool(True)}) + batch_size, vocab_size = T.int32(is_size_var=True), T.int32(is_size_var=True) + logits = T.match_buffer(var_logits, (batch_size, vocab_size)) + num_seq = T.int32(is_size_var=True) + seq_ids = T.match_buffer(var_seq_ids, (num_seq,), "int32") + bitmask = T.match_buffer(var_bitmask, (batch_size, (vocab_size + 31) // 32), "int32") + # with T.block("root"): + for fused_s_v_0 in T.thread_binding((num_seq * vocab_size + 1023) // 1024, thread="blockIdx.x"): + for fused_s_v_1 in T.thread_binding(1024, thread="threadIdx.x"): + with T.block("block"): + vs = T.axis.spatial(num_seq, (fused_s_v_0 * 1024 + fused_s_v_1) // vocab_size) + vv = T.axis.spatial(vocab_size, (fused_s_v_0 * 1024 + fused_s_v_1) % vocab_size) + T.where(fused_s_v_0 * 1024 + fused_s_v_1 < num_seq * vocab_size) + T.reads(bitmask[seq_ids[vs], vv // 32], seq_ids[vs], logits[seq_ids[vs], vv]) + T.writes(logits[seq_ids[vs], vv]) + logits[seq_ids[vs], vv] = T.if_then_else(T.bitwise_and(T.shift_right(bitmask[seq_ids[vs], vv // 32], vv % 32), 1) == 1, logits[seq_ids[vs], vv], T.float32(-3.4028234663852886e+38)) + + @T.prim_func + def apply_logit_bias_inplace(var_logits: T.handle, var_pos2seq_id: T.handle, var_token_ids: T.handle, var_logit_bias: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": T.bool(True), "tir.noalias": T.bool(True)}) + batch_size, vocab_size = T.int32(is_size_var=True), T.int32(is_size_var=True) + logits = T.match_buffer(var_logits, (batch_size, vocab_size)) + num_token = T.int32(is_size_var=True) + pos2seq_id = T.match_buffer(var_pos2seq_id, (num_token,), "int32") + token_ids = T.match_buffer(var_token_ids, (num_token,), "int32") + logit_bias = T.match_buffer(var_logit_bias, (num_token,)) + # with T.block("root"): + for p0 in T.thread_binding((num_token + 1023) // 1024, thread="blockIdx.x"): + for p1 in T.thread_binding(1024, thread="threadIdx.x"): + with T.block("block"): + vp = T.axis.spatial(num_token, p0 * 1024 + p1) + T.where(p0 * 1024 + p1 < num_token) + T.reads(logits[pos2seq_id[vp], token_ids[vp]], pos2seq_id[vp], token_ids[vp], logit_bias[vp]) + T.writes(logits[pos2seq_id[vp], token_ids[vp]]) + logits[pos2seq_id[vp], token_ids[vp]] = logits[pos2seq_id[vp], token_ids[vp]] + logit_bias[vp] + + @T.prim_func + def apply_penalty_inplace(var_logits: T.handle, var_seq_ids: T.handle, var_pos2seq_id: T.handle, var_token_ids: T.handle, var_token_cnt: T.handle, var_penalties: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": T.bool(True), "tir.noalias": T.bool(True)}) + batch_size, vocab_size = T.int32(is_size_var=True), T.int32(is_size_var=True) + logits = T.match_buffer(var_logits, (batch_size, vocab_size)) + num_seq = T.int32(is_size_var=True) + seq_ids = T.match_buffer(var_seq_ids, (num_seq,), "int32") + num_token = T.int32(is_size_var=True) + pos2seq_id = T.match_buffer(var_pos2seq_id, (num_token,), "int32") + token_ids = T.match_buffer(var_token_ids, (num_token,), "int32") + token_cnt = T.match_buffer(var_token_cnt, (num_token,), "int32") + penalties = T.match_buffer(var_penalties, (num_seq, 3)) + # with T.block("root"): + for p0 in T.thread_binding((num_token + 1023) // 1024, thread="blockIdx.x"): + for p1 in T.thread_binding(1024, thread="threadIdx.x"): + with T.block("block"): + vp = T.axis.spatial(num_token, p0 * 1024 + p1) + T.where(p0 * 1024 + p1 < num_token) + T.reads(logits[seq_ids[pos2seq_id[vp]], token_ids[vp]], seq_ids[pos2seq_id[vp]], pos2seq_id[vp], token_ids[vp], penalties[pos2seq_id[vp], 0:3], token_cnt[vp]) + T.writes(logits[seq_ids[pos2seq_id[vp]], token_ids[vp]]) + logits[seq_ids[pos2seq_id[vp]], token_ids[vp]] = logits[seq_ids[pos2seq_id[vp]], token_ids[vp]] - (penalties[pos2seq_id[vp], 0] + T.Cast("float32", token_cnt[vp]) * penalties[pos2seq_id[vp], 1]) + logits[seq_ids[pos2seq_id[vp]], token_ids[vp]] = T.if_then_else(logits[seq_ids[pos2seq_id[vp]], token_ids[vp]] > T.float32(0), logits[seq_ids[pos2seq_id[vp]], token_ids[vp]] * penalties[pos2seq_id[vp], 2], logits[seq_ids[pos2seq_id[vp]], token_ids[vp]] / penalties[pos2seq_id[vp], 2]) + + @T.prim_func(private=True) + def argsort_thrust(var_probs: T.handle, var_lv: T.handle, var_topk_gpu_v1: T.handle): + T.func_attr({"tir.noalias": T.bool(True)}) + batch_size, vocab_size = T.int64(), T.int64() + data_buf = T.match_buffer(var_probs, (batch_size, vocab_size), align=8) + workspace_buf = T.match_buffer(var_lv, (T.int64(8) * (batch_size * vocab_size * T.int64(4)) + T.int64(8388608) + batch_size * vocab_size * T.int64(12),), "uint8", align=8) + indices_buf = T.match_buffer(var_topk_gpu_v1, (batch_size, vocab_size), "int32", align=8) + # with T.block("root"): + value_buf = T.alloc_buffer((batch_size, vocab_size), align=8) + with T.block("topk_gpu"): + T.reads() + T.writes() + T.call_packed("tvm.contrib.thrust.sort", T.tvm_stack_make_array(data_buf.data, T.tvm_stack_make_shape(batch_size, vocab_size), 0, 2, T.float32(0), T.int64(0)), T.tvm_stack_make_array(value_buf.data, T.tvm_stack_make_shape(batch_size, vocab_size), 0, 2, T.float32(0), T.int64(0)), T.tvm_stack_make_array(indices_buf.data, T.tvm_stack_make_shape(batch_size, vocab_size), 0, 2, 0, T.int64(0)), 0, T.tvm_stack_make_array(workspace_buf.data, T.tvm_stack_make_shape(T.int64(8) * (batch_size * vocab_size * T.int64(4)) + T.int64(8388608) + batch_size * vocab_size * T.int64(12)), 0, 1, T.uint8(0), T.int64(0))) + + @T.prim_func + def batch_decode_paged_kv(_0: T.int32, Q_handle: T.handle, pages_handle: T.handle, page_table_indptr_handle: T.handle, page_table_values_handle: T.handle, var_length_info: T.handle, k_rope_pos_offset_handle: T.handle, q_rope_position_handle: T.handle, output_handle: T.handle, lse_handle: T.handle, rotary_mode: T.int32, rope_scale: T.float32, rope_theta: T.float32, attn_score_scaling_factor: T.float32): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) + B = T.int32(is_size_var=True) + Q = T.match_buffer(Q_handle, (B, 20, 64), "float16") + max_num_pages = T.int32(is_size_var=True) + pages = T.match_buffer(pages_handle, (max_num_pages, 2, 20, 16, 64), "float16") + page_table_indptr = T.match_buffer(page_table_indptr_handle, (B + 1,), "int32", offset_factor=1) + nnz_pages = T.int32(is_size_var=True) + page_table_values = T.match_buffer(page_table_values_handle, (nnz_pages,), "int32", offset_factor=1) + length_info = T.match_buffer(var_length_info, (B,), "int32", offset_factor=1) + k_rope_pos_offset = T.match_buffer(k_rope_pos_offset_handle, (B,), "int32", offset_factor=1) + q_rope_position = T.match_buffer(q_rope_position_handle, (B,), "int32", offset_factor=1) + output = T.match_buffer(output_handle, (B, 20, 64), "float16") + lse = T.match_buffer(lse_handle, (B, 20)) + # with T.block("root"): + sm_scale: T.float32 = T.float32(0.18033688011112042) + for bx in T.thread_binding(B, thread="blockIdx.x"): + for fused_by_bz in T.thread_binding(20, thread="blockIdx.y"): + for ty in T.thread_binding(1, thread="threadIdx.y"): + for tx in T.thread_binding(16, thread="threadIdx.x"): + for tz in T.thread_binding(32, thread="threadIdx.z"): + with T.block("attn"): + T.reads(page_table_indptr[bx:bx + 2], length_info[bx], q_rope_position[bx], Q[bx, fused_by_bz // 20 + ty + fused_by_bz % 20, tx * 4 - 32:tx * 4 - 32 + 68]) + T.writes(output[bx, fused_by_bz % 20 + fused_by_bz // 20 + ty, tx * 4:tx * 4 + 4], lse[bx, fused_by_bz % 20 + fused_by_bz // 20 + ty]) + Q_local = T.alloc_buffer((4,), "float16", scope="local") + kv_chunk_len = T.alloc_buffer((1,), "int32", scope="local") + K_smem = T.alloc_buffer((64, 64), "float16", scope="shared") + V_smem = T.alloc_buffer((64, 64), "float16", scope="shared") + O_allreduce = T.alloc_buffer((32, 1, 64), scope="shared") + md_allreduce = T.alloc_buffer((32, 1, 2), scope="shared") + S_reduce_local = T.alloc_buffer((1,), scope="local") + t0 = T.alloc_buffer((1,), scope="local") + S_local = T.alloc_buffer((2,), scope="local") + QK_local = T.alloc_buffer((4,), scope="local") + V_local = T.alloc_buffer((4,), "float16", scope="local") + m_prev = T.alloc_buffer((1,), scope="local") + d_prev = T.alloc_buffer((1,), scope="local") + other_m = T.alloc_buffer((1,), scope="local") + other_d = T.alloc_buffer((1,), scope="local") + exp_mprev = T.alloc_buffer((1,), scope="local") + exp_otherm = T.alloc_buffer((1,), scope="local") + other_o = T.alloc_buffer((4,), scope="local") + st_m = T.alloc_buffer((1,), scope="local") + st_d = T.alloc_buffer((1,), scope="local") + O_local = T.alloc_buffer((4,), scope="local") + by: T.int32 = fused_by_bz % 20 + bz: T.int32 = fused_by_bz // 20 + batch_idx: T.int32 = bx + cur_page_indptr_begin: T.int32 = page_table_indptr[batch_idx] + cur_page_indptr_end: T.int32 = page_table_indptr[batch_idx + 1] + kv_chunk_len[0] = T.if_then_else(cur_page_indptr_begin != cur_page_indptr_end, (cur_page_indptr_end - cur_page_indptr_begin - 1) * 16 + length_info[batch_idx], 0) + st_m[0] = T.float32(-50000) + st_d[0] = T.float32(1) + for vec in T.vectorized(4): + O_local[vec] = T.float32(0) + for vec in T.vectorized(4): + Q_local[vec] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", q_rope_position[batch_idx]) * rope_scale / T.pow(rope_theta, T.Cast("float32", (tx * 4 + vec) * 2 % 64) / T.float32(64))) * T.Cast("float32", Q[bx, by + bz + ty, tx * 4 + vec]) + T.sin(T.Cast("float32", q_rope_position[batch_idx]) * rope_scale / T.pow(rope_theta, T.Cast("float32", (tx * 4 + vec) * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(tx * 4 + vec < 32, Q[bx, by + bz + ty, tx * 4 + vec + 32] * T.float16(-1), Q[bx, by + bz + ty, tx * 4 + vec - 32]))), Q[bx, by + bz + ty, tx * 4 + vec]) + for iterator in range((kv_chunk_len[0] + 63) // 64): + tile_start_s: T.int32 = (tz + ty) * 2 + tile_start_g: T.int32 = (iterator * 32 + tz + ty) * 2 + for j in range(2): + with T.block("KV_load"): + T.reads() + T.writes() + row_g: T.int32 = tile_start_g + j + if row_g < kv_chunk_len[0]: + seq_offset: T.int32 = row_g + page_no: T.int32 = page_table_values[cur_page_indptr_begin + seq_offset // 16] + page_offset: T.int32 = seq_offset % 16 + for vec in T.vectorized(4): + K_smem[tile_start_s + j, tx * 4 + vec] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", k_rope_pos_offset[batch_idx] + row_g) * rope_scale / T.pow(rope_theta, T.Cast("float32", (tx * 4 + vec) * 2 % 64) / T.float32(64))) * T.Cast("float32", pages[page_no, 0, by, page_offset, tx * 4 + vec]) + T.sin(T.Cast("float32", k_rope_pos_offset[batch_idx] + row_g) * rope_scale / T.pow(rope_theta, T.Cast("float32", (tx * 4 + vec) * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(tx * 4 + vec < 32, pages[page_no, 0, by, page_offset, tx * 4 + vec + 32] * T.float16(-1), pages[page_no, 0, by, page_offset, tx * 4 + vec - 32]))), pages[page_no, 0, by, page_offset, tx * 4 + vec]) + V_smem[tile_start_s + j, tx * 4 + vec] = pages[page_no, 1, by, page_offset, tx * 4 + vec] + else: + for vec in T.vectorized(4): + K_smem[tile_start_s + j, tx * 4 + vec] = T.float16(0) + V_smem[tile_start_s + j, tx * 4 + vec] = T.float16(0) + T.tvm_storage_sync("shared") + m_prev[0] = st_m[0] + for j in range(2): + for vec in T.vectorized(4): + QK_local[vec] = T.Cast("float32", Q_local[vec]) * T.Cast("float32", K_smem[tz * 2 + j, tx * 4 + vec]) * attn_score_scaling_factor * sm_scale + S_reduce_local[0] = T.float32(0) + for vec in T.unroll(4): + S_reduce_local[0] = S_reduce_local[0] + QK_local[vec] + with T.block("block_cross_thread"): + T.reads(S_reduce_local[0]) + T.writes(t0[0]) + T.attr(T.comm_reducer(lambda x0, y0: x0 + y0, [T.float32(0)]), "reduce_scope", T.reinterpret("handle", T.uint64(0))) + T.tvm_thread_allreduce(T.uint32(1), S_reduce_local[0], T.bool(True), t0[0], tx) + S_local[j] = T.float32(-50000) + if (iterator * 32 + tz) * 2 + j < kv_chunk_len[0]: + S_local[j] = t0[0] + st_m[0] = T.max(st_m[0], S_local[j]) + o_scale: T.float32 = T.exp2(m_prev[0] - st_m[0]) + st_d[0] = st_d[0] * o_scale + for j in range(2): + S_local[j] = T.exp2(S_local[j] - st_m[0]) + st_d[0] = st_d[0] + S_local[j] + for j in T.vectorized(4): + O_local[j] = O_local[j] * o_scale + for j in range(2): + for vec in T.vectorized(4): + V_local[vec] = V_smem[tz * 2 + j, tx * 4 + vec] + for vec in T.vectorized(4): + O_local[vec] = O_local[vec] + T.Cast("float32", V_local[vec]) * S_local[j] + for vec in T.vectorized(4): + O_allreduce[tz, ty, tx * 4 + vec] = O_local[vec] + md_allreduce[tz, ty, 0] = st_m[0] + md_allreduce[tz, ty, 1] = st_d[0] + T.tvm_storage_sync("shared") + st_m[0] = T.float32(-50000) + st_d[0] = T.float32(1) + for vec in T.vectorized(4): + O_local[vec] = T.float32(0) + for j in range(32): + m_prev[0] = st_m[0] + d_prev[0] = st_d[0] + other_m[0] = md_allreduce[j, ty, 0] + other_d[0] = md_allreduce[j, ty, 1] + for vec in T.vectorized(4): + other_o[vec] = O_allreduce[j, ty, tx * 4 + vec] + st_m[0] = T.max(st_m[0], other_m[0]) + st_d[0] = d_prev[0] * T.exp2(m_prev[0] - st_m[0]) + other_d[0] * T.exp2(other_m[0] - st_m[0]) + exp_mprev[0] = T.exp2(m_prev[0] - st_m[0]) + exp_otherm[0] = T.exp2(other_m[0] - st_m[0]) + for vec in T.vectorized(4): + O_local[vec] = O_local[vec] * exp_mprev[0] + other_o[vec] * exp_otherm[0] + for vec in T.vectorized(4): + O_local[vec] = O_local[vec] / st_d[0] + for vec in T.vectorized(4): + output[batch_idx, by + bz + ty, tx * 4 + vec] = T.Cast("float16", O_local[vec]) + lse[batch_idx, by + bz + ty] = st_m[0] + T.log2(st_d[0]) + + @T.prim_func + def batch_decode_paged_kv_sliding_window(_0: T.int32, Q_handle: T.handle, pages_handle: T.handle, page_table_indptr_handle: T.handle, page_table_values_handle: T.handle, var_length_info: T.handle, k_rope_pos_offset_handle: T.handle, q_rope_position_handle: T.handle, output_handle: T.handle, lse_handle: T.handle, rotary_mode: T.int32, rope_scale: T.float32, rope_theta: T.float32, attn_score_scaling_factor: T.float32): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) + B = T.int32(is_size_var=True) + Q = T.match_buffer(Q_handle, (B, 20, 64), "float16") + max_num_pages = T.int32(is_size_var=True) + pages = T.match_buffer(pages_handle, (max_num_pages, 2, 20, 16, 64), "float16") + page_table_indptr = T.match_buffer(page_table_indptr_handle, (B + 1,), "int32", offset_factor=1) + nnz_pages = T.int32(is_size_var=True) + page_table_values = T.match_buffer(page_table_values_handle, (nnz_pages,), "int32", offset_factor=1) + length_info = T.match_buffer(var_length_info, (3, B), "int32", offset_factor=1) + k_rope_pos_offset = T.match_buffer(k_rope_pos_offset_handle, (B,), "int32", offset_factor=1) + q_rope_position = T.match_buffer(q_rope_position_handle, (B,), "int32", offset_factor=1) + output = T.match_buffer(output_handle, (B, 20, 64), "float16") + lse = T.match_buffer(lse_handle, (B, 20)) + # with T.block("root"): + sm_scale: T.float32 = T.float32(0.18033688011112042) + for bx in T.thread_binding(B, thread="blockIdx.x"): + for fused_by_bz in T.thread_binding(20, thread="blockIdx.y"): + for ty in T.thread_binding(1, thread="threadIdx.y"): + for tx in T.thread_binding(16, thread="threadIdx.x"): + for tz in T.thread_binding(32, thread="threadIdx.z"): + with T.block("attn"): + T.reads(page_table_indptr[bx:bx + 2], length_info[0:3, bx], q_rope_position[bx], Q[bx, fused_by_bz // 20 + ty + fused_by_bz % 20, tx * 4 - 32:tx * 4 - 32 + 68]) + T.writes(output[bx, fused_by_bz % 20 + fused_by_bz // 20 + ty, tx * 4:tx * 4 + 4], lse[bx, fused_by_bz % 20 + fused_by_bz // 20 + ty]) + Q_local = T.alloc_buffer((4,), "float16", scope="local") + kv_chunk_len = T.alloc_buffer((1,), "int32", scope="local") + K_smem = T.alloc_buffer((64, 64), "float16", scope="shared") + V_smem = T.alloc_buffer((64, 64), "float16", scope="shared") + O_allreduce = T.alloc_buffer((32, 1, 64), scope="shared") + md_allreduce = T.alloc_buffer((32, 1, 2), scope="shared") + S_reduce_local = T.alloc_buffer((1,), scope="local") + t0 = T.alloc_buffer((1,), scope="local") + S_local = T.alloc_buffer((2,), scope="local") + QK_local = T.alloc_buffer((4,), scope="local") + V_local = T.alloc_buffer((4,), "float16", scope="local") + m_prev = T.alloc_buffer((1,), scope="local") + d_prev = T.alloc_buffer((1,), scope="local") + other_m = T.alloc_buffer((1,), scope="local") + other_d = T.alloc_buffer((1,), scope="local") + exp_mprev = T.alloc_buffer((1,), scope="local") + exp_otherm = T.alloc_buffer((1,), scope="local") + other_o = T.alloc_buffer((4,), scope="local") + st_m = T.alloc_buffer((1,), scope="local") + st_d = T.alloc_buffer((1,), scope="local") + O_local = T.alloc_buffer((4,), scope="local") + by: T.int32 = fused_by_bz % 20 + bz: T.int32 = fused_by_bz // 20 + batch_idx: T.int32 = bx + cur_page_indptr_begin: T.int32 = page_table_indptr[batch_idx] + cur_page_indptr_end: T.int32 = page_table_indptr[batch_idx + 1] + kv_chunk_len[0] = T.if_then_else(cur_page_indptr_begin != cur_page_indptr_end, (cur_page_indptr_end - cur_page_indptr_begin - 1) * 16 + length_info[0, batch_idx] - length_info[1, batch_idx] + length_info[2, batch_idx], 0) + st_m[0] = T.float32(-50000) + st_d[0] = T.float32(1) + for vec in T.vectorized(4): + O_local[vec] = T.float32(0) + for vec in T.vectorized(4): + Q_local[vec] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", q_rope_position[batch_idx]) * rope_scale / T.pow(rope_theta, T.Cast("float32", (tx * 4 + vec) * 2 % 64) / T.float32(64))) * T.Cast("float32", Q[bx, by + bz + ty, tx * 4 + vec]) + T.sin(T.Cast("float32", q_rope_position[batch_idx]) * rope_scale / T.pow(rope_theta, T.Cast("float32", (tx * 4 + vec) * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(tx * 4 + vec < 32, Q[bx, by + bz + ty, tx * 4 + vec + 32] * T.float16(-1), Q[bx, by + bz + ty, tx * 4 + vec - 32]))), Q[bx, by + bz + ty, tx * 4 + vec]) + for iterator in range((kv_chunk_len[0] + 63) // 64): + tile_start_s: T.int32 = (tz + ty) * 2 + tile_start_g: T.int32 = (iterator * 32 + tz + ty) * 2 + for j in range(2): + with T.block("KV_load"): + T.reads() + T.writes() + row_g: T.int32 = tile_start_g + j + if row_g < kv_chunk_len[0]: + seq_offset: T.int32 = T.if_then_else(row_g < length_info[2, batch_idx], row_g, row_g - length_info[2, batch_idx] + length_info[1, batch_idx]) + page_no: T.int32 = page_table_values[cur_page_indptr_begin + seq_offset // 16] + page_offset: T.int32 = seq_offset % 16 + for vec in T.vectorized(4): + K_smem[tile_start_s + j, tx * 4 + vec] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", k_rope_pos_offset[batch_idx] + row_g) * rope_scale / T.pow(rope_theta, T.Cast("float32", (tx * 4 + vec) * 2 % 64) / T.float32(64))) * T.Cast("float32", pages[page_no, 0, by, page_offset, tx * 4 + vec]) + T.sin(T.Cast("float32", k_rope_pos_offset[batch_idx] + row_g) * rope_scale / T.pow(rope_theta, T.Cast("float32", (tx * 4 + vec) * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(tx * 4 + vec < 32, pages[page_no, 0, by, page_offset, tx * 4 + vec + 32] * T.float16(-1), pages[page_no, 0, by, page_offset, tx * 4 + vec - 32]))), pages[page_no, 0, by, page_offset, tx * 4 + vec]) + V_smem[tile_start_s + j, tx * 4 + vec] = pages[page_no, 1, by, page_offset, tx * 4 + vec] + else: + for vec in T.vectorized(4): + K_smem[tile_start_s + j, tx * 4 + vec] = T.float16(0) + V_smem[tile_start_s + j, tx * 4 + vec] = T.float16(0) + T.tvm_storage_sync("shared") + m_prev[0] = st_m[0] + for j in range(2): + for vec in T.vectorized(4): + QK_local[vec] = T.Cast("float32", Q_local[vec]) * T.Cast("float32", K_smem[tz * 2 + j, tx * 4 + vec]) * attn_score_scaling_factor * sm_scale + S_reduce_local[0] = T.float32(0) + for vec in T.unroll(4): + S_reduce_local[0] = S_reduce_local[0] + QK_local[vec] + with T.block("block_cross_thread"): + T.reads(S_reduce_local[0]) + T.writes(t0[0]) + T.attr(T.comm_reducer(lambda x0, y0: x0 + y0, [T.float32(0)]), "reduce_scope", T.reinterpret("handle", T.uint64(0))) + T.tvm_thread_allreduce(T.uint32(1), S_reduce_local[0], T.bool(True), t0[0], tx) + S_local[j] = T.float32(-50000) + if (iterator * 32 + tz) * 2 + j < kv_chunk_len[0]: + S_local[j] = t0[0] + st_m[0] = T.max(st_m[0], S_local[j]) + o_scale: T.float32 = T.exp2(m_prev[0] - st_m[0]) + st_d[0] = st_d[0] * o_scale + for j in range(2): + S_local[j] = T.exp2(S_local[j] - st_m[0]) + st_d[0] = st_d[0] + S_local[j] + for j in T.vectorized(4): + O_local[j] = O_local[j] * o_scale + for j in range(2): + for vec in T.vectorized(4): + V_local[vec] = V_smem[tz * 2 + j, tx * 4 + vec] + for vec in T.vectorized(4): + O_local[vec] = O_local[vec] + T.Cast("float32", V_local[vec]) * S_local[j] + for vec in T.vectorized(4): + O_allreduce[tz, ty, tx * 4 + vec] = O_local[vec] + md_allreduce[tz, ty, 0] = st_m[0] + md_allreduce[tz, ty, 1] = st_d[0] + T.tvm_storage_sync("shared") + st_m[0] = T.float32(-50000) + st_d[0] = T.float32(1) + for vec in T.vectorized(4): + O_local[vec] = T.float32(0) + for j in range(32): + m_prev[0] = st_m[0] + d_prev[0] = st_d[0] + other_m[0] = md_allreduce[j, ty, 0] + other_d[0] = md_allreduce[j, ty, 1] + for vec in T.vectorized(4): + other_o[vec] = O_allreduce[j, ty, tx * 4 + vec] + st_m[0] = T.max(st_m[0], other_m[0]) + st_d[0] = d_prev[0] * T.exp2(m_prev[0] - st_m[0]) + other_d[0] * T.exp2(other_m[0] - st_m[0]) + exp_mprev[0] = T.exp2(m_prev[0] - st_m[0]) + exp_otherm[0] = T.exp2(other_m[0] - st_m[0]) + for vec in T.vectorized(4): + O_local[vec] = O_local[vec] * exp_mprev[0] + other_o[vec] * exp_otherm[0] + for vec in T.vectorized(4): + O_local[vec] = O_local[vec] / st_d[0] + for vec in T.vectorized(4): + output[batch_idx, by + bz + ty, tx * 4 + vec] = T.Cast("float16", O_local[vec]) + lse[batch_idx, by + bz + ty] = st_m[0] + T.log2(st_d[0]) + + @T.prim_func + def batch_prefill_paged_kv(_0: T.int32, var_q: T.handle, var_q_indptr: T.handle, var_pages: T.handle, var_page_indptr: T.handle, var_page_values: T.handle, var_length_info: T.handle, var_k_rope_pos_offset: T.handle, var_q_rope_position: T.handle, var_output: T.handle, var_lse: T.handle, causal: T.int32, rotary_mode: T.int32, rope_scale: T.float32, rope_theta: T.float32, attn_score_scaling_factor: T.float32): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) + total_len = T.int32(is_size_var=True) + q = T.match_buffer(var_q, (total_len, 20, 64), "float16") + batch_size = T.int32(is_size_var=True) + q_indptr = T.match_buffer(var_q_indptr, (batch_size + 1,), "int32", offset_factor=1) + max_num_pages = T.int32(is_size_var=True) + pages = T.match_buffer(var_pages, (max_num_pages, 2, 20, 16, 64), "float16") + page_indptr = T.match_buffer(var_page_indptr, (batch_size + 1,), "int32", offset_factor=1) + nnz_pages = T.int32(is_size_var=True) + page_values = T.match_buffer(var_page_values, (nnz_pages,), "int32", offset_factor=1) + length_info = T.match_buffer(var_length_info, (batch_size,), "int32", offset_factor=1) + k_rope_pos_offset = T.match_buffer(var_k_rope_pos_offset, (batch_size,), "int32", offset_factor=1) + q_rope_position = T.match_buffer(var_q_rope_position, (total_len,), "int32", offset_factor=1) + output = T.match_buffer(var_output, (total_len, 20, 64), "float16") + lse = T.match_buffer(var_lse, (total_len, 20)) + # with T.block("root"): + for lbx in T.thread_binding(16, thread="blockIdx.x"): + for lby in T.thread_binding(20, thread="blockIdx.y"): + for lty in T.thread_binding(4, thread="threadIdx.y"): + for ltx in T.thread_binding(32, thread="threadIdx.x"): + with T.block("attn"): + bx, by, ty, tx = T.axis.remap("SSSS", [lbx, lby, lty, ltx]) + T.reads() + T.writes() + tile_id = T.alloc_buffer((1,), "int32", scope="local") + batch_idx = T.alloc_buffer((1,), "int32", scope="local") + batch_tiles = T.alloc_buffer((1,), "int32", scope="local") + batch_rows = T.alloc_buffer((1,), "int32", scope="local") + iterator = T.alloc_buffer((1,), "int32", scope="local") + kv_chunk_len = T.alloc_buffer((1,), "int32", scope="local") + Q_smem = T.alloc_buffer((32, 64), "float16", scope="shared") + K_smem = T.alloc_buffer((16, 64), "float16", scope="shared") + V_smem = T.alloc_buffer((16, 64), "float16", scope="shared") + S_smem = T.alloc_buffer((32, 16), scope="shared") + S_local = T.alloc_buffer((32, 16), scope="local") + O_local = T.alloc_buffer((32, 64), scope="local") + m_smem = T.alloc_buffer((32,), scope="shared") + m_prev_smem = T.alloc_buffer((32,), scope="shared") + d_smem = T.alloc_buffer((32,), scope="shared") + m_new = T.alloc_buffer((1,), scope="local") + m_prev = T.alloc_buffer((1,), scope="local") + d_new = T.alloc_buffer((1,), scope="local") + tile_id[0] = bx + batch_idx[0] = 0 + batch_rows[0] = q_indptr[1] - q_indptr[0] + batch_tiles[0] = (batch_rows[0] + 32 - 1) // 32 + while T.tvm_thread_invariant(batch_idx[0] < batch_size): + while tile_id[0] >= batch_tiles[0] and batch_idx[0] < batch_size: + tile_id[0] = tile_id[0] - batch_tiles[0] + batch_idx[0] = batch_idx[0] + 1 + if batch_idx[0] < batch_size: + b_idx: T.int32 = batch_idx[0] + batch_rows[0] = q_indptr[b_idx + 1] - q_indptr[b_idx] + batch_tiles[0] = (batch_rows[0] + 32 - 1) // 32 + if T.tvm_thread_invariant(batch_idx[0] < batch_size): + b_idx: T.int32 = batch_idx[0] + LH_start: T.int32 = tile_id[0] * 32 + q_indptr_val: T.int32 = q_indptr[b_idx] + cur_page_indptr_begin: T.int32 = page_indptr[b_idx] + cur_page_indptr_end: T.int32 = page_indptr[b_idx + 1] + kv_chunk_len[0] = T.if_then_else(cur_page_indptr_begin != cur_page_indptr_end, (cur_page_indptr_end - cur_page_indptr_begin - 1) * 16 + length_info[b_idx], 0) + T.tvm_storage_sync("shared") + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + m_smem[row] = T.float32(-50000) + d_smem[row] = T.float32(1) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(4, 4): + with T.block("O_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + T.reads() + T.writes(O_local[i, j]) + O_local[i, j] = T.float32(0) + T.tvm_storage_sync("shared") + for li_lj_fused_0 in range(4): + for li_lj_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for li_lj_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for li_lj_fused_3 in T.vectorized(4): + with T.block("Q_load"): + i = T.axis.spatial(32, (li_lj_fused_0 * 512 + li_lj_fused_1 * 128 + li_lj_fused_2 * 4 + li_lj_fused_3) // 64) + j = T.axis.spatial(64, (li_lj_fused_0 * 512 + li_lj_fused_1 * 128 + li_lj_fused_2 * 4 + li_lj_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = q_indptr_val + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + Q_smem[i, j] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", q[cur_L, cur_H_qo, j]) + T.sin(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(j < 32, q[cur_L, cur_H_qo, j + 32] * T.float16(-1), q[cur_L, cur_H_qo, j - 32]))), q[cur_L, cur_H_qo, j]) + else: + Q_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + for iterator_1 in range((kv_chunk_len[0] + 15) // 16): + L_kv_start: T.int32 = iterator_1 * 16 + for lz_ly_fused_0 in range(2): + for lz_ly_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for lz_ly_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for lz_ly_fused_3 in T.vectorized(4): + with T.block("K_load"): + i = T.axis.spatial(16, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) // 64) + j = T.axis.spatial(64, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = L_kv_start + i + if cur_L < kv_chunk_len[0]: + seq_offset: T.int32 = cur_L + page_no: T.int32 = page_values[cur_page_indptr_begin + seq_offset // 16] + page_offset: T.int32 = seq_offset % 16 + K_smem[i, j] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", k_rope_pos_offset[b_idx] + cur_L) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", pages[page_no, 0, by, page_offset, j]) + T.sin(T.Cast("float32", k_rope_pos_offset[b_idx] + cur_L) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(j < 32, pages[page_no, 0, by, page_offset, j + 32] * T.float16(-1), pages[page_no, 0, by, page_offset, j - 32]))), pages[page_no, 0, by, page_offset, j]) + else: + K_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + for lz_ly_fused_0 in range(2): + for lz_ly_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for lz_ly_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for lz_ly_fused_3 in T.vectorized(4): + with T.block("V_load"): + i = T.axis.spatial(16, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) // 64) + j = T.axis.spatial(64, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = L_kv_start + i + if cur_L < kv_chunk_len[0]: + seq_offset: T.int32 = cur_L + page_no: T.int32 = page_values[cur_page_indptr_begin + seq_offset // 16] + page_offset: T.int32 = seq_offset % 16 + V_smem[i, j] = pages[page_no, 1, by, page_offset, j] + else: + V_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + with T.block(""): + T.reads(Q_smem[0:32, 0:64], K_smem[0:16, 0:64]) + T.writes(S_local[0:32, 0:16]) + for li_0_lj_0_fused_0_init in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1_init in T.thread_binding(32, thread="threadIdx.x"): + for li_1_init, lj_1_init in T.grid(2, 2): + with T.block("S_gemm_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) // 8 * 2 + li_1_init) + j = T.axis.spatial(16, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) % 8 * 2 + lj_1_init) + T.reads() + T.writes(S_local[i, j]) + S_local[i, j] = T.float32(0) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for lk_0, li_1, lj_1, lk_1 in T.grid(8, 2, 2, 8): + with T.block("S_gemm_update"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 8 * 2 + li_1) + j = T.axis.spatial(16, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 8 * 2 + lj_1) + k = T.axis.reduce(64, lk_0 * 8 + lk_1) + T.reads(S_local[i, j], Q_smem[i, k], K_smem[j, k]) + T.writes(S_local[i, j]) + S_local[i, j] = S_local[i, j] + T.Cast("float32", Q_smem[i, k]) * T.Cast("float32", K_smem[j, k]) * attn_score_scaling_factor * T.float32(0.18033688011112042) + T.tvm_storage_sync("shared") + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(2, 2): + with T.block("S_store"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 8 * 2 + li_1) + j = T.axis.spatial(16, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 8 * 2 + lj_1) + T.reads(S_local[i, j]) + T.writes(S_smem[i, j]) + S_smem[i, j] = S_local[i, j] + T.tvm_storage_sync("shared") + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + with T.block("update1"): + T.reads(m_smem[row], kv_chunk_len[0], q_indptr[b_idx:b_idx + 2], m_new[i], S_smem[row, 0:16], d_smem[row], m_prev[i]) + T.writes(m_prev[i], m_new[i], d_new[i]) + m_prev[i] = m_smem[row] + m_new[i] = m_smem[row] + row_: T.int32 = LH_start + row + for j in range(16): + if T.if_then_else(causal > 0, L_kv_start + j < kv_chunk_len[0] - (q_indptr[b_idx + 1] - q_indptr[b_idx]) + row_ + 1, L_kv_start + j < kv_chunk_len[0]): + m_new[i] = T.max(m_new[i], S_smem[row, j]) + d_new[i] = d_smem[row] * T.exp2(m_prev[i] - m_new[i]) + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + with T.block("update"): + T.reads(kv_chunk_len[0], q_indptr[b_idx:b_idx + 2], S_smem[row, 0:16], m_new[i]) + T.writes(S_smem[row, 0:16]) + for j in range(16): + if row < 32: + row_: T.int32 = LH_start + row + if T.if_then_else(causal > 0, L_kv_start + j < kv_chunk_len[0] - (q_indptr[b_idx + 1] - q_indptr[b_idx]) + row_ + 1, L_kv_start + j < kv_chunk_len[0]): + S_smem[row, j] = T.exp2(S_smem[row, j] - m_new[i]) + else: + S_smem[row, j] = T.exp2(T.float32(-50000) - m_new[i]) + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + with T.block("update"): + T.reads(d_new[i], S_smem[row, 0:16], m_new[i], m_prev[i]) + T.writes(d_new[i], m_smem[row], d_smem[row], m_prev_smem[row]) + for j in range(16): + d_new[i] = d_new[i] + S_smem[row, j] + m_smem[row] = m_new[i] + d_smem[row] = d_new[i] + m_prev_smem[row] = m_prev[i] + T.tvm_storage_sync("shared") + with T.block(""): + T.reads(m_prev_smem[0:32], m_smem[0:32], S_smem[0:32, 0:16], V_smem[0:16, 0:64]) + T.writes(O_local[0:32, 0:64]) + for li_0_lj_0_fused_0_init in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1_init in T.thread_binding(32, thread="threadIdx.x"): + for li_1_init, lj_1_init in T.grid(4, 4): + with T.block("O_gemm_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) // 16 * 4 + li_1_init) + j = T.axis.spatial(64, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) % 16 * 4 + lj_1_init) + T.reads() + T.writes(O_local[i, j]) + O_local[i, j] = O_local[i, j] * T.exp2(m_prev_smem[i] - m_smem[i]) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for lk_0, lk_1, li_1, lj_1 in T.grid(2, 8, 4, 4): + with T.block("O_gemm_update"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + k = T.axis.reduce(16, lk_0 * 8 + lk_1) + T.reads(O_local[i, j], m_prev_smem[i], m_smem[i], S_smem[i, k], V_smem[k, j]) + T.writes(O_local[i, j]) + O_local[i, j] = O_local[i, j] + S_smem[i, k] * T.Cast("float32", V_smem[k, j]) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(4, 4): + with T.block("O_store"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + T.reads(q_indptr[b_idx:b_idx + 2], O_local[i, j], d_smem[i]) + T.writes(output[q_indptr[b_idx] + (LH_start + i), by, j]) + cur_L: T.int32 = q_indptr[b_idx] + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + output[cur_L, cur_H_qo, j] = T.Cast("float16", O_local[i, j] / d_smem[i]) + for li_0 in range(1): + for li_1 in T.thread_binding(4, thread="threadIdx.y"): + for li_2 in T.thread_binding(32, thread="threadIdx.x"): + with T.block("lse_store"): + i = T.axis.spatial(32, li_0 * 128 + li_1 * 32 + li_2) + T.where((li_0 * 4 + li_1) * 32 + li_2 < 32) + T.reads(q_indptr[b_idx:b_idx + 2], m_smem[i], d_smem[i]) + T.writes(lse[q_indptr[b_idx] + (LH_start + i), by]) + cur_L: T.int32 = q_indptr[b_idx] + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + lse[cur_L, cur_H_qo] = m_smem[i] + T.log2(d_smem[i]) + tile_id[0] = tile_id[0] + 16 + + @T.prim_func + def batch_prefill_paged_kv_sliding_window(_0: T.int32, var_q: T.handle, var_q_indptr: T.handle, var_pages: T.handle, var_page_indptr: T.handle, var_page_values: T.handle, var_length_info: T.handle, var_k_rope_pos_offset: T.handle, var_q_rope_position: T.handle, var_output: T.handle, var_lse: T.handle, causal: T.int32, rotary_mode: T.int32, rope_scale: T.float32, rope_theta: T.float32, attn_score_scaling_factor: T.float32): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) + total_len = T.int32(is_size_var=True) + q = T.match_buffer(var_q, (total_len, 20, 64), "float16") + batch_size = T.int32(is_size_var=True) + q_indptr = T.match_buffer(var_q_indptr, (batch_size + 1,), "int32", offset_factor=1) + max_num_pages = T.int32(is_size_var=True) + pages = T.match_buffer(var_pages, (max_num_pages, 2, 20, 16, 64), "float16") + page_indptr = T.match_buffer(var_page_indptr, (batch_size + 1,), "int32", offset_factor=1) + nnz_pages = T.int32(is_size_var=True) + page_values = T.match_buffer(var_page_values, (nnz_pages,), "int32", offset_factor=1) + length_info = T.match_buffer(var_length_info, (3, batch_size), "int32", offset_factor=1) + k_rope_pos_offset = T.match_buffer(var_k_rope_pos_offset, (batch_size,), "int32", offset_factor=1) + q_rope_position = T.match_buffer(var_q_rope_position, (total_len,), "int32", offset_factor=1) + output = T.match_buffer(var_output, (total_len, 20, 64), "float16") + lse = T.match_buffer(var_lse, (total_len, 20)) + # with T.block("root"): + for lbx in T.thread_binding(16, thread="blockIdx.x"): + for lby in T.thread_binding(20, thread="blockIdx.y"): + for lty in T.thread_binding(4, thread="threadIdx.y"): + for ltx in T.thread_binding(32, thread="threadIdx.x"): + with T.block("attn"): + bx, by, ty, tx = T.axis.remap("SSSS", [lbx, lby, lty, ltx]) + T.reads() + T.writes() + tile_id = T.alloc_buffer((1,), "int32", scope="local") + batch_idx = T.alloc_buffer((1,), "int32", scope="local") + batch_tiles = T.alloc_buffer((1,), "int32", scope="local") + batch_rows = T.alloc_buffer((1,), "int32", scope="local") + iterator = T.alloc_buffer((1,), "int32", scope="local") + kv_chunk_len = T.alloc_buffer((1,), "int32", scope="local") + Q_smem = T.alloc_buffer((32, 64), "float16", scope="shared") + K_smem = T.alloc_buffer((16, 64), "float16", scope="shared") + V_smem = T.alloc_buffer((16, 64), "float16", scope="shared") + S_smem = T.alloc_buffer((32, 16), scope="shared") + S_local = T.alloc_buffer((32, 16), scope="local") + O_local = T.alloc_buffer((32, 64), scope="local") + m_smem = T.alloc_buffer((32,), scope="shared") + m_prev_smem = T.alloc_buffer((32,), scope="shared") + d_smem = T.alloc_buffer((32,), scope="shared") + m_new = T.alloc_buffer((1,), scope="local") + m_prev = T.alloc_buffer((1,), scope="local") + d_new = T.alloc_buffer((1,), scope="local") + tile_id[0] = bx + batch_idx[0] = 0 + batch_rows[0] = q_indptr[1] - q_indptr[0] + batch_tiles[0] = (batch_rows[0] + 32 - 1) // 32 + while T.tvm_thread_invariant(batch_idx[0] < batch_size): + while tile_id[0] >= batch_tiles[0] and batch_idx[0] < batch_size: + tile_id[0] = tile_id[0] - batch_tiles[0] + batch_idx[0] = batch_idx[0] + 1 + if batch_idx[0] < batch_size: + b_idx: T.int32 = batch_idx[0] + batch_rows[0] = q_indptr[b_idx + 1] - q_indptr[b_idx] + batch_tiles[0] = (batch_rows[0] + 32 - 1) // 32 + if T.tvm_thread_invariant(batch_idx[0] < batch_size): + b_idx: T.int32 = batch_idx[0] + LH_start: T.int32 = tile_id[0] * 32 + q_indptr_val: T.int32 = q_indptr[b_idx] + cur_page_indptr_begin: T.int32 = page_indptr[b_idx] + cur_page_indptr_end: T.int32 = page_indptr[b_idx + 1] + kv_chunk_len[0] = T.if_then_else(cur_page_indptr_begin != cur_page_indptr_end, (cur_page_indptr_end - cur_page_indptr_begin - 1) * 16 + length_info[0, b_idx] - length_info[1, b_idx] + length_info[2, b_idx], 0) + T.tvm_storage_sync("shared") + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + m_smem[row] = T.float32(-50000) + d_smem[row] = T.float32(1) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(4, 4): + with T.block("O_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + T.reads() + T.writes(O_local[i, j]) + O_local[i, j] = T.float32(0) + T.tvm_storage_sync("shared") + for li_lj_fused_0 in range(4): + for li_lj_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for li_lj_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for li_lj_fused_3 in T.vectorized(4): + with T.block("Q_load"): + i = T.axis.spatial(32, (li_lj_fused_0 * 512 + li_lj_fused_1 * 128 + li_lj_fused_2 * 4 + li_lj_fused_3) // 64) + j = T.axis.spatial(64, (li_lj_fused_0 * 512 + li_lj_fused_1 * 128 + li_lj_fused_2 * 4 + li_lj_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = q_indptr_val + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + Q_smem[i, j] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", q[cur_L, cur_H_qo, j]) + T.sin(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(j < 32, q[cur_L, cur_H_qo, j + 32] * T.float16(-1), q[cur_L, cur_H_qo, j - 32]))), q[cur_L, cur_H_qo, j]) + else: + Q_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + for iterator_1 in range((kv_chunk_len[0] + 15) // 16): + L_kv_start: T.int32 = iterator_1 * 16 + for lz_ly_fused_0 in range(2): + for lz_ly_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for lz_ly_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for lz_ly_fused_3 in T.vectorized(4): + with T.block("K_load"): + i = T.axis.spatial(16, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) // 64) + j = T.axis.spatial(64, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = L_kv_start + i + if cur_L < kv_chunk_len[0]: + seq_offset: T.int32 = T.if_then_else(cur_L < length_info[2, b_idx], cur_L, cur_L - length_info[2, b_idx] + length_info[1, b_idx]) + page_no: T.int32 = page_values[cur_page_indptr_begin + seq_offset // 16] + page_offset: T.int32 = seq_offset % 16 + K_smem[i, j] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", k_rope_pos_offset[b_idx] + cur_L) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", pages[page_no, 0, by, page_offset, j]) + T.sin(T.Cast("float32", k_rope_pos_offset[b_idx] + cur_L) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(j < 32, pages[page_no, 0, by, page_offset, j + 32] * T.float16(-1), pages[page_no, 0, by, page_offset, j - 32]))), pages[page_no, 0, by, page_offset, j]) + else: + K_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + for lz_ly_fused_0 in range(2): + for lz_ly_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for lz_ly_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for lz_ly_fused_3 in T.vectorized(4): + with T.block("V_load"): + i = T.axis.spatial(16, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) // 64) + j = T.axis.spatial(64, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = L_kv_start + i + if cur_L < kv_chunk_len[0]: + seq_offset: T.int32 = T.if_then_else(cur_L < length_info[2, b_idx], cur_L, cur_L - length_info[2, b_idx] + length_info[1, b_idx]) + page_no: T.int32 = page_values[cur_page_indptr_begin + seq_offset // 16] + page_offset: T.int32 = seq_offset % 16 + V_smem[i, j] = pages[page_no, 1, by, page_offset, j] + else: + V_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + with T.block(""): + T.reads(Q_smem[0:32, 0:64], K_smem[0:16, 0:64]) + T.writes(S_local[0:32, 0:16]) + for li_0_lj_0_fused_0_init in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1_init in T.thread_binding(32, thread="threadIdx.x"): + for li_1_init, lj_1_init in T.grid(2, 2): + with T.block("S_gemm_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) // 8 * 2 + li_1_init) + j = T.axis.spatial(16, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) % 8 * 2 + lj_1_init) + T.reads() + T.writes(S_local[i, j]) + S_local[i, j] = T.float32(0) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for lk_0, li_1, lj_1, lk_1 in T.grid(8, 2, 2, 8): + with T.block("S_gemm_update"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 8 * 2 + li_1) + j = T.axis.spatial(16, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 8 * 2 + lj_1) + k = T.axis.reduce(64, lk_0 * 8 + lk_1) + T.reads(S_local[i, j], Q_smem[i, k], K_smem[j, k]) + T.writes(S_local[i, j]) + S_local[i, j] = S_local[i, j] + T.Cast("float32", Q_smem[i, k]) * T.Cast("float32", K_smem[j, k]) * attn_score_scaling_factor * T.float32(0.18033688011112042) + T.tvm_storage_sync("shared") + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(2, 2): + with T.block("S_store"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 8 * 2 + li_1) + j = T.axis.spatial(16, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 8 * 2 + lj_1) + T.reads(S_local[i, j]) + T.writes(S_smem[i, j]) + S_smem[i, j] = S_local[i, j] + T.tvm_storage_sync("shared") + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + with T.block("update1"): + T.reads(m_smem[row], kv_chunk_len[0], q_indptr[b_idx:b_idx + 2], m_new[i], S_smem[row, 0:16], d_smem[row], m_prev[i]) + T.writes(m_prev[i], m_new[i], d_new[i]) + m_prev[i] = m_smem[row] + m_new[i] = m_smem[row] + row_: T.int32 = LH_start + row + for j in range(16): + if T.if_then_else(causal > 0, L_kv_start + j < kv_chunk_len[0] - (q_indptr[b_idx + 1] - q_indptr[b_idx]) + row_ + 1, L_kv_start + j < kv_chunk_len[0]): + m_new[i] = T.max(m_new[i], S_smem[row, j]) + d_new[i] = d_smem[row] * T.exp2(m_prev[i] - m_new[i]) + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + with T.block("update"): + T.reads(kv_chunk_len[0], q_indptr[b_idx:b_idx + 2], S_smem[row, 0:16], m_new[i]) + T.writes(S_smem[row, 0:16]) + for j in range(16): + if row < 32: + row_: T.int32 = LH_start + row + if T.if_then_else(causal > 0, L_kv_start + j < kv_chunk_len[0] - (q_indptr[b_idx + 1] - q_indptr[b_idx]) + row_ + 1, L_kv_start + j < kv_chunk_len[0]): + S_smem[row, j] = T.exp2(S_smem[row, j] - m_new[i]) + else: + S_smem[row, j] = T.exp2(T.float32(-50000) - m_new[i]) + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + with T.block("update"): + T.reads(d_new[i], S_smem[row, 0:16], m_new[i], m_prev[i]) + T.writes(d_new[i], m_smem[row], d_smem[row], m_prev_smem[row]) + for j in range(16): + d_new[i] = d_new[i] + S_smem[row, j] + m_smem[row] = m_new[i] + d_smem[row] = d_new[i] + m_prev_smem[row] = m_prev[i] + T.tvm_storage_sync("shared") + with T.block(""): + T.reads(m_prev_smem[0:32], m_smem[0:32], S_smem[0:32, 0:16], V_smem[0:16, 0:64]) + T.writes(O_local[0:32, 0:64]) + for li_0_lj_0_fused_0_init in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1_init in T.thread_binding(32, thread="threadIdx.x"): + for li_1_init, lj_1_init in T.grid(4, 4): + with T.block("O_gemm_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) // 16 * 4 + li_1_init) + j = T.axis.spatial(64, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) % 16 * 4 + lj_1_init) + T.reads() + T.writes(O_local[i, j]) + O_local[i, j] = O_local[i, j] * T.exp2(m_prev_smem[i] - m_smem[i]) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for lk_0, lk_1, li_1, lj_1 in T.grid(2, 8, 4, 4): + with T.block("O_gemm_update"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + k = T.axis.reduce(16, lk_0 * 8 + lk_1) + T.reads(O_local[i, j], m_prev_smem[i], m_smem[i], S_smem[i, k], V_smem[k, j]) + T.writes(O_local[i, j]) + O_local[i, j] = O_local[i, j] + S_smem[i, k] * T.Cast("float32", V_smem[k, j]) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(4, 4): + with T.block("O_store"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + T.reads(q_indptr[b_idx:b_idx + 2], O_local[i, j], d_smem[i]) + T.writes(output[q_indptr[b_idx] + (LH_start + i), by, j]) + cur_L: T.int32 = q_indptr[b_idx] + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + output[cur_L, cur_H_qo, j] = T.Cast("float16", O_local[i, j] / d_smem[i]) + for li_0 in range(1): + for li_1 in T.thread_binding(4, thread="threadIdx.y"): + for li_2 in T.thread_binding(32, thread="threadIdx.x"): + with T.block("lse_store"): + i = T.axis.spatial(32, li_0 * 128 + li_1 * 32 + li_2) + T.where((li_0 * 4 + li_1) * 32 + li_2 < 32) + T.reads(q_indptr[b_idx:b_idx + 2], m_smem[i], d_smem[i]) + T.writes(lse[q_indptr[b_idx] + (LH_start + i), by]) + cur_L: T.int32 = q_indptr[b_idx] + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + lse[cur_L, cur_H_qo] = m_smem[i] + T.log2(d_smem[i]) + tile_id[0] = tile_id[0] + 16 + + @T.prim_func + def batch_prefill_ragged_kv(var_q: T.handle, var_q_indptr: T.handle, var_k: T.handle, var_v: T.handle, var_kv_indptr: T.handle, var_q_rope_position: T.handle, var_k_rope_pos_offset: T.handle, var_output: T.handle, var_lse: T.handle, causal: T.int32, rotary_mode: T.int32, rope_scale: T.float32, rope_theta: T.float32, attn_score_scaling_factor: T.float32): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) + qo_len = T.int32(is_size_var=True) + q = T.match_buffer(var_q, (qo_len, 20, 64), "float16") + batch_size = T.int32(is_size_var=True) + q_indptr = T.match_buffer(var_q_indptr, (batch_size + 1,), "int32", offset_factor=1) + kv_len = T.int32(is_size_var=True) + k = T.match_buffer(var_k, (kv_len, 20, 64), "float16") + v = T.match_buffer(var_v, (kv_len, 20, 64), "float16") + kv_indptr = T.match_buffer(var_kv_indptr, (batch_size + 1,), "int32", offset_factor=1) + q_rope_position = T.match_buffer(var_q_rope_position, (qo_len,), "int32", offset_factor=1) + k_rope_pos_offset = T.match_buffer(var_k_rope_pos_offset, (batch_size,), "int32", offset_factor=1) + output = T.match_buffer(var_output, (qo_len, 20, 64), "float16") + lse = T.match_buffer(var_lse, (qo_len, 20)) + # with T.block("root"): + for lbx in T.thread_binding(16, thread="blockIdx.x"): + for lby in T.thread_binding(20, thread="blockIdx.y"): + for lty in T.thread_binding(4, thread="threadIdx.y"): + for ltx in T.thread_binding(32, thread="threadIdx.x"): + with T.block("attn"): + bx, by, ty, tx = T.axis.remap("SSSS", [lbx, lby, lty, ltx]) + T.reads() + T.writes() + tile_id = T.alloc_buffer((1,), "int32", scope="local") + batch_idx = T.alloc_buffer((1,), "int32", scope="local") + batch_tiles = T.alloc_buffer((1,), "int32", scope="local") + batch_rows = T.alloc_buffer((1,), "int32", scope="local") + iterator = T.alloc_buffer((1,), "int32", scope="local") + kv_chunk_len = T.alloc_buffer((1,), "int32", scope="local") + Q_smem = T.alloc_buffer((32, 64), "float16", scope="shared") + K_smem = T.alloc_buffer((16, 64), "float16", scope="shared") + V_smem = T.alloc_buffer((16, 64), "float16", scope="shared") + S_smem = T.alloc_buffer((32, 16), scope="shared") + S_local = T.alloc_buffer((32, 16), scope="local") + O_local = T.alloc_buffer((32, 64), scope="local") + m_smem = T.alloc_buffer((32,), scope="shared") + m_prev_smem = T.alloc_buffer((32,), scope="shared") + d_smem = T.alloc_buffer((32,), scope="shared") + m_new = T.alloc_buffer((1,), scope="local") + m_prev = T.alloc_buffer((1,), scope="local") + d_new = T.alloc_buffer((1,), scope="local") + tile_id[0] = bx + batch_idx[0] = 0 + batch_rows[0] = q_indptr[1] - q_indptr[0] + batch_tiles[0] = (batch_rows[0] + 32 - 1) // 32 + while T.tvm_thread_invariant(batch_idx[0] < batch_size): + while tile_id[0] >= batch_tiles[0] and batch_idx[0] < batch_size: + tile_id[0] = tile_id[0] - batch_tiles[0] + batch_idx[0] = batch_idx[0] + 1 + if batch_idx[0] < batch_size: + b_idx: T.int32 = batch_idx[0] + batch_rows[0] = q_indptr[b_idx + 1] - q_indptr[b_idx] + batch_tiles[0] = (batch_rows[0] + 32 - 1) // 32 + if T.tvm_thread_invariant(batch_idx[0] < batch_size): + b_idx: T.int32 = batch_idx[0] + q_indptr_val: T.int32 = q_indptr[b_idx] + LH_start: T.int32 = tile_id[0] * 32 + kv_chunk_len[0] = kv_indptr[b_idx + 1] - kv_indptr[b_idx] + T.tvm_storage_sync("shared") + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + m_smem[row] = T.float32(-50000) + d_smem[row] = T.float32(1) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(4, 4): + with T.block("O_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + T.reads() + T.writes(O_local[i, j]) + O_local[i, j] = T.float32(0) + T.tvm_storage_sync("shared") + for li_lj_fused_0 in range(4): + for li_lj_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for li_lj_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for li_lj_fused_3 in T.vectorized(4): + with T.block("Q_load"): + i = T.axis.spatial(32, (li_lj_fused_0 * 512 + li_lj_fused_1 * 128 + li_lj_fused_2 * 4 + li_lj_fused_3) // 64) + j = T.axis.spatial(64, (li_lj_fused_0 * 512 + li_lj_fused_1 * 128 + li_lj_fused_2 * 4 + li_lj_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = q_indptr_val + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + Q_smem[i, j] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", q[cur_L, cur_H_qo, j]) + T.sin(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(j < 32, q[cur_L, cur_H_qo, j + 32] * T.float16(-1), q[cur_L, cur_H_qo, j - 32]))), q[cur_L, cur_H_qo, j]) + else: + Q_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + for iterator_1 in range((kv_chunk_len[0] + 15) // 16): + L_kv_start: T.int32 = iterator_1 * 16 + L_kv_base: T.int32 = kv_indptr[b_idx] + for lz_ly_fused_0 in range(2): + for lz_ly_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for lz_ly_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for lz_ly_fused_3 in T.vectorized(4): + with T.block("K_load"): + i = T.axis.spatial(16, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) // 64) + j = T.axis.spatial(64, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = L_kv_start + i + if cur_L < kv_chunk_len[0]: + K_smem[i, j] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", k_rope_pos_offset[b_idx] + cur_L) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", k[L_kv_base + cur_L, by, j]) + T.sin(T.Cast("float32", k_rope_pos_offset[b_idx] + cur_L) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(j < 32, k[L_kv_base + cur_L, by, j + 32] * T.float16(-1), k[L_kv_base + cur_L, by, j - 32]))), k[L_kv_base + cur_L, by, j]) + else: + K_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + for lz_ly_fused_0 in range(2): + for lz_ly_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for lz_ly_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for lz_ly_fused_3 in T.vectorized(4): + with T.block("V_load"): + i = T.axis.spatial(16, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) // 64) + j = T.axis.spatial(64, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = L_kv_start + i + if cur_L < kv_chunk_len[0]: + V_smem[i, j] = v[L_kv_base + cur_L, by, j] + else: + V_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + with T.block(""): + T.reads(Q_smem[0:32, 0:64], K_smem[0:16, 0:64]) + T.writes(S_local[0:32, 0:16]) + for li_0_lj_0_fused_0_init in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1_init in T.thread_binding(32, thread="threadIdx.x"): + for li_1_init, lj_1_init in T.grid(2, 2): + with T.block("S_gemm_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) // 8 * 2 + li_1_init) + j = T.axis.spatial(16, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) % 8 * 2 + lj_1_init) + T.reads() + T.writes(S_local[i, j]) + S_local[i, j] = T.float32(0) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for lk_0, li_1, lj_1, lk_1 in T.grid(8, 2, 2, 8): + with T.block("S_gemm_update"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 8 * 2 + li_1) + j = T.axis.spatial(16, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 8 * 2 + lj_1) + k_1 = T.axis.reduce(64, lk_0 * 8 + lk_1) + T.reads(S_local[i, j], Q_smem[i, k_1], K_smem[j, k_1]) + T.writes(S_local[i, j]) + S_local[i, j] = S_local[i, j] + T.Cast("float32", Q_smem[i, k_1]) * T.Cast("float32", K_smem[j, k_1]) * attn_score_scaling_factor * T.float32(0.18033688011112042) + T.tvm_storage_sync("shared") + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(2, 2): + with T.block("S_store"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 8 * 2 + li_1) + j = T.axis.spatial(16, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 8 * 2 + lj_1) + T.reads(S_local[i, j]) + T.writes(S_smem[i, j]) + S_smem[i, j] = S_local[i, j] + T.tvm_storage_sync("shared") + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + with T.block("update1"): + T.reads(m_smem[row], kv_chunk_len[0], q_indptr[b_idx:b_idx + 2], m_new[i], S_smem[row, 0:16], d_smem[row], m_prev[i]) + T.writes(m_prev[i], m_new[i], d_new[i]) + m_prev[i] = m_smem[row] + m_new[i] = m_smem[row] + row_: T.int32 = LH_start + row + for j in range(16): + if T.if_then_else(causal > 0, L_kv_start + j < kv_chunk_len[0] - (q_indptr[b_idx + 1] - q_indptr[b_idx]) + row_ + 1, L_kv_start + j < kv_chunk_len[0]): + m_new[i] = T.max(m_new[i], S_smem[row, j]) + d_new[i] = d_smem[row] * T.exp2(m_prev[i] - m_new[i]) + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + with T.block("update"): + T.reads(kv_chunk_len[0], q_indptr[b_idx:b_idx + 2], S_smem[row, 0:16], m_new[i]) + T.writes(S_smem[row, 0:16]) + for j in range(16): + if row < 32: + row_: T.int32 = LH_start + row + if T.if_then_else(causal > 0, L_kv_start + j < kv_chunk_len[0] - (q_indptr[b_idx + 1] - q_indptr[b_idx]) + row_ + 1, L_kv_start + j < kv_chunk_len[0]): + S_smem[row, j] = T.exp2(S_smem[row, j] - m_new[i]) + else: + S_smem[row, j] = T.exp2(T.float32(-50000) - m_new[i]) + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + with T.block("update"): + T.reads(d_new[i], S_smem[row, 0:16], m_new[i], m_prev[i]) + T.writes(d_new[i], m_smem[row], d_smem[row], m_prev_smem[row]) + for j in range(16): + d_new[i] = d_new[i] + S_smem[row, j] + m_smem[row] = m_new[i] + d_smem[row] = d_new[i] + m_prev_smem[row] = m_prev[i] + T.tvm_storage_sync("shared") + with T.block(""): + T.reads(m_prev_smem[0:32], m_smem[0:32], S_smem[0:32, 0:16], V_smem[0:16, 0:64]) + T.writes(O_local[0:32, 0:64]) + for li_0_lj_0_fused_0_init in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1_init in T.thread_binding(32, thread="threadIdx.x"): + for li_1_init, lj_1_init in T.grid(4, 4): + with T.block("O_gemm_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) // 16 * 4 + li_1_init) + j = T.axis.spatial(64, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) % 16 * 4 + lj_1_init) + T.reads() + T.writes(O_local[i, j]) + O_local[i, j] = O_local[i, j] * T.exp2(m_prev_smem[i] - m_smem[i]) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for lk_0, lk_1, li_1, lj_1 in T.grid(2, 8, 4, 4): + with T.block("O_gemm_update"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + k_1 = T.axis.reduce(16, lk_0 * 8 + lk_1) + T.reads(O_local[i, j], m_prev_smem[i], m_smem[i], S_smem[i, k_1], V_smem[k_1, j]) + T.writes(O_local[i, j]) + O_local[i, j] = O_local[i, j] + S_smem[i, k_1] * T.Cast("float32", V_smem[k_1, j]) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(4, 4): + with T.block("O_store"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + T.reads(q_indptr[b_idx:b_idx + 2], O_local[i, j], d_smem[i]) + T.writes(output[q_indptr[b_idx] + (LH_start + i), by, j]) + cur_L: T.int32 = q_indptr[b_idx] + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + output[cur_L, cur_H_qo, j] = T.Cast("float16", O_local[i, j] / d_smem[i]) + for li_0 in range(1): + for li_1 in T.thread_binding(4, thread="threadIdx.y"): + for li_2 in T.thread_binding(32, thread="threadIdx.x"): + with T.block("lse_store"): + i = T.axis.spatial(32, li_0 * 128 + li_1 * 32 + li_2) + T.where((li_0 * 4 + li_1) * 32 + li_2 < 32) + T.reads(q_indptr[b_idx:b_idx + 2], m_smem[i], d_smem[i]) + T.writes(lse[q_indptr[b_idx] + (LH_start + i), by]) + cur_L: T.int32 = q_indptr[b_idx] + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + lse[cur_L, cur_H_qo] = m_smem[i] + T.log2(d_smem[i]) + tile_id[0] = tile_id[0] + 16 + + @T.prim_func + def batch_tree_attn(var_q: T.handle, var_q_indptr: T.handle, var_k: T.handle, var_v: T.handle, var_kv_indptr: T.handle, var_q_rope_position: T.handle, var_mn_indptr: T.handle, var_mask: T.handle, var_output: T.handle, var_lse: T.handle, rotary_mode: T.int32, rope_scale: T.float32, rope_theta: T.float32, attn_score_scaling_factor: T.float32, batch_size: T.int32): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) + qo_len = T.int32(is_size_var=True) + q = T.match_buffer(var_q, (qo_len, 20, 64), "float16") + q_indptr = T.match_buffer(var_q_indptr, (batch_size + 1,), "int32", offset_factor=1) + kv_len = T.int32(is_size_var=True) + k = T.match_buffer(var_k, (kv_len, 20, 64), "float16") + v = T.match_buffer(var_v, (kv_len, 20, 64), "float16") + kv_indptr = T.match_buffer(var_kv_indptr, (batch_size + 1,), "int32", offset_factor=1) + q_rope_position = T.match_buffer(var_q_rope_position, (qo_len,), "int32", offset_factor=1) + mn_indptr = T.match_buffer(var_mn_indptr, (batch_size + 1,), "int32", offset_factor=1) + tree_size = T.int32(is_size_var=True) + mask = T.match_buffer(var_mask, (tree_size,), "int32", offset_factor=1) + output = T.match_buffer(var_output, (qo_len, 20, 64), "float16") + lse = T.match_buffer(var_lse, (qo_len, 20)) + # with T.block("root"): + for lbx in T.thread_binding(16, thread="blockIdx.x"): + for lby in T.thread_binding(20, thread="blockIdx.y"): + for lty in T.thread_binding(4, thread="threadIdx.y"): + for ltx in T.thread_binding(32, thread="threadIdx.x"): + with T.block("attn"): + bx, by, ty, tx = T.axis.remap("SSSS", [lbx, lby, lty, ltx]) + T.reads() + T.writes() + tile_id = T.alloc_buffer((1,), "int32", scope="local") + batch_idx = T.alloc_buffer((1,), "int32", scope="local") + batch_tiles = T.alloc_buffer((1,), "int32", scope="local") + batch_rows = T.alloc_buffer((1,), "int32", scope="local") + iterator = T.alloc_buffer((1,), "int32", scope="local") + kv_chunk_len = T.alloc_buffer((1,), "int32", scope="local") + Q_smem = T.alloc_buffer((32, 64), "float16", scope="shared") + K_smem = T.alloc_buffer((16, 64), "float16", scope="shared") + V_smem = T.alloc_buffer((16, 64), "float16", scope="shared") + S_smem = T.alloc_buffer((32, 16), scope="shared") + S_local = T.alloc_buffer((32, 16), scope="local") + O_local = T.alloc_buffer((32, 64), scope="local") + m_smem = T.alloc_buffer((32,), scope="shared") + m_prev_smem = T.alloc_buffer((32,), scope="shared") + d_smem = T.alloc_buffer((32,), scope="shared") + m_new = T.alloc_buffer((1,), scope="local") + m_prev = T.alloc_buffer((1,), scope="local") + d_new = T.alloc_buffer((1,), scope="local") + tile_id[0] = bx + batch_idx[0] = 0 + batch_rows[0] = q_indptr[1] - q_indptr[0] + batch_tiles[0] = (batch_rows[0] + 32 - 1) // 32 + while T.tvm_thread_invariant(batch_idx[0] < batch_size): + while tile_id[0] >= batch_tiles[0] and batch_idx[0] < batch_size: + tile_id[0] = tile_id[0] - batch_tiles[0] + batch_idx[0] = batch_idx[0] + 1 + if batch_idx[0] < batch_size: + b_idx: T.int32 = batch_idx[0] + batch_rows[0] = q_indptr[b_idx + 1] - q_indptr[b_idx] + batch_tiles[0] = (batch_rows[0] + 32 - 1) // 32 + if T.tvm_thread_invariant(batch_idx[0] < batch_size): + b_idx: T.int32 = batch_idx[0] + LH_start: T.int32 = tile_id[0] * 32 + q_indptr_val: T.int32 = q_indptr[b_idx] + kv_chunk_len[0] = kv_indptr[b_idx + 1] - kv_indptr[b_idx] + T.tvm_storage_sync("shared") + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + m_smem[row] = T.float32(-50000) + d_smem[row] = T.float32(1) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(4, 4): + with T.block("O_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + T.reads() + T.writes(O_local[i, j]) + O_local[i, j] = T.float32(0) + T.tvm_storage_sync("shared") + for li_lj_fused_0 in range(4): + for li_lj_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for li_lj_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for li_lj_fused_3 in T.vectorized(4): + with T.block("Q_load"): + i = T.axis.spatial(32, (li_lj_fused_0 * 512 + li_lj_fused_1 * 128 + li_lj_fused_2 * 4 + li_lj_fused_3) // 64) + j = T.axis.spatial(64, (li_lj_fused_0 * 512 + li_lj_fused_1 * 128 + li_lj_fused_2 * 4 + li_lj_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = q_indptr_val + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + Q_smem[i, j] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64)))) * q[cur_L, cur_H_qo, j] + T.Cast("float16", T.sin(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64)))) * T.if_then_else(j < 32, q[cur_L, cur_H_qo, j + 32] * T.float16(-1), q[cur_L, cur_H_qo, j - 32]), q[cur_L, cur_H_qo, j]) + else: + Q_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + for iterator_1 in range((kv_chunk_len[0] + 15) // 16): + L_kv_start: T.int32 = iterator_1 * 16 + L_kv_base: T.int32 = kv_indptr[b_idx] + for lz_ly_fused_0 in range(2): + for lz_ly_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for lz_ly_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for lz_ly_fused_3 in T.vectorized(4): + with T.block("KV_load"): + i = T.axis.spatial(16, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) // 64) + j = T.axis.spatial(64, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = L_kv_base + L_kv_start + i + if L_kv_start + i < kv_chunk_len[0]: + K_smem[i, j] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64)))) * k[cur_L, by, j] + T.Cast("float16", T.sin(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64)))) * T.if_then_else(j < 32, k[cur_L, by, j + 32] * T.float16(-1), k[cur_L, by, j - 32]), k[cur_L, by, j]) + V_smem[i, j] = v[cur_L, by, j] + else: + K_smem[i, j] = T.float16(0) + V_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + with T.block(""): + T.reads(Q_smem[0:32, 0:64], K_smem[0:16, 0:64]) + T.writes(S_local[0:32, 0:16]) + for li_0_lj_0_fused_0_init in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1_init in T.thread_binding(32, thread="threadIdx.x"): + for li_1_init, lj_1_init in T.grid(2, 2): + with T.block("S_gemm_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) // 8 * 2 + li_1_init) + j = T.axis.spatial(16, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) % 8 * 2 + lj_1_init) + T.reads() + T.writes(S_local[i, j]) + S_local[i, j] = T.float32(0) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for lk_0, li_1, lj_1, lk_1 in T.grid(8, 2, 2, 8): + with T.block("S_gemm_update"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 8 * 2 + li_1) + j = T.axis.spatial(16, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 8 * 2 + lj_1) + k_1 = T.axis.reduce(64, lk_0 * 8 + lk_1) + T.reads(S_local[i, j], Q_smem[i, k_1], K_smem[j, k_1]) + T.writes(S_local[i, j]) + S_local[i, j] = S_local[i, j] + T.Cast("float32", Q_smem[i, k_1]) * T.Cast("float32", K_smem[j, k_1]) * attn_score_scaling_factor * T.float32(0.18033688011112042) + T.tvm_storage_sync("shared") + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(2, 2): + with T.block("S_store"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 8 * 2 + li_1) + j = T.axis.spatial(16, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 8 * 2 + lj_1) + T.reads(S_local[i, j]) + T.writes(S_smem[i, j]) + S_smem[i, j] = S_local[i, j] + T.tvm_storage_sync("shared") + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + with T.block("update1"): + T.reads(m_smem[row], kv_chunk_len[0], mask[mn_indptr[b_idx] + (LH_start + row) * (q_indptr[b_idx + 1] - q_indptr[b_idx]) + L_kv_start:mn_indptr[b_idx] + (LH_start + row) * (q_indptr[b_idx + 1] - q_indptr[b_idx]) + L_kv_start + 16], mn_indptr[b_idx], q_indptr[b_idx:b_idx + 2], m_new[i], S_smem[row, 0:16], d_smem[row], m_prev[i]) + T.writes(m_prev[i], m_new[i], d_new[i]) + m_prev[i] = m_smem[row] + m_new[i] = m_smem[row] + row_: T.int32 = LH_start + row + for j in range(16): + if L_kv_start + j < kv_chunk_len[0] and mask[mn_indptr[b_idx] + row_ * (q_indptr[b_idx + 1] - q_indptr[b_idx]) + (L_kv_start + j)] == 1: + m_new[i] = T.max(m_new[i], S_smem[row, j]) + d_new[i] = d_smem[row] * T.exp2(m_prev[i] - m_new[i]) + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + with T.block("update"): + T.reads(kv_chunk_len[0], mask[mn_indptr[b_idx] + (LH_start + row) * (q_indptr[b_idx + 1] - q_indptr[b_idx]) + L_kv_start:mn_indptr[b_idx] + (LH_start + row) * (q_indptr[b_idx + 1] - q_indptr[b_idx]) + L_kv_start + 16], mn_indptr[b_idx], q_indptr[b_idx:b_idx + 2], S_smem[row, 0:16], m_new[i]) + T.writes(S_smem[row, 0:16]) + for j in range(16): + if row < 32: + row_: T.int32 = LH_start + row + if L_kv_start + j < kv_chunk_len[0] and mask[mn_indptr[b_idx] + row_ * (q_indptr[b_idx + 1] - q_indptr[b_idx]) + (L_kv_start + j)] == 1: + S_smem[row, j] = T.exp2(S_smem[row, j] - m_new[i]) + else: + S_smem[row, j] = T.exp2(T.float32(-50000) - m_new[i]) + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + with T.block("update"): + T.reads(d_new[i], S_smem[row, 0:16], m_new[i], m_prev[i]) + T.writes(d_new[i], m_smem[row], d_smem[row], m_prev_smem[row]) + for j in range(16): + d_new[i] = d_new[i] + S_smem[row, j] + m_smem[row] = m_new[i] + d_smem[row] = d_new[i] + m_prev_smem[row] = m_prev[i] + T.tvm_storage_sync("shared") + with T.block(""): + T.reads(m_prev_smem[0:32], m_smem[0:32], S_smem[0:32, 0:16], V_smem[0:16, 0:64]) + T.writes(O_local[0:32, 0:64]) + for li_0_lj_0_fused_0_init in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1_init in T.thread_binding(32, thread="threadIdx.x"): + for li_1_init, lj_1_init in T.grid(4, 4): + with T.block("O_gemm_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) // 16 * 4 + li_1_init) + j = T.axis.spatial(64, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) % 16 * 4 + lj_1_init) + T.reads() + T.writes(O_local[i, j]) + O_local[i, j] = O_local[i, j] * T.exp2(m_prev_smem[i] - m_smem[i]) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for lk_0, lk_1, li_1, lj_1 in T.grid(2, 8, 4, 4): + with T.block("O_gemm_update"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + k_1 = T.axis.reduce(16, lk_0 * 8 + lk_1) + T.reads(O_local[i, j], m_prev_smem[i], m_smem[i], S_smem[i, k_1], V_smem[k_1, j]) + T.writes(O_local[i, j]) + O_local[i, j] = O_local[i, j] + S_smem[i, k_1] * T.Cast("float32", V_smem[k_1, j]) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(4, 4): + with T.block("O_store"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + T.reads(q_indptr[b_idx:b_idx + 2], O_local[i, j], d_smem[i]) + T.writes(output[q_indptr[b_idx] + (LH_start + i), by, j]) + cur_L: T.int32 = q_indptr[b_idx] + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + output[cur_L, cur_H_qo, j] = T.Cast("float16", O_local[i, j] / d_smem[i]) + for li_0 in range(1): + for li_1 in T.thread_binding(4, thread="threadIdx.y"): + for li_2 in T.thread_binding(32, thread="threadIdx.x"): + with T.block("lse_store"): + i = T.axis.spatial(32, li_0 * 128 + li_1 * 32 + li_2) + T.where((li_0 * 4 + li_1) * 32 + li_2 < 32) + T.reads(q_indptr[b_idx:b_idx + 2], m_smem[i], d_smem[i]) + T.writes(lse[q_indptr[b_idx] + (LH_start + i), by]) + cur_L: T.int32 = q_indptr[b_idx] + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + lse[cur_L, cur_H_qo] = m_smem[i] + T.log2(d_smem[i]) + tile_id[0] = tile_id[0] + 16 + + @T.prim_func(private=True) + def batch_verify_on_gpu_single_kernel(var_draft_probs: T.handle, var_draft_tokens: T.handle, var_model_probs: T.handle, var_token_tree_first_child: T.handle, var_token_tree_next_sibling: T.handle, var_uniform_samples: T.handle, var_token_tree_parent_ptr: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + num_nodes, vocab_size = T.int32(is_size_var=True), T.int64() + draft_probs = T.match_buffer(var_draft_probs, (num_nodes, vocab_size)) + draft_tokens = T.match_buffer(var_draft_tokens, (num_nodes,), "int32") + model_probs = T.match_buffer(var_model_probs, (num_nodes, vocab_size)) + token_tree_first_child = T.match_buffer(var_token_tree_first_child, (num_nodes,), "int32") + token_tree_next_sibling = T.match_buffer(var_token_tree_next_sibling, (num_nodes,), "int32") + uniform_samples = T.match_buffer(var_uniform_samples, (num_nodes,)) + nbatch = T.int32(is_size_var=True) + token_tree_parent_ptr = T.match_buffer(var_token_tree_parent_ptr, (nbatch,), "int32") + # with T.block("root"): + child_ptr = T.alloc_buffer((1,), "int32", scope="local") + parent_ptr = T.alloc_buffer((1,), "int32", scope="local") + child_token = T.alloc_buffer((1,), "int32", scope="local") + done = T.alloc_buffer((1,), "bool", scope="local") + psum = T.alloc_buffer((1,), scope="local") + t0 = T.alloc_buffer((1,), scope="local") + model_prob_local = T.alloc_buffer((1,), scope="local") + draft_prob_local = T.alloc_buffer((1,), scope="local") + p_child = T.alloc_buffer((1,), scope="local") + q_child = T.alloc_buffer((1,), scope="local") + uniform_sample = T.alloc_buffer((1,), scope="local") + pred_shared = T.alloc_buffer((1,), "bool", scope="shared") + pred_local = T.alloc_buffer((1,), "bool", scope="local") + for _bx in T.thread_binding(nbatch, thread="blockIdx.x"): + for _tx in T.thread_binding(1024, thread="threadIdx.x"): + with T.block("CTA"): + b, tx = T.axis.remap("SS", [_bx, _tx]) + T.reads(token_tree_parent_ptr[b], token_tree_first_child[T.min(parent_ptr[0], child_ptr[0]):T.min(parent_ptr[0], child_ptr[0]) + (T.max(parent_ptr[0], child_ptr[0]) + 1 - T.min(parent_ptr[0], child_ptr[0]))], parent_ptr[0], done[0], child_ptr[0], draft_tokens[child_ptr[0]], model_probs[parent_ptr[0], T.min(T.Cast("int64", child_token[0]), T.Cast("int64", tx)):T.min(T.Cast("int64", child_token[0]), T.Cast("int64", tx)) + (T.max(T.Cast("int64", child_token[0]), (vocab_size + T.int64(1023)) // T.int64(1024) * T.int64(1024) + T.Cast("int64", tx) - T.int64(1024)) + T.int64(1) - T.min(T.Cast("int64", child_token[0]), T.Cast("int64", tx)))], child_token[0], draft_probs[child_ptr[0], T.min(T.Cast("int64", child_token[0]), T.Cast("int64", tx)):T.min(T.Cast("int64", child_token[0]), T.Cast("int64", tx)) + (T.max(T.Cast("int64", child_token[0]), (vocab_size + T.int64(1023)) // T.int64(1024) * T.int64(1024) + T.Cast("int64", tx) - T.int64(1024)) + T.int64(1) - T.min(T.Cast("int64", child_token[0]), T.Cast("int64", tx)))], uniform_samples[child_ptr[0]], p_child[0], uniform_sample[0], q_child[0], pred_shared[0], pred_local[0], model_prob_local[0], draft_prob_local[0], psum[0], t0[0], token_tree_next_sibling[child_ptr[0]]) + T.writes(parent_ptr[0], child_ptr[0], done[0], child_token[0], p_child[0], q_child[0], uniform_sample[0], pred_shared[0], pred_local[0], psum[0], model_prob_local[0], draft_prob_local[0], t0[0], model_probs[parent_ptr[0], T.Cast("int64", tx):T.Cast("int64", tx) + ((vocab_size + T.int64(1023)) // T.int64(1024) * T.int64(1024) - T.int64(1023))], token_tree_parent_ptr[b]) + parent_ptr[0] = token_tree_parent_ptr[b] + child_ptr[0] = token_tree_first_child[parent_ptr[0]] + done[0] = T.bool(False) + while not done[0]: + T.tvm_storage_sync("shared") + if child_ptr[0] == -1: + done[0] = T.bool(True) + T.tvm_storage_sync("shared") + else: + if tx == 0: + child_token[0] = draft_tokens[child_ptr[0]] + p_child[0] = model_probs[parent_ptr[0], child_token[0]] + q_child[0] = draft_probs[child_ptr[0], child_token[0]] + uniform_sample[0] = uniform_samples[child_ptr[0]] + pred_shared[0] = p_child[0] >= uniform_sample[0] * q_child[0] + T.tvm_storage_sync("shared") + pred_local[0] = pred_shared[0] + if pred_local[0]: + parent_ptr[0] = child_ptr[0] + child_ptr[0] = token_tree_first_child[child_ptr[0]] + else: + psum[0] = T.float32(0) + for i in range((vocab_size + T.int64(1023)) // T.int64(1024)): + if i * T.int64(1024) + T.Cast("int64", tx) < vocab_size: + model_prob_local[0] = model_probs[parent_ptr[0], i * T.int64(1024) + T.Cast("int64", tx)] + draft_prob_local[0] = draft_probs[child_ptr[0], i * T.int64(1024) + T.Cast("int64", tx)] + model_prob_local[0] = T.max(model_prob_local[0] - draft_prob_local[0], T.float32(0)) + psum[0] = psum[0] + model_prob_local[0] + with T.block("block_cross_thread"): + T.reads(psum[0]) + T.writes(t0[0]) + T.attr(T.comm_reducer(lambda x0, y0: x0 + y0, [T.float32(0)]), "reduce_scope", T.reinterpret("handle", T.uint64(0))) + T.tvm_thread_allreduce(T.uint32(1), psum[0], T.bool(True), t0[0], tx) + if t0[0] < T.float32(9.9999999999999995e-08): + parent_ptr[0] = child_ptr[0] + child_ptr[0] = token_tree_first_child[child_ptr[0]] + else: + for i in range((vocab_size + T.int64(1023)) // T.int64(1024)): + if i * T.int64(1024) + T.Cast("int64", tx) < vocab_size: + model_prob_local[0] = model_probs[parent_ptr[0], i * T.int64(1024) + T.Cast("int64", tx)] + draft_prob_local[0] = draft_probs[child_ptr[0], i * T.int64(1024) + T.Cast("int64", tx)] + model_prob_local[0] = T.max(model_prob_local[0] - draft_prob_local[0], T.float32(0)) + model_probs[parent_ptr[0], i * T.int64(1024) + T.Cast("int64", tx)] = model_prob_local[0] / t0[0] + child_ptr[0] = token_tree_next_sibling[child_ptr[0]] + if tx == 0: + token_tree_parent_ptr[b] = parent_ptr[0] + + @T.prim_func + def chunk_lse(var_A: T.handle, var_temperature: T.handle, var_chunked_sum: T.handle, var_chunked_max: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.noalias": T.bool(True)}) + batch_size, vocab_size = T.int64(is_size_var=True), T.int64(is_size_var=True) + A = T.match_buffer(var_A, (batch_size, vocab_size)) + temperature = T.match_buffer(var_temperature, (batch_size,)) + num_chunks = T.int64(is_size_var=True) + chunked_sum = T.match_buffer(var_chunked_sum, (batch_size, num_chunks)) + chunked_max = T.match_buffer(var_chunked_max, (batch_size, num_chunks)) + # with T.block("root"): + A_pad = T.alloc_buffer((batch_size, num_chunks, T.int64(4096))) + temp_max = T.alloc_buffer((batch_size, num_chunks)) + temp_sum = T.alloc_buffer((batch_size, num_chunks)) + for l0, l1, l2 in T.grid(batch_size, num_chunks, T.int64(4096)): + with T.block("pad"): + v0, v1, v2 = T.axis.remap("SSS", [l0, l1, l2]) + T.reads(temperature[v0], A[v0, v1 * T.int64(4096) + v2]) + T.writes(A_pad[v0, v1, v2]) + A_pad[v0, v1, v2] = T.if_then_else(v1 * T.int64(4096) + v2 < vocab_size, T.if_then_else(temperature[v0] > T.float32(1.0000000000000001e-05), A[v0, v1 * T.int64(4096) + v2] / temperature[v0], A[v0, v1 * T.int64(4096) + v2]), T.float32(-3.4028234663852886e+38)) + for l0, l1, l2 in T.grid(batch_size, num_chunks, T.int64(4096)): + with T.block("max"): + v0, v1, v2 = T.axis.remap("SSR", [l0, l1, l2]) + T.reads(A_pad[v0, v1, v2]) + T.writes(temp_max[v0, v1]) + with T.init(): + temp_max[v0, v1] = T.float32(-3.4028234663852886e+38) + temp_max[v0, v1] = T.max(temp_max[v0, v1], A_pad[v0, v1, v2]) + for l0, l1, l2 in T.grid(batch_size, num_chunks, T.int64(4096)): + with T.block("sum_exp"): + v0, v1, v2 = T.axis.remap("SSR", [l0, l1, l2]) + T.reads(temperature[v0], A_pad[v0, v1, v2], temp_max[v0, v1]) + T.writes(temp_sum[v0, v1]) + with T.init(): + temp_sum[v0, v1] = T.float32(0) + temp_sum[v0, v1] = temp_sum[v0, v1] + T.if_then_else(v1 * T.int64(4096) + v2 < vocab_size, T.Select(temperature[v0] > T.float32(1.0000000000000001e-05), T.exp(A_pad[v0, v1, v2] - temp_max[v0, v1]), T.Cast("float32", A_pad[v0, v1, v2] == temp_max[v0, v1])), T.float32(0)) + for l0, l1, l2 in T.grid(batch_size, num_chunks, T.int64(1)): + with T.block("log"): + v0, v1, v2 = T.axis.remap("SSS", [l0, l1, l2]) + T.reads(temperature[v0], temp_sum[v0, v1], temp_max[v0, v1]) + T.writes(chunked_sum[v0, v1], chunked_max[v0, v1]) + chunked_sum[v0, v1] = T.Select(temperature[v0] > T.float32(1.0000000000000001e-05), T.log(temp_sum[v0, v1]), temp_sum[v0, v1]) + chunked_max[v0, v1] = temp_max[v0, v1] + + @T.prim_func + def compact_kv_copy(var_pages: T.handle, var_copy_length_indptr: T.handle, var_copy_src_dst_pos: T.handle, batch_size: T.int32): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) + num_pages = T.int32() + pages = T.match_buffer(var_pages, (num_pages, 2, 20, 16, 64), "float16") + copy_length_indptr = T.match_buffer(var_copy_length_indptr, (batch_size + 1,), "int32", offset_factor=1) + total_copy_length = T.int32() + copy_src_dst_pos = T.match_buffer(var_copy_src_dst_pos, (2, total_copy_length), "int32", offset_factor=1) + with T.block("root"): + T.reads() + T.writes() + for bhd_o in T.thread_binding((batch_size * 1280 + 1023) // 1024, thread="blockIdx.x"): + for bhd_i in T.thread_binding(1024, thread="threadIdx.x"): + b: T.int32 = (bhd_o * 1024 + bhd_i) // 1280 + h: T.int32 = (bhd_o * 1024 + bhd_i) // 64 % 20 + d: T.int32 = (bhd_o * 1024 + bhd_i) % 64 + if bhd_o * 1024 + bhd_i < batch_size * 20 * 64: + for i in range(copy_length_indptr[b + 1] - copy_length_indptr[b]): + src_pos: T.int32 = copy_src_dst_pos[0, copy_length_indptr[b] + i] + dst_pos: T.int32 = copy_src_dst_pos[1, copy_length_indptr[b] + i] + pages[dst_pos // 16, 0, h, dst_pos % 16, d] = pages[src_pos // 16, 0, h, src_pos % 16, d] + pages[dst_pos // 16, 1, h, dst_pos % 16, d] = pages[src_pos // 16, 1, h, src_pos % 16, d] + + @T.prim_func(private=True) + def concatenate(var_reshape710: T.handle, var_reshape711: T.handle, var_reshape712: T.handle, var_T_concat: T.handle): + T.func_attr({"tir.noalias": T.bool(True)}) + batch_size = T.int64() + reshape710 = T.match_buffer(var_reshape710, (batch_size, T.int64(1), T.int64(20), T.int64(64)), "float16") + reshape711 = T.match_buffer(var_reshape711, (batch_size, T.int64(1), T.int64(20), T.int64(64)), "float16") + reshape712 = T.match_buffer(var_reshape712, (batch_size, T.int64(1), T.int64(20), T.int64(64)), "float16") + T_concat = T.match_buffer(var_T_concat, (batch_size, T.int64(1), T.int64(60), T.int64(64)), "float16") + # with T.block("root"): + for ax0, ax1, ax2, ax3 in T.grid(batch_size, T.int64(1), T.int64(60), T.int64(64)): + with T.block("T_concat"): + v_ax0, v_ax1, v_ax2, v_ax3 = T.axis.remap("SSSS", [ax0, ax1, ax2, ax3]) + T.reads(reshape712[v_ax0, v_ax1, v_ax2 - T.int64(40), v_ax3], reshape711[v_ax0, v_ax1, v_ax2 - T.int64(20), v_ax3], reshape710[v_ax0, v_ax1, v_ax2, v_ax3]) + T.writes(T_concat[v_ax0, v_ax1, v_ax2, v_ax3]) + T_concat[v_ax0, v_ax1, v_ax2, v_ax3] = T.if_then_else(T.int64(40) <= v_ax2, reshape712[v_ax0, v_ax1, v_ax2 - T.int64(40), v_ax3], T.if_then_else(T.int64(20) <= v_ax2, reshape711[v_ax0, v_ax1, v_ax2 - T.int64(20), v_ax3], reshape710[v_ax0, v_ax1, v_ax2, v_ax3])) + + @T.prim_func(private=True) + def concatenate1(var_reshape387: T.handle, var_reshape388: T.handle, var_reshape389: T.handle, var_T_concat: T.handle): + T.func_attr({"tir.noalias": T.bool(True)}) + seq_len = T.int64() + reshape387 = T.match_buffer(var_reshape387, (T.int64(1), seq_len, T.int64(20), T.int64(64)), "float16") + reshape388 = T.match_buffer(var_reshape388, (T.int64(1), seq_len, T.int64(20), T.int64(64)), "float16") + reshape389 = T.match_buffer(var_reshape389, (T.int64(1), seq_len, T.int64(20), T.int64(64)), "float16") + T_concat = T.match_buffer(var_T_concat, (T.int64(1), seq_len, T.int64(60), T.int64(64)), "float16") + # with T.block("root"): + for ax0, ax1, ax2, ax3 in T.grid(T.int64(1), seq_len, T.int64(60), T.int64(64)): + with T.block("T_concat"): + v_ax0, v_ax1, v_ax2, v_ax3 = T.axis.remap("SSSS", [ax0, ax1, ax2, ax3]) + T.reads(reshape389[v_ax0, v_ax1, v_ax2 - T.int64(40), v_ax3], reshape388[v_ax0, v_ax1, v_ax2 - T.int64(20), v_ax3], reshape387[v_ax0, v_ax1, v_ax2, v_ax3]) + T.writes(T_concat[v_ax0, v_ax1, v_ax2, v_ax3]) + T_concat[v_ax0, v_ax1, v_ax2, v_ax3] = T.if_then_else(T.int64(40) <= v_ax2, reshape389[v_ax0, v_ax1, v_ax2 - T.int64(40), v_ax3], T.if_then_else(T.int64(20) <= v_ax2, reshape388[v_ax0, v_ax1, v_ax2 - T.int64(20), v_ax3], reshape387[v_ax0, v_ax1, v_ax2, v_ax3])) + + @T.prim_func + def copy_single_page(var_pages: T.handle, src_page_id: T.int64, tgt_page_id: T.int64, copy_length: T.int64): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) + num_pages, page_size = T.int32(), T.int64() + pages = T.match_buffer(var_pages, (num_pages, 2, 20, page_size, 64), "float16") + # with T.block("root"): + for b in T.thread_binding((copy_length * T.int64(1280) + T.int64(1023)) // T.int64(1024), thread="blockIdx.x"): + for t in T.thread_binding(1024, thread="threadIdx.x"): + with T.block("copy"): + vh = T.axis.spatial(20, T.Cast("int32", (b * T.int64(1024) + T.Cast("int64", t)) // (copy_length * T.int64(64)))) + vp = T.axis.spatial(copy_length, (b * T.int64(1024) + T.Cast("int64", t)) % (copy_length * T.int64(64)) // T.int64(64)) + vd = T.axis.spatial(64, T.Cast("int32", (b * T.int64(1024) + T.Cast("int64", t)) % T.int64(64))) + T.reads(pages[src_page_id, 0:2, vh, vp, vd]) + T.writes(pages[tgt_page_id, 0:2, vh, vp, vd]) + pages[tgt_page_id, 0, vh, vp, vd] = pages[src_page_id, 0, vh, vp, vd] + pages[tgt_page_id, 1, vh, vp, vd] = pages[src_page_id, 1, vh, vp, vd] + + @T.prim_func(private=True) + def cumsum(var_sorted_probs: T.handle, var_lv1: T.handle, var_exclusive_scan_thrust: T.handle): + T.func_attr({"tir.noalias": T.bool(True)}) + batch_size, vocab_size = T.int64(), T.int64() + data_buf = T.match_buffer(var_sorted_probs, (batch_size, vocab_size), align=8) + workspace_buf = T.match_buffer(var_lv1, (T.int64(8) * (batch_size * vocab_size * T.int64(4)) + T.int64(8388608) + batch_size * vocab_size * T.int64(12),), "uint8", align=8) + output_buf = T.match_buffer(var_exclusive_scan_thrust, (batch_size, vocab_size), align=8) + with T.block("exclusive_scan_thrust"): + T.reads() + T.writes() + T.call_packed("tvm.contrib.thrust.sum_scan", T.tvm_stack_make_array(data_buf.data, T.tvm_stack_make_shape(batch_size, vocab_size), 0, 2, T.float32(0), T.int64(0)), T.tvm_stack_make_array(output_buf.data, T.tvm_stack_make_shape(batch_size, vocab_size), 0, 2, T.float32(0), T.int64(0)), T.bool(False), T.tvm_stack_make_array(workspace_buf.data, T.tvm_stack_make_shape(T.int64(8) * (batch_size * vocab_size * T.int64(4)) + T.int64(8388608) + batch_size * vocab_size * T.int64(12)), 0, 1, T.uint8(0), T.int64(0))) + + @T.prim_func + def full(var_result: T.handle, value: T.int32): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32})}) + batch_size = T.int32(is_size_var=True) + result = T.match_buffer(var_result, (batch_size, 1), "int32") + # with T.block("root"): + for i in range(batch_size): + with T.block("block"): + vi = T.axis.spatial(batch_size, i) + T.reads() + T.writes(result[vi, 0]) + result[vi, 0] = value + + @T.prim_func(private=True) + def fused_NT_matmul1_add8_gelu2(layer_norm358: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16"), model_decoder_layers_0_fc1_weight5: T.Buffer((T.int64(5120), T.int64(1280)), "float16"), model_decoder_layers_0_fc1_bias5: T.Buffer((T.int64(5120),), "float16"), T_multiply_intermediate: T.Buffer((T.int64(1), T.int64(1), T.int64(5120)), "float16")): + T.func_attr({"tir.noalias": T.bool(True)}) + # with T.block("root"): + NT_matmul_intermediate = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(5120)), "float16") + T_add_intermediate = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(5120)), "float16") + T_multiply = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(5120)), "float16") + compute = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(5120))) + compute_1 = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(5120))) + compute_2 = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(5120)), "float16") + T_multiply_1 = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(5120)), "float16") + T_add = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(5120)), "float16") + for i0, i1, i2, k in T.grid(T.int64(1), T.int64(1), T.int64(5120), T.int64(1280)): + with T.block("NT_matmul"): + v_i0, v_i1, v_i2, v_k = T.axis.remap("SSSR", [i0, i1, i2, k]) + T.reads(layer_norm358[v_i0, v_i1, v_k], model_decoder_layers_0_fc1_weight5[v_i2, v_k]) + T.writes(NT_matmul_intermediate[v_i0, v_i1, v_i2]) + with T.init(): + NT_matmul_intermediate[v_i0, v_i1, v_i2] = T.float16(0) + NT_matmul_intermediate[v_i0, v_i1, v_i2] = NT_matmul_intermediate[v_i0, v_i1, v_i2] + layer_norm358[v_i0, v_i1, v_k] * model_decoder_layers_0_fc1_weight5[v_i2, v_k] + for ax0, ax1, ax2 in T.grid(T.int64(1), T.int64(1), T.int64(5120)): + with T.block("T_add"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(NT_matmul_intermediate[v_ax0, v_ax1, v_ax2], model_decoder_layers_0_fc1_bias5[v_ax2]) + T.writes(T_add_intermediate[v_ax0, v_ax1, v_ax2]) + T_add_intermediate[v_ax0, v_ax1, v_ax2] = NT_matmul_intermediate[v_ax0, v_ax1, v_ax2] + model_decoder_layers_0_fc1_bias5[v_ax2] + for ax0, ax1, ax2 in T.grid(T.int64(1), T.int64(1), T.int64(5120)): + with T.block("T_multiply"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(T_add_intermediate[v_ax0, v_ax1, v_ax2]) + T.writes(T_multiply[v_ax0, v_ax1, v_ax2]) + T_multiply[v_ax0, v_ax1, v_ax2] = T_add_intermediate[v_ax0, v_ax1, v_ax2] * T.float16(0.70710678118654757) + for i0, i1, i2 in T.grid(T.int64(1), T.int64(1), T.int64(5120)): + with T.block("compute"): + v_i0, v_i1, v_i2 = T.axis.remap("SSS", [i0, i1, i2]) + T.reads(T_multiply[v_i0, v_i1, v_i2]) + T.writes(compute[v_i0, v_i1, v_i2]) + compute[v_i0, v_i1, v_i2] = T.Cast("float32", T_multiply[v_i0, v_i1, v_i2]) + for i0, i1, i2 in T.grid(T.int64(1), T.int64(1), T.int64(5120)): + with T.block("compute_1"): + v_i0, v_i1, v_i2 = T.axis.remap("SSS", [i0, i1, i2]) + T.reads(compute[v_i0, v_i1, v_i2]) + T.writes(compute_1[v_i0, v_i1, v_i2]) + compute_1[v_i0, v_i1, v_i2] = T.erf(compute[v_i0, v_i1, v_i2]) + for i0, i1, i2 in T.grid(T.int64(1), T.int64(1), T.int64(5120)): + with T.block("compute_2"): + v_i0, v_i1, v_i2 = T.axis.remap("SSS", [i0, i1, i2]) + T.reads(compute_1[v_i0, v_i1, v_i2]) + T.writes(compute_2[v_i0, v_i1, v_i2]) + compute_2[v_i0, v_i1, v_i2] = T.Cast("float16", compute_1[v_i0, v_i1, v_i2]) + for ax0, ax1, ax2 in T.grid(T.int64(1), T.int64(1), T.int64(5120)): + with T.block("T_multiply_1"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(compute_2[v_ax0, v_ax1, v_ax2]) + T.writes(T_multiply_1[v_ax0, v_ax1, v_ax2]) + T_multiply_1[v_ax0, v_ax1, v_ax2] = compute_2[v_ax0, v_ax1, v_ax2] * T.float16(0.5) + for ax0, ax1, ax2 in T.grid(T.int64(1), T.int64(1), T.int64(5120)): + with T.block("T_add_1"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(T_multiply_1[v_ax0, v_ax1, v_ax2]) + T.writes(T_add[v_ax0, v_ax1, v_ax2]) + T_add[v_ax0, v_ax1, v_ax2] = T.float16(0.5) + T_multiply_1[v_ax0, v_ax1, v_ax2] + for ax0, ax1, ax2 in T.grid(T.int64(1), T.int64(1), T.int64(5120)): + with T.block("T_multiply_2"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(T_add_intermediate[v_ax0, v_ax1, v_ax2], T_add[v_ax0, v_ax1, v_ax2]) + T.writes(T_multiply_intermediate[v_ax0, v_ax1, v_ax2]) + T_multiply_intermediate[v_ax0, v_ax1, v_ax2] = T_add_intermediate[v_ax0, v_ax1, v_ax2] * T_add[v_ax0, v_ax1, v_ax2] + + @T.prim_func(private=True) + def fused_NT_matmul2_add7_add6(gelu130: T.Buffer((T.int64(1), T.int64(1), T.int64(5120)), "float16"), model_decoder_layers_0_fc2_weight5: T.Buffer((T.int64(1280), T.int64(5120)), "float16"), model_decoder_layers_0_fc2_bias5: T.Buffer((T.int64(1280),), "float16"), add1227: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16"), T_add_intermediate_1: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16")): + T.func_attr({"tir.noalias": T.bool(True)}) + # with T.block("root"): + NT_matmul_intermediate = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16") + T_add_intermediate = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16") + for i0, i1, i2, k in T.grid(T.int64(1), T.int64(1), T.int64(1280), T.int64(5120)): + with T.block("NT_matmul"): + v_i0, v_i1, v_i2, v_k = T.axis.remap("SSSR", [i0, i1, i2, k]) + T.reads(gelu130[v_i0, v_i1, v_k], model_decoder_layers_0_fc2_weight5[v_i2, v_k]) + T.writes(NT_matmul_intermediate[v_i0, v_i1, v_i2]) + with T.init(): + NT_matmul_intermediate[v_i0, v_i1, v_i2] = T.float16(0) + NT_matmul_intermediate[v_i0, v_i1, v_i2] = NT_matmul_intermediate[v_i0, v_i1, v_i2] + gelu130[v_i0, v_i1, v_k] * model_decoder_layers_0_fc2_weight5[v_i2, v_k] + for ax0, ax1, ax2 in T.grid(T.int64(1), T.int64(1), T.int64(1280)): + with T.block("T_add"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(NT_matmul_intermediate[v_ax0, v_ax1, v_ax2], model_decoder_layers_0_fc2_bias5[v_ax2]) + T.writes(T_add_intermediate[v_ax0, v_ax1, v_ax2]) + T_add_intermediate[v_ax0, v_ax1, v_ax2] = NT_matmul_intermediate[v_ax0, v_ax1, v_ax2] + model_decoder_layers_0_fc2_bias5[v_ax2] + for ax0, ax1, ax2 in T.grid(T.int64(1), T.int64(1), T.int64(1280)): + with T.block("T_add_1"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(add1227[v_ax0, v_ax1, v_ax2], T_add_intermediate[v_ax0, v_ax1, v_ax2]) + T.writes(T_add_intermediate_1[v_ax0, v_ax1, v_ax2]) + T_add_intermediate_1[v_ax0, v_ax1, v_ax2] = add1227[v_ax0, v_ax1, v_ax2] + T_add_intermediate[v_ax0, v_ax1, v_ax2] + + @T.prim_func(private=True) + def fused_NT_matmul_add7(layer_norm356: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16"), model_decoder_layers_0_self_attn_q_proj_weight5: T.Buffer((T.int64(1280), T.int64(1280)), "float16"), model_decoder_layers_0_self_attn_q_proj_bias5: T.Buffer((T.int64(1280),), "float16"), T_add_intermediate: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16")): + T.func_attr({"tir.noalias": T.bool(True)}) + # with T.block("root"): + NT_matmul_intermediate = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16") + for i0, i1, i2, k in T.grid(T.int64(1), T.int64(1), T.int64(1280), T.int64(1280)): + with T.block("NT_matmul"): + v_i0, v_i1, v_i2, v_k = T.axis.remap("SSSR", [i0, i1, i2, k]) + T.reads(layer_norm356[v_i0, v_i1, v_k], model_decoder_layers_0_self_attn_q_proj_weight5[v_i2, v_k]) + T.writes(NT_matmul_intermediate[v_i0, v_i1, v_i2]) + with T.init(): + NT_matmul_intermediate[v_i0, v_i1, v_i2] = T.float16(0) + NT_matmul_intermediate[v_i0, v_i1, v_i2] = NT_matmul_intermediate[v_i0, v_i1, v_i2] + layer_norm356[v_i0, v_i1, v_k] * model_decoder_layers_0_self_attn_q_proj_weight5[v_i2, v_k] + for ax0, ax1, ax2 in T.grid(T.int64(1), T.int64(1), T.int64(1280)): + with T.block("T_add"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(NT_matmul_intermediate[v_ax0, v_ax1, v_ax2], model_decoder_layers_0_self_attn_q_proj_bias5[v_ax2]) + T.writes(T_add_intermediate[v_ax0, v_ax1, v_ax2]) + T_add_intermediate[v_ax0, v_ax1, v_ax2] = NT_matmul_intermediate[v_ax0, v_ax1, v_ax2] + model_decoder_layers_0_self_attn_q_proj_bias5[v_ax2] + + @T.prim_func(private=True) + def fused_NT_matmul_add7_add6(reshape1361: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16"), model_decoder_layers_0_self_attn_out_proj_weight5: T.Buffer((T.int64(1280), T.int64(1280)), "float16"), model_decoder_layers_0_self_attn_out_proj_bias5: T.Buffer((T.int64(1280),), "float16"), add1220: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16"), T_add_intermediate_1: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16")): + T.func_attr({"tir.noalias": T.bool(True)}) + # with T.block("root"): + NT_matmul_intermediate = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16") + T_add_intermediate = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16") + for i0, i1, i2, k in T.grid(T.int64(1), T.int64(1), T.int64(1280), T.int64(1280)): + with T.block("NT_matmul"): + v_i0, v_i1, v_i2, v_k = T.axis.remap("SSSR", [i0, i1, i2, k]) + T.reads(reshape1361[v_i0, v_i1, v_k], model_decoder_layers_0_self_attn_out_proj_weight5[v_i2, v_k]) + T.writes(NT_matmul_intermediate[v_i0, v_i1, v_i2]) + with T.init(): + NT_matmul_intermediate[v_i0, v_i1, v_i2] = T.float16(0) + NT_matmul_intermediate[v_i0, v_i1, v_i2] = NT_matmul_intermediate[v_i0, v_i1, v_i2] + reshape1361[v_i0, v_i1, v_k] * model_decoder_layers_0_self_attn_out_proj_weight5[v_i2, v_k] + for ax0, ax1, ax2 in T.grid(T.int64(1), T.int64(1), T.int64(1280)): + with T.block("T_add"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(NT_matmul_intermediate[v_ax0, v_ax1, v_ax2], model_decoder_layers_0_self_attn_out_proj_bias5[v_ax2]) + T.writes(T_add_intermediate[v_ax0, v_ax1, v_ax2]) + T_add_intermediate[v_ax0, v_ax1, v_ax2] = NT_matmul_intermediate[v_ax0, v_ax1, v_ax2] + model_decoder_layers_0_self_attn_out_proj_bias5[v_ax2] + for ax0, ax1, ax2 in T.grid(T.int64(1), T.int64(1), T.int64(1280)): + with T.block("T_add_1"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(add1220[v_ax0, v_ax1, v_ax2], T_add_intermediate[v_ax0, v_ax1, v_ax2]) + T.writes(T_add_intermediate_1[v_ax0, v_ax1, v_ax2]) + T_add_intermediate_1[v_ax0, v_ax1, v_ax2] = add1220[v_ax0, v_ax1, v_ax2] + T_add_intermediate[v_ax0, v_ax1, v_ax2] + + @T.prim_func(private=True) + def fused_add4_maximum_minimum(p_add4: T.handle, p_lv611: T.handle, p_output0: T.handle): + T.func_attr({"tir.noalias": T.bool(True)}) + batch_size = T.int64() + add4 = T.match_buffer(p_add4, (batch_size, T.int64(1500), T.int64(1280)), "float16") + lv611 = T.match_buffer(p_lv611, (batch_size, T.int64(1500), T.int64(1280)), "float16") + T_minimum_intermediate = T.match_buffer(p_output0, (batch_size, T.int64(1500), T.int64(1280)), "float16") + # with T.block("root"): + T_add_intermediate = T.alloc_buffer((batch_size, T.int64(1500), T.int64(1280)), "float16") + T_maximum_intermediate = T.alloc_buffer((batch_size, T.int64(1500), T.int64(1280)), "float16") + for ax0, ax1, ax2 in T.grid(batch_size, T.int64(1500), T.int64(1280)): + with T.block("T_add"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(add4[v_ax0, v_ax1, v_ax2], lv611[v_ax0, v_ax1, v_ax2]) + T.writes(T_add_intermediate[v_ax0, v_ax1, v_ax2]) + T_add_intermediate[v_ax0, v_ax1, v_ax2] = add4[v_ax0, v_ax1, v_ax2] + lv611[v_ax0, v_ax1, v_ax2] + for ax0, ax1, ax2 in T.grid(batch_size, T.int64(1500), T.int64(1280)): + with T.block("T_maximum"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(T_add_intermediate[v_ax0, v_ax1, v_ax2]) + T.writes(T_maximum_intermediate[v_ax0, v_ax1, v_ax2]) + T_maximum_intermediate[v_ax0, v_ax1, v_ax2] = T.max(T_add_intermediate[v_ax0, v_ax1, v_ax2], T.float16(-65504)) + for ax0, ax1, ax2 in T.grid(batch_size, T.int64(1500), T.int64(1280)): + with T.block("T_minimum"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(T_maximum_intermediate[v_ax0, v_ax1, v_ax2]) + T.writes(T_minimum_intermediate[v_ax0, v_ax1, v_ax2]) + T_minimum_intermediate[v_ax0, v_ax1, v_ax2] = T.min(T_maximum_intermediate[v_ax0, v_ax1, v_ax2], T.float16(65504)) + + @T.prim_func(private=True) + def fused_conv1d1_add2_gelu1(p_gelu: T.handle, model_encoder_conv2_weight: T.Buffer((T.int64(1280), T.int64(1280), T.int64(3)), "float16"), lv3: T.Buffer((T.int64(1), T.int64(1280), T.int64(1)), "float16"), p_output0: T.handle): + T.func_attr({"tir.noalias": T.bool(True)}) + batch_size = T.int64() + gelu = T.match_buffer(p_gelu, (batch_size, T.int64(1280), T.int64(3000)), "float16") + T_multiply_intermediate = T.match_buffer(p_output0, (batch_size, T.int64(1280), T.int64(1500)), "float16") + # with T.block("root"): + pad_temp = T.alloc_buffer((batch_size, T.int64(1280), T.int64(3002)), "float16") + conv1d_ncw_intermediate = T.alloc_buffer((batch_size, T.int64(1280), T.int64(1500)), "float16") + T_add_intermediate = T.alloc_buffer((batch_size, T.int64(1280), T.int64(1500)), "float16") + T_multiply = T.alloc_buffer((batch_size, T.int64(1280), T.int64(1500)), "float16") + compute = T.alloc_buffer((batch_size, T.int64(1280), T.int64(1500))) + compute_1 = T.alloc_buffer((batch_size, T.int64(1280), T.int64(1500))) + compute_2 = T.alloc_buffer((batch_size, T.int64(1280), T.int64(1500)), "float16") + T_multiply_1 = T.alloc_buffer((batch_size, T.int64(1280), T.int64(1500)), "float16") + T_add = T.alloc_buffer((batch_size, T.int64(1280), T.int64(1500)), "float16") + for i0, i1, i2 in T.grid(batch_size, T.int64(1280), T.int64(3002)): + with T.block("pad_temp"): + v_i0, v_i1, v_i2 = T.axis.remap("SSS", [i0, i1, i2]) + T.reads(gelu[v_i0, v_i1, v_i2 - T.int64(1)]) + T.writes(pad_temp[v_i0, v_i1, v_i2]) + pad_temp[v_i0, v_i1, v_i2] = T.if_then_else(T.int64(1) <= v_i2 and v_i2 < T.int64(3001), gelu[v_i0, v_i1, v_i2 - T.int64(1)], T.float16(0)) + for nn, ff, yy, rc, ry in T.grid(batch_size, T.int64(1280), T.int64(1500), T.int64(1280), T.int64(3)): + with T.block("conv1d_ncw"): + v_nn, v_ff, v_yy, v_rc, v_ry = T.axis.remap("SSSRR", [nn, ff, yy, rc, ry]) + T.reads(pad_temp[v_nn, v_rc, v_yy * T.int64(2) + v_ry], model_encoder_conv2_weight[v_ff, v_rc, v_ry]) + T.writes(conv1d_ncw_intermediate[v_nn, v_ff, v_yy]) + with T.init(): + conv1d_ncw_intermediate[v_nn, v_ff, v_yy] = T.float16(0) + conv1d_ncw_intermediate[v_nn, v_ff, v_yy] = conv1d_ncw_intermediate[v_nn, v_ff, v_yy] + pad_temp[v_nn, v_rc, v_yy * T.int64(2) + v_ry] * model_encoder_conv2_weight[v_ff, v_rc, v_ry] + for ax0, ax1, ax2 in T.grid(batch_size, T.int64(1280), T.int64(1500)): + with T.block("T_add"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(conv1d_ncw_intermediate[v_ax0, v_ax1, v_ax2], lv3[T.int64(0), v_ax1, T.int64(0)]) + T.writes(T_add_intermediate[v_ax0, v_ax1, v_ax2]) + T_add_intermediate[v_ax0, v_ax1, v_ax2] = conv1d_ncw_intermediate[v_ax0, v_ax1, v_ax2] + lv3[T.int64(0), v_ax1, T.int64(0)] + for ax0, ax1, ax2 in T.grid(batch_size, T.int64(1280), T.int64(1500)): + with T.block("T_multiply"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(T_add_intermediate[v_ax0, v_ax1, v_ax2]) + T.writes(T_multiply[v_ax0, v_ax1, v_ax2]) + T_multiply[v_ax0, v_ax1, v_ax2] = T_add_intermediate[v_ax0, v_ax1, v_ax2] * T.float16(0.70710678118654757) + for i0, i1, i2 in T.grid(batch_size, T.int64(1280), T.int64(1500)): + with T.block("compute"): + v_i0, v_i1, v_i2 = T.axis.remap("SSS", [i0, i1, i2]) + T.reads(T_multiply[v_i0, v_i1, v_i2]) + T.writes(compute[v_i0, v_i1, v_i2]) + compute[v_i0, v_i1, v_i2] = T.Cast("float32", T_multiply[v_i0, v_i1, v_i2]) + for i0, i1, i2 in T.grid(batch_size, T.int64(1280), T.int64(1500)): + with T.block("compute_1"): + v_i0, v_i1, v_i2 = T.axis.remap("SSS", [i0, i1, i2]) + T.reads(compute[v_i0, v_i1, v_i2]) + T.writes(compute_1[v_i0, v_i1, v_i2]) + compute_1[v_i0, v_i1, v_i2] = T.erf(compute[v_i0, v_i1, v_i2]) + for i0, i1, i2 in T.grid(batch_size, T.int64(1280), T.int64(1500)): + with T.block("compute_2"): + v_i0, v_i1, v_i2 = T.axis.remap("SSS", [i0, i1, i2]) + T.reads(compute_1[v_i0, v_i1, v_i2]) + T.writes(compute_2[v_i0, v_i1, v_i2]) + compute_2[v_i0, v_i1, v_i2] = T.Cast("float16", compute_1[v_i0, v_i1, v_i2]) + for ax0, ax1, ax2 in T.grid(batch_size, T.int64(1280), T.int64(1500)): + with T.block("T_multiply_1"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(compute_2[v_ax0, v_ax1, v_ax2]) + T.writes(T_multiply_1[v_ax0, v_ax1, v_ax2]) + T_multiply_1[v_ax0, v_ax1, v_ax2] = compute_2[v_ax0, v_ax1, v_ax2] * T.float16(0.5) + for ax0, ax1, ax2 in T.grid(batch_size, T.int64(1280), T.int64(1500)): + with T.block("T_add_1"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(T_multiply_1[v_ax0, v_ax1, v_ax2]) + T.writes(T_add[v_ax0, v_ax1, v_ax2]) + T_add[v_ax0, v_ax1, v_ax2] = T.float16(0.5) + T_multiply_1[v_ax0, v_ax1, v_ax2] + for ax0, ax1, ax2 in T.grid(batch_size, T.int64(1280), T.int64(1500)): + with T.block("T_multiply_2"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(T_add_intermediate[v_ax0, v_ax1, v_ax2], T_add[v_ax0, v_ax1, v_ax2]) + T.writes(T_multiply_intermediate[v_ax0, v_ax1, v_ax2]) + T_multiply_intermediate[v_ax0, v_ax1, v_ax2] = T_add_intermediate[v_ax0, v_ax1, v_ax2] * T_add[v_ax0, v_ax1, v_ax2] + + @T.prim_func(private=True) + def fused_conv1d_add1_gelu(p_input_features: T.handle, model_encoder_conv1_weight: T.Buffer((T.int64(1280), T.int64(128), T.int64(3)), "float16"), lv1: T.Buffer((T.int64(1), T.int64(1280), T.int64(1)), "float16"), p_output0: T.handle): + T.func_attr({"tir.noalias": T.bool(True)}) + batch_size = T.int64() + input_features = T.match_buffer(p_input_features, (batch_size, T.int64(128), T.int64(3000)), "float16") + T_multiply_intermediate = T.match_buffer(p_output0, (batch_size, T.int64(1280), T.int64(3000)), "float16") + # with T.block("root"): + pad_temp = T.alloc_buffer((batch_size, T.int64(128), T.int64(3002)), "float16") + conv1d_ncw_intermediate = T.alloc_buffer((batch_size, T.int64(1280), T.int64(3000)), "float16") + T_add_intermediate = T.alloc_buffer((batch_size, T.int64(1280), T.int64(3000)), "float16") + T_multiply = T.alloc_buffer((batch_size, T.int64(1280), T.int64(3000)), "float16") + compute = T.alloc_buffer((batch_size, T.int64(1280), T.int64(3000))) + compute_1 = T.alloc_buffer((batch_size, T.int64(1280), T.int64(3000))) + compute_2 = T.alloc_buffer((batch_size, T.int64(1280), T.int64(3000)), "float16") + T_multiply_1 = T.alloc_buffer((batch_size, T.int64(1280), T.int64(3000)), "float16") + T_add = T.alloc_buffer((batch_size, T.int64(1280), T.int64(3000)), "float16") + for i0, i1, i2 in T.grid(batch_size, T.int64(128), T.int64(3002)): + with T.block("pad_temp"): + v_i0, v_i1, v_i2 = T.axis.remap("SSS", [i0, i1, i2]) + T.reads(input_features[v_i0, v_i1, v_i2 - T.int64(1)]) + T.writes(pad_temp[v_i0, v_i1, v_i2]) + pad_temp[v_i0, v_i1, v_i2] = T.if_then_else(T.int64(1) <= v_i2 and v_i2 < T.int64(3001), input_features[v_i0, v_i1, v_i2 - T.int64(1)], T.float16(0)) + for nn, ff, yy, rc, ry in T.grid(batch_size, T.int64(1280), T.int64(3000), T.int64(128), T.int64(3)): + with T.block("conv1d_ncw"): + v_nn, v_ff, v_yy, v_rc, v_ry = T.axis.remap("SSSRR", [nn, ff, yy, rc, ry]) + T.reads(pad_temp[v_nn, v_rc, v_yy + v_ry], model_encoder_conv1_weight[v_ff, v_rc, v_ry]) + T.writes(conv1d_ncw_intermediate[v_nn, v_ff, v_yy]) + with T.init(): + conv1d_ncw_intermediate[v_nn, v_ff, v_yy] = T.float16(0) + conv1d_ncw_intermediate[v_nn, v_ff, v_yy] = conv1d_ncw_intermediate[v_nn, v_ff, v_yy] + pad_temp[v_nn, v_rc, v_yy + v_ry] * model_encoder_conv1_weight[v_ff, v_rc, v_ry] + for ax0, ax1, ax2 in T.grid(batch_size, T.int64(1280), T.int64(3000)): + with T.block("T_add"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(conv1d_ncw_intermediate[v_ax0, v_ax1, v_ax2], lv1[T.int64(0), v_ax1, T.int64(0)]) + T.writes(T_add_intermediate[v_ax0, v_ax1, v_ax2]) + T_add_intermediate[v_ax0, v_ax1, v_ax2] = conv1d_ncw_intermediate[v_ax0, v_ax1, v_ax2] + lv1[T.int64(0), v_ax1, T.int64(0)] + for ax0, ax1, ax2 in T.grid(batch_size, T.int64(1280), T.int64(3000)): + with T.block("T_multiply"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(T_add_intermediate[v_ax0, v_ax1, v_ax2]) + T.writes(T_multiply[v_ax0, v_ax1, v_ax2]) + T_multiply[v_ax0, v_ax1, v_ax2] = T_add_intermediate[v_ax0, v_ax1, v_ax2] * T.float16(0.70710678118654757) + for i0, i1, i2 in T.grid(batch_size, T.int64(1280), T.int64(3000)): + with T.block("compute"): + v_i0, v_i1, v_i2 = T.axis.remap("SSS", [i0, i1, i2]) + T.reads(T_multiply[v_i0, v_i1, v_i2]) + T.writes(compute[v_i0, v_i1, v_i2]) + compute[v_i0, v_i1, v_i2] = T.Cast("float32", T_multiply[v_i0, v_i1, v_i2]) + for i0, i1, i2 in T.grid(batch_size, T.int64(1280), T.int64(3000)): + with T.block("compute_1"): + v_i0, v_i1, v_i2 = T.axis.remap("SSS", [i0, i1, i2]) + T.reads(compute[v_i0, v_i1, v_i2]) + T.writes(compute_1[v_i0, v_i1, v_i2]) + compute_1[v_i0, v_i1, v_i2] = T.erf(compute[v_i0, v_i1, v_i2]) + for i0, i1, i2 in T.grid(batch_size, T.int64(1280), T.int64(3000)): + with T.block("compute_2"): + v_i0, v_i1, v_i2 = T.axis.remap("SSS", [i0, i1, i2]) + T.reads(compute_1[v_i0, v_i1, v_i2]) + T.writes(compute_2[v_i0, v_i1, v_i2]) + compute_2[v_i0, v_i1, v_i2] = T.Cast("float16", compute_1[v_i0, v_i1, v_i2]) + for ax0, ax1, ax2 in T.grid(batch_size, T.int64(1280), T.int64(3000)): + with T.block("T_multiply_1"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(compute_2[v_ax0, v_ax1, v_ax2]) + T.writes(T_multiply_1[v_ax0, v_ax1, v_ax2]) + T_multiply_1[v_ax0, v_ax1, v_ax2] = compute_2[v_ax0, v_ax1, v_ax2] * T.float16(0.5) + for ax0, ax1, ax2 in T.grid(batch_size, T.int64(1280), T.int64(3000)): + with T.block("T_add_1"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(T_multiply_1[v_ax0, v_ax1, v_ax2]) + T.writes(T_add[v_ax0, v_ax1, v_ax2]) + T_add[v_ax0, v_ax1, v_ax2] = T.float16(0.5) + T_multiply_1[v_ax0, v_ax1, v_ax2] + for ax0, ax1, ax2 in T.grid(batch_size, T.int64(1280), T.int64(3000)): + with T.block("T_multiply_2"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(T_add_intermediate[v_ax0, v_ax1, v_ax2], T_add[v_ax0, v_ax1, v_ax2]) + T.writes(T_multiply_intermediate[v_ax0, v_ax1, v_ax2]) + T_multiply_intermediate[v_ax0, v_ax1, v_ax2] = T_add_intermediate[v_ax0, v_ax1, v_ax2] * T_add[v_ax0, v_ax1, v_ax2] + + @T.prim_func(private=True) + def fused_reshape20_reshape20_add6(take7: T.Buffer((T.int64(1), T.int64(1280)), "float16"), take8: T.Buffer((T.int64(1), T.int64(1280)), "float16"), T_add_intermediate: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16")): + T.func_attr({"tir.noalias": T.bool(True)}) + # with T.block("root"): + T_reshape_intermediate = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16") + T_reshape_intermediate_1 = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16") + for ax0, ax1, ax2 in T.grid(T.int64(1), T.int64(1), T.int64(1280)): + with T.block("T_reshape"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(take7[T.int64(0), v_ax2 % T.int64(1280)]) + T.writes(T_reshape_intermediate[v_ax0, v_ax1, v_ax2]) + T_reshape_intermediate[v_ax0, v_ax1, v_ax2] = take7[T.int64(0), v_ax2 % T.int64(1280)] + for ax0, ax1, ax2 in T.grid(T.int64(1), T.int64(1), T.int64(1280)): + with T.block("T_reshape_1"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(take8[T.int64(0), v_ax2 % T.int64(1280)]) + T.writes(T_reshape_intermediate_1[v_ax0, v_ax1, v_ax2]) + T_reshape_intermediate_1[v_ax0, v_ax1, v_ax2] = take8[T.int64(0), v_ax2 % T.int64(1280)] + for ax0, ax1, ax2 in T.grid(T.int64(1), T.int64(1), T.int64(1280)): + with T.block("T_add"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(T_reshape_intermediate[v_ax0, v_ax1, v_ax2], T_reshape_intermediate_1[v_ax0, v_ax1, v_ax2]) + T.writes(T_add_intermediate[v_ax0, v_ax1, v_ax2]) + T_add_intermediate[v_ax0, v_ax1, v_ax2] = T_reshape_intermediate[v_ax0, v_ax1, v_ax2] + T_reshape_intermediate_1[v_ax0, v_ax1, v_ax2] + + @T.prim_func(private=True) + def fused_reshape21_reshape21_reshape21_concatenate2_reshape22(add1221: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16"), lv1: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16"), add1222: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16"), T_reshape_intermediate_1_2_3: T.Buffer((T.int64(1), T.int64(60), T.int64(64)), "float16")): + T.func_attr({"tir.noalias": T.bool(True)}) + # with T.block("root"): + T_reshape_intermediate = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(20), T.int64(64)), "float16") + T_reshape_intermediate_1 = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(20), T.int64(64)), "float16") + T_reshape_intermediate_1_2 = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(20), T.int64(64)), "float16") + T_concat_intermediate = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(60), T.int64(64)), "float16") + for ax0, ax1, ax2, ax3 in T.grid(T.int64(1), T.int64(1), T.int64(20), T.int64(64)): + with T.block("T_reshape"): + v_ax0, v_ax1, v_ax2, v_ax3 = T.axis.remap("SSSS", [ax0, ax1, ax2, ax3]) + T.reads(add1221[T.int64(0), T.int64(0), (v_ax2 * T.int64(64) + v_ax3) % T.int64(1280)]) + T.writes(T_reshape_intermediate[v_ax0, v_ax1, v_ax2, v_ax3]) + T_reshape_intermediate[v_ax0, v_ax1, v_ax2, v_ax3] = add1221[T.int64(0), T.int64(0), (v_ax2 * T.int64(64) + v_ax3) % T.int64(1280)] + for ax0, ax1, ax2, ax3 in T.grid(T.int64(1), T.int64(1), T.int64(20), T.int64(64)): + with T.block("T_reshape_1"): + v_ax0, v_ax1, v_ax2, v_ax3 = T.axis.remap("SSSS", [ax0, ax1, ax2, ax3]) + T.reads(lv1[T.int64(0), T.int64(0), (v_ax2 * T.int64(64) + v_ax3) % T.int64(1280)]) + T.writes(T_reshape_intermediate_1[v_ax0, v_ax1, v_ax2, v_ax3]) + T_reshape_intermediate_1[v_ax0, v_ax1, v_ax2, v_ax3] = lv1[T.int64(0), T.int64(0), (v_ax2 * T.int64(64) + v_ax3) % T.int64(1280)] + for ax0, ax1, ax2, ax3 in T.grid(T.int64(1), T.int64(1), T.int64(20), T.int64(64)): + with T.block("T_reshape_2"): + v_ax0, v_ax1, v_ax2, v_ax3 = T.axis.remap("SSSS", [ax0, ax1, ax2, ax3]) + T.reads(add1222[T.int64(0), T.int64(0), (v_ax2 * T.int64(64) + v_ax3) % T.int64(1280)]) + T.writes(T_reshape_intermediate_1_2[v_ax0, v_ax1, v_ax2, v_ax3]) + T_reshape_intermediate_1_2[v_ax0, v_ax1, v_ax2, v_ax3] = add1222[T.int64(0), T.int64(0), (v_ax2 * T.int64(64) + v_ax3) % T.int64(1280)] + for ax0, ax1, ax2, ax3 in T.grid(T.int64(1), T.int64(1), T.int64(60), T.int64(64)): + with T.block("T_concat"): + v_ax0, v_ax1, v_ax2, v_ax3 = T.axis.remap("SSSS", [ax0, ax1, ax2, ax3]) + T.reads(T_reshape_intermediate_1_2[v_ax0, v_ax1, v_ax2 - T.int64(40), v_ax3], T_reshape_intermediate_1[v_ax0, v_ax1, v_ax2 - T.int64(20), v_ax3], T_reshape_intermediate[v_ax0, v_ax1, v_ax2, v_ax3]) + T.writes(T_concat_intermediate[v_ax0, v_ax1, v_ax2, v_ax3]) + T_concat_intermediate[v_ax0, v_ax1, v_ax2, v_ax3] = T.if_then_else(T.int64(40) <= v_ax2, T_reshape_intermediate_1_2[v_ax0, v_ax1, v_ax2 - T.int64(40), v_ax3], T.if_then_else(T.int64(20) <= v_ax2, T_reshape_intermediate_1[v_ax0, v_ax1, v_ax2 - T.int64(20), v_ax3], T_reshape_intermediate[v_ax0, v_ax1, v_ax2, v_ax3])) + for ax0, ax1, ax2 in T.grid(T.int64(1), T.int64(60), T.int64(64)): + with T.block("T_reshape_3"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(T_concat_intermediate[T.int64(0), T.int64(0), (v_ax2 // T.int64(64) + v_ax1) % T.int64(60), v_ax2 % T.int64(64)]) + T.writes(T_reshape_intermediate_1_2_3[v_ax0, v_ax1, v_ax2]) + T_reshape_intermediate_1_2_3[v_ax0, v_ax1, v_ax2] = T_concat_intermediate[T.int64(0), T.int64(0), (v_ax2 // T.int64(64) + v_ax1) % T.int64(60), v_ax2 % T.int64(64)] + + @T.prim_func(private=True) + def fused_reshape21_reshape25(add1225: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16"), T_reshape_intermediate_1: T.Buffer((T.int64(1), T.int64(20), T.int64(64)), "float16")): + T.func_attr({"tir.noalias": T.bool(True)}) + # with T.block("root"): + T_reshape_intermediate = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(20), T.int64(64)), "float16") + for ax0, ax1, ax2, ax3 in T.grid(T.int64(1), T.int64(1), T.int64(20), T.int64(64)): + with T.block("T_reshape"): + v_ax0, v_ax1, v_ax2, v_ax3 = T.axis.remap("SSSS", [ax0, ax1, ax2, ax3]) + T.reads(add1225[T.int64(0), T.int64(0), (v_ax2 * T.int64(64) + v_ax3) % T.int64(1280)]) + T.writes(T_reshape_intermediate[v_ax0, v_ax1, v_ax2, v_ax3]) + T_reshape_intermediate[v_ax0, v_ax1, v_ax2, v_ax3] = add1225[T.int64(0), T.int64(0), (v_ax2 * T.int64(64) + v_ax3) % T.int64(1280)] + for ax0, ax1, ax2 in T.grid(T.int64(1), T.int64(20), T.int64(64)): + with T.block("T_reshape_1"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(T_reshape_intermediate[T.int64(0), T.int64(0), (v_ax2 // T.int64(64) + v_ax1) % T.int64(20), v_ax2 % T.int64(64)]) + T.writes(T_reshape_intermediate_1[v_ax0, v_ax1, v_ax2]) + T_reshape_intermediate_1[v_ax0, v_ax1, v_ax2] = T_reshape_intermediate[T.int64(0), T.int64(0), (v_ax2 // T.int64(64) + v_ax1) % T.int64(20), v_ax2 % T.int64(64)] + + @T.prim_func(private=True) + def fused_reshape23_reshape24(lv265: T.Buffer((T.int64(1), T.int64(20), T.int64(64)), "float16"), T_reshape_intermediate_1: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16")): + T.func_attr({"tir.noalias": T.bool(True)}) + # with T.block("root"): + T_reshape_intermediate = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(20), T.int64(64)), "float16") + for ax0, ax1, ax2, ax3 in T.grid(T.int64(1), T.int64(1), T.int64(20), T.int64(64)): + with T.block("T_reshape"): + v_ax0, v_ax1, v_ax2, v_ax3 = T.axis.remap("SSSS", [ax0, ax1, ax2, ax3]) + T.reads(lv265[T.int64(0), (v_ax3 // T.int64(64) + v_ax2) % T.int64(20), v_ax3 % T.int64(64)]) + T.writes(T_reshape_intermediate[v_ax0, v_ax1, v_ax2, v_ax3]) + T_reshape_intermediate[v_ax0, v_ax1, v_ax2, v_ax3] = lv265[T.int64(0), (v_ax3 // T.int64(64) + v_ax2) % T.int64(20), v_ax3 % T.int64(64)] + for ax0, ax1, ax2 in T.grid(T.int64(1), T.int64(1), T.int64(1280)): + with T.block("T_reshape_1"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(T_reshape_intermediate[T.int64(0), T.int64(0), v_ax2 % T.int64(1280) // T.int64(64), v_ax2 % T.int64(64)]) + T.writes(T_reshape_intermediate_1[v_ax0, v_ax1, v_ax2]) + T_reshape_intermediate_1[v_ax0, v_ax1, v_ax2] = T_reshape_intermediate[T.int64(0), T.int64(0), v_ax2 % T.int64(1280) // T.int64(64), v_ax2 % T.int64(64)] + + @T.prim_func(private=True) + def fused_reshape9(packed_params_1: T.Buffer((T.int64(1280),), "float16"), T_reshape_intermediate: T.Buffer((T.int64(1), T.int64(1280), T.int64(1)), "float16")): + T.func_attr({"tir.noalias": T.bool(True)}) + # with T.block("root"): + for ax0, ax1, ax2 in T.grid(T.int64(1), T.int64(1280), T.int64(1)): + with T.block("T_reshape"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(packed_params_1[(v_ax1 + v_ax2) % T.int64(1280)]) + T.writes(T_reshape_intermediate[v_ax0, v_ax1, v_ax2]) + T_reshape_intermediate[v_ax0, v_ax1, v_ax2] = packed_params_1[(v_ax1 + v_ax2) % T.int64(1280)] + + @T.prim_func + def fused_rope(var_qkv: T.handle, var_position_map: T.handle, var_q: T.handle, var_k: T.handle, var_v: T.handle, apply_rope: T.int32): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.noalias": T.bool(True)}) + seq_len = T.int64() + qkv = T.match_buffer(var_qkv, (seq_len, 60, 64), "float16") + position_map = T.match_buffer(var_position_map, (seq_len,), "int32", offset_factor=1) + q = T.match_buffer(var_q, (seq_len, 20, 64), "float16") + k = T.match_buffer(var_k, (seq_len, 20, 64), "float16") + v = T.match_buffer(var_v, (seq_len, 20, 64), "float16") + # with T.block("root"): + for iters_0, iters_1, iters_2 in T.grid(seq_len, 60, 64): + with T.block("llama_fused_rope"): + s, h, d = T.axis.remap("SSS", [iters_0, iters_1, iters_2]) + T.reads(position_map[s], qkv[s, h, d - 32:d - 32 + 65]) + T.writes(q[s, h, d], k[s, h - 20, d], v[s, h - 40, d]) + if h < 20: + q[s, h, d] = T.if_then_else(apply_rope > 0 and d < 64, T.Cast("float16", T.cos(T.Cast("float32", position_map[s]) / T.pow(T.float32(1), T.Cast("float32", d * 2 % 64) / T.float32(64))) * T.Cast("float32", qkv[s, h, d]) + T.sin(T.Cast("float32", position_map[s]) / T.pow(T.float32(1), T.Cast("float32", d * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(d < 32, qkv[s, h, d + 32] * T.float16(-1), qkv[s, h, d - 32]))), qkv[s, h, d]) + else: + if h < 40: + k[s, h - 20, d] = T.if_then_else(apply_rope > 0 and d < 64, T.Cast("float16", T.cos(T.Cast("float32", position_map[s]) / T.pow(T.float32(1), T.Cast("float32", d * 2 % 64) / T.float32(64))) * T.Cast("float32", qkv[s, h, d]) + T.sin(T.Cast("float32", position_map[s]) / T.pow(T.float32(1), T.Cast("float32", d * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(d < 32, qkv[s, h, d + 32] * T.float16(-1), qkv[s, h, d - 32]))), qkv[s, h, d]) + else: + v[s, h - 40, d] = qkv[s, h, d] + + @T.prim_func(private=True) + def fused_transpose_add3(packed_params_4: T.Buffer((T.int64(1500), T.int64(1280)), "float16"), p_gelu1: T.handle, p_output0: T.handle): + T.func_attr({"tir.noalias": T.bool(True)}) + batch_size = T.int64() + gelu1 = T.match_buffer(p_gelu1, (batch_size, T.int64(1280), T.int64(1500)), "float16") + T_add_intermediate = T.match_buffer(p_output0, (batch_size, T.int64(1500), T.int64(1280)), "float16") + # with T.block("root"): + T_transpose_intermediate = T.alloc_buffer((batch_size, T.int64(1500), T.int64(1280)), "float16") + for ax0, ax1, ax2 in T.grid(batch_size, T.int64(1500), T.int64(1280)): + with T.block("T_transpose"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(gelu1[v_ax0, v_ax2, v_ax1]) + T.writes(T_transpose_intermediate[v_ax0, v_ax1, v_ax2]) + T_transpose_intermediate[v_ax0, v_ax1, v_ax2] = gelu1[v_ax0, v_ax2, v_ax1] + for ax0, ax1, ax2 in T.grid(batch_size, T.int64(1500), T.int64(1280)): + with T.block("T_add"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(T_transpose_intermediate[v_ax0, v_ax1, v_ax2], packed_params_4[v_ax1, v_ax2]) + T.writes(T_add_intermediate[v_ax0, v_ax1, v_ax2]) + T_add_intermediate[v_ax0, v_ax1, v_ax2] = T_transpose_intermediate[v_ax0, v_ax1, v_ax2] + packed_params_4[v_ax1, v_ax2] + + @T.prim_func + def gather_probs(var_src: T.handle, var_indices: T.handle, var_dst: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.noalias": T.bool(True)}) + m, n = T.int32(is_size_var=True), T.int32(is_size_var=True) + src = T.match_buffer(var_src, (m, n)) + batch_size = T.int32(is_size_var=True) + indices = T.match_buffer(var_indices, (batch_size,), "int32") + dst = T.match_buffer(var_dst, (batch_size, n)) + # with T.block("root"): + for b, j in T.grid(batch_size, n): + with T.block("gather_2d"): + vb, vj = T.axis.remap("SS", [b, j]) + T.reads(src[indices[vb], vj], indices[vb]) + T.writes(dst[vb, vj]) + dst[vb, vj] = src[indices[vb], vj] + + @T.prim_func(private=True) + def get_index_from_sorted(A: T.handle, B: T.handle, C: T.handle, D: T.handle, E: T.handle, F: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32})}) + batch, vocab_size = T.int64(), T.int64() + cumsum_sorted = T.match_buffer(A, (batch, vocab_size)) + indices = T.match_buffer(B, (batch, vocab_size), "int32") + renorm_prob = T.match_buffer(C, (batch, 1)) + out_batch = T.int64() + usample = T.match_buffer(D, (out_batch, 1)) + sample_indices = T.match_buffer(E, (out_batch, 1), "int32") + output_index = T.match_buffer(F, (out_batch, 1), "int32") + # with T.block("root"): + for ax0, ax1 in T.grid(out_batch, vocab_size): + with T.block("T_get_index_from_sorted"): + v_ax0, v_ax1 = T.axis.remap("SS", [ax0, ax1]) + T.reads(usample[v_ax0, T.int64(0)], cumsum_sorted[sample_indices[v_ax0, T.int64(0)], v_ax1 - T.int64(1):v_ax1 - T.int64(1) + T.int64(2)], sample_indices[v_ax0, T.int64(0)], renorm_prob[sample_indices[v_ax0, T.int64(0)], 0], indices[sample_indices[v_ax0, T.int64(0)], T.min(T.int64(0), v_ax1):T.min(T.int64(0), v_ax1) + (T.max(T.int64(0), v_ax1) + T.int64(1) - T.min(T.int64(0), v_ax1))]) + T.writes(output_index[v_ax0, 0]) + if usample[v_ax0, T.int64(0)] < cumsum_sorted[sample_indices[v_ax0, T.int64(0)], v_ax1] / renorm_prob[sample_indices[v_ax0, T.int64(0)], 0] or v_ax1 + T.int64(1) == vocab_size: + if v_ax1 == T.int64(0): + output_index[v_ax0, 0] = indices[sample_indices[v_ax0, T.int64(0)], 0] + else: + if usample[v_ax0, T.int64(0)] >= cumsum_sorted[sample_indices[v_ax0, T.int64(0)], v_ax1 - T.int64(1)] / renorm_prob[sample_indices[v_ax0, T.int64(0)], 0]: + output_index[v_ax0, 0] = indices[sample_indices[v_ax0, T.int64(0)], v_ax1] + + @T.prim_func(private=True) + def get_renorm_prob(A: T.handle, B: T.handle, C: T.handle, D: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32})}) + batch, vocab_size = T.int64(), T.int64() + cumsum_sorted = T.match_buffer(A, (batch, vocab_size)) + top_p = T.match_buffer(B, (batch, 1)) + top_k = T.match_buffer(C, (batch, 1), "int32") + renorm_prob = T.match_buffer(D, (batch, 1)) + # with T.block("root"): + for ax0, ax1 in T.grid(batch, vocab_size): + with T.block("T_get_renorm_prob"): + v_ax0, v_ax1 = T.axis.remap("SS", [ax0, ax1]) + T.reads(cumsum_sorted[v_ax0, T.min(T.min(T.int64(0), v_ax1), v_ax1 + T.int64(1)):T.min(T.min(T.int64(0), v_ax1), v_ax1 + T.int64(1)) + (T.max(T.max(T.int64(0), v_ax1), v_ax1 + T.int64(1)) + T.int64(1) - T.min(T.min(T.int64(0), v_ax1), v_ax1 + T.int64(1)))], top_p[v_ax0, 0], top_k[v_ax0, 0]) + T.writes(renorm_prob[v_ax0, 0]) + if not (cumsum_sorted[v_ax0, 0] < top_p[v_ax0, 0] and top_k[v_ax0, 0] > 1): + renorm_prob[v_ax0, 0] = cumsum_sorted[v_ax0, 0] + else: + if cumsum_sorted[v_ax0, v_ax1] < top_p[v_ax0, 0] and v_ax1 + T.int64(1) < T.Cast("int64", top_k[v_ax0, 0]): + if v_ax1 + T.int64(1) == vocab_size: + renorm_prob[v_ax0, 0] = cumsum_sorted[v_ax0, v_ax1] + else: + if not (cumsum_sorted[v_ax0, v_ax1 + T.int64(1)] < top_p[v_ax0, 0] and v_ax1 + T.int64(1) + T.int64(1) < T.Cast("int64", top_k[v_ax0, 0])): + renorm_prob[v_ax0, 0] = cumsum_sorted[v_ax0, v_ax1 + T.int64(1)] + + @T.prim_func(private=True) + def index(var_layer_norm355: T.handle, index: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16")): + T.func_attr({"target": T.target({"arch": "sm_89", "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.noalias": T.bool(True)}) + seq_len = T.int64() + layer_norm355 = T.match_buffer(var_layer_norm355, (T.int64(1), seq_len, T.int64(1280)), "float16") + # with T.block("root"): + for i, _, k in T.grid(T.int64(1), T.int64(1), T.int64(1280)): + with T.block("index"): + v_i, v__, v_k = T.axis.remap("SSS", [i, _, k]) + T.reads(layer_norm355[v_i, seq_len - T.int64(1), v_k]) + T.writes(index[v_i, v__, v_k]) + index[v_i, v__, v_k] = layer_norm355[v_i, seq_len - T.int64(1), v_k] + + @T.prim_func(private=True) + def layer_norm(var_add578: T.handle, model_decoder_layers_0_self_attn_layer_norm_weight3: T.Buffer((T.int64(1280),), "float16"), model_decoder_layers_0_self_attn_layer_norm_bias3: T.Buffer((T.int64(1280),), "float16"), var_T_layer_norm: T.handle): + T.func_attr({"tir.noalias": T.bool(True)}) + batch_size = T.int64() + add578 = T.match_buffer(var_add578, (batch_size, T.int64(1), T.int64(1280)), "float16") + T_layer_norm = T.match_buffer(var_T_layer_norm, (batch_size, T.int64(1), T.int64(1280)), "float16") + # with T.block("root"): + add578_red_temp_v0 = T.alloc_buffer((batch_size, T.int64(1))) + add578_red_temp_v1 = T.alloc_buffer((batch_size, T.int64(1))) + for ax0, ax1, k2 in T.grid(batch_size, T.int64(1), T.int64(1280)): + with T.block("add578_red_temp"): + v_ax0, v_ax1, v_k2 = T.axis.remap("SSR", [ax0, ax1, k2]) + T.reads(add578[v_ax0, v_ax1, v_k2]) + T.writes(add578_red_temp_v0[v_ax0, v_ax1], add578_red_temp_v1[v_ax0, v_ax1]) + with T.init(): + add578_red_temp_v0[v_ax0, v_ax1] = T.float32(0) + add578_red_temp_v1[v_ax0, v_ax1] = T.float32(0) + v_add578_red_temp_v0: T.float32 = add578_red_temp_v0[v_ax0, v_ax1] + T.Cast("float32", add578[v_ax0, v_ax1, v_k2]) + v_add578_red_temp_v1: T.float32 = add578_red_temp_v1[v_ax0, v_ax1] + T.Cast("float32", add578[v_ax0, v_ax1, v_k2]) * T.Cast("float32", add578[v_ax0, v_ax1, v_k2]) + add578_red_temp_v0[v_ax0, v_ax1] = v_add578_red_temp_v0 + add578_red_temp_v1[v_ax0, v_ax1] = v_add578_red_temp_v1 + for ax0, ax1, ax2 in T.grid(batch_size, T.int64(1), T.int64(1280)): + with T.block("T_layer_norm"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(add578[v_ax0, v_ax1, v_ax2], add578_red_temp_v0[v_ax0, v_ax1], add578_red_temp_v1[v_ax0, v_ax1], model_decoder_layers_0_self_attn_layer_norm_weight3[v_ax2], model_decoder_layers_0_self_attn_layer_norm_bias3[v_ax2]) + T.writes(T_layer_norm[v_ax0, v_ax1, v_ax2]) + T_layer_norm[v_ax0, v_ax1, v_ax2] = T.Cast("float16", (T.Cast("float32", add578[v_ax0, v_ax1, v_ax2]) - add578_red_temp_v0[v_ax0, v_ax1] * T.float32(0.00078125000000000004)) * T.rsqrt(add578_red_temp_v1[v_ax0, v_ax1] * T.float32(0.00078125000000000004) - add578_red_temp_v0[v_ax0, v_ax1] * T.float32(0.00078125000000000004) * (add578_red_temp_v0[v_ax0, v_ax1] * T.float32(0.00078125000000000004)) + T.float32(1.0000000000000001e-05))) * model_decoder_layers_0_self_attn_layer_norm_weight3[v_ax2] + model_decoder_layers_0_self_attn_layer_norm_bias3[v_ax2] + + @T.prim_func(private=True) + def layer_norm1(var_add: T.handle, model_encoder_layers_0_self_attn_layer_norm_weight: T.Buffer((T.int64(1280),), "float16"), model_encoder_layers_0_self_attn_layer_norm_bias: T.Buffer((T.int64(1280),), "float16"), var_T_layer_norm: T.handle): + T.func_attr({"tir.noalias": T.bool(True)}) + batch_size = T.int64() + add = T.match_buffer(var_add, (batch_size, T.int64(1500), T.int64(1280)), "float16") + T_layer_norm = T.match_buffer(var_T_layer_norm, (batch_size, T.int64(1500), T.int64(1280)), "float16") + # with T.block("root"): + add_red_temp_v0 = T.alloc_buffer((batch_size, T.int64(1500))) + add_red_temp_v1 = T.alloc_buffer((batch_size, T.int64(1500))) + for ax0, ax1, k2 in T.grid(batch_size, T.int64(1500), T.int64(1280)): + with T.block("add_red_temp"): + v_ax0, v_ax1, v_k2 = T.axis.remap("SSR", [ax0, ax1, k2]) + T.reads(add[v_ax0, v_ax1, v_k2]) + T.writes(add_red_temp_v0[v_ax0, v_ax1], add_red_temp_v1[v_ax0, v_ax1]) + with T.init(): + add_red_temp_v0[v_ax0, v_ax1] = T.float32(0) + add_red_temp_v1[v_ax0, v_ax1] = T.float32(0) + v_add_red_temp_v0: T.float32 = add_red_temp_v0[v_ax0, v_ax1] + T.Cast("float32", add[v_ax0, v_ax1, v_k2]) + v_add_red_temp_v1: T.float32 = add_red_temp_v1[v_ax0, v_ax1] + T.Cast("float32", add[v_ax0, v_ax1, v_k2]) * T.Cast("float32", add[v_ax0, v_ax1, v_k2]) + add_red_temp_v0[v_ax0, v_ax1] = v_add_red_temp_v0 + add_red_temp_v1[v_ax0, v_ax1] = v_add_red_temp_v1 + for ax0, ax1, ax2 in T.grid(batch_size, T.int64(1500), T.int64(1280)): + with T.block("T_layer_norm"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(add[v_ax0, v_ax1, v_ax2], add_red_temp_v0[v_ax0, v_ax1], add_red_temp_v1[v_ax0, v_ax1], model_encoder_layers_0_self_attn_layer_norm_weight[v_ax2], model_encoder_layers_0_self_attn_layer_norm_bias[v_ax2]) + T.writes(T_layer_norm[v_ax0, v_ax1, v_ax2]) + T_layer_norm[v_ax0, v_ax1, v_ax2] = T.Cast("float16", (T.Cast("float32", add[v_ax0, v_ax1, v_ax2]) - add_red_temp_v0[v_ax0, v_ax1] * T.float32(0.00078125000000000004)) * T.rsqrt(add_red_temp_v1[v_ax0, v_ax1] * T.float32(0.00078125000000000004) - add_red_temp_v0[v_ax0, v_ax1] * T.float32(0.00078125000000000004) * (add_red_temp_v0[v_ax0, v_ax1] * T.float32(0.00078125000000000004)) + T.float32(1.0000000000000001e-05))) * model_encoder_layers_0_self_attn_layer_norm_weight[v_ax2] + model_encoder_layers_0_self_attn_layer_norm_bias[v_ax2] + + @T.prim_func(private=True) + def layer_norm2(var_add257: T.handle, model_decoder_layers_0_self_attn_layer_norm_weight2: T.Buffer((T.int64(1280),), "float16"), model_decoder_layers_0_self_attn_layer_norm_bias2: T.Buffer((T.int64(1280),), "float16"), var_T_layer_norm: T.handle): + T.func_attr({"tir.noalias": T.bool(True)}) + seq_len = T.int64() + add257 = T.match_buffer(var_add257, (T.int64(1), seq_len, T.int64(1280)), "float16") + T_layer_norm = T.match_buffer(var_T_layer_norm, (T.int64(1), seq_len, T.int64(1280)), "float16") + # with T.block("root"): + add257_red_temp_v0 = T.alloc_buffer((T.int64(1), seq_len)) + add257_red_temp_v1 = T.alloc_buffer((T.int64(1), seq_len)) + for ax0, ax1, k2 in T.grid(T.int64(1), seq_len, T.int64(1280)): + with T.block("add257_red_temp"): + v_ax0, v_ax1, v_k2 = T.axis.remap("SSR", [ax0, ax1, k2]) + T.reads(add257[v_ax0, v_ax1, v_k2]) + T.writes(add257_red_temp_v0[v_ax0, v_ax1], add257_red_temp_v1[v_ax0, v_ax1]) + with T.init(): + add257_red_temp_v0[v_ax0, v_ax1] = T.float32(0) + add257_red_temp_v1[v_ax0, v_ax1] = T.float32(0) + v_add257_red_temp_v0: T.float32 = add257_red_temp_v0[v_ax0, v_ax1] + T.Cast("float32", add257[v_ax0, v_ax1, v_k2]) + v_add257_red_temp_v1: T.float32 = add257_red_temp_v1[v_ax0, v_ax1] + T.Cast("float32", add257[v_ax0, v_ax1, v_k2]) * T.Cast("float32", add257[v_ax0, v_ax1, v_k2]) + add257_red_temp_v0[v_ax0, v_ax1] = v_add257_red_temp_v0 + add257_red_temp_v1[v_ax0, v_ax1] = v_add257_red_temp_v1 + for ax0, ax1, ax2 in T.grid(T.int64(1), seq_len, T.int64(1280)): + with T.block("T_layer_norm"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(add257[v_ax0, v_ax1, v_ax2], add257_red_temp_v0[v_ax0, v_ax1], add257_red_temp_v1[v_ax0, v_ax1], model_decoder_layers_0_self_attn_layer_norm_weight2[v_ax2], model_decoder_layers_0_self_attn_layer_norm_bias2[v_ax2]) + T.writes(T_layer_norm[v_ax0, v_ax1, v_ax2]) + T_layer_norm[v_ax0, v_ax1, v_ax2] = T.Cast("float16", (T.Cast("float32", add257[v_ax0, v_ax1, v_ax2]) - add257_red_temp_v0[v_ax0, v_ax1] * T.float32(0.00078125000000000004)) * T.rsqrt(add257_red_temp_v1[v_ax0, v_ax1] * T.float32(0.00078125000000000004) - add257_red_temp_v0[v_ax0, v_ax1] * T.float32(0.00078125000000000004) * (add257_red_temp_v0[v_ax0, v_ax1] * T.float32(0.00078125000000000004)) + T.float32(1.0000000000000001e-05))) * model_decoder_layers_0_self_attn_layer_norm_weight2[v_ax2] + model_decoder_layers_0_self_attn_layer_norm_bias2[v_ax2] + + @T.prim_func(private=True) + def layer_norm3(add1220: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16"), model_decoder_layers_0_self_attn_layer_norm_weight5: T.Buffer((T.int64(1280),), "float16"), model_decoder_layers_0_self_attn_layer_norm_bias5: T.Buffer((T.int64(1280),), "float16"), T_layer_norm: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16")): + T.func_attr({"tir.noalias": T.bool(True)}) + # with T.block("root"): + add1220_red_temp_v0 = T.alloc_buffer((T.int64(1), T.int64(1))) + add1220_red_temp_v1 = T.alloc_buffer((T.int64(1), T.int64(1))) + for ax0, ax1, k2 in T.grid(T.int64(1), T.int64(1), T.int64(1280)): + with T.block("add1220_red_temp"): + v_ax0, v_ax1, v_k2 = T.axis.remap("SSR", [ax0, ax1, k2]) + T.reads(add1220[v_ax0, v_ax1, v_k2]) + T.writes(add1220_red_temp_v0[v_ax0, v_ax1], add1220_red_temp_v1[v_ax0, v_ax1]) + with T.init(): + add1220_red_temp_v0[v_ax0, v_ax1] = T.float32(0) + add1220_red_temp_v1[v_ax0, v_ax1] = T.float32(0) + v_add1220_red_temp_v0: T.float32 = add1220_red_temp_v0[v_ax0, v_ax1] + T.Cast("float32", add1220[v_ax0, v_ax1, v_k2]) + v_add1220_red_temp_v1: T.float32 = add1220_red_temp_v1[v_ax0, v_ax1] + T.Cast("float32", add1220[v_ax0, v_ax1, v_k2]) * T.Cast("float32", add1220[v_ax0, v_ax1, v_k2]) + add1220_red_temp_v0[v_ax0, v_ax1] = v_add1220_red_temp_v0 + add1220_red_temp_v1[v_ax0, v_ax1] = v_add1220_red_temp_v1 + for ax0, ax1, ax2 in T.grid(T.int64(1), T.int64(1), T.int64(1280)): + with T.block("T_layer_norm"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(add1220[v_ax0, v_ax1, v_ax2], add1220_red_temp_v0[v_ax0, v_ax1], add1220_red_temp_v1[v_ax0, v_ax1], model_decoder_layers_0_self_attn_layer_norm_weight5[v_ax2], model_decoder_layers_0_self_attn_layer_norm_bias5[v_ax2]) + T.writes(T_layer_norm[v_ax0, v_ax1, v_ax2]) + T_layer_norm[v_ax0, v_ax1, v_ax2] = T.Cast("float16", (T.Cast("float32", add1220[v_ax0, v_ax1, v_ax2]) - add1220_red_temp_v0[v_ax0, v_ax1] * T.float32(0.00078125000000000004)) * T.rsqrt(add1220_red_temp_v1[v_ax0, v_ax1] * T.float32(0.00078125000000000004) - add1220_red_temp_v0[v_ax0, v_ax1] * T.float32(0.00078125000000000004) * (add1220_red_temp_v0[v_ax0, v_ax1] * T.float32(0.00078125000000000004)) + T.float32(1.0000000000000001e-05))) * model_decoder_layers_0_self_attn_layer_norm_weight5[v_ax2] + model_decoder_layers_0_self_attn_layer_norm_bias5[v_ax2] + + @T.prim_func + def merge_state_inplace(v: T.handle, s: T.handle, v_other: T.handle, s_other: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) + N, H, D = T.int32(is_size_var=True), T.int32(is_size_var=True), T.int32(is_size_var=True) + V = T.match_buffer(v, (N, H, D), "float16") + S = T.match_buffer(s, (N, H)) + V_other = T.match_buffer(v_other, (N, H, D), "float16") + S_other = T.match_buffer(s_other, (N, H)) + # with T.block("root"): + for bx in T.thread_binding(N, thread="blockIdx.x"): + for by in T.thread_binding(1, thread="blockIdx.y"): + for ty in T.thread_binding(20, thread="threadIdx.y"): + for tx in T.thread_binding(16, thread="threadIdx.x"): + with T.block("merge"): + T.reads(S[bx, ty + by * 20], S_other[bx, ty + by * 20], V[bx, ty + by * 20, tx * 4:tx * 4 + 4], V_other[bx, ty + by * 20, tx * 4:tx * 4 + 4]) + T.writes(V[bx, ty + by * 20, tx * 4:tx * 4 + 4], S[bx, ty + by * 20]) + s_val = T.alloc_buffer((1,), scope="local") + s_other_val = T.alloc_buffer((1,), scope="local") + s_max = T.alloc_buffer((1,), scope="local") + scale = T.alloc_buffer((1,), scope="local") + other_scale = T.alloc_buffer((1,), scope="local") + v_vec = T.alloc_buffer((4,), "float16", scope="local") + v_other_vec = T.alloc_buffer((4,), "float16", scope="local") + s_val[0] = S[bx, ty + by * 20] + s_other_val[0] = S_other[bx, ty + by * 20] + s_max[0] = T.max(s_val[0], s_other_val[0]) + s_val[0] = T.exp2(s_val[0] - s_max[0]) + s_other_val[0] = T.exp2(s_other_val[0] - s_max[0]) + scale[0] = s_val[0] / (s_val[0] + s_other_val[0]) + other_scale[0] = s_other_val[0] / (s_val[0] + s_other_val[0]) + for vec in T.vectorized(4): + v_vec[vec] = V[bx, ty + by * 20, tx * 4 + vec] + for vec in T.vectorized(4): + v_other_vec[vec] = V_other[bx, ty + by * 20, tx * 4 + vec] + for vec in range(4): + v_vec[vec] = T.Cast("float16", T.Cast("float32", v_vec[vec]) * scale[0] + T.Cast("float32", v_other_vec[vec]) * other_scale[0]) + for vec in T.vectorized(4): + V[bx, ty + by * 20, tx * 4 + vec] = v_vec[vec] + S[bx, ty + by * 20] = T.log2(s_val[0] + s_other_val[0]) + s_max[0] + + @T.prim_func + def parallel_sampling_from_prob(var_prob: T.handle, var_uniform_samples: T.handle, var_row_indices: T.handle, var_sampled_token_ids: T.handle): + T.func_attr({"tir.is_scheduled": 1}) + n, vocab_size = T.int64(), T.int64() + prob = T.match_buffer(var_prob, (n, vocab_size)) + batch_size = T.int64() + uniform_samples = T.match_buffer(var_uniform_samples, (batch_size, 1)) + row_indices = T.match_buffer(var_row_indices, (batch_size, 1), "int32") + token_ids = T.match_buffer(var_sampled_token_ids, (batch_size, 1), "int32") + # with T.block("root"): + aggregate = T.alloc_buffer((), scope="local") + sample_id_local = T.alloc_buffer((), "int32", scope="local") + step_iter = T.alloc_buffer((), "int32", scope="local") + for bx in T.thread_binding(batch_size, thread="blockIdx.x"): + row_idx: T.int32 = row_indices[bx, 0] + for ty in T.thread_binding(T.int64(4), thread="threadIdx.y"): + for tx in T.thread_binding(T.int64(32), thread="threadIdx.x"): + u: T.float32 = uniform_samples[bx, 0] + aggregate[()] = T.Cast("float32", 0) + step_iter[()] = 0 + while T.tvm_thread_invariant((step_iter[()] == 0 or aggregate[()] < u - T.float32(9.9999999999999995e-07)) and T.Cast("int64", step_iter[()]) < (vocab_size + T.int64(512) - T.int64(1)) // T.int64(512)): + with T.block(""): + T.reads(step_iter[()], prob[row_idx, T.Cast("int64", step_iter[()]) * T.int64(512) + ty * T.int64(128) + tx * T.int64(4):T.Cast("int64", step_iter[()]) * T.int64(512) + ty * T.int64(128) + tx * T.int64(4) + T.int64(4)], aggregate[()]) + T.writes(sample_id_local[()], aggregate[()]) + prob_gt_threshold = T.alloc_buffer((T.int64(4),), scope="local") + cumsum = T.alloc_buffer((T.int64(512),), scope="shared") + greater_than_u = T.alloc_buffer((T.int64(4),), "bool", scope="local") + mask = T.alloc_buffer((T.int64(4),), "bool", scope="local") + valid = T.alloc_buffer((T.int64(4),), "bool", scope="local") + indices = T.alloc_buffer((T.int64(4),), "int32", scope="local") + step_aggregate = T.alloc_buffer((), scope="local") + for v in T.unroll(T.int64(4)): + idx: T.int64 = T.Cast("int64", step_iter[()]) * T.int64(512) + ty * T.int64(128) + tx * T.int64(4) + v + prob_local: T.float32 = T.if_then_else(idx < vocab_size, prob[row_idx, idx], T.Cast("float32", 0)) + prob_gt_threshold[v] = T.if_then_else(prob_local > T.float32(0), prob_local, T.Cast("float32", 0)) + valid[v] = prob_local > T.float32(0) and idx < vocab_size + with T.block(""): + T.reads(prob_gt_threshold[T.int64(0):T.int64(4)]) + T.writes(step_aggregate[()]) + local_sum = T.alloc_buffer((), scope="local") + shared_buf = T.alloc_buffer((T.int64(128),), scope="shared") + idx: T.int64 = ty * T.int64(32) + tx + local_sum[()] = T.Cast("float32", 0) + for i in T.unroll(T.int64(4)): + local_sum[()] = local_sum[()] + prob_gt_threshold[i] + shared_buf[idx] = local_sum[()] + for i in T.unroll(T.int64(7)): + if idx % T.shift_left(T.int64(1), i + T.int64(1)) == T.int64(0): + shared_buf[idx] = shared_buf[idx] + shared_buf[idx + T.shift_left(T.int64(1), i)] + step_aggregate[()] = shared_buf[0] + if T.tvm_thread_invariant(aggregate[()] + step_aggregate[()] >= u - T.float32(9.9999999999999995e-07)): + for i in T.unroll(T.int64(1), T.int64(4)): + prob_gt_threshold[i] = prob_gt_threshold[i] + prob_gt_threshold[i - T.int64(1)] + for i in T.vectorized(T.int64(4)): + cumsum[ty * T.int64(128) + tx * T.int64(4) + i] = prob_gt_threshold[i] + for i in T.unroll(T.int64(5)): + for j in T.vectorized(T.int64(4)): + idx: T.int64 = ty * T.int64(128) + tx * T.int64(4) + if tx >= T.shift_left(T.int64(1), i): + cumsum[idx + j] = cumsum[idx + j] + cumsum[idx - T.shift_left(T.int64(1), i) * T.int64(4) + T.int64(4) - T.int64(1)] + for i in T.unroll(T.int64(1), T.int64(4)): + for j in T.vectorized(T.int64(4)): + if ty == T.int64(0): + idx: T.int64 = i * T.int64(128) + tx * T.int64(4) + cumsum[idx + j] = cumsum[idx + j] + cumsum[i * T.int64(128) - T.int64(1)] + for v in T.unroll(T.int64(4)): + greater_than_u[v] = cumsum[ty * T.int64(128) + tx * T.int64(4) + v] + aggregate[()] >= u - T.float32(9.9999999999999995e-07) + with T.block(""): + T.reads(greater_than_u[T.int64(0):T.int64(4)]) + T.writes(mask[T.int64(0):T.int64(4)]) + shared_buf = T.alloc_buffer((T.int64(128),), "bool", scope="shared") + tx_idx: T.int64 = ty * T.int64(32) + tx + shared_buf[tx_idx] = greater_than_u[T.int64(3)] + mask[0] = T.if_then_else(tx_idx != T.int64(0), T.Cast("int8", greater_than_u[0]) != T.Cast("int8", shared_buf[tx_idx - T.int64(1)]), greater_than_u[0]) + for i in T.unroll(T.int64(1), T.int64(4)): + mask[i] = T.Cast("int8", greater_than_u[i]) != T.Cast("int8", greater_than_u[i - T.int64(1)]) + for v in T.unroll(T.int64(4)): + mask[v] = mask[v] and valid[v] + indices[v] = T.Cast("int32", T.Cast("int64", step_iter[()]) * T.int64(512) + ty * T.int64(128) + tx * T.int64(4) + v) + with T.block(""): + T.reads(mask[T.int64(0):T.int64(4)], indices[T.int64(0):T.int64(4)]) + T.writes(sample_id_local[()]) + local_sum = T.alloc_buffer((), "int32", scope="local") + shared_buf = T.alloc_buffer((T.int64(128),), "int32", scope="shared") + idx: T.int64 = ty * T.int64(32) + tx + local_sum[()] = T.Cast("int32", vocab_size - T.int64(1)) + for i in T.unroll(T.int64(4)): + if mask[i]: + local_sum[()] = T.min(local_sum[()], indices[i]) + shared_buf[idx] = local_sum[()] + for i in T.unroll(T.int64(7)): + if idx % T.shift_left(T.int64(1), i + T.int64(1)) == T.int64(0): + shared_buf[idx] = T.min(shared_buf[idx], shared_buf[idx + T.shift_left(T.int64(1), i)]) + sample_id_local[()] = shared_buf[0] + aggregate[()] = aggregate[()] + step_aggregate[()] + step_iter[()] = step_iter[()] + 1 + if tx == T.int64(0) and ty == T.int64(0): + token_ids[bx, 0] = sample_id_local[()] + + @T.prim_func(private=True) + def reshape(var_lv: T.handle, var_T_reshape: T.handle): + T.func_attr({"tir.noalias": T.bool(True)}) + batch_size = T.int64() + lv = T.match_buffer(var_lv, (batch_size, T.int64(1500), T.int64(1280)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (batch_size, T.int64(1500), T.int64(20), T.int64(64)), "float16") + # with T.block("root"): + for ax0, ax1, ax2, ax3 in T.grid(batch_size, T.int64(1500), T.int64(20), T.int64(64)): + with T.block("T_reshape"): + v_ax0, v_ax1, v_ax2, v_ax3 = T.axis.remap("SSSS", [ax0, ax1, ax2, ax3]) + T.reads(lv[(((v_ax2 * T.int64(64) + v_ax3) // T.int64(1280) + v_ax1) // T.int64(1500) + v_ax0) % batch_size, ((v_ax2 * T.int64(64) + v_ax3) // T.int64(1280) + v_ax1) % T.int64(1500), (v_ax2 * T.int64(64) + v_ax3) % T.int64(1280)]) + T.writes(T_reshape[v_ax0, v_ax1, v_ax2, v_ax3]) + T_reshape[v_ax0, v_ax1, v_ax2, v_ax3] = lv[(((v_ax2 * T.int64(64) + v_ax3) // T.int64(1280) + v_ax1) // T.int64(1500) + v_ax0) % batch_size, ((v_ax2 * T.int64(64) + v_ax3) // T.int64(1280) + v_ax1) % T.int64(1500), (v_ax2 * T.int64(64) + v_ax3) % T.int64(1280)] + + @T.prim_func(private=True) + def reshape1(var_reshape256: T.handle, var_T_reshape: T.handle): + T.func_attr({"tir.noalias": T.bool(True)}) + batch_size = T.int64() + reshape256 = T.match_buffer(var_reshape256, (batch_size, T.int64(1500), T.int64(20), T.int64(64)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (batch_size * T.int64(1500), T.int64(20), T.int64(64)), "float16") + # with T.block("root"): + for ax0, ax1, ax2 in T.grid(batch_size * T.int64(1500), T.int64(20), T.int64(64)): + with T.block("T_reshape"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(reshape256[((v_ax2 // T.int64(64) + v_ax1) // T.int64(20) + v_ax0) // T.int64(1500) % batch_size, ((v_ax2 // T.int64(64) + v_ax1) // T.int64(20) + v_ax0) % T.int64(1500), (v_ax2 // T.int64(64) + v_ax1) % T.int64(20), v_ax2 % T.int64(64)]) + T.writes(T_reshape[v_ax0, v_ax1, v_ax2]) + T_reshape[v_ax0, v_ax1, v_ax2] = reshape256[((v_ax2 // T.int64(64) + v_ax1) // T.int64(20) + v_ax0) // T.int64(1500) % batch_size, ((v_ax2 // T.int64(64) + v_ax1) // T.int64(20) + v_ax0) % T.int64(1500), (v_ax2 // T.int64(64) + v_ax1) % T.int64(20), v_ax2 % T.int64(64)] + + @T.prim_func(private=True) + def reshape10(var_lv4: T.handle, var_T_reshape: T.handle): + T.func_attr({"tir.noalias": T.bool(True)}) + batch_size = T.int64() + lv4 = T.match_buffer(var_lv4, (batch_size * T.int64(1500), T.int64(20), T.int64(64)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (batch_size, T.int64(1500), T.int64(20), T.int64(64)), "float16") + # with T.block("root"): + for ax0, ax1, ax2, ax3 in T.grid(batch_size, T.int64(1500), T.int64(20), T.int64(64)): + with T.block("T_reshape"): + v_ax0, v_ax1, v_ax2, v_ax3 = T.axis.remap("SSSS", [ax0, ax1, ax2, ax3]) + T.reads(lv4[(v_ax0 * T.int64(1500) + (v_ax3 // T.int64(64) + v_ax2) // T.int64(20) + v_ax1) % (batch_size * T.int64(1500)), (v_ax3 // T.int64(64) + v_ax2) % T.int64(20), v_ax3 % T.int64(64)]) + T.writes(T_reshape[v_ax0, v_ax1, v_ax2, v_ax3]) + T_reshape[v_ax0, v_ax1, v_ax2, v_ax3] = lv4[(v_ax0 * T.int64(1500) + (v_ax3 // T.int64(64) + v_ax2) // T.int64(20) + v_ax1) % (batch_size * T.int64(1500)), (v_ax3 // T.int64(64) + v_ax2) % T.int64(20), v_ax3 % T.int64(64)] + + @T.prim_func(private=True) + def reshape11(var_reshape6: T.handle, var_T_reshape: T.handle): + T.func_attr({"tir.noalias": T.bool(True)}) + batch_size = T.int64() + reshape6 = T.match_buffer(var_reshape6, (batch_size, T.int64(1500), T.int64(20), T.int64(64)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (batch_size, T.int64(1500), T.int64(1280)), "float16") + # with T.block("root"): + for ax0, ax1, ax2 in T.grid(batch_size, T.int64(1500), T.int64(1280)): + with T.block("T_reshape"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(reshape6[((v_ax2 // T.int64(1280) + v_ax1) // T.int64(1500) + v_ax0) % batch_size, (v_ax2 // T.int64(1280) + v_ax1) % T.int64(1500), v_ax2 % T.int64(1280) // T.int64(64), v_ax2 % T.int64(64)]) + T.writes(T_reshape[v_ax0, v_ax1, v_ax2]) + T_reshape[v_ax0, v_ax1, v_ax2] = reshape6[((v_ax2 // T.int64(1280) + v_ax1) // T.int64(1500) + v_ax0) % batch_size, (v_ax2 // T.int64(1280) + v_ax1) % T.int64(1500), v_ax2 % T.int64(1280) // T.int64(64), v_ax2 % T.int64(64)] + + @T.prim_func(private=True) + def reshape12(var_input_ids: T.handle, var_T_reshape: T.handle): + T.func_attr({"tir.noalias": T.bool(True)}) + seq_len = T.int64() + input_ids = T.match_buffer(var_input_ids, (T.int64(1), seq_len), "int32") + T_reshape = T.match_buffer(var_T_reshape, (seq_len,), "int32") + # with T.block("root"): + for ax0 in range(seq_len): + with T.block("T_reshape"): + v_ax0 = T.axis.spatial(seq_len, ax0) + T.reads(input_ids[T.int64(0), v_ax0 % seq_len]) + T.writes(T_reshape[v_ax0]) + T_reshape[v_ax0] = input_ids[T.int64(0), v_ax0 % seq_len] + + @T.prim_func(private=True) + def reshape13(var_take: T.handle, var_T_reshape: T.handle): + T.func_attr({"tir.noalias": T.bool(True)}) + seq_len = T.int64() + take = T.match_buffer(var_take, (seq_len, T.int64(1280)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (T.int64(1), seq_len, T.int64(1280)), "float16") + # with T.block("root"): + for ax0, ax1, ax2 in T.grid(T.int64(1), seq_len, T.int64(1280)): + with T.block("T_reshape"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(take[(v_ax2 // T.int64(1280) + v_ax0 * seq_len + v_ax1) % seq_len, v_ax2 % T.int64(1280)]) + T.writes(T_reshape[v_ax0, v_ax1, v_ax2]) + T_reshape[v_ax0, v_ax1, v_ax2] = take[(v_ax2 // T.int64(1280) + v_ax0 * seq_len + v_ax1) % seq_len, v_ax2 % T.int64(1280)] + + @T.prim_func(private=True) + def reshape14(var_lv416: T.handle, var_T_reshape: T.handle): + T.func_attr({"tir.noalias": T.bool(True)}) + seq_len = T.int64() + lv416 = T.match_buffer(var_lv416, (T.int64(1), seq_len, T.int64(1280)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (T.int64(1), seq_len, T.int64(20), T.int64(64)), "float16") + # with T.block("root"): + for ax0, ax1, ax2, ax3 in T.grid(T.int64(1), seq_len, T.int64(20), T.int64(64)): + with T.block("T_reshape"): + v_ax0, v_ax1, v_ax2, v_ax3 = T.axis.remap("SSSS", [ax0, ax1, ax2, ax3]) + T.reads(lv416[T.int64(0), ((v_ax2 * T.int64(64) + v_ax3) // T.int64(1280) + v_ax0 * seq_len + v_ax1) % seq_len, (v_ax2 * T.int64(64) + v_ax3) % T.int64(1280)]) + T.writes(T_reshape[v_ax0, v_ax1, v_ax2, v_ax3]) + T_reshape[v_ax0, v_ax1, v_ax2, v_ax3] = lv416[T.int64(0), ((v_ax2 * T.int64(64) + v_ax3) // T.int64(1280) + v_ax0 * seq_len + v_ax1) % seq_len, (v_ax2 * T.int64(64) + v_ax3) % T.int64(1280)] + + @T.prim_func(private=True) + def reshape15(var_concat: T.handle, var_T_reshape: T.handle): + T.func_attr({"tir.noalias": T.bool(True)}) + seq_len = T.int64() + concat = T.match_buffer(var_concat, (T.int64(1), seq_len, T.int64(60), T.int64(64)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (seq_len, T.int64(60), T.int64(64)), "float16") + # with T.block("root"): + for ax0, ax1, ax2 in T.grid(seq_len, T.int64(60), T.int64(64)): + with T.block("T_reshape"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(concat[T.int64(0), ((v_ax2 // T.int64(64) + v_ax1) // T.int64(60) + v_ax0) % seq_len, (v_ax2 // T.int64(64) + v_ax1) % T.int64(60), v_ax2 % T.int64(64)]) + T.writes(T_reshape[v_ax0, v_ax1, v_ax2]) + T_reshape[v_ax0, v_ax1, v_ax2] = concat[T.int64(0), ((v_ax2 // T.int64(64) + v_ax1) // T.int64(60) + v_ax0) % seq_len, (v_ax2 // T.int64(64) + v_ax1) % T.int64(60), v_ax2 % T.int64(64)] + + @T.prim_func(private=True) + def reshape16(var_lv69: T.handle, var_T_reshape: T.handle): + T.func_attr({"tir.noalias": T.bool(True)}) + seq_len = T.int64() + lv69 = T.match_buffer(var_lv69, (seq_len, T.int64(20), T.int64(64)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (T.int64(1), seq_len, T.int64(20), T.int64(64)), "float16") + # with T.block("root"): + for ax0, ax1, ax2, ax3 in T.grid(T.int64(1), seq_len, T.int64(20), T.int64(64)): + with T.block("T_reshape"): + v_ax0, v_ax1, v_ax2, v_ax3 = T.axis.remap("SSSS", [ax0, ax1, ax2, ax3]) + T.reads(lv69[((v_ax3 // T.int64(64) + v_ax2) // T.int64(20) + v_ax0 * seq_len + v_ax1) % seq_len, (v_ax3 // T.int64(64) + v_ax2) % T.int64(20), v_ax3 % T.int64(64)]) + T.writes(T_reshape[v_ax0, v_ax1, v_ax2, v_ax3]) + T_reshape[v_ax0, v_ax1, v_ax2, v_ax3] = lv69[((v_ax3 // T.int64(64) + v_ax2) // T.int64(20) + v_ax0 * seq_len + v_ax1) % seq_len, (v_ax3 // T.int64(64) + v_ax2) % T.int64(20), v_ax3 % T.int64(64)] + + @T.prim_func(private=True) + def reshape17(var_reshape391: T.handle, var_T_reshape: T.handle): + T.func_attr({"tir.noalias": T.bool(True)}) + seq_len = T.int64() + reshape391 = T.match_buffer(var_reshape391, (T.int64(1), seq_len, T.int64(20), T.int64(64)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (T.int64(1), seq_len, T.int64(1280)), "float16") + # with T.block("root"): + for ax0, ax1, ax2 in T.grid(T.int64(1), seq_len, T.int64(1280)): + with T.block("T_reshape"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(reshape391[T.int64(0), (v_ax2 // T.int64(1280) + v_ax0 * seq_len + v_ax1) % seq_len, v_ax2 % T.int64(1280) // T.int64(64), v_ax2 % T.int64(64)]) + T.writes(T_reshape[v_ax0, v_ax1, v_ax2]) + T_reshape[v_ax0, v_ax1, v_ax2] = reshape391[T.int64(0), (v_ax2 // T.int64(1280) + v_ax0 * seq_len + v_ax1) % seq_len, v_ax2 % T.int64(1280) // T.int64(64), v_ax2 % T.int64(64)] + + @T.prim_func(private=True) + def reshape18(var_reshape393: T.handle, var_T_reshape: T.handle): + T.func_attr({"tir.noalias": T.bool(True)}) + seq_len = T.int64() + reshape393 = T.match_buffer(var_reshape393, (T.int64(1), seq_len, T.int64(20), T.int64(64)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (seq_len, T.int64(20), T.int64(64)), "float16") + # with T.block("root"): + for ax0, ax1, ax2 in T.grid(seq_len, T.int64(20), T.int64(64)): + with T.block("T_reshape"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(reshape393[T.int64(0), ((v_ax2 // T.int64(64) + v_ax1) // T.int64(20) + v_ax0) % seq_len, (v_ax2 // T.int64(64) + v_ax1) % T.int64(20), v_ax2 % T.int64(64)]) + T.writes(T_reshape[v_ax0, v_ax1, v_ax2]) + T_reshape[v_ax0, v_ax1, v_ax2] = reshape393[T.int64(0), ((v_ax2 // T.int64(64) + v_ax1) // T.int64(20) + v_ax0) % seq_len, (v_ax2 // T.int64(64) + v_ax1) % T.int64(20), v_ax2 % T.int64(64)] + + @T.prim_func(private=True) + def reshape19(input_ids: T.Buffer((T.int64(1), T.int64(1)), "int32"), T_reshape: T.Buffer((T.int64(1),), "int32")): + T.func_attr({"tir.noalias": T.bool(True)}) + # with T.block("root"): + for ax0 in range(T.int64(1)): + with T.block("T_reshape"): + v_ax0 = T.axis.spatial(T.int64(1), ax0) + T.reads(input_ids[T.int64(0), T.int64(0)]) + T.writes(T_reshape[v_ax0]) + T_reshape[v_ax0] = input_ids[T.int64(0), T.int64(0)] + + @T.prim_func(private=True) + def reshape2(var_input_ids: T.handle, var_T_reshape: T.handle): + T.func_attr({"tir.noalias": T.bool(True)}) + batch_size = T.int64() + input_ids = T.match_buffer(var_input_ids, (batch_size, T.int64(1)), "int32") + T_reshape = T.match_buffer(var_T_reshape, (batch_size,), "int32") + # with T.block("root"): + for ax0 in range(batch_size): + with T.block("T_reshape"): + v_ax0 = T.axis.spatial(batch_size, ax0) + T.reads(input_ids[v_ax0 % batch_size, T.int64(0)]) + T.writes(T_reshape[v_ax0]) + T_reshape[v_ax0] = input_ids[v_ax0 % batch_size, T.int64(0)] + + @T.prim_func(private=True) + def reshape3(var_take3: T.handle, var_T_reshape: T.handle): + T.func_attr({"tir.noalias": T.bool(True)}) + batch_size = T.int64() + take3 = T.match_buffer(var_take3, (batch_size, T.int64(1280)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (batch_size, T.int64(1), T.int64(1280)), "float16") + # with T.block("root"): + for ax0, ax1, ax2 in T.grid(batch_size, T.int64(1), T.int64(1280)): + with T.block("T_reshape"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(take3[(v_ax2 // T.int64(1280) + v_ax0 + v_ax1) % batch_size, v_ax2 % T.int64(1280)]) + T.writes(T_reshape[v_ax0, v_ax1, v_ax2]) + T_reshape[v_ax0, v_ax1, v_ax2] = take3[(v_ax2 // T.int64(1280) + v_ax0 + v_ax1) % batch_size, v_ax2 % T.int64(1280)] + + @T.prim_func(private=True) + def reshape4(var_lv224: T.handle, var_T_reshape: T.handle): + T.func_attr({"tir.noalias": T.bool(True)}) + batch_size = T.int64() + lv224 = T.match_buffer(var_lv224, (batch_size, T.int64(1), T.int64(1280)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (batch_size, T.int64(1), T.int64(20), T.int64(64)), "float16") + # with T.block("root"): + for ax0, ax1, ax2, ax3 in T.grid(batch_size, T.int64(1), T.int64(20), T.int64(64)): + with T.block("T_reshape"): + v_ax0, v_ax1, v_ax2, v_ax3 = T.axis.remap("SSSS", [ax0, ax1, ax2, ax3]) + T.reads(lv224[((v_ax2 * T.int64(64) + v_ax3) // T.int64(1280) + v_ax0 + v_ax1) % batch_size, T.int64(0), (v_ax2 * T.int64(64) + v_ax3) % T.int64(1280)]) + T.writes(T_reshape[v_ax0, v_ax1, v_ax2, v_ax3]) + T_reshape[v_ax0, v_ax1, v_ax2, v_ax3] = lv224[((v_ax2 * T.int64(64) + v_ax3) // T.int64(1280) + v_ax0 + v_ax1) % batch_size, T.int64(0), (v_ax2 * T.int64(64) + v_ax3) % T.int64(1280)] + + @T.prim_func(private=True) + def reshape5(var_concat32: T.handle, var_T_reshape: T.handle): + T.func_attr({"tir.noalias": T.bool(True)}) + batch_size = T.int64() + concat32 = T.match_buffer(var_concat32, (batch_size, T.int64(1), T.int64(60), T.int64(64)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (batch_size, T.int64(60), T.int64(64)), "float16") + # with T.block("root"): + for ax0, ax1, ax2 in T.grid(batch_size, T.int64(60), T.int64(64)): + with T.block("T_reshape"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(concat32[((v_ax2 // T.int64(64) + v_ax1) // T.int64(60) + v_ax0) % batch_size, T.int64(0), (v_ax2 // T.int64(64) + v_ax1) % T.int64(60), v_ax2 % T.int64(64)]) + T.writes(T_reshape[v_ax0, v_ax1, v_ax2]) + T_reshape[v_ax0, v_ax1, v_ax2] = concat32[((v_ax2 // T.int64(64) + v_ax1) // T.int64(60) + v_ax0) % batch_size, T.int64(0), (v_ax2 // T.int64(64) + v_ax1) % T.int64(60), v_ax2 % T.int64(64)] + + @T.prim_func(private=True) + def reshape6(var_lv134: T.handle, var_T_reshape: T.handle): + T.func_attr({"tir.noalias": T.bool(True)}) + batch_size = T.int64() + lv134 = T.match_buffer(var_lv134, (batch_size, T.int64(20), T.int64(64)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (batch_size, T.int64(1), T.int64(20), T.int64(64)), "float16") + # with T.block("root"): + for ax0, ax1, ax2, ax3 in T.grid(batch_size, T.int64(1), T.int64(20), T.int64(64)): + with T.block("T_reshape"): + v_ax0, v_ax1, v_ax2, v_ax3 = T.axis.remap("SSSS", [ax0, ax1, ax2, ax3]) + T.reads(lv134[((v_ax3 // T.int64(64) + v_ax2) // T.int64(20) + v_ax0 + v_ax1) % batch_size, (v_ax3 // T.int64(64) + v_ax2) % T.int64(20), v_ax3 % T.int64(64)]) + T.writes(T_reshape[v_ax0, v_ax1, v_ax2, v_ax3]) + T_reshape[v_ax0, v_ax1, v_ax2, v_ax3] = lv134[((v_ax3 // T.int64(64) + v_ax2) // T.int64(20) + v_ax0 + v_ax1) % batch_size, (v_ax3 // T.int64(64) + v_ax2) % T.int64(20), v_ax3 % T.int64(64)] + + @T.prim_func(private=True) + def reshape7(var_reshape714: T.handle, var_T_reshape: T.handle): + T.func_attr({"tir.noalias": T.bool(True)}) + batch_size = T.int64() + reshape714 = T.match_buffer(var_reshape714, (batch_size, T.int64(1), T.int64(20), T.int64(64)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (batch_size, T.int64(1), T.int64(1280)), "float16") + # with T.block("root"): + for ax0, ax1, ax2 in T.grid(batch_size, T.int64(1), T.int64(1280)): + with T.block("T_reshape"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(reshape714[(v_ax2 // T.int64(1280) + v_ax0 + v_ax1) % batch_size, T.int64(0), v_ax2 % T.int64(1280) // T.int64(64), v_ax2 % T.int64(64)]) + T.writes(T_reshape[v_ax0, v_ax1, v_ax2]) + T_reshape[v_ax0, v_ax1, v_ax2] = reshape714[(v_ax2 // T.int64(1280) + v_ax0 + v_ax1) % batch_size, T.int64(0), v_ax2 % T.int64(1280) // T.int64(64), v_ax2 % T.int64(64)] + + @T.prim_func(private=True) + def reshape8(var_reshape716: T.handle, var_T_reshape: T.handle): + T.func_attr({"tir.noalias": T.bool(True)}) + batch_size = T.int64() + reshape716 = T.match_buffer(var_reshape716, (batch_size, T.int64(1), T.int64(20), T.int64(64)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (batch_size, T.int64(20), T.int64(64)), "float16") + # with T.block("root"): + for ax0, ax1, ax2 in T.grid(batch_size, T.int64(20), T.int64(64)): + with T.block("T_reshape"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(reshape716[((v_ax2 // T.int64(64) + v_ax1) // T.int64(20) + v_ax0) % batch_size, T.int64(0), (v_ax2 // T.int64(64) + v_ax1) % T.int64(20), v_ax2 % T.int64(64)]) + T.writes(T_reshape[v_ax0, v_ax1, v_ax2]) + T_reshape[v_ax0, v_ax1, v_ax2] = reshape716[((v_ax2 // T.int64(64) + v_ax1) // T.int64(20) + v_ax0) % batch_size, T.int64(0), (v_ax2 // T.int64(64) + v_ax1) % T.int64(20), v_ax2 % T.int64(64)] + + @T.prim_func + def sampler_take_probs_tir(var_unsorted_probs: T.handle, var_sorted_indices: T.handle, var_sample_indices: T.handle, var_sampling_results: T.handle, var_top_prob_offsets: T.handle, var_sampled_values: T.handle, var_top_prob_probs: T.handle, var_top_prob_indices: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32})}) + batch_size, vocab_size = T.int32(is_size_var=True), T.int32(is_size_var=True) + unsorted_probs = T.match_buffer(var_unsorted_probs, (batch_size, vocab_size)) + sorted_indices = T.match_buffer(var_sorted_indices, (batch_size, vocab_size), "int32") + num_samples = T.int32(is_size_var=True) + sample_indices = T.match_buffer(var_sample_indices, (num_samples,), "int32") + sampling_results = T.match_buffer(var_sampling_results, (num_samples,), "int32") + num_positions = T.int32(is_size_var=True) + top_prob_offsets = T.match_buffer(var_top_prob_offsets, (num_positions,), "int32") + sampled_values = T.match_buffer(var_sampled_values, (num_samples,)) + top_prob_probs = T.match_buffer(var_top_prob_probs, (num_positions,)) + top_prob_indices = T.match_buffer(var_top_prob_indices, (num_positions,), "int32") + # with T.block("root"): + for i in range(num_positions + num_samples): + with T.block("block"): + vi = T.axis.spatial(num_positions + num_samples, i) + T.reads(top_prob_offsets[vi], sorted_indices[top_prob_offsets[vi] // vocab_size, top_prob_offsets[vi] % vocab_size], unsorted_probs[T.min(top_prob_offsets[vi] // vocab_size, sample_indices[vi - num_positions]):T.min(top_prob_offsets[vi] // vocab_size, sample_indices[vi - num_positions]) + (T.max(top_prob_offsets[vi] // vocab_size, sample_indices[vi - num_positions]) + 1 - T.min(top_prob_offsets[vi] // vocab_size, sample_indices[vi - num_positions])), T.min(sorted_indices[top_prob_offsets[vi] // vocab_size, top_prob_offsets[vi] % vocab_size], sampling_results[vi - num_positions]):T.min(sorted_indices[top_prob_offsets[vi] // vocab_size, top_prob_offsets[vi] % vocab_size], sampling_results[vi - num_positions]) + (T.max(sorted_indices[top_prob_offsets[vi] // vocab_size, top_prob_offsets[vi] % vocab_size], sampling_results[vi - num_positions]) + 1 - T.min(sorted_indices[top_prob_offsets[vi] // vocab_size, top_prob_offsets[vi] % vocab_size], sampling_results[vi - num_positions]))], sample_indices[vi - num_positions], sampling_results[vi - num_positions]) + T.writes(top_prob_indices[vi], top_prob_probs[vi], sampled_values[vi - num_positions]) + if vi < num_positions: + row: T.int32 = top_prob_offsets[vi] // vocab_size + col: T.int32 = top_prob_offsets[vi] % vocab_size + top_prob_indices[vi] = sorted_indices[row, col] + top_prob_probs[vi] = unsorted_probs[row, sorted_indices[row, col]] + else: + vj: T.int32 = vi - num_positions + sampled_values[vj] = unsorted_probs[sample_indices[vj], sampling_results[vj]] + + @T.prim_func + def scatter_probs(var_src: T.handle, var_indices: T.handle, var_dst: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.noalias": T.bool(True)}) + batch_size, n = T.int32(is_size_var=True), T.int32(is_size_var=True) + src = T.match_buffer(var_src, (batch_size, n)) + indices = T.match_buffer(var_indices, (batch_size,), "int32") + m = T.int32(is_size_var=True) + dst = T.match_buffer(var_dst, (m, n)) + # with T.block("root"): + for b, j in T.grid(batch_size, n): + with T.block("scatter_2d"): + vb, vj = T.axis.remap("SS", [b, j]) + T.reads(src[vb, vj], indices[vb]) + T.writes(dst[indices[vb], vj]) + dst[indices[vb], vj] = src[vb, vj] + + @T.prim_func + def softmax_with_chunked_sum(var_A: T.handle, var_temperature: T.handle, var_chunked_sum: T.handle, var_chunked_max: T.handle, var_softmax: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + batch_size, vocab_size = T.int64(is_size_var=True), T.int64(is_size_var=True) + A = T.match_buffer(var_A, (batch_size, vocab_size)) + temperature = T.match_buffer(var_temperature, (batch_size,)) + num_chunks = T.int64(is_size_var=True) + chunked_sum = T.match_buffer(var_chunked_sum, (batch_size, num_chunks)) + chunked_max = T.match_buffer(var_chunked_max, (batch_size, num_chunks)) + softmax = T.match_buffer(var_softmax, (batch_size, vocab_size)) + # with T.block("root"): + temp_max_shared = T.alloc_buffer((batch_size,), scope="shared") + temp_sum_shared = T.alloc_buffer((batch_size,), scope="shared") + for l0_l1_fused in T.thread_binding(batch_size * num_chunks, thread="blockIdx.x"): + for ax0_1 in T.thread_binding(T.int64(32), thread="threadIdx.x"): + for ax0_0 in T.serial((num_chunks + T.int64(31)) // T.int64(32), annotations={"pragma_auto_unroll_max_step": 64, "pragma_unroll_explicit": 1}): + with T.block("max"): + v0 = T.axis.spatial(batch_size, l0_l1_fused % (num_chunks * batch_size) // num_chunks) + v1 = T.axis.reduce(num_chunks, ax0_0 * T.int64(32) + ax0_1) + T.where(ax0_0 * T.int64(32) + ax0_1 < num_chunks) + T.reads(chunked_max[v0, v1]) + T.writes(temp_max_shared[v0]) + with T.init(): + temp_max_shared[v0] = T.float32(-3.4028234663852886e+38) + temp_max_shared[v0] = T.max(temp_max_shared[v0], chunked_max[v0, v1]) + for ax0_1 in T.thread_binding(T.int64(32), thread="threadIdx.x"): + for ax0_0 in T.serial((num_chunks + T.int64(31)) // T.int64(32), annotations={"pragma_auto_unroll_max_step": 64, "pragma_unroll_explicit": 1}): + with T.block("sum_exp"): + v0 = T.axis.spatial(batch_size, l0_l1_fused % (num_chunks * batch_size) // num_chunks) + v1 = T.axis.reduce(num_chunks, ax0_0 * T.int64(32) + ax0_1) + T.where(ax0_0 * T.int64(32) + ax0_1 < num_chunks) + T.reads(temperature[v0], chunked_sum[v0, v1], chunked_max[v0, v1], temp_max_shared[v0]) + T.writes(temp_sum_shared[v0]) + with T.init(): + temp_sum_shared[v0] = T.float32(0) + temp_sum_shared[v0] = temp_sum_shared[v0] + T.Select(temperature[v0] > T.float32(1.0000000000000001e-05), T.exp(chunked_sum[v0, v1] + chunked_max[v0, v1] - temp_max_shared[v0]), T.Cast("float32", chunked_max[v0, v1] == temp_max_shared[v0]) * chunked_sum[v0, v1]) + for l2_0 in T.serial(T.int64(4), annotations={"pragma_auto_unroll_max_step": 64, "pragma_unroll_explicit": 1}): + for l2_1 in T.thread_binding(T.int64(32), thread="threadIdx.y"): + for l2_2 in T.thread_binding(T.int64(32), thread="threadIdx.x"): + with T.block("log_pad"): + v0 = T.axis.spatial(batch_size, l0_l1_fused % (num_chunks * batch_size) // num_chunks) + v1 = T.axis.spatial(num_chunks, l0_l1_fused % num_chunks) + v2 = T.axis.spatial(T.int64(4096), l2_0 * T.int64(1024) + l2_1 * T.int64(32) + l2_2) + T.reads(temperature[v0], A[v0, v1 * T.int64(4096) + v2], temp_sum_shared[v0], temp_max_shared[v0]) + T.writes(softmax[v0, v1 * T.int64(4096) + v2]) + if v1 * T.int64(4096) + v2 < vocab_size: + softmax[v0, v1 * T.int64(4096) + v2] = T.if_then_else(temperature[v0] > T.float32(1.0000000000000001e-05), T.exp(A[v0, v1 * T.int64(4096) + v2] / temperature[v0] - (T.log(temp_sum_shared[v0]) + temp_max_shared[v0])), T.Cast("float32", A[v0, v1 * T.int64(4096) + v2] == temp_max_shared[v0]) / temp_sum_shared[v0]) + + @T.prim_func(private=True) + def take(model_decoder_embed_tokens_weight3: T.Buffer((T.int64(51866), T.int64(1280)), "float16"), var_reshape707: T.handle, var_T_take: T.handle): + T.func_attr({"tir.noalias": T.bool(True)}) + batch_size = T.int64() + reshape707 = T.match_buffer(var_reshape707, (batch_size,), "int32") + T_take = T.match_buffer(var_T_take, (batch_size, T.int64(1280)), "float16") + # with T.block("root"): + for ax0, ax1 in T.grid(batch_size, T.int64(1280)): + with T.block("T_take"): + v_ax0, v_ax1 = T.axis.remap("SS", [ax0, ax1]) + T.reads(model_decoder_embed_tokens_weight3[reshape707[v_ax0], v_ax1], reshape707[v_ax0]) + T.writes(T_take[v_ax0, v_ax1]) + T_take[v_ax0, v_ax1] = model_decoder_embed_tokens_weight3[reshape707[v_ax0], v_ax1] + + @T.prim_func(private=True) + def take1(model_decoder_embed_positions_weight3: T.Buffer((T.int64(448), T.int64(1280)), "float16"), var_lv133: T.handle, var_T_take: T.handle): + T.func_attr({"tir.noalias": T.bool(True)}) + batch_size = T.int64() + lv133 = T.match_buffer(var_lv133, (batch_size,), "int32") + T_take = T.match_buffer(var_T_take, (batch_size, T.int64(1280)), "float16") + # with T.block("root"): + for ax0, ax1 in T.grid(batch_size, T.int64(1280)): + with T.block("T_take"): + v_ax0, v_ax1 = T.axis.remap("SS", [ax0, ax1]) + T.reads(model_decoder_embed_positions_weight3[lv133[v_ax0], v_ax1], lv133[v_ax0]) + T.writes(T_take[v_ax0, v_ax1]) + T_take[v_ax0, v_ax1] = model_decoder_embed_positions_weight3[lv133[v_ax0], v_ax1] + + @T.prim_func(private=True) + def take2(var_layer_norm161: T.handle, var_logit_positions: T.handle, var_T_take: T.handle): + T.func_attr({"tir.noalias": T.bool(True)}) + seq_len = T.int64() + layer_norm161 = T.match_buffer(var_layer_norm161, (T.int64(1), seq_len, T.int64(1280)), "float16") + batch_size = T.int64() + logit_positions = T.match_buffer(var_logit_positions, (batch_size,), "int32") + T_take = T.match_buffer(var_T_take, (T.int64(1), batch_size, T.int64(1280)), "float16") + # with T.block("root"): + for ax0, ax1, ax2 in T.grid(T.int64(1), batch_size, T.int64(1280)): + with T.block("T_take"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(layer_norm161[v_ax0, logit_positions[v_ax1], v_ax2], logit_positions[v_ax1]) + T.writes(T_take[v_ax0, v_ax1, v_ax2]) + T_take[v_ax0, v_ax1, v_ax2] = layer_norm161[v_ax0, logit_positions[v_ax1], v_ax2] + + @T.prim_func(private=True) + def take3(model_decoder_embed_tokens_weight5: T.Buffer((T.int64(51866), T.int64(1280)), "float16"), reshape1353: T.Buffer((T.int64(1),), "int32"), T_take: T.Buffer((T.int64(1), T.int64(1280)), "float16")): + T.func_attr({"tir.noalias": T.bool(True)}) + # with T.block("root"): + for ax0, ax1 in T.grid(T.int64(1), T.int64(1280)): + with T.block("T_take"): + v_ax0, v_ax1 = T.axis.remap("SS", [ax0, ax1]) + T.reads(model_decoder_embed_tokens_weight5[reshape1353[v_ax0], v_ax1], reshape1353[v_ax0]) + T.writes(T_take[v_ax0, v_ax1]) + T_take[v_ax0, v_ax1] = model_decoder_embed_tokens_weight5[reshape1353[v_ax0], v_ax1] + + @T.prim_func(private=True) + def take4(model_decoder_embed_positions_weight5: T.Buffer((T.int64(448), T.int64(1280)), "float16"), lv264: T.Buffer((T.int64(1),), "int32"), T_take: T.Buffer((T.int64(1), T.int64(1280)), "float16")): + T.func_attr({"tir.noalias": T.bool(True)}) + # with T.block("root"): + for ax0, ax1 in T.grid(T.int64(1), T.int64(1280)): + with T.block("T_take"): + v_ax0, v_ax1 = T.axis.remap("SS", [ax0, ax1]) + T.reads(model_decoder_embed_positions_weight5[lv264[v_ax0], v_ax1], lv264[v_ax0]) + T.writes(T_take[v_ax0, v_ax1]) + T_take[v_ax0, v_ax1] = model_decoder_embed_positions_weight5[lv264[v_ax0], v_ax1] + + @T.prim_func(private=True) + def take_sorted_probs(var_probs: T.handle, var_lv1: T.handle, var_take_sorted_probs: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.noalias": T.bool(True)}) + batch_size, vocab_size = T.int64(), T.int64() + probs = T.match_buffer(var_probs, (batch_size, vocab_size)) + lv1 = T.match_buffer(var_lv1, (batch_size, vocab_size), "int32") + batch_size_1, vocab_size_1 = T.int64(), T.int64() + take_sorted_probs = T.match_buffer(var_take_sorted_probs, (batch_size_1, vocab_size_1)) + # with T.block("root"): + for i, j in T.grid(batch_size_1, vocab_size_1): + with T.block("take_sorted_probs"): + v_i, v_j = T.axis.remap("SS", [i, j]) + T.reads(probs[v_i, lv1[v_i, v_j]], lv1[v_i, v_j]) + T.writes(take_sorted_probs[v_i, v_j]) + take_sorted_probs[v_i, v_j] = probs[v_i, lv1[v_i, v_j]] + + @T.prim_func + def tir_kv_cache_debug_get_kv(var_pages: T.handle, var_position_map: T.handle, var_k_data: T.handle, var_v_data: T.handle, layer_id: T.int64): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.noalias": T.bool(True)}) + num_pages, page_size = T.int64(), T.int64(is_size_var=True) + pages = T.match_buffer(var_pages, (num_pages, 2, 20, page_size, 64), "float16") + seqlen = T.int64(is_size_var=True) + position_map = T.match_buffer(var_position_map, (seqlen,), "int32", offset_factor=1) + k_data = T.match_buffer(var_k_data, (32, seqlen, 20, 64), "float16") + v_data = T.match_buffer(var_v_data, (32, seqlen, 20, 64), "float16") + # with T.block("root"): + for p, h, d in T.grid(seqlen, 20, 64): + with T.block("copy0"): + vp, vh, vd = T.axis.remap("SSS", [p, h, d]) + T.reads(position_map[vp], pages[T.Cast("int64", position_map[vp]) // page_size, 0:2, vh, T.Cast("int64", position_map[vp]) % page_size, vd]) + T.writes(k_data[layer_id, vp, vh, vd], v_data[layer_id, vp, vh, vd]) + position: T.int32 = position_map[vp] + k_data[layer_id, vp, vh, vd] = pages[T.Cast("int64", position) // page_size, 0, vh, T.Cast("int64", position) % page_size, vd] + v_data[layer_id, vp, vh, vd] = pages[T.Cast("int64", position) // page_size, 1, vh, T.Cast("int64", position) % page_size, vd] + + @T.prim_func + def tir_kv_cache_transpose_append(var_pages: T.handle, var_k_data: T.handle, var_v_data: T.handle, var_position_map: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.noalias": T.bool(True)}) + num_pages = T.int64() + pages = T.match_buffer(var_pages, (num_pages, 2, 20, 16, 64), "float16") + ntoken = T.int64(is_size_var=True) + k_data = T.match_buffer(var_k_data, (ntoken, 20, 64), "float16") + v_data = T.match_buffer(var_v_data, (ntoken, 20, 64), "float16") + position_map = T.match_buffer(var_position_map, (ntoken,), "int32", offset_factor=1) + # with T.block("root"): + for global_pos, h, f in T.grid(ntoken, 20, 64): + if position_map[global_pos] != -1: + with T.block("k_transpose_append"): + vgpos, vh, vf = T.axis.remap("SSS", [global_pos, h, f]) + T.reads(position_map[vgpos], k_data[vgpos, vh, vf]) + T.writes(pages[position_map[vgpos] // 16, 0, vh, position_map[vgpos] % 16, vf]) + position: T.int32 = position_map[vgpos] + pages[position // 16, 0, vh, position % 16, vf] = k_data[vgpos, vh, vf] + with T.block("v_transpose_append"): + vgpos, vh, vf = T.axis.remap("SSS", [global_pos, h, f]) + T.reads(position_map[vgpos], v_data[vgpos, vh, vf]) + T.writes(pages[position_map[vgpos] // 16, 1, vh, position_map[vgpos] % 16, vf]) + position: T.int32 = position_map[vgpos] + pages[position // 16, 1, vh, position % 16, vf] = v_data[vgpos, vh, vf] + + @T.prim_func(private=True) + def top_p_pivot_cutoff(var_prob: T.handle, var_top_p_arr: T.handle, var_init_pivots: T.handle, var_final_pivot: T.handle, var_final_lsum: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + B, N = T.int32(), T.int32() + prob = T.match_buffer(var_prob, (B, N)) + top_p_arr = T.match_buffer(var_top_p_arr, (B,)) + init_pivots = T.match_buffer(var_init_pivots, (B, 3)) + final_pivot = T.match_buffer(var_final_pivot, (B,)) + final_lsum = T.match_buffer(var_final_lsum, (B,)) + # with T.block("root"): + pivot = T.alloc_buffer((3,), scope="local") + top_p = T.alloc_buffer((1,), scope="local") + L = T.alloc_buffer((1,), scope="shared") + R_1 = T.alloc_buffer((1,), scope="shared") + L_local = T.alloc_buffer((1,), scope="local") + R_local = T.alloc_buffer((1,), scope="local") + q = T.alloc_buffer((1,), scope="local") + lsum = T.alloc_buffer((3,), scope="local") + lmin_broadcast = T.alloc_buffer((1,), scope="shared") + lmin_broadcast_local = T.alloc_buffer((1,), scope="local") + lmin = T.alloc_buffer((3,), scope="local") + cmin = T.alloc_buffer((3,), "int32", scope="local") + total_sum = T.alloc_buffer((1,), scope="local") + it = T.alloc_buffer((1,), "int32", scope="local") + es_local = T.alloc_buffer((1,), "bool", scope="local") + es = T.alloc_buffer((1,), "bool", scope="shared") + find_pivot_local = T.alloc_buffer((1,), "bool", scope="local") + find_pivot = T.alloc_buffer((1,), "bool", scope="shared") + total_sum_reduce = T.alloc_buffer((1,), scope="local") + lsum_reduce = T.alloc_buffer((1,), scope="local") + lmin_reduce = T.alloc_buffer((1,), scope="local") + cmin_reduce = T.alloc_buffer((1,), "int32", scope="local") + for _bx in T.thread_binding(B, thread="blockIdx.x"): + for _tx in T.thread_binding(1024, thread="threadIdx.x"): + with T.block("CTA"): + b, tx = T.axis.remap("SS", [_bx, _tx]) + T.reads(top_p_arr[b], top_p[0], L[0], R_1[0], init_pivots[b, 0:3], L_local[0], R_local[0], find_pivot_local[0], it[0], es_local[0], prob[b, it[0] * 1024 + tx], total_sum[0], q[0], pivot[T.min(0, it[0]):T.min(0, it[0]) + (T.max(2, it[0]) + 1 - T.min(0, it[0]))], lsum[T.min(0, it[0]):T.min(0, it[0]) + (T.max(2, it[0]) + 1 - T.min(0, it[0]))], lmin[T.min(0, it[0]):T.min(0, it[0]) + (T.max(2, it[0]) + 1 - T.min(0, it[0]))], cmin[T.min(0, it[0]):T.min(0, it[0]) + (T.max(2, it[0]) + 1 - T.min(0, it[0]))], total_sum_reduce[0], es[0], lmin_reduce[0], lmin_broadcast[0], lmin_broadcast_local[0], lsum_reduce[0], cmin_reduce[0], find_pivot[0]) + T.writes(top_p[0], L[0], R_1[0], find_pivot[0], L_local[0], R_local[0], pivot[0:3], find_pivot_local[0], final_lsum[b], final_pivot[b], lsum[0:3], lmin[0:3], cmin[0:3], total_sum[0], it[0], es_local[0], q[0], total_sum_reduce[0], es[0], lsum_reduce[0], lmin_reduce[0], lmin_broadcast[0], lmin_broadcast_local[0], cmin_reduce[0]) + top_p[0] = top_p_arr[b] + if tx == 0: + L[0] = T.float32(1) - top_p[0] + R_1[0] = T.float32(9.9999999999999995e-08) + find_pivot[0] = T.bool(False) + T.tvm_storage_sync("shared") + L_local[0] = L[0] + R_local[0] = R_1[0] + for i in T.unroll(3): + pivot[i] = init_pivots[b, i] + find_pivot_local[0] = T.bool(False) + if L_local[0] - R_local[0] <= T.float32(9.9999999999999995e-08): + if tx == 0: + final_lsum[b] = T.float32(1) + final_pivot[b] = T.float32(0) + find_pivot_local[0] = T.bool(True) + while T.tvm_thread_invariant(L_local[0] - R_local[0] > T.float32(9.9999999999999995e-08) and not find_pivot_local[0]): + T.tvm_storage_sync("shared") + for pidx in T.unroll(3): + lsum[pidx] = T.float32(0) + lmin[pidx] = T.float32(3.4028234663852886e+38) + cmin[pidx] = 0 + total_sum[0] = T.float32(0) + it[0] = 0 + es_local[0] = T.bool(False) + while it[0] < (N + 1024 - 1) // 1024 and not es_local[0]: + q[0] = T.if_then_else(it[0] * 1024 + tx < N, prob[b, it[0] * 1024 + tx], T.float32(0)) + total_sum[0] = total_sum[0] + q[0] + for pidx in T.unroll(3): + if q[0] >= pivot[pidx]: + lsum[pidx] = lsum[pidx] + q[0] + if lmin[pidx] > q[0]: + lmin[pidx] = q[0] + cmin[pidx] = 1 + else: + if lmin[pidx] == q[0]: + cmin[pidx] = cmin[pidx] + 1 + it[0] = it[0] + 1 + if it[0] % 32 == 0: + with T.block("block_cross_thread"): + T.reads(total_sum[0]) + T.writes(total_sum_reduce[0]) + T.attr(T.comm_reducer(lambda x0, y0: x0 + y0, [T.float32(0)]), "reduce_scope", T.reinterpret("handle", T.uint64(0))) + T.tvm_thread_allreduce(T.uint32(1), total_sum[0], T.bool(True), total_sum_reduce[0], tx) + if tx == 0: + es[0] = T.float32(1) - total_sum_reduce[0] < pivot[2] + T.tvm_storage_sync("shared") + es_local[0] = es[0] + T.tvm_storage_sync("shared") + for pidx in range(3): + with T.block("block_cross_thread"): + T.reads(lsum[pidx]) + T.writes(lsum_reduce[0]) + T.attr(T.comm_reducer(lambda x0, y0: x0 + y0, [T.float32(0)]), "reduce_scope", T.reinterpret("handle", T.uint64(0))) + T.tvm_thread_allreduce(T.uint32(1), lsum[pidx], T.bool(True), lsum_reduce[0], tx) + with T.block("block_cross_thread"): + T.reads(lmin[pidx]) + T.writes(lmin_reduce[0]) + T.attr(T.comm_reducer(lambda x0, y0: T.min(x0, y0), [T.float32(0)]), "reduce_scope", T.reinterpret("handle", T.uint64(0))) + T.tvm_thread_allreduce(T.uint32(1), lmin[pidx], T.bool(True), lmin_reduce[0], tx) + if tx == 0: + lmin_broadcast[0] = lmin_reduce[0] + T.tvm_storage_sync("shared") + lmin_broadcast_local[0] = lmin_broadcast[0] + if lmin[pidx] > lmin_broadcast_local[0]: + cmin[pidx] = 0 + if tx == 0: + lsum[pidx] = lsum_reduce[0] + lmin[pidx] = lmin_reduce[0] + with T.block("block_cross_thread"): + T.reads(cmin[pidx]) + T.writes(cmin_reduce[0]) + T.attr(T.comm_reducer(lambda x0, y0: x0 + y0, [0]), "reduce_scope", T.reinterpret("handle", T.uint64(0))) + T.tvm_thread_allreduce(T.uint32(1), cmin[pidx], T.bool(True), cmin_reduce[0], tx) + if tx == 0: + cmin[pidx] = cmin_reduce[0] + T.tvm_storage_sync("shared") + if tx == 0: + it[0] = 0 + while it[0] < 3 and not find_pivot_local[0]: + if lsum[it[0]] >= top_p[0] and top_p[0] > lsum[it[0]] - T.Cast("float32", cmin[it[0]]) * lmin[it[0]]: + find_pivot[0] = T.bool(True) + find_pivot_local[0] = T.bool(True) + final_pivot[b] = pivot[it[0]] + final_lsum[b] = lsum[it[0]] + else: + if lsum[it[0]] - lmin[it[0]] * T.Cast("float32", cmin[it[0]]) >= top_p[0]: + R_1[0] = pivot[it[0]] + final_lsum[b] = lsum[it[0]] + else: + if lsum[it[0]] < top_p[0]: + L[0] = pivot[it[0]] + it[0] = it[0] + 1 + T.tvm_storage_sync("shared") + L_local[0] = L[0] + R_local[0] = R_1[0] + find_pivot_local[0] = find_pivot[0] + for pidx in T.unroll(3): + pivot[pidx] = L[0] - T.Cast("float32", pidx + 1) * (L_local[0] - R_local[0]) / T.float32(4) + if tx == 0: + if not find_pivot_local[0]: + final_pivot[b] = R_local[0] + if R_local[0] == T.float32(9.9999999999999995e-08): + final_lsum[b] = lsum[2] + + @T.prim_func(private=True) + def top_p_renorm_after_cutoff(var_prob: T.handle, var_final_pivot: T.handle, var_final_lsum: T.handle, var_renorm_prob: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + B, N = T.int32(), T.int32() + prob = T.match_buffer(var_prob, (B, N)) + final_pivot = T.match_buffer(var_final_pivot, (B,)) + final_lsum = T.match_buffer(var_final_lsum, (B,)) + renorm_prob = T.match_buffer(var_renorm_prob, (B, N)) + # with T.block("root"): + pivot = T.alloc_buffer((1,), scope="local") + lsum = T.alloc_buffer((1,), scope="local") + for _by in T.thread_binding(B, thread="blockIdx.y"): + for _bx in T.thread_binding((B + 511) // B, thread="blockIdx.x"): + for _tx in T.thread_binding(1024, thread="threadIdx.x"): + with T.block("CTA"): + by, bx, tx = T.axis.remap("SSS", [_by, _bx, _tx]) + T.reads(final_pivot[by], final_lsum[by], prob[by, T.Select(0 <= (B + 511) // B, 0, (((B + 511) // B * 1024 + N - 1) // ((B + 511) // B * 1024) - 1) * ((B + 511) // B)) * 1024 + bx * 1024 + tx:T.Select(0 <= (B + 511) // B, 0, (((B + 511) // B * 1024 + N - 1) // ((B + 511) // B * 1024) - 1) * ((B + 511) // B)) * 1024 + bx * 1024 + tx + (T.Select(0 <= (B + 511) // B, (N - 1) // ((B + 511) // B * 1024) * ((B + 511) // B), 0 - (((B + 511) // B * 1024 + N - 1) // ((B + 511) // B * 1024) - 1) * ((B + 511) // B)) * 1024 + 1)], pivot[0], lsum[0]) + T.writes(pivot[0], lsum[0], renorm_prob[by, T.Select(0 <= (B + 511) // B, 0, (((B + 511) // B * 1024 + N - 1) // ((B + 511) // B * 1024) - 1) * ((B + 511) // B)) * 1024 + bx * 1024 + tx:T.Select(0 <= (B + 511) // B, 0, (((B + 511) // B * 1024 + N - 1) // ((B + 511) // B * 1024) - 1) * ((B + 511) // B)) * 1024 + bx * 1024 + tx + (T.Select(0 <= (B + 511) // B, (N - 1) // ((B + 511) // B * 1024) * ((B + 511) // B), 0 - (((B + 511) // B * 1024 + N - 1) // ((B + 511) // B * 1024) - 1) * ((B + 511) // B)) * 1024 + 1)]) + pivot[0] = final_pivot[by] + lsum[0] = final_lsum[by] + for i in range(((B + 511) // B * 1024 + N - 1) // ((B + 511) // B * 1024)): + if i * ((512 + B - 1) // B) * 1024 + bx * 1024 + tx < N: + renorm_prob[by, i * ((512 + B - 1) // B) * 1024 + bx * 1024 + tx] = T.if_then_else(prob[by, i * ((512 + B - 1) // B) * 1024 + bx * 1024 + tx] >= pivot[0], prob[by, i * ((512 + B - 1) // B) * 1024 + bx * 1024 + tx] / lsum[0], T.float32(0)) + + @R.function + def argsort_probs(probs: R.Tensor(("batch_size", "vocab_size"), dtype="float32")) -> R.Tuple(R.Tensor(("batch_size", "vocab_size"), dtype="float32"), R.Tensor(("batch_size", "vocab_size"), dtype="int32")): + batch_size = T.int64() + vocab_size = T.int64() + R.func_attr({"relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "num_positions": 48, "num_samples": 8}}) + cls = Module + with R.dataflow(): + lv: R.Tensor((8 * (batch_size * vocab_size * 4) + 8388608 + batch_size * vocab_size * 12,), dtype="uint8") = R.builtin.alloc_tensor(R.shape([8 * (batch_size * vocab_size * 4) + 8388608 + batch_size * vocab_size * 12]), R.dtype("uint8"), R.prim_value(0), R.str("global")) + lv1 = R.call_tir(cls.argsort_thrust, (probs, lv), out_sinfo=R.Tensor((batch_size, vocab_size), dtype="int32")) + lv2 = R.call_tir(cls.take_sorted_probs, (probs, lv1), out_sinfo=R.Tensor((batch_size, vocab_size), dtype="float32")) + gv1: R.Tuple(R.Tensor((batch_size, vocab_size), dtype="float32"), R.Tensor((batch_size, vocab_size), dtype="int32")) = lv2, lv1 + R.output(gv1) + return gv1 + + @R.function + def batch_compute_cross_attn_kv(encoder_hidden_states: R.Tensor(("batch_size", 1500, 1280), dtype="float16"), paged_kv_cache: R.Object, packed_params: R.Tuple(R.Tensor((1280, 128, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1500, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), 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R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), 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R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"))) -> R.Object: + batch_size = T.int64() + R.func_attr({"num_input": 2, "relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "seq_len": 15000, "total_seq_len": 1500}}) + cls = Module + with R.dataflow(): + model_decoder_layers_0_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[498] + model_decoder_layers_0_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[499] + model_decoder_layers_0_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[500] + model_decoder_layers_1_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[522] + model_decoder_layers_1_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[523] + model_decoder_layers_1_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[524] + model_decoder_layers_2_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[546] + model_decoder_layers_2_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[547] + model_decoder_layers_2_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[548] + model_decoder_layers_3_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[570] + model_decoder_layers_3_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[571] + model_decoder_layers_3_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[572] + model_decoder_layers_4_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[594] + model_decoder_layers_4_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[595] + model_decoder_layers_4_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[596] + model_decoder_layers_5_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[618] + model_decoder_layers_5_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[619] + model_decoder_layers_5_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[620] + model_decoder_layers_6_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[642] + model_decoder_layers_6_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[643] + model_decoder_layers_6_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[644] + model_decoder_layers_7_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[666] + model_decoder_layers_7_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[667] + model_decoder_layers_7_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[668] + model_decoder_layers_8_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[690] + model_decoder_layers_8_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[691] + model_decoder_layers_8_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[692] + model_decoder_layers_9_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[714] + model_decoder_layers_9_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[715] + model_decoder_layers_9_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[716] + model_decoder_layers_10_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[738] + model_decoder_layers_10_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[739] + model_decoder_layers_10_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[740] + model_decoder_layers_11_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[762] + model_decoder_layers_11_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[763] + model_decoder_layers_11_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[764] + model_decoder_layers_12_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[786] + model_decoder_layers_12_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[787] + model_decoder_layers_12_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[788] + model_decoder_layers_13_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[810] + model_decoder_layers_13_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[811] + model_decoder_layers_13_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[812] + model_decoder_layers_14_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[834] + model_decoder_layers_14_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[835] + model_decoder_layers_14_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[836] + model_decoder_layers_15_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[858] + model_decoder_layers_15_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[859] + model_decoder_layers_15_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[860] + model_decoder_layers_16_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[882] + model_decoder_layers_16_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[883] + model_decoder_layers_16_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[884] + model_decoder_layers_17_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[906] + model_decoder_layers_17_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[907] + model_decoder_layers_17_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[908] + model_decoder_layers_18_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[930] + model_decoder_layers_18_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[931] + model_decoder_layers_18_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[932] + model_decoder_layers_19_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[954] + model_decoder_layers_19_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[955] + model_decoder_layers_19_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[956] + model_decoder_layers_20_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[978] + model_decoder_layers_20_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[979] + model_decoder_layers_20_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[980] + model_decoder_layers_21_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1002] + model_decoder_layers_21_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1003] + model_decoder_layers_21_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1004] + model_decoder_layers_22_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1026] + model_decoder_layers_22_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1027] + model_decoder_layers_22_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1028] + model_decoder_layers_23_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1050] + model_decoder_layers_23_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1051] + model_decoder_layers_23_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1052] + model_decoder_layers_24_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1074] + model_decoder_layers_24_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1075] + model_decoder_layers_24_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1076] + model_decoder_layers_25_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1098] + model_decoder_layers_25_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1099] + model_decoder_layers_25_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1100] + model_decoder_layers_26_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1122] + model_decoder_layers_26_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1123] + model_decoder_layers_26_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1124] + model_decoder_layers_27_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1146] + model_decoder_layers_27_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1147] + model_decoder_layers_27_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1148] + model_decoder_layers_28_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1170] + model_decoder_layers_28_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1171] + model_decoder_layers_28_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1172] + model_decoder_layers_29_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1194] + model_decoder_layers_29_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1195] + model_decoder_layers_29_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1196] + model_decoder_layers_30_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1218] + model_decoder_layers_30_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1219] + model_decoder_layers_30_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1220] + model_decoder_layers_31_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1242] + model_decoder_layers_31_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1243] + model_decoder_layers_31_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1244] + lv = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_0_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape256 = R.call_tir(cls.reshape, (lv,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_0_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_0_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape257 = R.call_tir(cls.reshape, (lv_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape258 = R.call_tir(cls.reshape1, (reshape256,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape259 = R.call_tir(cls.reshape1, (reshape257,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv36: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", paged_kv_cache, R.prim_value(0), reshape258, reshape259, sinfo_args=(R.Object,)) + lv1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_1_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape260 = R.call_tir(cls.reshape, (lv1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv1_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_1_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_1_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape261 = R.call_tir(cls.reshape, (lv1_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape262 = R.call_tir(cls.reshape1, (reshape260,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape263 = R.call_tir(cls.reshape1, (reshape261,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv37: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv36, R.prim_value(1), reshape262, reshape263, sinfo_args=(R.Object,)) + lv2 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_2_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape264 = R.call_tir(cls.reshape, (lv2,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv2_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_2_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_2_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape265 = R.call_tir(cls.reshape, (lv2_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape266 = R.call_tir(cls.reshape1, (reshape264,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape267 = R.call_tir(cls.reshape1, (reshape265,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv38: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv37, R.prim_value(2), reshape266, reshape267, sinfo_args=(R.Object,)) + lv3 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_3_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape268 = R.call_tir(cls.reshape, (lv3,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv3_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_3_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_3_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape269 = R.call_tir(cls.reshape, (lv3_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape270 = R.call_tir(cls.reshape1, (reshape268,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape271 = R.call_tir(cls.reshape1, (reshape269,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv39: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv38, R.prim_value(3), reshape270, reshape271, sinfo_args=(R.Object,)) + lv4 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_4_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape272 = R.call_tir(cls.reshape, (lv4,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv4_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_4_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_4_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape273 = R.call_tir(cls.reshape, (lv4_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape274 = R.call_tir(cls.reshape1, (reshape272,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape275 = R.call_tir(cls.reshape1, (reshape273,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv40: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv39, R.prim_value(4), reshape274, reshape275, sinfo_args=(R.Object,)) + lv5 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_5_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape276 = R.call_tir(cls.reshape, (lv5,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv5_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_5_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_5_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape277 = R.call_tir(cls.reshape, (lv5_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape278 = R.call_tir(cls.reshape1, (reshape276,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape279 = R.call_tir(cls.reshape1, (reshape277,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv41: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv40, R.prim_value(5), reshape278, reshape279, sinfo_args=(R.Object,)) + lv6 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_6_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape280 = R.call_tir(cls.reshape, (lv6,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv6_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_6_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_6_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape281 = R.call_tir(cls.reshape, (lv6_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape282 = R.call_tir(cls.reshape1, (reshape280,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape283 = R.call_tir(cls.reshape1, (reshape281,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv42: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv41, R.prim_value(6), reshape282, reshape283, sinfo_args=(R.Object,)) + lv7 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_7_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape284 = R.call_tir(cls.reshape, (lv7,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv7_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_7_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_7_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape285 = R.call_tir(cls.reshape, (lv7_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape286 = R.call_tir(cls.reshape1, (reshape284,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape287 = R.call_tir(cls.reshape1, (reshape285,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv43: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv42, R.prim_value(7), reshape286, reshape287, sinfo_args=(R.Object,)) + lv8 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_8_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape288 = R.call_tir(cls.reshape, (lv8,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv8_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_8_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_8_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape289 = R.call_tir(cls.reshape, (lv8_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape290 = R.call_tir(cls.reshape1, (reshape288,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape291 = R.call_tir(cls.reshape1, (reshape289,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv44: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv43, R.prim_value(8), reshape290, reshape291, sinfo_args=(R.Object,)) + lv9 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_9_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape292 = R.call_tir(cls.reshape, (lv9,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv9_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_9_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_9_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape293 = R.call_tir(cls.reshape, (lv9_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape294 = R.call_tir(cls.reshape1, (reshape292,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape295 = R.call_tir(cls.reshape1, (reshape293,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv45: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv44, R.prim_value(9), reshape294, reshape295, sinfo_args=(R.Object,)) + lv10 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_10_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape296 = R.call_tir(cls.reshape, (lv10,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv10_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_10_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_10_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape297 = R.call_tir(cls.reshape, (lv10_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape298 = R.call_tir(cls.reshape1, (reshape296,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape299 = R.call_tir(cls.reshape1, (reshape297,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv46: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv45, R.prim_value(10), reshape298, reshape299, sinfo_args=(R.Object,)) + lv11 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_11_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape300 = R.call_tir(cls.reshape, (lv11,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv11_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_11_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_11_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape301 = R.call_tir(cls.reshape, (lv11_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape302 = R.call_tir(cls.reshape1, (reshape300,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape303 = R.call_tir(cls.reshape1, (reshape301,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv47: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv46, R.prim_value(11), reshape302, reshape303, sinfo_args=(R.Object,)) + lv12 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_12_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape304 = R.call_tir(cls.reshape, (lv12,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv12_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_12_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_12_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape305 = R.call_tir(cls.reshape, (lv12_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape306 = R.call_tir(cls.reshape1, (reshape304,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape307 = R.call_tir(cls.reshape1, (reshape305,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv48: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv47, R.prim_value(12), reshape306, reshape307, sinfo_args=(R.Object,)) + lv13 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_13_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape308 = R.call_tir(cls.reshape, (lv13,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv13_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_13_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_13_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape309 = R.call_tir(cls.reshape, (lv13_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape310 = R.call_tir(cls.reshape1, (reshape308,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape311 = R.call_tir(cls.reshape1, (reshape309,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv49: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv48, R.prim_value(13), reshape310, reshape311, sinfo_args=(R.Object,)) + lv14 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_14_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape312 = R.call_tir(cls.reshape, (lv14,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv14_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_14_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_14_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape313 = R.call_tir(cls.reshape, (lv14_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape314 = R.call_tir(cls.reshape1, (reshape312,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape315 = R.call_tir(cls.reshape1, (reshape313,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv50: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv49, R.prim_value(14), reshape314, reshape315, sinfo_args=(R.Object,)) + lv15 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_15_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape316 = R.call_tir(cls.reshape, (lv15,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv15_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_15_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_15_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape317 = R.call_tir(cls.reshape, (lv15_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape318 = R.call_tir(cls.reshape1, (reshape316,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape319 = R.call_tir(cls.reshape1, (reshape317,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv51: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv50, R.prim_value(15), reshape318, reshape319, sinfo_args=(R.Object,)) + lv16 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_16_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape320 = R.call_tir(cls.reshape, (lv16,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv16_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_16_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_16_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape321 = R.call_tir(cls.reshape, (lv16_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape322 = R.call_tir(cls.reshape1, (reshape320,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape323 = R.call_tir(cls.reshape1, (reshape321,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv52: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv51, R.prim_value(16), reshape322, reshape323, sinfo_args=(R.Object,)) + lv17 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_17_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape324 = R.call_tir(cls.reshape, (lv17,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv17_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_17_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_17_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape325 = R.call_tir(cls.reshape, (lv17_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape326 = R.call_tir(cls.reshape1, (reshape324,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape327 = R.call_tir(cls.reshape1, (reshape325,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv53: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv52, R.prim_value(17), reshape326, reshape327, sinfo_args=(R.Object,)) + lv18 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_18_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape328 = R.call_tir(cls.reshape, (lv18,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv18_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_18_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_18_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape329 = R.call_tir(cls.reshape, (lv18_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape330 = R.call_tir(cls.reshape1, (reshape328,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape331 = R.call_tir(cls.reshape1, (reshape329,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv54: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv53, R.prim_value(18), reshape330, reshape331, sinfo_args=(R.Object,)) + lv19 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_19_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape332 = R.call_tir(cls.reshape, (lv19,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv19_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_19_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_19_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape333 = R.call_tir(cls.reshape, (lv19_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape334 = R.call_tir(cls.reshape1, (reshape332,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape335 = R.call_tir(cls.reshape1, (reshape333,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv55: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv54, R.prim_value(19), reshape334, reshape335, sinfo_args=(R.Object,)) + lv20 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_20_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape336 = R.call_tir(cls.reshape, (lv20,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv20_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_20_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_20_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape337 = R.call_tir(cls.reshape, (lv20_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape338 = R.call_tir(cls.reshape1, (reshape336,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape339 = R.call_tir(cls.reshape1, (reshape337,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv56: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv55, R.prim_value(20), reshape338, reshape339, sinfo_args=(R.Object,)) + lv21 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_21_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape340 = R.call_tir(cls.reshape, (lv21,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv21_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_21_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_21_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape341 = R.call_tir(cls.reshape, (lv21_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape342 = R.call_tir(cls.reshape1, (reshape340,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape343 = R.call_tir(cls.reshape1, (reshape341,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv57: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv56, R.prim_value(21), reshape342, reshape343, sinfo_args=(R.Object,)) + lv22 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_22_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape344 = R.call_tir(cls.reshape, (lv22,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv22_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_22_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_22_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape345 = R.call_tir(cls.reshape, (lv22_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape346 = R.call_tir(cls.reshape1, (reshape344,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape347 = R.call_tir(cls.reshape1, (reshape345,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv58: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv57, R.prim_value(22), reshape346, reshape347, sinfo_args=(R.Object,)) + lv23 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_23_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape348 = R.call_tir(cls.reshape, (lv23,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv23_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_23_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_23_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape349 = R.call_tir(cls.reshape, (lv23_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape350 = R.call_tir(cls.reshape1, (reshape348,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape351 = R.call_tir(cls.reshape1, (reshape349,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv59: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv58, R.prim_value(23), reshape350, reshape351, sinfo_args=(R.Object,)) + lv24 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_24_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape352 = R.call_tir(cls.reshape, (lv24,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv24_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_24_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_24_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape353 = R.call_tir(cls.reshape, (lv24_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape354 = R.call_tir(cls.reshape1, (reshape352,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape355 = R.call_tir(cls.reshape1, (reshape353,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv60: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv59, R.prim_value(24), reshape354, reshape355, sinfo_args=(R.Object,)) + lv25 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_25_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape356 = R.call_tir(cls.reshape, (lv25,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv25_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_25_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_25_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape357 = R.call_tir(cls.reshape, (lv25_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape358 = R.call_tir(cls.reshape1, (reshape356,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape359 = R.call_tir(cls.reshape1, (reshape357,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv61: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv60, R.prim_value(25), reshape358, reshape359, sinfo_args=(R.Object,)) + lv26 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_26_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape360 = R.call_tir(cls.reshape, (lv26,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv26_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_26_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_26_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape361 = R.call_tir(cls.reshape, (lv26_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape362 = R.call_tir(cls.reshape1, (reshape360,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape363 = R.call_tir(cls.reshape1, (reshape361,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv62: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv61, R.prim_value(26), reshape362, reshape363, sinfo_args=(R.Object,)) + lv27 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_27_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape364 = R.call_tir(cls.reshape, (lv27,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv27_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_27_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_27_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape365 = R.call_tir(cls.reshape, (lv27_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape366 = R.call_tir(cls.reshape1, (reshape364,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape367 = R.call_tir(cls.reshape1, (reshape365,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv63: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv62, R.prim_value(27), reshape366, reshape367, sinfo_args=(R.Object,)) + lv28 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_28_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape368 = R.call_tir(cls.reshape, (lv28,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv28_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_28_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_28_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape369 = R.call_tir(cls.reshape, (lv28_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape370 = R.call_tir(cls.reshape1, (reshape368,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape371 = R.call_tir(cls.reshape1, (reshape369,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv64: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv63, R.prim_value(28), reshape370, reshape371, sinfo_args=(R.Object,)) + lv29 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_29_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape372 = R.call_tir(cls.reshape, (lv29,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv29_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_29_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_29_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape373 = R.call_tir(cls.reshape, (lv29_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape374 = R.call_tir(cls.reshape1, (reshape372,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape375 = R.call_tir(cls.reshape1, (reshape373,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv65: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv64, R.prim_value(29), reshape374, reshape375, sinfo_args=(R.Object,)) + lv30 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_30_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape376 = R.call_tir(cls.reshape, (lv30,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv30_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_30_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_30_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape377 = R.call_tir(cls.reshape, (lv30_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape378 = R.call_tir(cls.reshape1, (reshape376,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape379 = R.call_tir(cls.reshape1, (reshape377,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv66: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv65, R.prim_value(30), reshape378, reshape379, sinfo_args=(R.Object,)) + lv31 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_31_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape380 = R.call_tir(cls.reshape, (lv31,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv31_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_31_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_31_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape381 = R.call_tir(cls.reshape, (lv31_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape382 = R.call_tir(cls.reshape1, (reshape380,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape383 = R.call_tir(cls.reshape1, (reshape381,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + gv1: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv66, R.prim_value(31), reshape382, reshape383, sinfo_args=(R.Object,)) + R.output(gv1) + return gv1 + + @R.function + def batch_decode(input_ids: R.Tensor(("batch_size", 1), dtype="int32"), paged_kv_cache: R.Object, packed_params: R.Tuple(R.Tensor((1280, 128, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1500, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), 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R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"))) -> R.Tensor(("batch_size", 1, 51866), dtype="float32"): + batch_size = T.int64() + R.func_attr({"num_input": 2, "relax.memory_plan_dynamic_func_output": 1, "relax.rewrite_cuda_graph.capture_symbolic_vars": ["batch_size"], "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "seq_len": 15000, "total_seq_len": 1500}}) + cls = Module + with R.dataflow(): + model_decoder_embed_tokens_weight3: R.Tensor((51866, 1280), dtype="float16") = packed_params[487] + model_decoder_embed_positions_weight3: R.Tensor((448, 1280), dtype="float16") = packed_params[488] + model_decoder_layers_0_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[489] + model_decoder_layers_0_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[490] + model_decoder_layers_0_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[491] + model_decoder_layers_0_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[492] + model_decoder_layers_0_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[493] + model_decoder_layers_0_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[494] + model_decoder_layers_0_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[495] + model_decoder_layers_0_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[496] + model_decoder_layers_0_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[497] + model_decoder_layers_0_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[501] + model_decoder_layers_0_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[502] + model_decoder_layers_0_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[503] + model_decoder_layers_0_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[504] + model_decoder_layers_0_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[505] + model_decoder_layers_0_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[506] + model_decoder_layers_0_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[507] + model_decoder_layers_0_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[508] + model_decoder_layers_0_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[509] + model_decoder_layers_0_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[510] + model_decoder_layers_0_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[511] + model_decoder_layers_0_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[512] + model_decoder_layers_1_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[513] + model_decoder_layers_1_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[514] + model_decoder_layers_1_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[515] + model_decoder_layers_1_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[516] + model_decoder_layers_1_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[517] + model_decoder_layers_1_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[518] + model_decoder_layers_1_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[519] + model_decoder_layers_1_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[520] + model_decoder_layers_1_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[521] + model_decoder_layers_1_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[525] + model_decoder_layers_1_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[526] + model_decoder_layers_1_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[527] + model_decoder_layers_1_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[528] + model_decoder_layers_1_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[529] + model_decoder_layers_1_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[530] + model_decoder_layers_1_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[531] + model_decoder_layers_1_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[532] + model_decoder_layers_1_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[533] + model_decoder_layers_1_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[534] + model_decoder_layers_1_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[535] + model_decoder_layers_1_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[536] + model_decoder_layers_2_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[537] + model_decoder_layers_2_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[538] + model_decoder_layers_2_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[539] + model_decoder_layers_2_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[540] + model_decoder_layers_2_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[541] + model_decoder_layers_2_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[542] + model_decoder_layers_2_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[543] + model_decoder_layers_2_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[544] + model_decoder_layers_2_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[545] + model_decoder_layers_2_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[549] + model_decoder_layers_2_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[550] + model_decoder_layers_2_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[551] + model_decoder_layers_2_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[552] + model_decoder_layers_2_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[553] + model_decoder_layers_2_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[554] + model_decoder_layers_2_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[555] + model_decoder_layers_2_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[556] + model_decoder_layers_2_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[557] + model_decoder_layers_2_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[558] + model_decoder_layers_2_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[559] + model_decoder_layers_2_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[560] + model_decoder_layers_3_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[561] + model_decoder_layers_3_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[562] + model_decoder_layers_3_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[563] + model_decoder_layers_3_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[564] + model_decoder_layers_3_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[565] + model_decoder_layers_3_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[566] + model_decoder_layers_3_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[567] + model_decoder_layers_3_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[568] + model_decoder_layers_3_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[569] + model_decoder_layers_3_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[573] + model_decoder_layers_3_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[574] + model_decoder_layers_3_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[575] + model_decoder_layers_3_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[576] + model_decoder_layers_3_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[577] + model_decoder_layers_3_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[578] + model_decoder_layers_3_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[579] + model_decoder_layers_3_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[580] + model_decoder_layers_3_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[581] + model_decoder_layers_3_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[582] + model_decoder_layers_3_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[583] + model_decoder_layers_3_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[584] + model_decoder_layers_4_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[585] + model_decoder_layers_4_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[586] + model_decoder_layers_4_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[587] + model_decoder_layers_4_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[588] + model_decoder_layers_4_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[589] + model_decoder_layers_4_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[590] + model_decoder_layers_4_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[591] + model_decoder_layers_4_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[592] + model_decoder_layers_4_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[593] + model_decoder_layers_4_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[597] + model_decoder_layers_4_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[598] + model_decoder_layers_4_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[599] + model_decoder_layers_4_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[600] + model_decoder_layers_4_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[601] + model_decoder_layers_4_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[602] + model_decoder_layers_4_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[603] + model_decoder_layers_4_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[604] + model_decoder_layers_4_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[605] + model_decoder_layers_4_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[606] + model_decoder_layers_4_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[607] + model_decoder_layers_4_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[608] + model_decoder_layers_5_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[609] + model_decoder_layers_5_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[610] + model_decoder_layers_5_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[611] + model_decoder_layers_5_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[612] + model_decoder_layers_5_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[613] + model_decoder_layers_5_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[614] + model_decoder_layers_5_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[615] + model_decoder_layers_5_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[616] + model_decoder_layers_5_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[617] + model_decoder_layers_5_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[621] + model_decoder_layers_5_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[622] + model_decoder_layers_5_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[623] + model_decoder_layers_5_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[624] + model_decoder_layers_5_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[625] + model_decoder_layers_5_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[626] + model_decoder_layers_5_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[627] + model_decoder_layers_5_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[628] + model_decoder_layers_5_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[629] + model_decoder_layers_5_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[630] + model_decoder_layers_5_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[631] + model_decoder_layers_5_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[632] + model_decoder_layers_6_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[633] + model_decoder_layers_6_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[634] + model_decoder_layers_6_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[635] + model_decoder_layers_6_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[636] + model_decoder_layers_6_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[637] + model_decoder_layers_6_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[638] + model_decoder_layers_6_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[639] + model_decoder_layers_6_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[640] + model_decoder_layers_6_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[641] + model_decoder_layers_6_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[645] + model_decoder_layers_6_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[646] + model_decoder_layers_6_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[647] + model_decoder_layers_6_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[648] + model_decoder_layers_6_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[649] + model_decoder_layers_6_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[650] + model_decoder_layers_6_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[651] + model_decoder_layers_6_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[652] + model_decoder_layers_6_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[653] + model_decoder_layers_6_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[654] + model_decoder_layers_6_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[655] + model_decoder_layers_6_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[656] + model_decoder_layers_7_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[657] + model_decoder_layers_7_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[658] + model_decoder_layers_7_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[659] + model_decoder_layers_7_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[660] + model_decoder_layers_7_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[661] + model_decoder_layers_7_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[662] + model_decoder_layers_7_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[663] + model_decoder_layers_7_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[664] + model_decoder_layers_7_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[665] + model_decoder_layers_7_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[669] + model_decoder_layers_7_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[670] + model_decoder_layers_7_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[671] + model_decoder_layers_7_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[672] + model_decoder_layers_7_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[673] + model_decoder_layers_7_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[674] + model_decoder_layers_7_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[675] + model_decoder_layers_7_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[676] + model_decoder_layers_7_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[677] + model_decoder_layers_7_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[678] + model_decoder_layers_7_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[679] + model_decoder_layers_7_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[680] + model_decoder_layers_8_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[681] + model_decoder_layers_8_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[682] + model_decoder_layers_8_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[683] + model_decoder_layers_8_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[684] + model_decoder_layers_8_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[685] + model_decoder_layers_8_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[686] + model_decoder_layers_8_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[687] + model_decoder_layers_8_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[688] + model_decoder_layers_8_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[689] + model_decoder_layers_8_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[693] + model_decoder_layers_8_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[694] + model_decoder_layers_8_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[695] + model_decoder_layers_8_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[696] + model_decoder_layers_8_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[697] + model_decoder_layers_8_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[698] + model_decoder_layers_8_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[699] + model_decoder_layers_8_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[700] + model_decoder_layers_8_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[701] + model_decoder_layers_8_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[702] + model_decoder_layers_8_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[703] + model_decoder_layers_8_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[704] + model_decoder_layers_9_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[705] + model_decoder_layers_9_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[706] + model_decoder_layers_9_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[707] + model_decoder_layers_9_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[708] + model_decoder_layers_9_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[709] + model_decoder_layers_9_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[710] + model_decoder_layers_9_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[711] + model_decoder_layers_9_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[712] + model_decoder_layers_9_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[713] + model_decoder_layers_9_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[717] + model_decoder_layers_9_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[718] + model_decoder_layers_9_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[719] + model_decoder_layers_9_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[720] + model_decoder_layers_9_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[721] + model_decoder_layers_9_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[722] + model_decoder_layers_9_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[723] + model_decoder_layers_9_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[724] + model_decoder_layers_9_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[725] + model_decoder_layers_9_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[726] + model_decoder_layers_9_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[727] + model_decoder_layers_9_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[728] + model_decoder_layers_10_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[729] + model_decoder_layers_10_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[730] + model_decoder_layers_10_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[731] + model_decoder_layers_10_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[732] + model_decoder_layers_10_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[733] + model_decoder_layers_10_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[734] + model_decoder_layers_10_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[735] + model_decoder_layers_10_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[736] + model_decoder_layers_10_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[737] + model_decoder_layers_10_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[741] + model_decoder_layers_10_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[742] + model_decoder_layers_10_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[743] + model_decoder_layers_10_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[744] + model_decoder_layers_10_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[745] + model_decoder_layers_10_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[746] + model_decoder_layers_10_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[747] + model_decoder_layers_10_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[748] + model_decoder_layers_10_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[749] + model_decoder_layers_10_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[750] + model_decoder_layers_10_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[751] + model_decoder_layers_10_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[752] + model_decoder_layers_11_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[753] + model_decoder_layers_11_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[754] + model_decoder_layers_11_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[755] + model_decoder_layers_11_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[756] + model_decoder_layers_11_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[757] + model_decoder_layers_11_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[758] + model_decoder_layers_11_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[759] + model_decoder_layers_11_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[760] + model_decoder_layers_11_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[761] + model_decoder_layers_11_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[765] + model_decoder_layers_11_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[766] + model_decoder_layers_11_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[767] + model_decoder_layers_11_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[768] + model_decoder_layers_11_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[769] + model_decoder_layers_11_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[770] + model_decoder_layers_11_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[771] + model_decoder_layers_11_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[772] + model_decoder_layers_11_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[773] + model_decoder_layers_11_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[774] + model_decoder_layers_11_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[775] + model_decoder_layers_11_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[776] + model_decoder_layers_12_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[777] + model_decoder_layers_12_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[778] + model_decoder_layers_12_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[779] + model_decoder_layers_12_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[780] + model_decoder_layers_12_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[781] + model_decoder_layers_12_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[782] + model_decoder_layers_12_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[783] + model_decoder_layers_12_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[784] + model_decoder_layers_12_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[785] + model_decoder_layers_12_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[789] + model_decoder_layers_12_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[790] + model_decoder_layers_12_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[791] + model_decoder_layers_12_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[792] + model_decoder_layers_12_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[793] + model_decoder_layers_12_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[794] + model_decoder_layers_12_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[795] + model_decoder_layers_12_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[796] + model_decoder_layers_12_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[797] + model_decoder_layers_12_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[798] + model_decoder_layers_12_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[799] + model_decoder_layers_12_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[800] + model_decoder_layers_13_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[801] + model_decoder_layers_13_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[802] + model_decoder_layers_13_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[803] + model_decoder_layers_13_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[804] + model_decoder_layers_13_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[805] + model_decoder_layers_13_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[806] + model_decoder_layers_13_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[807] + model_decoder_layers_13_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[808] + model_decoder_layers_13_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[809] + model_decoder_layers_13_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[813] + model_decoder_layers_13_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[814] + model_decoder_layers_13_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[815] + model_decoder_layers_13_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[816] + model_decoder_layers_13_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[817] + model_decoder_layers_13_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[818] + model_decoder_layers_13_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[819] + model_decoder_layers_13_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[820] + model_decoder_layers_13_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[821] + model_decoder_layers_13_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[822] + model_decoder_layers_13_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[823] + model_decoder_layers_13_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[824] + model_decoder_layers_14_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[825] + model_decoder_layers_14_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[826] + model_decoder_layers_14_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[827] + model_decoder_layers_14_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[828] + model_decoder_layers_14_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[829] + model_decoder_layers_14_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[830] + model_decoder_layers_14_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[831] + model_decoder_layers_14_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[832] + model_decoder_layers_14_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[833] + model_decoder_layers_14_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[837] + model_decoder_layers_14_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[838] + model_decoder_layers_14_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[839] + model_decoder_layers_14_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[840] + model_decoder_layers_14_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[841] + model_decoder_layers_14_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[842] + model_decoder_layers_14_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[843] + model_decoder_layers_14_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[844] + model_decoder_layers_14_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[845] + model_decoder_layers_14_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[846] + model_decoder_layers_14_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[847] + model_decoder_layers_14_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[848] + model_decoder_layers_15_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[849] + model_decoder_layers_15_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[850] + model_decoder_layers_15_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[851] + model_decoder_layers_15_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[852] + model_decoder_layers_15_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[853] + model_decoder_layers_15_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[854] + model_decoder_layers_15_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[855] + model_decoder_layers_15_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[856] + model_decoder_layers_15_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[857] + model_decoder_layers_15_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[861] + model_decoder_layers_15_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[862] + model_decoder_layers_15_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[863] + model_decoder_layers_15_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[864] + model_decoder_layers_15_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[865] + model_decoder_layers_15_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[866] + model_decoder_layers_15_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[867] + model_decoder_layers_15_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[868] + model_decoder_layers_15_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[869] + model_decoder_layers_15_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[870] + model_decoder_layers_15_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[871] + model_decoder_layers_15_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[872] + model_decoder_layers_16_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[873] + model_decoder_layers_16_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[874] + model_decoder_layers_16_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[875] + model_decoder_layers_16_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[876] + model_decoder_layers_16_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[877] + model_decoder_layers_16_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[878] + model_decoder_layers_16_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[879] + model_decoder_layers_16_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[880] + model_decoder_layers_16_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[881] + model_decoder_layers_16_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[885] + model_decoder_layers_16_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[886] + model_decoder_layers_16_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[887] + model_decoder_layers_16_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[888] + model_decoder_layers_16_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[889] + model_decoder_layers_16_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[890] + model_decoder_layers_16_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[891] + model_decoder_layers_16_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[892] + model_decoder_layers_16_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[893] + model_decoder_layers_16_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[894] + model_decoder_layers_16_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[895] + model_decoder_layers_16_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[896] + model_decoder_layers_17_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[897] + model_decoder_layers_17_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[898] + model_decoder_layers_17_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[899] + model_decoder_layers_17_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[900] + model_decoder_layers_17_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[901] + model_decoder_layers_17_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[902] + model_decoder_layers_17_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[903] + model_decoder_layers_17_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[904] + model_decoder_layers_17_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[905] + model_decoder_layers_17_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[909] + model_decoder_layers_17_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[910] + model_decoder_layers_17_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[911] + model_decoder_layers_17_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[912] + model_decoder_layers_17_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[913] + model_decoder_layers_17_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[914] + model_decoder_layers_17_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[915] + model_decoder_layers_17_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[916] + model_decoder_layers_17_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[917] + model_decoder_layers_17_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[918] + model_decoder_layers_17_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[919] + model_decoder_layers_17_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[920] + model_decoder_layers_18_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[921] + model_decoder_layers_18_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[922] + model_decoder_layers_18_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[923] + model_decoder_layers_18_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[924] + model_decoder_layers_18_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[925] + model_decoder_layers_18_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[926] + model_decoder_layers_18_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[927] + model_decoder_layers_18_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[928] + model_decoder_layers_18_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[929] + model_decoder_layers_18_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[933] + model_decoder_layers_18_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[934] + model_decoder_layers_18_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[935] + model_decoder_layers_18_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[936] + model_decoder_layers_18_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[937] + model_decoder_layers_18_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[938] + model_decoder_layers_18_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[939] + model_decoder_layers_18_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[940] + model_decoder_layers_18_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[941] + model_decoder_layers_18_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[942] + model_decoder_layers_18_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[943] + model_decoder_layers_18_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[944] + model_decoder_layers_19_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[945] + model_decoder_layers_19_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[946] + model_decoder_layers_19_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[947] + model_decoder_layers_19_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[948] + model_decoder_layers_19_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[949] + model_decoder_layers_19_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[950] + model_decoder_layers_19_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[951] + model_decoder_layers_19_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[952] + model_decoder_layers_19_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[953] + model_decoder_layers_19_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[957] + model_decoder_layers_19_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[958] + model_decoder_layers_19_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[959] + model_decoder_layers_19_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[960] + model_decoder_layers_19_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[961] + model_decoder_layers_19_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[962] + model_decoder_layers_19_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[963] + model_decoder_layers_19_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[964] + model_decoder_layers_19_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[965] + model_decoder_layers_19_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[966] + model_decoder_layers_19_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[967] + model_decoder_layers_19_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[968] + model_decoder_layers_20_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[969] + model_decoder_layers_20_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[970] + model_decoder_layers_20_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[971] + model_decoder_layers_20_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[972] + model_decoder_layers_20_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[973] + model_decoder_layers_20_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[974] + model_decoder_layers_20_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[975] + model_decoder_layers_20_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[976] + model_decoder_layers_20_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[977] + model_decoder_layers_20_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[981] + model_decoder_layers_20_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[982] + model_decoder_layers_20_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[983] + model_decoder_layers_20_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[984] + model_decoder_layers_20_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[985] + model_decoder_layers_20_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[986] + model_decoder_layers_20_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[987] + model_decoder_layers_20_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[988] + model_decoder_layers_20_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[989] + model_decoder_layers_20_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[990] + model_decoder_layers_20_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[991] + model_decoder_layers_20_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[992] + model_decoder_layers_21_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[993] + model_decoder_layers_21_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[994] + model_decoder_layers_21_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[995] + model_decoder_layers_21_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[996] + model_decoder_layers_21_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[997] + model_decoder_layers_21_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[998] + model_decoder_layers_21_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[999] + model_decoder_layers_21_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1000] + model_decoder_layers_21_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1001] + model_decoder_layers_21_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1005] + model_decoder_layers_21_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1006] + model_decoder_layers_21_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1007] + model_decoder_layers_21_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1008] + model_decoder_layers_21_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1009] + model_decoder_layers_21_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1010] + model_decoder_layers_21_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1011] + model_decoder_layers_21_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1012] + model_decoder_layers_21_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1013] + model_decoder_layers_21_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1014] + model_decoder_layers_21_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1015] + model_decoder_layers_21_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1016] + model_decoder_layers_22_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1017] + model_decoder_layers_22_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1018] + model_decoder_layers_22_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1019] + model_decoder_layers_22_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1020] + model_decoder_layers_22_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1021] + model_decoder_layers_22_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1022] + model_decoder_layers_22_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1023] + model_decoder_layers_22_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1024] + model_decoder_layers_22_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1025] + model_decoder_layers_22_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1029] + model_decoder_layers_22_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1030] + model_decoder_layers_22_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1031] + model_decoder_layers_22_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1032] + model_decoder_layers_22_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1033] + model_decoder_layers_22_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1034] + model_decoder_layers_22_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1035] + model_decoder_layers_22_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1036] + model_decoder_layers_22_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1037] + model_decoder_layers_22_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1038] + model_decoder_layers_22_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1039] + model_decoder_layers_22_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1040] + model_decoder_layers_23_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1041] + model_decoder_layers_23_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1042] + model_decoder_layers_23_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1043] + model_decoder_layers_23_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1044] + model_decoder_layers_23_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1045] + model_decoder_layers_23_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1046] + model_decoder_layers_23_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1047] + model_decoder_layers_23_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1048] + model_decoder_layers_23_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1049] + model_decoder_layers_23_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1053] + model_decoder_layers_23_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1054] + model_decoder_layers_23_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1055] + model_decoder_layers_23_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1056] + model_decoder_layers_23_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1057] + model_decoder_layers_23_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1058] + model_decoder_layers_23_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1059] + model_decoder_layers_23_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1060] + model_decoder_layers_23_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1061] + model_decoder_layers_23_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1062] + model_decoder_layers_23_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1063] + model_decoder_layers_23_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1064] + model_decoder_layers_24_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1065] + model_decoder_layers_24_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1066] + model_decoder_layers_24_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1067] + model_decoder_layers_24_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1068] + model_decoder_layers_24_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1069] + model_decoder_layers_24_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1070] + model_decoder_layers_24_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1071] + model_decoder_layers_24_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1072] + model_decoder_layers_24_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1073] + model_decoder_layers_24_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1077] + model_decoder_layers_24_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1078] + model_decoder_layers_24_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1079] + model_decoder_layers_24_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1080] + model_decoder_layers_24_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1081] + model_decoder_layers_24_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1082] + model_decoder_layers_24_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1083] + model_decoder_layers_24_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1084] + model_decoder_layers_24_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1085] + model_decoder_layers_24_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1086] + model_decoder_layers_24_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1087] + model_decoder_layers_24_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1088] + model_decoder_layers_25_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1089] + model_decoder_layers_25_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1090] + model_decoder_layers_25_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1091] + model_decoder_layers_25_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1092] + model_decoder_layers_25_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1093] + model_decoder_layers_25_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1094] + model_decoder_layers_25_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1095] + model_decoder_layers_25_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1096] + model_decoder_layers_25_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1097] + model_decoder_layers_25_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1101] + model_decoder_layers_25_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1102] + model_decoder_layers_25_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1103] + model_decoder_layers_25_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1104] + model_decoder_layers_25_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1105] + model_decoder_layers_25_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1106] + model_decoder_layers_25_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1107] + model_decoder_layers_25_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1108] + model_decoder_layers_25_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1109] + model_decoder_layers_25_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1110] + model_decoder_layers_25_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1111] + model_decoder_layers_25_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1112] + model_decoder_layers_26_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1113] + model_decoder_layers_26_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1114] + model_decoder_layers_26_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1115] + model_decoder_layers_26_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1116] + model_decoder_layers_26_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1117] + model_decoder_layers_26_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1118] + model_decoder_layers_26_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1119] + model_decoder_layers_26_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1120] + model_decoder_layers_26_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1121] + model_decoder_layers_26_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1125] + model_decoder_layers_26_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1126] + model_decoder_layers_26_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1127] + model_decoder_layers_26_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1128] + model_decoder_layers_26_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1129] + model_decoder_layers_26_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1130] + model_decoder_layers_26_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1131] + model_decoder_layers_26_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1132] + model_decoder_layers_26_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1133] + model_decoder_layers_26_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1134] + model_decoder_layers_26_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1135] + model_decoder_layers_26_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1136] + model_decoder_layers_27_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1137] + model_decoder_layers_27_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1138] + model_decoder_layers_27_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1139] + model_decoder_layers_27_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1140] + model_decoder_layers_27_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1141] + model_decoder_layers_27_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1142] + model_decoder_layers_27_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1143] + model_decoder_layers_27_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1144] + model_decoder_layers_27_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1145] + model_decoder_layers_27_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1149] + model_decoder_layers_27_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1150] + model_decoder_layers_27_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1151] + model_decoder_layers_27_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1152] + model_decoder_layers_27_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1153] + model_decoder_layers_27_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1154] + model_decoder_layers_27_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1155] + model_decoder_layers_27_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1156] + model_decoder_layers_27_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1157] + model_decoder_layers_27_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1158] + model_decoder_layers_27_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1159] + model_decoder_layers_27_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1160] + model_decoder_layers_28_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1161] + model_decoder_layers_28_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1162] + model_decoder_layers_28_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1163] + model_decoder_layers_28_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1164] + model_decoder_layers_28_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1165] + model_decoder_layers_28_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1166] + model_decoder_layers_28_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1167] + model_decoder_layers_28_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1168] + model_decoder_layers_28_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1169] + model_decoder_layers_28_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1173] + model_decoder_layers_28_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1174] + model_decoder_layers_28_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1175] + model_decoder_layers_28_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1176] + model_decoder_layers_28_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1177] + model_decoder_layers_28_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1178] + model_decoder_layers_28_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1179] + model_decoder_layers_28_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1180] + model_decoder_layers_28_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1181] + model_decoder_layers_28_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1182] + model_decoder_layers_28_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1183] + model_decoder_layers_28_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1184] + model_decoder_layers_29_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1185] + model_decoder_layers_29_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1186] + model_decoder_layers_29_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1187] + model_decoder_layers_29_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1188] + model_decoder_layers_29_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1189] + model_decoder_layers_29_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1190] + model_decoder_layers_29_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1191] + model_decoder_layers_29_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1192] + model_decoder_layers_29_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1193] + model_decoder_layers_29_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1197] + model_decoder_layers_29_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1198] + model_decoder_layers_29_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1199] + model_decoder_layers_29_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1200] + model_decoder_layers_29_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1201] + model_decoder_layers_29_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1202] + model_decoder_layers_29_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1203] + model_decoder_layers_29_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1204] + model_decoder_layers_29_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1205] + model_decoder_layers_29_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1206] + model_decoder_layers_29_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1207] + model_decoder_layers_29_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1208] + model_decoder_layers_30_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1209] + model_decoder_layers_30_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1210] + model_decoder_layers_30_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1211] + model_decoder_layers_30_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1212] + model_decoder_layers_30_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1213] + model_decoder_layers_30_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1214] + model_decoder_layers_30_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1215] + model_decoder_layers_30_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1216] + model_decoder_layers_30_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1217] + model_decoder_layers_30_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1221] + model_decoder_layers_30_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1222] + model_decoder_layers_30_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1223] + model_decoder_layers_30_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1224] + model_decoder_layers_30_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1225] + model_decoder_layers_30_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1226] + model_decoder_layers_30_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1227] + model_decoder_layers_30_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1228] + model_decoder_layers_30_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1229] + model_decoder_layers_30_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1230] + model_decoder_layers_30_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1231] + model_decoder_layers_30_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1232] + model_decoder_layers_31_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1233] + model_decoder_layers_31_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1234] + model_decoder_layers_31_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1235] + model_decoder_layers_31_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1236] + model_decoder_layers_31_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1237] + model_decoder_layers_31_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1238] + model_decoder_layers_31_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1239] + model_decoder_layers_31_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1240] + model_decoder_layers_31_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1241] + model_decoder_layers_31_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1245] + model_decoder_layers_31_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1246] + model_decoder_layers_31_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1247] + model_decoder_layers_31_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1248] + model_decoder_layers_31_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1249] + model_decoder_layers_31_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1250] + model_decoder_layers_31_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1251] + model_decoder_layers_31_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1252] + model_decoder_layers_31_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1253] + model_decoder_layers_31_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1254] + model_decoder_layers_31_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1255] + model_decoder_layers_31_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1256] + model_decoder_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1257] + model_decoder_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1258] + reshape707 = R.call_tir(cls.reshape2, (input_ids,), out_sinfo=R.Tensor((batch_size,), dtype="int32")) + take3 = R.call_tir(cls.take, (model_decoder_embed_tokens_weight3, reshape707), out_sinfo=R.Tensor((batch_size, 1280), dtype="float16")) + reshape708 = R.call_tir(cls.reshape3, (take3,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv133: R.Tensor((batch_size,), dtype="int32") = R.call_pure_packed("vm.builtin.attention_kv_cache_get_query_positions", paged_kv_cache, sinfo_args=(R.Tensor((batch_size,), dtype="int32"),)) + take4 = R.call_tir(cls.take1, (model_decoder_embed_positions_weight3, lv133), out_sinfo=R.Tensor((batch_size, 1280), dtype="float16")) + reshape709 = R.call_tir(cls.reshape3, (take4,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add578 = R.call_tir(cls.add, (reshape708, reshape709), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm162 = R.call_tir(cls.layer_norm, (add578, model_decoder_layers_0_self_attn_layer_norm_weight3, model_decoder_layers_0_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv224 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_0_self_attn_q_proj_weight3, layer_norm162, model_decoder_layers_0_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape710 = R.call_tir(cls.reshape4, (lv224,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv65 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_0_self_attn_k_proj_weight3, layer_norm162), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape711 = R.call_tir(cls.reshape4, (lv65,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv225 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_0_self_attn_v_proj_weight3, layer_norm162, model_decoder_layers_0_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape712 = R.call_tir(cls.reshape4, (lv225,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat32 = R.call_tir(cls.concatenate, (reshape710, reshape711, reshape712), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape713 = R.call_tir(cls.reshape5, (concat32,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv134 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), reshape713), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape714 = R.call_tir(cls.reshape6, (lv134,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape715 = R.call_tir(cls.reshape7, (reshape714,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv226 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_0_self_attn_out_proj_weight3, reshape715, model_decoder_layers_0_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add582 = R.call_tir(cls.add, (add578, lv226), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm163 = R.call_tir(cls.layer_norm, (add582, model_decoder_layers_0_encoder_attn_layer_norm_weight3, model_decoder_layers_0_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv227 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_0_encoder_attn_q_proj_weight3, layer_norm163, model_decoder_layers_0_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape716 = R.call_tir(cls.reshape4, (lv227,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape717 = R.call_tir(cls.reshape8, (reshape716,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv135 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), reshape717), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape718 = R.call_tir(cls.reshape6, (lv135,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape719 = R.call_tir(cls.reshape7, (reshape718,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv228 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_0_encoder_attn_out_proj_weight3, reshape719, model_decoder_layers_0_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add585 = R.call_tir(cls.add, (add582, lv228), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm164 = R.call_tir(cls.layer_norm, (add585, model_decoder_layers_0_final_layer_norm_weight3, model_decoder_layers_0_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv32 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_0_fc1_weight3, layer_norm164, model_decoder_layers_0_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv229 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_0_fc2_weight3, lv32, model_decoder_layers_0_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add588 = R.call_tir(cls.add, (add585, lv229), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm165 = R.call_tir(cls.layer_norm, (add588, model_decoder_layers_1_self_attn_layer_norm_weight3, model_decoder_layers_1_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv230 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_1_self_attn_q_proj_weight3, layer_norm165, model_decoder_layers_1_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape720 = R.call_tir(cls.reshape4, (lv230,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv66 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_1_self_attn_k_proj_weight3, layer_norm165), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape721 = R.call_tir(cls.reshape4, (lv66,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv231 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_1_self_attn_v_proj_weight3, layer_norm165, model_decoder_layers_1_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape722 = R.call_tir(cls.reshape4, (lv231,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat33 = R.call_tir(cls.concatenate, (reshape720, reshape721, reshape722), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape723 = R.call_tir(cls.reshape5, (concat33,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv136 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), reshape723), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape724 = R.call_tir(cls.reshape6, (lv136,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape725 = R.call_tir(cls.reshape7, (reshape724,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv232 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_1_self_attn_out_proj_weight3, reshape725, model_decoder_layers_1_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add592 = R.call_tir(cls.add, (add588, lv232), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm166 = R.call_tir(cls.layer_norm, (add592, model_decoder_layers_1_encoder_attn_layer_norm_weight3, model_decoder_layers_1_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv233 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_1_encoder_attn_q_proj_weight3, layer_norm166, model_decoder_layers_1_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape726 = R.call_tir(cls.reshape4, (lv233,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape727 = R.call_tir(cls.reshape8, (reshape726,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv137 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), reshape727), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape728 = R.call_tir(cls.reshape6, (lv137,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape729 = R.call_tir(cls.reshape7, (reshape728,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv234 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_1_encoder_attn_out_proj_weight3, reshape729, model_decoder_layers_1_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add595 = R.call_tir(cls.add, (add592, lv234), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm167 = R.call_tir(cls.layer_norm, (add595, model_decoder_layers_1_final_layer_norm_weight3, model_decoder_layers_1_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv33 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_1_fc1_weight3, layer_norm167, model_decoder_layers_1_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv235 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_1_fc2_weight3, lv33, model_decoder_layers_1_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add598 = R.call_tir(cls.add, (add595, lv235), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm168 = R.call_tir(cls.layer_norm, (add598, model_decoder_layers_2_self_attn_layer_norm_weight3, model_decoder_layers_2_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv236 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_2_self_attn_q_proj_weight3, layer_norm168, model_decoder_layers_2_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape730 = R.call_tir(cls.reshape4, (lv236,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv67 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_2_self_attn_k_proj_weight3, layer_norm168), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape731 = R.call_tir(cls.reshape4, (lv67,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv237 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_2_self_attn_v_proj_weight3, layer_norm168, model_decoder_layers_2_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape732 = R.call_tir(cls.reshape4, (lv237,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat34 = R.call_tir(cls.concatenate, (reshape730, reshape731, reshape732), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape733 = R.call_tir(cls.reshape5, (concat34,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv138 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), reshape733), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape734 = R.call_tir(cls.reshape6, (lv138,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape735 = R.call_tir(cls.reshape7, (reshape734,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv238 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_2_self_attn_out_proj_weight3, reshape735, model_decoder_layers_2_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add602 = R.call_tir(cls.add, (add598, lv238), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm169 = R.call_tir(cls.layer_norm, (add602, model_decoder_layers_2_encoder_attn_layer_norm_weight3, model_decoder_layers_2_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv239 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_2_encoder_attn_q_proj_weight3, layer_norm169, model_decoder_layers_2_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape736 = R.call_tir(cls.reshape4, (lv239,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape737 = R.call_tir(cls.reshape8, (reshape736,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv139 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), reshape737), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape738 = R.call_tir(cls.reshape6, (lv139,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape739 = R.call_tir(cls.reshape7, (reshape738,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv240 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_2_encoder_attn_out_proj_weight3, reshape739, model_decoder_layers_2_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add605 = R.call_tir(cls.add, (add602, lv240), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm170 = R.call_tir(cls.layer_norm, (add605, model_decoder_layers_2_final_layer_norm_weight3, model_decoder_layers_2_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv34 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_2_fc1_weight3, layer_norm170, model_decoder_layers_2_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv241 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_2_fc2_weight3, lv34, model_decoder_layers_2_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add608 = R.call_tir(cls.add, (add605, lv241), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm171 = R.call_tir(cls.layer_norm, (add608, model_decoder_layers_3_self_attn_layer_norm_weight3, model_decoder_layers_3_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv242 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_3_self_attn_q_proj_weight3, layer_norm171, model_decoder_layers_3_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape740 = R.call_tir(cls.reshape4, (lv242,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv68 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_3_self_attn_k_proj_weight3, layer_norm171), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape741 = R.call_tir(cls.reshape4, (lv68,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv243 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_3_self_attn_v_proj_weight3, layer_norm171, model_decoder_layers_3_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape742 = R.call_tir(cls.reshape4, (lv243,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat35 = R.call_tir(cls.concatenate, (reshape740, reshape741, reshape742), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape743 = R.call_tir(cls.reshape5, (concat35,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv140 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), reshape743), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape744 = R.call_tir(cls.reshape6, (lv140,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape745 = R.call_tir(cls.reshape7, (reshape744,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv244 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_3_self_attn_out_proj_weight3, reshape745, model_decoder_layers_3_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add612 = R.call_tir(cls.add, (add608, lv244), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm172 = R.call_tir(cls.layer_norm, (add612, model_decoder_layers_3_encoder_attn_layer_norm_weight3, model_decoder_layers_3_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv245 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_3_encoder_attn_q_proj_weight3, layer_norm172, model_decoder_layers_3_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape746 = R.call_tir(cls.reshape4, (lv245,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape747 = R.call_tir(cls.reshape8, (reshape746,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv141 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), reshape747), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape748 = R.call_tir(cls.reshape6, (lv141,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape749 = R.call_tir(cls.reshape7, (reshape748,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv246 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_3_encoder_attn_out_proj_weight3, reshape749, model_decoder_layers_3_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add615 = R.call_tir(cls.add, (add612, lv246), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm173 = R.call_tir(cls.layer_norm, (add615, model_decoder_layers_3_final_layer_norm_weight3, model_decoder_layers_3_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv35 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_3_fc1_weight3, layer_norm173, model_decoder_layers_3_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv247 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_3_fc2_weight3, lv35, model_decoder_layers_3_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add618 = R.call_tir(cls.add, (add615, lv247), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm174 = R.call_tir(cls.layer_norm, (add618, model_decoder_layers_4_self_attn_layer_norm_weight3, model_decoder_layers_4_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv248 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_4_self_attn_q_proj_weight3, layer_norm174, model_decoder_layers_4_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape750 = R.call_tir(cls.reshape4, (lv248,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv69 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_4_self_attn_k_proj_weight3, layer_norm174), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape751 = R.call_tir(cls.reshape4, (lv69,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv249 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_4_self_attn_v_proj_weight3, layer_norm174, model_decoder_layers_4_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape752 = R.call_tir(cls.reshape4, (lv249,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat36 = R.call_tir(cls.concatenate, (reshape750, reshape751, reshape752), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape753 = R.call_tir(cls.reshape5, (concat36,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv142 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), reshape753), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape754 = R.call_tir(cls.reshape6, (lv142,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape755 = R.call_tir(cls.reshape7, (reshape754,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv250 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_4_self_attn_out_proj_weight3, reshape755, model_decoder_layers_4_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add622 = R.call_tir(cls.add, (add618, lv250), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm175 = R.call_tir(cls.layer_norm, (add622, model_decoder_layers_4_encoder_attn_layer_norm_weight3, model_decoder_layers_4_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv251 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_4_encoder_attn_q_proj_weight3, layer_norm175, model_decoder_layers_4_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape756 = R.call_tir(cls.reshape4, (lv251,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape757 = R.call_tir(cls.reshape8, (reshape756,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv143 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), reshape757), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape758 = R.call_tir(cls.reshape6, (lv143,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape759 = R.call_tir(cls.reshape7, (reshape758,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv252 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_4_encoder_attn_out_proj_weight3, reshape759, model_decoder_layers_4_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add625 = R.call_tir(cls.add, (add622, lv252), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm176 = R.call_tir(cls.layer_norm, (add625, model_decoder_layers_4_final_layer_norm_weight3, model_decoder_layers_4_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv36 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_4_fc1_weight3, layer_norm176, model_decoder_layers_4_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv253 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_4_fc2_weight3, lv36, model_decoder_layers_4_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add628 = R.call_tir(cls.add, (add625, lv253), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm177 = R.call_tir(cls.layer_norm, (add628, model_decoder_layers_5_self_attn_layer_norm_weight3, model_decoder_layers_5_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv254 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_5_self_attn_q_proj_weight3, layer_norm177, model_decoder_layers_5_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape760 = R.call_tir(cls.reshape4, (lv254,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv70 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_5_self_attn_k_proj_weight3, layer_norm177), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape761 = R.call_tir(cls.reshape4, (lv70,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv255 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_5_self_attn_v_proj_weight3, layer_norm177, model_decoder_layers_5_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape762 = R.call_tir(cls.reshape4, (lv255,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat37 = R.call_tir(cls.concatenate, (reshape760, reshape761, reshape762), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape763 = R.call_tir(cls.reshape5, (concat37,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv144 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), reshape763), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape764 = R.call_tir(cls.reshape6, (lv144,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape765 = R.call_tir(cls.reshape7, (reshape764,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv256 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_5_self_attn_out_proj_weight3, reshape765, model_decoder_layers_5_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add632 = R.call_tir(cls.add, (add628, lv256), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm178 = R.call_tir(cls.layer_norm, (add632, model_decoder_layers_5_encoder_attn_layer_norm_weight3, model_decoder_layers_5_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv257 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_5_encoder_attn_q_proj_weight3, layer_norm178, model_decoder_layers_5_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape766 = R.call_tir(cls.reshape4, (lv257,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape767 = R.call_tir(cls.reshape8, (reshape766,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv145 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), reshape767), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape768 = R.call_tir(cls.reshape6, (lv145,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape769 = R.call_tir(cls.reshape7, (reshape768,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv258 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_5_encoder_attn_out_proj_weight3, reshape769, model_decoder_layers_5_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add635 = R.call_tir(cls.add, (add632, lv258), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm179 = R.call_tir(cls.layer_norm, (add635, model_decoder_layers_5_final_layer_norm_weight3, model_decoder_layers_5_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv37 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_5_fc1_weight3, layer_norm179, model_decoder_layers_5_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv259 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_5_fc2_weight3, lv37, model_decoder_layers_5_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add638 = R.call_tir(cls.add, (add635, lv259), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm180 = R.call_tir(cls.layer_norm, (add638, model_decoder_layers_6_self_attn_layer_norm_weight3, model_decoder_layers_6_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv260 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_6_self_attn_q_proj_weight3, layer_norm180, model_decoder_layers_6_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape770 = R.call_tir(cls.reshape4, (lv260,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv71 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_6_self_attn_k_proj_weight3, layer_norm180), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape771 = R.call_tir(cls.reshape4, (lv71,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv261 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_6_self_attn_v_proj_weight3, layer_norm180, model_decoder_layers_6_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape772 = R.call_tir(cls.reshape4, (lv261,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat38 = R.call_tir(cls.concatenate, (reshape770, reshape771, reshape772), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape773 = R.call_tir(cls.reshape5, (concat38,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv146 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), reshape773), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape774 = R.call_tir(cls.reshape6, (lv146,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape775 = R.call_tir(cls.reshape7, (reshape774,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv262 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_6_self_attn_out_proj_weight3, reshape775, model_decoder_layers_6_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add642 = R.call_tir(cls.add, (add638, lv262), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm181 = R.call_tir(cls.layer_norm, (add642, model_decoder_layers_6_encoder_attn_layer_norm_weight3, model_decoder_layers_6_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv263 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_6_encoder_attn_q_proj_weight3, layer_norm181, model_decoder_layers_6_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape776 = R.call_tir(cls.reshape4, (lv263,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape777 = R.call_tir(cls.reshape8, (reshape776,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv147 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), reshape777), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape778 = R.call_tir(cls.reshape6, (lv147,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape779 = R.call_tir(cls.reshape7, (reshape778,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv264 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_6_encoder_attn_out_proj_weight3, reshape779, model_decoder_layers_6_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add645 = R.call_tir(cls.add, (add642, lv264), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm182 = R.call_tir(cls.layer_norm, (add645, model_decoder_layers_6_final_layer_norm_weight3, model_decoder_layers_6_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv38 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_6_fc1_weight3, layer_norm182, model_decoder_layers_6_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv265 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_6_fc2_weight3, lv38, model_decoder_layers_6_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add648 = R.call_tir(cls.add, (add645, lv265), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm183 = R.call_tir(cls.layer_norm, (add648, model_decoder_layers_7_self_attn_layer_norm_weight3, model_decoder_layers_7_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv266 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_7_self_attn_q_proj_weight3, layer_norm183, model_decoder_layers_7_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape780 = R.call_tir(cls.reshape4, (lv266,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv72 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_7_self_attn_k_proj_weight3, layer_norm183), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape781 = R.call_tir(cls.reshape4, (lv72,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv267 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_7_self_attn_v_proj_weight3, layer_norm183, model_decoder_layers_7_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape782 = R.call_tir(cls.reshape4, (lv267,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat39 = R.call_tir(cls.concatenate, (reshape780, reshape781, reshape782), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape783 = R.call_tir(cls.reshape5, (concat39,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv148 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), reshape783), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape784 = R.call_tir(cls.reshape6, (lv148,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape785 = R.call_tir(cls.reshape7, (reshape784,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv268 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_7_self_attn_out_proj_weight3, reshape785, model_decoder_layers_7_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add652 = R.call_tir(cls.add, (add648, lv268), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm184 = R.call_tir(cls.layer_norm, (add652, model_decoder_layers_7_encoder_attn_layer_norm_weight3, model_decoder_layers_7_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv269 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_7_encoder_attn_q_proj_weight3, layer_norm184, model_decoder_layers_7_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape786 = R.call_tir(cls.reshape4, (lv269,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape787 = R.call_tir(cls.reshape8, (reshape786,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv149 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), reshape787), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape788 = R.call_tir(cls.reshape6, (lv149,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape789 = R.call_tir(cls.reshape7, (reshape788,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv270 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_7_encoder_attn_out_proj_weight3, reshape789, model_decoder_layers_7_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add655 = R.call_tir(cls.add, (add652, lv270), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm185 = R.call_tir(cls.layer_norm, (add655, model_decoder_layers_7_final_layer_norm_weight3, model_decoder_layers_7_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv39 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_7_fc1_weight3, layer_norm185, model_decoder_layers_7_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv271 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_7_fc2_weight3, lv39, model_decoder_layers_7_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add658 = R.call_tir(cls.add, (add655, lv271), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm186 = R.call_tir(cls.layer_norm, (add658, model_decoder_layers_8_self_attn_layer_norm_weight3, model_decoder_layers_8_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv272 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_8_self_attn_q_proj_weight3, layer_norm186, model_decoder_layers_8_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape790 = R.call_tir(cls.reshape4, (lv272,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv73 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_8_self_attn_k_proj_weight3, layer_norm186), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape791 = R.call_tir(cls.reshape4, (lv73,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv273 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_8_self_attn_v_proj_weight3, layer_norm186, model_decoder_layers_8_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape792 = R.call_tir(cls.reshape4, (lv273,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat40 = R.call_tir(cls.concatenate, (reshape790, reshape791, reshape792), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape793 = R.call_tir(cls.reshape5, (concat40,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv150 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), reshape793), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape794 = R.call_tir(cls.reshape6, (lv150,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape795 = R.call_tir(cls.reshape7, (reshape794,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv274 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_8_self_attn_out_proj_weight3, reshape795, model_decoder_layers_8_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add662 = R.call_tir(cls.add, (add658, lv274), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm187 = R.call_tir(cls.layer_norm, (add662, model_decoder_layers_8_encoder_attn_layer_norm_weight3, model_decoder_layers_8_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv275 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_8_encoder_attn_q_proj_weight3, layer_norm187, model_decoder_layers_8_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape796 = R.call_tir(cls.reshape4, (lv275,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape797 = R.call_tir(cls.reshape8, (reshape796,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv151 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), reshape797), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape798 = R.call_tir(cls.reshape6, (lv151,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape799 = R.call_tir(cls.reshape7, (reshape798,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv276 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_8_encoder_attn_out_proj_weight3, reshape799, model_decoder_layers_8_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add665 = R.call_tir(cls.add, (add662, lv276), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm188 = R.call_tir(cls.layer_norm, (add665, model_decoder_layers_8_final_layer_norm_weight3, model_decoder_layers_8_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv40 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_8_fc1_weight3, layer_norm188, model_decoder_layers_8_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv277 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_8_fc2_weight3, lv40, model_decoder_layers_8_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add668 = R.call_tir(cls.add, (add665, lv277), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm189 = R.call_tir(cls.layer_norm, (add668, model_decoder_layers_9_self_attn_layer_norm_weight3, model_decoder_layers_9_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv278 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_9_self_attn_q_proj_weight3, layer_norm189, model_decoder_layers_9_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape800 = R.call_tir(cls.reshape4, (lv278,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv74 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_9_self_attn_k_proj_weight3, layer_norm189), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape801 = R.call_tir(cls.reshape4, (lv74,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv279 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_9_self_attn_v_proj_weight3, layer_norm189, model_decoder_layers_9_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape802 = R.call_tir(cls.reshape4, (lv279,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat41 = R.call_tir(cls.concatenate, (reshape800, reshape801, reshape802), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape803 = R.call_tir(cls.reshape5, (concat41,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv152 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), reshape803), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape804 = R.call_tir(cls.reshape6, (lv152,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape805 = R.call_tir(cls.reshape7, (reshape804,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv280 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_9_self_attn_out_proj_weight3, reshape805, model_decoder_layers_9_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add672 = R.call_tir(cls.add, (add668, lv280), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm190 = R.call_tir(cls.layer_norm, (add672, model_decoder_layers_9_encoder_attn_layer_norm_weight3, model_decoder_layers_9_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv281 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_9_encoder_attn_q_proj_weight3, layer_norm190, model_decoder_layers_9_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape806 = R.call_tir(cls.reshape4, (lv281,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape807 = R.call_tir(cls.reshape8, (reshape806,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv153 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), reshape807), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape808 = R.call_tir(cls.reshape6, (lv153,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape809 = R.call_tir(cls.reshape7, (reshape808,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv282 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_9_encoder_attn_out_proj_weight3, reshape809, model_decoder_layers_9_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add675 = R.call_tir(cls.add, (add672, lv282), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm191 = R.call_tir(cls.layer_norm, (add675, model_decoder_layers_9_final_layer_norm_weight3, model_decoder_layers_9_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv41 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_9_fc1_weight3, layer_norm191, model_decoder_layers_9_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv283 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_9_fc2_weight3, lv41, model_decoder_layers_9_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add678 = R.call_tir(cls.add, (add675, lv283), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm192 = R.call_tir(cls.layer_norm, (add678, model_decoder_layers_10_self_attn_layer_norm_weight3, model_decoder_layers_10_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv284 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_10_self_attn_q_proj_weight3, layer_norm192, model_decoder_layers_10_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape810 = R.call_tir(cls.reshape4, (lv284,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv75 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_10_self_attn_k_proj_weight3, layer_norm192), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape811 = R.call_tir(cls.reshape4, (lv75,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv285 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_10_self_attn_v_proj_weight3, layer_norm192, model_decoder_layers_10_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape812 = R.call_tir(cls.reshape4, (lv285,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat42 = R.call_tir(cls.concatenate, (reshape810, reshape811, reshape812), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape813 = R.call_tir(cls.reshape5, (concat42,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv154 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), reshape813), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape814 = R.call_tir(cls.reshape6, (lv154,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape815 = R.call_tir(cls.reshape7, (reshape814,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv286 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_10_self_attn_out_proj_weight3, reshape815, model_decoder_layers_10_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add682 = R.call_tir(cls.add, (add678, lv286), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm193 = R.call_tir(cls.layer_norm, (add682, model_decoder_layers_10_encoder_attn_layer_norm_weight3, model_decoder_layers_10_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv287 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_10_encoder_attn_q_proj_weight3, layer_norm193, model_decoder_layers_10_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape816 = R.call_tir(cls.reshape4, (lv287,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape817 = R.call_tir(cls.reshape8, (reshape816,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv155 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), reshape817), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape818 = R.call_tir(cls.reshape6, (lv155,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape819 = R.call_tir(cls.reshape7, (reshape818,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv288 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_10_encoder_attn_out_proj_weight3, reshape819, model_decoder_layers_10_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add685 = R.call_tir(cls.add, (add682, lv288), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm194 = R.call_tir(cls.layer_norm, (add685, model_decoder_layers_10_final_layer_norm_weight3, model_decoder_layers_10_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv42 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_10_fc1_weight3, layer_norm194, model_decoder_layers_10_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv289 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_10_fc2_weight3, lv42, model_decoder_layers_10_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add688 = R.call_tir(cls.add, (add685, lv289), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm195 = R.call_tir(cls.layer_norm, (add688, model_decoder_layers_11_self_attn_layer_norm_weight3, model_decoder_layers_11_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv290 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_11_self_attn_q_proj_weight3, layer_norm195, model_decoder_layers_11_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape820 = R.call_tir(cls.reshape4, (lv290,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv76 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_11_self_attn_k_proj_weight3, layer_norm195), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape821 = R.call_tir(cls.reshape4, (lv76,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv291 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_11_self_attn_v_proj_weight3, layer_norm195, model_decoder_layers_11_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape822 = R.call_tir(cls.reshape4, (lv291,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat43 = R.call_tir(cls.concatenate, (reshape820, reshape821, reshape822), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape823 = R.call_tir(cls.reshape5, (concat43,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv156 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), reshape823), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape824 = R.call_tir(cls.reshape6, (lv156,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape825 = R.call_tir(cls.reshape7, (reshape824,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv292 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_11_self_attn_out_proj_weight3, reshape825, model_decoder_layers_11_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add692 = R.call_tir(cls.add, (add688, lv292), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm196 = R.call_tir(cls.layer_norm, (add692, model_decoder_layers_11_encoder_attn_layer_norm_weight3, model_decoder_layers_11_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv293 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_11_encoder_attn_q_proj_weight3, layer_norm196, model_decoder_layers_11_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape826 = R.call_tir(cls.reshape4, (lv293,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape827 = R.call_tir(cls.reshape8, (reshape826,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv157 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), reshape827), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape828 = R.call_tir(cls.reshape6, (lv157,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape829 = R.call_tir(cls.reshape7, (reshape828,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv294 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_11_encoder_attn_out_proj_weight3, reshape829, model_decoder_layers_11_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add695 = R.call_tir(cls.add, (add692, lv294), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm197 = R.call_tir(cls.layer_norm, (add695, model_decoder_layers_11_final_layer_norm_weight3, model_decoder_layers_11_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv43 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_11_fc1_weight3, layer_norm197, model_decoder_layers_11_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv295 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_11_fc2_weight3, lv43, model_decoder_layers_11_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add698 = R.call_tir(cls.add, (add695, lv295), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm198 = R.call_tir(cls.layer_norm, (add698, model_decoder_layers_12_self_attn_layer_norm_weight3, model_decoder_layers_12_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv296 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_12_self_attn_q_proj_weight3, layer_norm198, model_decoder_layers_12_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape830 = R.call_tir(cls.reshape4, (lv296,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv77 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_12_self_attn_k_proj_weight3, layer_norm198), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape831 = R.call_tir(cls.reshape4, (lv77,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv297 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_12_self_attn_v_proj_weight3, layer_norm198, model_decoder_layers_12_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape832 = R.call_tir(cls.reshape4, (lv297,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat44 = R.call_tir(cls.concatenate, (reshape830, reshape831, reshape832), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape833 = R.call_tir(cls.reshape5, (concat44,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv158 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), reshape833), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape834 = R.call_tir(cls.reshape6, (lv158,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape835 = R.call_tir(cls.reshape7, (reshape834,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv298 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_12_self_attn_out_proj_weight3, reshape835, model_decoder_layers_12_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add702 = R.call_tir(cls.add, (add698, lv298), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm199 = R.call_tir(cls.layer_norm, (add702, model_decoder_layers_12_encoder_attn_layer_norm_weight3, model_decoder_layers_12_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv299 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_12_encoder_attn_q_proj_weight3, layer_norm199, model_decoder_layers_12_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape836 = R.call_tir(cls.reshape4, (lv299,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape837 = R.call_tir(cls.reshape8, (reshape836,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv159 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), reshape837), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape838 = R.call_tir(cls.reshape6, (lv159,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape839 = R.call_tir(cls.reshape7, (reshape838,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv300 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_12_encoder_attn_out_proj_weight3, reshape839, model_decoder_layers_12_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add705 = R.call_tir(cls.add, (add702, lv300), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm200 = R.call_tir(cls.layer_norm, (add705, model_decoder_layers_12_final_layer_norm_weight3, model_decoder_layers_12_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv44 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_12_fc1_weight3, layer_norm200, model_decoder_layers_12_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv301 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_12_fc2_weight3, lv44, model_decoder_layers_12_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add708 = R.call_tir(cls.add, (add705, lv301), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm201 = R.call_tir(cls.layer_norm, (add708, model_decoder_layers_13_self_attn_layer_norm_weight3, model_decoder_layers_13_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv302 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_13_self_attn_q_proj_weight3, layer_norm201, model_decoder_layers_13_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape840 = R.call_tir(cls.reshape4, (lv302,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv78 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_13_self_attn_k_proj_weight3, layer_norm201), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape841 = R.call_tir(cls.reshape4, (lv78,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv303 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_13_self_attn_v_proj_weight3, layer_norm201, model_decoder_layers_13_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape842 = R.call_tir(cls.reshape4, (lv303,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat45 = R.call_tir(cls.concatenate, (reshape840, reshape841, reshape842), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape843 = R.call_tir(cls.reshape5, (concat45,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv160 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), reshape843), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape844 = R.call_tir(cls.reshape6, (lv160,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape845 = R.call_tir(cls.reshape7, (reshape844,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv304 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_13_self_attn_out_proj_weight3, reshape845, model_decoder_layers_13_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add712 = R.call_tir(cls.add, (add708, lv304), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm202 = R.call_tir(cls.layer_norm, (add712, model_decoder_layers_13_encoder_attn_layer_norm_weight3, model_decoder_layers_13_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv305 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_13_encoder_attn_q_proj_weight3, layer_norm202, model_decoder_layers_13_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape846 = R.call_tir(cls.reshape4, (lv305,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape847 = R.call_tir(cls.reshape8, (reshape846,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv161 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), reshape847), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape848 = R.call_tir(cls.reshape6, (lv161,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape849 = R.call_tir(cls.reshape7, (reshape848,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv306 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_13_encoder_attn_out_proj_weight3, reshape849, model_decoder_layers_13_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add715 = R.call_tir(cls.add, (add712, lv306), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm203 = R.call_tir(cls.layer_norm, (add715, model_decoder_layers_13_final_layer_norm_weight3, model_decoder_layers_13_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv45 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_13_fc1_weight3, layer_norm203, model_decoder_layers_13_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv307 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_13_fc2_weight3, lv45, model_decoder_layers_13_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add718 = R.call_tir(cls.add, (add715, lv307), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm204 = R.call_tir(cls.layer_norm, (add718, model_decoder_layers_14_self_attn_layer_norm_weight3, model_decoder_layers_14_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv308 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_14_self_attn_q_proj_weight3, layer_norm204, model_decoder_layers_14_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape850 = R.call_tir(cls.reshape4, (lv308,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv79 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_14_self_attn_k_proj_weight3, layer_norm204), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape851 = R.call_tir(cls.reshape4, (lv79,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv309 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_14_self_attn_v_proj_weight3, layer_norm204, model_decoder_layers_14_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape852 = R.call_tir(cls.reshape4, (lv309,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat46 = R.call_tir(cls.concatenate, (reshape850, reshape851, reshape852), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape853 = R.call_tir(cls.reshape5, (concat46,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv162 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), reshape853), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape854 = R.call_tir(cls.reshape6, (lv162,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape855 = R.call_tir(cls.reshape7, (reshape854,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv310 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_14_self_attn_out_proj_weight3, reshape855, model_decoder_layers_14_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add722 = R.call_tir(cls.add, (add718, lv310), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm205 = R.call_tir(cls.layer_norm, (add722, model_decoder_layers_14_encoder_attn_layer_norm_weight3, model_decoder_layers_14_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv311 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_14_encoder_attn_q_proj_weight3, layer_norm205, model_decoder_layers_14_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape856 = R.call_tir(cls.reshape4, (lv311,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape857 = R.call_tir(cls.reshape8, (reshape856,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv163 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), reshape857), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape858 = R.call_tir(cls.reshape6, (lv163,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape859 = R.call_tir(cls.reshape7, (reshape858,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv312 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_14_encoder_attn_out_proj_weight3, reshape859, model_decoder_layers_14_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add725 = R.call_tir(cls.add, (add722, lv312), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm206 = R.call_tir(cls.layer_norm, (add725, model_decoder_layers_14_final_layer_norm_weight3, model_decoder_layers_14_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv46 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_14_fc1_weight3, layer_norm206, model_decoder_layers_14_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv313 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_14_fc2_weight3, lv46, model_decoder_layers_14_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add728 = R.call_tir(cls.add, (add725, lv313), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm207 = R.call_tir(cls.layer_norm, (add728, model_decoder_layers_15_self_attn_layer_norm_weight3, model_decoder_layers_15_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv314 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_15_self_attn_q_proj_weight3, layer_norm207, model_decoder_layers_15_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape860 = R.call_tir(cls.reshape4, (lv314,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv80 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_15_self_attn_k_proj_weight3, layer_norm207), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape861 = R.call_tir(cls.reshape4, (lv80,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv315 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_15_self_attn_v_proj_weight3, layer_norm207, model_decoder_layers_15_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape862 = R.call_tir(cls.reshape4, (lv315,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat47 = R.call_tir(cls.concatenate, (reshape860, reshape861, reshape862), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape863 = R.call_tir(cls.reshape5, (concat47,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv164 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), reshape863), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape864 = R.call_tir(cls.reshape6, (lv164,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape865 = R.call_tir(cls.reshape7, (reshape864,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv316 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_15_self_attn_out_proj_weight3, reshape865, model_decoder_layers_15_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add732 = R.call_tir(cls.add, (add728, lv316), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm208 = R.call_tir(cls.layer_norm, (add732, model_decoder_layers_15_encoder_attn_layer_norm_weight3, model_decoder_layers_15_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv317 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_15_encoder_attn_q_proj_weight3, layer_norm208, model_decoder_layers_15_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape866 = R.call_tir(cls.reshape4, (lv317,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape867 = R.call_tir(cls.reshape8, (reshape866,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv165 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), reshape867), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape868 = R.call_tir(cls.reshape6, (lv165,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape869 = R.call_tir(cls.reshape7, (reshape868,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv318 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_15_encoder_attn_out_proj_weight3, reshape869, model_decoder_layers_15_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add735 = R.call_tir(cls.add, (add732, lv318), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm209 = R.call_tir(cls.layer_norm, (add735, model_decoder_layers_15_final_layer_norm_weight3, model_decoder_layers_15_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv47 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_15_fc1_weight3, layer_norm209, model_decoder_layers_15_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv319 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_15_fc2_weight3, lv47, model_decoder_layers_15_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add738 = R.call_tir(cls.add, (add735, lv319), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm210 = R.call_tir(cls.layer_norm, (add738, model_decoder_layers_16_self_attn_layer_norm_weight3, model_decoder_layers_16_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv320 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_16_self_attn_q_proj_weight3, layer_norm210, model_decoder_layers_16_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape870 = R.call_tir(cls.reshape4, (lv320,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv81 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_16_self_attn_k_proj_weight3, layer_norm210), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape871 = R.call_tir(cls.reshape4, (lv81,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv321 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_16_self_attn_v_proj_weight3, layer_norm210, model_decoder_layers_16_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape872 = R.call_tir(cls.reshape4, (lv321,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat48 = R.call_tir(cls.concatenate, (reshape870, reshape871, reshape872), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape873 = R.call_tir(cls.reshape5, (concat48,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv166 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), reshape873), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape874 = R.call_tir(cls.reshape6, (lv166,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape875 = R.call_tir(cls.reshape7, (reshape874,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv322 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_16_self_attn_out_proj_weight3, reshape875, model_decoder_layers_16_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add742 = R.call_tir(cls.add, (add738, lv322), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm211 = R.call_tir(cls.layer_norm, (add742, model_decoder_layers_16_encoder_attn_layer_norm_weight3, model_decoder_layers_16_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv323 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_16_encoder_attn_q_proj_weight3, layer_norm211, model_decoder_layers_16_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape876 = R.call_tir(cls.reshape4, (lv323,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape877 = R.call_tir(cls.reshape8, (reshape876,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv167 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), reshape877), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape878 = R.call_tir(cls.reshape6, (lv167,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape879 = R.call_tir(cls.reshape7, (reshape878,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv324 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_16_encoder_attn_out_proj_weight3, reshape879, model_decoder_layers_16_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add745 = R.call_tir(cls.add, (add742, lv324), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm212 = R.call_tir(cls.layer_norm, (add745, model_decoder_layers_16_final_layer_norm_weight3, model_decoder_layers_16_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv48 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_16_fc1_weight3, layer_norm212, model_decoder_layers_16_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv325 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_16_fc2_weight3, lv48, model_decoder_layers_16_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add748 = R.call_tir(cls.add, (add745, lv325), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm213 = R.call_tir(cls.layer_norm, (add748, model_decoder_layers_17_self_attn_layer_norm_weight3, model_decoder_layers_17_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv326 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_17_self_attn_q_proj_weight3, layer_norm213, model_decoder_layers_17_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape880 = R.call_tir(cls.reshape4, (lv326,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv82 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_17_self_attn_k_proj_weight3, layer_norm213), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape881 = R.call_tir(cls.reshape4, (lv82,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv327 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_17_self_attn_v_proj_weight3, layer_norm213, model_decoder_layers_17_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape882 = R.call_tir(cls.reshape4, (lv327,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat49 = R.call_tir(cls.concatenate, (reshape880, reshape881, reshape882), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape883 = R.call_tir(cls.reshape5, (concat49,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv168 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), reshape883), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape884 = R.call_tir(cls.reshape6, (lv168,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape885 = R.call_tir(cls.reshape7, (reshape884,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv328 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_17_self_attn_out_proj_weight3, reshape885, model_decoder_layers_17_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add752 = R.call_tir(cls.add, (add748, lv328), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm214 = R.call_tir(cls.layer_norm, (add752, model_decoder_layers_17_encoder_attn_layer_norm_weight3, model_decoder_layers_17_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv329 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_17_encoder_attn_q_proj_weight3, layer_norm214, model_decoder_layers_17_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape886 = R.call_tir(cls.reshape4, (lv329,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape887 = R.call_tir(cls.reshape8, (reshape886,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv169 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), reshape887), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape888 = R.call_tir(cls.reshape6, (lv169,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape889 = R.call_tir(cls.reshape7, (reshape888,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv330 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_17_encoder_attn_out_proj_weight3, reshape889, model_decoder_layers_17_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add755 = R.call_tir(cls.add, (add752, lv330), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm215 = R.call_tir(cls.layer_norm, (add755, model_decoder_layers_17_final_layer_norm_weight3, model_decoder_layers_17_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv49 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_17_fc1_weight3, layer_norm215, model_decoder_layers_17_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv331 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_17_fc2_weight3, lv49, model_decoder_layers_17_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add758 = R.call_tir(cls.add, (add755, lv331), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm216 = R.call_tir(cls.layer_norm, (add758, model_decoder_layers_18_self_attn_layer_norm_weight3, model_decoder_layers_18_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv332 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_18_self_attn_q_proj_weight3, layer_norm216, model_decoder_layers_18_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape890 = R.call_tir(cls.reshape4, (lv332,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv83 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_18_self_attn_k_proj_weight3, layer_norm216), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape891 = R.call_tir(cls.reshape4, (lv83,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv333 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_18_self_attn_v_proj_weight3, layer_norm216, model_decoder_layers_18_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape892 = R.call_tir(cls.reshape4, (lv333,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat50 = R.call_tir(cls.concatenate, (reshape890, reshape891, reshape892), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape893 = R.call_tir(cls.reshape5, (concat50,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv170 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), reshape893), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape894 = R.call_tir(cls.reshape6, (lv170,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape895 = R.call_tir(cls.reshape7, (reshape894,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv334 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_18_self_attn_out_proj_weight3, reshape895, model_decoder_layers_18_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add762 = R.call_tir(cls.add, (add758, lv334), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm217 = R.call_tir(cls.layer_norm, (add762, model_decoder_layers_18_encoder_attn_layer_norm_weight3, model_decoder_layers_18_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv335 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_18_encoder_attn_q_proj_weight3, layer_norm217, model_decoder_layers_18_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape896 = R.call_tir(cls.reshape4, (lv335,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape897 = R.call_tir(cls.reshape8, (reshape896,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv171 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), reshape897), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape898 = R.call_tir(cls.reshape6, (lv171,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape899 = R.call_tir(cls.reshape7, (reshape898,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv336 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_18_encoder_attn_out_proj_weight3, reshape899, model_decoder_layers_18_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add765 = R.call_tir(cls.add, (add762, lv336), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm218 = R.call_tir(cls.layer_norm, (add765, model_decoder_layers_18_final_layer_norm_weight3, model_decoder_layers_18_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv50 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_18_fc1_weight3, layer_norm218, model_decoder_layers_18_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv337 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_18_fc2_weight3, lv50, model_decoder_layers_18_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add768 = R.call_tir(cls.add, (add765, lv337), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm219 = R.call_tir(cls.layer_norm, (add768, model_decoder_layers_19_self_attn_layer_norm_weight3, model_decoder_layers_19_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv338 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_19_self_attn_q_proj_weight3, layer_norm219, model_decoder_layers_19_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape900 = R.call_tir(cls.reshape4, (lv338,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv84 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_19_self_attn_k_proj_weight3, layer_norm219), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape901 = R.call_tir(cls.reshape4, (lv84,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv339 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_19_self_attn_v_proj_weight3, layer_norm219, model_decoder_layers_19_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape902 = R.call_tir(cls.reshape4, (lv339,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat51 = R.call_tir(cls.concatenate, (reshape900, reshape901, reshape902), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape903 = R.call_tir(cls.reshape5, (concat51,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv172 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), reshape903), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape904 = R.call_tir(cls.reshape6, (lv172,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape905 = R.call_tir(cls.reshape7, (reshape904,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv340 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_19_self_attn_out_proj_weight3, reshape905, model_decoder_layers_19_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add772 = R.call_tir(cls.add, (add768, lv340), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm220 = R.call_tir(cls.layer_norm, (add772, model_decoder_layers_19_encoder_attn_layer_norm_weight3, model_decoder_layers_19_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv341 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_19_encoder_attn_q_proj_weight3, layer_norm220, model_decoder_layers_19_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape906 = R.call_tir(cls.reshape4, (lv341,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape907 = R.call_tir(cls.reshape8, (reshape906,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv173 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), reshape907), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape908 = R.call_tir(cls.reshape6, (lv173,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape909 = R.call_tir(cls.reshape7, (reshape908,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv342 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_19_encoder_attn_out_proj_weight3, reshape909, model_decoder_layers_19_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add775 = R.call_tir(cls.add, (add772, lv342), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm221 = R.call_tir(cls.layer_norm, (add775, model_decoder_layers_19_final_layer_norm_weight3, model_decoder_layers_19_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv51 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_19_fc1_weight3, layer_norm221, model_decoder_layers_19_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv343 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_19_fc2_weight3, lv51, model_decoder_layers_19_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add778 = R.call_tir(cls.add, (add775, lv343), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm222 = R.call_tir(cls.layer_norm, (add778, model_decoder_layers_20_self_attn_layer_norm_weight3, model_decoder_layers_20_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv344 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_20_self_attn_q_proj_weight3, layer_norm222, model_decoder_layers_20_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape910 = R.call_tir(cls.reshape4, (lv344,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv85 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_20_self_attn_k_proj_weight3, layer_norm222), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape911 = R.call_tir(cls.reshape4, (lv85,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv345 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_20_self_attn_v_proj_weight3, layer_norm222, model_decoder_layers_20_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape912 = R.call_tir(cls.reshape4, (lv345,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat52 = R.call_tir(cls.concatenate, (reshape910, reshape911, reshape912), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape913 = R.call_tir(cls.reshape5, (concat52,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv174 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), reshape913), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape914 = R.call_tir(cls.reshape6, (lv174,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape915 = R.call_tir(cls.reshape7, (reshape914,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv346 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_20_self_attn_out_proj_weight3, reshape915, model_decoder_layers_20_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add782 = R.call_tir(cls.add, (add778, lv346), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm223 = R.call_tir(cls.layer_norm, (add782, model_decoder_layers_20_encoder_attn_layer_norm_weight3, model_decoder_layers_20_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv347 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_20_encoder_attn_q_proj_weight3, layer_norm223, model_decoder_layers_20_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape916 = R.call_tir(cls.reshape4, (lv347,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape917 = R.call_tir(cls.reshape8, (reshape916,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv175 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), reshape917), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape918 = R.call_tir(cls.reshape6, (lv175,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape919 = R.call_tir(cls.reshape7, (reshape918,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv348 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_20_encoder_attn_out_proj_weight3, reshape919, model_decoder_layers_20_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add785 = R.call_tir(cls.add, (add782, lv348), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm224 = R.call_tir(cls.layer_norm, (add785, model_decoder_layers_20_final_layer_norm_weight3, model_decoder_layers_20_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv52 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_20_fc1_weight3, layer_norm224, model_decoder_layers_20_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv349 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_20_fc2_weight3, lv52, model_decoder_layers_20_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add788 = R.call_tir(cls.add, (add785, lv349), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm225 = R.call_tir(cls.layer_norm, (add788, model_decoder_layers_21_self_attn_layer_norm_weight3, model_decoder_layers_21_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv350 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_21_self_attn_q_proj_weight3, layer_norm225, model_decoder_layers_21_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape920 = R.call_tir(cls.reshape4, (lv350,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv86 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_21_self_attn_k_proj_weight3, layer_norm225), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape921 = R.call_tir(cls.reshape4, (lv86,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv351 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_21_self_attn_v_proj_weight3, layer_norm225, model_decoder_layers_21_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape922 = R.call_tir(cls.reshape4, (lv351,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat53 = R.call_tir(cls.concatenate, (reshape920, reshape921, reshape922), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape923 = R.call_tir(cls.reshape5, (concat53,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv176 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), reshape923), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape924 = R.call_tir(cls.reshape6, (lv176,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape925 = R.call_tir(cls.reshape7, (reshape924,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv352 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_21_self_attn_out_proj_weight3, reshape925, model_decoder_layers_21_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add792 = R.call_tir(cls.add, (add788, lv352), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm226 = R.call_tir(cls.layer_norm, (add792, model_decoder_layers_21_encoder_attn_layer_norm_weight3, model_decoder_layers_21_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv353 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_21_encoder_attn_q_proj_weight3, layer_norm226, model_decoder_layers_21_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape926 = R.call_tir(cls.reshape4, (lv353,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape927 = R.call_tir(cls.reshape8, (reshape926,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv177 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), reshape927), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape928 = R.call_tir(cls.reshape6, (lv177,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape929 = R.call_tir(cls.reshape7, (reshape928,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv354 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_21_encoder_attn_out_proj_weight3, reshape929, model_decoder_layers_21_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add795 = R.call_tir(cls.add, (add792, lv354), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm227 = R.call_tir(cls.layer_norm, (add795, model_decoder_layers_21_final_layer_norm_weight3, model_decoder_layers_21_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv53 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_21_fc1_weight3, layer_norm227, model_decoder_layers_21_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv355 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_21_fc2_weight3, lv53, model_decoder_layers_21_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add798 = R.call_tir(cls.add, (add795, lv355), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm228 = R.call_tir(cls.layer_norm, (add798, model_decoder_layers_22_self_attn_layer_norm_weight3, model_decoder_layers_22_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv356 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_22_self_attn_q_proj_weight3, layer_norm228, model_decoder_layers_22_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape930 = R.call_tir(cls.reshape4, (lv356,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv87 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_22_self_attn_k_proj_weight3, layer_norm228), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape931 = R.call_tir(cls.reshape4, (lv87,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv357 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_22_self_attn_v_proj_weight3, layer_norm228, model_decoder_layers_22_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape932 = R.call_tir(cls.reshape4, (lv357,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat54 = R.call_tir(cls.concatenate, (reshape930, reshape931, reshape932), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape933 = R.call_tir(cls.reshape5, (concat54,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv178 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), reshape933), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape934 = R.call_tir(cls.reshape6, (lv178,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape935 = R.call_tir(cls.reshape7, (reshape934,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv358 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_22_self_attn_out_proj_weight3, reshape935, model_decoder_layers_22_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add802 = R.call_tir(cls.add, (add798, lv358), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm229 = R.call_tir(cls.layer_norm, (add802, model_decoder_layers_22_encoder_attn_layer_norm_weight3, model_decoder_layers_22_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv359 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_22_encoder_attn_q_proj_weight3, layer_norm229, model_decoder_layers_22_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape936 = R.call_tir(cls.reshape4, (lv359,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape937 = R.call_tir(cls.reshape8, (reshape936,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv179 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), reshape937), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape938 = R.call_tir(cls.reshape6, (lv179,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape939 = R.call_tir(cls.reshape7, (reshape938,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv360 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_22_encoder_attn_out_proj_weight3, reshape939, model_decoder_layers_22_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add805 = R.call_tir(cls.add, (add802, lv360), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm230 = R.call_tir(cls.layer_norm, (add805, model_decoder_layers_22_final_layer_norm_weight3, model_decoder_layers_22_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv54 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_22_fc1_weight3, layer_norm230, model_decoder_layers_22_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv361 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_22_fc2_weight3, lv54, model_decoder_layers_22_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add808 = R.call_tir(cls.add, (add805, lv361), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm231 = R.call_tir(cls.layer_norm, (add808, model_decoder_layers_23_self_attn_layer_norm_weight3, model_decoder_layers_23_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv362 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_23_self_attn_q_proj_weight3, layer_norm231, model_decoder_layers_23_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape940 = R.call_tir(cls.reshape4, (lv362,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv88 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_23_self_attn_k_proj_weight3, layer_norm231), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape941 = R.call_tir(cls.reshape4, (lv88,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv363 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_23_self_attn_v_proj_weight3, layer_norm231, model_decoder_layers_23_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape942 = R.call_tir(cls.reshape4, (lv363,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat55 = R.call_tir(cls.concatenate, (reshape940, reshape941, reshape942), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape943 = R.call_tir(cls.reshape5, (concat55,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv180 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), reshape943), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape944 = R.call_tir(cls.reshape6, (lv180,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape945 = R.call_tir(cls.reshape7, (reshape944,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv364 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_23_self_attn_out_proj_weight3, reshape945, model_decoder_layers_23_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add812 = R.call_tir(cls.add, (add808, lv364), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm232 = R.call_tir(cls.layer_norm, (add812, model_decoder_layers_23_encoder_attn_layer_norm_weight3, model_decoder_layers_23_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv365 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_23_encoder_attn_q_proj_weight3, layer_norm232, model_decoder_layers_23_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape946 = R.call_tir(cls.reshape4, (lv365,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape947 = R.call_tir(cls.reshape8, (reshape946,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv181 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), reshape947), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape948 = R.call_tir(cls.reshape6, (lv181,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape949 = R.call_tir(cls.reshape7, (reshape948,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv366 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_23_encoder_attn_out_proj_weight3, reshape949, model_decoder_layers_23_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add815 = R.call_tir(cls.add, (add812, lv366), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm233 = R.call_tir(cls.layer_norm, (add815, model_decoder_layers_23_final_layer_norm_weight3, model_decoder_layers_23_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv55 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_23_fc1_weight3, layer_norm233, model_decoder_layers_23_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv367 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_23_fc2_weight3, lv55, model_decoder_layers_23_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add818 = R.call_tir(cls.add, (add815, lv367), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm234 = R.call_tir(cls.layer_norm, (add818, model_decoder_layers_24_self_attn_layer_norm_weight3, model_decoder_layers_24_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv368 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_24_self_attn_q_proj_weight3, layer_norm234, model_decoder_layers_24_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape950 = R.call_tir(cls.reshape4, (lv368,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv89 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_24_self_attn_k_proj_weight3, layer_norm234), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape951 = R.call_tir(cls.reshape4, (lv89,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv369 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_24_self_attn_v_proj_weight3, layer_norm234, model_decoder_layers_24_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape952 = R.call_tir(cls.reshape4, (lv369,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat56 = R.call_tir(cls.concatenate, (reshape950, reshape951, reshape952), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape953 = R.call_tir(cls.reshape5, (concat56,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv182 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), reshape953), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape954 = R.call_tir(cls.reshape6, (lv182,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape955 = R.call_tir(cls.reshape7, (reshape954,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv370 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_24_self_attn_out_proj_weight3, reshape955, model_decoder_layers_24_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add822 = R.call_tir(cls.add, (add818, lv370), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm235 = R.call_tir(cls.layer_norm, (add822, model_decoder_layers_24_encoder_attn_layer_norm_weight3, model_decoder_layers_24_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv371 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_24_encoder_attn_q_proj_weight3, layer_norm235, model_decoder_layers_24_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape956 = R.call_tir(cls.reshape4, (lv371,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape957 = R.call_tir(cls.reshape8, (reshape956,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv183 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), reshape957), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape958 = R.call_tir(cls.reshape6, (lv183,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape959 = R.call_tir(cls.reshape7, (reshape958,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv372 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_24_encoder_attn_out_proj_weight3, reshape959, model_decoder_layers_24_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add825 = R.call_tir(cls.add, (add822, lv372), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm236 = R.call_tir(cls.layer_norm, (add825, model_decoder_layers_24_final_layer_norm_weight3, model_decoder_layers_24_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv56 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_24_fc1_weight3, layer_norm236, model_decoder_layers_24_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv373 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_24_fc2_weight3, lv56, model_decoder_layers_24_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add828 = R.call_tir(cls.add, (add825, lv373), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm237 = R.call_tir(cls.layer_norm, (add828, model_decoder_layers_25_self_attn_layer_norm_weight3, model_decoder_layers_25_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv374 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_25_self_attn_q_proj_weight3, layer_norm237, model_decoder_layers_25_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape960 = R.call_tir(cls.reshape4, (lv374,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv90 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_25_self_attn_k_proj_weight3, layer_norm237), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape961 = R.call_tir(cls.reshape4, (lv90,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv375 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_25_self_attn_v_proj_weight3, layer_norm237, model_decoder_layers_25_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape962 = R.call_tir(cls.reshape4, (lv375,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat57 = R.call_tir(cls.concatenate, (reshape960, reshape961, reshape962), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape963 = R.call_tir(cls.reshape5, (concat57,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv184 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), reshape963), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape964 = R.call_tir(cls.reshape6, (lv184,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape965 = R.call_tir(cls.reshape7, (reshape964,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv376 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_25_self_attn_out_proj_weight3, reshape965, model_decoder_layers_25_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add832 = R.call_tir(cls.add, (add828, lv376), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm238 = R.call_tir(cls.layer_norm, (add832, model_decoder_layers_25_encoder_attn_layer_norm_weight3, model_decoder_layers_25_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv377 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_25_encoder_attn_q_proj_weight3, layer_norm238, model_decoder_layers_25_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape966 = R.call_tir(cls.reshape4, (lv377,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape967 = R.call_tir(cls.reshape8, (reshape966,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv185 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), reshape967), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape968 = R.call_tir(cls.reshape6, (lv185,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape969 = R.call_tir(cls.reshape7, (reshape968,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv378 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_25_encoder_attn_out_proj_weight3, reshape969, model_decoder_layers_25_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add835 = R.call_tir(cls.add, (add832, lv378), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm239 = R.call_tir(cls.layer_norm, (add835, model_decoder_layers_25_final_layer_norm_weight3, model_decoder_layers_25_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv57 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_25_fc1_weight3, layer_norm239, model_decoder_layers_25_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv379 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_25_fc2_weight3, lv57, model_decoder_layers_25_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add838 = R.call_tir(cls.add, (add835, lv379), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm240 = R.call_tir(cls.layer_norm, (add838, model_decoder_layers_26_self_attn_layer_norm_weight3, model_decoder_layers_26_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv380 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_26_self_attn_q_proj_weight3, layer_norm240, model_decoder_layers_26_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape970 = R.call_tir(cls.reshape4, (lv380,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv91 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_26_self_attn_k_proj_weight3, layer_norm240), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape971 = R.call_tir(cls.reshape4, (lv91,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv381 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_26_self_attn_v_proj_weight3, layer_norm240, model_decoder_layers_26_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape972 = R.call_tir(cls.reshape4, (lv381,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat58 = R.call_tir(cls.concatenate, (reshape970, reshape971, reshape972), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape973 = R.call_tir(cls.reshape5, (concat58,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv186 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), reshape973), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape974 = R.call_tir(cls.reshape6, (lv186,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape975 = R.call_tir(cls.reshape7, (reshape974,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv382 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_26_self_attn_out_proj_weight3, reshape975, model_decoder_layers_26_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add842 = R.call_tir(cls.add, (add838, lv382), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm241 = R.call_tir(cls.layer_norm, (add842, model_decoder_layers_26_encoder_attn_layer_norm_weight3, model_decoder_layers_26_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv383 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_26_encoder_attn_q_proj_weight3, layer_norm241, model_decoder_layers_26_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape976 = R.call_tir(cls.reshape4, (lv383,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape977 = R.call_tir(cls.reshape8, (reshape976,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv187 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), reshape977), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape978 = R.call_tir(cls.reshape6, (lv187,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape979 = R.call_tir(cls.reshape7, (reshape978,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv384 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_26_encoder_attn_out_proj_weight3, reshape979, model_decoder_layers_26_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add845 = R.call_tir(cls.add, (add842, lv384), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm242 = R.call_tir(cls.layer_norm, (add845, model_decoder_layers_26_final_layer_norm_weight3, model_decoder_layers_26_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv58 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_26_fc1_weight3, layer_norm242, model_decoder_layers_26_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv385 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_26_fc2_weight3, lv58, model_decoder_layers_26_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add848 = R.call_tir(cls.add, (add845, lv385), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm243 = R.call_tir(cls.layer_norm, (add848, model_decoder_layers_27_self_attn_layer_norm_weight3, model_decoder_layers_27_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv386 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_27_self_attn_q_proj_weight3, layer_norm243, model_decoder_layers_27_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape980 = R.call_tir(cls.reshape4, (lv386,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv92 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_27_self_attn_k_proj_weight3, layer_norm243), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape981 = R.call_tir(cls.reshape4, (lv92,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv387 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_27_self_attn_v_proj_weight3, layer_norm243, model_decoder_layers_27_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape982 = R.call_tir(cls.reshape4, (lv387,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat59 = R.call_tir(cls.concatenate, (reshape980, reshape981, reshape982), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape983 = R.call_tir(cls.reshape5, (concat59,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv188 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), reshape983), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape984 = R.call_tir(cls.reshape6, (lv188,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape985 = R.call_tir(cls.reshape7, (reshape984,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv388 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_27_self_attn_out_proj_weight3, reshape985, model_decoder_layers_27_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add852 = R.call_tir(cls.add, (add848, lv388), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm244 = R.call_tir(cls.layer_norm, (add852, model_decoder_layers_27_encoder_attn_layer_norm_weight3, model_decoder_layers_27_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv389 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_27_encoder_attn_q_proj_weight3, layer_norm244, model_decoder_layers_27_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape986 = R.call_tir(cls.reshape4, (lv389,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape987 = R.call_tir(cls.reshape8, (reshape986,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv189 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), reshape987), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape988 = R.call_tir(cls.reshape6, (lv189,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape989 = R.call_tir(cls.reshape7, (reshape988,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv390 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_27_encoder_attn_out_proj_weight3, reshape989, model_decoder_layers_27_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add855 = R.call_tir(cls.add, (add852, lv390), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm245 = R.call_tir(cls.layer_norm, (add855, model_decoder_layers_27_final_layer_norm_weight3, model_decoder_layers_27_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv59 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_27_fc1_weight3, layer_norm245, model_decoder_layers_27_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv391 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_27_fc2_weight3, lv59, model_decoder_layers_27_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add858 = R.call_tir(cls.add, (add855, lv391), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm246 = R.call_tir(cls.layer_norm, (add858, model_decoder_layers_28_self_attn_layer_norm_weight3, model_decoder_layers_28_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv392 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_28_self_attn_q_proj_weight3, layer_norm246, model_decoder_layers_28_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape990 = R.call_tir(cls.reshape4, (lv392,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv93 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_28_self_attn_k_proj_weight3, layer_norm246), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape991 = R.call_tir(cls.reshape4, (lv93,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv393 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_28_self_attn_v_proj_weight3, layer_norm246, model_decoder_layers_28_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape992 = R.call_tir(cls.reshape4, (lv393,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat60 = R.call_tir(cls.concatenate, (reshape990, reshape991, reshape992), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape993 = R.call_tir(cls.reshape5, (concat60,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv190 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), reshape993), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape994 = R.call_tir(cls.reshape6, (lv190,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape995 = R.call_tir(cls.reshape7, (reshape994,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv394 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_28_self_attn_out_proj_weight3, reshape995, model_decoder_layers_28_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add862 = R.call_tir(cls.add, (add858, lv394), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm247 = R.call_tir(cls.layer_norm, (add862, model_decoder_layers_28_encoder_attn_layer_norm_weight3, model_decoder_layers_28_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv395 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_28_encoder_attn_q_proj_weight3, layer_norm247, model_decoder_layers_28_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape996 = R.call_tir(cls.reshape4, (lv395,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape997 = R.call_tir(cls.reshape8, (reshape996,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv191 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), reshape997), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape998 = R.call_tir(cls.reshape6, (lv191,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape999 = R.call_tir(cls.reshape7, (reshape998,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv396 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_28_encoder_attn_out_proj_weight3, reshape999, model_decoder_layers_28_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add865 = R.call_tir(cls.add, (add862, lv396), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm248 = R.call_tir(cls.layer_norm, (add865, model_decoder_layers_28_final_layer_norm_weight3, model_decoder_layers_28_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv60 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_28_fc1_weight3, layer_norm248, model_decoder_layers_28_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv397 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_28_fc2_weight3, lv60, model_decoder_layers_28_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add868 = R.call_tir(cls.add, (add865, lv397), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm249 = R.call_tir(cls.layer_norm, (add868, model_decoder_layers_29_self_attn_layer_norm_weight3, model_decoder_layers_29_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv398 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_29_self_attn_q_proj_weight3, layer_norm249, model_decoder_layers_29_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape1000 = R.call_tir(cls.reshape4, (lv398,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv94 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_29_self_attn_k_proj_weight3, layer_norm249), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape1001 = R.call_tir(cls.reshape4, (lv94,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv399 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_29_self_attn_v_proj_weight3, layer_norm249, model_decoder_layers_29_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape1002 = R.call_tir(cls.reshape4, (lv399,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat61 = R.call_tir(cls.concatenate, (reshape1000, reshape1001, reshape1002), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape1003 = R.call_tir(cls.reshape5, (concat61,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv192 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), reshape1003), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape1004 = R.call_tir(cls.reshape6, (lv192,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape1005 = R.call_tir(cls.reshape7, (reshape1004,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv400 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_29_self_attn_out_proj_weight3, reshape1005, model_decoder_layers_29_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add872 = R.call_tir(cls.add, (add868, lv400), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm250 = R.call_tir(cls.layer_norm, (add872, model_decoder_layers_29_encoder_attn_layer_norm_weight3, model_decoder_layers_29_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv401 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_29_encoder_attn_q_proj_weight3, layer_norm250, model_decoder_layers_29_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape1006 = R.call_tir(cls.reshape4, (lv401,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape1007 = R.call_tir(cls.reshape8, (reshape1006,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv193 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), reshape1007), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape1008 = R.call_tir(cls.reshape6, (lv193,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape1009 = R.call_tir(cls.reshape7, (reshape1008,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv402 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_29_encoder_attn_out_proj_weight3, reshape1009, model_decoder_layers_29_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add875 = R.call_tir(cls.add, (add872, lv402), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm251 = R.call_tir(cls.layer_norm, (add875, model_decoder_layers_29_final_layer_norm_weight3, model_decoder_layers_29_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv61 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_29_fc1_weight3, layer_norm251, model_decoder_layers_29_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv403 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_29_fc2_weight3, lv61, model_decoder_layers_29_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add878 = R.call_tir(cls.add, (add875, lv403), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm252 = R.call_tir(cls.layer_norm, (add878, model_decoder_layers_30_self_attn_layer_norm_weight3, model_decoder_layers_30_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv404 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_30_self_attn_q_proj_weight3, layer_norm252, model_decoder_layers_30_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape1010 = R.call_tir(cls.reshape4, (lv404,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv95 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_30_self_attn_k_proj_weight3, layer_norm252), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape1011 = R.call_tir(cls.reshape4, (lv95,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv405 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_30_self_attn_v_proj_weight3, layer_norm252, model_decoder_layers_30_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape1012 = R.call_tir(cls.reshape4, (lv405,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat62 = R.call_tir(cls.concatenate, (reshape1010, reshape1011, reshape1012), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape1013 = R.call_tir(cls.reshape5, (concat62,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv194 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), reshape1013), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape1014 = R.call_tir(cls.reshape6, (lv194,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape1015 = R.call_tir(cls.reshape7, (reshape1014,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv406 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_30_self_attn_out_proj_weight3, reshape1015, model_decoder_layers_30_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add882 = R.call_tir(cls.add, (add878, lv406), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm253 = R.call_tir(cls.layer_norm, (add882, model_decoder_layers_30_encoder_attn_layer_norm_weight3, model_decoder_layers_30_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv407 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_30_encoder_attn_q_proj_weight3, layer_norm253, model_decoder_layers_30_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape1016 = R.call_tir(cls.reshape4, (lv407,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape1017 = R.call_tir(cls.reshape8, (reshape1016,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv195 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), reshape1017), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape1018 = R.call_tir(cls.reshape6, (lv195,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape1019 = R.call_tir(cls.reshape7, (reshape1018,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv408 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_30_encoder_attn_out_proj_weight3, reshape1019, model_decoder_layers_30_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add885 = R.call_tir(cls.add, (add882, lv408), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm254 = R.call_tir(cls.layer_norm, (add885, model_decoder_layers_30_final_layer_norm_weight3, model_decoder_layers_30_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv62 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_30_fc1_weight3, layer_norm254, model_decoder_layers_30_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv409 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_30_fc2_weight3, lv62, model_decoder_layers_30_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add888 = R.call_tir(cls.add, (add885, lv409), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm255 = R.call_tir(cls.layer_norm, (add888, model_decoder_layers_31_self_attn_layer_norm_weight3, model_decoder_layers_31_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv410 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_31_self_attn_q_proj_weight3, layer_norm255, model_decoder_layers_31_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape1020 = R.call_tir(cls.reshape4, (lv410,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv96 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_31_self_attn_k_proj_weight3, layer_norm255), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape1021 = R.call_tir(cls.reshape4, (lv96,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv411 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_31_self_attn_v_proj_weight3, layer_norm255, model_decoder_layers_31_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape1022 = R.call_tir(cls.reshape4, (lv411,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat63 = R.call_tir(cls.concatenate, (reshape1020, reshape1021, reshape1022), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape1023 = R.call_tir(cls.reshape5, (concat63,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv196 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), reshape1023), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape1024 = R.call_tir(cls.reshape6, (lv196,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape1025 = R.call_tir(cls.reshape7, (reshape1024,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv412 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_31_self_attn_out_proj_weight3, reshape1025, model_decoder_layers_31_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add892 = R.call_tir(cls.add, (add888, lv412), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm256 = R.call_tir(cls.layer_norm, (add892, model_decoder_layers_31_encoder_attn_layer_norm_weight3, model_decoder_layers_31_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv413 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_31_encoder_attn_q_proj_weight3, layer_norm256, model_decoder_layers_31_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape1026 = R.call_tir(cls.reshape4, (lv413,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape1027 = R.call_tir(cls.reshape8, (reshape1026,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv197 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), reshape1027), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape1028 = R.call_tir(cls.reshape6, (lv197,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape1029 = R.call_tir(cls.reshape7, (reshape1028,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv414 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_31_encoder_attn_out_proj_weight3, reshape1029, model_decoder_layers_31_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add895 = R.call_tir(cls.add, (add892, lv414), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm257 = R.call_tir(cls.layer_norm, (add895, model_decoder_layers_31_final_layer_norm_weight3, model_decoder_layers_31_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv63 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_31_fc1_weight3, layer_norm257, model_decoder_layers_31_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv415 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_31_fc2_weight3, lv63, model_decoder_layers_31_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add898 = R.call_tir(cls.add, (add895, lv415), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm258 = R.call_tir(cls.layer_norm, (add898, model_decoder_layer_norm_weight3, model_decoder_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + gv3 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul4_cublas", (model_decoder_embed_tokens_weight3, layer_norm258), out_sinfo=R.Tensor((batch_size, 1, 51866), dtype="float32")) + R.output(gv3) + return gv3 + + @R.function + def batch_encode(input_features: R.Tensor(("batch_size", 128, 3000), dtype="float16"), paged_kv_cache: R.Object, packed_params: R.Tuple(R.Tensor((1280, 128, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1500, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), 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dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), 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R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"))) -> R.Tensor(("batch_size", 1500, 1280), dtype="float16"): + batch_size = T.int64() + R.func_attr({"num_input": 2, "relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "seq_len": 15000, "total_seq_len": 1500}}) + cls = Module + with R.dataflow(): + model_encoder_conv1_weight: R.Tensor((1280, 128, 3), dtype="float16") = packed_params[0] + lv: R.Tensor((1280,), dtype="float16") = packed_params[1] + lv1 = R.call_tir(cls.fused_reshape9, (lv,), out_sinfo=R.Tensor((1, 1280, 1), dtype="float16")) + model_encoder_conv2_weight: R.Tensor((1280, 1280, 3), dtype="float16") = packed_params[2] + lv2: R.Tensor((1280,), dtype="float16") = packed_params[3] + lv3 = R.call_tir(cls.fused_reshape9, (lv2,), out_sinfo=R.Tensor((1, 1280, 1), dtype="float16")) + lv4 = R.call_tir(cls.fused_conv1d_add1_gelu, (input_features, model_encoder_conv1_weight, lv1), out_sinfo=R.Tensor((batch_size, 1280, 3000), dtype="float16")) + lv5 = R.call_tir(cls.fused_conv1d1_add2_gelu1, (lv4, model_encoder_conv2_weight, lv3), out_sinfo=R.Tensor((batch_size, 1280, 1500), dtype="float16")) + lv6: R.Tensor((1500, 1280), dtype="float16") = packed_params[4] + lv7 = R.call_tir(cls.fused_transpose_add3, (lv6, lv5), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + model_encoder_layers_0_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[5] + model_encoder_layers_0_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[6] + model_encoder_layers_0_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[7] + model_encoder_layers_0_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[8] + model_encoder_layers_0_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[9] + model_encoder_layers_0_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[10] + model_encoder_layers_0_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[11] + model_encoder_layers_0_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[12] + model_encoder_layers_0_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[13] + model_encoder_layers_0_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[14] + model_encoder_layers_0_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[15] + model_encoder_layers_0_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[16] + model_encoder_layers_0_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[17] + model_encoder_layers_0_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[18] + model_encoder_layers_0_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[19] + model_encoder_layers_1_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[20] + model_encoder_layers_1_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[21] + model_encoder_layers_1_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[22] + model_encoder_layers_1_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[23] + model_encoder_layers_1_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[24] + model_encoder_layers_1_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[25] + model_encoder_layers_1_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[26] + model_encoder_layers_1_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[27] + model_encoder_layers_1_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[28] + model_encoder_layers_1_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[29] + model_encoder_layers_1_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[30] + model_encoder_layers_1_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[31] + model_encoder_layers_1_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[32] + model_encoder_layers_1_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[33] + model_encoder_layers_1_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[34] + model_encoder_layers_2_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[35] + model_encoder_layers_2_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[36] + model_encoder_layers_2_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[37] + model_encoder_layers_2_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[38] + model_encoder_layers_2_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[39] + model_encoder_layers_2_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[40] + model_encoder_layers_2_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[41] + model_encoder_layers_2_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[42] + model_encoder_layers_2_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[43] + model_encoder_layers_2_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[44] + model_encoder_layers_2_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[45] + model_encoder_layers_2_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[46] + model_encoder_layers_2_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[47] + model_encoder_layers_2_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[48] + model_encoder_layers_2_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[49] + model_encoder_layers_3_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[50] + model_encoder_layers_3_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[51] + model_encoder_layers_3_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[52] + model_encoder_layers_3_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[53] + model_encoder_layers_3_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[54] + model_encoder_layers_3_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[55] + model_encoder_layers_3_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[56] + model_encoder_layers_3_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[57] + model_encoder_layers_3_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[58] + model_encoder_layers_3_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[59] + model_encoder_layers_3_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[60] + model_encoder_layers_3_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[61] + model_encoder_layers_3_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[62] + model_encoder_layers_3_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[63] + model_encoder_layers_3_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[64] + model_encoder_layers_4_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[65] + model_encoder_layers_4_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[66] + model_encoder_layers_4_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[67] + model_encoder_layers_4_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[68] + model_encoder_layers_4_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[69] + model_encoder_layers_4_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[70] + model_encoder_layers_4_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[71] + model_encoder_layers_4_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[72] + model_encoder_layers_4_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[73] + model_encoder_layers_4_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[74] + model_encoder_layers_4_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[75] + model_encoder_layers_4_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[76] + model_encoder_layers_4_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[77] + model_encoder_layers_4_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[78] + model_encoder_layers_4_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[79] + model_encoder_layers_5_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[80] + model_encoder_layers_5_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[81] + model_encoder_layers_5_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[82] + model_encoder_layers_5_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[83] + model_encoder_layers_5_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[84] + model_encoder_layers_5_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[85] + model_encoder_layers_5_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[86] + model_encoder_layers_5_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[87] + model_encoder_layers_5_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[88] + model_encoder_layers_5_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[89] + model_encoder_layers_5_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[90] + model_encoder_layers_5_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[91] + model_encoder_layers_5_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[92] + model_encoder_layers_5_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[93] + model_encoder_layers_5_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[94] + model_encoder_layers_6_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[95] + model_encoder_layers_6_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[96] + model_encoder_layers_6_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[97] + model_encoder_layers_6_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[98] + model_encoder_layers_6_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[99] + model_encoder_layers_6_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[100] + model_encoder_layers_6_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[101] + model_encoder_layers_6_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[102] + model_encoder_layers_6_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[103] + model_encoder_layers_6_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[104] + model_encoder_layers_6_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[105] + model_encoder_layers_6_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[106] + model_encoder_layers_6_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[107] + model_encoder_layers_6_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[108] + model_encoder_layers_6_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[109] + model_encoder_layers_7_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[110] + model_encoder_layers_7_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[111] + model_encoder_layers_7_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[112] + model_encoder_layers_7_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[113] + model_encoder_layers_7_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[114] + model_encoder_layers_7_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[115] + model_encoder_layers_7_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[116] + model_encoder_layers_7_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[117] + model_encoder_layers_7_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[118] + model_encoder_layers_7_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[119] + model_encoder_layers_7_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[120] + model_encoder_layers_7_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[121] + model_encoder_layers_7_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[122] + model_encoder_layers_7_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[123] + model_encoder_layers_7_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[124] + model_encoder_layers_8_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[125] + model_encoder_layers_8_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[126] + model_encoder_layers_8_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[127] + model_encoder_layers_8_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[128] + model_encoder_layers_8_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[129] + model_encoder_layers_8_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[130] + model_encoder_layers_8_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[131] + model_encoder_layers_8_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[132] + model_encoder_layers_8_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[133] + model_encoder_layers_8_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[134] + model_encoder_layers_8_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[135] + model_encoder_layers_8_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[136] + model_encoder_layers_8_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[137] + model_encoder_layers_8_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[138] + model_encoder_layers_8_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[139] + model_encoder_layers_9_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[140] + model_encoder_layers_9_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[141] + model_encoder_layers_9_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[142] + model_encoder_layers_9_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[143] + model_encoder_layers_9_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[144] + model_encoder_layers_9_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[145] + model_encoder_layers_9_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[146] + model_encoder_layers_9_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[147] + model_encoder_layers_9_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[148] + model_encoder_layers_9_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[149] + model_encoder_layers_9_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[150] + model_encoder_layers_9_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[151] + model_encoder_layers_9_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[152] + model_encoder_layers_9_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[153] + model_encoder_layers_9_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[154] + model_encoder_layers_10_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[155] + model_encoder_layers_10_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[156] + model_encoder_layers_10_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[157] + model_encoder_layers_10_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[158] + model_encoder_layers_10_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[159] + model_encoder_layers_10_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[160] + model_encoder_layers_10_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[161] + model_encoder_layers_10_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[162] + model_encoder_layers_10_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[163] + model_encoder_layers_10_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[164] + model_encoder_layers_10_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[165] + model_encoder_layers_10_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[166] + model_encoder_layers_10_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[167] + model_encoder_layers_10_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[168] + model_encoder_layers_10_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[169] + model_encoder_layers_11_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[170] + model_encoder_layers_11_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[171] + model_encoder_layers_11_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[172] + model_encoder_layers_11_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[173] + model_encoder_layers_11_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[174] + model_encoder_layers_11_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[175] + model_encoder_layers_11_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[176] + model_encoder_layers_11_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[177] + model_encoder_layers_11_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[178] + model_encoder_layers_11_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[179] + model_encoder_layers_11_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[180] + model_encoder_layers_11_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[181] + model_encoder_layers_11_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[182] + model_encoder_layers_11_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[183] + model_encoder_layers_11_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[184] + model_encoder_layers_12_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[185] + model_encoder_layers_12_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[186] + model_encoder_layers_12_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[187] + model_encoder_layers_12_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[188] + model_encoder_layers_12_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[189] + model_encoder_layers_12_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[190] + model_encoder_layers_12_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[191] + model_encoder_layers_12_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[192] + model_encoder_layers_12_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[193] + model_encoder_layers_12_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[194] + model_encoder_layers_12_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[195] + model_encoder_layers_12_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[196] + model_encoder_layers_12_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[197] + model_encoder_layers_12_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[198] + model_encoder_layers_12_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[199] + model_encoder_layers_13_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[200] + model_encoder_layers_13_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[201] + model_encoder_layers_13_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[202] + model_encoder_layers_13_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[203] + model_encoder_layers_13_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[204] + model_encoder_layers_13_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[205] + model_encoder_layers_13_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[206] + model_encoder_layers_13_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[207] + model_encoder_layers_13_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[208] + model_encoder_layers_13_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[209] + model_encoder_layers_13_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[210] + model_encoder_layers_13_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[211] + model_encoder_layers_13_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[212] + model_encoder_layers_13_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[213] + model_encoder_layers_13_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[214] + model_encoder_layers_14_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[215] + model_encoder_layers_14_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[216] + model_encoder_layers_14_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[217] + model_encoder_layers_14_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[218] + model_encoder_layers_14_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[219] + model_encoder_layers_14_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[220] + model_encoder_layers_14_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[221] + model_encoder_layers_14_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[222] + model_encoder_layers_14_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[223] + model_encoder_layers_14_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[224] + model_encoder_layers_14_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[225] + model_encoder_layers_14_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[226] + model_encoder_layers_14_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[227] + model_encoder_layers_14_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[228] + model_encoder_layers_14_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[229] + model_encoder_layers_15_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[230] + model_encoder_layers_15_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[231] + model_encoder_layers_15_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[232] + model_encoder_layers_15_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[233] + model_encoder_layers_15_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[234] + model_encoder_layers_15_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[235] + model_encoder_layers_15_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[236] + model_encoder_layers_15_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[237] + model_encoder_layers_15_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[238] + model_encoder_layers_15_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[239] + model_encoder_layers_15_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[240] + model_encoder_layers_15_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[241] + model_encoder_layers_15_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[242] + model_encoder_layers_15_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[243] + model_encoder_layers_15_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[244] + model_encoder_layers_16_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[245] + model_encoder_layers_16_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[246] + model_encoder_layers_16_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[247] + model_encoder_layers_16_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[248] + model_encoder_layers_16_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[249] + model_encoder_layers_16_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[250] + model_encoder_layers_16_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[251] + model_encoder_layers_16_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[252] + model_encoder_layers_16_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[253] + model_encoder_layers_16_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[254] + model_encoder_layers_16_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[255] + model_encoder_layers_16_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[256] + model_encoder_layers_16_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[257] + model_encoder_layers_16_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[258] + model_encoder_layers_16_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[259] + model_encoder_layers_17_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[260] + model_encoder_layers_17_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[261] + model_encoder_layers_17_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[262] + model_encoder_layers_17_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[263] + model_encoder_layers_17_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[264] + model_encoder_layers_17_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[265] + model_encoder_layers_17_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[266] + model_encoder_layers_17_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[267] + model_encoder_layers_17_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[268] + model_encoder_layers_17_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[269] + model_encoder_layers_17_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[270] + model_encoder_layers_17_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[271] + model_encoder_layers_17_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[272] + model_encoder_layers_17_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[273] + model_encoder_layers_17_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[274] + model_encoder_layers_18_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[275] + model_encoder_layers_18_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[276] + model_encoder_layers_18_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[277] + model_encoder_layers_18_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[278] + model_encoder_layers_18_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[279] + model_encoder_layers_18_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[280] + model_encoder_layers_18_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[281] + model_encoder_layers_18_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[282] + model_encoder_layers_18_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[283] + model_encoder_layers_18_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[284] + model_encoder_layers_18_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[285] + model_encoder_layers_18_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[286] + model_encoder_layers_18_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[287] + model_encoder_layers_18_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[288] + model_encoder_layers_18_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[289] + model_encoder_layers_19_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[290] + model_encoder_layers_19_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[291] + model_encoder_layers_19_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[292] + model_encoder_layers_19_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[293] + model_encoder_layers_19_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[294] + model_encoder_layers_19_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[295] + model_encoder_layers_19_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[296] + model_encoder_layers_19_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[297] + model_encoder_layers_19_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[298] + model_encoder_layers_19_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[299] + model_encoder_layers_19_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[300] + model_encoder_layers_19_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[301] + model_encoder_layers_19_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[302] + model_encoder_layers_19_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[303] + model_encoder_layers_19_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[304] + model_encoder_layers_20_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[305] + model_encoder_layers_20_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[306] + model_encoder_layers_20_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[307] + model_encoder_layers_20_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[308] + model_encoder_layers_20_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[309] + model_encoder_layers_20_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[310] + model_encoder_layers_20_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[311] + model_encoder_layers_20_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[312] + model_encoder_layers_20_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[313] + model_encoder_layers_20_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[314] + model_encoder_layers_20_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[315] + model_encoder_layers_20_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[316] + model_encoder_layers_20_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[317] + model_encoder_layers_20_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[318] + model_encoder_layers_20_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[319] + model_encoder_layers_21_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[320] + model_encoder_layers_21_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[321] + model_encoder_layers_21_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[322] + model_encoder_layers_21_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[323] + model_encoder_layers_21_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[324] + model_encoder_layers_21_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[325] + model_encoder_layers_21_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[326] + model_encoder_layers_21_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[327] + model_encoder_layers_21_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[328] + model_encoder_layers_21_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[329] + model_encoder_layers_21_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[330] + model_encoder_layers_21_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[331] + model_encoder_layers_21_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[332] + model_encoder_layers_21_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[333] + model_encoder_layers_21_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[334] + model_encoder_layers_22_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[335] + model_encoder_layers_22_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[336] + model_encoder_layers_22_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[337] + model_encoder_layers_22_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[338] + model_encoder_layers_22_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[339] + model_encoder_layers_22_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[340] + model_encoder_layers_22_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[341] + model_encoder_layers_22_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[342] + model_encoder_layers_22_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[343] + model_encoder_layers_22_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[344] + model_encoder_layers_22_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[345] + model_encoder_layers_22_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[346] + model_encoder_layers_22_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[347] + model_encoder_layers_22_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[348] + model_encoder_layers_22_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[349] + model_encoder_layers_23_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[350] + model_encoder_layers_23_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[351] + model_encoder_layers_23_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[352] + model_encoder_layers_23_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[353] + model_encoder_layers_23_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[354] + model_encoder_layers_23_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[355] + model_encoder_layers_23_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[356] + model_encoder_layers_23_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[357] + model_encoder_layers_23_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[358] + model_encoder_layers_23_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[359] + model_encoder_layers_23_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[360] + model_encoder_layers_23_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[361] + model_encoder_layers_23_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[362] + model_encoder_layers_23_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[363] + model_encoder_layers_23_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[364] + model_encoder_layers_24_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[365] + model_encoder_layers_24_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[366] + model_encoder_layers_24_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[367] + model_encoder_layers_24_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[368] + model_encoder_layers_24_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[369] + model_encoder_layers_24_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[370] + model_encoder_layers_24_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[371] + model_encoder_layers_24_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[372] + model_encoder_layers_24_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[373] + model_encoder_layers_24_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[374] + model_encoder_layers_24_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[375] + model_encoder_layers_24_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[376] + model_encoder_layers_24_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[377] + model_encoder_layers_24_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[378] + model_encoder_layers_24_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[379] + model_encoder_layers_25_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[380] + model_encoder_layers_25_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[381] + model_encoder_layers_25_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[382] + model_encoder_layers_25_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[383] + model_encoder_layers_25_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[384] + model_encoder_layers_25_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[385] + model_encoder_layers_25_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[386] + model_encoder_layers_25_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[387] + model_encoder_layers_25_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[388] + model_encoder_layers_25_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[389] + model_encoder_layers_25_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[390] + model_encoder_layers_25_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[391] + model_encoder_layers_25_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[392] + model_encoder_layers_25_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[393] + model_encoder_layers_25_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[394] + model_encoder_layers_26_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[395] + model_encoder_layers_26_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[396] + model_encoder_layers_26_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[397] + model_encoder_layers_26_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[398] + model_encoder_layers_26_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[399] + model_encoder_layers_26_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[400] + model_encoder_layers_26_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[401] + model_encoder_layers_26_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[402] + model_encoder_layers_26_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[403] + model_encoder_layers_26_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[404] + model_encoder_layers_26_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[405] + model_encoder_layers_26_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[406] + model_encoder_layers_26_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[407] + model_encoder_layers_26_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[408] + model_encoder_layers_26_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[409] + model_encoder_layers_27_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[410] + model_encoder_layers_27_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[411] + model_encoder_layers_27_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[412] + model_encoder_layers_27_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[413] + model_encoder_layers_27_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[414] + model_encoder_layers_27_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[415] + model_encoder_layers_27_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[416] + model_encoder_layers_27_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[417] + model_encoder_layers_27_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[418] + model_encoder_layers_27_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[419] + model_encoder_layers_27_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[420] + model_encoder_layers_27_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[421] + model_encoder_layers_27_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[422] + model_encoder_layers_27_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[423] + model_encoder_layers_27_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[424] + model_encoder_layers_28_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[425] + model_encoder_layers_28_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[426] + model_encoder_layers_28_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[427] + model_encoder_layers_28_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[428] + model_encoder_layers_28_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[429] + model_encoder_layers_28_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[430] + model_encoder_layers_28_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[431] + model_encoder_layers_28_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[432] + model_encoder_layers_28_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[433] + model_encoder_layers_28_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[434] + model_encoder_layers_28_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[435] + model_encoder_layers_28_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[436] + model_encoder_layers_28_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[437] + model_encoder_layers_28_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[438] + model_encoder_layers_28_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[439] + model_encoder_layers_29_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[440] + model_encoder_layers_29_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[441] + model_encoder_layers_29_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[442] + model_encoder_layers_29_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[443] + model_encoder_layers_29_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[444] + model_encoder_layers_29_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[445] + model_encoder_layers_29_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[446] + model_encoder_layers_29_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[447] + model_encoder_layers_29_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[448] + model_encoder_layers_29_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[449] + model_encoder_layers_29_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[450] + model_encoder_layers_29_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[451] + model_encoder_layers_29_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[452] + model_encoder_layers_29_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[453] + model_encoder_layers_29_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[454] + model_encoder_layers_30_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[455] + model_encoder_layers_30_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[456] + model_encoder_layers_30_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[457] + model_encoder_layers_30_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[458] + model_encoder_layers_30_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[459] + model_encoder_layers_30_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[460] + model_encoder_layers_30_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[461] + model_encoder_layers_30_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[462] + model_encoder_layers_30_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[463] + model_encoder_layers_30_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[464] + model_encoder_layers_30_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[465] + model_encoder_layers_30_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[466] + model_encoder_layers_30_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[467] + model_encoder_layers_30_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[468] + model_encoder_layers_30_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[469] + model_encoder_layers_31_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[470] + model_encoder_layers_31_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[471] + model_encoder_layers_31_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[472] + model_encoder_layers_31_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[473] + model_encoder_layers_31_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[474] + model_encoder_layers_31_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[475] + model_encoder_layers_31_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[476] + model_encoder_layers_31_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[477] + model_encoder_layers_31_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[478] + model_encoder_layers_31_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[479] + model_encoder_layers_31_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[480] + model_encoder_layers_31_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[481] + model_encoder_layers_31_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[482] + model_encoder_layers_31_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[483] + model_encoder_layers_31_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[484] + model_encoder_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[485] + model_encoder_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[486] + layer_norm = R.call_tir(cls.layer_norm1, (lv7, model_encoder_layers_0_self_attn_layer_norm_weight, model_encoder_layers_0_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv608 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_0_self_attn_q_proj_weight, layer_norm, model_encoder_layers_0_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape = R.call_tir(cls.reshape, (lv608,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv131 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_0_self_attn_k_proj_weight, layer_norm), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape1 = R.call_tir(cls.reshape, (lv131,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv609 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_0_self_attn_v_proj_weight, layer_norm, model_encoder_layers_0_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape2 = R.call_tir(cls.reshape, (lv609,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape3 = R.call_tir(cls.reshape1, (reshape,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape4 = R.call_tir(cls.reshape1, (reshape1,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape5 = R.call_tir(cls.reshape1, (reshape2,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv4_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), reshape3, reshape4, reshape5), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape6 = R.call_tir(cls.reshape10, (lv4_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape7 = R.call_tir(cls.reshape11, (reshape6,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv610 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_0_self_attn_out_proj_weight, reshape7, model_encoder_layers_0_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add4 = R.call_tir(cls.add4, (lv7, lv610), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm1 = R.call_tir(cls.layer_norm1, (add4, model_encoder_layers_0_final_layer_norm_weight, model_encoder_layers_0_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv96 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_0_fc1_weight, layer_norm1, model_encoder_layers_0_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv611 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_0_fc2_weight, lv96, model_encoder_layers_0_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv8 = R.call_tir(cls.fused_add4_maximum_minimum, (add4, lv611), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm2 = R.call_tir(cls.layer_norm1, (lv8, model_encoder_layers_1_self_attn_layer_norm_weight, model_encoder_layers_1_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv612 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_1_self_attn_q_proj_weight, layer_norm2, model_encoder_layers_1_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape8 = R.call_tir(cls.reshape, (lv612,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv132 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_1_self_attn_k_proj_weight, layer_norm2), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape9 = R.call_tir(cls.reshape, (lv132,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv613 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_1_self_attn_v_proj_weight, layer_norm2, model_encoder_layers_1_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape10 = R.call_tir(cls.reshape, (lv613,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape11 = R.call_tir(cls.reshape1, (reshape8,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape12 = R.call_tir(cls.reshape1, (reshape9,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape13 = R.call_tir(cls.reshape1, (reshape10,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv5_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), reshape11, reshape12, reshape13), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape14 = R.call_tir(cls.reshape10, (lv5_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape15 = R.call_tir(cls.reshape11, (reshape14,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv614 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_1_self_attn_out_proj_weight, reshape15, model_encoder_layers_1_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add11 = R.call_tir(cls.add4, (lv8, lv614), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm3 = R.call_tir(cls.layer_norm1, (add11, model_encoder_layers_1_final_layer_norm_weight, model_encoder_layers_1_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv97 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_1_fc1_weight, layer_norm3, model_encoder_layers_1_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv615 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_1_fc2_weight, lv97, model_encoder_layers_1_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv9 = R.call_tir(cls.fused_add4_maximum_minimum, (add11, lv615), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm4 = R.call_tir(cls.layer_norm1, (lv9, model_encoder_layers_2_self_attn_layer_norm_weight, model_encoder_layers_2_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv616 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_2_self_attn_q_proj_weight, layer_norm4, model_encoder_layers_2_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape16 = R.call_tir(cls.reshape, (lv616,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv133 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_2_self_attn_k_proj_weight, layer_norm4), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape17 = R.call_tir(cls.reshape, (lv133,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv617 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_2_self_attn_v_proj_weight, layer_norm4, model_encoder_layers_2_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape18 = R.call_tir(cls.reshape, (lv617,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape19 = R.call_tir(cls.reshape1, (reshape16,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape20 = R.call_tir(cls.reshape1, (reshape17,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape21 = R.call_tir(cls.reshape1, (reshape18,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv6_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), reshape19, reshape20, reshape21), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape22 = R.call_tir(cls.reshape10, (lv6_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape23 = R.call_tir(cls.reshape11, (reshape22,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv618 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_2_self_attn_out_proj_weight, reshape23, model_encoder_layers_2_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add18 = R.call_tir(cls.add4, (lv9, lv618), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm5 = R.call_tir(cls.layer_norm1, (add18, model_encoder_layers_2_final_layer_norm_weight, model_encoder_layers_2_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv98 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_2_fc1_weight, layer_norm5, model_encoder_layers_2_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv619 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_2_fc2_weight, lv98, model_encoder_layers_2_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv10 = R.call_tir(cls.fused_add4_maximum_minimum, (add18, lv619), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm6 = R.call_tir(cls.layer_norm1, (lv10, model_encoder_layers_3_self_attn_layer_norm_weight, model_encoder_layers_3_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv620 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_3_self_attn_q_proj_weight, layer_norm6, model_encoder_layers_3_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape24 = R.call_tir(cls.reshape, (lv620,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv134 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_3_self_attn_k_proj_weight, layer_norm6), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape25 = R.call_tir(cls.reshape, (lv134,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv621 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_3_self_attn_v_proj_weight, layer_norm6, model_encoder_layers_3_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape26 = R.call_tir(cls.reshape, (lv621,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape27 = R.call_tir(cls.reshape1, (reshape24,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape28 = R.call_tir(cls.reshape1, (reshape25,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape29 = R.call_tir(cls.reshape1, (reshape26,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv7_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), reshape27, reshape28, reshape29), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape30 = R.call_tir(cls.reshape10, (lv7_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape31 = R.call_tir(cls.reshape11, (reshape30,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv622 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_3_self_attn_out_proj_weight, reshape31, model_encoder_layers_3_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add25 = R.call_tir(cls.add4, (lv10, lv622), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm7 = R.call_tir(cls.layer_norm1, (add25, model_encoder_layers_3_final_layer_norm_weight, model_encoder_layers_3_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv99 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_3_fc1_weight, layer_norm7, model_encoder_layers_3_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv623 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_3_fc2_weight, lv99, model_encoder_layers_3_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv11 = R.call_tir(cls.fused_add4_maximum_minimum, (add25, lv623), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm8 = R.call_tir(cls.layer_norm1, (lv11, model_encoder_layers_4_self_attn_layer_norm_weight, model_encoder_layers_4_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv624 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_4_self_attn_q_proj_weight, layer_norm8, model_encoder_layers_4_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape32 = R.call_tir(cls.reshape, (lv624,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv135 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_4_self_attn_k_proj_weight, layer_norm8), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape33 = R.call_tir(cls.reshape, (lv135,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv625 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_4_self_attn_v_proj_weight, layer_norm8, model_encoder_layers_4_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape34 = R.call_tir(cls.reshape, (lv625,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape35 = R.call_tir(cls.reshape1, (reshape32,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape36 = R.call_tir(cls.reshape1, (reshape33,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape37 = R.call_tir(cls.reshape1, (reshape34,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv8_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), reshape35, reshape36, reshape37), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape38 = R.call_tir(cls.reshape10, (lv8_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape39 = R.call_tir(cls.reshape11, (reshape38,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv626 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_4_self_attn_out_proj_weight, reshape39, model_encoder_layers_4_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add32 = R.call_tir(cls.add4, (lv11, lv626), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm9 = R.call_tir(cls.layer_norm1, (add32, model_encoder_layers_4_final_layer_norm_weight, model_encoder_layers_4_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv100 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_4_fc1_weight, layer_norm9, model_encoder_layers_4_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv627 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_4_fc2_weight, lv100, model_encoder_layers_4_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv12 = R.call_tir(cls.fused_add4_maximum_minimum, (add32, lv627), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm10 = R.call_tir(cls.layer_norm1, (lv12, model_encoder_layers_5_self_attn_layer_norm_weight, model_encoder_layers_5_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv628 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_5_self_attn_q_proj_weight, layer_norm10, model_encoder_layers_5_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape40 = R.call_tir(cls.reshape, (lv628,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv136 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_5_self_attn_k_proj_weight, layer_norm10), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape41 = R.call_tir(cls.reshape, (lv136,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv629 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_5_self_attn_v_proj_weight, layer_norm10, model_encoder_layers_5_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape42 = R.call_tir(cls.reshape, (lv629,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape43 = R.call_tir(cls.reshape1, (reshape40,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape44 = R.call_tir(cls.reshape1, (reshape41,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape45 = R.call_tir(cls.reshape1, (reshape42,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv9_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), reshape43, reshape44, reshape45), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape46 = R.call_tir(cls.reshape10, (lv9_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape47 = R.call_tir(cls.reshape11, (reshape46,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv630 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_5_self_attn_out_proj_weight, reshape47, model_encoder_layers_5_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add39 = R.call_tir(cls.add4, (lv12, lv630), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm11 = R.call_tir(cls.layer_norm1, (add39, model_encoder_layers_5_final_layer_norm_weight, model_encoder_layers_5_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv101 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_5_fc1_weight, layer_norm11, model_encoder_layers_5_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv631 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_5_fc2_weight, lv101, model_encoder_layers_5_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv13 = R.call_tir(cls.fused_add4_maximum_minimum, (add39, lv631), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm12 = R.call_tir(cls.layer_norm1, (lv13, model_encoder_layers_6_self_attn_layer_norm_weight, model_encoder_layers_6_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv632 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_6_self_attn_q_proj_weight, layer_norm12, model_encoder_layers_6_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape48 = R.call_tir(cls.reshape, (lv632,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv137 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_6_self_attn_k_proj_weight, layer_norm12), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape49 = R.call_tir(cls.reshape, (lv137,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv633 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_6_self_attn_v_proj_weight, layer_norm12, model_encoder_layers_6_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape50 = R.call_tir(cls.reshape, (lv633,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape51 = R.call_tir(cls.reshape1, (reshape48,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape52 = R.call_tir(cls.reshape1, (reshape49,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape53 = R.call_tir(cls.reshape1, (reshape50,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv10_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), reshape51, reshape52, reshape53), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape54 = R.call_tir(cls.reshape10, (lv10_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape55 = R.call_tir(cls.reshape11, (reshape54,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv634 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_6_self_attn_out_proj_weight, reshape55, model_encoder_layers_6_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add46 = R.call_tir(cls.add4, (lv13, lv634), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm13 = R.call_tir(cls.layer_norm1, (add46, model_encoder_layers_6_final_layer_norm_weight, model_encoder_layers_6_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv102 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_6_fc1_weight, layer_norm13, model_encoder_layers_6_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv635 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_6_fc2_weight, lv102, model_encoder_layers_6_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv14 = R.call_tir(cls.fused_add4_maximum_minimum, (add46, lv635), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm14 = R.call_tir(cls.layer_norm1, (lv14, model_encoder_layers_7_self_attn_layer_norm_weight, model_encoder_layers_7_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv636 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_7_self_attn_q_proj_weight, layer_norm14, model_encoder_layers_7_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape56 = R.call_tir(cls.reshape, (lv636,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv138 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_7_self_attn_k_proj_weight, layer_norm14), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape57 = R.call_tir(cls.reshape, (lv138,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv637 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_7_self_attn_v_proj_weight, layer_norm14, model_encoder_layers_7_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape58 = R.call_tir(cls.reshape, (lv637,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape59 = R.call_tir(cls.reshape1, (reshape56,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape60 = R.call_tir(cls.reshape1, (reshape57,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape61 = R.call_tir(cls.reshape1, (reshape58,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv11_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), reshape59, reshape60, reshape61), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape62 = R.call_tir(cls.reshape10, (lv11_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape63 = R.call_tir(cls.reshape11, (reshape62,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv638 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_7_self_attn_out_proj_weight, reshape63, model_encoder_layers_7_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add53 = R.call_tir(cls.add4, (lv14, lv638), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm15 = R.call_tir(cls.layer_norm1, (add53, model_encoder_layers_7_final_layer_norm_weight, model_encoder_layers_7_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv103 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_7_fc1_weight, layer_norm15, model_encoder_layers_7_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv639 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_7_fc2_weight, lv103, model_encoder_layers_7_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv15 = R.call_tir(cls.fused_add4_maximum_minimum, (add53, lv639), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm16 = R.call_tir(cls.layer_norm1, (lv15, model_encoder_layers_8_self_attn_layer_norm_weight, model_encoder_layers_8_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv640 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_8_self_attn_q_proj_weight, layer_norm16, model_encoder_layers_8_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape64 = R.call_tir(cls.reshape, (lv640,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv139 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_8_self_attn_k_proj_weight, layer_norm16), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape65 = R.call_tir(cls.reshape, (lv139,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv641 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_8_self_attn_v_proj_weight, layer_norm16, model_encoder_layers_8_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape66 = R.call_tir(cls.reshape, (lv641,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape67 = R.call_tir(cls.reshape1, (reshape64,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape68 = R.call_tir(cls.reshape1, (reshape65,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape69 = R.call_tir(cls.reshape1, (reshape66,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv12_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), reshape67, reshape68, reshape69), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape70 = R.call_tir(cls.reshape10, (lv12_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape71 = R.call_tir(cls.reshape11, (reshape70,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv642 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_8_self_attn_out_proj_weight, reshape71, model_encoder_layers_8_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add60 = R.call_tir(cls.add4, (lv15, lv642), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm17 = R.call_tir(cls.layer_norm1, (add60, model_encoder_layers_8_final_layer_norm_weight, model_encoder_layers_8_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv104 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_8_fc1_weight, layer_norm17, model_encoder_layers_8_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv643 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_8_fc2_weight, lv104, model_encoder_layers_8_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv16 = R.call_tir(cls.fused_add4_maximum_minimum, (add60, lv643), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm18 = R.call_tir(cls.layer_norm1, (lv16, model_encoder_layers_9_self_attn_layer_norm_weight, model_encoder_layers_9_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv644 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_9_self_attn_q_proj_weight, layer_norm18, model_encoder_layers_9_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape72 = R.call_tir(cls.reshape, (lv644,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv140 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_9_self_attn_k_proj_weight, layer_norm18), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape73 = R.call_tir(cls.reshape, (lv140,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv645 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_9_self_attn_v_proj_weight, layer_norm18, model_encoder_layers_9_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape74 = R.call_tir(cls.reshape, (lv645,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape75 = R.call_tir(cls.reshape1, (reshape72,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape76 = R.call_tir(cls.reshape1, (reshape73,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape77 = R.call_tir(cls.reshape1, (reshape74,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv13_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), reshape75, reshape76, reshape77), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape78 = R.call_tir(cls.reshape10, (lv13_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape79 = R.call_tir(cls.reshape11, (reshape78,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv646 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_9_self_attn_out_proj_weight, reshape79, model_encoder_layers_9_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add67 = R.call_tir(cls.add4, (lv16, lv646), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm19 = R.call_tir(cls.layer_norm1, (add67, model_encoder_layers_9_final_layer_norm_weight, model_encoder_layers_9_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv105 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_9_fc1_weight, layer_norm19, model_encoder_layers_9_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv647 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_9_fc2_weight, lv105, model_encoder_layers_9_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv17 = R.call_tir(cls.fused_add4_maximum_minimum, (add67, lv647), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm20 = R.call_tir(cls.layer_norm1, (lv17, model_encoder_layers_10_self_attn_layer_norm_weight, model_encoder_layers_10_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv648 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_10_self_attn_q_proj_weight, layer_norm20, model_encoder_layers_10_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape80 = R.call_tir(cls.reshape, (lv648,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv141 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_10_self_attn_k_proj_weight, layer_norm20), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape81 = R.call_tir(cls.reshape, (lv141,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv649 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_10_self_attn_v_proj_weight, layer_norm20, model_encoder_layers_10_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape82 = R.call_tir(cls.reshape, (lv649,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape83 = R.call_tir(cls.reshape1, (reshape80,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape84 = R.call_tir(cls.reshape1, (reshape81,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape85 = R.call_tir(cls.reshape1, (reshape82,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv14_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), reshape83, reshape84, reshape85), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape86 = R.call_tir(cls.reshape10, (lv14_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape87 = R.call_tir(cls.reshape11, (reshape86,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv650 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_10_self_attn_out_proj_weight, reshape87, model_encoder_layers_10_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add74 = R.call_tir(cls.add4, (lv17, lv650), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm21 = R.call_tir(cls.layer_norm1, (add74, model_encoder_layers_10_final_layer_norm_weight, model_encoder_layers_10_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv106 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_10_fc1_weight, layer_norm21, model_encoder_layers_10_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv651 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_10_fc2_weight, lv106, model_encoder_layers_10_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv18 = R.call_tir(cls.fused_add4_maximum_minimum, (add74, lv651), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm22 = R.call_tir(cls.layer_norm1, (lv18, model_encoder_layers_11_self_attn_layer_norm_weight, model_encoder_layers_11_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv652 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_11_self_attn_q_proj_weight, layer_norm22, model_encoder_layers_11_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape88 = R.call_tir(cls.reshape, (lv652,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv142 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_11_self_attn_k_proj_weight, layer_norm22), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape89 = R.call_tir(cls.reshape, (lv142,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv653 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_11_self_attn_v_proj_weight, layer_norm22, model_encoder_layers_11_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape90 = R.call_tir(cls.reshape, (lv653,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape91 = R.call_tir(cls.reshape1, (reshape88,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape92 = R.call_tir(cls.reshape1, (reshape89,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape93 = R.call_tir(cls.reshape1, (reshape90,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv15_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), reshape91, reshape92, reshape93), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape94 = R.call_tir(cls.reshape10, (lv15_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape95 = R.call_tir(cls.reshape11, (reshape94,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv654 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_11_self_attn_out_proj_weight, reshape95, model_encoder_layers_11_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add81 = R.call_tir(cls.add4, (lv18, lv654), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm23 = R.call_tir(cls.layer_norm1, (add81, model_encoder_layers_11_final_layer_norm_weight, model_encoder_layers_11_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv107 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_11_fc1_weight, layer_norm23, model_encoder_layers_11_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv655 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_11_fc2_weight, lv107, model_encoder_layers_11_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv19 = R.call_tir(cls.fused_add4_maximum_minimum, (add81, lv655), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm24 = R.call_tir(cls.layer_norm1, (lv19, model_encoder_layers_12_self_attn_layer_norm_weight, model_encoder_layers_12_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv656 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_12_self_attn_q_proj_weight, layer_norm24, model_encoder_layers_12_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape96 = R.call_tir(cls.reshape, (lv656,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv143 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_12_self_attn_k_proj_weight, layer_norm24), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape97 = R.call_tir(cls.reshape, (lv143,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv657 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_12_self_attn_v_proj_weight, layer_norm24, model_encoder_layers_12_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape98 = R.call_tir(cls.reshape, (lv657,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape99 = R.call_tir(cls.reshape1, (reshape96,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape100 = R.call_tir(cls.reshape1, (reshape97,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape101 = R.call_tir(cls.reshape1, (reshape98,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv16_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), reshape99, reshape100, reshape101), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape102 = R.call_tir(cls.reshape10, (lv16_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape103 = R.call_tir(cls.reshape11, (reshape102,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv658 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_12_self_attn_out_proj_weight, reshape103, model_encoder_layers_12_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add88 = R.call_tir(cls.add4, (lv19, lv658), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm25 = R.call_tir(cls.layer_norm1, (add88, model_encoder_layers_12_final_layer_norm_weight, model_encoder_layers_12_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv108 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_12_fc1_weight, layer_norm25, model_encoder_layers_12_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv659 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_12_fc2_weight, lv108, model_encoder_layers_12_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv20 = R.call_tir(cls.fused_add4_maximum_minimum, (add88, lv659), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm26 = R.call_tir(cls.layer_norm1, (lv20, model_encoder_layers_13_self_attn_layer_norm_weight, model_encoder_layers_13_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv660 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_13_self_attn_q_proj_weight, layer_norm26, model_encoder_layers_13_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape104 = R.call_tir(cls.reshape, (lv660,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv144 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_13_self_attn_k_proj_weight, layer_norm26), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape105 = R.call_tir(cls.reshape, (lv144,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv661 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_13_self_attn_v_proj_weight, layer_norm26, model_encoder_layers_13_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape106 = R.call_tir(cls.reshape, (lv661,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape107 = R.call_tir(cls.reshape1, (reshape104,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape108 = R.call_tir(cls.reshape1, (reshape105,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape109 = R.call_tir(cls.reshape1, (reshape106,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv17_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), reshape107, reshape108, reshape109), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape110 = R.call_tir(cls.reshape10, (lv17_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape111 = R.call_tir(cls.reshape11, (reshape110,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv662 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_13_self_attn_out_proj_weight, reshape111, model_encoder_layers_13_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add95 = R.call_tir(cls.add4, (lv20, lv662), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm27 = R.call_tir(cls.layer_norm1, (add95, model_encoder_layers_13_final_layer_norm_weight, model_encoder_layers_13_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv109 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_13_fc1_weight, layer_norm27, model_encoder_layers_13_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv663 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_13_fc2_weight, lv109, model_encoder_layers_13_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv21 = R.call_tir(cls.fused_add4_maximum_minimum, (add95, lv663), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm28 = R.call_tir(cls.layer_norm1, (lv21, model_encoder_layers_14_self_attn_layer_norm_weight, model_encoder_layers_14_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv664 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_14_self_attn_q_proj_weight, layer_norm28, model_encoder_layers_14_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape112 = R.call_tir(cls.reshape, (lv664,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv145 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_14_self_attn_k_proj_weight, layer_norm28), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape113 = R.call_tir(cls.reshape, (lv145,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv665 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_14_self_attn_v_proj_weight, layer_norm28, model_encoder_layers_14_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape114 = R.call_tir(cls.reshape, (lv665,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape115 = R.call_tir(cls.reshape1, (reshape112,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape116 = R.call_tir(cls.reshape1, (reshape113,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape117 = R.call_tir(cls.reshape1, (reshape114,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv18_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), reshape115, reshape116, reshape117), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape118 = R.call_tir(cls.reshape10, (lv18_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape119 = R.call_tir(cls.reshape11, (reshape118,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv666 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_14_self_attn_out_proj_weight, reshape119, model_encoder_layers_14_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add102 = R.call_tir(cls.add4, (lv21, lv666), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm29 = R.call_tir(cls.layer_norm1, (add102, model_encoder_layers_14_final_layer_norm_weight, model_encoder_layers_14_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv110 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_14_fc1_weight, layer_norm29, model_encoder_layers_14_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv667 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_14_fc2_weight, lv110, model_encoder_layers_14_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv22 = R.call_tir(cls.fused_add4_maximum_minimum, (add102, lv667), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm30 = R.call_tir(cls.layer_norm1, (lv22, model_encoder_layers_15_self_attn_layer_norm_weight, model_encoder_layers_15_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv668 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_15_self_attn_q_proj_weight, layer_norm30, model_encoder_layers_15_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape120 = R.call_tir(cls.reshape, (lv668,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv146 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_15_self_attn_k_proj_weight, layer_norm30), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape121 = R.call_tir(cls.reshape, (lv146,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv669 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_15_self_attn_v_proj_weight, layer_norm30, model_encoder_layers_15_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape122 = R.call_tir(cls.reshape, (lv669,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape123 = R.call_tir(cls.reshape1, (reshape120,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape124 = R.call_tir(cls.reshape1, (reshape121,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape125 = R.call_tir(cls.reshape1, (reshape122,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv19_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), reshape123, reshape124, reshape125), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape126 = R.call_tir(cls.reshape10, (lv19_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape127 = R.call_tir(cls.reshape11, (reshape126,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv670 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_15_self_attn_out_proj_weight, reshape127, model_encoder_layers_15_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add109 = R.call_tir(cls.add4, (lv22, lv670), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm31 = R.call_tir(cls.layer_norm1, (add109, model_encoder_layers_15_final_layer_norm_weight, model_encoder_layers_15_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv111 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_15_fc1_weight, layer_norm31, model_encoder_layers_15_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv671 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_15_fc2_weight, lv111, model_encoder_layers_15_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv23 = R.call_tir(cls.fused_add4_maximum_minimum, (add109, lv671), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm32 = R.call_tir(cls.layer_norm1, (lv23, model_encoder_layers_16_self_attn_layer_norm_weight, model_encoder_layers_16_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv672 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_16_self_attn_q_proj_weight, layer_norm32, model_encoder_layers_16_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape128 = R.call_tir(cls.reshape, (lv672,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv147 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_16_self_attn_k_proj_weight, layer_norm32), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape129 = R.call_tir(cls.reshape, (lv147,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv673 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_16_self_attn_v_proj_weight, layer_norm32, model_encoder_layers_16_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape130 = R.call_tir(cls.reshape, (lv673,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape131 = R.call_tir(cls.reshape1, (reshape128,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape132 = R.call_tir(cls.reshape1, (reshape129,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape133 = R.call_tir(cls.reshape1, (reshape130,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv20_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), reshape131, reshape132, reshape133), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape134 = R.call_tir(cls.reshape10, (lv20_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape135 = R.call_tir(cls.reshape11, (reshape134,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv674 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_16_self_attn_out_proj_weight, reshape135, model_encoder_layers_16_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add116 = R.call_tir(cls.add4, (lv23, lv674), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm33 = R.call_tir(cls.layer_norm1, (add116, model_encoder_layers_16_final_layer_norm_weight, model_encoder_layers_16_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv112 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_16_fc1_weight, layer_norm33, model_encoder_layers_16_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv675 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_16_fc2_weight, lv112, model_encoder_layers_16_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv24 = R.call_tir(cls.fused_add4_maximum_minimum, (add116, lv675), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm34 = R.call_tir(cls.layer_norm1, (lv24, model_encoder_layers_17_self_attn_layer_norm_weight, model_encoder_layers_17_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv676 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_17_self_attn_q_proj_weight, layer_norm34, model_encoder_layers_17_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape136 = R.call_tir(cls.reshape, (lv676,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv148 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_17_self_attn_k_proj_weight, layer_norm34), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape137 = R.call_tir(cls.reshape, (lv148,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv677 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_17_self_attn_v_proj_weight, layer_norm34, model_encoder_layers_17_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape138 = R.call_tir(cls.reshape, (lv677,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape139 = R.call_tir(cls.reshape1, (reshape136,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape140 = R.call_tir(cls.reshape1, (reshape137,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape141 = R.call_tir(cls.reshape1, (reshape138,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv21_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), reshape139, reshape140, reshape141), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape142 = R.call_tir(cls.reshape10, (lv21_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape143 = R.call_tir(cls.reshape11, (reshape142,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv678 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_17_self_attn_out_proj_weight, reshape143, model_encoder_layers_17_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add123 = R.call_tir(cls.add4, (lv24, lv678), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm35 = R.call_tir(cls.layer_norm1, (add123, model_encoder_layers_17_final_layer_norm_weight, model_encoder_layers_17_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv113 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_17_fc1_weight, layer_norm35, model_encoder_layers_17_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv679 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_17_fc2_weight, lv113, model_encoder_layers_17_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv25 = R.call_tir(cls.fused_add4_maximum_minimum, (add123, lv679), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm36 = R.call_tir(cls.layer_norm1, (lv25, model_encoder_layers_18_self_attn_layer_norm_weight, model_encoder_layers_18_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv680 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_18_self_attn_q_proj_weight, layer_norm36, model_encoder_layers_18_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape144 = R.call_tir(cls.reshape, (lv680,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv149 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_18_self_attn_k_proj_weight, layer_norm36), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape145 = R.call_tir(cls.reshape, (lv149,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv681 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_18_self_attn_v_proj_weight, layer_norm36, model_encoder_layers_18_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape146 = R.call_tir(cls.reshape, (lv681,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape147 = R.call_tir(cls.reshape1, (reshape144,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape148 = R.call_tir(cls.reshape1, (reshape145,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape149 = R.call_tir(cls.reshape1, (reshape146,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv22_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), reshape147, reshape148, reshape149), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape150 = R.call_tir(cls.reshape10, (lv22_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape151 = R.call_tir(cls.reshape11, (reshape150,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv682 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_18_self_attn_out_proj_weight, reshape151, model_encoder_layers_18_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add130 = R.call_tir(cls.add4, (lv25, lv682), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm37 = R.call_tir(cls.layer_norm1, (add130, model_encoder_layers_18_final_layer_norm_weight, model_encoder_layers_18_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv114 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_18_fc1_weight, layer_norm37, model_encoder_layers_18_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv683 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_18_fc2_weight, lv114, model_encoder_layers_18_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv26 = R.call_tir(cls.fused_add4_maximum_minimum, (add130, lv683), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm38 = R.call_tir(cls.layer_norm1, (lv26, model_encoder_layers_19_self_attn_layer_norm_weight, model_encoder_layers_19_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv684 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_19_self_attn_q_proj_weight, layer_norm38, model_encoder_layers_19_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape152 = R.call_tir(cls.reshape, (lv684,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv150 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_19_self_attn_k_proj_weight, layer_norm38), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape153 = R.call_tir(cls.reshape, (lv150,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv685 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_19_self_attn_v_proj_weight, layer_norm38, model_encoder_layers_19_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape154 = R.call_tir(cls.reshape, (lv685,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape155 = R.call_tir(cls.reshape1, (reshape152,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape156 = R.call_tir(cls.reshape1, (reshape153,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape157 = R.call_tir(cls.reshape1, (reshape154,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv23_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), reshape155, reshape156, reshape157), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape158 = R.call_tir(cls.reshape10, (lv23_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape159 = R.call_tir(cls.reshape11, (reshape158,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv686 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_19_self_attn_out_proj_weight, reshape159, model_encoder_layers_19_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add137 = R.call_tir(cls.add4, (lv26, lv686), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm39 = R.call_tir(cls.layer_norm1, (add137, model_encoder_layers_19_final_layer_norm_weight, model_encoder_layers_19_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv115 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_19_fc1_weight, layer_norm39, model_encoder_layers_19_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv687 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_19_fc2_weight, lv115, model_encoder_layers_19_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv27 = R.call_tir(cls.fused_add4_maximum_minimum, (add137, lv687), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm40 = R.call_tir(cls.layer_norm1, (lv27, model_encoder_layers_20_self_attn_layer_norm_weight, model_encoder_layers_20_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv688 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_20_self_attn_q_proj_weight, layer_norm40, model_encoder_layers_20_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape160 = R.call_tir(cls.reshape, (lv688,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv151 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_20_self_attn_k_proj_weight, layer_norm40), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape161 = R.call_tir(cls.reshape, (lv151,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv689 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_20_self_attn_v_proj_weight, layer_norm40, model_encoder_layers_20_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape162 = R.call_tir(cls.reshape, (lv689,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape163 = R.call_tir(cls.reshape1, (reshape160,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape164 = R.call_tir(cls.reshape1, (reshape161,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape165 = R.call_tir(cls.reshape1, (reshape162,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv24_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), reshape163, reshape164, reshape165), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape166 = R.call_tir(cls.reshape10, (lv24_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape167 = R.call_tir(cls.reshape11, (reshape166,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv690 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_20_self_attn_out_proj_weight, reshape167, model_encoder_layers_20_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add144 = R.call_tir(cls.add4, (lv27, lv690), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm41 = R.call_tir(cls.layer_norm1, (add144, model_encoder_layers_20_final_layer_norm_weight, model_encoder_layers_20_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv116 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_20_fc1_weight, layer_norm41, model_encoder_layers_20_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv691 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_20_fc2_weight, lv116, model_encoder_layers_20_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv28 = R.call_tir(cls.fused_add4_maximum_minimum, (add144, lv691), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm42 = R.call_tir(cls.layer_norm1, (lv28, model_encoder_layers_21_self_attn_layer_norm_weight, model_encoder_layers_21_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv692 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_21_self_attn_q_proj_weight, layer_norm42, model_encoder_layers_21_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape168 = R.call_tir(cls.reshape, (lv692,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv152 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_21_self_attn_k_proj_weight, layer_norm42), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape169 = R.call_tir(cls.reshape, (lv152,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv693 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_21_self_attn_v_proj_weight, layer_norm42, model_encoder_layers_21_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape170 = R.call_tir(cls.reshape, (lv693,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape171 = R.call_tir(cls.reshape1, (reshape168,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape172 = R.call_tir(cls.reshape1, (reshape169,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape173 = R.call_tir(cls.reshape1, (reshape170,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv25_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), reshape171, reshape172, reshape173), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape174 = R.call_tir(cls.reshape10, (lv25_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape175 = R.call_tir(cls.reshape11, (reshape174,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv694 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_21_self_attn_out_proj_weight, reshape175, model_encoder_layers_21_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add151 = R.call_tir(cls.add4, (lv28, lv694), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm43 = R.call_tir(cls.layer_norm1, (add151, model_encoder_layers_21_final_layer_norm_weight, model_encoder_layers_21_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv117 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_21_fc1_weight, layer_norm43, model_encoder_layers_21_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv695 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_21_fc2_weight, lv117, model_encoder_layers_21_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv29 = R.call_tir(cls.fused_add4_maximum_minimum, (add151, lv695), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm44 = R.call_tir(cls.layer_norm1, (lv29, model_encoder_layers_22_self_attn_layer_norm_weight, model_encoder_layers_22_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv696 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_22_self_attn_q_proj_weight, layer_norm44, model_encoder_layers_22_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape176 = R.call_tir(cls.reshape, (lv696,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv153 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_22_self_attn_k_proj_weight, layer_norm44), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape177 = R.call_tir(cls.reshape, (lv153,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv697 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_22_self_attn_v_proj_weight, layer_norm44, model_encoder_layers_22_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape178 = R.call_tir(cls.reshape, (lv697,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape179 = R.call_tir(cls.reshape1, (reshape176,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape180 = R.call_tir(cls.reshape1, (reshape177,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape181 = R.call_tir(cls.reshape1, (reshape178,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv26_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), reshape179, reshape180, reshape181), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape182 = R.call_tir(cls.reshape10, (lv26_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape183 = R.call_tir(cls.reshape11, (reshape182,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv698 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_22_self_attn_out_proj_weight, reshape183, model_encoder_layers_22_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add158 = R.call_tir(cls.add4, (lv29, lv698), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm45 = R.call_tir(cls.layer_norm1, (add158, model_encoder_layers_22_final_layer_norm_weight, model_encoder_layers_22_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv118 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_22_fc1_weight, layer_norm45, model_encoder_layers_22_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv699 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_22_fc2_weight, lv118, model_encoder_layers_22_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv30 = R.call_tir(cls.fused_add4_maximum_minimum, (add158, lv699), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm46 = R.call_tir(cls.layer_norm1, (lv30, model_encoder_layers_23_self_attn_layer_norm_weight, model_encoder_layers_23_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv700 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_23_self_attn_q_proj_weight, layer_norm46, model_encoder_layers_23_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape184 = R.call_tir(cls.reshape, (lv700,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv154 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_23_self_attn_k_proj_weight, layer_norm46), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape185 = R.call_tir(cls.reshape, (lv154,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv701 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_23_self_attn_v_proj_weight, layer_norm46, model_encoder_layers_23_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape186 = R.call_tir(cls.reshape, (lv701,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape187 = R.call_tir(cls.reshape1, (reshape184,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape188 = R.call_tir(cls.reshape1, (reshape185,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape189 = R.call_tir(cls.reshape1, (reshape186,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv27_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), reshape187, reshape188, reshape189), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape190 = R.call_tir(cls.reshape10, (lv27_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape191 = R.call_tir(cls.reshape11, (reshape190,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv702 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_23_self_attn_out_proj_weight, reshape191, model_encoder_layers_23_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add165 = R.call_tir(cls.add4, (lv30, lv702), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm47 = R.call_tir(cls.layer_norm1, (add165, model_encoder_layers_23_final_layer_norm_weight, model_encoder_layers_23_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv119 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_23_fc1_weight, layer_norm47, model_encoder_layers_23_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv703 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_23_fc2_weight, lv119, model_encoder_layers_23_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv31 = R.call_tir(cls.fused_add4_maximum_minimum, (add165, lv703), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm48 = R.call_tir(cls.layer_norm1, (lv31, model_encoder_layers_24_self_attn_layer_norm_weight, model_encoder_layers_24_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv704 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_24_self_attn_q_proj_weight, layer_norm48, model_encoder_layers_24_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape192 = R.call_tir(cls.reshape, (lv704,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv155 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_24_self_attn_k_proj_weight, layer_norm48), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape193 = R.call_tir(cls.reshape, (lv155,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv705 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_24_self_attn_v_proj_weight, layer_norm48, model_encoder_layers_24_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape194 = R.call_tir(cls.reshape, (lv705,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape195 = R.call_tir(cls.reshape1, (reshape192,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape196 = R.call_tir(cls.reshape1, (reshape193,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape197 = R.call_tir(cls.reshape1, (reshape194,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv28_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), reshape195, reshape196, reshape197), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape198 = R.call_tir(cls.reshape10, (lv28_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape199 = R.call_tir(cls.reshape11, (reshape198,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv706 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_24_self_attn_out_proj_weight, reshape199, model_encoder_layers_24_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add172 = R.call_tir(cls.add4, (lv31, lv706), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm49 = R.call_tir(cls.layer_norm1, (add172, model_encoder_layers_24_final_layer_norm_weight, model_encoder_layers_24_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv120 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_24_fc1_weight, layer_norm49, model_encoder_layers_24_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv707 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_24_fc2_weight, lv120, model_encoder_layers_24_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv32 = R.call_tir(cls.fused_add4_maximum_minimum, (add172, lv707), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm50 = R.call_tir(cls.layer_norm1, (lv32, model_encoder_layers_25_self_attn_layer_norm_weight, model_encoder_layers_25_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv708 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_25_self_attn_q_proj_weight, layer_norm50, model_encoder_layers_25_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape200 = R.call_tir(cls.reshape, (lv708,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv156 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_25_self_attn_k_proj_weight, layer_norm50), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape201 = R.call_tir(cls.reshape, (lv156,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv709 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_25_self_attn_v_proj_weight, layer_norm50, model_encoder_layers_25_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape202 = R.call_tir(cls.reshape, (lv709,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape203 = R.call_tir(cls.reshape1, (reshape200,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape204 = R.call_tir(cls.reshape1, (reshape201,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape205 = R.call_tir(cls.reshape1, (reshape202,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv29_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), reshape203, reshape204, reshape205), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape206 = R.call_tir(cls.reshape10, (lv29_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape207 = R.call_tir(cls.reshape11, (reshape206,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv710 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_25_self_attn_out_proj_weight, reshape207, model_encoder_layers_25_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add179 = R.call_tir(cls.add4, (lv32, lv710), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm51 = R.call_tir(cls.layer_norm1, (add179, model_encoder_layers_25_final_layer_norm_weight, model_encoder_layers_25_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv121 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_25_fc1_weight, layer_norm51, model_encoder_layers_25_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv711 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_25_fc2_weight, lv121, model_encoder_layers_25_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv33 = R.call_tir(cls.fused_add4_maximum_minimum, (add179, lv711), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm52 = R.call_tir(cls.layer_norm1, (lv33, model_encoder_layers_26_self_attn_layer_norm_weight, model_encoder_layers_26_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv712 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_26_self_attn_q_proj_weight, layer_norm52, model_encoder_layers_26_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape208 = R.call_tir(cls.reshape, (lv712,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv157 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_26_self_attn_k_proj_weight, layer_norm52), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape209 = R.call_tir(cls.reshape, (lv157,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv713 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_26_self_attn_v_proj_weight, layer_norm52, model_encoder_layers_26_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape210 = R.call_tir(cls.reshape, (lv713,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape211 = R.call_tir(cls.reshape1, (reshape208,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape212 = R.call_tir(cls.reshape1, (reshape209,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape213 = R.call_tir(cls.reshape1, (reshape210,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv30_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), reshape211, reshape212, reshape213), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape214 = R.call_tir(cls.reshape10, (lv30_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape215 = R.call_tir(cls.reshape11, (reshape214,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv714 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_26_self_attn_out_proj_weight, reshape215, model_encoder_layers_26_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add186 = R.call_tir(cls.add4, (lv33, lv714), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm53 = R.call_tir(cls.layer_norm1, (add186, model_encoder_layers_26_final_layer_norm_weight, model_encoder_layers_26_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv122 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_26_fc1_weight, layer_norm53, model_encoder_layers_26_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv715 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_26_fc2_weight, lv122, model_encoder_layers_26_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv34 = R.call_tir(cls.fused_add4_maximum_minimum, (add186, lv715), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm54 = R.call_tir(cls.layer_norm1, (lv34, model_encoder_layers_27_self_attn_layer_norm_weight, model_encoder_layers_27_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv716 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_27_self_attn_q_proj_weight, layer_norm54, model_encoder_layers_27_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape216 = R.call_tir(cls.reshape, (lv716,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv158 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_27_self_attn_k_proj_weight, layer_norm54), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape217 = R.call_tir(cls.reshape, (lv158,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv717 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_27_self_attn_v_proj_weight, layer_norm54, model_encoder_layers_27_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape218 = R.call_tir(cls.reshape, (lv717,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape219 = R.call_tir(cls.reshape1, (reshape216,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape220 = R.call_tir(cls.reshape1, (reshape217,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape221 = R.call_tir(cls.reshape1, (reshape218,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv31_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), reshape219, reshape220, reshape221), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape222 = R.call_tir(cls.reshape10, (lv31_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape223 = R.call_tir(cls.reshape11, (reshape222,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv718 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_27_self_attn_out_proj_weight, reshape223, model_encoder_layers_27_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add193 = R.call_tir(cls.add4, (lv34, lv718), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm55 = R.call_tir(cls.layer_norm1, (add193, model_encoder_layers_27_final_layer_norm_weight, model_encoder_layers_27_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv123 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_27_fc1_weight, layer_norm55, model_encoder_layers_27_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv719 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_27_fc2_weight, lv123, model_encoder_layers_27_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv35 = R.call_tir(cls.fused_add4_maximum_minimum, (add193, lv719), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm56 = R.call_tir(cls.layer_norm1, (lv35, model_encoder_layers_28_self_attn_layer_norm_weight, model_encoder_layers_28_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv720 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_28_self_attn_q_proj_weight, layer_norm56, model_encoder_layers_28_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape224 = R.call_tir(cls.reshape, (lv720,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv159 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_28_self_attn_k_proj_weight, layer_norm56), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape225 = R.call_tir(cls.reshape, (lv159,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv721 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_28_self_attn_v_proj_weight, layer_norm56, model_encoder_layers_28_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape226 = R.call_tir(cls.reshape, (lv721,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape227 = R.call_tir(cls.reshape1, (reshape224,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape228 = R.call_tir(cls.reshape1, (reshape225,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape229 = R.call_tir(cls.reshape1, (reshape226,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv32_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), reshape227, reshape228, reshape229), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape230 = R.call_tir(cls.reshape10, (lv32_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape231 = R.call_tir(cls.reshape11, (reshape230,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv722 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_28_self_attn_out_proj_weight, reshape231, model_encoder_layers_28_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add200 = R.call_tir(cls.add4, (lv35, lv722), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm57 = R.call_tir(cls.layer_norm1, (add200, model_encoder_layers_28_final_layer_norm_weight, model_encoder_layers_28_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv124 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_28_fc1_weight, layer_norm57, model_encoder_layers_28_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv723 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_28_fc2_weight, lv124, model_encoder_layers_28_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv36 = R.call_tir(cls.fused_add4_maximum_minimum, (add200, lv723), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm58 = R.call_tir(cls.layer_norm1, (lv36, model_encoder_layers_29_self_attn_layer_norm_weight, model_encoder_layers_29_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv724 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_29_self_attn_q_proj_weight, layer_norm58, model_encoder_layers_29_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape232 = R.call_tir(cls.reshape, (lv724,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv160 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_29_self_attn_k_proj_weight, layer_norm58), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape233 = R.call_tir(cls.reshape, (lv160,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv725 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_29_self_attn_v_proj_weight, layer_norm58, model_encoder_layers_29_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape234 = R.call_tir(cls.reshape, (lv725,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape235 = R.call_tir(cls.reshape1, (reshape232,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape236 = R.call_tir(cls.reshape1, (reshape233,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape237 = R.call_tir(cls.reshape1, (reshape234,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv33_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), reshape235, reshape236, reshape237), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape238 = R.call_tir(cls.reshape10, (lv33_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape239 = R.call_tir(cls.reshape11, (reshape238,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv726 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_29_self_attn_out_proj_weight, reshape239, model_encoder_layers_29_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add207 = R.call_tir(cls.add4, (lv36, lv726), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm59 = R.call_tir(cls.layer_norm1, (add207, model_encoder_layers_29_final_layer_norm_weight, model_encoder_layers_29_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv125 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_29_fc1_weight, layer_norm59, model_encoder_layers_29_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv727 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_29_fc2_weight, lv125, model_encoder_layers_29_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv37 = R.call_tir(cls.fused_add4_maximum_minimum, (add207, lv727), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm60 = R.call_tir(cls.layer_norm1, (lv37, model_encoder_layers_30_self_attn_layer_norm_weight, model_encoder_layers_30_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv728 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_30_self_attn_q_proj_weight, layer_norm60, model_encoder_layers_30_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape240 = R.call_tir(cls.reshape, (lv728,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv161 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_30_self_attn_k_proj_weight, layer_norm60), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape241 = R.call_tir(cls.reshape, (lv161,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv729 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_30_self_attn_v_proj_weight, layer_norm60, model_encoder_layers_30_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape242 = R.call_tir(cls.reshape, (lv729,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape243 = R.call_tir(cls.reshape1, (reshape240,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape244 = R.call_tir(cls.reshape1, (reshape241,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape245 = R.call_tir(cls.reshape1, (reshape242,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv34_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), reshape243, reshape244, reshape245), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape246 = R.call_tir(cls.reshape10, (lv34_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape247 = R.call_tir(cls.reshape11, (reshape246,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv730 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_30_self_attn_out_proj_weight, reshape247, model_encoder_layers_30_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add214 = R.call_tir(cls.add4, (lv37, lv730), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm61 = R.call_tir(cls.layer_norm1, (add214, model_encoder_layers_30_final_layer_norm_weight, model_encoder_layers_30_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv126 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_30_fc1_weight, layer_norm61, model_encoder_layers_30_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv731 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_30_fc2_weight, lv126, model_encoder_layers_30_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv38 = R.call_tir(cls.fused_add4_maximum_minimum, (add214, lv731), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm62 = R.call_tir(cls.layer_norm1, (lv38, model_encoder_layers_31_self_attn_layer_norm_weight, model_encoder_layers_31_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv732 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_31_self_attn_q_proj_weight, layer_norm62, model_encoder_layers_31_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape248 = R.call_tir(cls.reshape, (lv732,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv162 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_31_self_attn_k_proj_weight, layer_norm62), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape249 = R.call_tir(cls.reshape, (lv162,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv733 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_31_self_attn_v_proj_weight, layer_norm62, model_encoder_layers_31_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape250 = R.call_tir(cls.reshape, (lv733,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape251 = R.call_tir(cls.reshape1, (reshape248,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape252 = R.call_tir(cls.reshape1, (reshape249,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape253 = R.call_tir(cls.reshape1, (reshape250,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv35_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), reshape251, reshape252, reshape253), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape254 = R.call_tir(cls.reshape10, (lv35_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape255 = R.call_tir(cls.reshape11, (reshape254,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv734 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_31_self_attn_out_proj_weight, reshape255, model_encoder_layers_31_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add221 = R.call_tir(cls.add4, (lv38, lv734), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm63 = R.call_tir(cls.layer_norm1, (add221, model_encoder_layers_31_final_layer_norm_weight, model_encoder_layers_31_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv127 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_31_fc1_weight, layer_norm63, model_encoder_layers_31_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv735 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_31_fc2_weight, lv127, model_encoder_layers_31_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv39 = R.call_tir(cls.fused_add4_maximum_minimum, (add221, lv735), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + gv = R.call_tir(cls.layer_norm1, (lv39, model_encoder_layer_norm_weight, model_encoder_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + R.output(gv) + return gv + + @R.function + def batch_prefill(input_ids: R.Tensor((1, "seq_len"), dtype="int32"), logit_positions: R.Tensor(("batch_size",), dtype="int32"), paged_kv_cache: R.Object, packed_params: R.Tuple(R.Tensor((1280, 128, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1500, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), 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R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"))) -> R.Tensor((1, "batch_size", 51866), dtype="float32"): + batch_size = T.int64() + seq_len = T.int64() + R.func_attr({"num_input": 3, "relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "seq_len": 15000, "total_seq_len": 1500}}) + cls = Module + with R.dataflow(): + model_decoder_embed_tokens_weight2: R.Tensor((51866, 1280), dtype="float16") = packed_params[487] + model_decoder_embed_positions_weight2: R.Tensor((448, 1280), dtype="float16") = packed_params[488] + model_decoder_layers_0_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[489] + model_decoder_layers_0_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[490] + model_decoder_layers_0_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[491] + model_decoder_layers_0_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[492] + model_decoder_layers_0_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[493] + model_decoder_layers_0_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[494] + model_decoder_layers_0_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[495] + model_decoder_layers_0_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[496] + model_decoder_layers_0_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[497] + model_decoder_layers_0_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[501] + model_decoder_layers_0_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[502] + model_decoder_layers_0_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[503] + model_decoder_layers_0_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[504] + model_decoder_layers_0_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[505] + model_decoder_layers_0_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[506] + model_decoder_layers_0_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[507] + model_decoder_layers_0_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[508] + model_decoder_layers_0_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[509] + model_decoder_layers_0_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[510] + model_decoder_layers_0_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[511] + model_decoder_layers_0_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[512] + model_decoder_layers_1_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[513] + model_decoder_layers_1_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[514] + model_decoder_layers_1_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[515] + model_decoder_layers_1_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[516] + model_decoder_layers_1_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[517] + model_decoder_layers_1_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[518] + model_decoder_layers_1_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[519] + model_decoder_layers_1_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[520] + model_decoder_layers_1_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[521] + model_decoder_layers_1_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[525] + model_decoder_layers_1_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[526] + model_decoder_layers_1_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[527] + model_decoder_layers_1_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[528] + model_decoder_layers_1_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[529] + model_decoder_layers_1_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[530] + model_decoder_layers_1_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[531] + model_decoder_layers_1_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[532] + model_decoder_layers_1_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[533] + model_decoder_layers_1_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[534] + model_decoder_layers_1_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[535] + model_decoder_layers_1_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[536] + model_decoder_layers_2_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[537] + model_decoder_layers_2_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[538] + model_decoder_layers_2_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[539] + model_decoder_layers_2_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[540] + model_decoder_layers_2_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[541] + model_decoder_layers_2_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[542] + model_decoder_layers_2_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[543] + model_decoder_layers_2_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[544] + model_decoder_layers_2_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[545] + model_decoder_layers_2_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[549] + model_decoder_layers_2_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[550] + model_decoder_layers_2_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[551] + model_decoder_layers_2_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[552] + model_decoder_layers_2_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[553] + model_decoder_layers_2_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[554] + model_decoder_layers_2_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[555] + model_decoder_layers_2_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[556] + model_decoder_layers_2_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[557] + model_decoder_layers_2_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[558] + model_decoder_layers_2_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[559] + model_decoder_layers_2_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[560] + model_decoder_layers_3_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[561] + model_decoder_layers_3_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[562] + model_decoder_layers_3_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[563] + model_decoder_layers_3_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[564] + model_decoder_layers_3_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[565] + model_decoder_layers_3_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[566] + model_decoder_layers_3_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[567] + model_decoder_layers_3_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[568] + model_decoder_layers_3_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[569] + model_decoder_layers_3_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[573] + model_decoder_layers_3_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[574] + model_decoder_layers_3_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[575] + model_decoder_layers_3_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[576] + model_decoder_layers_3_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[577] + model_decoder_layers_3_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[578] + model_decoder_layers_3_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[579] + model_decoder_layers_3_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[580] + model_decoder_layers_3_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[581] + model_decoder_layers_3_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[582] + model_decoder_layers_3_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[583] + model_decoder_layers_3_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[584] + model_decoder_layers_4_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[585] + model_decoder_layers_4_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[586] + model_decoder_layers_4_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[587] + model_decoder_layers_4_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[588] + model_decoder_layers_4_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[589] + model_decoder_layers_4_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[590] + model_decoder_layers_4_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[591] + model_decoder_layers_4_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[592] + model_decoder_layers_4_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[593] + model_decoder_layers_4_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[597] + model_decoder_layers_4_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[598] + model_decoder_layers_4_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[599] + model_decoder_layers_4_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[600] + model_decoder_layers_4_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[601] + model_decoder_layers_4_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[602] + model_decoder_layers_4_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[603] + model_decoder_layers_4_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[604] + model_decoder_layers_4_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[605] + model_decoder_layers_4_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[606] + model_decoder_layers_4_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[607] + model_decoder_layers_4_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[608] + model_decoder_layers_5_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[609] + model_decoder_layers_5_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[610] + model_decoder_layers_5_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[611] + model_decoder_layers_5_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[612] + model_decoder_layers_5_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[613] + model_decoder_layers_5_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[614] + model_decoder_layers_5_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[615] + model_decoder_layers_5_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[616] + model_decoder_layers_5_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[617] + model_decoder_layers_5_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[621] + model_decoder_layers_5_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[622] + model_decoder_layers_5_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[623] + model_decoder_layers_5_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[624] + model_decoder_layers_5_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[625] + model_decoder_layers_5_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[626] + model_decoder_layers_5_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[627] + model_decoder_layers_5_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[628] + model_decoder_layers_5_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[629] + model_decoder_layers_5_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[630] + model_decoder_layers_5_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[631] + model_decoder_layers_5_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[632] + model_decoder_layers_6_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[633] + model_decoder_layers_6_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[634] + model_decoder_layers_6_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[635] + model_decoder_layers_6_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[636] + model_decoder_layers_6_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[637] + model_decoder_layers_6_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[638] + model_decoder_layers_6_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[639] + model_decoder_layers_6_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[640] + model_decoder_layers_6_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[641] + model_decoder_layers_6_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[645] + model_decoder_layers_6_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[646] + model_decoder_layers_6_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[647] + model_decoder_layers_6_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[648] + model_decoder_layers_6_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[649] + model_decoder_layers_6_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[650] + model_decoder_layers_6_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[651] + model_decoder_layers_6_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[652] + model_decoder_layers_6_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[653] + model_decoder_layers_6_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[654] + model_decoder_layers_6_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[655] + model_decoder_layers_6_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[656] + model_decoder_layers_7_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[657] + model_decoder_layers_7_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[658] + model_decoder_layers_7_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[659] + model_decoder_layers_7_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[660] + model_decoder_layers_7_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[661] + model_decoder_layers_7_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[662] + model_decoder_layers_7_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[663] + model_decoder_layers_7_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[664] + model_decoder_layers_7_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[665] + model_decoder_layers_7_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[669] + model_decoder_layers_7_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[670] + model_decoder_layers_7_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[671] + model_decoder_layers_7_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[672] + model_decoder_layers_7_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[673] + model_decoder_layers_7_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[674] + model_decoder_layers_7_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[675] + model_decoder_layers_7_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[676] + model_decoder_layers_7_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[677] + model_decoder_layers_7_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[678] + model_decoder_layers_7_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[679] + model_decoder_layers_7_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[680] + model_decoder_layers_8_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[681] + model_decoder_layers_8_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[682] + model_decoder_layers_8_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[683] + model_decoder_layers_8_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[684] + model_decoder_layers_8_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[685] + model_decoder_layers_8_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[686] + model_decoder_layers_8_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[687] + model_decoder_layers_8_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[688] + model_decoder_layers_8_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[689] + model_decoder_layers_8_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[693] + model_decoder_layers_8_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[694] + model_decoder_layers_8_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[695] + model_decoder_layers_8_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[696] + model_decoder_layers_8_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[697] + model_decoder_layers_8_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[698] + model_decoder_layers_8_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[699] + model_decoder_layers_8_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[700] + model_decoder_layers_8_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[701] + model_decoder_layers_8_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[702] + model_decoder_layers_8_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[703] + model_decoder_layers_8_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[704] + model_decoder_layers_9_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[705] + model_decoder_layers_9_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[706] + model_decoder_layers_9_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[707] + model_decoder_layers_9_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[708] + model_decoder_layers_9_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[709] + model_decoder_layers_9_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[710] + model_decoder_layers_9_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[711] + model_decoder_layers_9_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[712] + model_decoder_layers_9_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[713] + model_decoder_layers_9_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[717] + model_decoder_layers_9_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[718] + model_decoder_layers_9_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[719] + model_decoder_layers_9_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[720] + model_decoder_layers_9_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[721] + model_decoder_layers_9_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[722] + model_decoder_layers_9_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[723] + model_decoder_layers_9_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[724] + model_decoder_layers_9_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[725] + model_decoder_layers_9_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[726] + model_decoder_layers_9_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[727] + model_decoder_layers_9_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[728] + model_decoder_layers_10_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[729] + model_decoder_layers_10_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[730] + model_decoder_layers_10_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[731] + model_decoder_layers_10_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[732] + model_decoder_layers_10_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[733] + model_decoder_layers_10_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[734] + model_decoder_layers_10_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[735] + model_decoder_layers_10_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[736] + model_decoder_layers_10_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[737] + model_decoder_layers_10_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[741] + model_decoder_layers_10_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[742] + model_decoder_layers_10_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[743] + model_decoder_layers_10_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[744] + model_decoder_layers_10_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[745] + model_decoder_layers_10_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[746] + model_decoder_layers_10_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[747] + model_decoder_layers_10_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[748] + model_decoder_layers_10_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[749] + model_decoder_layers_10_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[750] + model_decoder_layers_10_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[751] + model_decoder_layers_10_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[752] + model_decoder_layers_11_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[753] + model_decoder_layers_11_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[754] + model_decoder_layers_11_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[755] + model_decoder_layers_11_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[756] + model_decoder_layers_11_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[757] + model_decoder_layers_11_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[758] + model_decoder_layers_11_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[759] + model_decoder_layers_11_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[760] + model_decoder_layers_11_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[761] + model_decoder_layers_11_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[765] + model_decoder_layers_11_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[766] + model_decoder_layers_11_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[767] + model_decoder_layers_11_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[768] + model_decoder_layers_11_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[769] + model_decoder_layers_11_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[770] + model_decoder_layers_11_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[771] + model_decoder_layers_11_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[772] + model_decoder_layers_11_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[773] + model_decoder_layers_11_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[774] + model_decoder_layers_11_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[775] + model_decoder_layers_11_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[776] + model_decoder_layers_12_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[777] + model_decoder_layers_12_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[778] + model_decoder_layers_12_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[779] + model_decoder_layers_12_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[780] + model_decoder_layers_12_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[781] + model_decoder_layers_12_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[782] + model_decoder_layers_12_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[783] + model_decoder_layers_12_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[784] + model_decoder_layers_12_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[785] + model_decoder_layers_12_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[789] + model_decoder_layers_12_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[790] + model_decoder_layers_12_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[791] + model_decoder_layers_12_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[792] + model_decoder_layers_12_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[793] + model_decoder_layers_12_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[794] + model_decoder_layers_12_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[795] + model_decoder_layers_12_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[796] + model_decoder_layers_12_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[797] + model_decoder_layers_12_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[798] + model_decoder_layers_12_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[799] + model_decoder_layers_12_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[800] + model_decoder_layers_13_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[801] + model_decoder_layers_13_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[802] + model_decoder_layers_13_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[803] + model_decoder_layers_13_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[804] + model_decoder_layers_13_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[805] + model_decoder_layers_13_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[806] + model_decoder_layers_13_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[807] + model_decoder_layers_13_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[808] + model_decoder_layers_13_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[809] + model_decoder_layers_13_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[813] + model_decoder_layers_13_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[814] + model_decoder_layers_13_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[815] + model_decoder_layers_13_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[816] + model_decoder_layers_13_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[817] + model_decoder_layers_13_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[818] + model_decoder_layers_13_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[819] + model_decoder_layers_13_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[820] + model_decoder_layers_13_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[821] + model_decoder_layers_13_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[822] + model_decoder_layers_13_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[823] + model_decoder_layers_13_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[824] + model_decoder_layers_14_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[825] + model_decoder_layers_14_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[826] + model_decoder_layers_14_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[827] + model_decoder_layers_14_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[828] + model_decoder_layers_14_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[829] + model_decoder_layers_14_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[830] + model_decoder_layers_14_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[831] + model_decoder_layers_14_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[832] + model_decoder_layers_14_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[833] + model_decoder_layers_14_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[837] + model_decoder_layers_14_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[838] + model_decoder_layers_14_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[839] + model_decoder_layers_14_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[840] + model_decoder_layers_14_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[841] + model_decoder_layers_14_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[842] + model_decoder_layers_14_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[843] + model_decoder_layers_14_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[844] + model_decoder_layers_14_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[845] + model_decoder_layers_14_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[846] + model_decoder_layers_14_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[847] + model_decoder_layers_14_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[848] + model_decoder_layers_15_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[849] + model_decoder_layers_15_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[850] + model_decoder_layers_15_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[851] + model_decoder_layers_15_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[852] + model_decoder_layers_15_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[853] + model_decoder_layers_15_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[854] + model_decoder_layers_15_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[855] + model_decoder_layers_15_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[856] + model_decoder_layers_15_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[857] + model_decoder_layers_15_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[861] + model_decoder_layers_15_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[862] + model_decoder_layers_15_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[863] + model_decoder_layers_15_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[864] + model_decoder_layers_15_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[865] + model_decoder_layers_15_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[866] + model_decoder_layers_15_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[867] + model_decoder_layers_15_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[868] + model_decoder_layers_15_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[869] + model_decoder_layers_15_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[870] + model_decoder_layers_15_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[871] + model_decoder_layers_15_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[872] + model_decoder_layers_16_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[873] + model_decoder_layers_16_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[874] + model_decoder_layers_16_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[875] + model_decoder_layers_16_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[876] + model_decoder_layers_16_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[877] + model_decoder_layers_16_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[878] + model_decoder_layers_16_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[879] + model_decoder_layers_16_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[880] + model_decoder_layers_16_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[881] + model_decoder_layers_16_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[885] + model_decoder_layers_16_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[886] + model_decoder_layers_16_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[887] + model_decoder_layers_16_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[888] + model_decoder_layers_16_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[889] + model_decoder_layers_16_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[890] + model_decoder_layers_16_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[891] + model_decoder_layers_16_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[892] + model_decoder_layers_16_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[893] + model_decoder_layers_16_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[894] + model_decoder_layers_16_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[895] + model_decoder_layers_16_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[896] + model_decoder_layers_17_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[897] + model_decoder_layers_17_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[898] + model_decoder_layers_17_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[899] + model_decoder_layers_17_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[900] + model_decoder_layers_17_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[901] + model_decoder_layers_17_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[902] + model_decoder_layers_17_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[903] + model_decoder_layers_17_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[904] + model_decoder_layers_17_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[905] + model_decoder_layers_17_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[909] + model_decoder_layers_17_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[910] + model_decoder_layers_17_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[911] + model_decoder_layers_17_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[912] + model_decoder_layers_17_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[913] + model_decoder_layers_17_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[914] + model_decoder_layers_17_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[915] + model_decoder_layers_17_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[916] + model_decoder_layers_17_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[917] + model_decoder_layers_17_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[918] + model_decoder_layers_17_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[919] + model_decoder_layers_17_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[920] + model_decoder_layers_18_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[921] + model_decoder_layers_18_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[922] + model_decoder_layers_18_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[923] + model_decoder_layers_18_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[924] + model_decoder_layers_18_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[925] + model_decoder_layers_18_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[926] + model_decoder_layers_18_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[927] + model_decoder_layers_18_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[928] + model_decoder_layers_18_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[929] + model_decoder_layers_18_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[933] + model_decoder_layers_18_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[934] + model_decoder_layers_18_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[935] + model_decoder_layers_18_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[936] + model_decoder_layers_18_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[937] + model_decoder_layers_18_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[938] + model_decoder_layers_18_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[939] + model_decoder_layers_18_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[940] + model_decoder_layers_18_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[941] + model_decoder_layers_18_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[942] + model_decoder_layers_18_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[943] + model_decoder_layers_18_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[944] + model_decoder_layers_19_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[945] + model_decoder_layers_19_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[946] + model_decoder_layers_19_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[947] + model_decoder_layers_19_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[948] + model_decoder_layers_19_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[949] + model_decoder_layers_19_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[950] + model_decoder_layers_19_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[951] + model_decoder_layers_19_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[952] + model_decoder_layers_19_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[953] + model_decoder_layers_19_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[957] + model_decoder_layers_19_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[958] + model_decoder_layers_19_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[959] + model_decoder_layers_19_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[960] + model_decoder_layers_19_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[961] + model_decoder_layers_19_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[962] + model_decoder_layers_19_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[963] + model_decoder_layers_19_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[964] + model_decoder_layers_19_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[965] + model_decoder_layers_19_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[966] + model_decoder_layers_19_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[967] + model_decoder_layers_19_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[968] + model_decoder_layers_20_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[969] + model_decoder_layers_20_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[970] + model_decoder_layers_20_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[971] + model_decoder_layers_20_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[972] + model_decoder_layers_20_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[973] + model_decoder_layers_20_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[974] + model_decoder_layers_20_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[975] + model_decoder_layers_20_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[976] + model_decoder_layers_20_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[977] + model_decoder_layers_20_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[981] + model_decoder_layers_20_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[982] + model_decoder_layers_20_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[983] + model_decoder_layers_20_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[984] + model_decoder_layers_20_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[985] + model_decoder_layers_20_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[986] + model_decoder_layers_20_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[987] + model_decoder_layers_20_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[988] + model_decoder_layers_20_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[989] + model_decoder_layers_20_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[990] + model_decoder_layers_20_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[991] + model_decoder_layers_20_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[992] + model_decoder_layers_21_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[993] + model_decoder_layers_21_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[994] + model_decoder_layers_21_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[995] + model_decoder_layers_21_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[996] + model_decoder_layers_21_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[997] + model_decoder_layers_21_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[998] + model_decoder_layers_21_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[999] + model_decoder_layers_21_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1000] + model_decoder_layers_21_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1001] + model_decoder_layers_21_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1005] + model_decoder_layers_21_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1006] + model_decoder_layers_21_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1007] + model_decoder_layers_21_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1008] + model_decoder_layers_21_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1009] + model_decoder_layers_21_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1010] + model_decoder_layers_21_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1011] + model_decoder_layers_21_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1012] + model_decoder_layers_21_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1013] + model_decoder_layers_21_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1014] + model_decoder_layers_21_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1015] + model_decoder_layers_21_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1016] + model_decoder_layers_22_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1017] + model_decoder_layers_22_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1018] + model_decoder_layers_22_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1019] + model_decoder_layers_22_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1020] + model_decoder_layers_22_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1021] + model_decoder_layers_22_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1022] + model_decoder_layers_22_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1023] + model_decoder_layers_22_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1024] + model_decoder_layers_22_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1025] + model_decoder_layers_22_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1029] + model_decoder_layers_22_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1030] + model_decoder_layers_22_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1031] + model_decoder_layers_22_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1032] + model_decoder_layers_22_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1033] + model_decoder_layers_22_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1034] + model_decoder_layers_22_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1035] + model_decoder_layers_22_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1036] + model_decoder_layers_22_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1037] + model_decoder_layers_22_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1038] + model_decoder_layers_22_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1039] + model_decoder_layers_22_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1040] + model_decoder_layers_23_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1041] + model_decoder_layers_23_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1042] + model_decoder_layers_23_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1043] + model_decoder_layers_23_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1044] + model_decoder_layers_23_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1045] + model_decoder_layers_23_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1046] + model_decoder_layers_23_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1047] + model_decoder_layers_23_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1048] + model_decoder_layers_23_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1049] + model_decoder_layers_23_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1053] + model_decoder_layers_23_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1054] + model_decoder_layers_23_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1055] + model_decoder_layers_23_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1056] + model_decoder_layers_23_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1057] + model_decoder_layers_23_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1058] + model_decoder_layers_23_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1059] + model_decoder_layers_23_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1060] + model_decoder_layers_23_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1061] + model_decoder_layers_23_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1062] + model_decoder_layers_23_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1063] + model_decoder_layers_23_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1064] + model_decoder_layers_24_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1065] + model_decoder_layers_24_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1066] + model_decoder_layers_24_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1067] + model_decoder_layers_24_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1068] + model_decoder_layers_24_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1069] + model_decoder_layers_24_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1070] + model_decoder_layers_24_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1071] + model_decoder_layers_24_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1072] + model_decoder_layers_24_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1073] + model_decoder_layers_24_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1077] + model_decoder_layers_24_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1078] + model_decoder_layers_24_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1079] + model_decoder_layers_24_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1080] + model_decoder_layers_24_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1081] + model_decoder_layers_24_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1082] + model_decoder_layers_24_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1083] + model_decoder_layers_24_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1084] + model_decoder_layers_24_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1085] + model_decoder_layers_24_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1086] + model_decoder_layers_24_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1087] + model_decoder_layers_24_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1088] + model_decoder_layers_25_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1089] + model_decoder_layers_25_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1090] + model_decoder_layers_25_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1091] + model_decoder_layers_25_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1092] + model_decoder_layers_25_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1093] + model_decoder_layers_25_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1094] + model_decoder_layers_25_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1095] + model_decoder_layers_25_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1096] + model_decoder_layers_25_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1097] + model_decoder_layers_25_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1101] + model_decoder_layers_25_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1102] + model_decoder_layers_25_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1103] + model_decoder_layers_25_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1104] + model_decoder_layers_25_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1105] + model_decoder_layers_25_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1106] + model_decoder_layers_25_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1107] + model_decoder_layers_25_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1108] + model_decoder_layers_25_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1109] + model_decoder_layers_25_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1110] + model_decoder_layers_25_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1111] + model_decoder_layers_25_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1112] + model_decoder_layers_26_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1113] + model_decoder_layers_26_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1114] + model_decoder_layers_26_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1115] + model_decoder_layers_26_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1116] + model_decoder_layers_26_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1117] + model_decoder_layers_26_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1118] + model_decoder_layers_26_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1119] + model_decoder_layers_26_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1120] + model_decoder_layers_26_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1121] + model_decoder_layers_26_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1125] + model_decoder_layers_26_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1126] + model_decoder_layers_26_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1127] + model_decoder_layers_26_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1128] + model_decoder_layers_26_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1129] + model_decoder_layers_26_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1130] + model_decoder_layers_26_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1131] + model_decoder_layers_26_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1132] + model_decoder_layers_26_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1133] + model_decoder_layers_26_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1134] + model_decoder_layers_26_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1135] + model_decoder_layers_26_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1136] + model_decoder_layers_27_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1137] + model_decoder_layers_27_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1138] + model_decoder_layers_27_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1139] + model_decoder_layers_27_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1140] + model_decoder_layers_27_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1141] + model_decoder_layers_27_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1142] + model_decoder_layers_27_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1143] + model_decoder_layers_27_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1144] + model_decoder_layers_27_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1145] + model_decoder_layers_27_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1149] + model_decoder_layers_27_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1150] + model_decoder_layers_27_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1151] + model_decoder_layers_27_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1152] + model_decoder_layers_27_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1153] + model_decoder_layers_27_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1154] + model_decoder_layers_27_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1155] + model_decoder_layers_27_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1156] + model_decoder_layers_27_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1157] + model_decoder_layers_27_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1158] + model_decoder_layers_27_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1159] + model_decoder_layers_27_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1160] + model_decoder_layers_28_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1161] + model_decoder_layers_28_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1162] + model_decoder_layers_28_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1163] + model_decoder_layers_28_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1164] + model_decoder_layers_28_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1165] + model_decoder_layers_28_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1166] + model_decoder_layers_28_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1167] + model_decoder_layers_28_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1168] + model_decoder_layers_28_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1169] + model_decoder_layers_28_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1173] + model_decoder_layers_28_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1174] + model_decoder_layers_28_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1175] + model_decoder_layers_28_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1176] + model_decoder_layers_28_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1177] + model_decoder_layers_28_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1178] + model_decoder_layers_28_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1179] + model_decoder_layers_28_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1180] + model_decoder_layers_28_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1181] + model_decoder_layers_28_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1182] + model_decoder_layers_28_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1183] + model_decoder_layers_28_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1184] + model_decoder_layers_29_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1185] + model_decoder_layers_29_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1186] + model_decoder_layers_29_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1187] + model_decoder_layers_29_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1188] + model_decoder_layers_29_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1189] + model_decoder_layers_29_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1190] + model_decoder_layers_29_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1191] + model_decoder_layers_29_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1192] + model_decoder_layers_29_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1193] + model_decoder_layers_29_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1197] + model_decoder_layers_29_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1198] + model_decoder_layers_29_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1199] + model_decoder_layers_29_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1200] + model_decoder_layers_29_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1201] + model_decoder_layers_29_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1202] + model_decoder_layers_29_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1203] + model_decoder_layers_29_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1204] + model_decoder_layers_29_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1205] + model_decoder_layers_29_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1206] + model_decoder_layers_29_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1207] + model_decoder_layers_29_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1208] + model_decoder_layers_30_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1209] + model_decoder_layers_30_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1210] + model_decoder_layers_30_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1211] + model_decoder_layers_30_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1212] + model_decoder_layers_30_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1213] + model_decoder_layers_30_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1214] + model_decoder_layers_30_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1215] + model_decoder_layers_30_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1216] + model_decoder_layers_30_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1217] + model_decoder_layers_30_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1221] + model_decoder_layers_30_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1222] + model_decoder_layers_30_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1223] + model_decoder_layers_30_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1224] + model_decoder_layers_30_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1225] + model_decoder_layers_30_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1226] + model_decoder_layers_30_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1227] + model_decoder_layers_30_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1228] + model_decoder_layers_30_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1229] + model_decoder_layers_30_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1230] + model_decoder_layers_30_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1231] + model_decoder_layers_30_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1232] + model_decoder_layers_31_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1233] + model_decoder_layers_31_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1234] + model_decoder_layers_31_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1235] + model_decoder_layers_31_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1236] + model_decoder_layers_31_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1237] + model_decoder_layers_31_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1238] + model_decoder_layers_31_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1239] + model_decoder_layers_31_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1240] + model_decoder_layers_31_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1241] + model_decoder_layers_31_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1245] + model_decoder_layers_31_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1246] + model_decoder_layers_31_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1247] + model_decoder_layers_31_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1248] + model_decoder_layers_31_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1249] + model_decoder_layers_31_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1250] + model_decoder_layers_31_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1251] + model_decoder_layers_31_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1252] + model_decoder_layers_31_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1253] + model_decoder_layers_31_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1254] + model_decoder_layers_31_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1255] + model_decoder_layers_31_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1256] + model_decoder_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1257] + model_decoder_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1258] + reshape384 = R.call_tir(cls.reshape12, (input_ids,), out_sinfo=R.Tensor((seq_len,), dtype="int32")) + take = R.call_tir(cls.take, (model_decoder_embed_tokens_weight2, reshape384), out_sinfo=R.Tensor((seq_len, 1280), dtype="float16")) + reshape385 = R.call_tir(cls.reshape13, (take,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv68: R.Tensor((seq_len,), dtype="int32") = R.call_pure_packed("vm.builtin.attention_kv_cache_get_query_positions", paged_kv_cache, sinfo_args=(R.Tensor((seq_len,), dtype="int32"),)) + take1 = R.call_tir(cls.take1, (model_decoder_embed_positions_weight2, lv68), out_sinfo=R.Tensor((seq_len, 1280), dtype="float16")) + reshape386 = R.call_tir(cls.reshape13, (take1,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add257 = R.call_tir(cls.add5, (reshape385, reshape386), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm65 = R.call_tir(cls.layer_norm2, (add257, model_decoder_layers_0_self_attn_layer_norm_weight2, model_decoder_layers_0_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv416 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_0_self_attn_q_proj_weight2, layer_norm65, model_decoder_layers_0_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape387 = R.call_tir(cls.reshape14, (lv416,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv98 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_0_self_attn_k_proj_weight2, layer_norm65), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape388 = R.call_tir(cls.reshape14, (lv98,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv417 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_0_self_attn_v_proj_weight2, layer_norm65, model_decoder_layers_0_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape389 = R.call_tir(cls.reshape14, (lv417,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat = R.call_tir(cls.concatenate1, (reshape387, reshape388, reshape389), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape390 = R.call_tir(cls.reshape15, (concat,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv69 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), reshape390), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape391 = R.call_tir(cls.reshape16, (lv69,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape392 = R.call_tir(cls.reshape17, (reshape391,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv418 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_0_self_attn_out_proj_weight2, reshape392, model_decoder_layers_0_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add261 = R.call_tir(cls.add5, (add257, lv418), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm66 = R.call_tir(cls.layer_norm2, (add261, model_decoder_layers_0_encoder_attn_layer_norm_weight2, model_decoder_layers_0_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv419 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_0_encoder_attn_q_proj_weight2, layer_norm66, model_decoder_layers_0_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape393 = R.call_tir(cls.reshape14, (lv419,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape394 = R.call_tir(cls.reshape18, (reshape393,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv70 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), reshape394), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape395 = R.call_tir(cls.reshape16, (lv70,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape396 = R.call_tir(cls.reshape17, (reshape395,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv420 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_0_encoder_attn_out_proj_weight2, reshape396, model_decoder_layers_0_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add264 = R.call_tir(cls.add5, (add261, lv420), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm67 = R.call_tir(cls.layer_norm2, (add264, model_decoder_layers_0_final_layer_norm_weight2, model_decoder_layers_0_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv64 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_0_fc1_weight2, layer_norm67, model_decoder_layers_0_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv421 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_0_fc2_weight2, lv64, model_decoder_layers_0_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add267 = R.call_tir(cls.add5, (add264, lv421), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm68 = R.call_tir(cls.layer_norm2, (add267, model_decoder_layers_1_self_attn_layer_norm_weight2, model_decoder_layers_1_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv422 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_1_self_attn_q_proj_weight2, layer_norm68, model_decoder_layers_1_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape397 = R.call_tir(cls.reshape14, (lv422,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv99 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_1_self_attn_k_proj_weight2, layer_norm68), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape398 = R.call_tir(cls.reshape14, (lv99,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv423 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_1_self_attn_v_proj_weight2, layer_norm68, model_decoder_layers_1_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape399 = R.call_tir(cls.reshape14, (lv423,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat1 = R.call_tir(cls.concatenate1, (reshape397, reshape398, reshape399), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape400 = R.call_tir(cls.reshape15, (concat1,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv71 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), reshape400), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape401 = R.call_tir(cls.reshape16, (lv71,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape402 = R.call_tir(cls.reshape17, (reshape401,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv424 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_1_self_attn_out_proj_weight2, reshape402, model_decoder_layers_1_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add271 = R.call_tir(cls.add5, (add267, lv424), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm69 = R.call_tir(cls.layer_norm2, (add271, model_decoder_layers_1_encoder_attn_layer_norm_weight2, model_decoder_layers_1_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv425 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_1_encoder_attn_q_proj_weight2, layer_norm69, model_decoder_layers_1_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape403 = R.call_tir(cls.reshape14, (lv425,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape404 = R.call_tir(cls.reshape18, (reshape403,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv72 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), reshape404), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape405 = R.call_tir(cls.reshape16, (lv72,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape406 = R.call_tir(cls.reshape17, (reshape405,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv426 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_1_encoder_attn_out_proj_weight2, reshape406, model_decoder_layers_1_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add274 = R.call_tir(cls.add5, (add271, lv426), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm70 = R.call_tir(cls.layer_norm2, (add274, model_decoder_layers_1_final_layer_norm_weight2, model_decoder_layers_1_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv65 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_1_fc1_weight2, layer_norm70, model_decoder_layers_1_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv427 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_1_fc2_weight2, lv65, model_decoder_layers_1_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add277 = R.call_tir(cls.add5, (add274, lv427), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm71 = R.call_tir(cls.layer_norm2, (add277, model_decoder_layers_2_self_attn_layer_norm_weight2, model_decoder_layers_2_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv428 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_2_self_attn_q_proj_weight2, layer_norm71, model_decoder_layers_2_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape407 = R.call_tir(cls.reshape14, (lv428,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv100 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_2_self_attn_k_proj_weight2, layer_norm71), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape408 = R.call_tir(cls.reshape14, (lv100,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv429 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_2_self_attn_v_proj_weight2, layer_norm71, model_decoder_layers_2_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape409 = R.call_tir(cls.reshape14, (lv429,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat2 = R.call_tir(cls.concatenate1, (reshape407, reshape408, reshape409), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape410 = R.call_tir(cls.reshape15, (concat2,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv73 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), reshape410), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape411 = R.call_tir(cls.reshape16, (lv73,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape412 = R.call_tir(cls.reshape17, (reshape411,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv430 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_2_self_attn_out_proj_weight2, reshape412, model_decoder_layers_2_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add281 = R.call_tir(cls.add5, (add277, lv430), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm72 = R.call_tir(cls.layer_norm2, (add281, model_decoder_layers_2_encoder_attn_layer_norm_weight2, model_decoder_layers_2_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv431 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_2_encoder_attn_q_proj_weight2, layer_norm72, model_decoder_layers_2_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape413 = R.call_tir(cls.reshape14, (lv431,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape414 = R.call_tir(cls.reshape18, (reshape413,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv74 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), reshape414), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape415 = R.call_tir(cls.reshape16, (lv74,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape416 = R.call_tir(cls.reshape17, (reshape415,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv432 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_2_encoder_attn_out_proj_weight2, reshape416, model_decoder_layers_2_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add284 = R.call_tir(cls.add5, (add281, lv432), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm73 = R.call_tir(cls.layer_norm2, (add284, model_decoder_layers_2_final_layer_norm_weight2, model_decoder_layers_2_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv66 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_2_fc1_weight2, layer_norm73, model_decoder_layers_2_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv433 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_2_fc2_weight2, lv66, model_decoder_layers_2_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add287 = R.call_tir(cls.add5, (add284, lv433), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm74 = R.call_tir(cls.layer_norm2, (add287, model_decoder_layers_3_self_attn_layer_norm_weight2, model_decoder_layers_3_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv434 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_3_self_attn_q_proj_weight2, layer_norm74, model_decoder_layers_3_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape417 = R.call_tir(cls.reshape14, (lv434,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv101 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_3_self_attn_k_proj_weight2, layer_norm74), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape418 = R.call_tir(cls.reshape14, (lv101,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv435 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_3_self_attn_v_proj_weight2, layer_norm74, model_decoder_layers_3_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape419 = R.call_tir(cls.reshape14, (lv435,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat3 = R.call_tir(cls.concatenate1, (reshape417, reshape418, reshape419), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape420 = R.call_tir(cls.reshape15, (concat3,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv75 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), reshape420), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape421 = R.call_tir(cls.reshape16, (lv75,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape422 = R.call_tir(cls.reshape17, (reshape421,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv436 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_3_self_attn_out_proj_weight2, reshape422, model_decoder_layers_3_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add291 = R.call_tir(cls.add5, (add287, lv436), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm75 = R.call_tir(cls.layer_norm2, (add291, model_decoder_layers_3_encoder_attn_layer_norm_weight2, model_decoder_layers_3_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv437 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_3_encoder_attn_q_proj_weight2, layer_norm75, model_decoder_layers_3_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape423 = R.call_tir(cls.reshape14, (lv437,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape424 = R.call_tir(cls.reshape18, (reshape423,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv76 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), reshape424), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape425 = R.call_tir(cls.reshape16, (lv76,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape426 = R.call_tir(cls.reshape17, (reshape425,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv438 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_3_encoder_attn_out_proj_weight2, reshape426, model_decoder_layers_3_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add294 = R.call_tir(cls.add5, (add291, lv438), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm76 = R.call_tir(cls.layer_norm2, (add294, model_decoder_layers_3_final_layer_norm_weight2, model_decoder_layers_3_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv67 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_3_fc1_weight2, layer_norm76, model_decoder_layers_3_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv439 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_3_fc2_weight2, lv67, model_decoder_layers_3_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add297 = R.call_tir(cls.add5, (add294, lv439), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm77 = R.call_tir(cls.layer_norm2, (add297, model_decoder_layers_4_self_attn_layer_norm_weight2, model_decoder_layers_4_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv440 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_4_self_attn_q_proj_weight2, layer_norm77, model_decoder_layers_4_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape427 = R.call_tir(cls.reshape14, (lv440,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv102 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_4_self_attn_k_proj_weight2, layer_norm77), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape428 = R.call_tir(cls.reshape14, (lv102,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv441 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_4_self_attn_v_proj_weight2, layer_norm77, model_decoder_layers_4_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape429 = R.call_tir(cls.reshape14, (lv441,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat4 = R.call_tir(cls.concatenate1, (reshape427, reshape428, reshape429), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape430 = R.call_tir(cls.reshape15, (concat4,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv77 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), reshape430), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape431 = R.call_tir(cls.reshape16, (lv77,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape432 = R.call_tir(cls.reshape17, (reshape431,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv442 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_4_self_attn_out_proj_weight2, reshape432, model_decoder_layers_4_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add301 = R.call_tir(cls.add5, (add297, lv442), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm78 = R.call_tir(cls.layer_norm2, (add301, model_decoder_layers_4_encoder_attn_layer_norm_weight2, model_decoder_layers_4_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv443 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_4_encoder_attn_q_proj_weight2, layer_norm78, model_decoder_layers_4_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape433 = R.call_tir(cls.reshape14, (lv443,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape434 = R.call_tir(cls.reshape18, (reshape433,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv78 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), reshape434), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape435 = R.call_tir(cls.reshape16, (lv78,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape436 = R.call_tir(cls.reshape17, (reshape435,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv444 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_4_encoder_attn_out_proj_weight2, reshape436, model_decoder_layers_4_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add304 = R.call_tir(cls.add5, (add301, lv444), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm79 = R.call_tir(cls.layer_norm2, (add304, model_decoder_layers_4_final_layer_norm_weight2, model_decoder_layers_4_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv68_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_4_fc1_weight2, layer_norm79, model_decoder_layers_4_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv445 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_4_fc2_weight2, lv68_1, model_decoder_layers_4_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add307 = R.call_tir(cls.add5, (add304, lv445), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm80 = R.call_tir(cls.layer_norm2, (add307, model_decoder_layers_5_self_attn_layer_norm_weight2, model_decoder_layers_5_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv446 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_5_self_attn_q_proj_weight2, layer_norm80, model_decoder_layers_5_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape437 = R.call_tir(cls.reshape14, (lv446,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv103 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_5_self_attn_k_proj_weight2, layer_norm80), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape438 = R.call_tir(cls.reshape14, (lv103,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv447 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_5_self_attn_v_proj_weight2, layer_norm80, model_decoder_layers_5_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape439 = R.call_tir(cls.reshape14, (lv447,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat5 = R.call_tir(cls.concatenate1, (reshape437, reshape438, reshape439), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape440 = R.call_tir(cls.reshape15, (concat5,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv79 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), reshape440), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape441 = R.call_tir(cls.reshape16, (lv79,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape442 = R.call_tir(cls.reshape17, (reshape441,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv448 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_5_self_attn_out_proj_weight2, reshape442, model_decoder_layers_5_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add311 = R.call_tir(cls.add5, (add307, lv448), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm81 = R.call_tir(cls.layer_norm2, (add311, model_decoder_layers_5_encoder_attn_layer_norm_weight2, model_decoder_layers_5_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv449 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_5_encoder_attn_q_proj_weight2, layer_norm81, model_decoder_layers_5_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape443 = R.call_tir(cls.reshape14, (lv449,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape444 = R.call_tir(cls.reshape18, (reshape443,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv80 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), reshape444), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape445 = R.call_tir(cls.reshape16, (lv80,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape446 = R.call_tir(cls.reshape17, (reshape445,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv450 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_5_encoder_attn_out_proj_weight2, reshape446, model_decoder_layers_5_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add314 = R.call_tir(cls.add5, (add311, lv450), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm82 = R.call_tir(cls.layer_norm2, (add314, model_decoder_layers_5_final_layer_norm_weight2, model_decoder_layers_5_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv69_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_5_fc1_weight2, layer_norm82, model_decoder_layers_5_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv451 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_5_fc2_weight2, lv69_1, model_decoder_layers_5_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add317 = R.call_tir(cls.add5, (add314, lv451), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm83 = R.call_tir(cls.layer_norm2, (add317, model_decoder_layers_6_self_attn_layer_norm_weight2, model_decoder_layers_6_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv452 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_6_self_attn_q_proj_weight2, layer_norm83, model_decoder_layers_6_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape447 = R.call_tir(cls.reshape14, (lv452,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv104 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_6_self_attn_k_proj_weight2, layer_norm83), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape448 = R.call_tir(cls.reshape14, (lv104,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv453 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_6_self_attn_v_proj_weight2, layer_norm83, model_decoder_layers_6_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape449 = R.call_tir(cls.reshape14, (lv453,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat6 = R.call_tir(cls.concatenate1, (reshape447, reshape448, reshape449), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape450 = R.call_tir(cls.reshape15, (concat6,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv81 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), reshape450), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape451 = R.call_tir(cls.reshape16, (lv81,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape452 = R.call_tir(cls.reshape17, (reshape451,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv454 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_6_self_attn_out_proj_weight2, reshape452, model_decoder_layers_6_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add321 = R.call_tir(cls.add5, (add317, lv454), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm84 = R.call_tir(cls.layer_norm2, (add321, model_decoder_layers_6_encoder_attn_layer_norm_weight2, model_decoder_layers_6_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv455 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_6_encoder_attn_q_proj_weight2, layer_norm84, model_decoder_layers_6_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape453 = R.call_tir(cls.reshape14, (lv455,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape454 = R.call_tir(cls.reshape18, (reshape453,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv82 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), reshape454), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape455 = R.call_tir(cls.reshape16, (lv82,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape456 = R.call_tir(cls.reshape17, (reshape455,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv456 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_6_encoder_attn_out_proj_weight2, reshape456, model_decoder_layers_6_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add324 = R.call_tir(cls.add5, (add321, lv456), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm85 = R.call_tir(cls.layer_norm2, (add324, model_decoder_layers_6_final_layer_norm_weight2, model_decoder_layers_6_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv70_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_6_fc1_weight2, layer_norm85, model_decoder_layers_6_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv457 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_6_fc2_weight2, lv70_1, model_decoder_layers_6_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add327 = R.call_tir(cls.add5, (add324, lv457), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm86 = R.call_tir(cls.layer_norm2, (add327, model_decoder_layers_7_self_attn_layer_norm_weight2, model_decoder_layers_7_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv458 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_7_self_attn_q_proj_weight2, layer_norm86, model_decoder_layers_7_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape457 = R.call_tir(cls.reshape14, (lv458,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv105 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_7_self_attn_k_proj_weight2, layer_norm86), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape458 = R.call_tir(cls.reshape14, (lv105,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv459 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_7_self_attn_v_proj_weight2, layer_norm86, model_decoder_layers_7_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape459 = R.call_tir(cls.reshape14, (lv459,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat7 = R.call_tir(cls.concatenate1, (reshape457, reshape458, reshape459), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape460 = R.call_tir(cls.reshape15, (concat7,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv83 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), reshape460), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape461 = R.call_tir(cls.reshape16, (lv83,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape462 = R.call_tir(cls.reshape17, (reshape461,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv460 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_7_self_attn_out_proj_weight2, reshape462, model_decoder_layers_7_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add331 = R.call_tir(cls.add5, (add327, lv460), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm87 = R.call_tir(cls.layer_norm2, (add331, model_decoder_layers_7_encoder_attn_layer_norm_weight2, model_decoder_layers_7_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv461 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_7_encoder_attn_q_proj_weight2, layer_norm87, model_decoder_layers_7_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape463 = R.call_tir(cls.reshape14, (lv461,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape464 = R.call_tir(cls.reshape18, (reshape463,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv84 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), reshape464), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape465 = R.call_tir(cls.reshape16, (lv84,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape466 = R.call_tir(cls.reshape17, (reshape465,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv462 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_7_encoder_attn_out_proj_weight2, reshape466, model_decoder_layers_7_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add334 = R.call_tir(cls.add5, (add331, lv462), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm88 = R.call_tir(cls.layer_norm2, (add334, model_decoder_layers_7_final_layer_norm_weight2, model_decoder_layers_7_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv71_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_7_fc1_weight2, layer_norm88, model_decoder_layers_7_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv463 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_7_fc2_weight2, lv71_1, model_decoder_layers_7_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add337 = R.call_tir(cls.add5, (add334, lv463), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm89 = R.call_tir(cls.layer_norm2, (add337, model_decoder_layers_8_self_attn_layer_norm_weight2, model_decoder_layers_8_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv464 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_8_self_attn_q_proj_weight2, layer_norm89, model_decoder_layers_8_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape467 = R.call_tir(cls.reshape14, (lv464,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv106 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_8_self_attn_k_proj_weight2, layer_norm89), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape468 = R.call_tir(cls.reshape14, (lv106,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv465 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_8_self_attn_v_proj_weight2, layer_norm89, model_decoder_layers_8_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape469 = R.call_tir(cls.reshape14, (lv465,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat8 = R.call_tir(cls.concatenate1, (reshape467, reshape468, reshape469), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape470 = R.call_tir(cls.reshape15, (concat8,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv85 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), reshape470), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape471 = R.call_tir(cls.reshape16, (lv85,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape472 = R.call_tir(cls.reshape17, (reshape471,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv466 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_8_self_attn_out_proj_weight2, reshape472, model_decoder_layers_8_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add341 = R.call_tir(cls.add5, (add337, lv466), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm90 = R.call_tir(cls.layer_norm2, (add341, model_decoder_layers_8_encoder_attn_layer_norm_weight2, model_decoder_layers_8_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv467 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_8_encoder_attn_q_proj_weight2, layer_norm90, model_decoder_layers_8_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape473 = R.call_tir(cls.reshape14, (lv467,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape474 = R.call_tir(cls.reshape18, (reshape473,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv86 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), reshape474), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape475 = R.call_tir(cls.reshape16, (lv86,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape476 = R.call_tir(cls.reshape17, (reshape475,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv468 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_8_encoder_attn_out_proj_weight2, reshape476, model_decoder_layers_8_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add344 = R.call_tir(cls.add5, (add341, lv468), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm91 = R.call_tir(cls.layer_norm2, (add344, model_decoder_layers_8_final_layer_norm_weight2, model_decoder_layers_8_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv72_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_8_fc1_weight2, layer_norm91, model_decoder_layers_8_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv469 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_8_fc2_weight2, lv72_1, model_decoder_layers_8_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add347 = R.call_tir(cls.add5, (add344, lv469), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm92 = R.call_tir(cls.layer_norm2, (add347, model_decoder_layers_9_self_attn_layer_norm_weight2, model_decoder_layers_9_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv470 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_9_self_attn_q_proj_weight2, layer_norm92, model_decoder_layers_9_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape477 = R.call_tir(cls.reshape14, (lv470,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv107 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_9_self_attn_k_proj_weight2, layer_norm92), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape478 = R.call_tir(cls.reshape14, (lv107,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv471 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_9_self_attn_v_proj_weight2, layer_norm92, model_decoder_layers_9_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape479 = R.call_tir(cls.reshape14, (lv471,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat9 = R.call_tir(cls.concatenate1, (reshape477, reshape478, reshape479), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape480 = R.call_tir(cls.reshape15, (concat9,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv87 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), reshape480), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape481 = R.call_tir(cls.reshape16, (lv87,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape482 = R.call_tir(cls.reshape17, (reshape481,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv472 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_9_self_attn_out_proj_weight2, reshape482, model_decoder_layers_9_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add351 = R.call_tir(cls.add5, (add347, lv472), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm93 = R.call_tir(cls.layer_norm2, (add351, model_decoder_layers_9_encoder_attn_layer_norm_weight2, model_decoder_layers_9_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv473 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_9_encoder_attn_q_proj_weight2, layer_norm93, model_decoder_layers_9_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape483 = R.call_tir(cls.reshape14, (lv473,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape484 = R.call_tir(cls.reshape18, (reshape483,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv88 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), reshape484), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape485 = R.call_tir(cls.reshape16, (lv88,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape486 = R.call_tir(cls.reshape17, (reshape485,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv474 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_9_encoder_attn_out_proj_weight2, reshape486, model_decoder_layers_9_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add354 = R.call_tir(cls.add5, (add351, lv474), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm94 = R.call_tir(cls.layer_norm2, (add354, model_decoder_layers_9_final_layer_norm_weight2, model_decoder_layers_9_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv73_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_9_fc1_weight2, layer_norm94, model_decoder_layers_9_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv475 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_9_fc2_weight2, lv73_1, model_decoder_layers_9_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add357 = R.call_tir(cls.add5, (add354, lv475), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm95 = R.call_tir(cls.layer_norm2, (add357, model_decoder_layers_10_self_attn_layer_norm_weight2, model_decoder_layers_10_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv476 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_10_self_attn_q_proj_weight2, layer_norm95, model_decoder_layers_10_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape487 = R.call_tir(cls.reshape14, (lv476,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv108 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_10_self_attn_k_proj_weight2, layer_norm95), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape488 = R.call_tir(cls.reshape14, (lv108,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv477 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_10_self_attn_v_proj_weight2, layer_norm95, model_decoder_layers_10_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape489 = R.call_tir(cls.reshape14, (lv477,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat10 = R.call_tir(cls.concatenate1, (reshape487, reshape488, reshape489), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape490 = R.call_tir(cls.reshape15, (concat10,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv89 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), reshape490), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape491 = R.call_tir(cls.reshape16, (lv89,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape492 = R.call_tir(cls.reshape17, (reshape491,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv478 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_10_self_attn_out_proj_weight2, reshape492, model_decoder_layers_10_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add361 = R.call_tir(cls.add5, (add357, lv478), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm96 = R.call_tir(cls.layer_norm2, (add361, model_decoder_layers_10_encoder_attn_layer_norm_weight2, model_decoder_layers_10_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv479 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_10_encoder_attn_q_proj_weight2, layer_norm96, model_decoder_layers_10_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape493 = R.call_tir(cls.reshape14, (lv479,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape494 = R.call_tir(cls.reshape18, (reshape493,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv90 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), reshape494), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape495 = R.call_tir(cls.reshape16, (lv90,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape496 = R.call_tir(cls.reshape17, (reshape495,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv480 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_10_encoder_attn_out_proj_weight2, reshape496, model_decoder_layers_10_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add364 = R.call_tir(cls.add5, (add361, lv480), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm97 = R.call_tir(cls.layer_norm2, (add364, model_decoder_layers_10_final_layer_norm_weight2, model_decoder_layers_10_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv74_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_10_fc1_weight2, layer_norm97, model_decoder_layers_10_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv481 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_10_fc2_weight2, lv74_1, model_decoder_layers_10_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add367 = R.call_tir(cls.add5, (add364, lv481), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm98 = R.call_tir(cls.layer_norm2, (add367, model_decoder_layers_11_self_attn_layer_norm_weight2, model_decoder_layers_11_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv482 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_11_self_attn_q_proj_weight2, layer_norm98, model_decoder_layers_11_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape497 = R.call_tir(cls.reshape14, (lv482,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv109 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_11_self_attn_k_proj_weight2, layer_norm98), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape498 = R.call_tir(cls.reshape14, (lv109,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv483 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_11_self_attn_v_proj_weight2, layer_norm98, model_decoder_layers_11_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape499 = R.call_tir(cls.reshape14, (lv483,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat11 = R.call_tir(cls.concatenate1, (reshape497, reshape498, reshape499), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape500 = R.call_tir(cls.reshape15, (concat11,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv91 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), reshape500), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape501 = R.call_tir(cls.reshape16, (lv91,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape502 = R.call_tir(cls.reshape17, (reshape501,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv484 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_11_self_attn_out_proj_weight2, reshape502, model_decoder_layers_11_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add371 = R.call_tir(cls.add5, (add367, lv484), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm99 = R.call_tir(cls.layer_norm2, (add371, model_decoder_layers_11_encoder_attn_layer_norm_weight2, model_decoder_layers_11_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv485 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_11_encoder_attn_q_proj_weight2, layer_norm99, model_decoder_layers_11_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape503 = R.call_tir(cls.reshape14, (lv485,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape504 = R.call_tir(cls.reshape18, (reshape503,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv92 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), reshape504), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape505 = R.call_tir(cls.reshape16, (lv92,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape506 = R.call_tir(cls.reshape17, (reshape505,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv486 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_11_encoder_attn_out_proj_weight2, reshape506, model_decoder_layers_11_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add374 = R.call_tir(cls.add5, (add371, lv486), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm100 = R.call_tir(cls.layer_norm2, (add374, model_decoder_layers_11_final_layer_norm_weight2, model_decoder_layers_11_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv75_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_11_fc1_weight2, layer_norm100, model_decoder_layers_11_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv487 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_11_fc2_weight2, lv75_1, model_decoder_layers_11_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add377 = R.call_tir(cls.add5, (add374, lv487), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm101 = R.call_tir(cls.layer_norm2, (add377, model_decoder_layers_12_self_attn_layer_norm_weight2, model_decoder_layers_12_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv488 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_12_self_attn_q_proj_weight2, layer_norm101, model_decoder_layers_12_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape507 = R.call_tir(cls.reshape14, (lv488,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv110 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_12_self_attn_k_proj_weight2, layer_norm101), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape508 = R.call_tir(cls.reshape14, (lv110,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv489 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_12_self_attn_v_proj_weight2, layer_norm101, model_decoder_layers_12_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape509 = R.call_tir(cls.reshape14, (lv489,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat12 = R.call_tir(cls.concatenate1, (reshape507, reshape508, reshape509), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape510 = R.call_tir(cls.reshape15, (concat12,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv93 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), reshape510), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape511 = R.call_tir(cls.reshape16, (lv93,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape512 = R.call_tir(cls.reshape17, (reshape511,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv490 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_12_self_attn_out_proj_weight2, reshape512, model_decoder_layers_12_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add381 = R.call_tir(cls.add5, (add377, lv490), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm102 = R.call_tir(cls.layer_norm2, (add381, model_decoder_layers_12_encoder_attn_layer_norm_weight2, model_decoder_layers_12_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv491 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_12_encoder_attn_q_proj_weight2, layer_norm102, model_decoder_layers_12_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape513 = R.call_tir(cls.reshape14, (lv491,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape514 = R.call_tir(cls.reshape18, (reshape513,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv94 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), reshape514), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape515 = R.call_tir(cls.reshape16, (lv94,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape516 = R.call_tir(cls.reshape17, (reshape515,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv492 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_12_encoder_attn_out_proj_weight2, reshape516, model_decoder_layers_12_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add384 = R.call_tir(cls.add5, (add381, lv492), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm103 = R.call_tir(cls.layer_norm2, (add384, model_decoder_layers_12_final_layer_norm_weight2, model_decoder_layers_12_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv76_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_12_fc1_weight2, layer_norm103, model_decoder_layers_12_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv493 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_12_fc2_weight2, lv76_1, model_decoder_layers_12_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add387 = R.call_tir(cls.add5, (add384, lv493), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm104 = R.call_tir(cls.layer_norm2, (add387, model_decoder_layers_13_self_attn_layer_norm_weight2, model_decoder_layers_13_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv494 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_13_self_attn_q_proj_weight2, layer_norm104, model_decoder_layers_13_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape517 = R.call_tir(cls.reshape14, (lv494,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv111 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_13_self_attn_k_proj_weight2, layer_norm104), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape518 = R.call_tir(cls.reshape14, (lv111,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv495 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_13_self_attn_v_proj_weight2, layer_norm104, model_decoder_layers_13_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape519 = R.call_tir(cls.reshape14, (lv495,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat13 = R.call_tir(cls.concatenate1, (reshape517, reshape518, reshape519), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape520 = R.call_tir(cls.reshape15, (concat13,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv95 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), reshape520), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape521 = R.call_tir(cls.reshape16, (lv95,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape522 = R.call_tir(cls.reshape17, (reshape521,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv496 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_13_self_attn_out_proj_weight2, reshape522, model_decoder_layers_13_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add391 = R.call_tir(cls.add5, (add387, lv496), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm105 = R.call_tir(cls.layer_norm2, (add391, model_decoder_layers_13_encoder_attn_layer_norm_weight2, model_decoder_layers_13_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv497 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_13_encoder_attn_q_proj_weight2, layer_norm105, model_decoder_layers_13_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape523 = R.call_tir(cls.reshape14, (lv497,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape524 = R.call_tir(cls.reshape18, (reshape523,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv96 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), reshape524), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape525 = R.call_tir(cls.reshape16, (lv96,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape526 = R.call_tir(cls.reshape17, (reshape525,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv498 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_13_encoder_attn_out_proj_weight2, reshape526, model_decoder_layers_13_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add394 = R.call_tir(cls.add5, (add391, lv498), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm106 = R.call_tir(cls.layer_norm2, (add394, model_decoder_layers_13_final_layer_norm_weight2, model_decoder_layers_13_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv77_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_13_fc1_weight2, layer_norm106, model_decoder_layers_13_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv499 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_13_fc2_weight2, lv77_1, model_decoder_layers_13_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add397 = R.call_tir(cls.add5, (add394, lv499), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm107 = R.call_tir(cls.layer_norm2, (add397, model_decoder_layers_14_self_attn_layer_norm_weight2, model_decoder_layers_14_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv500 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_14_self_attn_q_proj_weight2, layer_norm107, model_decoder_layers_14_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape527 = R.call_tir(cls.reshape14, (lv500,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv112 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_14_self_attn_k_proj_weight2, layer_norm107), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape528 = R.call_tir(cls.reshape14, (lv112,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv501 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_14_self_attn_v_proj_weight2, layer_norm107, model_decoder_layers_14_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape529 = R.call_tir(cls.reshape14, (lv501,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat14 = R.call_tir(cls.concatenate1, (reshape527, reshape528, reshape529), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape530 = R.call_tir(cls.reshape15, (concat14,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv97 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), reshape530), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape531 = R.call_tir(cls.reshape16, (lv97,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape532 = R.call_tir(cls.reshape17, (reshape531,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv502 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_14_self_attn_out_proj_weight2, reshape532, model_decoder_layers_14_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add401 = R.call_tir(cls.add5, (add397, lv502), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm108 = R.call_tir(cls.layer_norm2, (add401, model_decoder_layers_14_encoder_attn_layer_norm_weight2, model_decoder_layers_14_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv503 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_14_encoder_attn_q_proj_weight2, layer_norm108, model_decoder_layers_14_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape533 = R.call_tir(cls.reshape14, (lv503,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape534 = R.call_tir(cls.reshape18, (reshape533,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv98_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), reshape534), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape535 = R.call_tir(cls.reshape16, (lv98_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape536 = R.call_tir(cls.reshape17, (reshape535,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv504 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_14_encoder_attn_out_proj_weight2, reshape536, model_decoder_layers_14_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add404 = R.call_tir(cls.add5, (add401, lv504), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm109 = R.call_tir(cls.layer_norm2, (add404, model_decoder_layers_14_final_layer_norm_weight2, model_decoder_layers_14_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv78_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_14_fc1_weight2, layer_norm109, model_decoder_layers_14_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv505 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_14_fc2_weight2, lv78_1, model_decoder_layers_14_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add407 = R.call_tir(cls.add5, (add404, lv505), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm110 = R.call_tir(cls.layer_norm2, (add407, model_decoder_layers_15_self_attn_layer_norm_weight2, model_decoder_layers_15_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv506 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_15_self_attn_q_proj_weight2, layer_norm110, model_decoder_layers_15_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape537 = R.call_tir(cls.reshape14, (lv506,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv113 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_15_self_attn_k_proj_weight2, layer_norm110), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape538 = R.call_tir(cls.reshape14, (lv113,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv507 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_15_self_attn_v_proj_weight2, layer_norm110, model_decoder_layers_15_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape539 = R.call_tir(cls.reshape14, (lv507,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat15 = R.call_tir(cls.concatenate1, (reshape537, reshape538, reshape539), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape540 = R.call_tir(cls.reshape15, (concat15,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv99_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), reshape540), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape541 = R.call_tir(cls.reshape16, (lv99_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape542 = R.call_tir(cls.reshape17, (reshape541,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv508 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_15_self_attn_out_proj_weight2, reshape542, model_decoder_layers_15_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add411 = R.call_tir(cls.add5, (add407, lv508), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm111 = R.call_tir(cls.layer_norm2, (add411, model_decoder_layers_15_encoder_attn_layer_norm_weight2, model_decoder_layers_15_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv509 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_15_encoder_attn_q_proj_weight2, layer_norm111, model_decoder_layers_15_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape543 = R.call_tir(cls.reshape14, (lv509,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape544 = R.call_tir(cls.reshape18, (reshape543,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv100_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), reshape544), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape545 = R.call_tir(cls.reshape16, (lv100_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape546 = R.call_tir(cls.reshape17, (reshape545,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv510 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_15_encoder_attn_out_proj_weight2, reshape546, model_decoder_layers_15_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add414 = R.call_tir(cls.add5, (add411, lv510), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm112 = R.call_tir(cls.layer_norm2, (add414, model_decoder_layers_15_final_layer_norm_weight2, model_decoder_layers_15_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv79_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_15_fc1_weight2, layer_norm112, model_decoder_layers_15_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv511 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_15_fc2_weight2, lv79_1, model_decoder_layers_15_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add417 = R.call_tir(cls.add5, (add414, lv511), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm113 = R.call_tir(cls.layer_norm2, (add417, model_decoder_layers_16_self_attn_layer_norm_weight2, model_decoder_layers_16_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv512 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_16_self_attn_q_proj_weight2, layer_norm113, model_decoder_layers_16_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape547 = R.call_tir(cls.reshape14, (lv512,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv114 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_16_self_attn_k_proj_weight2, layer_norm113), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape548 = R.call_tir(cls.reshape14, (lv114,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv513 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_16_self_attn_v_proj_weight2, layer_norm113, model_decoder_layers_16_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape549 = R.call_tir(cls.reshape14, (lv513,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat16 = R.call_tir(cls.concatenate1, (reshape547, reshape548, reshape549), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape550 = R.call_tir(cls.reshape15, (concat16,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv101_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), reshape550), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape551 = R.call_tir(cls.reshape16, (lv101_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape552 = R.call_tir(cls.reshape17, (reshape551,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv514 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_16_self_attn_out_proj_weight2, reshape552, model_decoder_layers_16_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add421 = R.call_tir(cls.add5, (add417, lv514), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm114 = R.call_tir(cls.layer_norm2, (add421, model_decoder_layers_16_encoder_attn_layer_norm_weight2, model_decoder_layers_16_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv515 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_16_encoder_attn_q_proj_weight2, layer_norm114, model_decoder_layers_16_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape553 = R.call_tir(cls.reshape14, (lv515,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape554 = R.call_tir(cls.reshape18, (reshape553,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv102_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), reshape554), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape555 = R.call_tir(cls.reshape16, (lv102_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape556 = R.call_tir(cls.reshape17, (reshape555,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv516 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_16_encoder_attn_out_proj_weight2, reshape556, model_decoder_layers_16_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add424 = R.call_tir(cls.add5, (add421, lv516), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm115 = R.call_tir(cls.layer_norm2, (add424, model_decoder_layers_16_final_layer_norm_weight2, model_decoder_layers_16_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv80_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_16_fc1_weight2, layer_norm115, model_decoder_layers_16_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv517 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_16_fc2_weight2, lv80_1, model_decoder_layers_16_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add427 = R.call_tir(cls.add5, (add424, lv517), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm116 = R.call_tir(cls.layer_norm2, (add427, model_decoder_layers_17_self_attn_layer_norm_weight2, model_decoder_layers_17_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv518 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_17_self_attn_q_proj_weight2, layer_norm116, model_decoder_layers_17_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape557 = R.call_tir(cls.reshape14, (lv518,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv115 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_17_self_attn_k_proj_weight2, layer_norm116), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape558 = R.call_tir(cls.reshape14, (lv115,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv519 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_17_self_attn_v_proj_weight2, layer_norm116, model_decoder_layers_17_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape559 = R.call_tir(cls.reshape14, (lv519,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat17 = R.call_tir(cls.concatenate1, (reshape557, reshape558, reshape559), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape560 = R.call_tir(cls.reshape15, (concat17,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv103_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), reshape560), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape561 = R.call_tir(cls.reshape16, (lv103_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape562 = R.call_tir(cls.reshape17, (reshape561,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv520 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_17_self_attn_out_proj_weight2, reshape562, model_decoder_layers_17_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add431 = R.call_tir(cls.add5, (add427, lv520), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm117 = R.call_tir(cls.layer_norm2, (add431, model_decoder_layers_17_encoder_attn_layer_norm_weight2, model_decoder_layers_17_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv521 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_17_encoder_attn_q_proj_weight2, layer_norm117, model_decoder_layers_17_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape563 = R.call_tir(cls.reshape14, (lv521,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape564 = R.call_tir(cls.reshape18, (reshape563,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv104_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), reshape564), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape565 = R.call_tir(cls.reshape16, (lv104_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape566 = R.call_tir(cls.reshape17, (reshape565,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv522 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_17_encoder_attn_out_proj_weight2, reshape566, model_decoder_layers_17_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add434 = R.call_tir(cls.add5, (add431, lv522), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm118 = R.call_tir(cls.layer_norm2, (add434, model_decoder_layers_17_final_layer_norm_weight2, model_decoder_layers_17_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv81_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_17_fc1_weight2, layer_norm118, model_decoder_layers_17_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv523 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_17_fc2_weight2, lv81_1, model_decoder_layers_17_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add437 = R.call_tir(cls.add5, (add434, lv523), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm119 = R.call_tir(cls.layer_norm2, (add437, model_decoder_layers_18_self_attn_layer_norm_weight2, model_decoder_layers_18_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv524 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_18_self_attn_q_proj_weight2, layer_norm119, model_decoder_layers_18_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape567 = R.call_tir(cls.reshape14, (lv524,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv116 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_18_self_attn_k_proj_weight2, layer_norm119), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape568 = R.call_tir(cls.reshape14, (lv116,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv525 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_18_self_attn_v_proj_weight2, layer_norm119, model_decoder_layers_18_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape569 = R.call_tir(cls.reshape14, (lv525,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat18 = R.call_tir(cls.concatenate1, (reshape567, reshape568, reshape569), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape570 = R.call_tir(cls.reshape15, (concat18,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv105_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), reshape570), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape571 = R.call_tir(cls.reshape16, (lv105_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape572 = R.call_tir(cls.reshape17, (reshape571,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv526 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_18_self_attn_out_proj_weight2, reshape572, model_decoder_layers_18_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add441 = R.call_tir(cls.add5, (add437, lv526), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm120 = R.call_tir(cls.layer_norm2, (add441, model_decoder_layers_18_encoder_attn_layer_norm_weight2, model_decoder_layers_18_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv527 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_18_encoder_attn_q_proj_weight2, layer_norm120, model_decoder_layers_18_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape573 = R.call_tir(cls.reshape14, (lv527,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape574 = R.call_tir(cls.reshape18, (reshape573,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv106_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), reshape574), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape575 = R.call_tir(cls.reshape16, (lv106_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape576 = R.call_tir(cls.reshape17, (reshape575,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv528 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_18_encoder_attn_out_proj_weight2, reshape576, model_decoder_layers_18_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add444 = R.call_tir(cls.add5, (add441, lv528), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm121 = R.call_tir(cls.layer_norm2, (add444, model_decoder_layers_18_final_layer_norm_weight2, model_decoder_layers_18_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv82_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_18_fc1_weight2, layer_norm121, model_decoder_layers_18_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv529 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_18_fc2_weight2, lv82_1, model_decoder_layers_18_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add447 = R.call_tir(cls.add5, (add444, lv529), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm122 = R.call_tir(cls.layer_norm2, (add447, model_decoder_layers_19_self_attn_layer_norm_weight2, model_decoder_layers_19_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv530 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_19_self_attn_q_proj_weight2, layer_norm122, model_decoder_layers_19_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape577 = R.call_tir(cls.reshape14, (lv530,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv117 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_19_self_attn_k_proj_weight2, layer_norm122), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape578 = R.call_tir(cls.reshape14, (lv117,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv531 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_19_self_attn_v_proj_weight2, layer_norm122, model_decoder_layers_19_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape579 = R.call_tir(cls.reshape14, (lv531,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat19 = R.call_tir(cls.concatenate1, (reshape577, reshape578, reshape579), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape580 = R.call_tir(cls.reshape15, (concat19,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv107_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), reshape580), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape581 = R.call_tir(cls.reshape16, (lv107_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape582 = R.call_tir(cls.reshape17, (reshape581,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv532 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_19_self_attn_out_proj_weight2, reshape582, model_decoder_layers_19_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add451 = R.call_tir(cls.add5, (add447, lv532), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm123 = R.call_tir(cls.layer_norm2, (add451, model_decoder_layers_19_encoder_attn_layer_norm_weight2, model_decoder_layers_19_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv533 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_19_encoder_attn_q_proj_weight2, layer_norm123, model_decoder_layers_19_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape583 = R.call_tir(cls.reshape14, (lv533,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape584 = R.call_tir(cls.reshape18, (reshape583,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv108_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), reshape584), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape585 = R.call_tir(cls.reshape16, (lv108_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape586 = R.call_tir(cls.reshape17, (reshape585,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv534 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_19_encoder_attn_out_proj_weight2, reshape586, model_decoder_layers_19_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add454 = R.call_tir(cls.add5, (add451, lv534), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm124 = R.call_tir(cls.layer_norm2, (add454, model_decoder_layers_19_final_layer_norm_weight2, model_decoder_layers_19_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv83_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_19_fc1_weight2, layer_norm124, model_decoder_layers_19_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv535 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_19_fc2_weight2, lv83_1, model_decoder_layers_19_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add457 = R.call_tir(cls.add5, (add454, lv535), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm125 = R.call_tir(cls.layer_norm2, (add457, model_decoder_layers_20_self_attn_layer_norm_weight2, model_decoder_layers_20_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv536 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_20_self_attn_q_proj_weight2, layer_norm125, model_decoder_layers_20_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape587 = R.call_tir(cls.reshape14, (lv536,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv118 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_20_self_attn_k_proj_weight2, layer_norm125), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape588 = R.call_tir(cls.reshape14, (lv118,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv537 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_20_self_attn_v_proj_weight2, layer_norm125, model_decoder_layers_20_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape589 = R.call_tir(cls.reshape14, (lv537,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat20 = R.call_tir(cls.concatenate1, (reshape587, reshape588, reshape589), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape590 = R.call_tir(cls.reshape15, (concat20,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv109_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), reshape590), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape591 = R.call_tir(cls.reshape16, (lv109_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape592 = R.call_tir(cls.reshape17, (reshape591,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv538 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_20_self_attn_out_proj_weight2, reshape592, model_decoder_layers_20_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add461 = R.call_tir(cls.add5, (add457, lv538), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm126 = R.call_tir(cls.layer_norm2, (add461, model_decoder_layers_20_encoder_attn_layer_norm_weight2, model_decoder_layers_20_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv539 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_20_encoder_attn_q_proj_weight2, layer_norm126, model_decoder_layers_20_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape593 = R.call_tir(cls.reshape14, (lv539,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape594 = R.call_tir(cls.reshape18, (reshape593,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv110_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), reshape594), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape595 = R.call_tir(cls.reshape16, (lv110_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape596 = R.call_tir(cls.reshape17, (reshape595,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv540 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_20_encoder_attn_out_proj_weight2, reshape596, model_decoder_layers_20_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add464 = R.call_tir(cls.add5, (add461, lv540), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm127 = R.call_tir(cls.layer_norm2, (add464, model_decoder_layers_20_final_layer_norm_weight2, model_decoder_layers_20_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv84_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_20_fc1_weight2, layer_norm127, model_decoder_layers_20_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv541 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_20_fc2_weight2, lv84_1, model_decoder_layers_20_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add467 = R.call_tir(cls.add5, (add464, lv541), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm128 = R.call_tir(cls.layer_norm2, (add467, model_decoder_layers_21_self_attn_layer_norm_weight2, model_decoder_layers_21_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv542 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_21_self_attn_q_proj_weight2, layer_norm128, model_decoder_layers_21_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape597 = R.call_tir(cls.reshape14, (lv542,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv119 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_21_self_attn_k_proj_weight2, layer_norm128), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape598 = R.call_tir(cls.reshape14, (lv119,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv543 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_21_self_attn_v_proj_weight2, layer_norm128, model_decoder_layers_21_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape599 = R.call_tir(cls.reshape14, (lv543,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat21 = R.call_tir(cls.concatenate1, (reshape597, reshape598, reshape599), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape600 = R.call_tir(cls.reshape15, (concat21,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv111_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), reshape600), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape601 = R.call_tir(cls.reshape16, (lv111_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape602 = R.call_tir(cls.reshape17, (reshape601,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv544 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_21_self_attn_out_proj_weight2, reshape602, model_decoder_layers_21_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add471 = R.call_tir(cls.add5, (add467, lv544), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm129 = R.call_tir(cls.layer_norm2, (add471, model_decoder_layers_21_encoder_attn_layer_norm_weight2, model_decoder_layers_21_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv545 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_21_encoder_attn_q_proj_weight2, layer_norm129, model_decoder_layers_21_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape603 = R.call_tir(cls.reshape14, (lv545,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape604 = R.call_tir(cls.reshape18, (reshape603,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv112_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), reshape604), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape605 = R.call_tir(cls.reshape16, (lv112_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape606 = R.call_tir(cls.reshape17, (reshape605,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv546 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_21_encoder_attn_out_proj_weight2, reshape606, model_decoder_layers_21_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add474 = R.call_tir(cls.add5, (add471, lv546), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm130 = R.call_tir(cls.layer_norm2, (add474, model_decoder_layers_21_final_layer_norm_weight2, model_decoder_layers_21_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv85_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_21_fc1_weight2, layer_norm130, model_decoder_layers_21_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv547 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_21_fc2_weight2, lv85_1, model_decoder_layers_21_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add477 = R.call_tir(cls.add5, (add474, lv547), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm131 = R.call_tir(cls.layer_norm2, (add477, model_decoder_layers_22_self_attn_layer_norm_weight2, model_decoder_layers_22_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv548 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_22_self_attn_q_proj_weight2, layer_norm131, model_decoder_layers_22_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape607 = R.call_tir(cls.reshape14, (lv548,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv120 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_22_self_attn_k_proj_weight2, layer_norm131), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape608 = R.call_tir(cls.reshape14, (lv120,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv549 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_22_self_attn_v_proj_weight2, layer_norm131, model_decoder_layers_22_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape609 = R.call_tir(cls.reshape14, (lv549,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat22 = R.call_tir(cls.concatenate1, (reshape607, reshape608, reshape609), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape610 = R.call_tir(cls.reshape15, (concat22,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv113_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), reshape610), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape611 = R.call_tir(cls.reshape16, (lv113_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape612 = R.call_tir(cls.reshape17, (reshape611,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv550 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_22_self_attn_out_proj_weight2, reshape612, model_decoder_layers_22_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add481 = R.call_tir(cls.add5, (add477, lv550), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm132 = R.call_tir(cls.layer_norm2, (add481, model_decoder_layers_22_encoder_attn_layer_norm_weight2, model_decoder_layers_22_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv551 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_22_encoder_attn_q_proj_weight2, layer_norm132, model_decoder_layers_22_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape613 = R.call_tir(cls.reshape14, (lv551,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape614 = R.call_tir(cls.reshape18, (reshape613,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv114_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), reshape614), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape615 = R.call_tir(cls.reshape16, (lv114_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape616 = R.call_tir(cls.reshape17, (reshape615,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv552 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_22_encoder_attn_out_proj_weight2, reshape616, model_decoder_layers_22_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add484 = R.call_tir(cls.add5, (add481, lv552), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm133 = R.call_tir(cls.layer_norm2, (add484, model_decoder_layers_22_final_layer_norm_weight2, model_decoder_layers_22_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv86_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_22_fc1_weight2, layer_norm133, model_decoder_layers_22_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv553 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_22_fc2_weight2, lv86_1, model_decoder_layers_22_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add487 = R.call_tir(cls.add5, (add484, lv553), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm134 = R.call_tir(cls.layer_norm2, (add487, model_decoder_layers_23_self_attn_layer_norm_weight2, model_decoder_layers_23_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv554 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_23_self_attn_q_proj_weight2, layer_norm134, model_decoder_layers_23_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape617 = R.call_tir(cls.reshape14, (lv554,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv121 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_23_self_attn_k_proj_weight2, layer_norm134), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape618 = R.call_tir(cls.reshape14, (lv121,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv555 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_23_self_attn_v_proj_weight2, layer_norm134, model_decoder_layers_23_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape619 = R.call_tir(cls.reshape14, (lv555,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat23 = R.call_tir(cls.concatenate1, (reshape617, reshape618, reshape619), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape620 = R.call_tir(cls.reshape15, (concat23,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv115_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), reshape620), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape621 = R.call_tir(cls.reshape16, (lv115_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape622 = R.call_tir(cls.reshape17, (reshape621,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv556 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_23_self_attn_out_proj_weight2, reshape622, model_decoder_layers_23_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add491 = R.call_tir(cls.add5, (add487, lv556), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm135 = R.call_tir(cls.layer_norm2, (add491, model_decoder_layers_23_encoder_attn_layer_norm_weight2, model_decoder_layers_23_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv557 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_23_encoder_attn_q_proj_weight2, layer_norm135, model_decoder_layers_23_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape623 = R.call_tir(cls.reshape14, (lv557,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape624 = R.call_tir(cls.reshape18, (reshape623,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv116_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), reshape624), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape625 = R.call_tir(cls.reshape16, (lv116_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape626 = R.call_tir(cls.reshape17, (reshape625,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv558 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_23_encoder_attn_out_proj_weight2, reshape626, model_decoder_layers_23_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add494 = R.call_tir(cls.add5, (add491, lv558), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm136 = R.call_tir(cls.layer_norm2, (add494, model_decoder_layers_23_final_layer_norm_weight2, model_decoder_layers_23_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv87_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_23_fc1_weight2, layer_norm136, model_decoder_layers_23_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv559 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_23_fc2_weight2, lv87_1, model_decoder_layers_23_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add497 = R.call_tir(cls.add5, (add494, lv559), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm137 = R.call_tir(cls.layer_norm2, (add497, model_decoder_layers_24_self_attn_layer_norm_weight2, model_decoder_layers_24_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv560 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_24_self_attn_q_proj_weight2, layer_norm137, model_decoder_layers_24_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape627 = R.call_tir(cls.reshape14, (lv560,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv122 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_24_self_attn_k_proj_weight2, layer_norm137), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape628 = R.call_tir(cls.reshape14, (lv122,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv561 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_24_self_attn_v_proj_weight2, layer_norm137, model_decoder_layers_24_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape629 = R.call_tir(cls.reshape14, (lv561,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat24 = R.call_tir(cls.concatenate1, (reshape627, reshape628, reshape629), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape630 = R.call_tir(cls.reshape15, (concat24,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv117_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), reshape630), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape631 = R.call_tir(cls.reshape16, (lv117_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape632 = R.call_tir(cls.reshape17, (reshape631,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv562 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_24_self_attn_out_proj_weight2, reshape632, model_decoder_layers_24_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add501 = R.call_tir(cls.add5, (add497, lv562), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm138 = R.call_tir(cls.layer_norm2, (add501, model_decoder_layers_24_encoder_attn_layer_norm_weight2, model_decoder_layers_24_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv563 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_24_encoder_attn_q_proj_weight2, layer_norm138, model_decoder_layers_24_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape633 = R.call_tir(cls.reshape14, (lv563,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape634 = R.call_tir(cls.reshape18, (reshape633,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv118_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), reshape634), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape635 = R.call_tir(cls.reshape16, (lv118_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape636 = R.call_tir(cls.reshape17, (reshape635,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv564 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_24_encoder_attn_out_proj_weight2, reshape636, model_decoder_layers_24_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add504 = R.call_tir(cls.add5, (add501, lv564), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm139 = R.call_tir(cls.layer_norm2, (add504, model_decoder_layers_24_final_layer_norm_weight2, model_decoder_layers_24_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv88_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_24_fc1_weight2, layer_norm139, model_decoder_layers_24_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv565 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_24_fc2_weight2, lv88_1, model_decoder_layers_24_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add507 = R.call_tir(cls.add5, (add504, lv565), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm140 = R.call_tir(cls.layer_norm2, (add507, model_decoder_layers_25_self_attn_layer_norm_weight2, model_decoder_layers_25_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv566 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_25_self_attn_q_proj_weight2, layer_norm140, model_decoder_layers_25_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape637 = R.call_tir(cls.reshape14, (lv566,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv123 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_25_self_attn_k_proj_weight2, layer_norm140), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape638 = R.call_tir(cls.reshape14, (lv123,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv567 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_25_self_attn_v_proj_weight2, layer_norm140, model_decoder_layers_25_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape639 = R.call_tir(cls.reshape14, (lv567,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat25 = R.call_tir(cls.concatenate1, (reshape637, reshape638, reshape639), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape640 = R.call_tir(cls.reshape15, (concat25,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv119_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), reshape640), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape641 = R.call_tir(cls.reshape16, (lv119_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape642 = R.call_tir(cls.reshape17, (reshape641,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv568 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_25_self_attn_out_proj_weight2, reshape642, model_decoder_layers_25_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add511 = R.call_tir(cls.add5, (add507, lv568), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm141 = R.call_tir(cls.layer_norm2, (add511, model_decoder_layers_25_encoder_attn_layer_norm_weight2, model_decoder_layers_25_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv569 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_25_encoder_attn_q_proj_weight2, layer_norm141, model_decoder_layers_25_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape643 = R.call_tir(cls.reshape14, (lv569,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape644 = R.call_tir(cls.reshape18, (reshape643,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv120_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), reshape644), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape645 = R.call_tir(cls.reshape16, (lv120_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape646 = R.call_tir(cls.reshape17, (reshape645,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv570 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_25_encoder_attn_out_proj_weight2, reshape646, model_decoder_layers_25_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add514 = R.call_tir(cls.add5, (add511, lv570), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm142 = R.call_tir(cls.layer_norm2, (add514, model_decoder_layers_25_final_layer_norm_weight2, model_decoder_layers_25_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv89_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_25_fc1_weight2, layer_norm142, model_decoder_layers_25_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv571 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_25_fc2_weight2, lv89_1, model_decoder_layers_25_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add517 = R.call_tir(cls.add5, (add514, lv571), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm143 = R.call_tir(cls.layer_norm2, (add517, model_decoder_layers_26_self_attn_layer_norm_weight2, model_decoder_layers_26_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv572 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_26_self_attn_q_proj_weight2, layer_norm143, model_decoder_layers_26_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape647 = R.call_tir(cls.reshape14, (lv572,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv124 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_26_self_attn_k_proj_weight2, layer_norm143), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape648 = R.call_tir(cls.reshape14, (lv124,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv573 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_26_self_attn_v_proj_weight2, layer_norm143, model_decoder_layers_26_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape649 = R.call_tir(cls.reshape14, (lv573,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat26 = R.call_tir(cls.concatenate1, (reshape647, reshape648, reshape649), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape650 = R.call_tir(cls.reshape15, (concat26,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv121_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), reshape650), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape651 = R.call_tir(cls.reshape16, (lv121_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape652 = R.call_tir(cls.reshape17, (reshape651,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv574 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_26_self_attn_out_proj_weight2, reshape652, model_decoder_layers_26_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add521 = R.call_tir(cls.add5, (add517, lv574), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm144 = R.call_tir(cls.layer_norm2, (add521, model_decoder_layers_26_encoder_attn_layer_norm_weight2, model_decoder_layers_26_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv575 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_26_encoder_attn_q_proj_weight2, layer_norm144, model_decoder_layers_26_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape653 = R.call_tir(cls.reshape14, (lv575,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape654 = R.call_tir(cls.reshape18, (reshape653,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv122_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), reshape654), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape655 = R.call_tir(cls.reshape16, (lv122_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape656 = R.call_tir(cls.reshape17, (reshape655,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv576 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_26_encoder_attn_out_proj_weight2, reshape656, model_decoder_layers_26_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add524 = R.call_tir(cls.add5, (add521, lv576), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm145 = R.call_tir(cls.layer_norm2, (add524, model_decoder_layers_26_final_layer_norm_weight2, model_decoder_layers_26_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv90_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_26_fc1_weight2, layer_norm145, model_decoder_layers_26_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv577 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_26_fc2_weight2, lv90_1, model_decoder_layers_26_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add527 = R.call_tir(cls.add5, (add524, lv577), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm146 = R.call_tir(cls.layer_norm2, (add527, model_decoder_layers_27_self_attn_layer_norm_weight2, model_decoder_layers_27_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv578 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_27_self_attn_q_proj_weight2, layer_norm146, model_decoder_layers_27_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape657 = R.call_tir(cls.reshape14, (lv578,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv125 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_27_self_attn_k_proj_weight2, layer_norm146), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape658 = R.call_tir(cls.reshape14, (lv125,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv579 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_27_self_attn_v_proj_weight2, layer_norm146, model_decoder_layers_27_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape659 = R.call_tir(cls.reshape14, (lv579,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat27 = R.call_tir(cls.concatenate1, (reshape657, reshape658, reshape659), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape660 = R.call_tir(cls.reshape15, (concat27,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv123_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), reshape660), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape661 = R.call_tir(cls.reshape16, (lv123_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape662 = R.call_tir(cls.reshape17, (reshape661,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv580 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_27_self_attn_out_proj_weight2, reshape662, model_decoder_layers_27_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add531 = R.call_tir(cls.add5, (add527, lv580), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm147 = R.call_tir(cls.layer_norm2, (add531, model_decoder_layers_27_encoder_attn_layer_norm_weight2, model_decoder_layers_27_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv581 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_27_encoder_attn_q_proj_weight2, layer_norm147, model_decoder_layers_27_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape663 = R.call_tir(cls.reshape14, (lv581,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape664 = R.call_tir(cls.reshape18, (reshape663,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv124_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), reshape664), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape665 = R.call_tir(cls.reshape16, (lv124_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape666 = R.call_tir(cls.reshape17, (reshape665,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv582 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_27_encoder_attn_out_proj_weight2, reshape666, model_decoder_layers_27_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add534 = R.call_tir(cls.add5, (add531, lv582), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm148 = R.call_tir(cls.layer_norm2, (add534, model_decoder_layers_27_final_layer_norm_weight2, model_decoder_layers_27_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv91_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_27_fc1_weight2, layer_norm148, model_decoder_layers_27_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv583 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_27_fc2_weight2, lv91_1, model_decoder_layers_27_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add537 = R.call_tir(cls.add5, (add534, lv583), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm149 = R.call_tir(cls.layer_norm2, (add537, model_decoder_layers_28_self_attn_layer_norm_weight2, model_decoder_layers_28_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv584 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_28_self_attn_q_proj_weight2, layer_norm149, model_decoder_layers_28_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape667 = R.call_tir(cls.reshape14, (lv584,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv126 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_28_self_attn_k_proj_weight2, layer_norm149), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape668 = R.call_tir(cls.reshape14, (lv126,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv585 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_28_self_attn_v_proj_weight2, layer_norm149, model_decoder_layers_28_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape669 = R.call_tir(cls.reshape14, (lv585,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat28 = R.call_tir(cls.concatenate1, (reshape667, reshape668, reshape669), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape670 = R.call_tir(cls.reshape15, (concat28,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv125_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), reshape670), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape671 = R.call_tir(cls.reshape16, (lv125_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape672 = R.call_tir(cls.reshape17, (reshape671,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv586 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_28_self_attn_out_proj_weight2, reshape672, model_decoder_layers_28_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add541 = R.call_tir(cls.add5, (add537, lv586), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm150 = R.call_tir(cls.layer_norm2, (add541, model_decoder_layers_28_encoder_attn_layer_norm_weight2, model_decoder_layers_28_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv587 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_28_encoder_attn_q_proj_weight2, layer_norm150, model_decoder_layers_28_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape673 = R.call_tir(cls.reshape14, (lv587,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape674 = R.call_tir(cls.reshape18, (reshape673,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv126_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), reshape674), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape675 = R.call_tir(cls.reshape16, (lv126_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape676 = R.call_tir(cls.reshape17, (reshape675,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv588 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_28_encoder_attn_out_proj_weight2, reshape676, model_decoder_layers_28_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add544 = R.call_tir(cls.add5, (add541, lv588), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm151 = R.call_tir(cls.layer_norm2, (add544, model_decoder_layers_28_final_layer_norm_weight2, model_decoder_layers_28_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv92_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_28_fc1_weight2, layer_norm151, model_decoder_layers_28_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv589 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_28_fc2_weight2, lv92_1, model_decoder_layers_28_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add547 = R.call_tir(cls.add5, (add544, lv589), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm152 = R.call_tir(cls.layer_norm2, (add547, model_decoder_layers_29_self_attn_layer_norm_weight2, model_decoder_layers_29_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv590 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_29_self_attn_q_proj_weight2, layer_norm152, model_decoder_layers_29_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape677 = R.call_tir(cls.reshape14, (lv590,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv127 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_29_self_attn_k_proj_weight2, layer_norm152), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape678 = R.call_tir(cls.reshape14, (lv127,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv591 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_29_self_attn_v_proj_weight2, layer_norm152, model_decoder_layers_29_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape679 = R.call_tir(cls.reshape14, (lv591,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat29 = R.call_tir(cls.concatenate1, (reshape677, reshape678, reshape679), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape680 = R.call_tir(cls.reshape15, (concat29,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv127_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), reshape680), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape681 = R.call_tir(cls.reshape16, (lv127_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape682 = R.call_tir(cls.reshape17, (reshape681,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv592 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_29_self_attn_out_proj_weight2, reshape682, model_decoder_layers_29_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add551 = R.call_tir(cls.add5, (add547, lv592), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm153 = R.call_tir(cls.layer_norm2, (add551, model_decoder_layers_29_encoder_attn_layer_norm_weight2, model_decoder_layers_29_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv593 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_29_encoder_attn_q_proj_weight2, layer_norm153, model_decoder_layers_29_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape683 = R.call_tir(cls.reshape14, (lv593,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape684 = R.call_tir(cls.reshape18, (reshape683,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv128 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), reshape684), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape685 = R.call_tir(cls.reshape16, (lv128,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape686 = R.call_tir(cls.reshape17, (reshape685,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv594 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_29_encoder_attn_out_proj_weight2, reshape686, model_decoder_layers_29_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add554 = R.call_tir(cls.add5, (add551, lv594), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm154 = R.call_tir(cls.layer_norm2, (add554, model_decoder_layers_29_final_layer_norm_weight2, model_decoder_layers_29_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv93_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_29_fc1_weight2, layer_norm154, model_decoder_layers_29_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv595 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_29_fc2_weight2, lv93_1, model_decoder_layers_29_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add557 = R.call_tir(cls.add5, (add554, lv595), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm155 = R.call_tir(cls.layer_norm2, (add557, model_decoder_layers_30_self_attn_layer_norm_weight2, model_decoder_layers_30_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv596 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_30_self_attn_q_proj_weight2, layer_norm155, model_decoder_layers_30_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape687 = R.call_tir(cls.reshape14, (lv596,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv128_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_30_self_attn_k_proj_weight2, layer_norm155), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape688 = R.call_tir(cls.reshape14, (lv128_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv597 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_30_self_attn_v_proj_weight2, layer_norm155, model_decoder_layers_30_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape689 = R.call_tir(cls.reshape14, (lv597,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat30 = R.call_tir(cls.concatenate1, (reshape687, reshape688, reshape689), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape690 = R.call_tir(cls.reshape15, (concat30,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv129 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), reshape690), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape691 = R.call_tir(cls.reshape16, (lv129,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape692 = R.call_tir(cls.reshape17, (reshape691,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv598 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_30_self_attn_out_proj_weight2, reshape692, model_decoder_layers_30_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add561 = R.call_tir(cls.add5, (add557, lv598), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm156 = R.call_tir(cls.layer_norm2, (add561, model_decoder_layers_30_encoder_attn_layer_norm_weight2, model_decoder_layers_30_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv599 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_30_encoder_attn_q_proj_weight2, layer_norm156, model_decoder_layers_30_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape693 = R.call_tir(cls.reshape14, (lv599,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape694 = R.call_tir(cls.reshape18, (reshape693,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv130 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), reshape694), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape695 = R.call_tir(cls.reshape16, (lv130,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape696 = R.call_tir(cls.reshape17, (reshape695,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv600 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_30_encoder_attn_out_proj_weight2, reshape696, model_decoder_layers_30_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add564 = R.call_tir(cls.add5, (add561, lv600), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm157 = R.call_tir(cls.layer_norm2, (add564, model_decoder_layers_30_final_layer_norm_weight2, model_decoder_layers_30_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv94_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_30_fc1_weight2, layer_norm157, model_decoder_layers_30_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv601 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_30_fc2_weight2, lv94_1, model_decoder_layers_30_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add567 = R.call_tir(cls.add5, (add564, lv601), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm158 = R.call_tir(cls.layer_norm2, (add567, model_decoder_layers_31_self_attn_layer_norm_weight2, model_decoder_layers_31_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv602 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_31_self_attn_q_proj_weight2, layer_norm158, model_decoder_layers_31_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape697 = R.call_tir(cls.reshape14, (lv602,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv129_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_31_self_attn_k_proj_weight2, layer_norm158), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape698 = R.call_tir(cls.reshape14, (lv129_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv603 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_31_self_attn_v_proj_weight2, layer_norm158, model_decoder_layers_31_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape699 = R.call_tir(cls.reshape14, (lv603,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat31 = R.call_tir(cls.concatenate1, (reshape697, reshape698, reshape699), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape700 = R.call_tir(cls.reshape15, (concat31,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv131 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), reshape700), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape701 = R.call_tir(cls.reshape16, (lv131,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape702 = R.call_tir(cls.reshape17, (reshape701,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv604 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_31_self_attn_out_proj_weight2, reshape702, model_decoder_layers_31_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add571 = R.call_tir(cls.add5, (add567, lv604), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm159 = R.call_tir(cls.layer_norm2, (add571, model_decoder_layers_31_encoder_attn_layer_norm_weight2, model_decoder_layers_31_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv605 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_31_encoder_attn_q_proj_weight2, layer_norm159, model_decoder_layers_31_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape703 = R.call_tir(cls.reshape14, (lv605,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape704 = R.call_tir(cls.reshape18, (reshape703,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv132 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), reshape704), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape705 = R.call_tir(cls.reshape16, (lv132,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape706 = R.call_tir(cls.reshape17, (reshape705,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv606 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_31_encoder_attn_out_proj_weight2, reshape706, model_decoder_layers_31_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add574 = R.call_tir(cls.add5, (add571, lv606), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm160 = R.call_tir(cls.layer_norm2, (add574, model_decoder_layers_31_final_layer_norm_weight2, model_decoder_layers_31_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv95_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_31_fc1_weight2, layer_norm160, model_decoder_layers_31_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv607 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_31_fc2_weight2, lv95_1, model_decoder_layers_31_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add577 = R.call_tir(cls.add5, (add574, lv607), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm161 = R.call_tir(cls.layer_norm2, (add577, model_decoder_layer_norm_weight2, model_decoder_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + take2 = R.call_tir(cls.take2, (layer_norm161, logit_positions), out_sinfo=R.Tensor((1, batch_size, 1280), dtype="float16")) + gv2 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul5_cublas", (model_decoder_embed_tokens_weight2, take2), out_sinfo=R.Tensor((1, batch_size, 51866), dtype="float32")) + R.output(gv2) + return gv2 + + @R.function + def create_tir_paged_kv_cache(max_batch_size_: R.Shape(["max_batch_size"]), max_total_seq_len_: R.Shape(["max_total_seq_len"]), prefill_chunk_size_: R.Shape(["prefill_chunk_size"]), page_size_: R.Shape(["page_size"]), support_sliding_window_: R.Shape(["support_sliding_window"])) -> R.Object: + max_batch_size = T.int64() + max_total_seq_len = T.int64() + prefill_chunk_size = T.int64() + page_size = T.int64() + support_sliding_window = T.int64() + R.func_attr({"relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "seq_len": 15000, "total_seq_len": 1500}}) + cls = Module + paged_kv_cache: R.Object = R.call_pure_packed("vm.builtin.paged_attention_kv_cache_create_reduced", R.shape([max_batch_size, max_total_seq_len, prefill_chunk_size, page_size, support_sliding_window]), R.prim_value(32), R.prim_value(20), R.prim_value(20), R.prim_value(64), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.const(0, "float16"), cls.tir_kv_cache_transpose_append, cls.batch_prefill_paged_kv, cls.batch_decode_paged_kv, cls.batch_prefill_paged_kv_sliding_window, cls.batch_decode_paged_kv_sliding_window, cls.batch_prefill_ragged_kv, cls.merge_state_inplace, cls.fused_rope, cls.copy_single_page, cls.tir_kv_cache_debug_get_kv, cls.compact_kv_copy, cls.batch_tree_attn, sinfo_args=(R.Object,)) + return paged_kv_cache + + @R.function + def decode(input_ids: R.Tensor((1, 1), dtype="int32"), paged_kv_cache: R.Object, packed_params: R.Tuple(R.Tensor((1280, 128, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1500, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), 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R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"))) -> R.Tensor((1, 1, 51866), dtype="float32"): + R.func_attr({"num_input": 2, "relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "seq_len": 15000, "total_seq_len": 1500}}) + cls = Module + with R.dataflow(): + model_decoder_embed_tokens_weight5: R.Tensor((51866, 1280), dtype="float16") = packed_params[487] + model_decoder_embed_positions_weight5: R.Tensor((448, 1280), dtype="float16") = packed_params[488] + model_decoder_layers_0_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[489] + model_decoder_layers_0_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[490] + model_decoder_layers_0_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[491] + model_decoder_layers_0_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[492] + model_decoder_layers_0_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[493] + model_decoder_layers_0_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[494] + model_decoder_layers_0_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[495] + model_decoder_layers_0_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[496] + model_decoder_layers_0_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[497] + model_decoder_layers_0_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[501] + model_decoder_layers_0_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[502] + model_decoder_layers_0_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[503] + model_decoder_layers_0_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[504] + model_decoder_layers_0_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[505] + model_decoder_layers_0_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[506] + model_decoder_layers_0_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[507] + model_decoder_layers_0_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[508] + model_decoder_layers_0_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[509] + model_decoder_layers_0_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[510] + model_decoder_layers_0_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[511] + model_decoder_layers_0_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[512] + model_decoder_layers_1_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[513] + model_decoder_layers_1_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[514] + model_decoder_layers_1_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[515] + model_decoder_layers_1_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[516] + model_decoder_layers_1_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[517] + model_decoder_layers_1_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[518] + model_decoder_layers_1_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[519] + model_decoder_layers_1_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[520] + model_decoder_layers_1_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[521] + model_decoder_layers_1_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[525] + model_decoder_layers_1_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[526] + model_decoder_layers_1_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[527] + model_decoder_layers_1_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[528] + model_decoder_layers_1_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[529] + model_decoder_layers_1_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[530] + model_decoder_layers_1_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[531] + model_decoder_layers_1_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[532] + model_decoder_layers_1_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[533] + model_decoder_layers_1_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[534] + model_decoder_layers_1_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[535] + model_decoder_layers_1_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[536] + model_decoder_layers_2_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[537] + model_decoder_layers_2_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[538] + model_decoder_layers_2_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[539] + model_decoder_layers_2_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[540] + model_decoder_layers_2_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[541] + model_decoder_layers_2_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[542] + model_decoder_layers_2_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[543] + model_decoder_layers_2_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[544] + model_decoder_layers_2_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[545] + model_decoder_layers_2_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[549] + model_decoder_layers_2_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[550] + model_decoder_layers_2_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[551] + model_decoder_layers_2_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[552] + model_decoder_layers_2_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[553] + model_decoder_layers_2_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[554] + model_decoder_layers_2_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[555] + model_decoder_layers_2_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[556] + model_decoder_layers_2_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[557] + model_decoder_layers_2_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[558] + model_decoder_layers_2_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[559] + model_decoder_layers_2_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[560] + model_decoder_layers_3_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[561] + model_decoder_layers_3_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[562] + model_decoder_layers_3_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[563] + model_decoder_layers_3_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[564] + model_decoder_layers_3_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[565] + model_decoder_layers_3_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[566] + model_decoder_layers_3_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[567] + model_decoder_layers_3_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[568] + model_decoder_layers_3_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[569] + model_decoder_layers_3_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[573] + model_decoder_layers_3_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[574] + model_decoder_layers_3_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[575] + model_decoder_layers_3_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[576] + model_decoder_layers_3_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[577] + model_decoder_layers_3_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[578] + model_decoder_layers_3_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[579] + model_decoder_layers_3_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[580] + model_decoder_layers_3_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[581] + model_decoder_layers_3_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[582] + model_decoder_layers_3_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[583] + model_decoder_layers_3_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[584] + model_decoder_layers_4_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[585] + model_decoder_layers_4_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[586] + model_decoder_layers_4_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[587] + model_decoder_layers_4_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[588] + model_decoder_layers_4_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[589] + model_decoder_layers_4_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[590] + model_decoder_layers_4_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[591] + model_decoder_layers_4_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[592] + model_decoder_layers_4_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[593] + model_decoder_layers_4_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[597] + model_decoder_layers_4_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[598] + model_decoder_layers_4_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[599] + model_decoder_layers_4_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[600] + model_decoder_layers_4_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[601] + model_decoder_layers_4_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[602] + model_decoder_layers_4_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[603] + model_decoder_layers_4_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[604] + model_decoder_layers_4_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[605] + model_decoder_layers_4_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[606] + model_decoder_layers_4_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[607] + model_decoder_layers_4_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[608] + model_decoder_layers_5_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[609] + model_decoder_layers_5_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[610] + model_decoder_layers_5_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[611] + model_decoder_layers_5_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[612] + model_decoder_layers_5_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[613] + model_decoder_layers_5_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[614] + model_decoder_layers_5_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[615] + model_decoder_layers_5_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[616] + model_decoder_layers_5_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[617] + model_decoder_layers_5_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[621] + model_decoder_layers_5_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[622] + model_decoder_layers_5_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[623] + model_decoder_layers_5_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[624] + model_decoder_layers_5_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[625] + model_decoder_layers_5_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[626] + model_decoder_layers_5_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[627] + model_decoder_layers_5_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[628] + model_decoder_layers_5_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[629] + model_decoder_layers_5_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[630] + model_decoder_layers_5_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[631] + model_decoder_layers_5_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[632] + model_decoder_layers_6_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[633] + model_decoder_layers_6_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[634] + model_decoder_layers_6_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[635] + model_decoder_layers_6_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[636] + model_decoder_layers_6_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[637] + model_decoder_layers_6_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[638] + model_decoder_layers_6_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[639] + model_decoder_layers_6_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[640] + model_decoder_layers_6_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[641] + model_decoder_layers_6_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[645] + model_decoder_layers_6_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[646] + model_decoder_layers_6_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[647] + model_decoder_layers_6_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[648] + model_decoder_layers_6_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[649] + model_decoder_layers_6_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[650] + model_decoder_layers_6_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[651] + model_decoder_layers_6_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[652] + model_decoder_layers_6_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[653] + model_decoder_layers_6_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[654] + model_decoder_layers_6_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[655] + model_decoder_layers_6_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[656] + model_decoder_layers_7_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[657] + model_decoder_layers_7_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[658] + model_decoder_layers_7_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[659] + model_decoder_layers_7_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[660] + model_decoder_layers_7_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[661] + model_decoder_layers_7_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[662] + model_decoder_layers_7_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[663] + model_decoder_layers_7_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[664] + model_decoder_layers_7_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[665] + model_decoder_layers_7_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[669] + model_decoder_layers_7_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[670] + model_decoder_layers_7_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[671] + model_decoder_layers_7_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[672] + model_decoder_layers_7_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[673] + model_decoder_layers_7_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[674] + model_decoder_layers_7_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[675] + model_decoder_layers_7_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[676] + model_decoder_layers_7_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[677] + model_decoder_layers_7_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[678] + model_decoder_layers_7_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[679] + model_decoder_layers_7_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[680] + model_decoder_layers_8_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[681] + model_decoder_layers_8_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[682] + model_decoder_layers_8_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[683] + model_decoder_layers_8_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[684] + model_decoder_layers_8_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[685] + model_decoder_layers_8_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[686] + model_decoder_layers_8_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[687] + model_decoder_layers_8_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[688] + model_decoder_layers_8_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[689] + model_decoder_layers_8_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[693] + model_decoder_layers_8_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[694] + model_decoder_layers_8_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[695] + model_decoder_layers_8_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[696] + model_decoder_layers_8_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[697] + model_decoder_layers_8_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[698] + model_decoder_layers_8_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[699] + model_decoder_layers_8_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[700] + model_decoder_layers_8_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[701] + model_decoder_layers_8_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[702] + model_decoder_layers_8_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[703] + model_decoder_layers_8_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[704] + model_decoder_layers_9_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[705] + model_decoder_layers_9_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[706] + model_decoder_layers_9_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[707] + model_decoder_layers_9_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[708] + model_decoder_layers_9_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[709] + model_decoder_layers_9_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[710] + model_decoder_layers_9_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[711] + model_decoder_layers_9_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[712] + model_decoder_layers_9_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[713] + model_decoder_layers_9_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[717] + model_decoder_layers_9_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[718] + model_decoder_layers_9_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[719] + model_decoder_layers_9_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[720] + model_decoder_layers_9_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[721] + model_decoder_layers_9_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[722] + model_decoder_layers_9_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[723] + model_decoder_layers_9_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[724] + model_decoder_layers_9_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[725] + model_decoder_layers_9_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[726] + model_decoder_layers_9_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[727] + model_decoder_layers_9_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[728] + model_decoder_layers_10_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[729] + model_decoder_layers_10_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[730] + model_decoder_layers_10_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[731] + model_decoder_layers_10_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[732] + model_decoder_layers_10_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[733] + model_decoder_layers_10_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[734] + model_decoder_layers_10_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[735] + model_decoder_layers_10_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[736] + model_decoder_layers_10_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[737] + model_decoder_layers_10_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[741] + model_decoder_layers_10_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[742] + model_decoder_layers_10_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[743] + model_decoder_layers_10_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[744] + model_decoder_layers_10_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[745] + model_decoder_layers_10_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[746] + model_decoder_layers_10_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[747] + model_decoder_layers_10_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[748] + model_decoder_layers_10_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[749] + model_decoder_layers_10_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[750] + model_decoder_layers_10_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[751] + model_decoder_layers_10_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[752] + model_decoder_layers_11_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[753] + model_decoder_layers_11_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[754] + model_decoder_layers_11_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[755] + model_decoder_layers_11_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[756] + model_decoder_layers_11_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[757] + model_decoder_layers_11_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[758] + model_decoder_layers_11_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[759] + model_decoder_layers_11_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[760] + model_decoder_layers_11_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[761] + model_decoder_layers_11_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[765] + model_decoder_layers_11_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[766] + model_decoder_layers_11_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[767] + model_decoder_layers_11_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[768] + model_decoder_layers_11_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[769] + model_decoder_layers_11_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[770] + model_decoder_layers_11_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[771] + model_decoder_layers_11_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[772] + model_decoder_layers_11_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[773] + model_decoder_layers_11_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[774] + model_decoder_layers_11_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[775] + model_decoder_layers_11_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[776] + model_decoder_layers_12_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[777] + model_decoder_layers_12_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[778] + model_decoder_layers_12_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[779] + model_decoder_layers_12_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[780] + model_decoder_layers_12_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[781] + model_decoder_layers_12_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[782] + model_decoder_layers_12_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[783] + model_decoder_layers_12_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[784] + model_decoder_layers_12_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[785] + model_decoder_layers_12_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[789] + model_decoder_layers_12_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[790] + model_decoder_layers_12_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[791] + model_decoder_layers_12_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[792] + model_decoder_layers_12_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[793] + model_decoder_layers_12_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[794] + model_decoder_layers_12_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[795] + model_decoder_layers_12_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[796] + model_decoder_layers_12_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[797] + model_decoder_layers_12_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[798] + model_decoder_layers_12_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[799] + model_decoder_layers_12_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[800] + model_decoder_layers_13_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[801] + model_decoder_layers_13_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[802] + model_decoder_layers_13_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[803] + model_decoder_layers_13_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[804] + model_decoder_layers_13_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[805] + model_decoder_layers_13_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[806] + model_decoder_layers_13_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[807] + model_decoder_layers_13_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[808] + model_decoder_layers_13_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[809] + model_decoder_layers_13_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[813] + model_decoder_layers_13_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[814] + model_decoder_layers_13_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[815] + model_decoder_layers_13_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[816] + model_decoder_layers_13_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[817] + model_decoder_layers_13_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[818] + model_decoder_layers_13_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[819] + model_decoder_layers_13_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[820] + model_decoder_layers_13_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[821] + model_decoder_layers_13_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[822] + model_decoder_layers_13_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[823] + model_decoder_layers_13_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[824] + model_decoder_layers_14_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[825] + model_decoder_layers_14_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[826] + model_decoder_layers_14_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[827] + model_decoder_layers_14_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[828] + model_decoder_layers_14_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[829] + model_decoder_layers_14_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[830] + model_decoder_layers_14_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[831] + model_decoder_layers_14_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[832] + model_decoder_layers_14_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[833] + model_decoder_layers_14_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[837] + model_decoder_layers_14_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[838] + model_decoder_layers_14_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[839] + model_decoder_layers_14_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[840] + model_decoder_layers_14_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[841] + model_decoder_layers_14_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[842] + model_decoder_layers_14_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[843] + model_decoder_layers_14_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[844] + model_decoder_layers_14_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[845] + model_decoder_layers_14_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[846] + model_decoder_layers_14_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[847] + model_decoder_layers_14_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[848] + model_decoder_layers_15_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[849] + model_decoder_layers_15_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[850] + model_decoder_layers_15_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[851] + model_decoder_layers_15_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[852] + model_decoder_layers_15_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[853] + model_decoder_layers_15_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[854] + model_decoder_layers_15_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[855] + model_decoder_layers_15_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[856] + model_decoder_layers_15_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[857] + model_decoder_layers_15_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[861] + model_decoder_layers_15_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[862] + model_decoder_layers_15_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[863] + model_decoder_layers_15_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[864] + model_decoder_layers_15_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[865] + model_decoder_layers_15_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[866] + model_decoder_layers_15_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[867] + model_decoder_layers_15_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[868] + model_decoder_layers_15_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[869] + model_decoder_layers_15_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[870] + model_decoder_layers_15_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[871] + model_decoder_layers_15_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[872] + model_decoder_layers_16_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[873] + model_decoder_layers_16_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[874] + model_decoder_layers_16_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[875] + model_decoder_layers_16_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[876] + model_decoder_layers_16_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[877] + model_decoder_layers_16_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[878] + model_decoder_layers_16_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[879] + model_decoder_layers_16_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[880] + model_decoder_layers_16_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[881] + model_decoder_layers_16_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[885] + model_decoder_layers_16_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[886] + model_decoder_layers_16_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[887] + model_decoder_layers_16_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[888] + model_decoder_layers_16_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[889] + model_decoder_layers_16_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[890] + model_decoder_layers_16_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[891] + model_decoder_layers_16_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[892] + model_decoder_layers_16_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[893] + model_decoder_layers_16_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[894] + model_decoder_layers_16_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[895] + model_decoder_layers_16_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[896] + model_decoder_layers_17_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[897] + model_decoder_layers_17_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[898] + model_decoder_layers_17_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[899] + model_decoder_layers_17_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[900] + model_decoder_layers_17_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[901] + model_decoder_layers_17_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[902] + model_decoder_layers_17_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[903] + model_decoder_layers_17_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[904] + model_decoder_layers_17_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[905] + model_decoder_layers_17_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[909] + model_decoder_layers_17_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[910] + model_decoder_layers_17_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[911] + model_decoder_layers_17_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[912] + model_decoder_layers_17_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[913] + model_decoder_layers_17_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[914] + model_decoder_layers_17_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[915] + model_decoder_layers_17_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[916] + model_decoder_layers_17_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[917] + model_decoder_layers_17_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[918] + model_decoder_layers_17_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[919] + model_decoder_layers_17_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[920] + model_decoder_layers_18_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[921] + model_decoder_layers_18_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[922] + model_decoder_layers_18_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[923] + model_decoder_layers_18_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[924] + model_decoder_layers_18_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[925] + model_decoder_layers_18_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[926] + model_decoder_layers_18_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[927] + model_decoder_layers_18_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[928] + model_decoder_layers_18_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[929] + model_decoder_layers_18_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[933] + model_decoder_layers_18_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[934] + model_decoder_layers_18_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[935] + model_decoder_layers_18_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[936] + model_decoder_layers_18_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[937] + model_decoder_layers_18_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[938] + model_decoder_layers_18_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[939] + model_decoder_layers_18_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[940] + model_decoder_layers_18_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[941] + model_decoder_layers_18_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[942] + model_decoder_layers_18_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[943] + model_decoder_layers_18_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[944] + model_decoder_layers_19_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[945] + model_decoder_layers_19_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[946] + model_decoder_layers_19_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[947] + model_decoder_layers_19_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[948] + model_decoder_layers_19_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[949] + model_decoder_layers_19_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[950] + model_decoder_layers_19_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[951] + model_decoder_layers_19_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[952] + model_decoder_layers_19_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[953] + model_decoder_layers_19_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[957] + model_decoder_layers_19_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[958] + model_decoder_layers_19_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[959] + model_decoder_layers_19_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[960] + model_decoder_layers_19_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[961] + model_decoder_layers_19_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[962] + model_decoder_layers_19_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[963] + model_decoder_layers_19_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[964] + model_decoder_layers_19_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[965] + model_decoder_layers_19_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[966] + model_decoder_layers_19_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[967] + model_decoder_layers_19_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[968] + model_decoder_layers_20_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[969] + model_decoder_layers_20_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[970] + model_decoder_layers_20_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[971] + model_decoder_layers_20_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[972] + model_decoder_layers_20_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[973] + model_decoder_layers_20_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[974] + model_decoder_layers_20_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[975] + model_decoder_layers_20_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[976] + model_decoder_layers_20_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[977] + model_decoder_layers_20_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[981] + model_decoder_layers_20_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[982] + model_decoder_layers_20_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[983] + model_decoder_layers_20_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[984] + model_decoder_layers_20_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[985] + model_decoder_layers_20_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[986] + model_decoder_layers_20_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[987] + model_decoder_layers_20_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[988] + model_decoder_layers_20_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[989] + model_decoder_layers_20_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[990] + model_decoder_layers_20_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[991] + model_decoder_layers_20_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[992] + model_decoder_layers_21_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[993] + model_decoder_layers_21_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[994] + model_decoder_layers_21_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[995] + model_decoder_layers_21_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[996] + model_decoder_layers_21_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[997] + model_decoder_layers_21_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[998] + model_decoder_layers_21_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[999] + model_decoder_layers_21_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1000] + model_decoder_layers_21_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1001] + model_decoder_layers_21_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1005] + model_decoder_layers_21_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1006] + model_decoder_layers_21_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1007] + model_decoder_layers_21_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1008] + model_decoder_layers_21_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1009] + model_decoder_layers_21_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1010] + model_decoder_layers_21_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1011] + model_decoder_layers_21_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1012] + model_decoder_layers_21_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1013] + model_decoder_layers_21_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1014] + model_decoder_layers_21_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1015] + model_decoder_layers_21_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1016] + model_decoder_layers_22_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1017] + model_decoder_layers_22_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1018] + model_decoder_layers_22_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1019] + model_decoder_layers_22_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1020] + model_decoder_layers_22_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1021] + model_decoder_layers_22_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1022] + model_decoder_layers_22_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1023] + model_decoder_layers_22_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1024] + model_decoder_layers_22_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1025] + model_decoder_layers_22_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1029] + model_decoder_layers_22_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1030] + model_decoder_layers_22_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1031] + model_decoder_layers_22_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1032] + model_decoder_layers_22_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1033] + model_decoder_layers_22_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1034] + model_decoder_layers_22_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1035] + model_decoder_layers_22_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1036] + model_decoder_layers_22_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1037] + model_decoder_layers_22_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1038] + model_decoder_layers_22_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1039] + model_decoder_layers_22_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1040] + model_decoder_layers_23_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1041] + model_decoder_layers_23_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1042] + model_decoder_layers_23_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1043] + model_decoder_layers_23_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1044] + model_decoder_layers_23_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1045] + model_decoder_layers_23_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1046] + model_decoder_layers_23_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1047] + model_decoder_layers_23_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1048] + model_decoder_layers_23_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1049] + model_decoder_layers_23_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1053] + model_decoder_layers_23_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1054] + model_decoder_layers_23_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1055] + model_decoder_layers_23_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1056] + model_decoder_layers_23_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1057] + model_decoder_layers_23_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1058] + model_decoder_layers_23_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1059] + model_decoder_layers_23_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1060] + model_decoder_layers_23_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1061] + model_decoder_layers_23_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1062] + model_decoder_layers_23_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1063] + model_decoder_layers_23_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1064] + model_decoder_layers_24_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1065] + model_decoder_layers_24_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1066] + model_decoder_layers_24_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1067] + model_decoder_layers_24_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1068] + model_decoder_layers_24_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1069] + model_decoder_layers_24_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1070] + model_decoder_layers_24_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1071] + model_decoder_layers_24_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1072] + model_decoder_layers_24_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1073] + model_decoder_layers_24_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1077] + model_decoder_layers_24_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1078] + model_decoder_layers_24_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1079] + model_decoder_layers_24_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1080] + model_decoder_layers_24_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1081] + model_decoder_layers_24_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1082] + model_decoder_layers_24_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1083] + model_decoder_layers_24_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1084] + model_decoder_layers_24_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1085] + model_decoder_layers_24_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1086] + model_decoder_layers_24_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1087] + model_decoder_layers_24_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1088] + model_decoder_layers_25_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1089] + model_decoder_layers_25_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1090] + model_decoder_layers_25_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1091] + model_decoder_layers_25_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1092] + model_decoder_layers_25_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1093] + model_decoder_layers_25_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1094] + model_decoder_layers_25_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1095] + model_decoder_layers_25_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1096] + model_decoder_layers_25_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1097] + model_decoder_layers_25_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1101] + model_decoder_layers_25_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1102] + model_decoder_layers_25_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1103] + model_decoder_layers_25_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1104] + model_decoder_layers_25_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1105] + model_decoder_layers_25_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1106] + model_decoder_layers_25_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1107] + model_decoder_layers_25_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1108] + model_decoder_layers_25_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1109] + model_decoder_layers_25_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1110] + model_decoder_layers_25_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1111] + model_decoder_layers_25_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1112] + model_decoder_layers_26_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1113] + model_decoder_layers_26_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1114] + model_decoder_layers_26_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1115] + model_decoder_layers_26_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1116] + model_decoder_layers_26_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1117] + model_decoder_layers_26_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1118] + model_decoder_layers_26_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1119] + model_decoder_layers_26_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1120] + model_decoder_layers_26_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1121] + model_decoder_layers_26_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1125] + model_decoder_layers_26_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1126] + model_decoder_layers_26_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1127] + model_decoder_layers_26_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1128] + model_decoder_layers_26_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1129] + model_decoder_layers_26_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1130] + model_decoder_layers_26_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1131] + model_decoder_layers_26_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1132] + model_decoder_layers_26_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1133] + model_decoder_layers_26_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1134] + model_decoder_layers_26_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1135] + model_decoder_layers_26_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1136] + model_decoder_layers_27_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1137] + model_decoder_layers_27_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1138] + model_decoder_layers_27_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1139] + model_decoder_layers_27_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1140] + model_decoder_layers_27_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1141] + model_decoder_layers_27_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1142] + model_decoder_layers_27_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1143] + model_decoder_layers_27_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1144] + model_decoder_layers_27_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1145] + model_decoder_layers_27_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1149] + model_decoder_layers_27_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1150] + model_decoder_layers_27_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1151] + model_decoder_layers_27_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1152] + model_decoder_layers_27_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1153] + model_decoder_layers_27_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1154] + model_decoder_layers_27_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1155] + model_decoder_layers_27_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1156] + model_decoder_layers_27_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1157] + model_decoder_layers_27_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1158] + model_decoder_layers_27_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1159] + model_decoder_layers_27_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1160] + model_decoder_layers_28_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1161] + model_decoder_layers_28_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1162] + model_decoder_layers_28_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1163] + model_decoder_layers_28_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1164] + model_decoder_layers_28_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1165] + model_decoder_layers_28_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1166] + model_decoder_layers_28_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1167] + model_decoder_layers_28_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1168] + model_decoder_layers_28_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1169] + model_decoder_layers_28_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1173] + model_decoder_layers_28_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1174] + model_decoder_layers_28_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1175] + model_decoder_layers_28_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1176] + model_decoder_layers_28_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1177] + model_decoder_layers_28_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1178] + model_decoder_layers_28_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1179] + model_decoder_layers_28_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1180] + model_decoder_layers_28_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1181] + model_decoder_layers_28_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1182] + model_decoder_layers_28_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1183] + model_decoder_layers_28_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1184] + model_decoder_layers_29_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1185] + model_decoder_layers_29_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1186] + model_decoder_layers_29_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1187] + model_decoder_layers_29_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1188] + model_decoder_layers_29_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1189] + model_decoder_layers_29_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1190] + model_decoder_layers_29_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1191] + model_decoder_layers_29_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1192] + model_decoder_layers_29_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1193] + model_decoder_layers_29_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1197] + model_decoder_layers_29_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1198] + model_decoder_layers_29_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1199] + model_decoder_layers_29_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1200] + model_decoder_layers_29_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1201] + model_decoder_layers_29_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1202] + model_decoder_layers_29_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1203] + model_decoder_layers_29_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1204] + model_decoder_layers_29_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1205] + model_decoder_layers_29_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1206] + model_decoder_layers_29_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1207] + model_decoder_layers_29_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1208] + model_decoder_layers_30_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1209] + model_decoder_layers_30_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1210] + model_decoder_layers_30_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1211] + model_decoder_layers_30_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1212] + model_decoder_layers_30_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1213] + model_decoder_layers_30_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1214] + model_decoder_layers_30_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1215] + model_decoder_layers_30_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1216] + model_decoder_layers_30_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1217] + model_decoder_layers_30_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1221] + model_decoder_layers_30_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1222] + model_decoder_layers_30_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1223] + model_decoder_layers_30_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1224] + model_decoder_layers_30_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1225] + model_decoder_layers_30_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1226] + model_decoder_layers_30_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1227] + model_decoder_layers_30_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1228] + model_decoder_layers_30_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1229] + model_decoder_layers_30_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1230] + model_decoder_layers_30_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1231] + model_decoder_layers_30_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1232] + model_decoder_layers_31_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1233] + model_decoder_layers_31_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1234] + model_decoder_layers_31_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1235] + model_decoder_layers_31_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1236] + model_decoder_layers_31_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1237] + model_decoder_layers_31_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1238] + model_decoder_layers_31_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1239] + model_decoder_layers_31_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1240] + model_decoder_layers_31_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1241] + model_decoder_layers_31_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1245] + model_decoder_layers_31_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1246] + model_decoder_layers_31_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1247] + model_decoder_layers_31_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1248] + model_decoder_layers_31_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1249] + model_decoder_layers_31_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1250] + model_decoder_layers_31_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1251] + model_decoder_layers_31_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1252] + model_decoder_layers_31_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1253] + model_decoder_layers_31_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1254] + model_decoder_layers_31_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1255] + model_decoder_layers_31_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1256] + model_decoder_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1257] + model_decoder_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1258] + reshape1353 = R.call_tir(cls.reshape19, (input_ids,), out_sinfo=R.Tensor((1,), dtype="int32")) + take7 = R.call_tir(cls.take3, (model_decoder_embed_tokens_weight5, reshape1353), out_sinfo=R.Tensor((1, 1280), dtype="float16")) + lv264: R.Tensor((1,), dtype="int32") = R.call_pure_packed("vm.builtin.attention_kv_cache_get_query_positions", paged_kv_cache, sinfo_args=(R.Tensor((1,), dtype="int32"),)) + take8 = R.call_tir(cls.take4, (model_decoder_embed_positions_weight5, lv264), out_sinfo=R.Tensor((1, 1280), dtype="float16")) + lv40 = R.call_tir(cls.fused_reshape20_reshape20_add6, (take7, take8), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm356 = R.call_tir(cls.layer_norm3, (lv40, model_decoder_layers_0_self_attn_layer_norm_weight5, model_decoder_layers_0_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv41 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm356, model_decoder_layers_0_self_attn_q_proj_weight5, model_decoder_layers_0_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv1 = R.call_tir(cls.NT_matmul, (layer_norm356, model_decoder_layers_0_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv42 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm356, model_decoder_layers_0_self_attn_v_proj_weight5, model_decoder_layers_0_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv43 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv41, lv1, lv42), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv265 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), lv43), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv44 = R.call_tir(cls.fused_reshape23_reshape24, (lv265,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv45 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv44, model_decoder_layers_0_self_attn_out_proj_weight5, model_decoder_layers_0_self_attn_out_proj_bias5, lv40), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm357 = R.call_tir(cls.layer_norm3, (lv45, model_decoder_layers_0_encoder_attn_layer_norm_weight5, model_decoder_layers_0_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv46 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm357, model_decoder_layers_0_encoder_attn_q_proj_weight5, model_decoder_layers_0_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv47 = R.call_tir(cls.fused_reshape21_reshape25, (lv46,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv266 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), lv47), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv48 = R.call_tir(cls.fused_reshape23_reshape24, (lv266,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv49 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv48, model_decoder_layers_0_encoder_attn_out_proj_weight5, model_decoder_layers_0_encoder_attn_out_proj_bias5, lv45), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm358 = R.call_tir(cls.layer_norm3, (lv49, model_decoder_layers_0_final_layer_norm_weight5, model_decoder_layers_0_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv50 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm358, model_decoder_layers_0_fc1_weight5, model_decoder_layers_0_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv51 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv50, model_decoder_layers_0_fc2_weight5, model_decoder_layers_0_fc2_bias5, lv49), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm359 = R.call_tir(cls.layer_norm3, (lv51, model_decoder_layers_1_self_attn_layer_norm_weight5, model_decoder_layers_1_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv52 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm359, model_decoder_layers_1_self_attn_q_proj_weight5, model_decoder_layers_1_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv9 = R.call_tir(cls.NT_matmul, (layer_norm359, model_decoder_layers_1_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv53 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm359, model_decoder_layers_1_self_attn_v_proj_weight5, model_decoder_layers_1_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv54 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv52, lv9, lv53), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv267 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), lv54), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv55 = R.call_tir(cls.fused_reshape23_reshape24, (lv267,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv56 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv55, model_decoder_layers_1_self_attn_out_proj_weight5, model_decoder_layers_1_self_attn_out_proj_bias5, lv51), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm360 = R.call_tir(cls.layer_norm3, (lv56, model_decoder_layers_1_encoder_attn_layer_norm_weight5, model_decoder_layers_1_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv57 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm360, model_decoder_layers_1_encoder_attn_q_proj_weight5, model_decoder_layers_1_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv58 = R.call_tir(cls.fused_reshape21_reshape25, (lv57,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv268 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), lv58), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv59 = R.call_tir(cls.fused_reshape23_reshape24, (lv268,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv60 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv59, model_decoder_layers_1_encoder_attn_out_proj_weight5, model_decoder_layers_1_encoder_attn_out_proj_bias5, lv56), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm361 = R.call_tir(cls.layer_norm3, (lv60, model_decoder_layers_1_final_layer_norm_weight5, model_decoder_layers_1_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv61 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm361, model_decoder_layers_1_fc1_weight5, model_decoder_layers_1_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv62 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv61, model_decoder_layers_1_fc2_weight5, model_decoder_layers_1_fc2_bias5, lv60), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm362 = R.call_tir(cls.layer_norm3, (lv62, model_decoder_layers_2_self_attn_layer_norm_weight5, model_decoder_layers_2_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv63 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm362, model_decoder_layers_2_self_attn_q_proj_weight5, model_decoder_layers_2_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv17 = R.call_tir(cls.NT_matmul, (layer_norm362, model_decoder_layers_2_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv64 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm362, model_decoder_layers_2_self_attn_v_proj_weight5, model_decoder_layers_2_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv65 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv63, lv17, lv64), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv269 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), lv65), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv66 = R.call_tir(cls.fused_reshape23_reshape24, (lv269,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv67 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv66, model_decoder_layers_2_self_attn_out_proj_weight5, model_decoder_layers_2_self_attn_out_proj_bias5, lv62), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm363 = R.call_tir(cls.layer_norm3, (lv67, model_decoder_layers_2_encoder_attn_layer_norm_weight5, model_decoder_layers_2_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv68 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm363, model_decoder_layers_2_encoder_attn_q_proj_weight5, model_decoder_layers_2_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv69 = R.call_tir(cls.fused_reshape21_reshape25, (lv68,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv270 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), lv69), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv70 = R.call_tir(cls.fused_reshape23_reshape24, (lv270,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv71 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv70, model_decoder_layers_2_encoder_attn_out_proj_weight5, model_decoder_layers_2_encoder_attn_out_proj_bias5, lv67), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm364 = R.call_tir(cls.layer_norm3, (lv71, model_decoder_layers_2_final_layer_norm_weight5, model_decoder_layers_2_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv72 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm364, model_decoder_layers_2_fc1_weight5, model_decoder_layers_2_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv73 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv72, model_decoder_layers_2_fc2_weight5, model_decoder_layers_2_fc2_bias5, lv71), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm365 = R.call_tir(cls.layer_norm3, (lv73, model_decoder_layers_3_self_attn_layer_norm_weight5, model_decoder_layers_3_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv74 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm365, model_decoder_layers_3_self_attn_q_proj_weight5, model_decoder_layers_3_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv25 = R.call_tir(cls.NT_matmul, (layer_norm365, model_decoder_layers_3_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv75 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm365, model_decoder_layers_3_self_attn_v_proj_weight5, model_decoder_layers_3_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv76 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv74, lv25, lv75), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv271 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), lv76), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv77 = R.call_tir(cls.fused_reshape23_reshape24, (lv271,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv78 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv77, model_decoder_layers_3_self_attn_out_proj_weight5, model_decoder_layers_3_self_attn_out_proj_bias5, lv73), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm366 = R.call_tir(cls.layer_norm3, (lv78, model_decoder_layers_3_encoder_attn_layer_norm_weight5, model_decoder_layers_3_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv79 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm366, model_decoder_layers_3_encoder_attn_q_proj_weight5, model_decoder_layers_3_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv80 = R.call_tir(cls.fused_reshape21_reshape25, (lv79,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv272 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), lv80), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv81 = R.call_tir(cls.fused_reshape23_reshape24, (lv272,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv82 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv81, model_decoder_layers_3_encoder_attn_out_proj_weight5, model_decoder_layers_3_encoder_attn_out_proj_bias5, lv78), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm367 = R.call_tir(cls.layer_norm3, (lv82, model_decoder_layers_3_final_layer_norm_weight5, model_decoder_layers_3_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv83 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm367, model_decoder_layers_3_fc1_weight5, model_decoder_layers_3_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv84 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv83, model_decoder_layers_3_fc2_weight5, model_decoder_layers_3_fc2_bias5, lv82), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm368 = R.call_tir(cls.layer_norm3, (lv84, model_decoder_layers_4_self_attn_layer_norm_weight5, model_decoder_layers_4_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv85 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm368, model_decoder_layers_4_self_attn_q_proj_weight5, model_decoder_layers_4_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv33 = R.call_tir(cls.NT_matmul, (layer_norm368, model_decoder_layers_4_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv86 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm368, model_decoder_layers_4_self_attn_v_proj_weight5, model_decoder_layers_4_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv87 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv85, lv33, lv86), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv273 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), lv87), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv88 = R.call_tir(cls.fused_reshape23_reshape24, (lv273,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv89 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv88, model_decoder_layers_4_self_attn_out_proj_weight5, model_decoder_layers_4_self_attn_out_proj_bias5, lv84), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm369 = R.call_tir(cls.layer_norm3, (lv89, model_decoder_layers_4_encoder_attn_layer_norm_weight5, model_decoder_layers_4_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv90 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm369, model_decoder_layers_4_encoder_attn_q_proj_weight5, model_decoder_layers_4_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv91 = R.call_tir(cls.fused_reshape21_reshape25, (lv90,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv274 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), lv91), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv92 = R.call_tir(cls.fused_reshape23_reshape24, (lv274,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv93 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv92, model_decoder_layers_4_encoder_attn_out_proj_weight5, model_decoder_layers_4_encoder_attn_out_proj_bias5, lv89), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm370 = R.call_tir(cls.layer_norm3, (lv93, model_decoder_layers_4_final_layer_norm_weight5, model_decoder_layers_4_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv94 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm370, model_decoder_layers_4_fc1_weight5, model_decoder_layers_4_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv95 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv94, model_decoder_layers_4_fc2_weight5, model_decoder_layers_4_fc2_bias5, lv93), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm371 = R.call_tir(cls.layer_norm3, (lv95, model_decoder_layers_5_self_attn_layer_norm_weight5, model_decoder_layers_5_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv96 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm371, model_decoder_layers_5_self_attn_q_proj_weight5, model_decoder_layers_5_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv41_1 = R.call_tir(cls.NT_matmul, (layer_norm371, model_decoder_layers_5_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv97 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm371, model_decoder_layers_5_self_attn_v_proj_weight5, model_decoder_layers_5_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv98 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv96, lv41_1, lv97), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv275 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), lv98), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv99 = R.call_tir(cls.fused_reshape23_reshape24, (lv275,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv100 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv99, model_decoder_layers_5_self_attn_out_proj_weight5, model_decoder_layers_5_self_attn_out_proj_bias5, lv95), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm372 = R.call_tir(cls.layer_norm3, (lv100, model_decoder_layers_5_encoder_attn_layer_norm_weight5, model_decoder_layers_5_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv101 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm372, model_decoder_layers_5_encoder_attn_q_proj_weight5, model_decoder_layers_5_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv102 = R.call_tir(cls.fused_reshape21_reshape25, (lv101,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv276 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), lv102), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv103 = R.call_tir(cls.fused_reshape23_reshape24, (lv276,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv104 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv103, model_decoder_layers_5_encoder_attn_out_proj_weight5, model_decoder_layers_5_encoder_attn_out_proj_bias5, lv100), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm373 = R.call_tir(cls.layer_norm3, (lv104, model_decoder_layers_5_final_layer_norm_weight5, model_decoder_layers_5_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv105 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm373, model_decoder_layers_5_fc1_weight5, model_decoder_layers_5_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv106 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv105, model_decoder_layers_5_fc2_weight5, model_decoder_layers_5_fc2_bias5, lv104), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm374 = R.call_tir(cls.layer_norm3, (lv106, model_decoder_layers_6_self_attn_layer_norm_weight5, model_decoder_layers_6_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv107 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm374, model_decoder_layers_6_self_attn_q_proj_weight5, model_decoder_layers_6_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv49_1 = R.call_tir(cls.NT_matmul, (layer_norm374, model_decoder_layers_6_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv108 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm374, model_decoder_layers_6_self_attn_v_proj_weight5, model_decoder_layers_6_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv109 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv107, lv49_1, lv108), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv277 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), lv109), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv110 = R.call_tir(cls.fused_reshape23_reshape24, (lv277,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv111 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv110, model_decoder_layers_6_self_attn_out_proj_weight5, model_decoder_layers_6_self_attn_out_proj_bias5, lv106), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm375 = R.call_tir(cls.layer_norm3, (lv111, model_decoder_layers_6_encoder_attn_layer_norm_weight5, model_decoder_layers_6_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv112 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm375, model_decoder_layers_6_encoder_attn_q_proj_weight5, model_decoder_layers_6_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv113 = R.call_tir(cls.fused_reshape21_reshape25, (lv112,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv278 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), lv113), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv114 = R.call_tir(cls.fused_reshape23_reshape24, (lv278,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv115 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv114, model_decoder_layers_6_encoder_attn_out_proj_weight5, model_decoder_layers_6_encoder_attn_out_proj_bias5, lv111), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm376 = R.call_tir(cls.layer_norm3, (lv115, model_decoder_layers_6_final_layer_norm_weight5, model_decoder_layers_6_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv116 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm376, model_decoder_layers_6_fc1_weight5, model_decoder_layers_6_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv117 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv116, model_decoder_layers_6_fc2_weight5, model_decoder_layers_6_fc2_bias5, lv115), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm377 = R.call_tir(cls.layer_norm3, (lv117, model_decoder_layers_7_self_attn_layer_norm_weight5, model_decoder_layers_7_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv118 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm377, model_decoder_layers_7_self_attn_q_proj_weight5, model_decoder_layers_7_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv57_1 = R.call_tir(cls.NT_matmul, (layer_norm377, model_decoder_layers_7_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv119 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm377, model_decoder_layers_7_self_attn_v_proj_weight5, model_decoder_layers_7_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv120 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv118, lv57_1, lv119), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv279 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), lv120), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv121 = R.call_tir(cls.fused_reshape23_reshape24, (lv279,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv122 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv121, model_decoder_layers_7_self_attn_out_proj_weight5, model_decoder_layers_7_self_attn_out_proj_bias5, lv117), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm378 = R.call_tir(cls.layer_norm3, (lv122, model_decoder_layers_7_encoder_attn_layer_norm_weight5, model_decoder_layers_7_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv123 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm378, model_decoder_layers_7_encoder_attn_q_proj_weight5, model_decoder_layers_7_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv124 = R.call_tir(cls.fused_reshape21_reshape25, (lv123,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv280 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), lv124), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv125 = R.call_tir(cls.fused_reshape23_reshape24, (lv280,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv126 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv125, model_decoder_layers_7_encoder_attn_out_proj_weight5, model_decoder_layers_7_encoder_attn_out_proj_bias5, lv122), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm379 = R.call_tir(cls.layer_norm3, (lv126, model_decoder_layers_7_final_layer_norm_weight5, model_decoder_layers_7_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv127 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm379, model_decoder_layers_7_fc1_weight5, model_decoder_layers_7_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv128 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv127, model_decoder_layers_7_fc2_weight5, model_decoder_layers_7_fc2_bias5, lv126), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm380 = R.call_tir(cls.layer_norm3, (lv128, model_decoder_layers_8_self_attn_layer_norm_weight5, model_decoder_layers_8_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv129 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm380, model_decoder_layers_8_self_attn_q_proj_weight5, model_decoder_layers_8_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv65_1 = R.call_tir(cls.NT_matmul, (layer_norm380, model_decoder_layers_8_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv130 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm380, model_decoder_layers_8_self_attn_v_proj_weight5, model_decoder_layers_8_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv131 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv129, lv65_1, lv130), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv281 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), lv131), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv132 = R.call_tir(cls.fused_reshape23_reshape24, (lv281,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv133 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv132, model_decoder_layers_8_self_attn_out_proj_weight5, model_decoder_layers_8_self_attn_out_proj_bias5, lv128), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm381 = R.call_tir(cls.layer_norm3, (lv133, model_decoder_layers_8_encoder_attn_layer_norm_weight5, model_decoder_layers_8_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv134 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm381, model_decoder_layers_8_encoder_attn_q_proj_weight5, model_decoder_layers_8_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv135 = R.call_tir(cls.fused_reshape21_reshape25, (lv134,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv282 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), lv135), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv136 = R.call_tir(cls.fused_reshape23_reshape24, (lv282,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv137 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv136, model_decoder_layers_8_encoder_attn_out_proj_weight5, model_decoder_layers_8_encoder_attn_out_proj_bias5, lv133), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm382 = R.call_tir(cls.layer_norm3, (lv137, model_decoder_layers_8_final_layer_norm_weight5, model_decoder_layers_8_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv138 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm382, model_decoder_layers_8_fc1_weight5, model_decoder_layers_8_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv139 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv138, model_decoder_layers_8_fc2_weight5, model_decoder_layers_8_fc2_bias5, lv137), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm383 = R.call_tir(cls.layer_norm3, (lv139, model_decoder_layers_9_self_attn_layer_norm_weight5, model_decoder_layers_9_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv140 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm383, model_decoder_layers_9_self_attn_q_proj_weight5, model_decoder_layers_9_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv73_1 = R.call_tir(cls.NT_matmul, (layer_norm383, model_decoder_layers_9_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv141 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm383, model_decoder_layers_9_self_attn_v_proj_weight5, model_decoder_layers_9_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv142 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv140, lv73_1, lv141), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv283 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), lv142), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv143 = R.call_tir(cls.fused_reshape23_reshape24, (lv283,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv144 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv143, model_decoder_layers_9_self_attn_out_proj_weight5, model_decoder_layers_9_self_attn_out_proj_bias5, lv139), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm384 = R.call_tir(cls.layer_norm3, (lv144, model_decoder_layers_9_encoder_attn_layer_norm_weight5, model_decoder_layers_9_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv145 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm384, model_decoder_layers_9_encoder_attn_q_proj_weight5, model_decoder_layers_9_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv146 = R.call_tir(cls.fused_reshape21_reshape25, (lv145,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv284 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), lv146), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv147 = R.call_tir(cls.fused_reshape23_reshape24, (lv284,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv148 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv147, model_decoder_layers_9_encoder_attn_out_proj_weight5, model_decoder_layers_9_encoder_attn_out_proj_bias5, lv144), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm385 = R.call_tir(cls.layer_norm3, (lv148, model_decoder_layers_9_final_layer_norm_weight5, model_decoder_layers_9_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv149 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm385, model_decoder_layers_9_fc1_weight5, model_decoder_layers_9_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv150 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv149, model_decoder_layers_9_fc2_weight5, model_decoder_layers_9_fc2_bias5, lv148), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm386 = R.call_tir(cls.layer_norm3, (lv150, model_decoder_layers_10_self_attn_layer_norm_weight5, model_decoder_layers_10_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv151 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm386, model_decoder_layers_10_self_attn_q_proj_weight5, model_decoder_layers_10_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv81_1 = R.call_tir(cls.NT_matmul, (layer_norm386, model_decoder_layers_10_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv152 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm386, model_decoder_layers_10_self_attn_v_proj_weight5, model_decoder_layers_10_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv153 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv151, lv81_1, lv152), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv285 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), lv153), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv154 = R.call_tir(cls.fused_reshape23_reshape24, (lv285,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv155 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv154, model_decoder_layers_10_self_attn_out_proj_weight5, model_decoder_layers_10_self_attn_out_proj_bias5, lv150), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm387 = R.call_tir(cls.layer_norm3, (lv155, model_decoder_layers_10_encoder_attn_layer_norm_weight5, model_decoder_layers_10_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv156 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm387, model_decoder_layers_10_encoder_attn_q_proj_weight5, model_decoder_layers_10_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv157 = R.call_tir(cls.fused_reshape21_reshape25, (lv156,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv286 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), lv157), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv158 = R.call_tir(cls.fused_reshape23_reshape24, (lv286,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv159 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv158, model_decoder_layers_10_encoder_attn_out_proj_weight5, model_decoder_layers_10_encoder_attn_out_proj_bias5, lv155), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm388 = R.call_tir(cls.layer_norm3, (lv159, model_decoder_layers_10_final_layer_norm_weight5, model_decoder_layers_10_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv160 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm388, model_decoder_layers_10_fc1_weight5, model_decoder_layers_10_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv161 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv160, model_decoder_layers_10_fc2_weight5, model_decoder_layers_10_fc2_bias5, lv159), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm389 = R.call_tir(cls.layer_norm3, (lv161, model_decoder_layers_11_self_attn_layer_norm_weight5, model_decoder_layers_11_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv162 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm389, model_decoder_layers_11_self_attn_q_proj_weight5, model_decoder_layers_11_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv89_1 = R.call_tir(cls.NT_matmul, (layer_norm389, model_decoder_layers_11_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv163 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm389, model_decoder_layers_11_self_attn_v_proj_weight5, model_decoder_layers_11_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv164 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv162, lv89_1, lv163), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv287 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), lv164), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv165 = R.call_tir(cls.fused_reshape23_reshape24, (lv287,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv166 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv165, model_decoder_layers_11_self_attn_out_proj_weight5, model_decoder_layers_11_self_attn_out_proj_bias5, lv161), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm390 = R.call_tir(cls.layer_norm3, (lv166, model_decoder_layers_11_encoder_attn_layer_norm_weight5, model_decoder_layers_11_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv167 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm390, model_decoder_layers_11_encoder_attn_q_proj_weight5, model_decoder_layers_11_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv168 = R.call_tir(cls.fused_reshape21_reshape25, (lv167,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv288 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), lv168), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv169 = R.call_tir(cls.fused_reshape23_reshape24, (lv288,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv170 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv169, model_decoder_layers_11_encoder_attn_out_proj_weight5, model_decoder_layers_11_encoder_attn_out_proj_bias5, lv166), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm391 = R.call_tir(cls.layer_norm3, (lv170, model_decoder_layers_11_final_layer_norm_weight5, model_decoder_layers_11_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv171 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm391, model_decoder_layers_11_fc1_weight5, model_decoder_layers_11_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv172 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv171, model_decoder_layers_11_fc2_weight5, model_decoder_layers_11_fc2_bias5, lv170), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm392 = R.call_tir(cls.layer_norm3, (lv172, model_decoder_layers_12_self_attn_layer_norm_weight5, model_decoder_layers_12_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv173 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm392, model_decoder_layers_12_self_attn_q_proj_weight5, model_decoder_layers_12_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv97_1 = R.call_tir(cls.NT_matmul, (layer_norm392, model_decoder_layers_12_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv174 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm392, model_decoder_layers_12_self_attn_v_proj_weight5, model_decoder_layers_12_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv175 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv173, lv97_1, lv174), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv289 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), lv175), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv176 = R.call_tir(cls.fused_reshape23_reshape24, (lv289,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv177 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv176, model_decoder_layers_12_self_attn_out_proj_weight5, model_decoder_layers_12_self_attn_out_proj_bias5, lv172), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm393 = R.call_tir(cls.layer_norm3, (lv177, model_decoder_layers_12_encoder_attn_layer_norm_weight5, model_decoder_layers_12_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv178 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm393, model_decoder_layers_12_encoder_attn_q_proj_weight5, model_decoder_layers_12_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv179 = R.call_tir(cls.fused_reshape21_reshape25, (lv178,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv290 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), lv179), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv180 = R.call_tir(cls.fused_reshape23_reshape24, (lv290,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv181 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv180, model_decoder_layers_12_encoder_attn_out_proj_weight5, model_decoder_layers_12_encoder_attn_out_proj_bias5, lv177), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm394 = R.call_tir(cls.layer_norm3, (lv181, model_decoder_layers_12_final_layer_norm_weight5, model_decoder_layers_12_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv182 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm394, model_decoder_layers_12_fc1_weight5, model_decoder_layers_12_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv183 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv182, model_decoder_layers_12_fc2_weight5, model_decoder_layers_12_fc2_bias5, lv181), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm395 = R.call_tir(cls.layer_norm3, (lv183, model_decoder_layers_13_self_attn_layer_norm_weight5, model_decoder_layers_13_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv184 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm395, model_decoder_layers_13_self_attn_q_proj_weight5, model_decoder_layers_13_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv105_1 = R.call_tir(cls.NT_matmul, (layer_norm395, model_decoder_layers_13_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv185 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm395, model_decoder_layers_13_self_attn_v_proj_weight5, model_decoder_layers_13_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv186 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv184, lv105_1, lv185), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv291 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), lv186), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv187 = R.call_tir(cls.fused_reshape23_reshape24, (lv291,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv188 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv187, model_decoder_layers_13_self_attn_out_proj_weight5, model_decoder_layers_13_self_attn_out_proj_bias5, lv183), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm396 = R.call_tir(cls.layer_norm3, (lv188, model_decoder_layers_13_encoder_attn_layer_norm_weight5, model_decoder_layers_13_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv189 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm396, model_decoder_layers_13_encoder_attn_q_proj_weight5, model_decoder_layers_13_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv190 = R.call_tir(cls.fused_reshape21_reshape25, (lv189,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv292 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), lv190), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv191 = R.call_tir(cls.fused_reshape23_reshape24, (lv292,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv192 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv191, model_decoder_layers_13_encoder_attn_out_proj_weight5, model_decoder_layers_13_encoder_attn_out_proj_bias5, lv188), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm397 = R.call_tir(cls.layer_norm3, (lv192, model_decoder_layers_13_final_layer_norm_weight5, model_decoder_layers_13_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv193 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm397, model_decoder_layers_13_fc1_weight5, model_decoder_layers_13_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv194 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv193, model_decoder_layers_13_fc2_weight5, model_decoder_layers_13_fc2_bias5, lv192), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm398 = R.call_tir(cls.layer_norm3, (lv194, model_decoder_layers_14_self_attn_layer_norm_weight5, model_decoder_layers_14_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv195 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm398, model_decoder_layers_14_self_attn_q_proj_weight5, model_decoder_layers_14_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv113_1 = R.call_tir(cls.NT_matmul, (layer_norm398, model_decoder_layers_14_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv196 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm398, model_decoder_layers_14_self_attn_v_proj_weight5, model_decoder_layers_14_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv197 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv195, lv113_1, lv196), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv293 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), lv197), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv198 = R.call_tir(cls.fused_reshape23_reshape24, (lv293,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv199 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv198, model_decoder_layers_14_self_attn_out_proj_weight5, model_decoder_layers_14_self_attn_out_proj_bias5, lv194), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm399 = R.call_tir(cls.layer_norm3, (lv199, model_decoder_layers_14_encoder_attn_layer_norm_weight5, model_decoder_layers_14_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv200 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm399, model_decoder_layers_14_encoder_attn_q_proj_weight5, model_decoder_layers_14_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv201 = R.call_tir(cls.fused_reshape21_reshape25, (lv200,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv294 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), lv201), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv202 = R.call_tir(cls.fused_reshape23_reshape24, (lv294,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv203 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv202, model_decoder_layers_14_encoder_attn_out_proj_weight5, model_decoder_layers_14_encoder_attn_out_proj_bias5, lv199), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm400 = R.call_tir(cls.layer_norm3, (lv203, model_decoder_layers_14_final_layer_norm_weight5, model_decoder_layers_14_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv204 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm400, model_decoder_layers_14_fc1_weight5, model_decoder_layers_14_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv205 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv204, model_decoder_layers_14_fc2_weight5, model_decoder_layers_14_fc2_bias5, lv203), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm401 = R.call_tir(cls.layer_norm3, (lv205, model_decoder_layers_15_self_attn_layer_norm_weight5, model_decoder_layers_15_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv206 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm401, model_decoder_layers_15_self_attn_q_proj_weight5, model_decoder_layers_15_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv121_1 = R.call_tir(cls.NT_matmul, (layer_norm401, model_decoder_layers_15_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv207 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm401, model_decoder_layers_15_self_attn_v_proj_weight5, model_decoder_layers_15_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv208 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv206, lv121_1, lv207), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv295 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), lv208), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv209 = R.call_tir(cls.fused_reshape23_reshape24, (lv295,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv210 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv209, model_decoder_layers_15_self_attn_out_proj_weight5, model_decoder_layers_15_self_attn_out_proj_bias5, lv205), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm402 = R.call_tir(cls.layer_norm3, (lv210, model_decoder_layers_15_encoder_attn_layer_norm_weight5, model_decoder_layers_15_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv211 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm402, model_decoder_layers_15_encoder_attn_q_proj_weight5, model_decoder_layers_15_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv212 = R.call_tir(cls.fused_reshape21_reshape25, (lv211,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv296 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), lv212), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv213 = R.call_tir(cls.fused_reshape23_reshape24, (lv296,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv214 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv213, model_decoder_layers_15_encoder_attn_out_proj_weight5, model_decoder_layers_15_encoder_attn_out_proj_bias5, lv210), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm403 = R.call_tir(cls.layer_norm3, (lv214, model_decoder_layers_15_final_layer_norm_weight5, model_decoder_layers_15_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv215 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm403, model_decoder_layers_15_fc1_weight5, model_decoder_layers_15_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv216 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv215, model_decoder_layers_15_fc2_weight5, model_decoder_layers_15_fc2_bias5, lv214), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm404 = R.call_tir(cls.layer_norm3, (lv216, model_decoder_layers_16_self_attn_layer_norm_weight5, model_decoder_layers_16_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv217 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm404, model_decoder_layers_16_self_attn_q_proj_weight5, model_decoder_layers_16_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv129_1 = R.call_tir(cls.NT_matmul, (layer_norm404, model_decoder_layers_16_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv218 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm404, model_decoder_layers_16_self_attn_v_proj_weight5, model_decoder_layers_16_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv219 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv217, lv129_1, lv218), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv297 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), lv219), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv220 = R.call_tir(cls.fused_reshape23_reshape24, (lv297,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv221 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv220, model_decoder_layers_16_self_attn_out_proj_weight5, model_decoder_layers_16_self_attn_out_proj_bias5, lv216), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm405 = R.call_tir(cls.layer_norm3, (lv221, model_decoder_layers_16_encoder_attn_layer_norm_weight5, model_decoder_layers_16_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv222 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm405, model_decoder_layers_16_encoder_attn_q_proj_weight5, model_decoder_layers_16_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv223 = R.call_tir(cls.fused_reshape21_reshape25, (lv222,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv298 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), lv223), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv224 = R.call_tir(cls.fused_reshape23_reshape24, (lv298,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv225 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv224, model_decoder_layers_16_encoder_attn_out_proj_weight5, model_decoder_layers_16_encoder_attn_out_proj_bias5, lv221), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm406 = R.call_tir(cls.layer_norm3, (lv225, model_decoder_layers_16_final_layer_norm_weight5, model_decoder_layers_16_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv226 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm406, model_decoder_layers_16_fc1_weight5, model_decoder_layers_16_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv227 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv226, model_decoder_layers_16_fc2_weight5, model_decoder_layers_16_fc2_bias5, lv225), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm407 = R.call_tir(cls.layer_norm3, (lv227, model_decoder_layers_17_self_attn_layer_norm_weight5, model_decoder_layers_17_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv228 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm407, model_decoder_layers_17_self_attn_q_proj_weight5, model_decoder_layers_17_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv137_1 = R.call_tir(cls.NT_matmul, (layer_norm407, model_decoder_layers_17_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv229 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm407, model_decoder_layers_17_self_attn_v_proj_weight5, model_decoder_layers_17_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv230 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv228, lv137_1, lv229), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv299 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), lv230), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv231 = R.call_tir(cls.fused_reshape23_reshape24, (lv299,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv232 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv231, model_decoder_layers_17_self_attn_out_proj_weight5, model_decoder_layers_17_self_attn_out_proj_bias5, lv227), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm408 = R.call_tir(cls.layer_norm3, (lv232, model_decoder_layers_17_encoder_attn_layer_norm_weight5, model_decoder_layers_17_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv233 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm408, model_decoder_layers_17_encoder_attn_q_proj_weight5, model_decoder_layers_17_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv234 = R.call_tir(cls.fused_reshape21_reshape25, (lv233,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv300 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), lv234), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv235 = R.call_tir(cls.fused_reshape23_reshape24, (lv300,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv236 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv235, model_decoder_layers_17_encoder_attn_out_proj_weight5, model_decoder_layers_17_encoder_attn_out_proj_bias5, lv232), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm409 = R.call_tir(cls.layer_norm3, (lv236, model_decoder_layers_17_final_layer_norm_weight5, model_decoder_layers_17_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv237 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm409, model_decoder_layers_17_fc1_weight5, model_decoder_layers_17_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv238 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv237, model_decoder_layers_17_fc2_weight5, model_decoder_layers_17_fc2_bias5, lv236), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm410 = R.call_tir(cls.layer_norm3, (lv238, model_decoder_layers_18_self_attn_layer_norm_weight5, model_decoder_layers_18_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv239 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm410, model_decoder_layers_18_self_attn_q_proj_weight5, model_decoder_layers_18_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv145_1 = R.call_tir(cls.NT_matmul, (layer_norm410, model_decoder_layers_18_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv240 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm410, model_decoder_layers_18_self_attn_v_proj_weight5, model_decoder_layers_18_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv241 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv239, lv145_1, lv240), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv301 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), lv241), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv242 = R.call_tir(cls.fused_reshape23_reshape24, (lv301,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv243 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv242, model_decoder_layers_18_self_attn_out_proj_weight5, model_decoder_layers_18_self_attn_out_proj_bias5, lv238), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm411 = R.call_tir(cls.layer_norm3, (lv243, model_decoder_layers_18_encoder_attn_layer_norm_weight5, model_decoder_layers_18_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv244 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm411, model_decoder_layers_18_encoder_attn_q_proj_weight5, model_decoder_layers_18_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv245 = R.call_tir(cls.fused_reshape21_reshape25, (lv244,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv302 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), lv245), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv246 = R.call_tir(cls.fused_reshape23_reshape24, (lv302,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv247 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv246, model_decoder_layers_18_encoder_attn_out_proj_weight5, model_decoder_layers_18_encoder_attn_out_proj_bias5, lv243), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm412 = R.call_tir(cls.layer_norm3, (lv247, model_decoder_layers_18_final_layer_norm_weight5, model_decoder_layers_18_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv248 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm412, model_decoder_layers_18_fc1_weight5, model_decoder_layers_18_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv249 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv248, model_decoder_layers_18_fc2_weight5, model_decoder_layers_18_fc2_bias5, lv247), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm413 = R.call_tir(cls.layer_norm3, (lv249, model_decoder_layers_19_self_attn_layer_norm_weight5, model_decoder_layers_19_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv250 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm413, model_decoder_layers_19_self_attn_q_proj_weight5, model_decoder_layers_19_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv153_1 = R.call_tir(cls.NT_matmul, (layer_norm413, model_decoder_layers_19_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv251 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm413, model_decoder_layers_19_self_attn_v_proj_weight5, model_decoder_layers_19_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv252 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv250, lv153_1, lv251), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv303 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), lv252), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv253 = R.call_tir(cls.fused_reshape23_reshape24, (lv303,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv254 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv253, model_decoder_layers_19_self_attn_out_proj_weight5, model_decoder_layers_19_self_attn_out_proj_bias5, lv249), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm414 = R.call_tir(cls.layer_norm3, (lv254, model_decoder_layers_19_encoder_attn_layer_norm_weight5, model_decoder_layers_19_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv255 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm414, model_decoder_layers_19_encoder_attn_q_proj_weight5, model_decoder_layers_19_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv256 = R.call_tir(cls.fused_reshape21_reshape25, (lv255,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv304 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), lv256), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv257 = R.call_tir(cls.fused_reshape23_reshape24, (lv304,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv258 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv257, model_decoder_layers_19_encoder_attn_out_proj_weight5, model_decoder_layers_19_encoder_attn_out_proj_bias5, lv254), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm415 = R.call_tir(cls.layer_norm3, (lv258, model_decoder_layers_19_final_layer_norm_weight5, model_decoder_layers_19_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv259 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm415, model_decoder_layers_19_fc1_weight5, model_decoder_layers_19_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv260 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv259, model_decoder_layers_19_fc2_weight5, model_decoder_layers_19_fc2_bias5, lv258), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm416 = R.call_tir(cls.layer_norm3, (lv260, model_decoder_layers_20_self_attn_layer_norm_weight5, model_decoder_layers_20_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv261 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm416, model_decoder_layers_20_self_attn_q_proj_weight5, model_decoder_layers_20_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv161_1 = R.call_tir(cls.NT_matmul, (layer_norm416, model_decoder_layers_20_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv262 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm416, model_decoder_layers_20_self_attn_v_proj_weight5, model_decoder_layers_20_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv263 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv261, lv161_1, lv262), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv305 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), lv263), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv264_1 = R.call_tir(cls.fused_reshape23_reshape24, (lv305,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv265_1 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv264_1, model_decoder_layers_20_self_attn_out_proj_weight5, model_decoder_layers_20_self_attn_out_proj_bias5, lv260), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm417 = R.call_tir(cls.layer_norm3, (lv265_1, model_decoder_layers_20_encoder_attn_layer_norm_weight5, model_decoder_layers_20_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv266_1 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm417, model_decoder_layers_20_encoder_attn_q_proj_weight5, model_decoder_layers_20_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv267_1 = R.call_tir(cls.fused_reshape21_reshape25, (lv266_1,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv306 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), lv267_1), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv268_1 = R.call_tir(cls.fused_reshape23_reshape24, (lv306,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv269_1 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv268_1, model_decoder_layers_20_encoder_attn_out_proj_weight5, model_decoder_layers_20_encoder_attn_out_proj_bias5, lv265_1), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm418 = R.call_tir(cls.layer_norm3, (lv269_1, model_decoder_layers_20_final_layer_norm_weight5, model_decoder_layers_20_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv270_1 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm418, model_decoder_layers_20_fc1_weight5, model_decoder_layers_20_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv271_1 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv270_1, model_decoder_layers_20_fc2_weight5, model_decoder_layers_20_fc2_bias5, lv269_1), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm419 = R.call_tir(cls.layer_norm3, (lv271_1, model_decoder_layers_21_self_attn_layer_norm_weight5, model_decoder_layers_21_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv272_1 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm419, model_decoder_layers_21_self_attn_q_proj_weight5, model_decoder_layers_21_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv169_1 = R.call_tir(cls.NT_matmul, (layer_norm419, model_decoder_layers_21_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv273_1 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm419, model_decoder_layers_21_self_attn_v_proj_weight5, model_decoder_layers_21_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv274_1 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv272_1, lv169_1, lv273_1), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv307 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), lv274_1), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv275_1 = R.call_tir(cls.fused_reshape23_reshape24, (lv307,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv276_1 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv275_1, model_decoder_layers_21_self_attn_out_proj_weight5, model_decoder_layers_21_self_attn_out_proj_bias5, lv271_1), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm420 = R.call_tir(cls.layer_norm3, (lv276_1, model_decoder_layers_21_encoder_attn_layer_norm_weight5, model_decoder_layers_21_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv277_1 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm420, model_decoder_layers_21_encoder_attn_q_proj_weight5, model_decoder_layers_21_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv278_1 = R.call_tir(cls.fused_reshape21_reshape25, (lv277_1,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv308 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), lv278_1), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv279_1 = R.call_tir(cls.fused_reshape23_reshape24, (lv308,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv280_1 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv279_1, model_decoder_layers_21_encoder_attn_out_proj_weight5, model_decoder_layers_21_encoder_attn_out_proj_bias5, lv276_1), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm421 = R.call_tir(cls.layer_norm3, (lv280_1, model_decoder_layers_21_final_layer_norm_weight5, model_decoder_layers_21_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv281_1 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm421, model_decoder_layers_21_fc1_weight5, model_decoder_layers_21_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv282_1 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv281_1, model_decoder_layers_21_fc2_weight5, model_decoder_layers_21_fc2_bias5, lv280_1), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm422 = R.call_tir(cls.layer_norm3, (lv282_1, model_decoder_layers_22_self_attn_layer_norm_weight5, model_decoder_layers_22_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv283_1 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm422, model_decoder_layers_22_self_attn_q_proj_weight5, model_decoder_layers_22_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv177_1 = R.call_tir(cls.NT_matmul, (layer_norm422, model_decoder_layers_22_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv284_1 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm422, model_decoder_layers_22_self_attn_v_proj_weight5, model_decoder_layers_22_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv285_1 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv283_1, lv177_1, lv284_1), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv309 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), lv285_1), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv286_1 = R.call_tir(cls.fused_reshape23_reshape24, (lv309,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv287_1 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv286_1, model_decoder_layers_22_self_attn_out_proj_weight5, model_decoder_layers_22_self_attn_out_proj_bias5, lv282_1), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm423 = R.call_tir(cls.layer_norm3, (lv287_1, model_decoder_layers_22_encoder_attn_layer_norm_weight5, model_decoder_layers_22_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv288_1 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm423, model_decoder_layers_22_encoder_attn_q_proj_weight5, model_decoder_layers_22_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv289_1 = R.call_tir(cls.fused_reshape21_reshape25, (lv288_1,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv310 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), lv289_1), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv290_1 = R.call_tir(cls.fused_reshape23_reshape24, (lv310,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv291_1 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv290_1, model_decoder_layers_22_encoder_attn_out_proj_weight5, model_decoder_layers_22_encoder_attn_out_proj_bias5, lv287_1), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm424 = R.call_tir(cls.layer_norm3, (lv291_1, model_decoder_layers_22_final_layer_norm_weight5, model_decoder_layers_22_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv292_1 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm424, model_decoder_layers_22_fc1_weight5, model_decoder_layers_22_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv293_1 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv292_1, model_decoder_layers_22_fc2_weight5, model_decoder_layers_22_fc2_bias5, lv291_1), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm425 = R.call_tir(cls.layer_norm3, (lv293_1, model_decoder_layers_23_self_attn_layer_norm_weight5, model_decoder_layers_23_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv294_1 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm425, model_decoder_layers_23_self_attn_q_proj_weight5, model_decoder_layers_23_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv185_1 = R.call_tir(cls.NT_matmul, (layer_norm425, model_decoder_layers_23_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv295_1 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm425, model_decoder_layers_23_self_attn_v_proj_weight5, model_decoder_layers_23_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv296_1 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv294_1, lv185_1, lv295_1), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv311 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), lv296_1), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv297_1 = R.call_tir(cls.fused_reshape23_reshape24, (lv311,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv298_1 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv297_1, model_decoder_layers_23_self_attn_out_proj_weight5, model_decoder_layers_23_self_attn_out_proj_bias5, lv293_1), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm426 = R.call_tir(cls.layer_norm3, (lv298_1, model_decoder_layers_23_encoder_attn_layer_norm_weight5, model_decoder_layers_23_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv299_1 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm426, model_decoder_layers_23_encoder_attn_q_proj_weight5, model_decoder_layers_23_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv300_1 = R.call_tir(cls.fused_reshape21_reshape25, (lv299_1,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv312 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), lv300_1), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv301_1 = R.call_tir(cls.fused_reshape23_reshape24, (lv312,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv302_1 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv301_1, model_decoder_layers_23_encoder_attn_out_proj_weight5, model_decoder_layers_23_encoder_attn_out_proj_bias5, lv298_1), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm427 = R.call_tir(cls.layer_norm3, (lv302_1, model_decoder_layers_23_final_layer_norm_weight5, model_decoder_layers_23_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv303_1 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm427, model_decoder_layers_23_fc1_weight5, model_decoder_layers_23_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv304_1 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv303_1, model_decoder_layers_23_fc2_weight5, model_decoder_layers_23_fc2_bias5, lv302_1), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm428 = R.call_tir(cls.layer_norm3, (lv304_1, model_decoder_layers_24_self_attn_layer_norm_weight5, model_decoder_layers_24_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv305_1 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm428, model_decoder_layers_24_self_attn_q_proj_weight5, model_decoder_layers_24_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv193_1 = R.call_tir(cls.NT_matmul, (layer_norm428, model_decoder_layers_24_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv306_1 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm428, model_decoder_layers_24_self_attn_v_proj_weight5, model_decoder_layers_24_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv307_1 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv305_1, lv193_1, lv306_1), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv313 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), lv307_1), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv308_1 = R.call_tir(cls.fused_reshape23_reshape24, (lv313,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv309_1 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv308_1, model_decoder_layers_24_self_attn_out_proj_weight5, model_decoder_layers_24_self_attn_out_proj_bias5, lv304_1), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm429 = R.call_tir(cls.layer_norm3, (lv309_1, model_decoder_layers_24_encoder_attn_layer_norm_weight5, model_decoder_layers_24_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv310_1 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm429, model_decoder_layers_24_encoder_attn_q_proj_weight5, model_decoder_layers_24_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv311_1 = R.call_tir(cls.fused_reshape21_reshape25, (lv310_1,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv314 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), lv311_1), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv312_1 = R.call_tir(cls.fused_reshape23_reshape24, (lv314,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv313_1 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv312_1, model_decoder_layers_24_encoder_attn_out_proj_weight5, model_decoder_layers_24_encoder_attn_out_proj_bias5, lv309_1), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm430 = R.call_tir(cls.layer_norm3, (lv313_1, model_decoder_layers_24_final_layer_norm_weight5, model_decoder_layers_24_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv314_1 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm430, model_decoder_layers_24_fc1_weight5, model_decoder_layers_24_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv315 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv314_1, model_decoder_layers_24_fc2_weight5, model_decoder_layers_24_fc2_bias5, lv313_1), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm431 = R.call_tir(cls.layer_norm3, (lv315, model_decoder_layers_25_self_attn_layer_norm_weight5, model_decoder_layers_25_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv316 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm431, model_decoder_layers_25_self_attn_q_proj_weight5, model_decoder_layers_25_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv201_1 = R.call_tir(cls.NT_matmul, (layer_norm431, model_decoder_layers_25_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv317 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm431, model_decoder_layers_25_self_attn_v_proj_weight5, model_decoder_layers_25_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv318 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv316, lv201_1, lv317), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv315_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), lv318), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv319 = R.call_tir(cls.fused_reshape23_reshape24, (lv315_1,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv320 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv319, model_decoder_layers_25_self_attn_out_proj_weight5, model_decoder_layers_25_self_attn_out_proj_bias5, lv315), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm432 = R.call_tir(cls.layer_norm3, (lv320, model_decoder_layers_25_encoder_attn_layer_norm_weight5, model_decoder_layers_25_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv321 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm432, model_decoder_layers_25_encoder_attn_q_proj_weight5, model_decoder_layers_25_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv322 = R.call_tir(cls.fused_reshape21_reshape25, (lv321,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv316_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), lv322), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv323 = R.call_tir(cls.fused_reshape23_reshape24, (lv316_1,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv324 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv323, model_decoder_layers_25_encoder_attn_out_proj_weight5, model_decoder_layers_25_encoder_attn_out_proj_bias5, lv320), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm433 = R.call_tir(cls.layer_norm3, (lv324, model_decoder_layers_25_final_layer_norm_weight5, model_decoder_layers_25_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv325 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm433, model_decoder_layers_25_fc1_weight5, model_decoder_layers_25_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv326 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv325, model_decoder_layers_25_fc2_weight5, model_decoder_layers_25_fc2_bias5, lv324), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm434 = R.call_tir(cls.layer_norm3, (lv326, model_decoder_layers_26_self_attn_layer_norm_weight5, model_decoder_layers_26_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv327 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm434, model_decoder_layers_26_self_attn_q_proj_weight5, model_decoder_layers_26_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv209_1 = R.call_tir(cls.NT_matmul, (layer_norm434, model_decoder_layers_26_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv328 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm434, model_decoder_layers_26_self_attn_v_proj_weight5, model_decoder_layers_26_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv329 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv327, lv209_1, lv328), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv317_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), lv329), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv330 = R.call_tir(cls.fused_reshape23_reshape24, (lv317_1,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv331 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv330, model_decoder_layers_26_self_attn_out_proj_weight5, model_decoder_layers_26_self_attn_out_proj_bias5, lv326), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm435 = R.call_tir(cls.layer_norm3, (lv331, model_decoder_layers_26_encoder_attn_layer_norm_weight5, model_decoder_layers_26_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv332 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm435, model_decoder_layers_26_encoder_attn_q_proj_weight5, model_decoder_layers_26_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv333 = R.call_tir(cls.fused_reshape21_reshape25, (lv332,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv318_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), lv333), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv334 = R.call_tir(cls.fused_reshape23_reshape24, (lv318_1,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv335 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv334, model_decoder_layers_26_encoder_attn_out_proj_weight5, model_decoder_layers_26_encoder_attn_out_proj_bias5, lv331), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm436 = R.call_tir(cls.layer_norm3, (lv335, model_decoder_layers_26_final_layer_norm_weight5, model_decoder_layers_26_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv336 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm436, model_decoder_layers_26_fc1_weight5, model_decoder_layers_26_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv337 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv336, model_decoder_layers_26_fc2_weight5, model_decoder_layers_26_fc2_bias5, lv335), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm437 = R.call_tir(cls.layer_norm3, (lv337, model_decoder_layers_27_self_attn_layer_norm_weight5, model_decoder_layers_27_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv338 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm437, model_decoder_layers_27_self_attn_q_proj_weight5, model_decoder_layers_27_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv217_1 = R.call_tir(cls.NT_matmul, (layer_norm437, model_decoder_layers_27_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv339 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm437, model_decoder_layers_27_self_attn_v_proj_weight5, model_decoder_layers_27_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv340 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv338, lv217_1, lv339), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv319_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), lv340), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv341 = R.call_tir(cls.fused_reshape23_reshape24, (lv319_1,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv342 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv341, model_decoder_layers_27_self_attn_out_proj_weight5, model_decoder_layers_27_self_attn_out_proj_bias5, lv337), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm438 = R.call_tir(cls.layer_norm3, (lv342, model_decoder_layers_27_encoder_attn_layer_norm_weight5, model_decoder_layers_27_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv343 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm438, model_decoder_layers_27_encoder_attn_q_proj_weight5, model_decoder_layers_27_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv344 = R.call_tir(cls.fused_reshape21_reshape25, (lv343,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv320_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), lv344), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv345 = R.call_tir(cls.fused_reshape23_reshape24, (lv320_1,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv346 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv345, model_decoder_layers_27_encoder_attn_out_proj_weight5, model_decoder_layers_27_encoder_attn_out_proj_bias5, lv342), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm439 = R.call_tir(cls.layer_norm3, (lv346, model_decoder_layers_27_final_layer_norm_weight5, model_decoder_layers_27_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv347 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm439, model_decoder_layers_27_fc1_weight5, model_decoder_layers_27_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv348 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv347, model_decoder_layers_27_fc2_weight5, model_decoder_layers_27_fc2_bias5, lv346), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm440 = R.call_tir(cls.layer_norm3, (lv348, model_decoder_layers_28_self_attn_layer_norm_weight5, model_decoder_layers_28_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv349 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm440, model_decoder_layers_28_self_attn_q_proj_weight5, model_decoder_layers_28_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv225_1 = R.call_tir(cls.NT_matmul, (layer_norm440, model_decoder_layers_28_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv350 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm440, model_decoder_layers_28_self_attn_v_proj_weight5, model_decoder_layers_28_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv351 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv349, lv225_1, lv350), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv321_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), lv351), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv352 = R.call_tir(cls.fused_reshape23_reshape24, (lv321_1,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv353 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv352, model_decoder_layers_28_self_attn_out_proj_weight5, model_decoder_layers_28_self_attn_out_proj_bias5, lv348), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm441 = R.call_tir(cls.layer_norm3, (lv353, model_decoder_layers_28_encoder_attn_layer_norm_weight5, model_decoder_layers_28_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv354 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm441, model_decoder_layers_28_encoder_attn_q_proj_weight5, model_decoder_layers_28_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv355 = R.call_tir(cls.fused_reshape21_reshape25, (lv354,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv322_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), lv355), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv356 = R.call_tir(cls.fused_reshape23_reshape24, (lv322_1,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv357 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv356, model_decoder_layers_28_encoder_attn_out_proj_weight5, model_decoder_layers_28_encoder_attn_out_proj_bias5, lv353), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm442 = R.call_tir(cls.layer_norm3, (lv357, model_decoder_layers_28_final_layer_norm_weight5, model_decoder_layers_28_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv358 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm442, model_decoder_layers_28_fc1_weight5, model_decoder_layers_28_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv359 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv358, model_decoder_layers_28_fc2_weight5, model_decoder_layers_28_fc2_bias5, lv357), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm443 = R.call_tir(cls.layer_norm3, (lv359, model_decoder_layers_29_self_attn_layer_norm_weight5, model_decoder_layers_29_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv360 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm443, model_decoder_layers_29_self_attn_q_proj_weight5, model_decoder_layers_29_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv233_1 = R.call_tir(cls.NT_matmul, (layer_norm443, model_decoder_layers_29_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv361 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm443, model_decoder_layers_29_self_attn_v_proj_weight5, model_decoder_layers_29_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv362 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv360, lv233_1, lv361), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv323_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), lv362), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv363 = R.call_tir(cls.fused_reshape23_reshape24, (lv323_1,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv364 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv363, model_decoder_layers_29_self_attn_out_proj_weight5, model_decoder_layers_29_self_attn_out_proj_bias5, lv359), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm444 = R.call_tir(cls.layer_norm3, (lv364, model_decoder_layers_29_encoder_attn_layer_norm_weight5, model_decoder_layers_29_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv365 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm444, model_decoder_layers_29_encoder_attn_q_proj_weight5, model_decoder_layers_29_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv366 = R.call_tir(cls.fused_reshape21_reshape25, (lv365,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv324_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), lv366), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv367 = R.call_tir(cls.fused_reshape23_reshape24, (lv324_1,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv368 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv367, model_decoder_layers_29_encoder_attn_out_proj_weight5, model_decoder_layers_29_encoder_attn_out_proj_bias5, lv364), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm445 = R.call_tir(cls.layer_norm3, (lv368, model_decoder_layers_29_final_layer_norm_weight5, model_decoder_layers_29_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv369 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm445, model_decoder_layers_29_fc1_weight5, model_decoder_layers_29_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv370 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv369, model_decoder_layers_29_fc2_weight5, model_decoder_layers_29_fc2_bias5, lv368), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm446 = R.call_tir(cls.layer_norm3, (lv370, model_decoder_layers_30_self_attn_layer_norm_weight5, model_decoder_layers_30_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv371 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm446, model_decoder_layers_30_self_attn_q_proj_weight5, model_decoder_layers_30_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv241_1 = R.call_tir(cls.NT_matmul, (layer_norm446, model_decoder_layers_30_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv372 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm446, model_decoder_layers_30_self_attn_v_proj_weight5, model_decoder_layers_30_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv373 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv371, lv241_1, lv372), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv325_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), lv373), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv374 = R.call_tir(cls.fused_reshape23_reshape24, (lv325_1,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv375 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv374, model_decoder_layers_30_self_attn_out_proj_weight5, model_decoder_layers_30_self_attn_out_proj_bias5, lv370), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm447 = R.call_tir(cls.layer_norm3, (lv375, model_decoder_layers_30_encoder_attn_layer_norm_weight5, model_decoder_layers_30_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv376 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm447, model_decoder_layers_30_encoder_attn_q_proj_weight5, model_decoder_layers_30_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv377 = R.call_tir(cls.fused_reshape21_reshape25, (lv376,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv326_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), lv377), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv378 = R.call_tir(cls.fused_reshape23_reshape24, (lv326_1,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv379 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv378, model_decoder_layers_30_encoder_attn_out_proj_weight5, model_decoder_layers_30_encoder_attn_out_proj_bias5, lv375), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm448 = R.call_tir(cls.layer_norm3, (lv379, model_decoder_layers_30_final_layer_norm_weight5, model_decoder_layers_30_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv380 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm448, model_decoder_layers_30_fc1_weight5, model_decoder_layers_30_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv381 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv380, model_decoder_layers_30_fc2_weight5, model_decoder_layers_30_fc2_bias5, lv379), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm449 = R.call_tir(cls.layer_norm3, (lv381, model_decoder_layers_31_self_attn_layer_norm_weight5, model_decoder_layers_31_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv382 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm449, model_decoder_layers_31_self_attn_q_proj_weight5, model_decoder_layers_31_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv249_1 = R.call_tir(cls.NT_matmul, (layer_norm449, model_decoder_layers_31_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv383 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm449, model_decoder_layers_31_self_attn_v_proj_weight5, model_decoder_layers_31_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv384 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv382, lv249_1, lv383), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv327_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), lv384), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv385 = R.call_tir(cls.fused_reshape23_reshape24, (lv327_1,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv386 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv385, model_decoder_layers_31_self_attn_out_proj_weight5, model_decoder_layers_31_self_attn_out_proj_bias5, lv381), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm450 = R.call_tir(cls.layer_norm3, (lv386, model_decoder_layers_31_encoder_attn_layer_norm_weight5, model_decoder_layers_31_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv387 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm450, model_decoder_layers_31_encoder_attn_q_proj_weight5, model_decoder_layers_31_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv388 = R.call_tir(cls.fused_reshape21_reshape25, (lv387,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv328_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), lv388), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv389 = R.call_tir(cls.fused_reshape23_reshape24, (lv328_1,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv390 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv389, model_decoder_layers_31_encoder_attn_out_proj_weight5, model_decoder_layers_31_encoder_attn_out_proj_bias5, lv386), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm451 = R.call_tir(cls.layer_norm3, (lv390, model_decoder_layers_31_final_layer_norm_weight5, model_decoder_layers_31_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv391 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm451, model_decoder_layers_31_fc1_weight5, model_decoder_layers_31_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv392 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv391, model_decoder_layers_31_fc2_weight5, model_decoder_layers_31_fc2_bias5, lv390), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm452 = R.call_tir(cls.layer_norm3, (lv392, model_decoder_layer_norm_weight5, model_decoder_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + gv5 = R.call_tir(cls.NT_matmul3, (layer_norm452, model_decoder_embed_tokens_weight5), out_sinfo=R.Tensor((1, 1, 51866), dtype="float32")) + R.output(gv5) + return gv5 + + @R.function + def multinomial_from_uniform(probs: R.Tensor(("batch_size", "vocab_size"), dtype="float32"), uniform_samples: R.Tensor(("num_samples",), dtype="float32"), sample_indices: R.Tensor(("num_samples",), dtype="int32")) -> R.Tensor(("num_samples",), dtype="int32"): + num_samples = T.int64() + batch_size = T.int64() + vocab_size = T.int64() + R.func_attr({"relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "num_positions": 48, "num_samples": 8}}) + cls = Module + with R.dataflow(): + uniform_samples_1: R.Tensor((num_samples, 1), dtype="float32") = R.call_pure_packed("vm.builtin.reshape", uniform_samples, R.shape([num_samples, 1]), sinfo_args=(R.Tensor((num_samples, 1), dtype="float32"),)) + sample_indices_1: R.Tensor((num_samples, 1), dtype="int32") = R.call_pure_packed("vm.builtin.reshape", sample_indices, R.shape([num_samples, 1]), sinfo_args=(R.Tensor((num_samples, 1), dtype="int32"),)) + nn_multinomial_from_uniform = R.call_tir(cls.parallel_sampling_from_prob, (probs, uniform_samples_1, sample_indices_1), out_sinfo=R.Tensor((num_samples, 1), dtype="int32")) + gv: R.Tensor((num_samples,), dtype="int32") = R.call_pure_packed("vm.builtin.reshape", nn_multinomial_from_uniform, R.shape([num_samples]), sinfo_args=(R.Tensor((num_samples,), dtype="int32"),)) + R.output(gv) + return gv + + @R.function + def prefill(input_ids: R.Tensor((1, "seq_len"), dtype="int32"), paged_kv_cache: R.Object, packed_params: R.Tuple(R.Tensor((1280, 128, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1500, 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R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"))) -> R.Tensor((1, 1, 51866), dtype="float32"): + seq_len = T.int64() + R.func_attr({"num_input": 2, "relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "seq_len": 15000, "total_seq_len": 1500}}) + cls = Module + with R.dataflow(): + model_decoder_embed_tokens_weight4: R.Tensor((51866, 1280), dtype="float16") = packed_params[487] + model_decoder_embed_positions_weight4: R.Tensor((448, 1280), dtype="float16") = packed_params[488] + model_decoder_layers_0_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[489] + model_decoder_layers_0_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[490] + model_decoder_layers_0_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[491] + model_decoder_layers_0_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[492] + model_decoder_layers_0_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[493] + model_decoder_layers_0_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[494] + model_decoder_layers_0_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[495] + model_decoder_layers_0_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[496] + model_decoder_layers_0_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[497] + model_decoder_layers_0_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[501] + model_decoder_layers_0_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[502] + model_decoder_layers_0_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[503] + model_decoder_layers_0_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[504] + model_decoder_layers_0_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[505] + model_decoder_layers_0_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[506] + model_decoder_layers_0_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[507] + model_decoder_layers_0_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[508] + model_decoder_layers_0_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[509] + model_decoder_layers_0_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[510] + model_decoder_layers_0_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[511] + model_decoder_layers_0_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[512] + model_decoder_layers_1_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[513] + model_decoder_layers_1_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[514] + model_decoder_layers_1_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[515] + model_decoder_layers_1_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[516] + model_decoder_layers_1_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[517] + model_decoder_layers_1_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[518] + model_decoder_layers_1_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[519] + model_decoder_layers_1_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[520] + model_decoder_layers_1_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[521] + model_decoder_layers_1_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[525] + model_decoder_layers_1_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[526] + model_decoder_layers_1_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[527] + model_decoder_layers_1_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[528] + model_decoder_layers_1_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[529] + model_decoder_layers_1_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[530] + model_decoder_layers_1_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[531] + model_decoder_layers_1_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[532] + model_decoder_layers_1_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[533] + model_decoder_layers_1_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[534] + model_decoder_layers_1_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[535] + model_decoder_layers_1_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[536] + model_decoder_layers_2_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[537] + model_decoder_layers_2_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[538] + model_decoder_layers_2_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[539] + model_decoder_layers_2_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[540] + model_decoder_layers_2_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[541] + model_decoder_layers_2_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[542] + model_decoder_layers_2_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[543] + model_decoder_layers_2_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[544] + model_decoder_layers_2_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[545] + model_decoder_layers_2_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[549] + model_decoder_layers_2_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[550] + model_decoder_layers_2_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[551] + model_decoder_layers_2_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[552] + model_decoder_layers_2_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[553] + model_decoder_layers_2_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[554] + model_decoder_layers_2_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[555] + model_decoder_layers_2_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[556] + model_decoder_layers_2_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[557] + model_decoder_layers_2_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[558] + model_decoder_layers_2_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[559] + model_decoder_layers_2_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[560] + model_decoder_layers_3_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[561] + model_decoder_layers_3_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[562] + model_decoder_layers_3_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[563] + model_decoder_layers_3_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[564] + model_decoder_layers_3_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[565] + model_decoder_layers_3_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[566] + model_decoder_layers_3_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[567] + model_decoder_layers_3_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[568] + model_decoder_layers_3_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[569] + model_decoder_layers_3_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[573] + model_decoder_layers_3_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[574] + model_decoder_layers_3_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[575] + model_decoder_layers_3_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[576] + model_decoder_layers_3_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[577] + model_decoder_layers_3_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[578] + model_decoder_layers_3_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[579] + model_decoder_layers_3_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[580] + model_decoder_layers_3_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[581] + model_decoder_layers_3_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[582] + model_decoder_layers_3_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[583] + model_decoder_layers_3_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[584] + model_decoder_layers_4_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[585] + model_decoder_layers_4_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[586] + model_decoder_layers_4_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[587] + model_decoder_layers_4_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[588] + model_decoder_layers_4_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[589] + model_decoder_layers_4_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[590] + model_decoder_layers_4_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[591] + model_decoder_layers_4_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[592] + model_decoder_layers_4_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[593] + model_decoder_layers_4_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[597] + model_decoder_layers_4_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[598] + model_decoder_layers_4_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[599] + model_decoder_layers_4_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[600] + model_decoder_layers_4_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[601] + model_decoder_layers_4_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[602] + model_decoder_layers_4_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[603] + model_decoder_layers_4_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[604] + model_decoder_layers_4_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[605] + model_decoder_layers_4_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[606] + model_decoder_layers_4_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[607] + model_decoder_layers_4_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[608] + model_decoder_layers_5_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[609] + model_decoder_layers_5_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[610] + model_decoder_layers_5_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[611] + model_decoder_layers_5_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[612] + model_decoder_layers_5_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[613] + model_decoder_layers_5_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[614] + model_decoder_layers_5_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[615] + model_decoder_layers_5_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[616] + model_decoder_layers_5_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[617] + model_decoder_layers_5_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[621] + model_decoder_layers_5_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[622] + model_decoder_layers_5_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[623] + model_decoder_layers_5_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[624] + model_decoder_layers_5_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[625] + model_decoder_layers_5_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[626] + model_decoder_layers_5_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[627] + model_decoder_layers_5_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[628] + model_decoder_layers_5_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[629] + model_decoder_layers_5_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[630] + model_decoder_layers_5_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[631] + model_decoder_layers_5_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[632] + model_decoder_layers_6_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[633] + model_decoder_layers_6_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[634] + model_decoder_layers_6_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[635] + model_decoder_layers_6_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[636] + model_decoder_layers_6_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[637] + model_decoder_layers_6_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[638] + model_decoder_layers_6_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[639] + model_decoder_layers_6_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[640] + model_decoder_layers_6_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[641] + model_decoder_layers_6_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[645] + model_decoder_layers_6_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[646] + model_decoder_layers_6_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[647] + model_decoder_layers_6_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[648] + model_decoder_layers_6_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[649] + model_decoder_layers_6_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[650] + model_decoder_layers_6_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[651] + model_decoder_layers_6_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[652] + model_decoder_layers_6_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[653] + model_decoder_layers_6_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[654] + model_decoder_layers_6_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[655] + model_decoder_layers_6_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[656] + model_decoder_layers_7_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[657] + model_decoder_layers_7_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[658] + model_decoder_layers_7_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[659] + model_decoder_layers_7_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[660] + model_decoder_layers_7_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[661] + model_decoder_layers_7_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[662] + model_decoder_layers_7_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[663] + model_decoder_layers_7_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[664] + model_decoder_layers_7_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[665] + model_decoder_layers_7_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[669] + model_decoder_layers_7_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[670] + model_decoder_layers_7_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[671] + model_decoder_layers_7_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[672] + model_decoder_layers_7_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[673] + model_decoder_layers_7_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[674] + model_decoder_layers_7_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[675] + model_decoder_layers_7_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[676] + model_decoder_layers_7_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[677] + model_decoder_layers_7_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[678] + model_decoder_layers_7_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[679] + model_decoder_layers_7_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[680] + model_decoder_layers_8_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[681] + model_decoder_layers_8_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[682] + model_decoder_layers_8_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[683] + model_decoder_layers_8_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[684] + model_decoder_layers_8_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[685] + model_decoder_layers_8_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[686] + model_decoder_layers_8_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[687] + model_decoder_layers_8_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[688] + model_decoder_layers_8_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[689] + model_decoder_layers_8_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[693] + model_decoder_layers_8_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[694] + model_decoder_layers_8_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[695] + model_decoder_layers_8_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[696] + model_decoder_layers_8_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[697] + model_decoder_layers_8_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[698] + model_decoder_layers_8_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[699] + model_decoder_layers_8_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[700] + model_decoder_layers_8_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[701] + model_decoder_layers_8_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[702] + model_decoder_layers_8_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[703] + model_decoder_layers_8_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[704] + model_decoder_layers_9_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[705] + model_decoder_layers_9_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[706] + model_decoder_layers_9_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[707] + model_decoder_layers_9_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[708] + model_decoder_layers_9_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[709] + model_decoder_layers_9_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[710] + model_decoder_layers_9_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[711] + model_decoder_layers_9_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[712] + model_decoder_layers_9_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[713] + model_decoder_layers_9_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[717] + model_decoder_layers_9_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[718] + model_decoder_layers_9_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[719] + model_decoder_layers_9_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[720] + model_decoder_layers_9_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[721] + model_decoder_layers_9_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[722] + model_decoder_layers_9_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[723] + model_decoder_layers_9_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[724] + model_decoder_layers_9_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[725] + model_decoder_layers_9_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[726] + model_decoder_layers_9_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[727] + model_decoder_layers_9_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[728] + model_decoder_layers_10_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[729] + model_decoder_layers_10_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[730] + model_decoder_layers_10_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[731] + model_decoder_layers_10_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[732] + model_decoder_layers_10_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[733] + model_decoder_layers_10_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[734] + model_decoder_layers_10_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[735] + model_decoder_layers_10_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[736] + model_decoder_layers_10_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[737] + model_decoder_layers_10_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[741] + model_decoder_layers_10_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[742] + model_decoder_layers_10_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[743] + model_decoder_layers_10_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[744] + model_decoder_layers_10_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[745] + model_decoder_layers_10_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[746] + model_decoder_layers_10_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[747] + model_decoder_layers_10_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[748] + model_decoder_layers_10_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[749] + model_decoder_layers_10_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[750] + model_decoder_layers_10_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[751] + model_decoder_layers_10_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[752] + model_decoder_layers_11_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[753] + model_decoder_layers_11_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[754] + model_decoder_layers_11_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[755] + model_decoder_layers_11_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[756] + model_decoder_layers_11_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[757] + model_decoder_layers_11_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[758] + model_decoder_layers_11_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[759] + model_decoder_layers_11_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[760] + model_decoder_layers_11_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[761] + model_decoder_layers_11_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[765] + model_decoder_layers_11_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[766] + model_decoder_layers_11_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[767] + model_decoder_layers_11_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[768] + model_decoder_layers_11_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[769] + model_decoder_layers_11_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[770] + model_decoder_layers_11_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[771] + model_decoder_layers_11_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[772] + model_decoder_layers_11_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[773] + model_decoder_layers_11_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[774] + model_decoder_layers_11_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[775] + model_decoder_layers_11_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[776] + model_decoder_layers_12_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[777] + model_decoder_layers_12_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[778] + model_decoder_layers_12_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[779] + model_decoder_layers_12_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[780] + model_decoder_layers_12_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[781] + model_decoder_layers_12_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[782] + model_decoder_layers_12_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[783] + model_decoder_layers_12_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[784] + model_decoder_layers_12_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[785] + model_decoder_layers_12_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[789] + model_decoder_layers_12_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[790] + model_decoder_layers_12_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[791] + model_decoder_layers_12_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[792] + model_decoder_layers_12_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[793] + model_decoder_layers_12_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[794] + model_decoder_layers_12_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[795] + model_decoder_layers_12_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[796] + model_decoder_layers_12_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[797] + model_decoder_layers_12_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[798] + model_decoder_layers_12_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[799] + model_decoder_layers_12_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[800] + model_decoder_layers_13_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[801] + model_decoder_layers_13_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[802] + model_decoder_layers_13_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[803] + model_decoder_layers_13_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[804] + model_decoder_layers_13_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[805] + model_decoder_layers_13_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[806] + model_decoder_layers_13_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[807] + model_decoder_layers_13_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[808] + model_decoder_layers_13_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[809] + model_decoder_layers_13_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[813] + model_decoder_layers_13_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[814] + model_decoder_layers_13_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[815] + model_decoder_layers_13_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[816] + model_decoder_layers_13_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[817] + model_decoder_layers_13_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[818] + model_decoder_layers_13_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[819] + model_decoder_layers_13_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[820] + model_decoder_layers_13_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[821] + model_decoder_layers_13_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[822] + model_decoder_layers_13_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[823] + model_decoder_layers_13_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[824] + model_decoder_layers_14_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[825] + model_decoder_layers_14_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[826] + model_decoder_layers_14_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[827] + model_decoder_layers_14_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[828] + model_decoder_layers_14_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[829] + model_decoder_layers_14_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[830] + model_decoder_layers_14_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[831] + model_decoder_layers_14_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[832] + model_decoder_layers_14_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[833] + model_decoder_layers_14_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[837] + model_decoder_layers_14_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[838] + model_decoder_layers_14_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[839] + model_decoder_layers_14_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[840] + model_decoder_layers_14_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[841] + model_decoder_layers_14_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[842] + model_decoder_layers_14_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[843] + model_decoder_layers_14_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[844] + model_decoder_layers_14_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[845] + model_decoder_layers_14_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[846] + model_decoder_layers_14_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[847] + model_decoder_layers_14_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[848] + model_decoder_layers_15_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[849] + model_decoder_layers_15_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[850] + model_decoder_layers_15_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[851] + model_decoder_layers_15_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[852] + model_decoder_layers_15_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[853] + model_decoder_layers_15_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[854] + model_decoder_layers_15_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[855] + model_decoder_layers_15_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[856] + model_decoder_layers_15_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[857] + model_decoder_layers_15_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[861] + model_decoder_layers_15_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[862] + model_decoder_layers_15_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[863] + model_decoder_layers_15_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[864] + model_decoder_layers_15_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[865] + model_decoder_layers_15_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[866] + model_decoder_layers_15_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[867] + model_decoder_layers_15_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[868] + model_decoder_layers_15_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[869] + model_decoder_layers_15_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[870] + model_decoder_layers_15_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[871] + model_decoder_layers_15_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[872] + model_decoder_layers_16_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[873] + model_decoder_layers_16_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[874] + model_decoder_layers_16_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[875] + model_decoder_layers_16_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[876] + model_decoder_layers_16_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[877] + model_decoder_layers_16_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[878] + model_decoder_layers_16_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[879] + model_decoder_layers_16_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[880] + model_decoder_layers_16_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[881] + model_decoder_layers_16_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[885] + model_decoder_layers_16_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[886] + model_decoder_layers_16_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[887] + model_decoder_layers_16_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[888] + model_decoder_layers_16_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[889] + model_decoder_layers_16_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[890] + model_decoder_layers_16_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[891] + model_decoder_layers_16_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[892] + model_decoder_layers_16_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[893] + model_decoder_layers_16_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[894] + model_decoder_layers_16_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[895] + model_decoder_layers_16_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[896] + model_decoder_layers_17_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[897] + model_decoder_layers_17_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[898] + model_decoder_layers_17_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[899] + model_decoder_layers_17_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[900] + model_decoder_layers_17_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[901] + model_decoder_layers_17_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[902] + model_decoder_layers_17_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[903] + model_decoder_layers_17_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[904] + model_decoder_layers_17_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[905] + model_decoder_layers_17_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[909] + model_decoder_layers_17_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[910] + model_decoder_layers_17_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[911] + model_decoder_layers_17_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[912] + model_decoder_layers_17_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[913] + model_decoder_layers_17_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[914] + model_decoder_layers_17_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[915] + model_decoder_layers_17_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[916] + model_decoder_layers_17_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[917] + model_decoder_layers_17_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[918] + model_decoder_layers_17_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[919] + model_decoder_layers_17_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[920] + model_decoder_layers_18_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[921] + model_decoder_layers_18_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[922] + model_decoder_layers_18_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[923] + model_decoder_layers_18_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[924] + model_decoder_layers_18_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[925] + model_decoder_layers_18_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[926] + model_decoder_layers_18_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[927] + model_decoder_layers_18_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[928] + model_decoder_layers_18_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[929] + model_decoder_layers_18_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[933] + model_decoder_layers_18_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[934] + model_decoder_layers_18_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[935] + model_decoder_layers_18_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[936] + model_decoder_layers_18_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[937] + model_decoder_layers_18_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[938] + model_decoder_layers_18_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[939] + model_decoder_layers_18_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[940] + model_decoder_layers_18_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[941] + model_decoder_layers_18_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[942] + model_decoder_layers_18_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[943] + model_decoder_layers_18_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[944] + model_decoder_layers_19_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[945] + model_decoder_layers_19_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[946] + model_decoder_layers_19_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[947] + model_decoder_layers_19_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[948] + model_decoder_layers_19_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[949] + model_decoder_layers_19_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[950] + model_decoder_layers_19_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[951] + model_decoder_layers_19_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[952] + model_decoder_layers_19_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[953] + model_decoder_layers_19_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[957] + model_decoder_layers_19_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[958] + model_decoder_layers_19_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[959] + model_decoder_layers_19_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[960] + model_decoder_layers_19_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[961] + model_decoder_layers_19_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[962] + model_decoder_layers_19_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[963] + model_decoder_layers_19_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[964] + model_decoder_layers_19_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[965] + model_decoder_layers_19_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[966] + model_decoder_layers_19_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[967] + model_decoder_layers_19_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[968] + model_decoder_layers_20_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[969] + model_decoder_layers_20_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[970] + model_decoder_layers_20_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[971] + model_decoder_layers_20_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[972] + model_decoder_layers_20_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[973] + model_decoder_layers_20_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[974] + model_decoder_layers_20_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[975] + model_decoder_layers_20_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[976] + model_decoder_layers_20_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[977] + model_decoder_layers_20_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[981] + model_decoder_layers_20_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[982] + model_decoder_layers_20_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[983] + model_decoder_layers_20_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[984] + model_decoder_layers_20_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[985] + model_decoder_layers_20_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[986] + model_decoder_layers_20_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[987] + model_decoder_layers_20_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[988] + model_decoder_layers_20_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[989] + model_decoder_layers_20_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[990] + model_decoder_layers_20_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[991] + model_decoder_layers_20_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[992] + model_decoder_layers_21_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[993] + model_decoder_layers_21_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[994] + model_decoder_layers_21_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[995] + model_decoder_layers_21_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[996] + model_decoder_layers_21_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[997] + model_decoder_layers_21_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[998] + model_decoder_layers_21_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[999] + model_decoder_layers_21_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1000] + model_decoder_layers_21_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1001] + model_decoder_layers_21_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1005] + model_decoder_layers_21_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1006] + model_decoder_layers_21_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1007] + model_decoder_layers_21_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1008] + model_decoder_layers_21_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1009] + model_decoder_layers_21_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1010] + model_decoder_layers_21_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1011] + model_decoder_layers_21_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1012] + model_decoder_layers_21_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1013] + model_decoder_layers_21_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1014] + model_decoder_layers_21_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1015] + model_decoder_layers_21_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1016] + model_decoder_layers_22_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1017] + model_decoder_layers_22_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1018] + model_decoder_layers_22_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1019] + model_decoder_layers_22_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1020] + model_decoder_layers_22_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1021] + model_decoder_layers_22_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1022] + model_decoder_layers_22_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1023] + model_decoder_layers_22_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1024] + model_decoder_layers_22_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1025] + model_decoder_layers_22_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1029] + model_decoder_layers_22_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1030] + model_decoder_layers_22_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1031] + model_decoder_layers_22_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1032] + model_decoder_layers_22_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1033] + model_decoder_layers_22_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1034] + model_decoder_layers_22_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1035] + model_decoder_layers_22_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1036] + model_decoder_layers_22_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1037] + model_decoder_layers_22_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1038] + model_decoder_layers_22_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1039] + model_decoder_layers_22_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1040] + model_decoder_layers_23_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1041] + model_decoder_layers_23_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1042] + model_decoder_layers_23_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1043] + model_decoder_layers_23_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1044] + model_decoder_layers_23_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1045] + model_decoder_layers_23_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1046] + model_decoder_layers_23_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1047] + model_decoder_layers_23_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1048] + model_decoder_layers_23_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1049] + model_decoder_layers_23_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1053] + model_decoder_layers_23_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1054] + model_decoder_layers_23_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1055] + model_decoder_layers_23_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1056] + model_decoder_layers_23_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1057] + model_decoder_layers_23_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1058] + model_decoder_layers_23_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1059] + model_decoder_layers_23_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1060] + model_decoder_layers_23_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1061] + model_decoder_layers_23_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1062] + model_decoder_layers_23_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1063] + model_decoder_layers_23_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1064] + model_decoder_layers_24_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1065] + model_decoder_layers_24_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1066] + model_decoder_layers_24_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1067] + model_decoder_layers_24_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1068] + model_decoder_layers_24_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1069] + model_decoder_layers_24_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1070] + model_decoder_layers_24_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1071] + model_decoder_layers_24_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1072] + model_decoder_layers_24_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1073] + model_decoder_layers_24_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1077] + model_decoder_layers_24_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1078] + model_decoder_layers_24_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1079] + model_decoder_layers_24_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1080] + model_decoder_layers_24_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1081] + model_decoder_layers_24_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1082] + model_decoder_layers_24_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1083] + model_decoder_layers_24_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1084] + model_decoder_layers_24_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1085] + model_decoder_layers_24_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1086] + model_decoder_layers_24_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1087] + model_decoder_layers_24_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1088] + model_decoder_layers_25_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1089] + model_decoder_layers_25_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1090] + model_decoder_layers_25_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1091] + model_decoder_layers_25_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1092] + model_decoder_layers_25_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1093] + model_decoder_layers_25_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1094] + model_decoder_layers_25_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1095] + model_decoder_layers_25_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1096] + model_decoder_layers_25_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1097] + model_decoder_layers_25_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1101] + model_decoder_layers_25_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1102] + model_decoder_layers_25_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1103] + model_decoder_layers_25_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1104] + model_decoder_layers_25_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1105] + model_decoder_layers_25_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1106] + model_decoder_layers_25_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1107] + model_decoder_layers_25_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1108] + model_decoder_layers_25_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1109] + model_decoder_layers_25_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1110] + model_decoder_layers_25_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1111] + model_decoder_layers_25_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1112] + model_decoder_layers_26_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1113] + model_decoder_layers_26_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1114] + model_decoder_layers_26_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1115] + model_decoder_layers_26_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1116] + model_decoder_layers_26_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1117] + model_decoder_layers_26_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1118] + model_decoder_layers_26_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1119] + model_decoder_layers_26_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1120] + model_decoder_layers_26_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1121] + model_decoder_layers_26_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1125] + model_decoder_layers_26_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1126] + model_decoder_layers_26_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1127] + model_decoder_layers_26_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1128] + model_decoder_layers_26_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1129] + model_decoder_layers_26_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1130] + model_decoder_layers_26_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1131] + model_decoder_layers_26_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1132] + model_decoder_layers_26_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1133] + model_decoder_layers_26_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1134] + model_decoder_layers_26_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1135] + model_decoder_layers_26_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1136] + model_decoder_layers_27_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1137] + model_decoder_layers_27_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1138] + model_decoder_layers_27_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1139] + model_decoder_layers_27_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1140] + model_decoder_layers_27_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1141] + model_decoder_layers_27_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1142] + model_decoder_layers_27_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1143] + model_decoder_layers_27_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1144] + model_decoder_layers_27_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1145] + model_decoder_layers_27_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1149] + model_decoder_layers_27_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1150] + model_decoder_layers_27_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1151] + model_decoder_layers_27_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1152] + model_decoder_layers_27_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1153] + model_decoder_layers_27_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1154] + model_decoder_layers_27_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1155] + model_decoder_layers_27_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1156] + model_decoder_layers_27_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1157] + model_decoder_layers_27_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1158] + model_decoder_layers_27_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1159] + model_decoder_layers_27_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1160] + model_decoder_layers_28_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1161] + model_decoder_layers_28_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1162] + model_decoder_layers_28_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1163] + model_decoder_layers_28_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1164] + model_decoder_layers_28_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1165] + model_decoder_layers_28_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1166] + model_decoder_layers_28_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1167] + model_decoder_layers_28_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1168] + model_decoder_layers_28_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1169] + model_decoder_layers_28_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1173] + model_decoder_layers_28_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1174] + model_decoder_layers_28_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1175] + model_decoder_layers_28_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1176] + model_decoder_layers_28_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1177] + model_decoder_layers_28_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1178] + model_decoder_layers_28_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1179] + model_decoder_layers_28_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1180] + model_decoder_layers_28_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1181] + model_decoder_layers_28_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1182] + model_decoder_layers_28_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1183] + model_decoder_layers_28_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1184] + model_decoder_layers_29_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1185] + model_decoder_layers_29_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1186] + model_decoder_layers_29_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1187] + model_decoder_layers_29_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1188] + model_decoder_layers_29_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1189] + model_decoder_layers_29_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1190] + model_decoder_layers_29_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1191] + model_decoder_layers_29_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1192] + model_decoder_layers_29_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1193] + model_decoder_layers_29_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1197] + model_decoder_layers_29_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1198] + model_decoder_layers_29_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1199] + model_decoder_layers_29_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1200] + model_decoder_layers_29_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1201] + model_decoder_layers_29_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1202] + model_decoder_layers_29_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1203] + model_decoder_layers_29_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1204] + model_decoder_layers_29_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1205] + model_decoder_layers_29_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1206] + model_decoder_layers_29_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1207] + model_decoder_layers_29_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1208] + model_decoder_layers_30_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1209] + model_decoder_layers_30_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1210] + model_decoder_layers_30_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1211] + model_decoder_layers_30_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1212] + model_decoder_layers_30_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1213] + model_decoder_layers_30_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1214] + model_decoder_layers_30_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1215] + model_decoder_layers_30_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1216] + model_decoder_layers_30_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1217] + model_decoder_layers_30_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1221] + model_decoder_layers_30_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1222] + model_decoder_layers_30_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1223] + model_decoder_layers_30_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1224] + model_decoder_layers_30_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1225] + model_decoder_layers_30_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1226] + model_decoder_layers_30_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1227] + model_decoder_layers_30_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1228] + model_decoder_layers_30_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1229] + model_decoder_layers_30_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1230] + model_decoder_layers_30_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1231] + model_decoder_layers_30_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1232] + model_decoder_layers_31_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1233] + model_decoder_layers_31_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1234] + model_decoder_layers_31_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1235] + model_decoder_layers_31_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1236] + model_decoder_layers_31_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1237] + model_decoder_layers_31_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1238] + model_decoder_layers_31_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1239] + model_decoder_layers_31_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1240] + model_decoder_layers_31_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1241] + model_decoder_layers_31_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1245] + model_decoder_layers_31_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1246] + model_decoder_layers_31_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1247] + model_decoder_layers_31_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1248] + model_decoder_layers_31_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1249] + model_decoder_layers_31_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1250] + model_decoder_layers_31_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1251] + model_decoder_layers_31_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1252] + model_decoder_layers_31_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1253] + model_decoder_layers_31_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1254] + model_decoder_layers_31_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1255] + model_decoder_layers_31_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1256] + model_decoder_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1257] + model_decoder_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1258] + reshape1030 = R.call_tir(cls.reshape12, (input_ids,), out_sinfo=R.Tensor((seq_len,), dtype="int32")) + take5 = R.call_tir(cls.take, (model_decoder_embed_tokens_weight4, reshape1030), out_sinfo=R.Tensor((seq_len, 1280), dtype="float16")) + reshape1031 = R.call_tir(cls.reshape13, (take5,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv198: R.Tensor((seq_len,), dtype="int32") = R.call_pure_packed("vm.builtin.attention_kv_cache_get_query_positions", paged_kv_cache, sinfo_args=(R.Tensor((seq_len,), dtype="int32"),)) + take6 = R.call_tir(cls.take1, (model_decoder_embed_positions_weight4, lv198), out_sinfo=R.Tensor((seq_len, 1280), dtype="float16")) + reshape1032 = R.call_tir(cls.reshape13, (take6,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add899 = R.call_tir(cls.add5, (reshape1031, reshape1032), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm259 = R.call_tir(cls.layer_norm2, (add899, model_decoder_layers_0_self_attn_layer_norm_weight4, model_decoder_layers_0_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv32 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_0_self_attn_q_proj_weight4, layer_norm259, model_decoder_layers_0_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1033 = R.call_tir(cls.reshape14, (lv32,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv32_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_0_self_attn_k_proj_weight4, layer_norm259), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1034 = R.call_tir(cls.reshape14, (lv32_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv33 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_0_self_attn_v_proj_weight4, layer_norm259, model_decoder_layers_0_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1035 = R.call_tir(cls.reshape14, (lv33,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat64 = R.call_tir(cls.concatenate1, (reshape1033, reshape1034, reshape1035), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1036 = R.call_tir(cls.reshape15, (concat64,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv199 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), reshape1036), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1037 = R.call_tir(cls.reshape16, (lv199,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1038 = R.call_tir(cls.reshape17, (reshape1037,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv34 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_0_self_attn_out_proj_weight4, reshape1038, model_decoder_layers_0_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add903 = R.call_tir(cls.add5, (add899, lv34), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm260 = R.call_tir(cls.layer_norm2, (add903, model_decoder_layers_0_encoder_attn_layer_norm_weight4, model_decoder_layers_0_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv35 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_0_encoder_attn_q_proj_weight4, layer_norm260, model_decoder_layers_0_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1039 = R.call_tir(cls.reshape14, (lv35,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1040 = R.call_tir(cls.reshape18, (reshape1039,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv200 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), reshape1040), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1041 = R.call_tir(cls.reshape16, (lv200,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1042 = R.call_tir(cls.reshape17, (reshape1041,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv36 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_0_encoder_attn_out_proj_weight4, reshape1042, model_decoder_layers_0_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add906 = R.call_tir(cls.add5, (add903, lv36), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm261 = R.call_tir(cls.layer_norm2, (add906, model_decoder_layers_0_final_layer_norm_weight4, model_decoder_layers_0_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_0_fc1_weight4, layer_norm261, model_decoder_layers_0_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv37 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_0_fc2_weight4, lv, model_decoder_layers_0_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add909 = R.call_tir(cls.add5, (add906, lv37), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm262 = R.call_tir(cls.layer_norm2, (add909, model_decoder_layers_1_self_attn_layer_norm_weight4, model_decoder_layers_1_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv38 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_1_self_attn_q_proj_weight4, layer_norm262, model_decoder_layers_1_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1043 = R.call_tir(cls.reshape14, (lv38,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv33_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_1_self_attn_k_proj_weight4, layer_norm262), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1044 = R.call_tir(cls.reshape14, (lv33_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv39 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_1_self_attn_v_proj_weight4, layer_norm262, model_decoder_layers_1_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1045 = R.call_tir(cls.reshape14, (lv39,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat65 = R.call_tir(cls.concatenate1, (reshape1043, reshape1044, reshape1045), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1046 = R.call_tir(cls.reshape15, (concat65,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv201 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), reshape1046), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1047 = R.call_tir(cls.reshape16, (lv201,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1048 = R.call_tir(cls.reshape17, (reshape1047,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv40 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_1_self_attn_out_proj_weight4, reshape1048, model_decoder_layers_1_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add913 = R.call_tir(cls.add5, (add909, lv40), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm263 = R.call_tir(cls.layer_norm2, (add913, model_decoder_layers_1_encoder_attn_layer_norm_weight4, model_decoder_layers_1_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv41 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_1_encoder_attn_q_proj_weight4, layer_norm263, model_decoder_layers_1_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1049 = R.call_tir(cls.reshape14, (lv41,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1050 = R.call_tir(cls.reshape18, (reshape1049,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv202 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), reshape1050), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1051 = R.call_tir(cls.reshape16, (lv202,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1052 = R.call_tir(cls.reshape17, (reshape1051,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv42 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_1_encoder_attn_out_proj_weight4, reshape1052, model_decoder_layers_1_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add916 = R.call_tir(cls.add5, (add913, lv42), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm264 = R.call_tir(cls.layer_norm2, (add916, model_decoder_layers_1_final_layer_norm_weight4, model_decoder_layers_1_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_1_fc1_weight4, layer_norm264, model_decoder_layers_1_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv43 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_1_fc2_weight4, lv1, model_decoder_layers_1_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add919 = R.call_tir(cls.add5, (add916, lv43), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm265 = R.call_tir(cls.layer_norm2, (add919, model_decoder_layers_2_self_attn_layer_norm_weight4, model_decoder_layers_2_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv44 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_2_self_attn_q_proj_weight4, layer_norm265, model_decoder_layers_2_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1053 = R.call_tir(cls.reshape14, (lv44,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv34_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_2_self_attn_k_proj_weight4, layer_norm265), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1054 = R.call_tir(cls.reshape14, (lv34_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv45 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_2_self_attn_v_proj_weight4, layer_norm265, model_decoder_layers_2_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1055 = R.call_tir(cls.reshape14, (lv45,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat66 = R.call_tir(cls.concatenate1, (reshape1053, reshape1054, reshape1055), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1056 = R.call_tir(cls.reshape15, (concat66,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv203 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), reshape1056), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1057 = R.call_tir(cls.reshape16, (lv203,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1058 = R.call_tir(cls.reshape17, (reshape1057,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv46 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_2_self_attn_out_proj_weight4, reshape1058, model_decoder_layers_2_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add923 = R.call_tir(cls.add5, (add919, lv46), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm266 = R.call_tir(cls.layer_norm2, (add923, model_decoder_layers_2_encoder_attn_layer_norm_weight4, model_decoder_layers_2_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv47 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_2_encoder_attn_q_proj_weight4, layer_norm266, model_decoder_layers_2_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1059 = R.call_tir(cls.reshape14, (lv47,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1060 = R.call_tir(cls.reshape18, (reshape1059,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv204 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), reshape1060), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1061 = R.call_tir(cls.reshape16, (lv204,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1062 = R.call_tir(cls.reshape17, (reshape1061,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv48 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_2_encoder_attn_out_proj_weight4, reshape1062, model_decoder_layers_2_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add926 = R.call_tir(cls.add5, (add923, lv48), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm267 = R.call_tir(cls.layer_norm2, (add926, model_decoder_layers_2_final_layer_norm_weight4, model_decoder_layers_2_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv2 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_2_fc1_weight4, layer_norm267, model_decoder_layers_2_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv49 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_2_fc2_weight4, lv2, model_decoder_layers_2_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add929 = R.call_tir(cls.add5, (add926, lv49), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm268 = R.call_tir(cls.layer_norm2, (add929, model_decoder_layers_3_self_attn_layer_norm_weight4, model_decoder_layers_3_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv50 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_3_self_attn_q_proj_weight4, layer_norm268, model_decoder_layers_3_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1063 = R.call_tir(cls.reshape14, (lv50,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv35_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_3_self_attn_k_proj_weight4, layer_norm268), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1064 = R.call_tir(cls.reshape14, (lv35_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv51 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_3_self_attn_v_proj_weight4, layer_norm268, model_decoder_layers_3_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1065 = R.call_tir(cls.reshape14, (lv51,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat67 = R.call_tir(cls.concatenate1, (reshape1063, reshape1064, reshape1065), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1066 = R.call_tir(cls.reshape15, (concat67,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv205 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), reshape1066), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1067 = R.call_tir(cls.reshape16, (lv205,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1068 = R.call_tir(cls.reshape17, (reshape1067,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv52 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_3_self_attn_out_proj_weight4, reshape1068, model_decoder_layers_3_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add933 = R.call_tir(cls.add5, (add929, lv52), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm269 = R.call_tir(cls.layer_norm2, (add933, model_decoder_layers_3_encoder_attn_layer_norm_weight4, model_decoder_layers_3_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv53 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_3_encoder_attn_q_proj_weight4, layer_norm269, model_decoder_layers_3_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1069 = R.call_tir(cls.reshape14, (lv53,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1070 = R.call_tir(cls.reshape18, (reshape1069,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv206 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), reshape1070), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1071 = R.call_tir(cls.reshape16, (lv206,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1072 = R.call_tir(cls.reshape17, (reshape1071,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv54 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_3_encoder_attn_out_proj_weight4, reshape1072, model_decoder_layers_3_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add936 = R.call_tir(cls.add5, (add933, lv54), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm270 = R.call_tir(cls.layer_norm2, (add936, model_decoder_layers_3_final_layer_norm_weight4, model_decoder_layers_3_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv3 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_3_fc1_weight4, layer_norm270, model_decoder_layers_3_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv55 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_3_fc2_weight4, lv3, model_decoder_layers_3_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add939 = R.call_tir(cls.add5, (add936, lv55), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm271 = R.call_tir(cls.layer_norm2, (add939, model_decoder_layers_4_self_attn_layer_norm_weight4, model_decoder_layers_4_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv56 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_4_self_attn_q_proj_weight4, layer_norm271, model_decoder_layers_4_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1073 = R.call_tir(cls.reshape14, (lv56,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv36_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_4_self_attn_k_proj_weight4, layer_norm271), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1074 = R.call_tir(cls.reshape14, (lv36_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv57 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_4_self_attn_v_proj_weight4, layer_norm271, model_decoder_layers_4_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1075 = R.call_tir(cls.reshape14, (lv57,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat68 = R.call_tir(cls.concatenate1, (reshape1073, reshape1074, reshape1075), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1076 = R.call_tir(cls.reshape15, (concat68,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv207 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), reshape1076), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1077 = R.call_tir(cls.reshape16, (lv207,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1078 = R.call_tir(cls.reshape17, (reshape1077,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv58 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_4_self_attn_out_proj_weight4, reshape1078, model_decoder_layers_4_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add943 = R.call_tir(cls.add5, (add939, lv58), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm272 = R.call_tir(cls.layer_norm2, (add943, model_decoder_layers_4_encoder_attn_layer_norm_weight4, model_decoder_layers_4_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv59 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_4_encoder_attn_q_proj_weight4, layer_norm272, model_decoder_layers_4_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1079 = R.call_tir(cls.reshape14, (lv59,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1080 = R.call_tir(cls.reshape18, (reshape1079,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv208 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), reshape1080), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1081 = R.call_tir(cls.reshape16, (lv208,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1082 = R.call_tir(cls.reshape17, (reshape1081,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv60 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_4_encoder_attn_out_proj_weight4, reshape1082, model_decoder_layers_4_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add946 = R.call_tir(cls.add5, (add943, lv60), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm273 = R.call_tir(cls.layer_norm2, (add946, model_decoder_layers_4_final_layer_norm_weight4, model_decoder_layers_4_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv4 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_4_fc1_weight4, layer_norm273, model_decoder_layers_4_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv61 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_4_fc2_weight4, lv4, model_decoder_layers_4_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add949 = R.call_tir(cls.add5, (add946, lv61), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm274 = R.call_tir(cls.layer_norm2, (add949, model_decoder_layers_5_self_attn_layer_norm_weight4, model_decoder_layers_5_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv62 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_5_self_attn_q_proj_weight4, layer_norm274, model_decoder_layers_5_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1083 = R.call_tir(cls.reshape14, (lv62,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv37_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_5_self_attn_k_proj_weight4, layer_norm274), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1084 = R.call_tir(cls.reshape14, (lv37_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv63 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_5_self_attn_v_proj_weight4, layer_norm274, model_decoder_layers_5_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1085 = R.call_tir(cls.reshape14, (lv63,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat69 = R.call_tir(cls.concatenate1, (reshape1083, reshape1084, reshape1085), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1086 = R.call_tir(cls.reshape15, (concat69,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv209 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), reshape1086), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1087 = R.call_tir(cls.reshape16, (lv209,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1088 = R.call_tir(cls.reshape17, (reshape1087,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv64 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_5_self_attn_out_proj_weight4, reshape1088, model_decoder_layers_5_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add953 = R.call_tir(cls.add5, (add949, lv64), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm275 = R.call_tir(cls.layer_norm2, (add953, model_decoder_layers_5_encoder_attn_layer_norm_weight4, model_decoder_layers_5_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv65 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_5_encoder_attn_q_proj_weight4, layer_norm275, model_decoder_layers_5_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1089 = R.call_tir(cls.reshape14, (lv65,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1090 = R.call_tir(cls.reshape18, (reshape1089,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv210 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), reshape1090), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1091 = R.call_tir(cls.reshape16, (lv210,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1092 = R.call_tir(cls.reshape17, (reshape1091,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv66 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_5_encoder_attn_out_proj_weight4, reshape1092, model_decoder_layers_5_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add956 = R.call_tir(cls.add5, (add953, lv66), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm276 = R.call_tir(cls.layer_norm2, (add956, model_decoder_layers_5_final_layer_norm_weight4, model_decoder_layers_5_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv5 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_5_fc1_weight4, layer_norm276, model_decoder_layers_5_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv67 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_5_fc2_weight4, lv5, model_decoder_layers_5_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add959 = R.call_tir(cls.add5, (add956, lv67), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm277 = R.call_tir(cls.layer_norm2, (add959, model_decoder_layers_6_self_attn_layer_norm_weight4, model_decoder_layers_6_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv68 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_6_self_attn_q_proj_weight4, layer_norm277, model_decoder_layers_6_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1093 = R.call_tir(cls.reshape14, (lv68,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv38_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_6_self_attn_k_proj_weight4, layer_norm277), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1094 = R.call_tir(cls.reshape14, (lv38_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv69 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_6_self_attn_v_proj_weight4, layer_norm277, model_decoder_layers_6_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1095 = R.call_tir(cls.reshape14, (lv69,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat70 = R.call_tir(cls.concatenate1, (reshape1093, reshape1094, reshape1095), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1096 = R.call_tir(cls.reshape15, (concat70,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv211 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), reshape1096), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1097 = R.call_tir(cls.reshape16, (lv211,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1098 = R.call_tir(cls.reshape17, (reshape1097,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv70 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_6_self_attn_out_proj_weight4, reshape1098, model_decoder_layers_6_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add963 = R.call_tir(cls.add5, (add959, lv70), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm278 = R.call_tir(cls.layer_norm2, (add963, model_decoder_layers_6_encoder_attn_layer_norm_weight4, model_decoder_layers_6_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv71 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_6_encoder_attn_q_proj_weight4, layer_norm278, model_decoder_layers_6_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1099 = R.call_tir(cls.reshape14, (lv71,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1100 = R.call_tir(cls.reshape18, (reshape1099,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv212 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), reshape1100), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1101 = R.call_tir(cls.reshape16, (lv212,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1102 = R.call_tir(cls.reshape17, (reshape1101,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv72 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_6_encoder_attn_out_proj_weight4, reshape1102, model_decoder_layers_6_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add966 = R.call_tir(cls.add5, (add963, lv72), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm279 = R.call_tir(cls.layer_norm2, (add966, model_decoder_layers_6_final_layer_norm_weight4, model_decoder_layers_6_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv6 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_6_fc1_weight4, layer_norm279, model_decoder_layers_6_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv73 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_6_fc2_weight4, lv6, model_decoder_layers_6_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add969 = R.call_tir(cls.add5, (add966, lv73), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm280 = R.call_tir(cls.layer_norm2, (add969, model_decoder_layers_7_self_attn_layer_norm_weight4, model_decoder_layers_7_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv74 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_7_self_attn_q_proj_weight4, layer_norm280, model_decoder_layers_7_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1103 = R.call_tir(cls.reshape14, (lv74,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv39_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_7_self_attn_k_proj_weight4, layer_norm280), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1104 = R.call_tir(cls.reshape14, (lv39_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv75 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_7_self_attn_v_proj_weight4, layer_norm280, model_decoder_layers_7_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1105 = R.call_tir(cls.reshape14, (lv75,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat71 = R.call_tir(cls.concatenate1, (reshape1103, reshape1104, reshape1105), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1106 = R.call_tir(cls.reshape15, (concat71,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv213 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), reshape1106), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1107 = R.call_tir(cls.reshape16, (lv213,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1108 = R.call_tir(cls.reshape17, (reshape1107,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv76 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_7_self_attn_out_proj_weight4, reshape1108, model_decoder_layers_7_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add973 = R.call_tir(cls.add5, (add969, lv76), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm281 = R.call_tir(cls.layer_norm2, (add973, model_decoder_layers_7_encoder_attn_layer_norm_weight4, model_decoder_layers_7_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv77 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_7_encoder_attn_q_proj_weight4, layer_norm281, model_decoder_layers_7_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1109 = R.call_tir(cls.reshape14, (lv77,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1110 = R.call_tir(cls.reshape18, (reshape1109,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv214 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), reshape1110), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1111 = R.call_tir(cls.reshape16, (lv214,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1112 = R.call_tir(cls.reshape17, (reshape1111,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv78 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_7_encoder_attn_out_proj_weight4, reshape1112, model_decoder_layers_7_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add976 = R.call_tir(cls.add5, (add973, lv78), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm282 = R.call_tir(cls.layer_norm2, (add976, model_decoder_layers_7_final_layer_norm_weight4, model_decoder_layers_7_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv7 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_7_fc1_weight4, layer_norm282, model_decoder_layers_7_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv79 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_7_fc2_weight4, lv7, model_decoder_layers_7_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add979 = R.call_tir(cls.add5, (add976, lv79), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm283 = R.call_tir(cls.layer_norm2, (add979, model_decoder_layers_8_self_attn_layer_norm_weight4, model_decoder_layers_8_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv80 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_8_self_attn_q_proj_weight4, layer_norm283, model_decoder_layers_8_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1113 = R.call_tir(cls.reshape14, (lv80,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv40_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_8_self_attn_k_proj_weight4, layer_norm283), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1114 = R.call_tir(cls.reshape14, (lv40_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv81 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_8_self_attn_v_proj_weight4, layer_norm283, model_decoder_layers_8_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1115 = R.call_tir(cls.reshape14, (lv81,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat72 = R.call_tir(cls.concatenate1, (reshape1113, reshape1114, reshape1115), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1116 = R.call_tir(cls.reshape15, (concat72,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv215 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), reshape1116), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1117 = R.call_tir(cls.reshape16, (lv215,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1118 = R.call_tir(cls.reshape17, (reshape1117,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv82 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_8_self_attn_out_proj_weight4, reshape1118, model_decoder_layers_8_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add983 = R.call_tir(cls.add5, (add979, lv82), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm284 = R.call_tir(cls.layer_norm2, (add983, model_decoder_layers_8_encoder_attn_layer_norm_weight4, model_decoder_layers_8_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv83 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_8_encoder_attn_q_proj_weight4, layer_norm284, model_decoder_layers_8_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1119 = R.call_tir(cls.reshape14, (lv83,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1120 = R.call_tir(cls.reshape18, (reshape1119,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv216 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), reshape1120), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1121 = R.call_tir(cls.reshape16, (lv216,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1122 = R.call_tir(cls.reshape17, (reshape1121,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv84 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_8_encoder_attn_out_proj_weight4, reshape1122, model_decoder_layers_8_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add986 = R.call_tir(cls.add5, (add983, lv84), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm285 = R.call_tir(cls.layer_norm2, (add986, model_decoder_layers_8_final_layer_norm_weight4, model_decoder_layers_8_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv8 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_8_fc1_weight4, layer_norm285, model_decoder_layers_8_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv85 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_8_fc2_weight4, lv8, model_decoder_layers_8_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add989 = R.call_tir(cls.add5, (add986, lv85), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm286 = R.call_tir(cls.layer_norm2, (add989, model_decoder_layers_9_self_attn_layer_norm_weight4, model_decoder_layers_9_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv86 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_9_self_attn_q_proj_weight4, layer_norm286, model_decoder_layers_9_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1123 = R.call_tir(cls.reshape14, (lv86,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv41_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_9_self_attn_k_proj_weight4, layer_norm286), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1124 = R.call_tir(cls.reshape14, (lv41_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv87 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_9_self_attn_v_proj_weight4, layer_norm286, model_decoder_layers_9_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1125 = R.call_tir(cls.reshape14, (lv87,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat73 = R.call_tir(cls.concatenate1, (reshape1123, reshape1124, reshape1125), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1126 = R.call_tir(cls.reshape15, (concat73,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv217 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), reshape1126), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1127 = R.call_tir(cls.reshape16, (lv217,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1128 = R.call_tir(cls.reshape17, (reshape1127,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv88 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_9_self_attn_out_proj_weight4, reshape1128, model_decoder_layers_9_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add993 = R.call_tir(cls.add5, (add989, lv88), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm287 = R.call_tir(cls.layer_norm2, (add993, model_decoder_layers_9_encoder_attn_layer_norm_weight4, model_decoder_layers_9_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv89 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_9_encoder_attn_q_proj_weight4, layer_norm287, model_decoder_layers_9_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1129 = R.call_tir(cls.reshape14, (lv89,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1130 = R.call_tir(cls.reshape18, (reshape1129,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv218 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), reshape1130), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1131 = R.call_tir(cls.reshape16, (lv218,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1132 = R.call_tir(cls.reshape17, (reshape1131,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv90 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_9_encoder_attn_out_proj_weight4, reshape1132, model_decoder_layers_9_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add996 = R.call_tir(cls.add5, (add993, lv90), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm288 = R.call_tir(cls.layer_norm2, (add996, model_decoder_layers_9_final_layer_norm_weight4, model_decoder_layers_9_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv9 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_9_fc1_weight4, layer_norm288, model_decoder_layers_9_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv91 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_9_fc2_weight4, lv9, model_decoder_layers_9_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add999 = R.call_tir(cls.add5, (add996, lv91), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm289 = R.call_tir(cls.layer_norm2, (add999, model_decoder_layers_10_self_attn_layer_norm_weight4, model_decoder_layers_10_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv92 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_10_self_attn_q_proj_weight4, layer_norm289, model_decoder_layers_10_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1133 = R.call_tir(cls.reshape14, (lv92,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv42_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_10_self_attn_k_proj_weight4, layer_norm289), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1134 = R.call_tir(cls.reshape14, (lv42_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv93 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_10_self_attn_v_proj_weight4, layer_norm289, model_decoder_layers_10_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1135 = R.call_tir(cls.reshape14, (lv93,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat74 = R.call_tir(cls.concatenate1, (reshape1133, reshape1134, reshape1135), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1136 = R.call_tir(cls.reshape15, (concat74,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv219 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), reshape1136), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1137 = R.call_tir(cls.reshape16, (lv219,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1138 = R.call_tir(cls.reshape17, (reshape1137,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv94 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_10_self_attn_out_proj_weight4, reshape1138, model_decoder_layers_10_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1003 = R.call_tir(cls.add5, (add999, lv94), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm290 = R.call_tir(cls.layer_norm2, (add1003, model_decoder_layers_10_encoder_attn_layer_norm_weight4, model_decoder_layers_10_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv95 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_10_encoder_attn_q_proj_weight4, layer_norm290, model_decoder_layers_10_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1139 = R.call_tir(cls.reshape14, (lv95,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1140 = R.call_tir(cls.reshape18, (reshape1139,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv220 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), reshape1140), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1141 = R.call_tir(cls.reshape16, (lv220,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1142 = R.call_tir(cls.reshape17, (reshape1141,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv96 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_10_encoder_attn_out_proj_weight4, reshape1142, model_decoder_layers_10_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1006 = R.call_tir(cls.add5, (add1003, lv96), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm291 = R.call_tir(cls.layer_norm2, (add1006, model_decoder_layers_10_final_layer_norm_weight4, model_decoder_layers_10_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv10 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_10_fc1_weight4, layer_norm291, model_decoder_layers_10_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv97 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_10_fc2_weight4, lv10, model_decoder_layers_10_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1009 = R.call_tir(cls.add5, (add1006, lv97), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm292 = R.call_tir(cls.layer_norm2, (add1009, model_decoder_layers_11_self_attn_layer_norm_weight4, model_decoder_layers_11_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv98 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_11_self_attn_q_proj_weight4, layer_norm292, model_decoder_layers_11_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1143 = R.call_tir(cls.reshape14, (lv98,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv43_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_11_self_attn_k_proj_weight4, layer_norm292), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1144 = R.call_tir(cls.reshape14, (lv43_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv99 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_11_self_attn_v_proj_weight4, layer_norm292, model_decoder_layers_11_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1145 = R.call_tir(cls.reshape14, (lv99,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat75 = R.call_tir(cls.concatenate1, (reshape1143, reshape1144, reshape1145), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1146 = R.call_tir(cls.reshape15, (concat75,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv221 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), reshape1146), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1147 = R.call_tir(cls.reshape16, (lv221,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1148 = R.call_tir(cls.reshape17, (reshape1147,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv100 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_11_self_attn_out_proj_weight4, reshape1148, model_decoder_layers_11_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1013 = R.call_tir(cls.add5, (add1009, lv100), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm293 = R.call_tir(cls.layer_norm2, (add1013, model_decoder_layers_11_encoder_attn_layer_norm_weight4, model_decoder_layers_11_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv101 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_11_encoder_attn_q_proj_weight4, layer_norm293, model_decoder_layers_11_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1149 = R.call_tir(cls.reshape14, (lv101,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1150 = R.call_tir(cls.reshape18, (reshape1149,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv222 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), reshape1150), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1151 = R.call_tir(cls.reshape16, (lv222,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1152 = R.call_tir(cls.reshape17, (reshape1151,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv102 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_11_encoder_attn_out_proj_weight4, reshape1152, model_decoder_layers_11_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1016 = R.call_tir(cls.add5, (add1013, lv102), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm294 = R.call_tir(cls.layer_norm2, (add1016, model_decoder_layers_11_final_layer_norm_weight4, model_decoder_layers_11_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv11 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_11_fc1_weight4, layer_norm294, model_decoder_layers_11_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv103 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_11_fc2_weight4, lv11, model_decoder_layers_11_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1019 = R.call_tir(cls.add5, (add1016, lv103), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm295 = R.call_tir(cls.layer_norm2, (add1019, model_decoder_layers_12_self_attn_layer_norm_weight4, model_decoder_layers_12_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv104 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_12_self_attn_q_proj_weight4, layer_norm295, model_decoder_layers_12_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1153 = R.call_tir(cls.reshape14, (lv104,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv44_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_12_self_attn_k_proj_weight4, layer_norm295), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1154 = R.call_tir(cls.reshape14, (lv44_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv105 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_12_self_attn_v_proj_weight4, layer_norm295, model_decoder_layers_12_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1155 = R.call_tir(cls.reshape14, (lv105,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat76 = R.call_tir(cls.concatenate1, (reshape1153, reshape1154, reshape1155), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1156 = R.call_tir(cls.reshape15, (concat76,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv223 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), reshape1156), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1157 = R.call_tir(cls.reshape16, (lv223,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1158 = R.call_tir(cls.reshape17, (reshape1157,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv106 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_12_self_attn_out_proj_weight4, reshape1158, model_decoder_layers_12_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1023 = R.call_tir(cls.add5, (add1019, lv106), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm296 = R.call_tir(cls.layer_norm2, (add1023, model_decoder_layers_12_encoder_attn_layer_norm_weight4, model_decoder_layers_12_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv107 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_12_encoder_attn_q_proj_weight4, layer_norm296, model_decoder_layers_12_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1159 = R.call_tir(cls.reshape14, (lv107,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1160 = R.call_tir(cls.reshape18, (reshape1159,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv224 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), reshape1160), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1161 = R.call_tir(cls.reshape16, (lv224,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1162 = R.call_tir(cls.reshape17, (reshape1161,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv108 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_12_encoder_attn_out_proj_weight4, reshape1162, model_decoder_layers_12_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1026 = R.call_tir(cls.add5, (add1023, lv108), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm297 = R.call_tir(cls.layer_norm2, (add1026, model_decoder_layers_12_final_layer_norm_weight4, model_decoder_layers_12_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv12 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_12_fc1_weight4, layer_norm297, model_decoder_layers_12_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv109 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_12_fc2_weight4, lv12, model_decoder_layers_12_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1029 = R.call_tir(cls.add5, (add1026, lv109), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm298 = R.call_tir(cls.layer_norm2, (add1029, model_decoder_layers_13_self_attn_layer_norm_weight4, model_decoder_layers_13_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv110 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_13_self_attn_q_proj_weight4, layer_norm298, model_decoder_layers_13_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1163 = R.call_tir(cls.reshape14, (lv110,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv45_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_13_self_attn_k_proj_weight4, layer_norm298), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1164 = R.call_tir(cls.reshape14, (lv45_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv111 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_13_self_attn_v_proj_weight4, layer_norm298, model_decoder_layers_13_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1165 = R.call_tir(cls.reshape14, (lv111,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat77 = R.call_tir(cls.concatenate1, (reshape1163, reshape1164, reshape1165), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1166 = R.call_tir(cls.reshape15, (concat77,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv225 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), reshape1166), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1167 = R.call_tir(cls.reshape16, (lv225,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1168 = R.call_tir(cls.reshape17, (reshape1167,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv112 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_13_self_attn_out_proj_weight4, reshape1168, model_decoder_layers_13_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1033 = R.call_tir(cls.add5, (add1029, lv112), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm299 = R.call_tir(cls.layer_norm2, (add1033, model_decoder_layers_13_encoder_attn_layer_norm_weight4, model_decoder_layers_13_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv113 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_13_encoder_attn_q_proj_weight4, layer_norm299, model_decoder_layers_13_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1169 = R.call_tir(cls.reshape14, (lv113,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1170 = R.call_tir(cls.reshape18, (reshape1169,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv226 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), reshape1170), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1171 = R.call_tir(cls.reshape16, (lv226,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1172 = R.call_tir(cls.reshape17, (reshape1171,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv114 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_13_encoder_attn_out_proj_weight4, reshape1172, model_decoder_layers_13_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1036 = R.call_tir(cls.add5, (add1033, lv114), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm300 = R.call_tir(cls.layer_norm2, (add1036, model_decoder_layers_13_final_layer_norm_weight4, model_decoder_layers_13_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv13 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_13_fc1_weight4, layer_norm300, model_decoder_layers_13_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv115 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_13_fc2_weight4, lv13, model_decoder_layers_13_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1039 = R.call_tir(cls.add5, (add1036, lv115), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm301 = R.call_tir(cls.layer_norm2, (add1039, model_decoder_layers_14_self_attn_layer_norm_weight4, model_decoder_layers_14_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv116 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_14_self_attn_q_proj_weight4, layer_norm301, model_decoder_layers_14_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1173 = R.call_tir(cls.reshape14, (lv116,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv46_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_14_self_attn_k_proj_weight4, layer_norm301), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1174 = R.call_tir(cls.reshape14, (lv46_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv117 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_14_self_attn_v_proj_weight4, layer_norm301, model_decoder_layers_14_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1175 = R.call_tir(cls.reshape14, (lv117,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat78 = R.call_tir(cls.concatenate1, (reshape1173, reshape1174, reshape1175), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1176 = R.call_tir(cls.reshape15, (concat78,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv227 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), reshape1176), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1177 = R.call_tir(cls.reshape16, (lv227,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1178 = R.call_tir(cls.reshape17, (reshape1177,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv118 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_14_self_attn_out_proj_weight4, reshape1178, model_decoder_layers_14_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1043 = R.call_tir(cls.add5, (add1039, lv118), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm302 = R.call_tir(cls.layer_norm2, (add1043, model_decoder_layers_14_encoder_attn_layer_norm_weight4, model_decoder_layers_14_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv119 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_14_encoder_attn_q_proj_weight4, layer_norm302, model_decoder_layers_14_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1179 = R.call_tir(cls.reshape14, (lv119,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1180 = R.call_tir(cls.reshape18, (reshape1179,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv228 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), reshape1180), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1181 = R.call_tir(cls.reshape16, (lv228,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1182 = R.call_tir(cls.reshape17, (reshape1181,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv120 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_14_encoder_attn_out_proj_weight4, reshape1182, model_decoder_layers_14_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1046 = R.call_tir(cls.add5, (add1043, lv120), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm303 = R.call_tir(cls.layer_norm2, (add1046, model_decoder_layers_14_final_layer_norm_weight4, model_decoder_layers_14_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv14 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_14_fc1_weight4, layer_norm303, model_decoder_layers_14_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv121 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_14_fc2_weight4, lv14, model_decoder_layers_14_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1049 = R.call_tir(cls.add5, (add1046, lv121), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm304 = R.call_tir(cls.layer_norm2, (add1049, model_decoder_layers_15_self_attn_layer_norm_weight4, model_decoder_layers_15_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv122 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_15_self_attn_q_proj_weight4, layer_norm304, model_decoder_layers_15_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1183 = R.call_tir(cls.reshape14, (lv122,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv47_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_15_self_attn_k_proj_weight4, layer_norm304), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1184 = R.call_tir(cls.reshape14, (lv47_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv123 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_15_self_attn_v_proj_weight4, layer_norm304, model_decoder_layers_15_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1185 = R.call_tir(cls.reshape14, (lv123,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat79 = R.call_tir(cls.concatenate1, (reshape1183, reshape1184, reshape1185), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1186 = R.call_tir(cls.reshape15, (concat79,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv229 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), reshape1186), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1187 = R.call_tir(cls.reshape16, (lv229,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1188 = R.call_tir(cls.reshape17, (reshape1187,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv124 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_15_self_attn_out_proj_weight4, reshape1188, model_decoder_layers_15_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1053 = R.call_tir(cls.add5, (add1049, lv124), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm305 = R.call_tir(cls.layer_norm2, (add1053, model_decoder_layers_15_encoder_attn_layer_norm_weight4, model_decoder_layers_15_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv125 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_15_encoder_attn_q_proj_weight4, layer_norm305, model_decoder_layers_15_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1189 = R.call_tir(cls.reshape14, (lv125,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1190 = R.call_tir(cls.reshape18, (reshape1189,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv230 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), reshape1190), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1191 = R.call_tir(cls.reshape16, (lv230,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1192 = R.call_tir(cls.reshape17, (reshape1191,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv126 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_15_encoder_attn_out_proj_weight4, reshape1192, model_decoder_layers_15_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1056 = R.call_tir(cls.add5, (add1053, lv126), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm306 = R.call_tir(cls.layer_norm2, (add1056, model_decoder_layers_15_final_layer_norm_weight4, model_decoder_layers_15_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv15 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_15_fc1_weight4, layer_norm306, model_decoder_layers_15_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv127 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_15_fc2_weight4, lv15, model_decoder_layers_15_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1059 = R.call_tir(cls.add5, (add1056, lv127), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm307 = R.call_tir(cls.layer_norm2, (add1059, model_decoder_layers_16_self_attn_layer_norm_weight4, model_decoder_layers_16_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv128 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_16_self_attn_q_proj_weight4, layer_norm307, model_decoder_layers_16_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1193 = R.call_tir(cls.reshape14, (lv128,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv48_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_16_self_attn_k_proj_weight4, layer_norm307), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1194 = R.call_tir(cls.reshape14, (lv48_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv129 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_16_self_attn_v_proj_weight4, layer_norm307, model_decoder_layers_16_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1195 = R.call_tir(cls.reshape14, (lv129,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat80 = R.call_tir(cls.concatenate1, (reshape1193, reshape1194, reshape1195), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1196 = R.call_tir(cls.reshape15, (concat80,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv231 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), reshape1196), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1197 = R.call_tir(cls.reshape16, (lv231,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1198 = R.call_tir(cls.reshape17, (reshape1197,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv130 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_16_self_attn_out_proj_weight4, reshape1198, model_decoder_layers_16_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1063 = R.call_tir(cls.add5, (add1059, lv130), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm308 = R.call_tir(cls.layer_norm2, (add1063, model_decoder_layers_16_encoder_attn_layer_norm_weight4, model_decoder_layers_16_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv131 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_16_encoder_attn_q_proj_weight4, layer_norm308, model_decoder_layers_16_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1199 = R.call_tir(cls.reshape14, (lv131,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1200 = R.call_tir(cls.reshape18, (reshape1199,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv232 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), reshape1200), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1201 = R.call_tir(cls.reshape16, (lv232,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1202 = R.call_tir(cls.reshape17, (reshape1201,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv132 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_16_encoder_attn_out_proj_weight4, reshape1202, model_decoder_layers_16_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1066 = R.call_tir(cls.add5, (add1063, lv132), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm309 = R.call_tir(cls.layer_norm2, (add1066, model_decoder_layers_16_final_layer_norm_weight4, model_decoder_layers_16_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv16 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_16_fc1_weight4, layer_norm309, model_decoder_layers_16_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv133 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_16_fc2_weight4, lv16, model_decoder_layers_16_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1069 = R.call_tir(cls.add5, (add1066, lv133), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm310 = R.call_tir(cls.layer_norm2, (add1069, model_decoder_layers_17_self_attn_layer_norm_weight4, model_decoder_layers_17_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv134 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_17_self_attn_q_proj_weight4, layer_norm310, model_decoder_layers_17_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1203 = R.call_tir(cls.reshape14, (lv134,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv49_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_17_self_attn_k_proj_weight4, layer_norm310), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1204 = R.call_tir(cls.reshape14, (lv49_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv135 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_17_self_attn_v_proj_weight4, layer_norm310, model_decoder_layers_17_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1205 = R.call_tir(cls.reshape14, (lv135,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat81 = R.call_tir(cls.concatenate1, (reshape1203, reshape1204, reshape1205), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1206 = R.call_tir(cls.reshape15, (concat81,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv233 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), reshape1206), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1207 = R.call_tir(cls.reshape16, (lv233,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1208 = R.call_tir(cls.reshape17, (reshape1207,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv136 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_17_self_attn_out_proj_weight4, reshape1208, model_decoder_layers_17_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1073 = R.call_tir(cls.add5, (add1069, lv136), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm311 = R.call_tir(cls.layer_norm2, (add1073, model_decoder_layers_17_encoder_attn_layer_norm_weight4, model_decoder_layers_17_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv137 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_17_encoder_attn_q_proj_weight4, layer_norm311, model_decoder_layers_17_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1209 = R.call_tir(cls.reshape14, (lv137,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1210 = R.call_tir(cls.reshape18, (reshape1209,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv234 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), reshape1210), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1211 = R.call_tir(cls.reshape16, (lv234,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1212 = R.call_tir(cls.reshape17, (reshape1211,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv138 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_17_encoder_attn_out_proj_weight4, reshape1212, model_decoder_layers_17_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1076 = R.call_tir(cls.add5, (add1073, lv138), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm312 = R.call_tir(cls.layer_norm2, (add1076, model_decoder_layers_17_final_layer_norm_weight4, model_decoder_layers_17_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv17 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_17_fc1_weight4, layer_norm312, model_decoder_layers_17_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv139 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_17_fc2_weight4, lv17, model_decoder_layers_17_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1079 = R.call_tir(cls.add5, (add1076, lv139), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm313 = R.call_tir(cls.layer_norm2, (add1079, model_decoder_layers_18_self_attn_layer_norm_weight4, model_decoder_layers_18_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv140 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_18_self_attn_q_proj_weight4, layer_norm313, model_decoder_layers_18_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1213 = R.call_tir(cls.reshape14, (lv140,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv50_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_18_self_attn_k_proj_weight4, layer_norm313), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1214 = R.call_tir(cls.reshape14, (lv50_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv141 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_18_self_attn_v_proj_weight4, layer_norm313, model_decoder_layers_18_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1215 = R.call_tir(cls.reshape14, (lv141,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat82 = R.call_tir(cls.concatenate1, (reshape1213, reshape1214, reshape1215), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1216 = R.call_tir(cls.reshape15, (concat82,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv235 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), reshape1216), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1217 = R.call_tir(cls.reshape16, (lv235,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1218 = R.call_tir(cls.reshape17, (reshape1217,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv142 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_18_self_attn_out_proj_weight4, reshape1218, model_decoder_layers_18_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1083 = R.call_tir(cls.add5, (add1079, lv142), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm314 = R.call_tir(cls.layer_norm2, (add1083, model_decoder_layers_18_encoder_attn_layer_norm_weight4, model_decoder_layers_18_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv143 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_18_encoder_attn_q_proj_weight4, layer_norm314, model_decoder_layers_18_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1219 = R.call_tir(cls.reshape14, (lv143,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1220 = R.call_tir(cls.reshape18, (reshape1219,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv236 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), reshape1220), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1221 = R.call_tir(cls.reshape16, (lv236,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1222 = R.call_tir(cls.reshape17, (reshape1221,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv144 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_18_encoder_attn_out_proj_weight4, reshape1222, model_decoder_layers_18_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1086 = R.call_tir(cls.add5, (add1083, lv144), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm315 = R.call_tir(cls.layer_norm2, (add1086, model_decoder_layers_18_final_layer_norm_weight4, model_decoder_layers_18_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv18 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_18_fc1_weight4, layer_norm315, model_decoder_layers_18_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv145 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_18_fc2_weight4, lv18, model_decoder_layers_18_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1089 = R.call_tir(cls.add5, (add1086, lv145), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm316 = R.call_tir(cls.layer_norm2, (add1089, model_decoder_layers_19_self_attn_layer_norm_weight4, model_decoder_layers_19_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv146 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_19_self_attn_q_proj_weight4, layer_norm316, model_decoder_layers_19_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1223 = R.call_tir(cls.reshape14, (lv146,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv51_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_19_self_attn_k_proj_weight4, layer_norm316), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1224 = R.call_tir(cls.reshape14, (lv51_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv147 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_19_self_attn_v_proj_weight4, layer_norm316, model_decoder_layers_19_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1225 = R.call_tir(cls.reshape14, (lv147,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat83 = R.call_tir(cls.concatenate1, (reshape1223, reshape1224, reshape1225), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1226 = R.call_tir(cls.reshape15, (concat83,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv237 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), reshape1226), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1227 = R.call_tir(cls.reshape16, (lv237,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1228 = R.call_tir(cls.reshape17, (reshape1227,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv148 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_19_self_attn_out_proj_weight4, reshape1228, model_decoder_layers_19_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1093 = R.call_tir(cls.add5, (add1089, lv148), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm317 = R.call_tir(cls.layer_norm2, (add1093, model_decoder_layers_19_encoder_attn_layer_norm_weight4, model_decoder_layers_19_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv149 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_19_encoder_attn_q_proj_weight4, layer_norm317, model_decoder_layers_19_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1229 = R.call_tir(cls.reshape14, (lv149,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1230 = R.call_tir(cls.reshape18, (reshape1229,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv238 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), reshape1230), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1231 = R.call_tir(cls.reshape16, (lv238,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1232 = R.call_tir(cls.reshape17, (reshape1231,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv150 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_19_encoder_attn_out_proj_weight4, reshape1232, model_decoder_layers_19_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1096 = R.call_tir(cls.add5, (add1093, lv150), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm318 = R.call_tir(cls.layer_norm2, (add1096, model_decoder_layers_19_final_layer_norm_weight4, model_decoder_layers_19_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv19 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_19_fc1_weight4, layer_norm318, model_decoder_layers_19_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv151 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_19_fc2_weight4, lv19, model_decoder_layers_19_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1099 = R.call_tir(cls.add5, (add1096, lv151), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm319 = R.call_tir(cls.layer_norm2, (add1099, model_decoder_layers_20_self_attn_layer_norm_weight4, model_decoder_layers_20_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv152 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_20_self_attn_q_proj_weight4, layer_norm319, model_decoder_layers_20_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1233 = R.call_tir(cls.reshape14, (lv152,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv52_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_20_self_attn_k_proj_weight4, layer_norm319), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1234 = R.call_tir(cls.reshape14, (lv52_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv153 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_20_self_attn_v_proj_weight4, layer_norm319, model_decoder_layers_20_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1235 = R.call_tir(cls.reshape14, (lv153,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat84 = R.call_tir(cls.concatenate1, (reshape1233, reshape1234, reshape1235), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1236 = R.call_tir(cls.reshape15, (concat84,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv239 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), reshape1236), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1237 = R.call_tir(cls.reshape16, (lv239,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1238 = R.call_tir(cls.reshape17, (reshape1237,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv154 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_20_self_attn_out_proj_weight4, reshape1238, model_decoder_layers_20_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1103 = R.call_tir(cls.add5, (add1099, lv154), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm320 = R.call_tir(cls.layer_norm2, (add1103, model_decoder_layers_20_encoder_attn_layer_norm_weight4, model_decoder_layers_20_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv155 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_20_encoder_attn_q_proj_weight4, layer_norm320, model_decoder_layers_20_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1239 = R.call_tir(cls.reshape14, (lv155,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1240 = R.call_tir(cls.reshape18, (reshape1239,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv240 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), reshape1240), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1241 = R.call_tir(cls.reshape16, (lv240,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1242 = R.call_tir(cls.reshape17, (reshape1241,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv156 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_20_encoder_attn_out_proj_weight4, reshape1242, model_decoder_layers_20_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1106 = R.call_tir(cls.add5, (add1103, lv156), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm321 = R.call_tir(cls.layer_norm2, (add1106, model_decoder_layers_20_final_layer_norm_weight4, model_decoder_layers_20_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv20 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_20_fc1_weight4, layer_norm321, model_decoder_layers_20_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv157 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_20_fc2_weight4, lv20, model_decoder_layers_20_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1109 = R.call_tir(cls.add5, (add1106, lv157), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm322 = R.call_tir(cls.layer_norm2, (add1109, model_decoder_layers_21_self_attn_layer_norm_weight4, model_decoder_layers_21_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv158 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_21_self_attn_q_proj_weight4, layer_norm322, model_decoder_layers_21_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1243 = R.call_tir(cls.reshape14, (lv158,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv53_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_21_self_attn_k_proj_weight4, layer_norm322), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1244 = R.call_tir(cls.reshape14, (lv53_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv159 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_21_self_attn_v_proj_weight4, layer_norm322, model_decoder_layers_21_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1245 = R.call_tir(cls.reshape14, (lv159,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat85 = R.call_tir(cls.concatenate1, (reshape1243, reshape1244, reshape1245), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1246 = R.call_tir(cls.reshape15, (concat85,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv241 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), reshape1246), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1247 = R.call_tir(cls.reshape16, (lv241,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1248 = R.call_tir(cls.reshape17, (reshape1247,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv160 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_21_self_attn_out_proj_weight4, reshape1248, model_decoder_layers_21_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1113 = R.call_tir(cls.add5, (add1109, lv160), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm323 = R.call_tir(cls.layer_norm2, (add1113, model_decoder_layers_21_encoder_attn_layer_norm_weight4, model_decoder_layers_21_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv161 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_21_encoder_attn_q_proj_weight4, layer_norm323, model_decoder_layers_21_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1249 = R.call_tir(cls.reshape14, (lv161,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1250 = R.call_tir(cls.reshape18, (reshape1249,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv242 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), reshape1250), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1251 = R.call_tir(cls.reshape16, (lv242,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1252 = R.call_tir(cls.reshape17, (reshape1251,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv162 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_21_encoder_attn_out_proj_weight4, reshape1252, model_decoder_layers_21_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1116 = R.call_tir(cls.add5, (add1113, lv162), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm324 = R.call_tir(cls.layer_norm2, (add1116, model_decoder_layers_21_final_layer_norm_weight4, model_decoder_layers_21_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv21 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_21_fc1_weight4, layer_norm324, model_decoder_layers_21_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv163 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_21_fc2_weight4, lv21, model_decoder_layers_21_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1119 = R.call_tir(cls.add5, (add1116, lv163), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm325 = R.call_tir(cls.layer_norm2, (add1119, model_decoder_layers_22_self_attn_layer_norm_weight4, model_decoder_layers_22_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv164 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_22_self_attn_q_proj_weight4, layer_norm325, model_decoder_layers_22_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1253 = R.call_tir(cls.reshape14, (lv164,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv54_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_22_self_attn_k_proj_weight4, layer_norm325), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1254 = R.call_tir(cls.reshape14, (lv54_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv165 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_22_self_attn_v_proj_weight4, layer_norm325, model_decoder_layers_22_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1255 = R.call_tir(cls.reshape14, (lv165,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat86 = R.call_tir(cls.concatenate1, (reshape1253, reshape1254, reshape1255), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1256 = R.call_tir(cls.reshape15, (concat86,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv243 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), reshape1256), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1257 = R.call_tir(cls.reshape16, (lv243,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1258 = R.call_tir(cls.reshape17, (reshape1257,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv166 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_22_self_attn_out_proj_weight4, reshape1258, model_decoder_layers_22_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1123 = R.call_tir(cls.add5, (add1119, lv166), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm326 = R.call_tir(cls.layer_norm2, (add1123, model_decoder_layers_22_encoder_attn_layer_norm_weight4, model_decoder_layers_22_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv167 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_22_encoder_attn_q_proj_weight4, layer_norm326, model_decoder_layers_22_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1259 = R.call_tir(cls.reshape14, (lv167,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1260 = R.call_tir(cls.reshape18, (reshape1259,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv244 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), reshape1260), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1261 = R.call_tir(cls.reshape16, (lv244,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1262 = R.call_tir(cls.reshape17, (reshape1261,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv168 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_22_encoder_attn_out_proj_weight4, reshape1262, model_decoder_layers_22_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1126 = R.call_tir(cls.add5, (add1123, lv168), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm327 = R.call_tir(cls.layer_norm2, (add1126, model_decoder_layers_22_final_layer_norm_weight4, model_decoder_layers_22_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv22 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_22_fc1_weight4, layer_norm327, model_decoder_layers_22_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv169 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_22_fc2_weight4, lv22, model_decoder_layers_22_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1129 = R.call_tir(cls.add5, (add1126, lv169), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm328 = R.call_tir(cls.layer_norm2, (add1129, model_decoder_layers_23_self_attn_layer_norm_weight4, model_decoder_layers_23_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv170 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_23_self_attn_q_proj_weight4, layer_norm328, model_decoder_layers_23_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1263 = R.call_tir(cls.reshape14, (lv170,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv55_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_23_self_attn_k_proj_weight4, layer_norm328), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1264 = R.call_tir(cls.reshape14, (lv55_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv171 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_23_self_attn_v_proj_weight4, layer_norm328, model_decoder_layers_23_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1265 = R.call_tir(cls.reshape14, (lv171,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat87 = R.call_tir(cls.concatenate1, (reshape1263, reshape1264, reshape1265), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1266 = R.call_tir(cls.reshape15, (concat87,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv245 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), reshape1266), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1267 = R.call_tir(cls.reshape16, (lv245,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1268 = R.call_tir(cls.reshape17, (reshape1267,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv172 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_23_self_attn_out_proj_weight4, reshape1268, model_decoder_layers_23_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1133 = R.call_tir(cls.add5, (add1129, lv172), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm329 = R.call_tir(cls.layer_norm2, (add1133, model_decoder_layers_23_encoder_attn_layer_norm_weight4, model_decoder_layers_23_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv173 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_23_encoder_attn_q_proj_weight4, layer_norm329, model_decoder_layers_23_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1269 = R.call_tir(cls.reshape14, (lv173,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1270 = R.call_tir(cls.reshape18, (reshape1269,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv246 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), reshape1270), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1271 = R.call_tir(cls.reshape16, (lv246,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1272 = R.call_tir(cls.reshape17, (reshape1271,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv174 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_23_encoder_attn_out_proj_weight4, reshape1272, model_decoder_layers_23_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1136 = R.call_tir(cls.add5, (add1133, lv174), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm330 = R.call_tir(cls.layer_norm2, (add1136, model_decoder_layers_23_final_layer_norm_weight4, model_decoder_layers_23_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv23 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_23_fc1_weight4, layer_norm330, model_decoder_layers_23_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv175 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_23_fc2_weight4, lv23, model_decoder_layers_23_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1139 = R.call_tir(cls.add5, (add1136, lv175), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm331 = R.call_tir(cls.layer_norm2, (add1139, model_decoder_layers_24_self_attn_layer_norm_weight4, model_decoder_layers_24_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv176 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_24_self_attn_q_proj_weight4, layer_norm331, model_decoder_layers_24_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1273 = R.call_tir(cls.reshape14, (lv176,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv56_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_24_self_attn_k_proj_weight4, layer_norm331), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1274 = R.call_tir(cls.reshape14, (lv56_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv177 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_24_self_attn_v_proj_weight4, layer_norm331, model_decoder_layers_24_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1275 = R.call_tir(cls.reshape14, (lv177,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat88 = R.call_tir(cls.concatenate1, (reshape1273, reshape1274, reshape1275), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1276 = R.call_tir(cls.reshape15, (concat88,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv247 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), reshape1276), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1277 = R.call_tir(cls.reshape16, (lv247,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1278 = R.call_tir(cls.reshape17, (reshape1277,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv178 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_24_self_attn_out_proj_weight4, reshape1278, model_decoder_layers_24_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1143 = R.call_tir(cls.add5, (add1139, lv178), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm332 = R.call_tir(cls.layer_norm2, (add1143, model_decoder_layers_24_encoder_attn_layer_norm_weight4, model_decoder_layers_24_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv179 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_24_encoder_attn_q_proj_weight4, layer_norm332, model_decoder_layers_24_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1279 = R.call_tir(cls.reshape14, (lv179,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1280 = R.call_tir(cls.reshape18, (reshape1279,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv248 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), reshape1280), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1281 = R.call_tir(cls.reshape16, (lv248,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1282 = R.call_tir(cls.reshape17, (reshape1281,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv180 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_24_encoder_attn_out_proj_weight4, reshape1282, model_decoder_layers_24_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1146 = R.call_tir(cls.add5, (add1143, lv180), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm333 = R.call_tir(cls.layer_norm2, (add1146, model_decoder_layers_24_final_layer_norm_weight4, model_decoder_layers_24_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv24 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_24_fc1_weight4, layer_norm333, model_decoder_layers_24_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv181 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_24_fc2_weight4, lv24, model_decoder_layers_24_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1149 = R.call_tir(cls.add5, (add1146, lv181), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm334 = R.call_tir(cls.layer_norm2, (add1149, model_decoder_layers_25_self_attn_layer_norm_weight4, model_decoder_layers_25_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv182 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_25_self_attn_q_proj_weight4, layer_norm334, model_decoder_layers_25_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1283 = R.call_tir(cls.reshape14, (lv182,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv57_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_25_self_attn_k_proj_weight4, layer_norm334), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1284 = R.call_tir(cls.reshape14, (lv57_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv183 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_25_self_attn_v_proj_weight4, layer_norm334, model_decoder_layers_25_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1285 = R.call_tir(cls.reshape14, (lv183,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat89 = R.call_tir(cls.concatenate1, (reshape1283, reshape1284, reshape1285), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1286 = R.call_tir(cls.reshape15, (concat89,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv249 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), reshape1286), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1287 = R.call_tir(cls.reshape16, (lv249,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1288 = R.call_tir(cls.reshape17, (reshape1287,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv184 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_25_self_attn_out_proj_weight4, reshape1288, model_decoder_layers_25_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1153 = R.call_tir(cls.add5, (add1149, lv184), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm335 = R.call_tir(cls.layer_norm2, (add1153, model_decoder_layers_25_encoder_attn_layer_norm_weight4, model_decoder_layers_25_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv185 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_25_encoder_attn_q_proj_weight4, layer_norm335, model_decoder_layers_25_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1289 = R.call_tir(cls.reshape14, (lv185,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1290 = R.call_tir(cls.reshape18, (reshape1289,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv250 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), reshape1290), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1291 = R.call_tir(cls.reshape16, (lv250,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1292 = R.call_tir(cls.reshape17, (reshape1291,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv186 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_25_encoder_attn_out_proj_weight4, reshape1292, model_decoder_layers_25_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1156 = R.call_tir(cls.add5, (add1153, lv186), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm336 = R.call_tir(cls.layer_norm2, (add1156, model_decoder_layers_25_final_layer_norm_weight4, model_decoder_layers_25_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv25 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_25_fc1_weight4, layer_norm336, model_decoder_layers_25_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv187 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_25_fc2_weight4, lv25, model_decoder_layers_25_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1159 = R.call_tir(cls.add5, (add1156, lv187), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm337 = R.call_tir(cls.layer_norm2, (add1159, model_decoder_layers_26_self_attn_layer_norm_weight4, model_decoder_layers_26_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv188 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_26_self_attn_q_proj_weight4, layer_norm337, model_decoder_layers_26_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1293 = R.call_tir(cls.reshape14, (lv188,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv58_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_26_self_attn_k_proj_weight4, layer_norm337), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1294 = R.call_tir(cls.reshape14, (lv58_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv189 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_26_self_attn_v_proj_weight4, layer_norm337, model_decoder_layers_26_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1295 = R.call_tir(cls.reshape14, (lv189,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat90 = R.call_tir(cls.concatenate1, (reshape1293, reshape1294, reshape1295), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1296 = R.call_tir(cls.reshape15, (concat90,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv251 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), reshape1296), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1297 = R.call_tir(cls.reshape16, (lv251,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1298 = R.call_tir(cls.reshape17, (reshape1297,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv190 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_26_self_attn_out_proj_weight4, reshape1298, model_decoder_layers_26_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1163 = R.call_tir(cls.add5, (add1159, lv190), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm338 = R.call_tir(cls.layer_norm2, (add1163, model_decoder_layers_26_encoder_attn_layer_norm_weight4, model_decoder_layers_26_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv191 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_26_encoder_attn_q_proj_weight4, layer_norm338, model_decoder_layers_26_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1299 = R.call_tir(cls.reshape14, (lv191,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1300 = R.call_tir(cls.reshape18, (reshape1299,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv252 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), reshape1300), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1301 = R.call_tir(cls.reshape16, (lv252,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1302 = R.call_tir(cls.reshape17, (reshape1301,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv192 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_26_encoder_attn_out_proj_weight4, reshape1302, model_decoder_layers_26_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1166 = R.call_tir(cls.add5, (add1163, lv192), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm339 = R.call_tir(cls.layer_norm2, (add1166, model_decoder_layers_26_final_layer_norm_weight4, model_decoder_layers_26_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv26 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_26_fc1_weight4, layer_norm339, model_decoder_layers_26_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv193 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_26_fc2_weight4, lv26, model_decoder_layers_26_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1169 = R.call_tir(cls.add5, (add1166, lv193), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm340 = R.call_tir(cls.layer_norm2, (add1169, model_decoder_layers_27_self_attn_layer_norm_weight4, model_decoder_layers_27_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv194 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_27_self_attn_q_proj_weight4, layer_norm340, model_decoder_layers_27_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1303 = R.call_tir(cls.reshape14, (lv194,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv59_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_27_self_attn_k_proj_weight4, layer_norm340), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1304 = R.call_tir(cls.reshape14, (lv59_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv195 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_27_self_attn_v_proj_weight4, layer_norm340, model_decoder_layers_27_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1305 = R.call_tir(cls.reshape14, (lv195,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat91 = R.call_tir(cls.concatenate1, (reshape1303, reshape1304, reshape1305), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1306 = R.call_tir(cls.reshape15, (concat91,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv253 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), reshape1306), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1307 = R.call_tir(cls.reshape16, (lv253,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1308 = R.call_tir(cls.reshape17, (reshape1307,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv196 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_27_self_attn_out_proj_weight4, reshape1308, model_decoder_layers_27_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1173 = R.call_tir(cls.add5, (add1169, lv196), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm341 = R.call_tir(cls.layer_norm2, (add1173, model_decoder_layers_27_encoder_attn_layer_norm_weight4, model_decoder_layers_27_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv197 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_27_encoder_attn_q_proj_weight4, layer_norm341, model_decoder_layers_27_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1309 = R.call_tir(cls.reshape14, (lv197,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1310 = R.call_tir(cls.reshape18, (reshape1309,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv254 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), reshape1310), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1311 = R.call_tir(cls.reshape16, (lv254,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1312 = R.call_tir(cls.reshape17, (reshape1311,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv198_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_27_encoder_attn_out_proj_weight4, reshape1312, model_decoder_layers_27_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1176 = R.call_tir(cls.add5, (add1173, lv198_1), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm342 = R.call_tir(cls.layer_norm2, (add1176, model_decoder_layers_27_final_layer_norm_weight4, model_decoder_layers_27_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv27 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_27_fc1_weight4, layer_norm342, model_decoder_layers_27_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv199_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_27_fc2_weight4, lv27, model_decoder_layers_27_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1179 = R.call_tir(cls.add5, (add1176, lv199_1), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm343 = R.call_tir(cls.layer_norm2, (add1179, model_decoder_layers_28_self_attn_layer_norm_weight4, model_decoder_layers_28_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv200_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_28_self_attn_q_proj_weight4, layer_norm343, model_decoder_layers_28_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1313 = R.call_tir(cls.reshape14, (lv200_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv60_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_28_self_attn_k_proj_weight4, layer_norm343), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1314 = R.call_tir(cls.reshape14, (lv60_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv201_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_28_self_attn_v_proj_weight4, layer_norm343, model_decoder_layers_28_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1315 = R.call_tir(cls.reshape14, (lv201_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat92 = R.call_tir(cls.concatenate1, (reshape1313, reshape1314, reshape1315), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1316 = R.call_tir(cls.reshape15, (concat92,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv255 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), reshape1316), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1317 = R.call_tir(cls.reshape16, (lv255,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1318 = R.call_tir(cls.reshape17, (reshape1317,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv202_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_28_self_attn_out_proj_weight4, reshape1318, model_decoder_layers_28_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1183 = R.call_tir(cls.add5, (add1179, lv202_1), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm344 = R.call_tir(cls.layer_norm2, (add1183, model_decoder_layers_28_encoder_attn_layer_norm_weight4, model_decoder_layers_28_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv203_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_28_encoder_attn_q_proj_weight4, layer_norm344, model_decoder_layers_28_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1319 = R.call_tir(cls.reshape14, (lv203_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1320 = R.call_tir(cls.reshape18, (reshape1319,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv256 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), reshape1320), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1321 = R.call_tir(cls.reshape16, (lv256,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1322 = R.call_tir(cls.reshape17, (reshape1321,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv204_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_28_encoder_attn_out_proj_weight4, reshape1322, model_decoder_layers_28_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1186 = R.call_tir(cls.add5, (add1183, lv204_1), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm345 = R.call_tir(cls.layer_norm2, (add1186, model_decoder_layers_28_final_layer_norm_weight4, model_decoder_layers_28_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv28 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_28_fc1_weight4, layer_norm345, model_decoder_layers_28_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv205_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_28_fc2_weight4, lv28, model_decoder_layers_28_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1189 = R.call_tir(cls.add5, (add1186, lv205_1), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm346 = R.call_tir(cls.layer_norm2, (add1189, model_decoder_layers_29_self_attn_layer_norm_weight4, model_decoder_layers_29_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv206_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_29_self_attn_q_proj_weight4, layer_norm346, model_decoder_layers_29_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1323 = R.call_tir(cls.reshape14, (lv206_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv61_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_29_self_attn_k_proj_weight4, layer_norm346), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1324 = R.call_tir(cls.reshape14, (lv61_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv207_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_29_self_attn_v_proj_weight4, layer_norm346, model_decoder_layers_29_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1325 = R.call_tir(cls.reshape14, (lv207_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat93 = R.call_tir(cls.concatenate1, (reshape1323, reshape1324, reshape1325), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1326 = R.call_tir(cls.reshape15, (concat93,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv257 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), reshape1326), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1327 = R.call_tir(cls.reshape16, (lv257,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1328 = R.call_tir(cls.reshape17, (reshape1327,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv208_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_29_self_attn_out_proj_weight4, reshape1328, model_decoder_layers_29_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1193 = R.call_tir(cls.add5, (add1189, lv208_1), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm347 = R.call_tir(cls.layer_norm2, (add1193, model_decoder_layers_29_encoder_attn_layer_norm_weight4, model_decoder_layers_29_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv209_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_29_encoder_attn_q_proj_weight4, layer_norm347, model_decoder_layers_29_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1329 = R.call_tir(cls.reshape14, (lv209_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1330 = R.call_tir(cls.reshape18, (reshape1329,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv258 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), reshape1330), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1331 = R.call_tir(cls.reshape16, (lv258,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1332 = R.call_tir(cls.reshape17, (reshape1331,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv210_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_29_encoder_attn_out_proj_weight4, reshape1332, model_decoder_layers_29_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1196 = R.call_tir(cls.add5, (add1193, lv210_1), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm348 = R.call_tir(cls.layer_norm2, (add1196, model_decoder_layers_29_final_layer_norm_weight4, model_decoder_layers_29_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv29 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_29_fc1_weight4, layer_norm348, model_decoder_layers_29_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv211_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_29_fc2_weight4, lv29, model_decoder_layers_29_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1199 = R.call_tir(cls.add5, (add1196, lv211_1), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm349 = R.call_tir(cls.layer_norm2, (add1199, model_decoder_layers_30_self_attn_layer_norm_weight4, model_decoder_layers_30_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv212_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_30_self_attn_q_proj_weight4, layer_norm349, model_decoder_layers_30_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1333 = R.call_tir(cls.reshape14, (lv212_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv62_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_30_self_attn_k_proj_weight4, layer_norm349), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1334 = R.call_tir(cls.reshape14, (lv62_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv213_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_30_self_attn_v_proj_weight4, layer_norm349, model_decoder_layers_30_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1335 = R.call_tir(cls.reshape14, (lv213_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat94 = R.call_tir(cls.concatenate1, (reshape1333, reshape1334, reshape1335), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1336 = R.call_tir(cls.reshape15, (concat94,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv259 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), reshape1336), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1337 = R.call_tir(cls.reshape16, (lv259,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1338 = R.call_tir(cls.reshape17, (reshape1337,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv214_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_30_self_attn_out_proj_weight4, reshape1338, model_decoder_layers_30_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1203 = R.call_tir(cls.add5, (add1199, lv214_1), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm350 = R.call_tir(cls.layer_norm2, (add1203, model_decoder_layers_30_encoder_attn_layer_norm_weight4, model_decoder_layers_30_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv215_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_30_encoder_attn_q_proj_weight4, layer_norm350, model_decoder_layers_30_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1339 = R.call_tir(cls.reshape14, (lv215_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1340 = R.call_tir(cls.reshape18, (reshape1339,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv260 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), reshape1340), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1341 = R.call_tir(cls.reshape16, (lv260,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1342 = R.call_tir(cls.reshape17, (reshape1341,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv216_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_30_encoder_attn_out_proj_weight4, reshape1342, model_decoder_layers_30_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1206 = R.call_tir(cls.add5, (add1203, lv216_1), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm351 = R.call_tir(cls.layer_norm2, (add1206, model_decoder_layers_30_final_layer_norm_weight4, model_decoder_layers_30_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv30 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_30_fc1_weight4, layer_norm351, model_decoder_layers_30_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv217_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_30_fc2_weight4, lv30, model_decoder_layers_30_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1209 = R.call_tir(cls.add5, (add1206, lv217_1), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm352 = R.call_tir(cls.layer_norm2, (add1209, model_decoder_layers_31_self_attn_layer_norm_weight4, model_decoder_layers_31_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv218_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_31_self_attn_q_proj_weight4, layer_norm352, model_decoder_layers_31_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1343 = R.call_tir(cls.reshape14, (lv218_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv63_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_31_self_attn_k_proj_weight4, layer_norm352), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1344 = R.call_tir(cls.reshape14, (lv63_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv219_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_31_self_attn_v_proj_weight4, layer_norm352, model_decoder_layers_31_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1345 = R.call_tir(cls.reshape14, (lv219_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat95 = R.call_tir(cls.concatenate1, (reshape1343, reshape1344, reshape1345), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1346 = R.call_tir(cls.reshape15, (concat95,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv261 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), reshape1346), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1347 = R.call_tir(cls.reshape16, (lv261,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1348 = R.call_tir(cls.reshape17, (reshape1347,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv220_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_31_self_attn_out_proj_weight4, reshape1348, model_decoder_layers_31_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1213 = R.call_tir(cls.add5, (add1209, lv220_1), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm353 = R.call_tir(cls.layer_norm2, (add1213, model_decoder_layers_31_encoder_attn_layer_norm_weight4, model_decoder_layers_31_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv221_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_31_encoder_attn_q_proj_weight4, layer_norm353, model_decoder_layers_31_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1349 = R.call_tir(cls.reshape14, (lv221_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1350 = R.call_tir(cls.reshape18, (reshape1349,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv262 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), reshape1350), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1351 = R.call_tir(cls.reshape16, (lv262,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1352 = R.call_tir(cls.reshape17, (reshape1351,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv222_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_31_encoder_attn_out_proj_weight4, reshape1352, model_decoder_layers_31_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1216 = R.call_tir(cls.add5, (add1213, lv222_1), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm354 = R.call_tir(cls.layer_norm2, (add1216, model_decoder_layers_31_final_layer_norm_weight4, model_decoder_layers_31_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv31 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_31_fc1_weight4, layer_norm354, model_decoder_layers_31_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv223_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_31_fc2_weight4, lv31, model_decoder_layers_31_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1219 = R.call_tir(cls.add5, (add1216, lv223_1), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm355 = R.call_tir(cls.layer_norm2, (add1219, model_decoder_layer_norm_weight4, model_decoder_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv263 = R.call_tir(cls.index, (layer_norm355,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + gv4 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul2_cublas", (model_decoder_embed_tokens_weight4, lv263), out_sinfo=R.Tensor((1, 1, 51866), dtype="float32")) + R.output(gv4) + return gv4 + + @R.function + def renormalize_by_top_p(probs: R.Tensor(("batch_size", "vocab_size"), dtype="float32"), top_p: R.Tensor(("batch_size",), dtype="float32"), init_pivots: R.Tensor(("batch_size", 3), dtype="float32")) -> R.Tensor(("batch_size", "vocab_size"), dtype="float32"): + batch_size = T.int64() + vocab_size = T.int64() + R.func_attr({"relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "num_positions": 48, "num_samples": 8}}) + cls = Module + with R.dataflow(): + lv6 = R.call_tir(cls.top_p_pivot_cutoff, (probs, top_p, init_pivots), out_sinfo=[R.Tensor((batch_size,), dtype="float32"), R.Tensor((batch_size,), dtype="float32")]) + lv7: R.Tensor((batch_size,), dtype="float32") = lv6[0] + lv8: R.Tensor((batch_size,), dtype="float32") = lv6[1] + gv5 = R.call_tir(cls.top_p_renorm_after_cutoff, (probs, lv7, lv8), out_sinfo=R.Tensor((batch_size, vocab_size), dtype="float32")) + R.output(gv5) + return gv5 + + @R.function + def sample_with_top_p(sorted_probs: R.Tensor(("batch_size", "vocab_size"), dtype="float32"), sorted_indices: R.Tensor(("batch_size", "vocab_size"), dtype="int32"), uniform_samples: R.Tensor(("num_samples",), dtype="float32"), sample_indices: R.Tensor(("num_samples",), dtype="int32"), top_p: R.Tensor(("batch_size",), dtype="float32")) -> R.Tensor(("num_samples",), dtype="int32"): + num_samples = T.int64() + batch_size = T.int64() + vocab_size = T.int64() + R.func_attr({"relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "num_positions": 48, "num_samples": 8}}) + cls = Module + with R.dataflow(): + uniform_samples1: R.Tensor((num_samples, 1), dtype="float32") = R.call_pure_packed("vm.builtin.reshape", uniform_samples, R.shape([num_samples, 1]), sinfo_args=(R.Tensor((num_samples, 1), dtype="float32"),)) + sample_indices1: R.Tensor((num_samples, 1), dtype="int32") = R.call_pure_packed("vm.builtin.reshape", sample_indices, R.shape([num_samples, 1]), sinfo_args=(R.Tensor((num_samples, 1), dtype="int32"),)) + sample_indices2: R.Tensor((batch_size, 1), dtype="float32") = R.call_pure_packed("vm.builtin.reshape", top_p, R.shape([batch_size, 1]), sinfo_args=(R.Tensor((batch_size, 1), dtype="float32"),)) + lv3 = R.call_tir(cls.full, R.tuple(), out_sinfo=R.Tensor((batch_size, 1), dtype="int32"), tir_vars=R.shape([vocab_size])) + lv1: R.Tensor((8 * (batch_size * vocab_size * 4) + 8388608 + batch_size * vocab_size * 12,), dtype="uint8") = R.builtin.alloc_tensor(R.shape([8 * (batch_size * vocab_size * 4) + 8388608 + batch_size * vocab_size * 12]), R.dtype("uint8"), R.prim_value(0), R.str("global")) + cumsum = R.call_tir(cls.cumsum, (sorted_probs, lv1), out_sinfo=R.Tensor((batch_size, vocab_size), dtype="float32")) + lv4 = R.call_tir(cls.get_renorm_prob, (cumsum, sample_indices2, lv3), out_sinfo=R.Tensor((batch_size, 1), dtype="float32")) + lv5 = R.call_tir(cls.get_index_from_sorted, (cumsum, sorted_indices, lv4, uniform_samples1, sample_indices1), out_sinfo=R.Tensor((num_samples, 1), dtype="int32")) + gv2: R.Tensor((num_samples,), dtype="int32") = R.call_pure_packed("vm.builtin.reshape", lv5, R.shape([num_samples]), sinfo_args=(R.Tensor((num_samples,), dtype="int32"),)) + R.output(gv2) + return gv2 + + @R.function + def sampler_take_probs(unsorted_probs: R.Tensor(("batch_size", "vocab_size"), dtype="float32"), sorted_indices: R.Tensor(("batch_size", "vocab_size"), dtype="int32"), sample_indices: R.Tensor(("num_samples",), dtype="int32"), sampling_result: R.Tensor(("num_samples",), dtype="int32"), lobprob_offsets: R.Tensor(("num_positions",), dtype="int32")) -> R.Tuple(R.Tensor(("num_samples",), dtype="float32"), R.Tensor(("num_positions",), dtype="float32"), R.Tensor(("num_positions",), dtype="int32")): + num_samples = T.int64() + num_positions = T.int64() + batch_size = T.int64() + vocab_size = T.int64() + R.func_attr({"relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "num_positions": 48, "num_samples": 8}}) + cls = Module + with R.dataflow(): + gv3 = R.call_tir(cls.sampler_take_probs_tir, (unsorted_probs, sorted_indices, sample_indices, sampling_result, lobprob_offsets), out_sinfo=[R.Tensor((num_samples,), dtype="float32"), R.Tensor((num_positions,), dtype="float32"), R.Tensor((num_positions,), dtype="int32")]) + R.output(gv3) + return gv3 + + @R.function + def sampler_verify_draft_tokens(draft_probs: R.Tensor(("num_nodes", "vocab_size"), dtype="float32"), draft_tokens: R.Tensor(("num_nodes",), dtype="int32"), model_probs: R.Tensor(("num_nodes", "vocab_size"), dtype="float32"), token_tree_first_child: R.Tensor(("num_nodes",), dtype="int32"), token_tree_next_sibling: R.Tensor(("num_nodes",), dtype="int32"), uniform_samples: R.Tensor(("num_nodes",), dtype="float32"), token_tree_parent_ptr: R.Tensor(("nbatch",), dtype="int32")) -> R.Tuple(R.Tensor(("num_nodes", "vocab_size"), dtype="float32"), R.Tensor(("nbatch",), dtype="int32")): + num_nodes = T.int64() + vocab_size = T.int64() + nbatch = T.int64() + R.func_attr({"relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "num_positions": 48, "num_samples": 8}}) + cls = Module + with R.dataflow(): + gv4: R.Tuple(R.Tensor((num_nodes, vocab_size), dtype="float32"), R.Tensor((nbatch,), dtype="int32")) = R.call_tir_inplace(cls.batch_verify_on_gpu_single_kernel, (draft_probs, draft_tokens, model_probs, token_tree_first_child, token_tree_next_sibling, uniform_samples, token_tree_parent_ptr), out_sinfo=[R.Tensor((num_nodes, vocab_size), dtype="float32"), R.Tensor((nbatch,), dtype="int32")], inplace_indices=[2, 6]) + R.output(gv4) + return gv4 + + @R.function + def softmax_with_temperature(logits: R.Tensor(("batch_size", 1, "vocab_size"), dtype="float32"), temperature: R.Tensor(("batch_size",), dtype="float32")) -> R.Tensor(("batch_size", 1, "vocab_size"), dtype="float32"): + batch_size = T.int64() + vocab_size = T.int64() + R.func_attr({"relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "seq_len": 15000, "total_seq_len": 1500}}) + cls = Module + with R.dataflow(): + lv: R.Tensor((batch_size, vocab_size), dtype="float32") = R.call_pure_packed("vm.builtin.reshape", logits, R.shape([batch_size, vocab_size]), sinfo_args=(R.Tensor((batch_size, vocab_size), dtype="float32"),)) + lv1 = R.call_tir(cls.chunk_lse, (lv, temperature), out_sinfo=[R.Tensor((batch_size, (vocab_size + 4096 - 1) // 4096), dtype="float32"), R.Tensor((batch_size, (vocab_size + 4096 - 1) // 4096), dtype="float32")]) + lv2: R.Tensor((batch_size, (vocab_size + 4096 - 1) // 4096), dtype="float32") = lv1[0] + lv3: R.Tensor((batch_size, (vocab_size + 4096 - 1) // 4096), dtype="float32") = lv1[1] + lv4 = R.call_tir(cls.softmax_with_chunked_sum, (lv, temperature, lv2, lv3), out_sinfo=R.Tensor((batch_size, vocab_size), dtype="float32")) + gv: R.Tensor((batch_size, 1, vocab_size), dtype="float32") = R.call_pure_packed("vm.builtin.reshape", lv4, R.shape([batch_size, 1, vocab_size]), sinfo_args=(R.Tensor((batch_size, 1, vocab_size), dtype="float32"),)) + R.output(gv) + return gv + +# Metadata omitted. Use show_meta=True in script() method to show it. \ No newline at end of file diff --git a/debug/debug-phase4.py b/debug/debug-phase4.py new file mode 100644 index 0000000000000000000000000000000000000000..4f5382e343d8981dc731fcc3f252ffb96af23a33 --- /dev/null +++ b/debug/debug-phase4.py @@ -0,0 +1,10895 @@ +# from tvm.script import ir as I +# from tvm.script import tir as T +# from tvm.script import relax as R + +@I.ir_module +class Module: + I.module_attrs({"external_mods": [metadata["runtime.Module"][0], metadata["runtime.Module"][1], metadata["runtime.Module"][2], metadata["runtime.Module"][3], metadata["runtime.Module"][4], metadata["runtime.Module"][5], metadata["runtime.Module"][6], metadata["runtime.Module"][7], metadata["runtime.Module"][8], metadata["runtime.Module"][9], metadata["runtime.Module"][10], metadata["runtime.Module"][11], metadata["runtime.Module"][12], metadata["runtime.Module"][13], metadata["runtime.Module"][14]]}) + @T.prim_func(private=True) + def NT_matmul(layer_norm356: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16"), model_decoder_layers_0_self_attn_q_proj_weight5: T.Buffer((T.int64(1280), T.int64(1280)), "float16"), NT_matmul: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16")): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + # with T.block("root"): + NT_matmul_rf_local = T.alloc_buffer((T.int64(128), T.int64(1), T.int64(1), T.int64(1280)), "float16", scope="local") + NT_matmul_rf_local_1 = T.alloc_buffer((T.int64(32), T.int64(1), T.int64(1), T.int64(1280)), "float16", scope="local") + model_decoder_layers_0_self_attn_q_proj_weight5_local = T.alloc_buffer((T.int64(1280), T.int64(1280)), "float16", scope="local") + layer_norm356_shared = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16", scope="shared") + for u_fused_ax0_fused_fused_0 in T.thread_binding(T.int64(80), thread="blockIdx.x"): + for u_fused_ax0_fused_fused_1 in T.thread_binding(T.int64(16), thread="threadIdx.y"): + for ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 in T.thread_binding(T.int64(32), thread="threadIdx.x"): + for ax0, ax1 in T.grid(T.int64(1), T.int64(1)): + for ax2_0 in T.serial(T.int64(3), annotations={"pragma_unroll_explicit": 256, "pragma_vectorize": 1}): + for ax2_1 in T.thread_binding(T.int64(16), thread="threadIdx.y"): + for ax2_2 in T.thread_binding(T.int64(32), thread="threadIdx.x"): + for ax2_3 in T.vectorized(T.int64(1)): + with T.block("layer_norm356_shared"): + v0, v1 = T.axis.remap("SS", [ax0, ax1]) + v2 = T.axis.spatial(T.int64(1280), ax2_0 * T.int64(512) + ax2_1 * T.int64(32) + ax2_2 + ax2_3) + T.where((ax2_0 * T.int64(16) + ax2_1) * T.int64(32) + ax2_2 + ax2_3 < T.int64(1280)) + T.reads(layer_norm356[v0, v1, v2]) + T.writes(layer_norm356_shared[v0, v1, v2]) + layer_norm356_shared[v0, v1, v2] = layer_norm356[v0, v1, v2] + for u_fused_ax0_fused_fused_2_init in range(T.int64(1)): + for ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1_init in T.vectorized(T.int64(4)): + with T.block("NT_matmul_rf_init"): + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused = T.axis.spatial(T.int64(128), ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 * T.int64(4) + ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1_init) + v0 = T.axis.spatial(T.int64(1280), u_fused_ax0_fused_fused_0 * T.int64(16) + u_fused_ax0_fused_fused_1 + u_fused_ax0_fused_fused_2_init) + T.reads() + T.writes(NT_matmul_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0]) + NT_matmul_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0] = T.float16(0) + for ax1_fused_u_fused_0 in T.serial(T.int64(5), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): + for ax0_ax1_fused_0 in range(T.int64(4)): + for ax0_ax1_fused_1 in T.vectorized(T.int64(2)): + with T.block("model_decoder_layers_0_self_attn_q_proj_weight5_local"): + v0 = T.axis.spatial(T.int64(1280), u_fused_ax0_fused_fused_0 * T.int64(16) + u_fused_ax0_fused_fused_1) + v1 = T.axis.spatial(T.int64(1280), ax1_fused_u_fused_0 * T.int64(256) + ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 * T.int64(8) + ax0_ax1_fused_0 * T.int64(2) + ax0_ax1_fused_1) + T.reads(model_decoder_layers_0_self_attn_q_proj_weight5[v0, v1]) + T.writes(model_decoder_layers_0_self_attn_q_proj_weight5_local[v0, v1]) + model_decoder_layers_0_self_attn_q_proj_weight5_local[v0, v1] = model_decoder_layers_0_self_attn_q_proj_weight5[v0, v1] + for u_fused_ax0_fused_fused_2, ax1_fused_u_fused_2 in T.grid(T.int64(1), T.int64(2)): + for ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1 in T.vectorized(T.int64(4)): + with T.block("NT_matmul_rf_update"): + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused = T.axis.spatial(T.int64(128), ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 * T.int64(4) + ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1) + v0 = T.axis.spatial(T.int64(1280), u_fused_ax0_fused_fused_0 * T.int64(16) + u_fused_ax0_fused_fused_1 + u_fused_ax0_fused_fused_2) + vax1_fused_u_fused_0, vax1_fused_u_fused_2 = T.axis.remap("RR", [ax1_fused_u_fused_0, ax1_fused_u_fused_2]) + T.reads(NT_matmul_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0], layer_norm356_shared[T.int64(0), T.int64(0), vax1_fused_u_fused_0 * T.int64(256) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused // T.int64(4) * T.int64(8) + vax1_fused_u_fused_2 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused % T.int64(4)], model_decoder_layers_0_self_attn_q_proj_weight5_local[v0, vax1_fused_u_fused_0 * T.int64(256) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused // T.int64(4) * T.int64(8) + vax1_fused_u_fused_2 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused % T.int64(4)]) + T.writes(NT_matmul_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0]) + NT_matmul_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0] = NT_matmul_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0] + layer_norm356_shared[T.int64(0), T.int64(0), vax1_fused_u_fused_0 * T.int64(256) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused // T.int64(4) * T.int64(8) + vax1_fused_u_fused_2 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused % T.int64(4)] * model_decoder_layers_0_self_attn_q_proj_weight5_local[v0, vax1_fused_u_fused_0 * T.int64(256) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused // T.int64(4) * T.int64(8) + vax1_fused_u_fused_2 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused % T.int64(4)] + for ax2_fused_0_ax2_fused_1_fused in T.thread_binding(T.int64(16), thread="threadIdx.y"): + for ax0 in T.thread_binding(T.int64(32), thread="threadIdx.x"): + for ax2_fused_2_0 in T.serial(T.int64(1), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): + for ax2_fused_2_1 in T.vectorized(T.int64(1)): + with T.block("NT_matmul_rf_init"): + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 = T.axis.spatial(T.int64(32), ax0) + v0 = T.axis.spatial(T.int64(1280), u_fused_ax0_fused_fused_0 * T.int64(16) + ax2_fused_0_ax2_fused_1_fused + ax2_fused_2_0 + ax2_fused_2_1) + T.reads() + T.writes(NT_matmul_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0]) + NT_matmul_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0] = T.float16(0) + for ax1 in range(T.int64(4)): + with T.block("NT_matmul_rf_update"): + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1 = T.axis.remap("SR", [ax0, ax1]) + v0 = T.axis.spatial(T.int64(1280), u_fused_ax0_fused_fused_0 * T.int64(16) + ax2_fused_0_ax2_fused_1_fused + ax2_fused_2_0 + ax2_fused_2_1) + T.reads(NT_matmul_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0], NT_matmul_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1, T.int64(0), T.int64(0), v0]) + T.writes(NT_matmul_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0]) + NT_matmul_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0] = NT_matmul_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0] + NT_matmul_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1, T.int64(0), T.int64(0), v0] + for ax1_fused_2 in range(T.int64(1)): + for ax1_fused_0_ax1_fused_1_fused in T.thread_binding(T.int64(16), thread="threadIdx.y"): + for ax0 in T.thread_binding(T.int64(32), thread="threadIdx.x"): + with T.block("NT_matmul"): + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 = T.axis.reduce(T.int64(32), ax0) + v0 = T.axis.spatial(T.int64(1280), u_fused_ax0_fused_fused_0 * T.int64(16) + ax1_fused_0_ax1_fused_1_fused + ax1_fused_2) + T.reads(NT_matmul_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0]) + T.writes(NT_matmul[T.int64(0), T.int64(0), v0]) + with T.init(): + NT_matmul[T.int64(0), T.int64(0), v0] = T.float16(0) + NT_matmul[T.int64(0), T.int64(0), v0] = NT_matmul[T.int64(0), T.int64(0), v0] + NT_matmul_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0] + + @T.prim_func(private=True) + def NT_matmul3(layer_norm452: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16"), model_decoder_embed_tokens_weight5: T.Buffer((T.int64(51866), T.int64(1280)), "float16"), NT_matmul: T.Buffer((T.int64(1), T.int64(1), T.int64(51866)), "float32")): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + # with T.block("root"): + NT_matmul_rf_local = T.alloc_buffer((T.int64(256), T.int64(1), T.int64(1), T.int64(51866)), scope="local") + NT_matmul_rf_local_1 = T.alloc_buffer((T.int64(64), T.int64(1), T.int64(1), T.int64(51866)), scope="local") + model_decoder_embed_tokens_weight5_local = T.alloc_buffer((T.int64(51866), T.int64(1280)), "float16", scope="local") + layer_norm452_shared = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16", scope="shared") + for u_fused_ax0_fused_fused_0 in T.thread_binding(T.int64(12967), thread="blockIdx.x"): + for u_fused_ax0_fused_fused_1 in T.thread_binding(T.int64(4), thread="threadIdx.y"): + for ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 in T.thread_binding(T.int64(64), thread="threadIdx.x"): + for ax0, ax1 in T.grid(T.int64(1), T.int64(1)): + for ax2_0 in T.serial(T.int64(5), annotations={"pragma_unroll_explicit": 256, "pragma_vectorize": 1}): + for ax2_1 in T.thread_binding(T.int64(4), thread="threadIdx.y"): + for ax2_2 in T.thread_binding(T.int64(64), thread="threadIdx.x"): + for ax2_3 in T.vectorized(T.int64(1)): + with T.block("layer_norm452_shared"): + v0, v1 = T.axis.remap("SS", [ax0, ax1]) + v2 = T.axis.spatial(T.int64(1280), ax2_0 * T.int64(256) + ax2_1 * T.int64(64) + ax2_2 + ax2_3) + T.reads(layer_norm452[v0, v1, v2]) + T.writes(layer_norm452_shared[v0, v1, v2]) + layer_norm452_shared[v0, v1, v2] = layer_norm452[v0, v1, v2] + for u_fused_ax0_fused_fused_2_init in range(T.int64(1)): + for ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1_init in T.vectorized(T.int64(4)): + with T.block("NT_matmul_rf_init"): + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused = T.axis.spatial(T.int64(256), ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 * T.int64(4) + ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1_init) + v0 = T.axis.spatial(T.int64(51866), u_fused_ax0_fused_fused_0 * T.int64(4) + u_fused_ax0_fused_fused_1 + u_fused_ax0_fused_fused_2_init) + T.where(u_fused_ax0_fused_fused_0 * T.int64(4) + u_fused_ax0_fused_fused_1 + u_fused_ax0_fused_fused_2_init < T.int64(51866)) + T.reads() + T.writes(NT_matmul_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0]) + NT_matmul_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0] = T.float32(0) + for ax1_fused_u_fused_0 in T.serial(T.int64(5), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): + for ax0_ax1_fused_0 in range(T.int64(2)): + for ax0_ax1_fused_1 in T.vectorized(T.int64(2)): + with T.block("model_decoder_embed_tokens_weight5_local"): + v0 = T.axis.spatial(T.int64(51866), u_fused_ax0_fused_fused_0 * T.int64(4) + u_fused_ax0_fused_fused_1) + v1 = T.axis.spatial(T.int64(1280), ax1_fused_u_fused_0 * T.int64(256) + ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 * T.int64(4) + ax0_ax1_fused_0 * T.int64(2) + ax0_ax1_fused_1) + T.where(u_fused_ax0_fused_fused_0 * T.int64(4) + u_fused_ax0_fused_fused_1 < T.int64(51866)) + T.reads(model_decoder_embed_tokens_weight5[v0, v1]) + T.writes(model_decoder_embed_tokens_weight5_local[v0, v1]) + model_decoder_embed_tokens_weight5_local[v0, v1] = model_decoder_embed_tokens_weight5[v0, v1] + for u_fused_ax0_fused_fused_2, ax1_fused_u_fused_2 in T.grid(T.int64(1), T.int64(1)): + for ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1 in T.vectorized(T.int64(4)): + with T.block("NT_matmul_rf_update"): + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused = T.axis.spatial(T.int64(256), ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 * T.int64(4) + ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1) + v0 = T.axis.spatial(T.int64(51866), u_fused_ax0_fused_fused_0 * T.int64(4) + u_fused_ax0_fused_fused_1 + u_fused_ax0_fused_fused_2) + vax1_fused_u_fused_2, vax1_fused_u_fused_0 = T.axis.remap("RR", [ax1_fused_u_fused_2, ax1_fused_u_fused_0]) + T.where(u_fused_ax0_fused_fused_0 * T.int64(4) + u_fused_ax0_fused_fused_1 + u_fused_ax0_fused_fused_2 < T.int64(51866)) + T.reads(NT_matmul_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0], layer_norm452_shared[T.int64(0), T.int64(0), vax1_fused_u_fused_0 * T.int64(256) + vax1_fused_u_fused_2 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused], model_decoder_embed_tokens_weight5_local[v0, vax1_fused_u_fused_0 * T.int64(256) + vax1_fused_u_fused_2 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused]) + T.writes(NT_matmul_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0]) + NT_matmul_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0] = NT_matmul_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0] + T.Cast("float32", layer_norm452_shared[T.int64(0), T.int64(0), vax1_fused_u_fused_0 * T.int64(256) + vax1_fused_u_fused_2 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused]) * T.Cast("float32", model_decoder_embed_tokens_weight5_local[v0, vax1_fused_u_fused_0 * T.int64(256) + vax1_fused_u_fused_2 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused]) + for ax2_fused_0_ax2_fused_1_fused in T.thread_binding(T.int64(4), thread="threadIdx.y"): + for ax0 in T.thread_binding(T.int64(64), thread="threadIdx.x"): + for ax2_fused_2_0 in T.serial(T.int64(1), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): + for ax2_fused_2_1 in T.vectorized(T.int64(1)): + with T.block("NT_matmul_rf_init"): + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 = T.axis.spatial(T.int64(64), ax0) + v0 = T.axis.spatial(T.int64(51866), u_fused_ax0_fused_fused_0 * T.int64(4) + ax2_fused_0_ax2_fused_1_fused + ax2_fused_2_0 + ax2_fused_2_1) + T.where(u_fused_ax0_fused_fused_0 * T.int64(4) + (T.Mul(T.int64(0), T.int64(4)) + ax2_fused_0_ax2_fused_1_fused % T.int64(4) + (ax2_fused_2_0 + ax2_fused_2_1)) < T.int64(51866)) + T.reads() + T.writes(NT_matmul_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0]) + NT_matmul_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0] = T.float32(0) + for ax1 in range(T.int64(4)): + with T.block("NT_matmul_rf_update"): + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1 = T.axis.remap("SR", [ax0, ax1]) + v0 = T.axis.spatial(T.int64(51866), u_fused_ax0_fused_fused_0 * T.int64(4) + ax2_fused_0_ax2_fused_1_fused + ax2_fused_2_0 + ax2_fused_2_1) + T.where(u_fused_ax0_fused_fused_0 * T.int64(4) + (T.Mul(T.int64(0), T.int64(4)) + ax2_fused_0_ax2_fused_1_fused % T.int64(4) + (ax2_fused_2_0 + ax2_fused_2_1)) < T.int64(51866)) + T.reads(NT_matmul_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0], NT_matmul_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1, T.int64(0), T.int64(0), v0]) + T.writes(NT_matmul_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0]) + NT_matmul_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0] = NT_matmul_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0] + NT_matmul_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1, T.int64(0), T.int64(0), v0] + for ax1_fused_2 in range(T.int64(1)): + for ax1_fused_0_ax1_fused_1_fused in T.thread_binding(T.int64(4), thread="threadIdx.y"): + for ax0 in T.thread_binding(T.int64(64), thread="threadIdx.x"): + with T.block("NT_matmul"): + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 = T.axis.reduce(T.int64(64), ax0) + v0 = T.axis.spatial(T.int64(51866), u_fused_ax0_fused_fused_0 * T.int64(4) + ax1_fused_0_ax1_fused_1_fused + ax1_fused_2) + T.where(u_fused_ax0_fused_fused_0 * T.int64(4) + (T.Mul(T.int64(0), T.int64(4)) + ax1_fused_0_ax1_fused_1_fused % T.int64(4) + ax1_fused_2) < T.int64(51866)) + T.reads(NT_matmul_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0]) + T.writes(NT_matmul[T.int64(0), T.int64(0), v0]) + with T.init(): + NT_matmul[T.int64(0), T.int64(0), v0] = T.float32(0) + NT_matmul[T.int64(0), T.int64(0), v0] = NT_matmul[T.int64(0), T.int64(0), v0] + NT_matmul_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0] + + @T.prim_func(private=True) + def add(var_reshape708: T.handle, var_reshape709: T.handle, var_T_add: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + batch_size = T.int64() + reshape708 = T.match_buffer(var_reshape708, (batch_size, T.int64(1), T.int64(1280)), "float16") + reshape709 = T.match_buffer(var_reshape709, (batch_size, T.int64(1), T.int64(1280)), "float16") + T_add = T.match_buffer(var_T_add, (batch_size, T.int64(1), T.int64(1280)), "float16") + # with T.block("root"): + for ax0_ax1_fused_0 in T.thread_binding((batch_size * T.int64(1280) + T.int64(1023)) // T.int64(1024), thread="blockIdx.x"): + for ax0_ax1_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_add"): + v0 = T.axis.spatial(batch_size, (ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1) // T.int64(1280)) + v1 = T.axis.spatial(T.int64(1280), (ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1) % T.int64(1280)) + T.where(ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1 < batch_size * T.int64(1280)) + T.reads(reshape708[v0, T.int64(0), v1], reshape709[v0, T.int64(0), v1]) + T.writes(T_add[v0, T.int64(0), v1]) + T_add[v0, T.int64(0), v1] = reshape708[v0, T.int64(0), v1] + reshape709[v0, T.int64(0), v1] + + @T.prim_func(private=True) + def add4(var_add: T.handle, var_lv610: T.handle, var_T_add: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + batch_size = T.int64() + add = T.match_buffer(var_add, (batch_size, T.int64(1500), T.int64(1280)), "float16") + lv610 = T.match_buffer(var_lv610, (batch_size, T.int64(1500), T.int64(1280)), "float16") + T_add = T.match_buffer(var_T_add, (batch_size, T.int64(1500), T.int64(1280)), "float16") + # with T.block("root"): + for ax0_ax1_ax2_fused_0 in T.thread_binding(batch_size * T.int64(1875), thread="blockIdx.x"): + for ax0_ax1_ax2_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_add"): + v0 = T.axis.spatial(batch_size, (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) // T.int64(1920000)) + v1 = T.axis.spatial(T.int64(1500), (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) % T.int64(1920000) // T.int64(1280)) + v2 = T.axis.spatial(T.int64(1280), (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) % T.int64(1280)) + T.reads(add[v0, v1, v2], lv610[v0, v1, v2]) + T.writes(T_add[v0, v1, v2]) + T_add[v0, v1, v2] = add[v0, v1, v2] + lv610[v0, v1, v2] + + @T.prim_func(private=True) + def add5(var_reshape385: T.handle, var_reshape386: T.handle, var_T_add: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + seq_len = T.int64() + reshape385 = T.match_buffer(var_reshape385, (T.int64(1), seq_len, T.int64(1280)), "float16") + reshape386 = T.match_buffer(var_reshape386, (T.int64(1), seq_len, T.int64(1280)), "float16") + T_add = T.match_buffer(var_T_add, (T.int64(1), seq_len, T.int64(1280)), "float16") + # with T.block("root"): + for ax0_ax1_fused_0 in T.thread_binding((seq_len * T.int64(1280) + T.int64(1023)) // T.int64(1024), thread="blockIdx.x"): + for ax0_ax1_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_add"): + v0 = T.axis.spatial(seq_len, (ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1) // T.int64(1280)) + v1 = T.axis.spatial(T.int64(1280), (ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1) % T.int64(1280)) + T.where(ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1 < seq_len * T.int64(1280)) + T.reads(reshape385[T.int64(0), v0, v1], reshape386[T.int64(0), v0, v1]) + T.writes(T_add[T.int64(0), v0, v1]) + T_add[T.int64(0), v0, v1] = reshape385[T.int64(0), v0, v1] + reshape386[T.int64(0), v0, v1] + + @T.prim_func + def apply_bitmask_inplace(var_logits: T.handle, var_seq_ids: T.handle, var_bitmask: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": T.bool(True), "tir.noalias": T.bool(True)}) + batch_size, vocab_size = T.int32(is_size_var=True), T.int32(is_size_var=True) + logits = T.match_buffer(var_logits, (batch_size, vocab_size)) + num_seq = T.int32(is_size_var=True) + seq_ids = T.match_buffer(var_seq_ids, (num_seq,), "int32") + bitmask = T.match_buffer(var_bitmask, (batch_size, (vocab_size + 31) // 32), "int32") + # with T.block("root"): + for fused_s_v_0 in T.thread_binding((num_seq * vocab_size + 1023) // 1024, thread="blockIdx.x"): + for fused_s_v_1 in T.thread_binding(1024, thread="threadIdx.x"): + with T.block("block"): + vs = T.axis.spatial(num_seq, (fused_s_v_0 * 1024 + fused_s_v_1) // vocab_size) + vv = T.axis.spatial(vocab_size, (fused_s_v_0 * 1024 + fused_s_v_1) % vocab_size) + T.where(fused_s_v_0 * 1024 + fused_s_v_1 < num_seq * vocab_size) + T.reads(bitmask[seq_ids[vs], vv // 32], seq_ids[vs], logits[seq_ids[vs], vv]) + T.writes(logits[seq_ids[vs], vv]) + logits[seq_ids[vs], vv] = T.if_then_else(T.bitwise_and(T.shift_right(bitmask[seq_ids[vs], vv // 32], vv % 32), 1) == 1, logits[seq_ids[vs], vv], T.float32(-3.4028234663852886e+38)) + + @T.prim_func + def apply_logit_bias_inplace(var_logits: T.handle, var_pos2seq_id: T.handle, var_token_ids: T.handle, var_logit_bias: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": T.bool(True), "tir.noalias": T.bool(True)}) + batch_size, vocab_size = T.int32(is_size_var=True), T.int32(is_size_var=True) + logits = T.match_buffer(var_logits, (batch_size, vocab_size)) + num_token = T.int32(is_size_var=True) + pos2seq_id = T.match_buffer(var_pos2seq_id, (num_token,), "int32") + token_ids = T.match_buffer(var_token_ids, (num_token,), "int32") + logit_bias = T.match_buffer(var_logit_bias, (num_token,)) + # with T.block("root"): + for p0 in T.thread_binding((num_token + 1023) // 1024, thread="blockIdx.x"): + for p1 in T.thread_binding(1024, thread="threadIdx.x"): + with T.block("block"): + vp = T.axis.spatial(num_token, p0 * 1024 + p1) + T.where(p0 * 1024 + p1 < num_token) + T.reads(logits[pos2seq_id[vp], token_ids[vp]], pos2seq_id[vp], token_ids[vp], logit_bias[vp]) + T.writes(logits[pos2seq_id[vp], token_ids[vp]]) + logits[pos2seq_id[vp], token_ids[vp]] = logits[pos2seq_id[vp], token_ids[vp]] + logit_bias[vp] + + @T.prim_func + def apply_penalty_inplace(var_logits: T.handle, var_seq_ids: T.handle, var_pos2seq_id: T.handle, var_token_ids: T.handle, var_token_cnt: T.handle, var_penalties: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": T.bool(True), "tir.noalias": T.bool(True)}) + batch_size, vocab_size = T.int32(is_size_var=True), T.int32(is_size_var=True) + logits = T.match_buffer(var_logits, (batch_size, vocab_size)) + num_seq = T.int32(is_size_var=True) + seq_ids = T.match_buffer(var_seq_ids, (num_seq,), "int32") + num_token = T.int32(is_size_var=True) + pos2seq_id = T.match_buffer(var_pos2seq_id, (num_token,), "int32") + token_ids = T.match_buffer(var_token_ids, (num_token,), "int32") + token_cnt = T.match_buffer(var_token_cnt, (num_token,), "int32") + penalties = T.match_buffer(var_penalties, (num_seq, 3)) + # with T.block("root"): + for p0 in T.thread_binding((num_token + 1023) // 1024, thread="blockIdx.x"): + for p1 in T.thread_binding(1024, thread="threadIdx.x"): + with T.block("block"): + vp = T.axis.spatial(num_token, p0 * 1024 + p1) + T.where(p0 * 1024 + p1 < num_token) + T.reads(logits[seq_ids[pos2seq_id[vp]], token_ids[vp]], seq_ids[pos2seq_id[vp]], pos2seq_id[vp], token_ids[vp], penalties[pos2seq_id[vp], 0:3], token_cnt[vp]) + T.writes(logits[seq_ids[pos2seq_id[vp]], token_ids[vp]]) + logits[seq_ids[pos2seq_id[vp]], token_ids[vp]] = logits[seq_ids[pos2seq_id[vp]], token_ids[vp]] - (penalties[pos2seq_id[vp], 0] + T.Cast("float32", token_cnt[vp]) * penalties[pos2seq_id[vp], 1]) + logits[seq_ids[pos2seq_id[vp]], token_ids[vp]] = T.if_then_else(logits[seq_ids[pos2seq_id[vp]], token_ids[vp]] > T.float32(0), logits[seq_ids[pos2seq_id[vp]], token_ids[vp]] * penalties[pos2seq_id[vp], 2], logits[seq_ids[pos2seq_id[vp]], token_ids[vp]] / penalties[pos2seq_id[vp], 2]) + + @T.prim_func(private=True) + def argsort_thrust(var_probs: T.handle, var_lv: T.handle, var_topk_gpu_v1: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + batch_size, vocab_size = T.int64(), T.int64() + data_buf = T.match_buffer(var_probs, (batch_size, vocab_size), align=8) + workspace_buf = T.match_buffer(var_lv, (T.int64(8) * (batch_size * vocab_size * T.int64(4)) + T.int64(8388608) + batch_size * vocab_size * T.int64(12),), "uint8", align=8) + indices_buf = T.match_buffer(var_topk_gpu_v1, (batch_size, vocab_size), "int32", align=8) + # with T.block("root"): + value_buf = T.alloc_buffer((batch_size, vocab_size), align=8) + with T.block("topk_gpu"): + T.reads() + T.writes() + T.call_packed("tvm.contrib.thrust.sort", T.tvm_stack_make_array(data_buf.data, T.tvm_stack_make_shape(batch_size, vocab_size), 0, 2, T.float32(0), T.int64(0)), T.tvm_stack_make_array(value_buf.data, T.tvm_stack_make_shape(batch_size, vocab_size), 0, 2, T.float32(0), T.int64(0)), T.tvm_stack_make_array(indices_buf.data, T.tvm_stack_make_shape(batch_size, vocab_size), 0, 2, 0, T.int64(0)), 0, T.tvm_stack_make_array(workspace_buf.data, T.tvm_stack_make_shape(T.int64(8) * (batch_size * vocab_size * T.int64(4)) + T.int64(8388608) + batch_size * vocab_size * T.int64(12)), 0, 1, T.uint8(0), T.int64(0))) + + @T.prim_func + def batch_decode_paged_kv(_0: T.int32, Q_handle: T.handle, pages_handle: T.handle, page_table_indptr_handle: T.handle, page_table_values_handle: T.handle, var_length_info: T.handle, k_rope_pos_offset_handle: T.handle, q_rope_position_handle: T.handle, output_handle: T.handle, lse_handle: T.handle, rotary_mode: T.int32, rope_scale: T.float32, rope_theta: T.float32, attn_score_scaling_factor: T.float32): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) + B = T.int32(is_size_var=True) + Q = T.match_buffer(Q_handle, (B, 20, 64), "float16") + max_num_pages = T.int32(is_size_var=True) + pages = T.match_buffer(pages_handle, (max_num_pages, 2, 20, 16, 64), "float16") + page_table_indptr = T.match_buffer(page_table_indptr_handle, (B + 1,), "int32", offset_factor=1) + nnz_pages = T.int32(is_size_var=True) + page_table_values = T.match_buffer(page_table_values_handle, (nnz_pages,), "int32", offset_factor=1) + length_info = T.match_buffer(var_length_info, (B,), "int32", offset_factor=1) + k_rope_pos_offset = T.match_buffer(k_rope_pos_offset_handle, (B,), "int32", offset_factor=1) + q_rope_position = T.match_buffer(q_rope_position_handle, (B,), "int32", offset_factor=1) + output = T.match_buffer(output_handle, (B, 20, 64), "float16") + lse = T.match_buffer(lse_handle, (B, 20)) + # with T.block("root"): + sm_scale: T.float32 = T.float32(0.18033688011112042) + for bx in T.thread_binding(B, thread="blockIdx.x"): + for fused_by_bz in T.thread_binding(20, thread="blockIdx.y"): + for ty in T.thread_binding(1, thread="threadIdx.y"): + for tx in T.thread_binding(16, thread="threadIdx.x"): + for tz in T.thread_binding(32, thread="threadIdx.z"): + with T.block("attn"): + T.reads(page_table_indptr[bx:bx + 2], length_info[bx], q_rope_position[bx], Q[bx, fused_by_bz // 20 + ty + fused_by_bz % 20, tx * 4 - 32:tx * 4 - 32 + 68]) + T.writes(output[bx, fused_by_bz % 20 + fused_by_bz // 20 + ty, tx * 4:tx * 4 + 4], lse[bx, fused_by_bz % 20 + fused_by_bz // 20 + ty]) + Q_local = T.alloc_buffer((4,), "float16", scope="local") + kv_chunk_len = T.alloc_buffer((1,), "int32", scope="local") + K_smem = T.alloc_buffer((64, 64), "float16", scope="shared") + V_smem = T.alloc_buffer((64, 64), "float16", scope="shared") + O_allreduce = T.alloc_buffer((32, 1, 64), scope="shared") + md_allreduce = T.alloc_buffer((32, 1, 2), scope="shared") + S_reduce_local = T.alloc_buffer((1,), scope="local") + t0 = T.alloc_buffer((1,), scope="local") + S_local = T.alloc_buffer((2,), scope="local") + QK_local = T.alloc_buffer((4,), scope="local") + V_local = T.alloc_buffer((4,), "float16", scope="local") + m_prev = T.alloc_buffer((1,), scope="local") + d_prev = T.alloc_buffer((1,), scope="local") + other_m = T.alloc_buffer((1,), scope="local") + other_d = T.alloc_buffer((1,), scope="local") + exp_mprev = T.alloc_buffer((1,), scope="local") + exp_otherm = T.alloc_buffer((1,), scope="local") + other_o = T.alloc_buffer((4,), scope="local") + st_m = T.alloc_buffer((1,), scope="local") + st_d = T.alloc_buffer((1,), scope="local") + O_local = T.alloc_buffer((4,), scope="local") + by: T.int32 = fused_by_bz % 20 + bz: T.int32 = fused_by_bz // 20 + batch_idx: T.int32 = bx + cur_page_indptr_begin: T.int32 = page_table_indptr[batch_idx] + cur_page_indptr_end: T.int32 = page_table_indptr[batch_idx + 1] + kv_chunk_len[0] = T.if_then_else(cur_page_indptr_begin != cur_page_indptr_end, (cur_page_indptr_end - cur_page_indptr_begin - 1) * 16 + length_info[batch_idx], 0) + st_m[0] = T.float32(-50000) + st_d[0] = T.float32(1) + for vec in T.vectorized(4): + O_local[vec] = T.float32(0) + for vec in T.vectorized(4): + Q_local[vec] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", q_rope_position[batch_idx]) * rope_scale / T.pow(rope_theta, T.Cast("float32", (tx * 4 + vec) * 2 % 64) / T.float32(64))) * T.Cast("float32", Q[bx, by + bz + ty, tx * 4 + vec]) + T.sin(T.Cast("float32", q_rope_position[batch_idx]) * rope_scale / T.pow(rope_theta, T.Cast("float32", (tx * 4 + vec) * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(tx * 4 + vec < 32, Q[bx, by + bz + ty, tx * 4 + vec + 32] * T.float16(-1), Q[bx, by + bz + ty, tx * 4 + vec - 32]))), Q[bx, by + bz + ty, tx * 4 + vec]) + for iterator in range((kv_chunk_len[0] + 63) // 64): + tile_start_s: T.int32 = (tz + ty) * 2 + tile_start_g: T.int32 = (iterator * 32 + tz + ty) * 2 + for j in range(2): + with T.block("KV_load"): + T.reads() + T.writes() + row_g: T.int32 = tile_start_g + j + if row_g < kv_chunk_len[0]: + seq_offset: T.int32 = row_g + page_no: T.int32 = page_table_values[cur_page_indptr_begin + seq_offset // 16] + page_offset: T.int32 = seq_offset % 16 + for vec in T.vectorized(4): + K_smem[tile_start_s + j, tx * 4 + vec] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", k_rope_pos_offset[batch_idx] + row_g) * rope_scale / T.pow(rope_theta, T.Cast("float32", (tx * 4 + vec) * 2 % 64) / T.float32(64))) * T.Cast("float32", pages[page_no, 0, by, page_offset, tx * 4 + vec]) + T.sin(T.Cast("float32", k_rope_pos_offset[batch_idx] + row_g) * rope_scale / T.pow(rope_theta, T.Cast("float32", (tx * 4 + vec) * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(tx * 4 + vec < 32, pages[page_no, 0, by, page_offset, tx * 4 + vec + 32] * T.float16(-1), pages[page_no, 0, by, page_offset, tx * 4 + vec - 32]))), pages[page_no, 0, by, page_offset, tx * 4 + vec]) + V_smem[tile_start_s + j, tx * 4 + vec] = pages[page_no, 1, by, page_offset, tx * 4 + vec] + else: + for vec in T.vectorized(4): + K_smem[tile_start_s + j, tx * 4 + vec] = T.float16(0) + V_smem[tile_start_s + j, tx * 4 + vec] = T.float16(0) + T.tvm_storage_sync("shared") + m_prev[0] = st_m[0] + for j in range(2): + for vec in T.vectorized(4): + QK_local[vec] = T.Cast("float32", Q_local[vec]) * T.Cast("float32", K_smem[tz * 2 + j, tx * 4 + vec]) * attn_score_scaling_factor * sm_scale + S_reduce_local[0] = T.float32(0) + for vec in T.unroll(4): + S_reduce_local[0] = S_reduce_local[0] + QK_local[vec] + with T.block("block_cross_thread"): + T.reads(S_reduce_local[0]) + T.writes(t0[0]) + T.attr(T.comm_reducer(lambda x0, y0: x0 + y0, [T.float32(0)]), "reduce_scope", T.reinterpret("handle", T.uint64(0))) + T.tvm_thread_allreduce(T.uint32(1), S_reduce_local[0], T.bool(True), t0[0], tx) + S_local[j] = T.float32(-50000) + if (iterator * 32 + tz) * 2 + j < kv_chunk_len[0]: + S_local[j] = t0[0] + st_m[0] = T.max(st_m[0], S_local[j]) + o_scale: T.float32 = T.exp2(m_prev[0] - st_m[0]) + st_d[0] = st_d[0] * o_scale + for j in range(2): + S_local[j] = T.exp2(S_local[j] - st_m[0]) + st_d[0] = st_d[0] + S_local[j] + for j in T.vectorized(4): + O_local[j] = O_local[j] * o_scale + for j in range(2): + for vec in T.vectorized(4): + V_local[vec] = V_smem[tz * 2 + j, tx * 4 + vec] + for vec in T.vectorized(4): + O_local[vec] = O_local[vec] + T.Cast("float32", V_local[vec]) * S_local[j] + for vec in T.vectorized(4): + O_allreduce[tz, ty, tx * 4 + vec] = O_local[vec] + md_allreduce[tz, ty, 0] = st_m[0] + md_allreduce[tz, ty, 1] = st_d[0] + T.tvm_storage_sync("shared") + st_m[0] = T.float32(-50000) + st_d[0] = T.float32(1) + for vec in T.vectorized(4): + O_local[vec] = T.float32(0) + for j in range(32): + m_prev[0] = st_m[0] + d_prev[0] = st_d[0] + other_m[0] = md_allreduce[j, ty, 0] + other_d[0] = md_allreduce[j, ty, 1] + for vec in T.vectorized(4): + other_o[vec] = O_allreduce[j, ty, tx * 4 + vec] + st_m[0] = T.max(st_m[0], other_m[0]) + st_d[0] = d_prev[0] * T.exp2(m_prev[0] - st_m[0]) + other_d[0] * T.exp2(other_m[0] - st_m[0]) + exp_mprev[0] = T.exp2(m_prev[0] - st_m[0]) + exp_otherm[0] = T.exp2(other_m[0] - st_m[0]) + for vec in T.vectorized(4): + O_local[vec] = O_local[vec] * exp_mprev[0] + other_o[vec] * exp_otherm[0] + for vec in T.vectorized(4): + O_local[vec] = O_local[vec] / st_d[0] + for vec in T.vectorized(4): + output[batch_idx, by + bz + ty, tx * 4 + vec] = T.Cast("float16", O_local[vec]) + lse[batch_idx, by + bz + ty] = st_m[0] + T.log2(st_d[0]) + + @T.prim_func + def batch_decode_paged_kv_sliding_window(_0: T.int32, Q_handle: T.handle, pages_handle: T.handle, page_table_indptr_handle: T.handle, page_table_values_handle: T.handle, var_length_info: T.handle, k_rope_pos_offset_handle: T.handle, q_rope_position_handle: T.handle, output_handle: T.handle, lse_handle: T.handle, rotary_mode: T.int32, rope_scale: T.float32, rope_theta: T.float32, attn_score_scaling_factor: T.float32): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) + B = T.int32(is_size_var=True) + Q = T.match_buffer(Q_handle, (B, 20, 64), "float16") + max_num_pages = T.int32(is_size_var=True) + pages = T.match_buffer(pages_handle, (max_num_pages, 2, 20, 16, 64), "float16") + page_table_indptr = T.match_buffer(page_table_indptr_handle, (B + 1,), "int32", offset_factor=1) + nnz_pages = T.int32(is_size_var=True) + page_table_values = T.match_buffer(page_table_values_handle, (nnz_pages,), "int32", offset_factor=1) + length_info = T.match_buffer(var_length_info, (3, B), "int32", offset_factor=1) + k_rope_pos_offset = T.match_buffer(k_rope_pos_offset_handle, (B,), "int32", offset_factor=1) + q_rope_position = T.match_buffer(q_rope_position_handle, (B,), "int32", offset_factor=1) + output = T.match_buffer(output_handle, (B, 20, 64), "float16") + lse = T.match_buffer(lse_handle, (B, 20)) + # with T.block("root"): + sm_scale: T.float32 = T.float32(0.18033688011112042) + for bx in T.thread_binding(B, thread="blockIdx.x"): + for fused_by_bz in T.thread_binding(20, thread="blockIdx.y"): + for ty in T.thread_binding(1, thread="threadIdx.y"): + for tx in T.thread_binding(16, thread="threadIdx.x"): + for tz in T.thread_binding(32, thread="threadIdx.z"): + with T.block("attn"): + T.reads(page_table_indptr[bx:bx + 2], length_info[0:3, bx], q_rope_position[bx], Q[bx, fused_by_bz // 20 + ty + fused_by_bz % 20, tx * 4 - 32:tx * 4 - 32 + 68]) + T.writes(output[bx, fused_by_bz % 20 + fused_by_bz // 20 + ty, tx * 4:tx * 4 + 4], lse[bx, fused_by_bz % 20 + fused_by_bz // 20 + ty]) + Q_local = T.alloc_buffer((4,), "float16", scope="local") + kv_chunk_len = T.alloc_buffer((1,), "int32", scope="local") + K_smem = T.alloc_buffer((64, 64), "float16", scope="shared") + V_smem = T.alloc_buffer((64, 64), "float16", scope="shared") + O_allreduce = T.alloc_buffer((32, 1, 64), scope="shared") + md_allreduce = T.alloc_buffer((32, 1, 2), scope="shared") + S_reduce_local = T.alloc_buffer((1,), scope="local") + t0 = T.alloc_buffer((1,), scope="local") + S_local = T.alloc_buffer((2,), scope="local") + QK_local = T.alloc_buffer((4,), scope="local") + V_local = T.alloc_buffer((4,), "float16", scope="local") + m_prev = T.alloc_buffer((1,), scope="local") + d_prev = T.alloc_buffer((1,), scope="local") + other_m = T.alloc_buffer((1,), scope="local") + other_d = T.alloc_buffer((1,), scope="local") + exp_mprev = T.alloc_buffer((1,), scope="local") + exp_otherm = T.alloc_buffer((1,), scope="local") + other_o = T.alloc_buffer((4,), scope="local") + st_m = T.alloc_buffer((1,), scope="local") + st_d = T.alloc_buffer((1,), scope="local") + O_local = T.alloc_buffer((4,), scope="local") + by: T.int32 = fused_by_bz % 20 + bz: T.int32 = fused_by_bz // 20 + batch_idx: T.int32 = bx + cur_page_indptr_begin: T.int32 = page_table_indptr[batch_idx] + cur_page_indptr_end: T.int32 = page_table_indptr[batch_idx + 1] + kv_chunk_len[0] = T.if_then_else(cur_page_indptr_begin != cur_page_indptr_end, (cur_page_indptr_end - cur_page_indptr_begin - 1) * 16 + length_info[0, batch_idx] - length_info[1, batch_idx] + length_info[2, batch_idx], 0) + st_m[0] = T.float32(-50000) + st_d[0] = T.float32(1) + for vec in T.vectorized(4): + O_local[vec] = T.float32(0) + for vec in T.vectorized(4): + Q_local[vec] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", q_rope_position[batch_idx]) * rope_scale / T.pow(rope_theta, T.Cast("float32", (tx * 4 + vec) * 2 % 64) / T.float32(64))) * T.Cast("float32", Q[bx, by + bz + ty, tx * 4 + vec]) + T.sin(T.Cast("float32", q_rope_position[batch_idx]) * rope_scale / T.pow(rope_theta, T.Cast("float32", (tx * 4 + vec) * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(tx * 4 + vec < 32, Q[bx, by + bz + ty, tx * 4 + vec + 32] * T.float16(-1), Q[bx, by + bz + ty, tx * 4 + vec - 32]))), Q[bx, by + bz + ty, tx * 4 + vec]) + for iterator in range((kv_chunk_len[0] + 63) // 64): + tile_start_s: T.int32 = (tz + ty) * 2 + tile_start_g: T.int32 = (iterator * 32 + tz + ty) * 2 + for j in range(2): + with T.block("KV_load"): + T.reads() + T.writes() + row_g: T.int32 = tile_start_g + j + if row_g < kv_chunk_len[0]: + seq_offset: T.int32 = T.if_then_else(row_g < length_info[2, batch_idx], row_g, row_g - length_info[2, batch_idx] + length_info[1, batch_idx]) + page_no: T.int32 = page_table_values[cur_page_indptr_begin + seq_offset // 16] + page_offset: T.int32 = seq_offset % 16 + for vec in T.vectorized(4): + K_smem[tile_start_s + j, tx * 4 + vec] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", k_rope_pos_offset[batch_idx] + row_g) * rope_scale / T.pow(rope_theta, T.Cast("float32", (tx * 4 + vec) * 2 % 64) / T.float32(64))) * T.Cast("float32", pages[page_no, 0, by, page_offset, tx * 4 + vec]) + T.sin(T.Cast("float32", k_rope_pos_offset[batch_idx] + row_g) * rope_scale / T.pow(rope_theta, T.Cast("float32", (tx * 4 + vec) * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(tx * 4 + vec < 32, pages[page_no, 0, by, page_offset, tx * 4 + vec + 32] * T.float16(-1), pages[page_no, 0, by, page_offset, tx * 4 + vec - 32]))), pages[page_no, 0, by, page_offset, tx * 4 + vec]) + V_smem[tile_start_s + j, tx * 4 + vec] = pages[page_no, 1, by, page_offset, tx * 4 + vec] + else: + for vec in T.vectorized(4): + K_smem[tile_start_s + j, tx * 4 + vec] = T.float16(0) + V_smem[tile_start_s + j, tx * 4 + vec] = T.float16(0) + T.tvm_storage_sync("shared") + m_prev[0] = st_m[0] + for j in range(2): + for vec in T.vectorized(4): + QK_local[vec] = T.Cast("float32", Q_local[vec]) * T.Cast("float32", K_smem[tz * 2 + j, tx * 4 + vec]) * attn_score_scaling_factor * sm_scale + S_reduce_local[0] = T.float32(0) + for vec in T.unroll(4): + S_reduce_local[0] = S_reduce_local[0] + QK_local[vec] + with T.block("block_cross_thread"): + T.reads(S_reduce_local[0]) + T.writes(t0[0]) + T.attr(T.comm_reducer(lambda x0, y0: x0 + y0, [T.float32(0)]), "reduce_scope", T.reinterpret("handle", T.uint64(0))) + T.tvm_thread_allreduce(T.uint32(1), S_reduce_local[0], T.bool(True), t0[0], tx) + S_local[j] = T.float32(-50000) + if (iterator * 32 + tz) * 2 + j < kv_chunk_len[0]: + S_local[j] = t0[0] + st_m[0] = T.max(st_m[0], S_local[j]) + o_scale: T.float32 = T.exp2(m_prev[0] - st_m[0]) + st_d[0] = st_d[0] * o_scale + for j in range(2): + S_local[j] = T.exp2(S_local[j] - st_m[0]) + st_d[0] = st_d[0] + S_local[j] + for j in T.vectorized(4): + O_local[j] = O_local[j] * o_scale + for j in range(2): + for vec in T.vectorized(4): + V_local[vec] = V_smem[tz * 2 + j, tx * 4 + vec] + for vec in T.vectorized(4): + O_local[vec] = O_local[vec] + T.Cast("float32", V_local[vec]) * S_local[j] + for vec in T.vectorized(4): + O_allreduce[tz, ty, tx * 4 + vec] = O_local[vec] + md_allreduce[tz, ty, 0] = st_m[0] + md_allreduce[tz, ty, 1] = st_d[0] + T.tvm_storage_sync("shared") + st_m[0] = T.float32(-50000) + st_d[0] = T.float32(1) + for vec in T.vectorized(4): + O_local[vec] = T.float32(0) + for j in range(32): + m_prev[0] = st_m[0] + d_prev[0] = st_d[0] + other_m[0] = md_allreduce[j, ty, 0] + other_d[0] = md_allreduce[j, ty, 1] + for vec in T.vectorized(4): + other_o[vec] = O_allreduce[j, ty, tx * 4 + vec] + st_m[0] = T.max(st_m[0], other_m[0]) + st_d[0] = d_prev[0] * T.exp2(m_prev[0] - st_m[0]) + other_d[0] * T.exp2(other_m[0] - st_m[0]) + exp_mprev[0] = T.exp2(m_prev[0] - st_m[0]) + exp_otherm[0] = T.exp2(other_m[0] - st_m[0]) + for vec in T.vectorized(4): + O_local[vec] = O_local[vec] * exp_mprev[0] + other_o[vec] * exp_otherm[0] + for vec in T.vectorized(4): + O_local[vec] = O_local[vec] / st_d[0] + for vec in T.vectorized(4): + output[batch_idx, by + bz + ty, tx * 4 + vec] = T.Cast("float16", O_local[vec]) + lse[batch_idx, by + bz + ty] = st_m[0] + T.log2(st_d[0]) + + @T.prim_func + def batch_prefill_paged_kv(_0: T.int32, var_q: T.handle, var_q_indptr: T.handle, var_pages: T.handle, var_page_indptr: T.handle, var_page_values: T.handle, var_length_info: T.handle, var_k_rope_pos_offset: T.handle, var_q_rope_position: T.handle, var_output: T.handle, var_lse: T.handle, causal: T.int32, rotary_mode: T.int32, rope_scale: T.float32, rope_theta: T.float32, attn_score_scaling_factor: T.float32): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) + total_len = T.int32(is_size_var=True) + q = T.match_buffer(var_q, (total_len, 20, 64), "float16") + batch_size = T.int32(is_size_var=True) + q_indptr = T.match_buffer(var_q_indptr, (batch_size + 1,), "int32", offset_factor=1) + max_num_pages = T.int32(is_size_var=True) + pages = T.match_buffer(var_pages, (max_num_pages, 2, 20, 16, 64), "float16") + page_indptr = T.match_buffer(var_page_indptr, (batch_size + 1,), "int32", offset_factor=1) + nnz_pages = T.int32(is_size_var=True) + page_values = T.match_buffer(var_page_values, (nnz_pages,), "int32", offset_factor=1) + length_info = T.match_buffer(var_length_info, (batch_size,), "int32", offset_factor=1) + k_rope_pos_offset = T.match_buffer(var_k_rope_pos_offset, (batch_size,), "int32", offset_factor=1) + q_rope_position = T.match_buffer(var_q_rope_position, (total_len,), "int32", offset_factor=1) + output = T.match_buffer(var_output, (total_len, 20, 64), "float16") + lse = T.match_buffer(var_lse, (total_len, 20)) + # with T.block("root"): + for lbx in T.thread_binding(16, thread="blockIdx.x"): + for lby in T.thread_binding(20, thread="blockIdx.y"): + for lty in T.thread_binding(4, thread="threadIdx.y"): + for ltx in T.thread_binding(32, thread="threadIdx.x"): + with T.block("attn"): + bx, by, ty, tx = T.axis.remap("SSSS", [lbx, lby, lty, ltx]) + T.reads() + T.writes() + tile_id = T.alloc_buffer((1,), "int32", scope="local") + batch_idx = T.alloc_buffer((1,), "int32", scope="local") + batch_tiles = T.alloc_buffer((1,), "int32", scope="local") + batch_rows = T.alloc_buffer((1,), "int32", scope="local") + iterator = T.alloc_buffer((1,), "int32", scope="local") + kv_chunk_len = T.alloc_buffer((1,), "int32", scope="local") + Q_smem = T.alloc_buffer((32, 64), "float16", scope="shared") + K_smem = T.alloc_buffer((16, 64), "float16", scope="shared") + V_smem = T.alloc_buffer((16, 64), "float16", scope="shared") + S_smem = T.alloc_buffer((32, 16), scope="shared") + S_local = T.alloc_buffer((32, 16), scope="local") + O_local = T.alloc_buffer((32, 64), scope="local") + m_smem = T.alloc_buffer((32,), scope="shared") + m_prev_smem = T.alloc_buffer((32,), scope="shared") + d_smem = T.alloc_buffer((32,), scope="shared") + m_new = T.alloc_buffer((1,), scope="local") + m_prev = T.alloc_buffer((1,), scope="local") + d_new = T.alloc_buffer((1,), scope="local") + tile_id[0] = bx + batch_idx[0] = 0 + batch_rows[0] = q_indptr[1] - q_indptr[0] + batch_tiles[0] = (batch_rows[0] + 32 - 1) // 32 + while T.tvm_thread_invariant(batch_idx[0] < batch_size): + while tile_id[0] >= batch_tiles[0] and batch_idx[0] < batch_size: + tile_id[0] = tile_id[0] - batch_tiles[0] + batch_idx[0] = batch_idx[0] + 1 + if batch_idx[0] < batch_size: + b_idx: T.int32 = batch_idx[0] + batch_rows[0] = q_indptr[b_idx + 1] - q_indptr[b_idx] + batch_tiles[0] = (batch_rows[0] + 32 - 1) // 32 + if T.tvm_thread_invariant(batch_idx[0] < batch_size): + b_idx: T.int32 = batch_idx[0] + LH_start: T.int32 = tile_id[0] * 32 + q_indptr_val: T.int32 = q_indptr[b_idx] + cur_page_indptr_begin: T.int32 = page_indptr[b_idx] + cur_page_indptr_end: T.int32 = page_indptr[b_idx + 1] + kv_chunk_len[0] = T.if_then_else(cur_page_indptr_begin != cur_page_indptr_end, (cur_page_indptr_end - cur_page_indptr_begin - 1) * 16 + length_info[b_idx], 0) + T.tvm_storage_sync("shared") + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + m_smem[row] = T.float32(-50000) + d_smem[row] = T.float32(1) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(4, 4): + with T.block("O_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + T.reads() + T.writes(O_local[i, j]) + O_local[i, j] = T.float32(0) + T.tvm_storage_sync("shared") + for li_lj_fused_0 in range(4): + for li_lj_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for li_lj_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for li_lj_fused_3 in T.vectorized(4): + with T.block("Q_load"): + i = T.axis.spatial(32, (li_lj_fused_0 * 512 + li_lj_fused_1 * 128 + li_lj_fused_2 * 4 + li_lj_fused_3) // 64) + j = T.axis.spatial(64, (li_lj_fused_0 * 512 + li_lj_fused_1 * 128 + li_lj_fused_2 * 4 + li_lj_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = q_indptr_val + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + Q_smem[i, j] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", q[cur_L, cur_H_qo, j]) + T.sin(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(j < 32, q[cur_L, cur_H_qo, j + 32] * T.float16(-1), q[cur_L, cur_H_qo, j - 32]))), q[cur_L, cur_H_qo, j]) + else: + Q_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + for iterator_1 in range((kv_chunk_len[0] + 15) // 16): + L_kv_start: T.int32 = iterator_1 * 16 + for lz_ly_fused_0 in range(2): + for lz_ly_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for lz_ly_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for lz_ly_fused_3 in T.vectorized(4): + with T.block("K_load"): + i = T.axis.spatial(16, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) // 64) + j = T.axis.spatial(64, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = L_kv_start + i + if cur_L < kv_chunk_len[0]: + seq_offset: T.int32 = cur_L + page_no: T.int32 = page_values[cur_page_indptr_begin + seq_offset // 16] + page_offset: T.int32 = seq_offset % 16 + K_smem[i, j] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", k_rope_pos_offset[b_idx] + cur_L) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", pages[page_no, 0, by, page_offset, j]) + T.sin(T.Cast("float32", k_rope_pos_offset[b_idx] + cur_L) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(j < 32, pages[page_no, 0, by, page_offset, j + 32] * T.float16(-1), pages[page_no, 0, by, page_offset, j - 32]))), pages[page_no, 0, by, page_offset, j]) + else: + K_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + for lz_ly_fused_0 in range(2): + for lz_ly_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for lz_ly_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for lz_ly_fused_3 in T.vectorized(4): + with T.block("V_load"): + i = T.axis.spatial(16, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) // 64) + j = T.axis.spatial(64, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = L_kv_start + i + if cur_L < kv_chunk_len[0]: + seq_offset: T.int32 = cur_L + page_no: T.int32 = page_values[cur_page_indptr_begin + seq_offset // 16] + page_offset: T.int32 = seq_offset % 16 + V_smem[i, j] = pages[page_no, 1, by, page_offset, j] + else: + V_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + with T.block(""): + T.reads(Q_smem[0:32, 0:64], K_smem[0:16, 0:64]) + T.writes(S_local[0:32, 0:16]) + for li_0_lj_0_fused_0_init in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1_init in T.thread_binding(32, thread="threadIdx.x"): + for li_1_init, lj_1_init in T.grid(2, 2): + with T.block("S_gemm_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) // 8 * 2 + li_1_init) + j = T.axis.spatial(16, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) % 8 * 2 + lj_1_init) + T.reads() + T.writes(S_local[i, j]) + S_local[i, j] = T.float32(0) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for lk_0, li_1, lj_1, lk_1 in T.grid(8, 2, 2, 8): + with T.block("S_gemm_update"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 8 * 2 + li_1) + j = T.axis.spatial(16, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 8 * 2 + lj_1) + k = T.axis.reduce(64, lk_0 * 8 + lk_1) + T.reads(S_local[i, j], Q_smem[i, k], K_smem[j, k]) + T.writes(S_local[i, j]) + S_local[i, j] = S_local[i, j] + T.Cast("float32", Q_smem[i, k]) * T.Cast("float32", K_smem[j, k]) * attn_score_scaling_factor * T.float32(0.18033688011112042) + T.tvm_storage_sync("shared") + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(2, 2): + with T.block("S_store"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 8 * 2 + li_1) + j = T.axis.spatial(16, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 8 * 2 + lj_1) + T.reads(S_local[i, j]) + T.writes(S_smem[i, j]) + S_smem[i, j] = S_local[i, j] + T.tvm_storage_sync("shared") + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + with T.block("update1"): + T.reads(m_smem[row], kv_chunk_len[0], q_indptr[b_idx:b_idx + 2], m_new[i], S_smem[row, 0:16], d_smem[row], m_prev[i]) + T.writes(m_prev[i], m_new[i], d_new[i]) + m_prev[i] = m_smem[row] + m_new[i] = m_smem[row] + row_: T.int32 = LH_start + row + for j in range(16): + if T.if_then_else(causal > 0, L_kv_start + j < kv_chunk_len[0] - (q_indptr[b_idx + 1] - q_indptr[b_idx]) + row_ + 1, L_kv_start + j < kv_chunk_len[0]): + m_new[i] = T.max(m_new[i], S_smem[row, j]) + d_new[i] = d_smem[row] * T.exp2(m_prev[i] - m_new[i]) + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + with T.block("update"): + T.reads(kv_chunk_len[0], q_indptr[b_idx:b_idx + 2], S_smem[row, 0:16], m_new[i]) + T.writes(S_smem[row, 0:16]) + for j in range(16): + if row < 32: + row_: T.int32 = LH_start + row + if T.if_then_else(causal > 0, L_kv_start + j < kv_chunk_len[0] - (q_indptr[b_idx + 1] - q_indptr[b_idx]) + row_ + 1, L_kv_start + j < kv_chunk_len[0]): + S_smem[row, j] = T.exp2(S_smem[row, j] - m_new[i]) + else: + S_smem[row, j] = T.exp2(T.float32(-50000) - m_new[i]) + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + with T.block("update"): + T.reads(d_new[i], S_smem[row, 0:16], m_new[i], m_prev[i]) + T.writes(d_new[i], m_smem[row], d_smem[row], m_prev_smem[row]) + for j in range(16): + d_new[i] = d_new[i] + S_smem[row, j] + m_smem[row] = m_new[i] + d_smem[row] = d_new[i] + m_prev_smem[row] = m_prev[i] + T.tvm_storage_sync("shared") + with T.block(""): + T.reads(m_prev_smem[0:32], m_smem[0:32], S_smem[0:32, 0:16], V_smem[0:16, 0:64]) + T.writes(O_local[0:32, 0:64]) + for li_0_lj_0_fused_0_init in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1_init in T.thread_binding(32, thread="threadIdx.x"): + for li_1_init, lj_1_init in T.grid(4, 4): + with T.block("O_gemm_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) // 16 * 4 + li_1_init) + j = T.axis.spatial(64, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) % 16 * 4 + lj_1_init) + T.reads() + T.writes(O_local[i, j]) + O_local[i, j] = O_local[i, j] * T.exp2(m_prev_smem[i] - m_smem[i]) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for lk_0, lk_1, li_1, lj_1 in T.grid(2, 8, 4, 4): + with T.block("O_gemm_update"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + k = T.axis.reduce(16, lk_0 * 8 + lk_1) + T.reads(O_local[i, j], m_prev_smem[i], m_smem[i], S_smem[i, k], V_smem[k, j]) + T.writes(O_local[i, j]) + O_local[i, j] = O_local[i, j] + S_smem[i, k] * T.Cast("float32", V_smem[k, j]) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(4, 4): + with T.block("O_store"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + T.reads(q_indptr[b_idx:b_idx + 2], O_local[i, j], d_smem[i]) + T.writes(output[q_indptr[b_idx] + (LH_start + i), by, j]) + cur_L: T.int32 = q_indptr[b_idx] + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + output[cur_L, cur_H_qo, j] = T.Cast("float16", O_local[i, j] / d_smem[i]) + for li_0 in range(1): + for li_1 in T.thread_binding(4, thread="threadIdx.y"): + for li_2 in T.thread_binding(32, thread="threadIdx.x"): + with T.block("lse_store"): + i = T.axis.spatial(32, li_0 * 128 + li_1 * 32 + li_2) + T.where((li_0 * 4 + li_1) * 32 + li_2 < 32) + T.reads(q_indptr[b_idx:b_idx + 2], m_smem[i], d_smem[i]) + T.writes(lse[q_indptr[b_idx] + (LH_start + i), by]) + cur_L: T.int32 = q_indptr[b_idx] + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + lse[cur_L, cur_H_qo] = m_smem[i] + T.log2(d_smem[i]) + tile_id[0] = tile_id[0] + 16 + + @T.prim_func + def batch_prefill_paged_kv_sliding_window(_0: T.int32, var_q: T.handle, var_q_indptr: T.handle, var_pages: T.handle, var_page_indptr: T.handle, var_page_values: T.handle, var_length_info: T.handle, var_k_rope_pos_offset: T.handle, var_q_rope_position: T.handle, var_output: T.handle, var_lse: T.handle, causal: T.int32, rotary_mode: T.int32, rope_scale: T.float32, rope_theta: T.float32, attn_score_scaling_factor: T.float32): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) + total_len = T.int32(is_size_var=True) + q = T.match_buffer(var_q, (total_len, 20, 64), "float16") + batch_size = T.int32(is_size_var=True) + q_indptr = T.match_buffer(var_q_indptr, (batch_size + 1,), "int32", offset_factor=1) + max_num_pages = T.int32(is_size_var=True) + pages = T.match_buffer(var_pages, (max_num_pages, 2, 20, 16, 64), "float16") + page_indptr = T.match_buffer(var_page_indptr, (batch_size + 1,), "int32", offset_factor=1) + nnz_pages = T.int32(is_size_var=True) + page_values = T.match_buffer(var_page_values, (nnz_pages,), "int32", offset_factor=1) + length_info = T.match_buffer(var_length_info, (3, batch_size), "int32", offset_factor=1) + k_rope_pos_offset = T.match_buffer(var_k_rope_pos_offset, (batch_size,), "int32", offset_factor=1) + q_rope_position = T.match_buffer(var_q_rope_position, (total_len,), "int32", offset_factor=1) + output = T.match_buffer(var_output, (total_len, 20, 64), "float16") + lse = T.match_buffer(var_lse, (total_len, 20)) + # with T.block("root"): + for lbx in T.thread_binding(16, thread="blockIdx.x"): + for lby in T.thread_binding(20, thread="blockIdx.y"): + for lty in T.thread_binding(4, thread="threadIdx.y"): + for ltx in T.thread_binding(32, thread="threadIdx.x"): + with T.block("attn"): + bx, by, ty, tx = T.axis.remap("SSSS", [lbx, lby, lty, ltx]) + T.reads() + T.writes() + tile_id = T.alloc_buffer((1,), "int32", scope="local") + batch_idx = T.alloc_buffer((1,), "int32", scope="local") + batch_tiles = T.alloc_buffer((1,), "int32", scope="local") + batch_rows = T.alloc_buffer((1,), "int32", scope="local") + iterator = T.alloc_buffer((1,), "int32", scope="local") + kv_chunk_len = T.alloc_buffer((1,), "int32", scope="local") + Q_smem = T.alloc_buffer((32, 64), "float16", scope="shared") + K_smem = T.alloc_buffer((16, 64), "float16", scope="shared") + V_smem = T.alloc_buffer((16, 64), "float16", scope="shared") + S_smem = T.alloc_buffer((32, 16), scope="shared") + S_local = T.alloc_buffer((32, 16), scope="local") + O_local = T.alloc_buffer((32, 64), scope="local") + m_smem = T.alloc_buffer((32,), scope="shared") + m_prev_smem = T.alloc_buffer((32,), scope="shared") + d_smem = T.alloc_buffer((32,), scope="shared") + m_new = T.alloc_buffer((1,), scope="local") + m_prev = T.alloc_buffer((1,), scope="local") + d_new = T.alloc_buffer((1,), scope="local") + tile_id[0] = bx + batch_idx[0] = 0 + batch_rows[0] = q_indptr[1] - q_indptr[0] + batch_tiles[0] = (batch_rows[0] + 32 - 1) // 32 + while T.tvm_thread_invariant(batch_idx[0] < batch_size): + while tile_id[0] >= batch_tiles[0] and batch_idx[0] < batch_size: + tile_id[0] = tile_id[0] - batch_tiles[0] + batch_idx[0] = batch_idx[0] + 1 + if batch_idx[0] < batch_size: + b_idx: T.int32 = batch_idx[0] + batch_rows[0] = q_indptr[b_idx + 1] - q_indptr[b_idx] + batch_tiles[0] = (batch_rows[0] + 32 - 1) // 32 + if T.tvm_thread_invariant(batch_idx[0] < batch_size): + b_idx: T.int32 = batch_idx[0] + LH_start: T.int32 = tile_id[0] * 32 + q_indptr_val: T.int32 = q_indptr[b_idx] + cur_page_indptr_begin: T.int32 = page_indptr[b_idx] + cur_page_indptr_end: T.int32 = page_indptr[b_idx + 1] + kv_chunk_len[0] = T.if_then_else(cur_page_indptr_begin != cur_page_indptr_end, (cur_page_indptr_end - cur_page_indptr_begin - 1) * 16 + length_info[0, b_idx] - length_info[1, b_idx] + length_info[2, b_idx], 0) + T.tvm_storage_sync("shared") + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + m_smem[row] = T.float32(-50000) + d_smem[row] = T.float32(1) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(4, 4): + with T.block("O_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + T.reads() + T.writes(O_local[i, j]) + O_local[i, j] = T.float32(0) + T.tvm_storage_sync("shared") + for li_lj_fused_0 in range(4): + for li_lj_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for li_lj_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for li_lj_fused_3 in T.vectorized(4): + with T.block("Q_load"): + i = T.axis.spatial(32, (li_lj_fused_0 * 512 + li_lj_fused_1 * 128 + li_lj_fused_2 * 4 + li_lj_fused_3) // 64) + j = T.axis.spatial(64, (li_lj_fused_0 * 512 + li_lj_fused_1 * 128 + li_lj_fused_2 * 4 + li_lj_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = q_indptr_val + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + Q_smem[i, j] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", q[cur_L, cur_H_qo, j]) + T.sin(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(j < 32, q[cur_L, cur_H_qo, j + 32] * T.float16(-1), q[cur_L, cur_H_qo, j - 32]))), q[cur_L, cur_H_qo, j]) + else: + Q_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + for iterator_1 in range((kv_chunk_len[0] + 15) // 16): + L_kv_start: T.int32 = iterator_1 * 16 + for lz_ly_fused_0 in range(2): + for lz_ly_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for lz_ly_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for lz_ly_fused_3 in T.vectorized(4): + with T.block("K_load"): + i = T.axis.spatial(16, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) // 64) + j = T.axis.spatial(64, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = L_kv_start + i + if cur_L < kv_chunk_len[0]: + seq_offset: T.int32 = T.if_then_else(cur_L < length_info[2, b_idx], cur_L, cur_L - length_info[2, b_idx] + length_info[1, b_idx]) + page_no: T.int32 = page_values[cur_page_indptr_begin + seq_offset // 16] + page_offset: T.int32 = seq_offset % 16 + K_smem[i, j] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", k_rope_pos_offset[b_idx] + cur_L) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", pages[page_no, 0, by, page_offset, j]) + T.sin(T.Cast("float32", k_rope_pos_offset[b_idx] + cur_L) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(j < 32, pages[page_no, 0, by, page_offset, j + 32] * T.float16(-1), pages[page_no, 0, by, page_offset, j - 32]))), pages[page_no, 0, by, page_offset, j]) + else: + K_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + for lz_ly_fused_0 in range(2): + for lz_ly_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for lz_ly_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for lz_ly_fused_3 in T.vectorized(4): + with T.block("V_load"): + i = T.axis.spatial(16, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) // 64) + j = T.axis.spatial(64, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = L_kv_start + i + if cur_L < kv_chunk_len[0]: + seq_offset: T.int32 = T.if_then_else(cur_L < length_info[2, b_idx], cur_L, cur_L - length_info[2, b_idx] + length_info[1, b_idx]) + page_no: T.int32 = page_values[cur_page_indptr_begin + seq_offset // 16] + page_offset: T.int32 = seq_offset % 16 + V_smem[i, j] = pages[page_no, 1, by, page_offset, j] + else: + V_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + with T.block(""): + T.reads(Q_smem[0:32, 0:64], K_smem[0:16, 0:64]) + T.writes(S_local[0:32, 0:16]) + for li_0_lj_0_fused_0_init in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1_init in T.thread_binding(32, thread="threadIdx.x"): + for li_1_init, lj_1_init in T.grid(2, 2): + with T.block("S_gemm_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) // 8 * 2 + li_1_init) + j = T.axis.spatial(16, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) % 8 * 2 + lj_1_init) + T.reads() + T.writes(S_local[i, j]) + S_local[i, j] = T.float32(0) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for lk_0, li_1, lj_1, lk_1 in T.grid(8, 2, 2, 8): + with T.block("S_gemm_update"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 8 * 2 + li_1) + j = T.axis.spatial(16, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 8 * 2 + lj_1) + k = T.axis.reduce(64, lk_0 * 8 + lk_1) + T.reads(S_local[i, j], Q_smem[i, k], K_smem[j, k]) + T.writes(S_local[i, j]) + S_local[i, j] = S_local[i, j] + T.Cast("float32", Q_smem[i, k]) * T.Cast("float32", K_smem[j, k]) * attn_score_scaling_factor * T.float32(0.18033688011112042) + T.tvm_storage_sync("shared") + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(2, 2): + with T.block("S_store"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 8 * 2 + li_1) + j = T.axis.spatial(16, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 8 * 2 + lj_1) + T.reads(S_local[i, j]) + T.writes(S_smem[i, j]) + S_smem[i, j] = S_local[i, j] + T.tvm_storage_sync("shared") + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + with T.block("update1"): + T.reads(m_smem[row], kv_chunk_len[0], q_indptr[b_idx:b_idx + 2], m_new[i], S_smem[row, 0:16], d_smem[row], m_prev[i]) + T.writes(m_prev[i], m_new[i], d_new[i]) + m_prev[i] = m_smem[row] + m_new[i] = m_smem[row] + row_: T.int32 = LH_start + row + for j in range(16): + if T.if_then_else(causal > 0, L_kv_start + j < kv_chunk_len[0] - (q_indptr[b_idx + 1] - q_indptr[b_idx]) + row_ + 1, L_kv_start + j < kv_chunk_len[0]): + m_new[i] = T.max(m_new[i], S_smem[row, j]) + d_new[i] = d_smem[row] * T.exp2(m_prev[i] - m_new[i]) + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + with T.block("update"): + T.reads(kv_chunk_len[0], q_indptr[b_idx:b_idx + 2], S_smem[row, 0:16], m_new[i]) + T.writes(S_smem[row, 0:16]) + for j in range(16): + if row < 32: + row_: T.int32 = LH_start + row + if T.if_then_else(causal > 0, L_kv_start + j < kv_chunk_len[0] - (q_indptr[b_idx + 1] - q_indptr[b_idx]) + row_ + 1, L_kv_start + j < kv_chunk_len[0]): + S_smem[row, j] = T.exp2(S_smem[row, j] - m_new[i]) + else: + S_smem[row, j] = T.exp2(T.float32(-50000) - m_new[i]) + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + with T.block("update"): + T.reads(d_new[i], S_smem[row, 0:16], m_new[i], m_prev[i]) + T.writes(d_new[i], m_smem[row], d_smem[row], m_prev_smem[row]) + for j in range(16): + d_new[i] = d_new[i] + S_smem[row, j] + m_smem[row] = m_new[i] + d_smem[row] = d_new[i] + m_prev_smem[row] = m_prev[i] + T.tvm_storage_sync("shared") + with T.block(""): + T.reads(m_prev_smem[0:32], m_smem[0:32], S_smem[0:32, 0:16], V_smem[0:16, 0:64]) + T.writes(O_local[0:32, 0:64]) + for li_0_lj_0_fused_0_init in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1_init in T.thread_binding(32, thread="threadIdx.x"): + for li_1_init, lj_1_init in T.grid(4, 4): + with T.block("O_gemm_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) // 16 * 4 + li_1_init) + j = T.axis.spatial(64, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) % 16 * 4 + lj_1_init) + T.reads() + T.writes(O_local[i, j]) + O_local[i, j] = O_local[i, j] * T.exp2(m_prev_smem[i] - m_smem[i]) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for lk_0, lk_1, li_1, lj_1 in T.grid(2, 8, 4, 4): + with T.block("O_gemm_update"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + k = T.axis.reduce(16, lk_0 * 8 + lk_1) + T.reads(O_local[i, j], m_prev_smem[i], m_smem[i], S_smem[i, k], V_smem[k, j]) + T.writes(O_local[i, j]) + O_local[i, j] = O_local[i, j] + S_smem[i, k] * T.Cast("float32", V_smem[k, j]) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(4, 4): + with T.block("O_store"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + T.reads(q_indptr[b_idx:b_idx + 2], O_local[i, j], d_smem[i]) + T.writes(output[q_indptr[b_idx] + (LH_start + i), by, j]) + cur_L: T.int32 = q_indptr[b_idx] + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + output[cur_L, cur_H_qo, j] = T.Cast("float16", O_local[i, j] / d_smem[i]) + for li_0 in range(1): + for li_1 in T.thread_binding(4, thread="threadIdx.y"): + for li_2 in T.thread_binding(32, thread="threadIdx.x"): + with T.block("lse_store"): + i = T.axis.spatial(32, li_0 * 128 + li_1 * 32 + li_2) + T.where((li_0 * 4 + li_1) * 32 + li_2 < 32) + T.reads(q_indptr[b_idx:b_idx + 2], m_smem[i], d_smem[i]) + T.writes(lse[q_indptr[b_idx] + (LH_start + i), by]) + cur_L: T.int32 = q_indptr[b_idx] + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + lse[cur_L, cur_H_qo] = m_smem[i] + T.log2(d_smem[i]) + tile_id[0] = tile_id[0] + 16 + + @T.prim_func + def batch_prefill_ragged_kv(var_q: T.handle, var_q_indptr: T.handle, var_k: T.handle, var_v: T.handle, var_kv_indptr: T.handle, var_q_rope_position: T.handle, var_k_rope_pos_offset: T.handle, var_output: T.handle, var_lse: T.handle, causal: T.int32, rotary_mode: T.int32, rope_scale: T.float32, rope_theta: T.float32, attn_score_scaling_factor: T.float32): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) + qo_len = T.int32(is_size_var=True) + q = T.match_buffer(var_q, (qo_len, 20, 64), "float16") + batch_size = T.int32(is_size_var=True) + q_indptr = T.match_buffer(var_q_indptr, (batch_size + 1,), "int32", offset_factor=1) + kv_len = T.int32(is_size_var=True) + k = T.match_buffer(var_k, (kv_len, 20, 64), "float16") + v = T.match_buffer(var_v, (kv_len, 20, 64), "float16") + kv_indptr = T.match_buffer(var_kv_indptr, (batch_size + 1,), "int32", offset_factor=1) + q_rope_position = T.match_buffer(var_q_rope_position, (qo_len,), "int32", offset_factor=1) + k_rope_pos_offset = T.match_buffer(var_k_rope_pos_offset, (batch_size,), "int32", offset_factor=1) + output = T.match_buffer(var_output, (qo_len, 20, 64), "float16") + lse = T.match_buffer(var_lse, (qo_len, 20)) + # with T.block("root"): + for lbx in T.thread_binding(16, thread="blockIdx.x"): + for lby in T.thread_binding(20, thread="blockIdx.y"): + for lty in T.thread_binding(4, thread="threadIdx.y"): + for ltx in T.thread_binding(32, thread="threadIdx.x"): + with T.block("attn"): + bx, by, ty, tx = T.axis.remap("SSSS", [lbx, lby, lty, ltx]) + T.reads() + T.writes() + tile_id = T.alloc_buffer((1,), "int32", scope="local") + batch_idx = T.alloc_buffer((1,), "int32", scope="local") + batch_tiles = T.alloc_buffer((1,), "int32", scope="local") + batch_rows = T.alloc_buffer((1,), "int32", scope="local") + iterator = T.alloc_buffer((1,), "int32", scope="local") + kv_chunk_len = T.alloc_buffer((1,), "int32", scope="local") + Q_smem = T.alloc_buffer((32, 64), "float16", scope="shared") + K_smem = T.alloc_buffer((16, 64), "float16", scope="shared") + V_smem = T.alloc_buffer((16, 64), "float16", scope="shared") + S_smem = T.alloc_buffer((32, 16), scope="shared") + S_local = T.alloc_buffer((32, 16), scope="local") + O_local = T.alloc_buffer((32, 64), scope="local") + m_smem = T.alloc_buffer((32,), scope="shared") + m_prev_smem = T.alloc_buffer((32,), scope="shared") + d_smem = T.alloc_buffer((32,), scope="shared") + m_new = T.alloc_buffer((1,), scope="local") + m_prev = T.alloc_buffer((1,), scope="local") + d_new = T.alloc_buffer((1,), scope="local") + tile_id[0] = bx + batch_idx[0] = 0 + batch_rows[0] = q_indptr[1] - q_indptr[0] + batch_tiles[0] = (batch_rows[0] + 32 - 1) // 32 + while T.tvm_thread_invariant(batch_idx[0] < batch_size): + while tile_id[0] >= batch_tiles[0] and batch_idx[0] < batch_size: + tile_id[0] = tile_id[0] - batch_tiles[0] + batch_idx[0] = batch_idx[0] + 1 + if batch_idx[0] < batch_size: + b_idx: T.int32 = batch_idx[0] + batch_rows[0] = q_indptr[b_idx + 1] - q_indptr[b_idx] + batch_tiles[0] = (batch_rows[0] + 32 - 1) // 32 + if T.tvm_thread_invariant(batch_idx[0] < batch_size): + b_idx: T.int32 = batch_idx[0] + q_indptr_val: T.int32 = q_indptr[b_idx] + LH_start: T.int32 = tile_id[0] * 32 + kv_chunk_len[0] = kv_indptr[b_idx + 1] - kv_indptr[b_idx] + T.tvm_storage_sync("shared") + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + m_smem[row] = T.float32(-50000) + d_smem[row] = T.float32(1) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(4, 4): + with T.block("O_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + T.reads() + T.writes(O_local[i, j]) + O_local[i, j] = T.float32(0) + T.tvm_storage_sync("shared") + for li_lj_fused_0 in range(4): + for li_lj_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for li_lj_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for li_lj_fused_3 in T.vectorized(4): + with T.block("Q_load"): + i = T.axis.spatial(32, (li_lj_fused_0 * 512 + li_lj_fused_1 * 128 + li_lj_fused_2 * 4 + li_lj_fused_3) // 64) + j = T.axis.spatial(64, (li_lj_fused_0 * 512 + li_lj_fused_1 * 128 + li_lj_fused_2 * 4 + li_lj_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = q_indptr_val + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + Q_smem[i, j] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", q[cur_L, cur_H_qo, j]) + T.sin(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(j < 32, q[cur_L, cur_H_qo, j + 32] * T.float16(-1), q[cur_L, cur_H_qo, j - 32]))), q[cur_L, cur_H_qo, j]) + else: + Q_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + for iterator_1 in range((kv_chunk_len[0] + 15) // 16): + L_kv_start: T.int32 = iterator_1 * 16 + L_kv_base: T.int32 = kv_indptr[b_idx] + for lz_ly_fused_0 in range(2): + for lz_ly_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for lz_ly_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for lz_ly_fused_3 in T.vectorized(4): + with T.block("K_load"): + i = T.axis.spatial(16, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) // 64) + j = T.axis.spatial(64, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = L_kv_start + i + if cur_L < kv_chunk_len[0]: + K_smem[i, j] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", k_rope_pos_offset[b_idx] + cur_L) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", k[L_kv_base + cur_L, by, j]) + T.sin(T.Cast("float32", k_rope_pos_offset[b_idx] + cur_L) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(j < 32, k[L_kv_base + cur_L, by, j + 32] * T.float16(-1), k[L_kv_base + cur_L, by, j - 32]))), k[L_kv_base + cur_L, by, j]) + else: + K_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + for lz_ly_fused_0 in range(2): + for lz_ly_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for lz_ly_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for lz_ly_fused_3 in T.vectorized(4): + with T.block("V_load"): + i = T.axis.spatial(16, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) // 64) + j = T.axis.spatial(64, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = L_kv_start + i + if cur_L < kv_chunk_len[0]: + V_smem[i, j] = v[L_kv_base + cur_L, by, j] + else: + V_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + with T.block(""): + T.reads(Q_smem[0:32, 0:64], K_smem[0:16, 0:64]) + T.writes(S_local[0:32, 0:16]) + for li_0_lj_0_fused_0_init in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1_init in T.thread_binding(32, thread="threadIdx.x"): + for li_1_init, lj_1_init in T.grid(2, 2): + with T.block("S_gemm_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) // 8 * 2 + li_1_init) + j = T.axis.spatial(16, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) % 8 * 2 + lj_1_init) + T.reads() + T.writes(S_local[i, j]) + S_local[i, j] = T.float32(0) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for lk_0, li_1, lj_1, lk_1 in T.grid(8, 2, 2, 8): + with T.block("S_gemm_update"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 8 * 2 + li_1) + j = T.axis.spatial(16, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 8 * 2 + lj_1) + k_1 = T.axis.reduce(64, lk_0 * 8 + lk_1) + T.reads(S_local[i, j], Q_smem[i, k_1], K_smem[j, k_1]) + T.writes(S_local[i, j]) + S_local[i, j] = S_local[i, j] + T.Cast("float32", Q_smem[i, k_1]) * T.Cast("float32", K_smem[j, k_1]) * attn_score_scaling_factor * T.float32(0.18033688011112042) + T.tvm_storage_sync("shared") + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(2, 2): + with T.block("S_store"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 8 * 2 + li_1) + j = T.axis.spatial(16, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 8 * 2 + lj_1) + T.reads(S_local[i, j]) + T.writes(S_smem[i, j]) + S_smem[i, j] = S_local[i, j] + T.tvm_storage_sync("shared") + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + with T.block("update1"): + T.reads(m_smem[row], kv_chunk_len[0], q_indptr[b_idx:b_idx + 2], m_new[i], S_smem[row, 0:16], d_smem[row], m_prev[i]) + T.writes(m_prev[i], m_new[i], d_new[i]) + m_prev[i] = m_smem[row] + m_new[i] = m_smem[row] + row_: T.int32 = LH_start + row + for j in range(16): + if T.if_then_else(causal > 0, L_kv_start + j < kv_chunk_len[0] - (q_indptr[b_idx + 1] - q_indptr[b_idx]) + row_ + 1, L_kv_start + j < kv_chunk_len[0]): + m_new[i] = T.max(m_new[i], S_smem[row, j]) + d_new[i] = d_smem[row] * T.exp2(m_prev[i] - m_new[i]) + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + with T.block("update"): + T.reads(kv_chunk_len[0], q_indptr[b_idx:b_idx + 2], S_smem[row, 0:16], m_new[i]) + T.writes(S_smem[row, 0:16]) + for j in range(16): + if row < 32: + row_: T.int32 = LH_start + row + if T.if_then_else(causal > 0, L_kv_start + j < kv_chunk_len[0] - (q_indptr[b_idx + 1] - q_indptr[b_idx]) + row_ + 1, L_kv_start + j < kv_chunk_len[0]): + S_smem[row, j] = T.exp2(S_smem[row, j] - m_new[i]) + else: + S_smem[row, j] = T.exp2(T.float32(-50000) - m_new[i]) + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + with T.block("update"): + T.reads(d_new[i], S_smem[row, 0:16], m_new[i], m_prev[i]) + T.writes(d_new[i], m_smem[row], d_smem[row], m_prev_smem[row]) + for j in range(16): + d_new[i] = d_new[i] + S_smem[row, j] + m_smem[row] = m_new[i] + d_smem[row] = d_new[i] + m_prev_smem[row] = m_prev[i] + T.tvm_storage_sync("shared") + with T.block(""): + T.reads(m_prev_smem[0:32], m_smem[0:32], S_smem[0:32, 0:16], V_smem[0:16, 0:64]) + T.writes(O_local[0:32, 0:64]) + for li_0_lj_0_fused_0_init in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1_init in T.thread_binding(32, thread="threadIdx.x"): + for li_1_init, lj_1_init in T.grid(4, 4): + with T.block("O_gemm_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) // 16 * 4 + li_1_init) + j = T.axis.spatial(64, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) % 16 * 4 + lj_1_init) + T.reads() + T.writes(O_local[i, j]) + O_local[i, j] = O_local[i, j] * T.exp2(m_prev_smem[i] - m_smem[i]) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for lk_0, lk_1, li_1, lj_1 in T.grid(2, 8, 4, 4): + with T.block("O_gemm_update"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + k_1 = T.axis.reduce(16, lk_0 * 8 + lk_1) + T.reads(O_local[i, j], m_prev_smem[i], m_smem[i], S_smem[i, k_1], V_smem[k_1, j]) + T.writes(O_local[i, j]) + O_local[i, j] = O_local[i, j] + S_smem[i, k_1] * T.Cast("float32", V_smem[k_1, j]) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(4, 4): + with T.block("O_store"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + T.reads(q_indptr[b_idx:b_idx + 2], O_local[i, j], d_smem[i]) + T.writes(output[q_indptr[b_idx] + (LH_start + i), by, j]) + cur_L: T.int32 = q_indptr[b_idx] + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + output[cur_L, cur_H_qo, j] = T.Cast("float16", O_local[i, j] / d_smem[i]) + for li_0 in range(1): + for li_1 in T.thread_binding(4, thread="threadIdx.y"): + for li_2 in T.thread_binding(32, thread="threadIdx.x"): + with T.block("lse_store"): + i = T.axis.spatial(32, li_0 * 128 + li_1 * 32 + li_2) + T.where((li_0 * 4 + li_1) * 32 + li_2 < 32) + T.reads(q_indptr[b_idx:b_idx + 2], m_smem[i], d_smem[i]) + T.writes(lse[q_indptr[b_idx] + (LH_start + i), by]) + cur_L: T.int32 = q_indptr[b_idx] + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + lse[cur_L, cur_H_qo] = m_smem[i] + T.log2(d_smem[i]) + tile_id[0] = tile_id[0] + 16 + + @T.prim_func + def batch_tree_attn(var_q: T.handle, var_q_indptr: T.handle, var_k: T.handle, var_v: T.handle, var_kv_indptr: T.handle, var_q_rope_position: T.handle, var_mn_indptr: T.handle, var_mask: T.handle, var_output: T.handle, var_lse: T.handle, rotary_mode: T.int32, rope_scale: T.float32, rope_theta: T.float32, attn_score_scaling_factor: T.float32, batch_size: T.int32): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) + qo_len = T.int32(is_size_var=True) + q = T.match_buffer(var_q, (qo_len, 20, 64), "float16") + q_indptr = T.match_buffer(var_q_indptr, (batch_size + 1,), "int32", offset_factor=1) + kv_len = T.int32(is_size_var=True) + k = T.match_buffer(var_k, (kv_len, 20, 64), "float16") + v = T.match_buffer(var_v, (kv_len, 20, 64), "float16") + kv_indptr = T.match_buffer(var_kv_indptr, (batch_size + 1,), "int32", offset_factor=1) + q_rope_position = T.match_buffer(var_q_rope_position, (qo_len,), "int32", offset_factor=1) + mn_indptr = T.match_buffer(var_mn_indptr, (batch_size + 1,), "int32", offset_factor=1) + tree_size = T.int32(is_size_var=True) + mask = T.match_buffer(var_mask, (tree_size,), "int32", offset_factor=1) + output = T.match_buffer(var_output, (qo_len, 20, 64), "float16") + lse = T.match_buffer(var_lse, (qo_len, 20)) + # with T.block("root"): + for lbx in T.thread_binding(16, thread="blockIdx.x"): + for lby in T.thread_binding(20, thread="blockIdx.y"): + for lty in T.thread_binding(4, thread="threadIdx.y"): + for ltx in T.thread_binding(32, thread="threadIdx.x"): + with T.block("attn"): + bx, by, ty, tx = T.axis.remap("SSSS", [lbx, lby, lty, ltx]) + T.reads() + T.writes() + tile_id = T.alloc_buffer((1,), "int32", scope="local") + batch_idx = T.alloc_buffer((1,), "int32", scope="local") + batch_tiles = T.alloc_buffer((1,), "int32", scope="local") + batch_rows = T.alloc_buffer((1,), "int32", scope="local") + iterator = T.alloc_buffer((1,), "int32", scope="local") + kv_chunk_len = T.alloc_buffer((1,), "int32", scope="local") + Q_smem = T.alloc_buffer((32, 64), "float16", scope="shared") + K_smem = T.alloc_buffer((16, 64), "float16", scope="shared") + V_smem = T.alloc_buffer((16, 64), "float16", scope="shared") + S_smem = T.alloc_buffer((32, 16), scope="shared") + S_local = T.alloc_buffer((32, 16), scope="local") + O_local = T.alloc_buffer((32, 64), scope="local") + m_smem = T.alloc_buffer((32,), scope="shared") + m_prev_smem = T.alloc_buffer((32,), scope="shared") + d_smem = T.alloc_buffer((32,), scope="shared") + m_new = T.alloc_buffer((1,), scope="local") + m_prev = T.alloc_buffer((1,), scope="local") + d_new = T.alloc_buffer((1,), scope="local") + tile_id[0] = bx + batch_idx[0] = 0 + batch_rows[0] = q_indptr[1] - q_indptr[0] + batch_tiles[0] = (batch_rows[0] + 32 - 1) // 32 + while T.tvm_thread_invariant(batch_idx[0] < batch_size): + while tile_id[0] >= batch_tiles[0] and batch_idx[0] < batch_size: + tile_id[0] = tile_id[0] - batch_tiles[0] + batch_idx[0] = batch_idx[0] + 1 + if batch_idx[0] < batch_size: + b_idx: T.int32 = batch_idx[0] + batch_rows[0] = q_indptr[b_idx + 1] - q_indptr[b_idx] + batch_tiles[0] = (batch_rows[0] + 32 - 1) // 32 + if T.tvm_thread_invariant(batch_idx[0] < batch_size): + b_idx: T.int32 = batch_idx[0] + LH_start: T.int32 = tile_id[0] * 32 + q_indptr_val: T.int32 = q_indptr[b_idx] + kv_chunk_len[0] = kv_indptr[b_idx + 1] - kv_indptr[b_idx] + T.tvm_storage_sync("shared") + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + m_smem[row] = T.float32(-50000) + d_smem[row] = T.float32(1) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(4, 4): + with T.block("O_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + T.reads() + T.writes(O_local[i, j]) + O_local[i, j] = T.float32(0) + T.tvm_storage_sync("shared") + for li_lj_fused_0 in range(4): + for li_lj_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for li_lj_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for li_lj_fused_3 in T.vectorized(4): + with T.block("Q_load"): + i = T.axis.spatial(32, (li_lj_fused_0 * 512 + li_lj_fused_1 * 128 + li_lj_fused_2 * 4 + li_lj_fused_3) // 64) + j = T.axis.spatial(64, (li_lj_fused_0 * 512 + li_lj_fused_1 * 128 + li_lj_fused_2 * 4 + li_lj_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = q_indptr_val + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + Q_smem[i, j] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64)))) * q[cur_L, cur_H_qo, j] + T.Cast("float16", T.sin(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64)))) * T.if_then_else(j < 32, q[cur_L, cur_H_qo, j + 32] * T.float16(-1), q[cur_L, cur_H_qo, j - 32]), q[cur_L, cur_H_qo, j]) + else: + Q_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + for iterator_1 in range((kv_chunk_len[0] + 15) // 16): + L_kv_start: T.int32 = iterator_1 * 16 + L_kv_base: T.int32 = kv_indptr[b_idx] + for lz_ly_fused_0 in range(2): + for lz_ly_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for lz_ly_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for lz_ly_fused_3 in T.vectorized(4): + with T.block("KV_load"): + i = T.axis.spatial(16, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) // 64) + j = T.axis.spatial(64, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) % 64) + T.reads() + T.writes() + cur_L: T.int32 = L_kv_base + L_kv_start + i + if L_kv_start + i < kv_chunk_len[0]: + K_smem[i, j] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64)))) * k[cur_L, by, j] + T.Cast("float16", T.sin(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64)))) * T.if_then_else(j < 32, k[cur_L, by, j + 32] * T.float16(-1), k[cur_L, by, j - 32]), k[cur_L, by, j]) + V_smem[i, j] = v[cur_L, by, j] + else: + K_smem[i, j] = T.float16(0) + V_smem[i, j] = T.float16(0) + T.tvm_storage_sync("shared") + with T.block(""): + T.reads(Q_smem[0:32, 0:64], K_smem[0:16, 0:64]) + T.writes(S_local[0:32, 0:16]) + for li_0_lj_0_fused_0_init in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1_init in T.thread_binding(32, thread="threadIdx.x"): + for li_1_init, lj_1_init in T.grid(2, 2): + with T.block("S_gemm_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) // 8 * 2 + li_1_init) + j = T.axis.spatial(16, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) % 8 * 2 + lj_1_init) + T.reads() + T.writes(S_local[i, j]) + S_local[i, j] = T.float32(0) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for lk_0, li_1, lj_1, lk_1 in T.grid(8, 2, 2, 8): + with T.block("S_gemm_update"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 8 * 2 + li_1) + j = T.axis.spatial(16, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 8 * 2 + lj_1) + k_1 = T.axis.reduce(64, lk_0 * 8 + lk_1) + T.reads(S_local[i, j], Q_smem[i, k_1], K_smem[j, k_1]) + T.writes(S_local[i, j]) + S_local[i, j] = S_local[i, j] + T.Cast("float32", Q_smem[i, k_1]) * T.Cast("float32", K_smem[j, k_1]) * attn_score_scaling_factor * T.float32(0.18033688011112042) + T.tvm_storage_sync("shared") + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(2, 2): + with T.block("S_store"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 8 * 2 + li_1) + j = T.axis.spatial(16, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 8 * 2 + lj_1) + T.reads(S_local[i, j]) + T.writes(S_smem[i, j]) + S_smem[i, j] = S_local[i, j] + T.tvm_storage_sync("shared") + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + with T.block("update1"): + T.reads(m_smem[row], kv_chunk_len[0], mask[mn_indptr[b_idx] + (LH_start + row) * (q_indptr[b_idx + 1] - q_indptr[b_idx]) + L_kv_start:mn_indptr[b_idx] + (LH_start + row) * (q_indptr[b_idx + 1] - q_indptr[b_idx]) + L_kv_start + 16], mn_indptr[b_idx], q_indptr[b_idx:b_idx + 2], m_new[i], S_smem[row, 0:16], d_smem[row], m_prev[i]) + T.writes(m_prev[i], m_new[i], d_new[i]) + m_prev[i] = m_smem[row] + m_new[i] = m_smem[row] + row_: T.int32 = LH_start + row + for j in range(16): + if L_kv_start + j < kv_chunk_len[0] and mask[mn_indptr[b_idx] + row_ * (q_indptr[b_idx + 1] - q_indptr[b_idx]) + (L_kv_start + j)] == 1: + m_new[i] = T.max(m_new[i], S_smem[row, j]) + d_new[i] = d_smem[row] * T.exp2(m_prev[i] - m_new[i]) + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + with T.block("update"): + T.reads(kv_chunk_len[0], mask[mn_indptr[b_idx] + (LH_start + row) * (q_indptr[b_idx + 1] - q_indptr[b_idx]) + L_kv_start:mn_indptr[b_idx] + (LH_start + row) * (q_indptr[b_idx + 1] - q_indptr[b_idx]) + L_kv_start + 16], mn_indptr[b_idx], q_indptr[b_idx:b_idx + 2], S_smem[row, 0:16], m_new[i]) + T.writes(S_smem[row, 0:16]) + for j in range(16): + if row < 32: + row_: T.int32 = LH_start + row + if L_kv_start + j < kv_chunk_len[0] and mask[mn_indptr[b_idx] + row_ * (q_indptr[b_idx + 1] - q_indptr[b_idx]) + (L_kv_start + j)] == 1: + S_smem[row, j] = T.exp2(S_smem[row, j] - m_new[i]) + else: + S_smem[row, j] = T.exp2(T.float32(-50000) - m_new[i]) + for i in range(1): + row: T.int32 = i * 32 * 4 + ty * 32 + tx + if row < 32: + with T.block("update"): + T.reads(d_new[i], S_smem[row, 0:16], m_new[i], m_prev[i]) + T.writes(d_new[i], m_smem[row], d_smem[row], m_prev_smem[row]) + for j in range(16): + d_new[i] = d_new[i] + S_smem[row, j] + m_smem[row] = m_new[i] + d_smem[row] = d_new[i] + m_prev_smem[row] = m_prev[i] + T.tvm_storage_sync("shared") + with T.block(""): + T.reads(m_prev_smem[0:32], m_smem[0:32], S_smem[0:32, 0:16], V_smem[0:16, 0:64]) + T.writes(O_local[0:32, 0:64]) + for li_0_lj_0_fused_0_init in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1_init in T.thread_binding(32, thread="threadIdx.x"): + for li_1_init, lj_1_init in T.grid(4, 4): + with T.block("O_gemm_init"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) // 16 * 4 + li_1_init) + j = T.axis.spatial(64, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) % 16 * 4 + lj_1_init) + T.reads() + T.writes(O_local[i, j]) + O_local[i, j] = O_local[i, j] * T.exp2(m_prev_smem[i] - m_smem[i]) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for lk_0, lk_1, li_1, lj_1 in T.grid(2, 8, 4, 4): + with T.block("O_gemm_update"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + k_1 = T.axis.reduce(16, lk_0 * 8 + lk_1) + T.reads(O_local[i, j], m_prev_smem[i], m_smem[i], S_smem[i, k_1], V_smem[k_1, j]) + T.writes(O_local[i, j]) + O_local[i, j] = O_local[i, j] + S_smem[i, k_1] * T.Cast("float32", V_smem[k_1, j]) + for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): + for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): + for li_1, lj_1 in T.grid(4, 4): + with T.block("O_store"): + i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) + j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) + T.reads(q_indptr[b_idx:b_idx + 2], O_local[i, j], d_smem[i]) + T.writes(output[q_indptr[b_idx] + (LH_start + i), by, j]) + cur_L: T.int32 = q_indptr[b_idx] + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + output[cur_L, cur_H_qo, j] = T.Cast("float16", O_local[i, j] / d_smem[i]) + for li_0 in range(1): + for li_1 in T.thread_binding(4, thread="threadIdx.y"): + for li_2 in T.thread_binding(32, thread="threadIdx.x"): + with T.block("lse_store"): + i = T.axis.spatial(32, li_0 * 128 + li_1 * 32 + li_2) + T.where((li_0 * 4 + li_1) * 32 + li_2 < 32) + T.reads(q_indptr[b_idx:b_idx + 2], m_smem[i], d_smem[i]) + T.writes(lse[q_indptr[b_idx] + (LH_start + i), by]) + cur_L: T.int32 = q_indptr[b_idx] + (LH_start + i) + cur_H_qo: T.int32 = by + if cur_L < q_indptr[b_idx + 1]: + lse[cur_L, cur_H_qo] = m_smem[i] + T.log2(d_smem[i]) + tile_id[0] = tile_id[0] + 16 + + @T.prim_func(private=True) + def batch_verify_on_gpu_single_kernel(var_draft_probs: T.handle, var_draft_tokens: T.handle, var_model_probs: T.handle, var_token_tree_first_child: T.handle, var_token_tree_next_sibling: T.handle, var_uniform_samples: T.handle, var_token_tree_parent_ptr: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + num_nodes, vocab_size = T.int32(is_size_var=True), T.int64() + draft_probs = T.match_buffer(var_draft_probs, (num_nodes, vocab_size)) + draft_tokens = T.match_buffer(var_draft_tokens, (num_nodes,), "int32") + model_probs = T.match_buffer(var_model_probs, (num_nodes, vocab_size)) + token_tree_first_child = T.match_buffer(var_token_tree_first_child, (num_nodes,), "int32") + token_tree_next_sibling = T.match_buffer(var_token_tree_next_sibling, (num_nodes,), "int32") + uniform_samples = T.match_buffer(var_uniform_samples, (num_nodes,)) + nbatch = T.int32(is_size_var=True) + token_tree_parent_ptr = T.match_buffer(var_token_tree_parent_ptr, (nbatch,), "int32") + # with T.block("root"): + child_ptr = T.alloc_buffer((1,), "int32", scope="local") + parent_ptr = T.alloc_buffer((1,), "int32", scope="local") + child_token = T.alloc_buffer((1,), "int32", scope="local") + done = T.alloc_buffer((1,), "bool", scope="local") + psum = T.alloc_buffer((1,), scope="local") + t0 = T.alloc_buffer((1,), scope="local") + model_prob_local = T.alloc_buffer((1,), scope="local") + draft_prob_local = T.alloc_buffer((1,), scope="local") + p_child = T.alloc_buffer((1,), scope="local") + q_child = T.alloc_buffer((1,), scope="local") + uniform_sample = T.alloc_buffer((1,), scope="local") + pred_shared = T.alloc_buffer((1,), "bool", scope="shared") + pred_local = T.alloc_buffer((1,), "bool", scope="local") + for _bx in T.thread_binding(nbatch, thread="blockIdx.x"): + for _tx in T.thread_binding(1024, thread="threadIdx.x"): + with T.block("CTA"): + b, tx = T.axis.remap("SS", [_bx, _tx]) + T.reads(token_tree_parent_ptr[b], token_tree_first_child[T.min(parent_ptr[0], child_ptr[0]):T.min(parent_ptr[0], child_ptr[0]) + (T.max(parent_ptr[0], child_ptr[0]) + 1 - T.min(parent_ptr[0], child_ptr[0]))], parent_ptr[0], done[0], child_ptr[0], draft_tokens[child_ptr[0]], model_probs[parent_ptr[0], T.min(T.Cast("int64", child_token[0]), T.Cast("int64", tx)):T.min(T.Cast("int64", child_token[0]), T.Cast("int64", tx)) + (T.max(T.Cast("int64", child_token[0]), (vocab_size + T.int64(1023)) // T.int64(1024) * T.int64(1024) + T.Cast("int64", tx) - T.int64(1024)) + T.int64(1) - T.min(T.Cast("int64", child_token[0]), T.Cast("int64", tx)))], child_token[0], draft_probs[child_ptr[0], T.min(T.Cast("int64", child_token[0]), T.Cast("int64", tx)):T.min(T.Cast("int64", child_token[0]), T.Cast("int64", tx)) + (T.max(T.Cast("int64", child_token[0]), (vocab_size + T.int64(1023)) // T.int64(1024) * T.int64(1024) + T.Cast("int64", tx) - T.int64(1024)) + T.int64(1) - T.min(T.Cast("int64", child_token[0]), T.Cast("int64", tx)))], uniform_samples[child_ptr[0]], p_child[0], uniform_sample[0], q_child[0], pred_shared[0], pred_local[0], model_prob_local[0], draft_prob_local[0], psum[0], t0[0], token_tree_next_sibling[child_ptr[0]]) + T.writes(parent_ptr[0], child_ptr[0], done[0], child_token[0], p_child[0], q_child[0], uniform_sample[0], pred_shared[0], pred_local[0], psum[0], model_prob_local[0], draft_prob_local[0], t0[0], model_probs[parent_ptr[0], T.Cast("int64", tx):T.Cast("int64", tx) + ((vocab_size + T.int64(1023)) // T.int64(1024) * T.int64(1024) - T.int64(1023))], token_tree_parent_ptr[b]) + parent_ptr[0] = token_tree_parent_ptr[b] + child_ptr[0] = token_tree_first_child[parent_ptr[0]] + done[0] = T.bool(False) + while not done[0]: + T.tvm_storage_sync("shared") + if child_ptr[0] == -1: + done[0] = T.bool(True) + T.tvm_storage_sync("shared") + else: + if tx == 0: + child_token[0] = draft_tokens[child_ptr[0]] + p_child[0] = model_probs[parent_ptr[0], child_token[0]] + q_child[0] = draft_probs[child_ptr[0], child_token[0]] + uniform_sample[0] = uniform_samples[child_ptr[0]] + pred_shared[0] = p_child[0] >= uniform_sample[0] * q_child[0] + T.tvm_storage_sync("shared") + pred_local[0] = pred_shared[0] + if pred_local[0]: + parent_ptr[0] = child_ptr[0] + child_ptr[0] = token_tree_first_child[child_ptr[0]] + else: + psum[0] = T.float32(0) + for i in range((vocab_size + T.int64(1023)) // T.int64(1024)): + if i * T.int64(1024) + T.Cast("int64", tx) < vocab_size: + model_prob_local[0] = model_probs[parent_ptr[0], i * T.int64(1024) + T.Cast("int64", tx)] + draft_prob_local[0] = draft_probs[child_ptr[0], i * T.int64(1024) + T.Cast("int64", tx)] + model_prob_local[0] = T.max(model_prob_local[0] - draft_prob_local[0], T.float32(0)) + psum[0] = psum[0] + model_prob_local[0] + with T.block("block_cross_thread"): + T.reads(psum[0]) + T.writes(t0[0]) + T.attr(T.comm_reducer(lambda x0, y0: x0 + y0, [T.float32(0)]), "reduce_scope", T.reinterpret("handle", T.uint64(0))) + T.tvm_thread_allreduce(T.uint32(1), psum[0], T.bool(True), t0[0], tx) + if t0[0] < T.float32(9.9999999999999995e-08): + parent_ptr[0] = child_ptr[0] + child_ptr[0] = token_tree_first_child[child_ptr[0]] + else: + for i in range((vocab_size + T.int64(1023)) // T.int64(1024)): + if i * T.int64(1024) + T.Cast("int64", tx) < vocab_size: + model_prob_local[0] = model_probs[parent_ptr[0], i * T.int64(1024) + T.Cast("int64", tx)] + draft_prob_local[0] = draft_probs[child_ptr[0], i * T.int64(1024) + T.Cast("int64", tx)] + model_prob_local[0] = T.max(model_prob_local[0] - draft_prob_local[0], T.float32(0)) + model_probs[parent_ptr[0], i * T.int64(1024) + T.Cast("int64", tx)] = model_prob_local[0] / t0[0] + child_ptr[0] = token_tree_next_sibling[child_ptr[0]] + if tx == 0: + token_tree_parent_ptr[b] = parent_ptr[0] + + @T.prim_func + def chunk_lse(var_A: T.handle, var_temperature: T.handle, var_chunked_sum: T.handle, var_chunked_max: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + batch_size, vocab_size = T.int64(is_size_var=True), T.int64(is_size_var=True) + A = T.match_buffer(var_A, (batch_size, vocab_size)) + temperature = T.match_buffer(var_temperature, (batch_size,)) + num_chunks = T.int64(is_size_var=True) + chunked_sum = T.match_buffer(var_chunked_sum, (batch_size, num_chunks)) + chunked_max = T.match_buffer(var_chunked_max, (batch_size, num_chunks)) + # with T.block("root"): + temp_max_shared = T.alloc_buffer((batch_size, num_chunks), scope="shared") + temp_sum_shared = T.alloc_buffer((batch_size, num_chunks), scope="shared") + for ax0_ax1_fused in T.thread_binding(batch_size * num_chunks, thread="blockIdx.x"): + for ax0, ax1 in T.grid(T.int64(1), T.int64(1)): + for ax2_fused_1 in T.thread_binding(T.int64(256), thread="threadIdx.x"): + for ax2_fused_0 in T.serial(T.int64(16), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): + with T.block("max"): + v0 = T.axis.spatial(batch_size, ax0_ax1_fused % (num_chunks * batch_size) // num_chunks + ax0) + v1 = T.axis.spatial(num_chunks, ax0_ax1_fused % num_chunks + ax1) + v2 = T.axis.reduce(T.int64(4096), ax2_fused_0 * T.int64(256) + ax2_fused_1) + T.reads(temperature[v0], A[v0, v1 * T.int64(4096) + v2]) + T.writes(temp_max_shared[v0, v1]) + with T.init(): + temp_max_shared[v0, v1] = T.float32(-3.4028234663852886e+38) + temp_max_shared[v0, v1] = T.max(temp_max_shared[v0, v1], T.if_then_else(v1 * T.int64(4096) + v2 < vocab_size, T.if_then_else(temperature[v0] > T.float32(1.0000000000000001e-05), A[v0, v1 * T.int64(4096) + v2] / temperature[v0], A[v0, v1 * T.int64(4096) + v2]), T.float32(-3.4028234663852886e+38))) + for ax0, ax1 in T.grid(T.int64(1), T.int64(1)): + for ax2_fused_1 in T.thread_binding(T.int64(256), thread="threadIdx.x"): + for ax2_fused_0 in T.serial(T.int64(16), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): + with T.block("sum_exp"): + v0 = T.axis.spatial(batch_size, ax0_ax1_fused % (num_chunks * batch_size) // num_chunks + ax0) + v1 = T.axis.spatial(num_chunks, ax0_ax1_fused % num_chunks + ax1) + v2 = T.axis.reduce(T.int64(4096), ax2_fused_0 * T.int64(256) + ax2_fused_1) + T.reads(temperature[v0], A[v0, v1 * T.int64(4096) + v2], temp_max_shared[v0, v1]) + T.writes(temp_sum_shared[v0, v1]) + with T.init(): + temp_sum_shared[v0, v1] = T.float32(0) + temp_sum_shared[v0, v1] = temp_sum_shared[v0, v1] + T.if_then_else(v1 * T.int64(4096) + v2 < vocab_size, T.Select(temperature[v0] > T.float32(1.0000000000000001e-05), T.exp(T.if_then_else(v1 * T.int64(4096) + v2 < vocab_size, T.if_then_else(temperature[v0] > T.float32(1.0000000000000001e-05), A[v0, v1 * T.int64(4096) + v2] / temperature[v0], A[v0, v1 * T.int64(4096) + v2]), T.float32(-3.4028234663852886e+38)) - temp_max_shared[v0, v1]), T.Cast("float32", T.if_then_else(v1 * T.int64(4096) + v2 < vocab_size, T.if_then_else(temperature[v0] > T.float32(1.0000000000000001e-05), A[v0, v1 * T.int64(4096) + v2] / temperature[v0], A[v0, v1 * T.int64(4096) + v2]), T.float32(-3.4028234663852886e+38)) == temp_max_shared[v0, v1])), T.float32(0)) + for ax2_1 in T.thread_binding(T.int64(256), thread="threadIdx.x"): + for ax2_0 in T.serial(T.int64(1), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): + with T.block("log"): + v0 = T.axis.spatial(batch_size, ax0_ax1_fused % (num_chunks * batch_size) // num_chunks) + v1 = T.axis.spatial(num_chunks, ax0_ax1_fused % num_chunks) + v2 = T.axis.spatial(T.int64(1), ax2_0 * T.int64(256) + ax2_1) + T.where(ax2_0 * T.int64(256) + ax2_1 < T.int64(1)) + T.reads(temperature[v0], temp_sum_shared[v0, v1], temp_max_shared[v0, v1]) + T.writes(chunked_sum[v0, v1], chunked_max[v0, v1]) + chunked_sum[v0, v1] = T.Select(temperature[v0] > T.float32(1.0000000000000001e-05), T.log(temp_sum_shared[v0, v1]), temp_sum_shared[v0, v1]) + chunked_max[v0, v1] = temp_max_shared[v0, v1] + + @T.prim_func + def compact_kv_copy(var_pages: T.handle, var_copy_length_indptr: T.handle, var_copy_src_dst_pos: T.handle, batch_size: T.int32): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) + num_pages = T.int32() + pages = T.match_buffer(var_pages, (num_pages, 2, 20, 16, 64), "float16") + copy_length_indptr = T.match_buffer(var_copy_length_indptr, (batch_size + 1,), "int32", offset_factor=1) + total_copy_length = T.int32() + copy_src_dst_pos = T.match_buffer(var_copy_src_dst_pos, (2, total_copy_length), "int32", offset_factor=1) + with T.block("root"): + T.reads() + T.writes() + for bhd_o in T.thread_binding((batch_size * 1280 + 1023) // 1024, thread="blockIdx.x"): + for bhd_i in T.thread_binding(1024, thread="threadIdx.x"): + b: T.int32 = (bhd_o * 1024 + bhd_i) // 1280 + h: T.int32 = (bhd_o * 1024 + bhd_i) // 64 % 20 + d: T.int32 = (bhd_o * 1024 + bhd_i) % 64 + if bhd_o * 1024 + bhd_i < batch_size * 20 * 64: + for i in range(copy_length_indptr[b + 1] - copy_length_indptr[b]): + src_pos: T.int32 = copy_src_dst_pos[0, copy_length_indptr[b] + i] + dst_pos: T.int32 = copy_src_dst_pos[1, copy_length_indptr[b] + i] + pages[dst_pos // 16, 0, h, dst_pos % 16, d] = pages[src_pos // 16, 0, h, src_pos % 16, d] + pages[dst_pos // 16, 1, h, dst_pos % 16, d] = pages[src_pos // 16, 1, h, src_pos % 16, d] + + @T.prim_func(private=True) + def concatenate(var_reshape710: T.handle, var_reshape711: T.handle, var_reshape712: T.handle, var_T_concat: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + batch_size = T.int64() + reshape710 = T.match_buffer(var_reshape710, (batch_size, T.int64(1), T.int64(20), T.int64(64)), "float16") + reshape711 = T.match_buffer(var_reshape711, (batch_size, T.int64(1), T.int64(20), T.int64(64)), "float16") + reshape712 = T.match_buffer(var_reshape712, (batch_size, T.int64(1), T.int64(20), T.int64(64)), "float16") + T_concat = T.match_buffer(var_T_concat, (batch_size, T.int64(1), T.int64(60), T.int64(64)), "float16") + # with T.block("root"): + for ax0_ax1_ax2_fused_0 in T.thread_binding((batch_size * T.int64(3840) + T.int64(1023)) // T.int64(1024), thread="blockIdx.x"): + for ax0_ax1_ax2_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_concat"): + v0 = T.axis.spatial(batch_size, (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) // T.int64(3840)) + v1 = T.axis.spatial(T.int64(60), (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) % T.int64(3840) // T.int64(64)) + v2 = T.axis.spatial(T.int64(64), (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) % T.int64(64)) + T.where(ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1 < batch_size * T.int64(3840)) + T.reads(reshape712[v0, T.int64(0), v1 + T.int64(-40), v2], reshape711[v0, T.int64(0), v1 + T.int64(-20), v2], reshape710[v0, T.int64(0), v1, v2]) + T.writes(T_concat[v0, T.int64(0), v1, v2]) + T_concat[v0, T.int64(0), v1, v2] = T.if_then_else(T.int64(40) <= v1, reshape712[v0, T.int64(0), v1 - T.int64(40), v2], T.if_then_else(T.int64(20) <= v1, reshape711[v0, T.int64(0), v1 + T.int64(-20), v2], reshape710[v0, T.int64(0), v1, v2])) + + @T.prim_func(private=True) + def concatenate1(var_reshape387: T.handle, var_reshape388: T.handle, var_reshape389: T.handle, var_T_concat: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + seq_len = T.int64() + reshape387 = T.match_buffer(var_reshape387, (T.int64(1), seq_len, T.int64(20), T.int64(64)), "float16") + reshape388 = T.match_buffer(var_reshape388, (T.int64(1), seq_len, T.int64(20), T.int64(64)), "float16") + reshape389 = T.match_buffer(var_reshape389, (T.int64(1), seq_len, T.int64(20), T.int64(64)), "float16") + T_concat = T.match_buffer(var_T_concat, (T.int64(1), seq_len, T.int64(60), T.int64(64)), "float16") + # with T.block("root"): + for ax0_ax1_ax2_fused_0 in T.thread_binding((seq_len * T.int64(3840) + T.int64(1023)) // T.int64(1024), thread="blockIdx.x"): + for ax0_ax1_ax2_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_concat"): + v0 = T.axis.spatial(seq_len, (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) // T.int64(3840)) + v1 = T.axis.spatial(T.int64(60), (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) % T.int64(3840) // T.int64(64)) + v2 = T.axis.spatial(T.int64(64), (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) % T.int64(64)) + T.where(ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1 < seq_len * T.int64(3840)) + T.reads(reshape389[T.int64(0), v0, v1 + T.int64(-40), v2], reshape388[T.int64(0), v0, v1 + T.int64(-20), v2], reshape387[T.int64(0), v0, v1, v2]) + T.writes(T_concat[T.int64(0), v0, v1, v2]) + T_concat[T.int64(0), v0, v1, v2] = T.if_then_else(T.int64(40) <= v1, reshape389[T.int64(0), v0, v1 - T.int64(40), v2], T.if_then_else(T.int64(20) <= v1, reshape388[T.int64(0), v0, v1 + T.int64(-20), v2], reshape387[T.int64(0), v0, v1, v2])) + + @T.prim_func + def copy_single_page(var_pages: T.handle, src_page_id: T.int64, tgt_page_id: T.int64, copy_length: T.int64): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) + num_pages, page_size = T.int32(), T.int64() + pages = T.match_buffer(var_pages, (num_pages, 2, 20, page_size, 64), "float16") + # with T.block("root"): + for b in T.thread_binding((copy_length * T.int64(1280) + T.int64(1023)) // T.int64(1024), thread="blockIdx.x"): + for t in T.thread_binding(1024, thread="threadIdx.x"): + with T.block("copy"): + vh = T.axis.spatial(20, T.Cast("int32", (b * T.int64(1024) + T.Cast("int64", t)) // (copy_length * T.int64(64)))) + vp = T.axis.spatial(copy_length, (b * T.int64(1024) + T.Cast("int64", t)) % (copy_length * T.int64(64)) // T.int64(64)) + vd = T.axis.spatial(64, T.Cast("int32", (b * T.int64(1024) + T.Cast("int64", t)) % T.int64(64))) + T.reads(pages[src_page_id, 0:2, vh, vp, vd]) + T.writes(pages[tgt_page_id, 0:2, vh, vp, vd]) + pages[tgt_page_id, 0, vh, vp, vd] = pages[src_page_id, 0, vh, vp, vd] + pages[tgt_page_id, 1, vh, vp, vd] = pages[src_page_id, 1, vh, vp, vd] + + @T.prim_func(private=True) + def cumsum(var_sorted_probs: T.handle, var_lv1: T.handle, var_exclusive_scan_thrust: T.handle): + T.func_attr({"tir.noalias": T.bool(True)}) + batch_size, vocab_size = T.int64(), T.int64() + data_buf = T.match_buffer(var_sorted_probs, (batch_size, vocab_size), align=8) + workspace_buf = T.match_buffer(var_lv1, (T.int64(8) * (batch_size * vocab_size * T.int64(4)) + T.int64(8388608) + batch_size * vocab_size * T.int64(12),), "uint8", align=8) + output_buf = T.match_buffer(var_exclusive_scan_thrust, (batch_size, vocab_size), align=8) + with T.block("exclusive_scan_thrust"): + T.reads() + T.writes() + T.call_packed("tvm.contrib.thrust.sum_scan", T.tvm_stack_make_array(data_buf.data, T.tvm_stack_make_shape(batch_size, vocab_size), 0, 2, T.float32(0), T.int64(0)), T.tvm_stack_make_array(output_buf.data, T.tvm_stack_make_shape(batch_size, vocab_size), 0, 2, T.float32(0), T.int64(0)), T.bool(False), T.tvm_stack_make_array(workspace_buf.data, T.tvm_stack_make_shape(T.int64(8) * (batch_size * vocab_size * T.int64(4)) + T.int64(8388608) + batch_size * vocab_size * T.int64(12)), 0, 1, T.uint8(0), T.int64(0))) + + @T.prim_func + def full(var_result: T.handle, value: T.int32): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) + batch_size = T.int32(is_size_var=True) + result = T.match_buffer(var_result, (batch_size, 1), "int32") + # with T.block("root"): + for ax0_fused_0 in T.thread_binding((batch_size + 1023) // 1024, thread="blockIdx.x"): + for ax0_fused_1 in T.thread_binding(1024, thread="threadIdx.x"): + with T.block("block"): + v0 = T.axis.spatial(batch_size, ax0_fused_0 * 1024 + ax0_fused_1) + T.where(ax0_fused_0 * 1024 + ax0_fused_1 < batch_size) + T.reads() + T.writes(result[v0, 0]) + result[v0, 0] = value + + @T.prim_func(private=True) + def fused_NT_matmul1_add8_gelu2(layer_norm358: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16"), model_decoder_layers_0_fc1_weight5: T.Buffer((T.int64(5120), T.int64(1280)), "float16"), model_decoder_layers_0_fc1_bias5: T.Buffer((T.int64(5120),), "float16"), T_multiply_intermediate: T.Buffer((T.int64(1), T.int64(1), T.int64(5120)), "float16")): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + # with T.block("root"): + NT_matmul_intermediate_local = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(5120)), "float16", scope="local") + NT_matmul_intermediate_rf_local = T.alloc_buffer((T.int64(256), T.int64(1), T.int64(1), T.int64(5120)), "float16", scope="local") + NT_matmul_intermediate_rf_local_1 = T.alloc_buffer((T.int64(64), T.int64(1), T.int64(1), T.int64(5120)), "float16", scope="local") + model_decoder_layers_0_fc1_weight5_local = T.alloc_buffer((T.int64(5120), T.int64(1280)), "float16", scope="local") + layer_norm358_shared = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16", scope="shared") + for u_fused_ax0_fused_fused_0 in T.thread_binding(T.int64(1280), thread="blockIdx.x"): + for u_fused_ax0_fused_fused_1 in T.thread_binding(T.int64(4), thread="threadIdx.y"): + for ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 in T.thread_binding(T.int64(64), thread="threadIdx.x"): + for ax0, ax1 in T.grid(T.int64(1), T.int64(1)): + for ax2_0 in T.serial(T.int64(5), annotations={"pragma_unroll_explicit": 256, "pragma_vectorize": 1}): + for ax2_1 in T.thread_binding(T.int64(4), thread="threadIdx.y"): + for ax2_2 in T.thread_binding(T.int64(64), thread="threadIdx.x"): + for ax2_3 in T.vectorized(T.int64(1)): + with T.block("layer_norm358_shared"): + v0, v1 = T.axis.remap("SS", [ax0, ax1]) + v2 = T.axis.spatial(T.int64(1280), ax2_0 * T.int64(256) + ax2_1 * T.int64(64) + ax2_2 + ax2_3) + T.reads(layer_norm358[v0, v1, v2]) + T.writes(layer_norm358_shared[v0, v1, v2]) + layer_norm358_shared[v0, v1, v2] = layer_norm358[v0, v1, v2] + for u_fused_ax0_fused_fused_2_init in range(T.int64(1)): + for ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1_init in T.vectorized(T.int64(4)): + with T.block("NT_matmul_rf_init"): + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused = T.axis.spatial(T.int64(256), ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 * T.int64(4) + ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1_init) + v0 = T.axis.spatial(T.int64(5120), u_fused_ax0_fused_fused_0 * T.int64(4) + u_fused_ax0_fused_fused_1 + u_fused_ax0_fused_fused_2_init) + T.reads() + T.writes(NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0]) + NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0] = T.float16(0) + for ax1_fused_u_fused_0 in T.serial(T.int64(5), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): + for ax0_ax1_fused_0 in range(T.int64(2)): + for ax0_ax1_fused_1 in T.vectorized(T.int64(2)): + with T.block("model_decoder_layers_0_fc1_weight5_local"): + v0 = T.axis.spatial(T.int64(5120), u_fused_ax0_fused_fused_0 * T.int64(4) + u_fused_ax0_fused_fused_1) + v1 = T.axis.spatial(T.int64(1280), ax1_fused_u_fused_0 * T.int64(256) + ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 * T.int64(4) + ax0_ax1_fused_0 * T.int64(2) + ax0_ax1_fused_1) + T.reads(model_decoder_layers_0_fc1_weight5[v0, v1]) + T.writes(model_decoder_layers_0_fc1_weight5_local[v0, v1]) + model_decoder_layers_0_fc1_weight5_local[v0, v1] = model_decoder_layers_0_fc1_weight5[v0, v1] + for u_fused_ax0_fused_fused_2, ax1_fused_u_fused_2 in T.grid(T.int64(1), T.int64(1)): + for ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1 in T.vectorized(T.int64(4)): + with T.block("NT_matmul_rf_update"): + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused = T.axis.spatial(T.int64(256), ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 * T.int64(4) + ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1) + v0 = T.axis.spatial(T.int64(5120), u_fused_ax0_fused_fused_0 * T.int64(4) + u_fused_ax0_fused_fused_1 + u_fused_ax0_fused_fused_2) + vax1_fused_u_fused_2, vax1_fused_u_fused_0 = T.axis.remap("RR", [ax1_fused_u_fused_2, ax1_fused_u_fused_0]) + T.reads(NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0], layer_norm358_shared[T.int64(0), T.int64(0), vax1_fused_u_fused_0 * T.int64(256) + vax1_fused_u_fused_2 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused], model_decoder_layers_0_fc1_weight5_local[v0, vax1_fused_u_fused_0 * T.int64(256) + vax1_fused_u_fused_2 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused]) + T.writes(NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0]) + NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0] = NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0] + layer_norm358_shared[T.int64(0), T.int64(0), vax1_fused_u_fused_0 * T.int64(256) + vax1_fused_u_fused_2 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused] * model_decoder_layers_0_fc1_weight5_local[v0, vax1_fused_u_fused_0 * T.int64(256) + vax1_fused_u_fused_2 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused] + for ax2_fused_0_ax2_fused_1_fused in T.thread_binding(T.int64(4), thread="threadIdx.y"): + for ax0 in T.thread_binding(T.int64(64), thread="threadIdx.x"): + for ax2_fused_2_0 in T.serial(T.int64(1), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): + for ax2_fused_2_1 in T.vectorized(T.int64(1)): + with T.block("NT_matmul_rf_init"): + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 = T.axis.spatial(T.int64(64), ax0) + v0 = T.axis.spatial(T.int64(5120), u_fused_ax0_fused_fused_0 * T.int64(4) + ax2_fused_0_ax2_fused_1_fused + ax2_fused_2_0 + ax2_fused_2_1) + T.reads() + T.writes(NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0]) + NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0] = T.float16(0) + for ax1 in range(T.int64(4)): + with T.block("NT_matmul_rf_update"): + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1 = T.axis.remap("SR", [ax0, ax1]) + v0 = T.axis.spatial(T.int64(5120), u_fused_ax0_fused_fused_0 * T.int64(4) + ax2_fused_0_ax2_fused_1_fused + ax2_fused_2_0 + ax2_fused_2_1) + T.reads(NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0], NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1, T.int64(0), T.int64(0), v0]) + T.writes(NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0]) + NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0] = NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0] + NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1, T.int64(0), T.int64(0), v0] + for ax1_fused_2 in range(T.int64(1)): + for ax1_fused_0_ax1_fused_1_fused in T.thread_binding(T.int64(4), thread="threadIdx.y"): + for ax0 in T.thread_binding(T.int64(64), thread="threadIdx.x"): + with T.block("NT_matmul"): + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 = T.axis.reduce(T.int64(64), ax0) + v0 = T.axis.spatial(T.int64(5120), u_fused_ax0_fused_fused_0 * T.int64(4) + ax1_fused_0_ax1_fused_1_fused + ax1_fused_2) + T.reads(NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0]) + T.writes(NT_matmul_intermediate_local[T.int64(0), T.int64(0), v0]) + with T.init(): + NT_matmul_intermediate_local[T.int64(0), T.int64(0), v0] = T.float16(0) + NT_matmul_intermediate_local[T.int64(0), T.int64(0), v0] = NT_matmul_intermediate_local[T.int64(0), T.int64(0), v0] + NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0] + for ax0_fused_0_ax0_fused_1_fused in T.thread_binding(T.int64(4), thread="threadIdx.y"): + for ax0_fused_2 in range(T.int64(1)): + with T.block("T_multiply_2"): + v0 = T.axis.spatial(T.int64(5120), u_fused_ax0_fused_fused_0 * T.int64(4) + ax0_fused_0_ax0_fused_1_fused + ax0_fused_2) + T.reads(NT_matmul_intermediate_local[T.int64(0), T.int64(0), v0], model_decoder_layers_0_fc1_bias5[v0]) + T.writes(T_multiply_intermediate[T.int64(0), T.int64(0), v0]) + T_multiply_intermediate[T.int64(0), T.int64(0), v0] = (NT_matmul_intermediate_local[T.int64(0), T.int64(0), v0] + model_decoder_layers_0_fc1_bias5[v0]) * (T.float16(0.5) + T.Cast("float16", T.erf(T.Cast("float32", (NT_matmul_intermediate_local[T.int64(0), T.int64(0), v0] + model_decoder_layers_0_fc1_bias5[v0]) * T.float16(0.70710678118654757)))) * T.float16(0.5)) + + @T.prim_func(private=True) + def fused_NT_matmul2_add7_add6(gelu130: T.Buffer((T.int64(1), T.int64(1), T.int64(5120)), "float16"), model_decoder_layers_0_fc2_weight5: T.Buffer((T.int64(1280), T.int64(5120)), "float16"), model_decoder_layers_0_fc2_bias5: T.Buffer((T.int64(1280),), "float16"), add1227: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16"), T_add_intermediate_1: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16")): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + # with T.block("root"): + NT_matmul_intermediate_local = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16", scope="local") + NT_matmul_intermediate_rf_local = T.alloc_buffer((T.int64(128), T.int64(1), T.int64(1), T.int64(1280)), "float16", scope="local") + NT_matmul_intermediate_rf_local_1 = T.alloc_buffer((T.int64(32), T.int64(1), T.int64(1), T.int64(1280)), "float16", scope="local") + model_decoder_layers_0_fc2_weight5_local = T.alloc_buffer((T.int64(1280), T.int64(5120)), "float16", scope="local") + gelu130_shared = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(5120)), "float16", scope="shared") + for u_fused_ax0_fused_fused_0 in T.thread_binding(T.int64(80), thread="blockIdx.x"): + for u_fused_ax0_fused_fused_1 in T.thread_binding(T.int64(16), thread="threadIdx.y"): + for ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 in T.thread_binding(T.int64(32), thread="threadIdx.x"): + for ax0, ax1 in T.grid(T.int64(1), T.int64(1)): + for ax2_0 in T.serial(T.int64(5), annotations={"pragma_unroll_explicit": 256, "pragma_vectorize": 1}): + for ax2_1 in T.thread_binding(T.int64(16), thread="threadIdx.y"): + for ax2_2 in T.thread_binding(T.int64(32), thread="threadIdx.x"): + for ax2_3 in T.vectorized(T.int64(2)): + with T.block("gelu130_shared"): + v0, v1 = T.axis.remap("SS", [ax0, ax1]) + v2 = T.axis.spatial(T.int64(5120), ax2_0 * T.int64(1024) + ax2_1 * T.int64(64) + ax2_2 * T.int64(2) + ax2_3) + T.reads(gelu130[v0, v1, v2]) + T.writes(gelu130_shared[v0, v1, v2]) + gelu130_shared[v0, v1, v2] = gelu130[v0, v1, v2] + for u_fused_ax0_fused_fused_2_init in range(T.int64(1)): + for ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1_init in T.vectorized(T.int64(4)): + with T.block("NT_matmul_rf_init"): + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused = T.axis.spatial(T.int64(128), ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 * T.int64(4) + ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1_init) + v0 = T.axis.spatial(T.int64(1280), u_fused_ax0_fused_fused_0 * T.int64(16) + u_fused_ax0_fused_fused_1 + u_fused_ax0_fused_fused_2_init) + T.reads() + T.writes(NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0]) + NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0] = T.float16(0) + for ax1_fused_u_fused_0 in T.serial(T.int64(20), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): + for ax0_ax1_fused_0 in range(T.int64(4)): + for ax0_ax1_fused_1 in T.vectorized(T.int64(2)): + with T.block("model_decoder_layers_0_fc2_weight5_local"): + v0 = T.axis.spatial(T.int64(1280), u_fused_ax0_fused_fused_0 * T.int64(16) + u_fused_ax0_fused_fused_1) + v1 = T.axis.spatial(T.int64(5120), ax1_fused_u_fused_0 * T.int64(256) + ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 * T.int64(8) + ax0_ax1_fused_0 * T.int64(2) + ax0_ax1_fused_1) + T.reads(model_decoder_layers_0_fc2_weight5[v0, v1]) + T.writes(model_decoder_layers_0_fc2_weight5_local[v0, v1]) + model_decoder_layers_0_fc2_weight5_local[v0, v1] = model_decoder_layers_0_fc2_weight5[v0, v1] + for u_fused_ax0_fused_fused_2, ax1_fused_u_fused_2 in T.grid(T.int64(1), T.int64(2)): + for ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1 in T.vectorized(T.int64(4)): + with T.block("NT_matmul_rf_update"): + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused = T.axis.spatial(T.int64(128), ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 * T.int64(4) + ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1) + v0 = T.axis.spatial(T.int64(1280), u_fused_ax0_fused_fused_0 * T.int64(16) + u_fused_ax0_fused_fused_1 + u_fused_ax0_fused_fused_2) + vax1_fused_u_fused_0, vax1_fused_u_fused_2 = T.axis.remap("RR", [ax1_fused_u_fused_0, ax1_fused_u_fused_2]) + T.reads(NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0], gelu130_shared[T.int64(0), T.int64(0), vax1_fused_u_fused_0 * T.int64(256) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused // T.int64(4) * T.int64(8) + vax1_fused_u_fused_2 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused % T.int64(4)], model_decoder_layers_0_fc2_weight5_local[v0, vax1_fused_u_fused_0 * T.int64(256) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused // T.int64(4) * T.int64(8) + vax1_fused_u_fused_2 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused % T.int64(4)]) + T.writes(NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0]) + NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0] = NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0] + gelu130_shared[T.int64(0), T.int64(0), vax1_fused_u_fused_0 * T.int64(256) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused // T.int64(4) * T.int64(8) + vax1_fused_u_fused_2 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused % T.int64(4)] * model_decoder_layers_0_fc2_weight5_local[v0, vax1_fused_u_fused_0 * T.int64(256) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused // T.int64(4) * T.int64(8) + vax1_fused_u_fused_2 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused % T.int64(4)] + for ax2_fused_0_ax2_fused_1_fused in T.thread_binding(T.int64(16), thread="threadIdx.y"): + for ax0 in T.thread_binding(T.int64(32), thread="threadIdx.x"): + for ax2_fused_2_0 in T.serial(T.int64(1), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): + for ax2_fused_2_1 in T.vectorized(T.int64(1)): + with T.block("NT_matmul_rf_init"): + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 = T.axis.spatial(T.int64(32), ax0) + v0 = T.axis.spatial(T.int64(1280), u_fused_ax0_fused_fused_0 * T.int64(16) + ax2_fused_0_ax2_fused_1_fused + ax2_fused_2_0 + ax2_fused_2_1) + T.reads() + T.writes(NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0]) + NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0] = T.float16(0) + for ax1 in range(T.int64(4)): + with T.block("NT_matmul_rf_update"): + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1 = T.axis.remap("SR", [ax0, ax1]) + v0 = T.axis.spatial(T.int64(1280), u_fused_ax0_fused_fused_0 * T.int64(16) + ax2_fused_0_ax2_fused_1_fused + ax2_fused_2_0 + ax2_fused_2_1) + T.reads(NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0], NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1, T.int64(0), T.int64(0), v0]) + T.writes(NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0]) + NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0] = NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0] + NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1, T.int64(0), T.int64(0), v0] + for ax1_fused_2 in range(T.int64(1)): + for ax1_fused_0_ax1_fused_1_fused in T.thread_binding(T.int64(16), thread="threadIdx.y"): + for ax0 in T.thread_binding(T.int64(32), thread="threadIdx.x"): + with T.block("NT_matmul"): + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 = T.axis.reduce(T.int64(32), ax0) + v0 = T.axis.spatial(T.int64(1280), u_fused_ax0_fused_fused_0 * T.int64(16) + ax1_fused_0_ax1_fused_1_fused + ax1_fused_2) + T.reads(NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0]) + T.writes(NT_matmul_intermediate_local[T.int64(0), T.int64(0), v0]) + with T.init(): + NT_matmul_intermediate_local[T.int64(0), T.int64(0), v0] = T.float16(0) + NT_matmul_intermediate_local[T.int64(0), T.int64(0), v0] = NT_matmul_intermediate_local[T.int64(0), T.int64(0), v0] + NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0] + for ax0_fused_0_ax0_fused_1_fused in T.thread_binding(T.int64(16), thread="threadIdx.y"): + for ax0_fused_2 in range(T.int64(1)): + with T.block("T_add_1"): + v0 = T.axis.spatial(T.int64(1280), u_fused_ax0_fused_fused_0 * T.int64(16) + ax0_fused_0_ax0_fused_1_fused + ax0_fused_2) + T.reads(add1227[T.int64(0), T.int64(0), v0], NT_matmul_intermediate_local[T.int64(0), T.int64(0), v0], model_decoder_layers_0_fc2_bias5[v0]) + T.writes(T_add_intermediate_1[T.int64(0), T.int64(0), v0]) + T_add_intermediate_1[T.int64(0), T.int64(0), v0] = add1227[T.int64(0), T.int64(0), v0] + (NT_matmul_intermediate_local[T.int64(0), T.int64(0), v0] + model_decoder_layers_0_fc2_bias5[v0]) + + @T.prim_func(private=True) + def fused_NT_matmul_add7(layer_norm356: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16"), model_decoder_layers_0_self_attn_q_proj_weight5: T.Buffer((T.int64(1280), T.int64(1280)), "float16"), model_decoder_layers_0_self_attn_q_proj_bias5: T.Buffer((T.int64(1280),), "float16"), T_add_intermediate: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16")): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + # with T.block("root"): + NT_matmul_intermediate_local = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16", scope="local") + NT_matmul_intermediate_rf_local = T.alloc_buffer((T.int64(128), T.int64(1), T.int64(1), T.int64(1280)), "float16", scope="local") + NT_matmul_intermediate_rf_local_1 = T.alloc_buffer((T.int64(32), T.int64(1), T.int64(1), T.int64(1280)), "float16", scope="local") + model_decoder_layers_0_self_attn_q_proj_weight5_local = T.alloc_buffer((T.int64(1280), T.int64(1280)), "float16", scope="local") + layer_norm356_shared = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16", scope="shared") + for u_fused_ax0_fused_fused_0 in T.thread_binding(T.int64(80), thread="blockIdx.x"): + for u_fused_ax0_fused_fused_1 in T.thread_binding(T.int64(16), thread="threadIdx.y"): + for ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 in T.thread_binding(T.int64(32), thread="threadIdx.x"): + for ax0, ax1 in T.grid(T.int64(1), T.int64(1)): + for ax2_0 in T.serial(T.int64(3), annotations={"pragma_unroll_explicit": 256, "pragma_vectorize": 1}): + for ax2_1 in T.thread_binding(T.int64(16), thread="threadIdx.y"): + for ax2_2 in T.thread_binding(T.int64(32), thread="threadIdx.x"): + for ax2_3 in T.vectorized(T.int64(1)): + with T.block("layer_norm356_shared"): + v0, v1 = T.axis.remap("SS", [ax0, ax1]) + v2 = T.axis.spatial(T.int64(1280), ax2_0 * T.int64(512) + ax2_1 * T.int64(32) + ax2_2 + ax2_3) + T.where((ax2_0 * T.int64(16) + ax2_1) * T.int64(32) + ax2_2 + ax2_3 < T.int64(1280)) + T.reads(layer_norm356[v0, v1, v2]) + T.writes(layer_norm356_shared[v0, v1, v2]) + layer_norm356_shared[v0, v1, v2] = layer_norm356[v0, v1, v2] + for u_fused_ax0_fused_fused_2_init in range(T.int64(1)): + for ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1_init in T.vectorized(T.int64(4)): + with T.block("NT_matmul_rf_init"): + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused = T.axis.spatial(T.int64(128), ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 * T.int64(4) + ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1_init) + v0 = T.axis.spatial(T.int64(1280), u_fused_ax0_fused_fused_0 * T.int64(16) + u_fused_ax0_fused_fused_1 + u_fused_ax0_fused_fused_2_init) + T.reads() + T.writes(NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0]) + NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0] = T.float16(0) + for ax1_fused_u_fused_0 in T.serial(T.int64(5), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): + for ax0_ax1_fused_0 in range(T.int64(4)): + for ax0_ax1_fused_1 in T.vectorized(T.int64(2)): + with T.block("model_decoder_layers_0_self_attn_q_proj_weight5_local"): + v0 = T.axis.spatial(T.int64(1280), u_fused_ax0_fused_fused_0 * T.int64(16) + u_fused_ax0_fused_fused_1) + v1 = T.axis.spatial(T.int64(1280), ax1_fused_u_fused_0 * T.int64(256) + ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 * T.int64(8) + ax0_ax1_fused_0 * T.int64(2) + ax0_ax1_fused_1) + T.reads(model_decoder_layers_0_self_attn_q_proj_weight5[v0, v1]) + T.writes(model_decoder_layers_0_self_attn_q_proj_weight5_local[v0, v1]) + model_decoder_layers_0_self_attn_q_proj_weight5_local[v0, v1] = model_decoder_layers_0_self_attn_q_proj_weight5[v0, v1] + for u_fused_ax0_fused_fused_2, ax1_fused_u_fused_2 in T.grid(T.int64(1), T.int64(2)): + for ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1 in T.vectorized(T.int64(4)): + with T.block("NT_matmul_rf_update"): + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused = T.axis.spatial(T.int64(128), ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 * T.int64(4) + ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1) + v0 = T.axis.spatial(T.int64(1280), u_fused_ax0_fused_fused_0 * T.int64(16) + u_fused_ax0_fused_fused_1 + u_fused_ax0_fused_fused_2) + vax1_fused_u_fused_0, vax1_fused_u_fused_2 = T.axis.remap("RR", [ax1_fused_u_fused_0, ax1_fused_u_fused_2]) + T.reads(NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0], layer_norm356_shared[T.int64(0), T.int64(0), vax1_fused_u_fused_0 * T.int64(256) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused // T.int64(4) * T.int64(8) + vax1_fused_u_fused_2 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused % T.int64(4)], model_decoder_layers_0_self_attn_q_proj_weight5_local[v0, vax1_fused_u_fused_0 * T.int64(256) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused // T.int64(4) * T.int64(8) + vax1_fused_u_fused_2 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused % T.int64(4)]) + T.writes(NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0]) + NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0] = NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0] + layer_norm356_shared[T.int64(0), T.int64(0), vax1_fused_u_fused_0 * T.int64(256) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused // T.int64(4) * T.int64(8) + vax1_fused_u_fused_2 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused % T.int64(4)] * model_decoder_layers_0_self_attn_q_proj_weight5_local[v0, vax1_fused_u_fused_0 * T.int64(256) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused // T.int64(4) * T.int64(8) + vax1_fused_u_fused_2 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused % T.int64(4)] + for ax2_fused_0_ax2_fused_1_fused in T.thread_binding(T.int64(16), thread="threadIdx.y"): + for ax0 in T.thread_binding(T.int64(32), thread="threadIdx.x"): + for ax2_fused_2_0 in T.serial(T.int64(1), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): + for ax2_fused_2_1 in T.vectorized(T.int64(1)): + with T.block("NT_matmul_rf_init"): + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 = T.axis.spatial(T.int64(32), ax0) + v0 = T.axis.spatial(T.int64(1280), u_fused_ax0_fused_fused_0 * T.int64(16) + ax2_fused_0_ax2_fused_1_fused + ax2_fused_2_0 + ax2_fused_2_1) + T.reads() + T.writes(NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0]) + NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0] = T.float16(0) + for ax1 in range(T.int64(4)): + with T.block("NT_matmul_rf_update"): + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1 = T.axis.remap("SR", [ax0, ax1]) + v0 = T.axis.spatial(T.int64(1280), u_fused_ax0_fused_fused_0 * T.int64(16) + ax2_fused_0_ax2_fused_1_fused + ax2_fused_2_0 + ax2_fused_2_1) + T.reads(NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0], NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1, T.int64(0), T.int64(0), v0]) + T.writes(NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0]) + NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0] = NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0] + NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1, T.int64(0), T.int64(0), v0] + for ax1_fused_2 in range(T.int64(1)): + for ax1_fused_0_ax1_fused_1_fused in T.thread_binding(T.int64(16), thread="threadIdx.y"): + for ax0 in T.thread_binding(T.int64(32), thread="threadIdx.x"): + with T.block("NT_matmul"): + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 = T.axis.reduce(T.int64(32), ax0) + v0 = T.axis.spatial(T.int64(1280), u_fused_ax0_fused_fused_0 * T.int64(16) + ax1_fused_0_ax1_fused_1_fused + ax1_fused_2) + T.reads(NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0]) + T.writes(NT_matmul_intermediate_local[T.int64(0), T.int64(0), v0]) + with T.init(): + NT_matmul_intermediate_local[T.int64(0), T.int64(0), v0] = T.float16(0) + NT_matmul_intermediate_local[T.int64(0), T.int64(0), v0] = NT_matmul_intermediate_local[T.int64(0), T.int64(0), v0] + NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0] + for ax0_fused_0_ax0_fused_1_fused in T.thread_binding(T.int64(16), thread="threadIdx.y"): + for ax0_fused_2 in range(T.int64(1)): + with T.block("T_add"): + v0 = T.axis.spatial(T.int64(1280), u_fused_ax0_fused_fused_0 * T.int64(16) + ax0_fused_0_ax0_fused_1_fused + ax0_fused_2) + T.reads(NT_matmul_intermediate_local[T.int64(0), T.int64(0), v0], model_decoder_layers_0_self_attn_q_proj_bias5[v0]) + T.writes(T_add_intermediate[T.int64(0), T.int64(0), v0]) + T_add_intermediate[T.int64(0), T.int64(0), v0] = NT_matmul_intermediate_local[T.int64(0), T.int64(0), v0] + model_decoder_layers_0_self_attn_q_proj_bias5[v0] + + @T.prim_func(private=True) + def fused_NT_matmul_add7_add6(reshape1361: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16"), model_decoder_layers_0_self_attn_out_proj_weight5: T.Buffer((T.int64(1280), T.int64(1280)), "float16"), model_decoder_layers_0_self_attn_out_proj_bias5: T.Buffer((T.int64(1280),), "float16"), add1220: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16"), T_add_intermediate_1: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16")): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + # with T.block("root"): + NT_matmul_intermediate_local = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16", scope="local") + NT_matmul_intermediate_rf_local = T.alloc_buffer((T.int64(128), T.int64(1), T.int64(1), T.int64(1280)), "float16", scope="local") + NT_matmul_intermediate_rf_local_1 = T.alloc_buffer((T.int64(32), T.int64(1), T.int64(1), T.int64(1280)), "float16", scope="local") + model_decoder_layers_0_self_attn_out_proj_weight5_local = T.alloc_buffer((T.int64(1280), T.int64(1280)), "float16", scope="local") + reshape1361_shared = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16", scope="shared") + for u_fused_ax0_fused_fused_0 in T.thread_binding(T.int64(80), thread="blockIdx.x"): + for u_fused_ax0_fused_fused_1 in T.thread_binding(T.int64(16), thread="threadIdx.y"): + for ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 in T.thread_binding(T.int64(32), thread="threadIdx.x"): + for ax0, ax1 in T.grid(T.int64(1), T.int64(1)): + for ax2_0 in T.serial(T.int64(3), annotations={"pragma_unroll_explicit": 256, "pragma_vectorize": 1}): + for ax2_1 in T.thread_binding(T.int64(16), thread="threadIdx.y"): + for ax2_2 in T.thread_binding(T.int64(32), thread="threadIdx.x"): + for ax2_3 in T.vectorized(T.int64(1)): + with T.block("reshape1361_shared"): + v0, v1 = T.axis.remap("SS", [ax0, ax1]) + v2 = T.axis.spatial(T.int64(1280), ax2_0 * T.int64(512) + ax2_1 * T.int64(32) + ax2_2 + ax2_3) + T.where((ax2_0 * T.int64(16) + ax2_1) * T.int64(32) + ax2_2 + ax2_3 < T.int64(1280)) + T.reads(reshape1361[v0, v1, v2]) + T.writes(reshape1361_shared[v0, v1, v2]) + reshape1361_shared[v0, v1, v2] = reshape1361[v0, v1, v2] + for u_fused_ax0_fused_fused_2_init in range(T.int64(1)): + for ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1_init in T.vectorized(T.int64(4)): + with T.block("NT_matmul_rf_init"): + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused = T.axis.spatial(T.int64(128), ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 * T.int64(4) + ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1_init) + v0 = T.axis.spatial(T.int64(1280), u_fused_ax0_fused_fused_0 * T.int64(16) + u_fused_ax0_fused_fused_1 + u_fused_ax0_fused_fused_2_init) + T.reads() + T.writes(NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0]) + NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0] = T.float16(0) + for ax1_fused_u_fused_0 in T.serial(T.int64(5), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): + for ax0_ax1_fused_0 in range(T.int64(4)): + for ax0_ax1_fused_1 in T.vectorized(T.int64(2)): + with T.block("model_decoder_layers_0_self_attn_out_proj_weight5_local"): + v0 = T.axis.spatial(T.int64(1280), u_fused_ax0_fused_fused_0 * T.int64(16) + u_fused_ax0_fused_fused_1) + v1 = T.axis.spatial(T.int64(1280), ax1_fused_u_fused_0 * T.int64(256) + ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 * T.int64(8) + ax0_ax1_fused_0 * T.int64(2) + ax0_ax1_fused_1) + T.reads(model_decoder_layers_0_self_attn_out_proj_weight5[v0, v1]) + T.writes(model_decoder_layers_0_self_attn_out_proj_weight5_local[v0, v1]) + model_decoder_layers_0_self_attn_out_proj_weight5_local[v0, v1] = model_decoder_layers_0_self_attn_out_proj_weight5[v0, v1] + for u_fused_ax0_fused_fused_2, ax1_fused_u_fused_2 in T.grid(T.int64(1), T.int64(2)): + for ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1 in T.vectorized(T.int64(4)): + with T.block("NT_matmul_rf_update"): + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused = T.axis.spatial(T.int64(128), ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 * T.int64(4) + ax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1) + v0 = T.axis.spatial(T.int64(1280), u_fused_ax0_fused_fused_0 * T.int64(16) + u_fused_ax0_fused_fused_1 + u_fused_ax0_fused_fused_2) + vax1_fused_u_fused_0, vax1_fused_u_fused_2 = T.axis.remap("RR", [ax1_fused_u_fused_0, ax1_fused_u_fused_2]) + T.reads(NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0], reshape1361_shared[T.int64(0), T.int64(0), vax1_fused_u_fused_0 * T.int64(256) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused // T.int64(4) * T.int64(8) + vax1_fused_u_fused_2 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused % T.int64(4)], model_decoder_layers_0_self_attn_out_proj_weight5_local[v0, vax1_fused_u_fused_0 * T.int64(256) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused // T.int64(4) * T.int64(8) + vax1_fused_u_fused_2 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused % T.int64(4)]) + T.writes(NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0]) + NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0] = NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused, T.int64(0), T.int64(0), v0] + reshape1361_shared[T.int64(0), T.int64(0), vax1_fused_u_fused_0 * T.int64(256) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused // T.int64(4) * T.int64(8) + vax1_fused_u_fused_2 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused % T.int64(4)] * model_decoder_layers_0_self_attn_out_proj_weight5_local[v0, vax1_fused_u_fused_0 * T.int64(256) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused // T.int64(4) * T.int64(8) + vax1_fused_u_fused_2 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused % T.int64(4)] + for ax2_fused_0_ax2_fused_1_fused in T.thread_binding(T.int64(16), thread="threadIdx.y"): + for ax0 in T.thread_binding(T.int64(32), thread="threadIdx.x"): + for ax2_fused_2_0 in T.serial(T.int64(1), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): + for ax2_fused_2_1 in T.vectorized(T.int64(1)): + with T.block("NT_matmul_rf_init"): + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 = T.axis.spatial(T.int64(32), ax0) + v0 = T.axis.spatial(T.int64(1280), u_fused_ax0_fused_fused_0 * T.int64(16) + ax2_fused_0_ax2_fused_1_fused + ax2_fused_2_0 + ax2_fused_2_1) + T.reads() + T.writes(NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0]) + NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0] = T.float16(0) + for ax1 in range(T.int64(4)): + with T.block("NT_matmul_rf_update"): + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1 = T.axis.remap("SR", [ax0, ax1]) + v0 = T.axis.spatial(T.int64(1280), u_fused_ax0_fused_fused_0 * T.int64(16) + ax2_fused_0_ax2_fused_1_fused + ax2_fused_2_0 + ax2_fused_2_1) + T.reads(NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0], NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1, T.int64(0), T.int64(0), v0]) + T.writes(NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0]) + NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0] = NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0] + NT_matmul_intermediate_rf_local[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 * T.int64(4) + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_1, T.int64(0), T.int64(0), v0] + for ax1_fused_2 in range(T.int64(1)): + for ax1_fused_0_ax1_fused_1_fused in T.thread_binding(T.int64(16), thread="threadIdx.y"): + for ax0 in T.thread_binding(T.int64(32), thread="threadIdx.x"): + with T.block("NT_matmul"): + vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0 = T.axis.reduce(T.int64(32), ax0) + v0 = T.axis.spatial(T.int64(1280), u_fused_ax0_fused_fused_0 * T.int64(16) + ax1_fused_0_ax1_fused_1_fused + ax1_fused_2) + T.reads(NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0]) + T.writes(NT_matmul_intermediate_local[T.int64(0), T.int64(0), v0]) + with T.init(): + NT_matmul_intermediate_local[T.int64(0), T.int64(0), v0] = T.float16(0) + NT_matmul_intermediate_local[T.int64(0), T.int64(0), v0] = NT_matmul_intermediate_local[T.int64(0), T.int64(0), v0] + NT_matmul_intermediate_rf_local_1[vax1_fused_u_fused_1_ax1_fused_u_fused_3_fused_0, T.int64(0), T.int64(0), v0] + for ax0_fused_0_ax0_fused_1_fused in T.thread_binding(T.int64(16), thread="threadIdx.y"): + for ax0_fused_2 in range(T.int64(1)): + with T.block("T_add_1"): + v0 = T.axis.spatial(T.int64(1280), u_fused_ax0_fused_fused_0 * T.int64(16) + ax0_fused_0_ax0_fused_1_fused + ax0_fused_2) + T.reads(add1220[T.int64(0), T.int64(0), v0], NT_matmul_intermediate_local[T.int64(0), T.int64(0), v0], model_decoder_layers_0_self_attn_out_proj_bias5[v0]) + T.writes(T_add_intermediate_1[T.int64(0), T.int64(0), v0]) + T_add_intermediate_1[T.int64(0), T.int64(0), v0] = add1220[T.int64(0), T.int64(0), v0] + (NT_matmul_intermediate_local[T.int64(0), T.int64(0), v0] + model_decoder_layers_0_self_attn_out_proj_bias5[v0]) + + @T.prim_func(private=True) + def fused_add4_maximum_minimum(p_add4: T.handle, p_lv611: T.handle, p_output0: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + batch_size = T.int64() + add4 = T.match_buffer(p_add4, (batch_size, T.int64(1500), T.int64(1280)), "float16") + lv611 = T.match_buffer(p_lv611, (batch_size, T.int64(1500), T.int64(1280)), "float16") + T_minimum_intermediate = T.match_buffer(p_output0, (batch_size, T.int64(1500), T.int64(1280)), "float16") + # with T.block("root"): + for ax0_ax1_ax2_fused_0 in T.thread_binding(batch_size * T.int64(1875), thread="blockIdx.x"): + for ax0_ax1_ax2_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_minimum"): + v0 = T.axis.spatial(batch_size, (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) // T.int64(1920000)) + v1 = T.axis.spatial(T.int64(1500), (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) % T.int64(1920000) // T.int64(1280)) + v2 = T.axis.spatial(T.int64(1280), (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) % T.int64(1280)) + T.reads(add4[v0, v1, v2], lv611[v0, v1, v2]) + T.writes(T_minimum_intermediate[v0, v1, v2]) + T_minimum_intermediate[v0, v1, v2] = T.min(T.max(add4[v0, v1, v2] + lv611[v0, v1, v2], T.float16(-65504)), T.float16(65504)) + + @T.prim_func(private=True) + def fused_conv1d1_add2_gelu1(p_gelu: T.handle, model_encoder_conv2_weight: T.Buffer((T.int64(1280), T.int64(1280), T.int64(3)), "float16"), lv3: T.Buffer((T.int64(1), T.int64(1280), T.int64(1)), "float16"), p_output0: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + batch_size = T.int64() + gelu = T.match_buffer(p_gelu, (batch_size, T.int64(1280), T.int64(3000)), "float16") + T_multiply_intermediate = T.match_buffer(p_output0, (batch_size, T.int64(1280), T.int64(1500)), "float16") + # with T.block("root"): + conv1d_ncw_intermediate_shared = T.alloc_buffer((batch_size, T.int64(1280), T.int64(1500)), "float16", scope="shared") + for ax0_ax1_ax2_fused in T.thread_binding(batch_size * T.int64(1920000), thread="blockIdx.x"): + for ax0, ax1, ax2 in T.grid(T.int64(1), T.int64(1), T.int64(1)): + for ax3_ax4_fused_1 in T.thread_binding(T.int64(256), thread="threadIdx.x"): + for ax3_ax4_fused_0 in T.serial(T.int64(15), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): + with T.block("conv1d_ncw"): + v0 = T.axis.spatial(batch_size, ax0_ax1_ax2_fused // T.int64(1920000) + ax0) + v1 = T.axis.spatial(T.int64(1280), ax0_ax1_ax2_fused % T.int64(1920000) // T.int64(1500) + ax1) + v2 = T.axis.spatial(T.int64(1500), ax0_ax1_ax2_fused % T.int64(1500) + ax2) + v3 = T.axis.reduce(T.int64(1280), (ax3_ax4_fused_0 * T.int64(256) + ax3_ax4_fused_1) // T.int64(3)) + v4 = T.axis.reduce(T.int64(3), (ax3_ax4_fused_0 * T.int64(256) + ax3_ax4_fused_1) % T.int64(3)) + T.reads(gelu[v0, v3, v2 * T.int64(2) + v4 - T.int64(1)], model_encoder_conv2_weight[v1, v3, v4]) + T.writes(conv1d_ncw_intermediate_shared[v0, v1, v2]) + with T.init(): + conv1d_ncw_intermediate_shared[v0, v1, v2] = T.float16(0) + conv1d_ncw_intermediate_shared[v0, v1, v2] = conv1d_ncw_intermediate_shared[v0, v1, v2] + T.if_then_else(T.int64(1) <= v2 * T.int64(2) + v4 and v2 * T.int64(2) + v4 < T.int64(3001), gelu[v0, v3, v2 * T.int64(2) + v4 - T.int64(1)], T.float16(0)) * model_encoder_conv2_weight[v1, v3, v4] + for ax3 in range(T.int64(1)): + for ax4_1 in T.thread_binding(T.int64(256), thread="threadIdx.x"): + for ax4_0 in T.serial(T.int64(1), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): + with T.block("T_multiply_2"): + v0 = T.axis.spatial(batch_size, ax0_ax1_ax2_fused // T.int64(1920000)) + v1 = T.axis.spatial(T.int64(1280), ax0_ax1_ax2_fused % T.int64(1920000) // T.int64(1500)) + v2 = T.axis.spatial(T.int64(1500), ax0_ax1_ax2_fused % T.int64(1500)) + v3 = T.axis.spatial(T.int64(1), ax3) + v4 = T.axis.spatial(T.int64(1), ax4_0 * T.int64(256) + ax4_1) + T.where(ax4_0 * T.int64(256) + ax4_1 < T.int64(1)) + T.reads(conv1d_ncw_intermediate_shared[v0, v1, v2], lv3[T.int64(0), v1, T.int64(0)]) + T.writes(T_multiply_intermediate[v0, v1, v2]) + T_multiply_intermediate[v0, v1, v2] = (conv1d_ncw_intermediate_shared[v0, v1, v2] + lv3[T.int64(0), v1, T.int64(0)]) * (T.float16(0.5) + T.Cast("float16", T.erf(T.Cast("float32", (conv1d_ncw_intermediate_shared[v0, v1, v2] + lv3[T.int64(0), v1, T.int64(0)]) * T.float16(0.70710678118654757)))) * T.float16(0.5)) + + @T.prim_func(private=True) + def fused_conv1d_add1_gelu(p_input_features: T.handle, model_encoder_conv1_weight: T.Buffer((T.int64(1280), T.int64(128), T.int64(3)), "float16"), lv1: T.Buffer((T.int64(1), T.int64(1280), T.int64(1)), "float16"), p_output0: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + batch_size = T.int64() + input_features = T.match_buffer(p_input_features, (batch_size, T.int64(128), T.int64(3000)), "float16") + T_multiply_intermediate = T.match_buffer(p_output0, (batch_size, T.int64(1280), T.int64(3000)), "float16") + # with T.block("root"): + conv1d_ncw_intermediate_shared = T.alloc_buffer((batch_size, T.int64(1280), T.int64(3000)), "float16", scope="shared") + for ax0_ax1_ax2_fused in T.thread_binding(batch_size * T.int64(3840000), thread="blockIdx.x"): + for ax0, ax1, ax2 in T.grid(T.int64(1), T.int64(1), T.int64(1)): + for ax3_ax4_fused_1 in T.thread_binding(T.int64(256), thread="threadIdx.x"): + for ax3_ax4_fused_0 in T.serial(T.int64(2), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): + with T.block("conv1d_ncw"): + v0 = T.axis.spatial(batch_size, ax0_ax1_ax2_fused // T.int64(3840000) + ax0) + v1 = T.axis.spatial(T.int64(1280), ax0_ax1_ax2_fused % T.int64(3840000) // T.int64(3000) + ax1) + v2 = T.axis.spatial(T.int64(3000), ax0_ax1_ax2_fused % T.int64(3000) + ax2) + v3 = T.axis.reduce(T.int64(128), (ax3_ax4_fused_0 * T.int64(256) + ax3_ax4_fused_1) // T.int64(3)) + v4 = T.axis.reduce(T.int64(3), (ax3_ax4_fused_0 * T.int64(256) + ax3_ax4_fused_1) % T.int64(3)) + T.where(ax3_ax4_fused_0 * T.int64(256) + ax3_ax4_fused_1 < T.int64(384)) + T.reads(input_features[v0, v3, v2 + v4 - T.int64(1)], model_encoder_conv1_weight[v1, v3, v4]) + T.writes(conv1d_ncw_intermediate_shared[v0, v1, v2]) + with T.init(): + conv1d_ncw_intermediate_shared[v0, v1, v2] = T.float16(0) + conv1d_ncw_intermediate_shared[v0, v1, v2] = conv1d_ncw_intermediate_shared[v0, v1, v2] + T.if_then_else(T.int64(1) <= v2 + v4 and v2 + v4 < T.int64(3001), input_features[v0, v3, v2 + v4 - T.int64(1)], T.float16(0)) * model_encoder_conv1_weight[v1, v3, v4] + for ax3 in range(T.int64(1)): + for ax4_1 in T.thread_binding(T.int64(256), thread="threadIdx.x"): + for ax4_0 in T.serial(T.int64(1), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): + with T.block("T_multiply_2"): + v0 = T.axis.spatial(batch_size, ax0_ax1_ax2_fused // T.int64(3840000)) + v1 = T.axis.spatial(T.int64(1280), ax0_ax1_ax2_fused % T.int64(3840000) // T.int64(3000)) + v2 = T.axis.spatial(T.int64(3000), ax0_ax1_ax2_fused % T.int64(3000)) + v3 = T.axis.spatial(T.int64(1), ax3) + v4 = T.axis.spatial(T.int64(1), ax4_0 * T.int64(256) + ax4_1) + T.where(ax4_0 * T.int64(256) + ax4_1 < T.int64(1)) + T.reads(conv1d_ncw_intermediate_shared[v0, v1, v2], lv1[T.int64(0), v1, T.int64(0)]) + T.writes(T_multiply_intermediate[v0, v1, v2]) + T_multiply_intermediate[v0, v1, v2] = (conv1d_ncw_intermediate_shared[v0, v1, v2] + lv1[T.int64(0), v1, T.int64(0)]) * (T.float16(0.5) + T.Cast("float16", T.erf(T.Cast("float32", (conv1d_ncw_intermediate_shared[v0, v1, v2] + lv1[T.int64(0), v1, T.int64(0)]) * T.float16(0.70710678118654757)))) * T.float16(0.5)) + + @T.prim_func(private=True) + def fused_reshape20_reshape20_add6(take7: T.Buffer((T.int64(1), T.int64(1280)), "float16"), take8: T.Buffer((T.int64(1), T.int64(1280)), "float16"), T_add_intermediate: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16")): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + # with T.block("root"): + for ax0_fused_0 in T.thread_binding(T.int64(2), thread="blockIdx.x"): + for ax0_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_add"): + v0 = T.axis.spatial(T.int64(1280), ax0_fused_0 * T.int64(1024) + ax0_fused_1) + T.where(ax0_fused_0 * T.int64(1024) + ax0_fused_1 < T.int64(1280)) + T.reads(take7[T.int64(0), v0], take8[T.int64(0), v0]) + T.writes(T_add_intermediate[T.int64(0), T.int64(0), v0]) + T_add_intermediate[T.int64(0), T.int64(0), v0] = take7[T.int64(0), v0] + take8[T.int64(0), v0] + + @T.prim_func(private=True) + def fused_reshape21_reshape21_reshape21_concatenate2_reshape22(add1221: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16"), lv1: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16"), add1222: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16"), T_reshape_intermediate_1_2_3: T.Buffer((T.int64(1), T.int64(60), T.int64(64)), "float16")): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + # with T.block("root"): + for ax0_ax1_fused_0 in T.thread_binding(T.int64(4), thread="blockIdx.x"): + for ax0_ax1_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_reshape_3"): + v0 = T.axis.spatial(T.int64(60), (ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1) // T.int64(64)) + v1 = T.axis.spatial(T.int64(64), (ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1) % T.int64(64)) + T.where(ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1 < T.int64(3840)) + T.reads(add1222[T.int64(0), T.int64(0), (v0 - T.int64(40)) * T.int64(64) + v1], lv1[T.int64(0), T.int64(0), (v0 + T.int64(-20)) * T.int64(64) + v1], add1221[T.int64(0), T.int64(0), v0 * T.int64(64) + v1]) + T.writes(T_reshape_intermediate_1_2_3[T.int64(0), v0, v1]) + T_reshape_intermediate_1_2_3[T.int64(0), v0, v1] = T.if_then_else(T.int64(40) <= v0, add1222[T.int64(0), T.int64(0), (v0 - T.int64(40)) * T.int64(64) + v1], T.if_then_else(T.int64(20) <= v0, lv1[T.int64(0), T.int64(0), (v0 + T.int64(-20)) * T.int64(64) + v1], add1221[T.int64(0), T.int64(0), v0 * T.int64(64) + v1])) + + @T.prim_func(private=True) + def fused_reshape21_reshape25(add1225: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16"), T_reshape_intermediate_1: T.Buffer((T.int64(1), T.int64(20), T.int64(64)), "float16")): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + # with T.block("root"): + for ax0_ax1_fused_0 in T.thread_binding(T.int64(2), thread="blockIdx.x"): + for ax0_ax1_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_reshape_1"): + v0 = T.axis.spatial(T.int64(20), (ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1) // T.int64(64)) + v1 = T.axis.spatial(T.int64(64), (ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1) % T.int64(64)) + T.where(ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1 < T.int64(1280)) + T.reads(add1225[T.int64(0), T.int64(0), v0 * T.int64(64) + v1]) + T.writes(T_reshape_intermediate_1[T.int64(0), v0, v1]) + T_reshape_intermediate_1[T.int64(0), v0, v1] = add1225[T.int64(0), T.int64(0), v0 * T.int64(64) + v1] + + @T.prim_func(private=True) + def fused_reshape23_reshape24(lv265: T.Buffer((T.int64(1), T.int64(20), T.int64(64)), "float16"), T_reshape_intermediate_1: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16")): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + # with T.block("root"): + for ax0_fused_0 in T.thread_binding(T.int64(2), thread="blockIdx.x"): + for ax0_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_reshape_1"): + v0 = T.axis.spatial(T.int64(1280), ax0_fused_0 * T.int64(1024) + ax0_fused_1) + T.where(ax0_fused_0 * T.int64(1024) + ax0_fused_1 < T.int64(1280)) + T.reads(lv265[T.int64(0), v0 // T.int64(64), v0 % T.int64(64)]) + T.writes(T_reshape_intermediate_1[T.int64(0), T.int64(0), v0]) + T_reshape_intermediate_1[T.int64(0), T.int64(0), v0] = lv265[T.int64(0), v0 // T.int64(64), v0 % T.int64(64)] + + @T.prim_func(private=True) + def fused_reshape9(packed_params_1: T.Buffer((T.int64(1280),), "float16"), T_reshape_intermediate: T.Buffer((T.int64(1), T.int64(1280), T.int64(1)), "float16")): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + # with T.block("root"): + for ax0_fused_0 in T.thread_binding(T.int64(2), thread="blockIdx.x"): + for ax0_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_reshape"): + v0 = T.axis.spatial(T.int64(1280), ax0_fused_0 * T.int64(1024) + ax0_fused_1) + T.where(ax0_fused_0 * T.int64(1024) + ax0_fused_1 < T.int64(1280)) + T.reads(packed_params_1[v0]) + T.writes(T_reshape_intermediate[T.int64(0), v0, T.int64(0)]) + T_reshape_intermediate[T.int64(0), v0, T.int64(0)] = packed_params_1[v0] + + @T.prim_func + def fused_rope(var_qkv: T.handle, var_position_map: T.handle, var_q: T.handle, var_k: T.handle, var_v: T.handle, apply_rope: T.int32): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + seq_len = T.int64() + qkv = T.match_buffer(var_qkv, (seq_len, 60, 64), "float16") + position_map = T.match_buffer(var_position_map, (seq_len,), "int32", offset_factor=1) + q = T.match_buffer(var_q, (seq_len, 20, 64), "float16") + k = T.match_buffer(var_k, (seq_len, 20, 64), "float16") + v = T.match_buffer(var_v, (seq_len, 20, 64), "float16") + # with T.block("root"): + for iters_0_iters_1_iters_2_fused_0 in T.thread_binding((seq_len * T.int64(3840) + T.int64(1023)) // T.int64(1024), thread="blockIdx.x"): + for iters_0_iters_1_iters_2_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("llama_fused_rope"): + s = T.axis.spatial(seq_len, (iters_0_iters_1_iters_2_fused_0 * T.int64(1024) + iters_0_iters_1_iters_2_fused_1) // T.int64(3840)) + h = T.axis.spatial(60, T.Cast("int32", (iters_0_iters_1_iters_2_fused_0 * T.int64(1024) + iters_0_iters_1_iters_2_fused_1) % T.int64(3840) // T.int64(64))) + d = T.axis.spatial(64, T.Cast("int32", (iters_0_iters_1_iters_2_fused_0 * T.int64(1024) + iters_0_iters_1_iters_2_fused_1) % T.int64(64))) + T.where(iters_0_iters_1_iters_2_fused_0 * T.int64(1024) + iters_0_iters_1_iters_2_fused_1 < seq_len * T.int64(3840)) + T.reads(position_map[s], qkv[s, h, d - 32:d - 32 + 65]) + T.writes(q[s, h, d], k[s, h - 20, d], v[s, h - 40, d]) + if h < 20: + q[s, h, d] = T.if_then_else(apply_rope > 0 and d < 64, T.Cast("float16", T.cos(T.Cast("float32", position_map[s]) / T.pow(T.float32(1), T.Cast("float32", d * 2 % 64) / T.float32(64))) * T.Cast("float32", qkv[s, h, d]) + T.sin(T.Cast("float32", position_map[s]) / T.pow(T.float32(1), T.Cast("float32", d * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(d < 32, qkv[s, h, d + 32] * T.float16(-1), qkv[s, h, d - 32]))), qkv[s, h, d]) + else: + if h < 40: + k[s, h - 20, d] = T.if_then_else(apply_rope > 0 and d < 64, T.Cast("float16", T.cos(T.Cast("float32", position_map[s]) / T.pow(T.float32(1), T.Cast("float32", d * 2 % 64) / T.float32(64))) * T.Cast("float32", qkv[s, h, d]) + T.sin(T.Cast("float32", position_map[s]) / T.pow(T.float32(1), T.Cast("float32", d * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(d < 32, qkv[s, h, d + 32] * T.float16(-1), qkv[s, h, d - 32]))), qkv[s, h, d]) + else: + v[s, h - 40, d] = qkv[s, h, d] + + @T.prim_func(private=True) + def fused_transpose_add3(packed_params_4: T.Buffer((T.int64(1500), T.int64(1280)), "float16"), p_gelu1: T.handle, p_output0: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + batch_size = T.int64() + gelu1 = T.match_buffer(p_gelu1, (batch_size, T.int64(1280), T.int64(1500)), "float16") + T_add_intermediate = T.match_buffer(p_output0, (batch_size, T.int64(1500), T.int64(1280)), "float16") + # with T.block("root"): + for ax0_ax1_ax2_fused_0 in T.thread_binding(batch_size * T.int64(1875), thread="blockIdx.x"): + for ax0_ax1_ax2_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_add"): + v0 = T.axis.spatial(batch_size, (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) // T.int64(1920000)) + v1 = T.axis.spatial(T.int64(1500), (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) % T.int64(1920000) // T.int64(1280)) + v2 = T.axis.spatial(T.int64(1280), (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) % T.int64(1280)) + T.reads(gelu1[v0, v2, v1], packed_params_4[v1, v2]) + T.writes(T_add_intermediate[v0, v1, v2]) + T_add_intermediate[v0, v1, v2] = gelu1[v0, v2, v1] + packed_params_4[v1, v2] + + @T.prim_func + def gather_probs(var_src: T.handle, var_indices: T.handle, var_dst: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + m, n = T.int32(is_size_var=True), T.int32(is_size_var=True) + src = T.match_buffer(var_src, (m, n)) + batch_size = T.int32(is_size_var=True) + indices = T.match_buffer(var_indices, (batch_size,), "int32") + dst = T.match_buffer(var_dst, (batch_size, n)) + # with T.block("root"): + for ax0_ax1_fused_0 in T.thread_binding((batch_size * n + 1023) // 1024, thread="blockIdx.x"): + for ax0_ax1_fused_1 in T.thread_binding(1024, thread="threadIdx.x"): + with T.block("gather_2d"): + v0 = T.axis.spatial(batch_size, (ax0_ax1_fused_0 * 1024 + ax0_ax1_fused_1) % (n * batch_size) // n) + v1 = T.axis.spatial(n, (ax0_ax1_fused_0 * 1024 + ax0_ax1_fused_1) % n) + T.where(ax0_ax1_fused_0 * 1024 + ax0_ax1_fused_1 < batch_size * n) + T.reads(src[indices[v0], v1], indices[v0]) + T.writes(dst[v0, v1]) + dst[v0, v1] = src[indices[v0], v1] + + @T.prim_func(private=True) + def get_index_from_sorted(A: T.handle, B: T.handle, C: T.handle, D: T.handle, E: T.handle, F: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) + batch, vocab_size = T.int64(), T.int64() + cumsum_sorted = T.match_buffer(A, (batch, vocab_size)) + indices = T.match_buffer(B, (batch, vocab_size), "int32") + renorm_prob = T.match_buffer(C, (batch, 1)) + out_batch = T.int64() + usample = T.match_buffer(D, (out_batch, 1)) + sample_indices = T.match_buffer(E, (out_batch, 1), "int32") + output_index = T.match_buffer(F, (out_batch, 1), "int32") + # with T.block("root"): + for ax0_ax1_fused_0 in T.thread_binding((out_batch * vocab_size + T.int64(1023)) // T.int64(1024), thread="blockIdx.x"): + for ax0_ax1_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_get_index_from_sorted"): + v0 = T.axis.spatial(out_batch, (ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1) % (vocab_size * out_batch) // vocab_size) + v1 = T.axis.spatial(vocab_size, (ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1) % vocab_size) + T.where(ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1 < out_batch * vocab_size) + T.reads(usample[v0, T.int64(0)], cumsum_sorted[sample_indices[v0, T.int64(0)], v1 - T.int64(1):v1 - T.int64(1) + T.int64(2)], sample_indices[v0, T.int64(0)], renorm_prob[sample_indices[v0, T.int64(0)], 0], indices[sample_indices[v0, T.int64(0)], T.min(T.int64(0), v1):T.min(T.int64(0), v1) + (v1 + T.int64(1))]) + T.writes(output_index[v0, 0]) + if usample[v0, T.int64(0)] < cumsum_sorted[sample_indices[v0, T.int64(0)], v1] / renorm_prob[sample_indices[v0, T.int64(0)], 0] or v1 + T.int64(1) == vocab_size: + if v1 == T.int64(0): + output_index[v0, 0] = indices[sample_indices[v0, T.int64(0)], 0] + else: + if usample[v0, T.int64(0)] >= cumsum_sorted[sample_indices[v0, T.int64(0)], v1 - T.int64(1)] / renorm_prob[sample_indices[v0, T.int64(0)], 0]: + output_index[v0, 0] = indices[sample_indices[v0, T.int64(0)], v1] + + @T.prim_func(private=True) + def get_renorm_prob(A: T.handle, B: T.handle, C: T.handle, D: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) + batch, vocab_size = T.int64(), T.int64() + cumsum_sorted = T.match_buffer(A, (batch, vocab_size)) + top_p = T.match_buffer(B, (batch, 1)) + top_k = T.match_buffer(C, (batch, 1), "int32") + renorm_prob = T.match_buffer(D, (batch, 1)) + # with T.block("root"): + for ax0_ax1_fused_0 in T.thread_binding((batch * vocab_size + T.int64(1023)) // T.int64(1024), thread="blockIdx.x"): + for ax0_ax1_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_get_renorm_prob"): + v0 = T.axis.spatial(batch, (ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1) % (vocab_size * batch) // vocab_size) + v1 = T.axis.spatial(vocab_size, (ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1) % vocab_size) + T.where(ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1 < batch * vocab_size) + T.reads(cumsum_sorted[v0, T.min(T.min(T.int64(0), v1), v1 + T.int64(1)):T.min(T.min(T.int64(0), v1), v1 + T.int64(1)) + (v1 + T.int64(2))], top_p[v0, 0], top_k[v0, 0]) + T.writes(renorm_prob[v0, 0]) + if not (cumsum_sorted[v0, 0] < top_p[v0, 0] and top_k[v0, 0] > 1): + renorm_prob[v0, 0] = cumsum_sorted[v0, 0] + else: + if cumsum_sorted[v0, v1] < top_p[v0, 0] and v1 + T.int64(1) < T.Cast("int64", top_k[v0, 0]): + if v1 + T.int64(1) == vocab_size: + renorm_prob[v0, 0] = cumsum_sorted[v0, v1] + else: + if not (cumsum_sorted[v0, v1 + T.int64(1)] < top_p[v0, 0] and v1 + T.int64(1) + T.int64(1) < T.Cast("int64", top_k[v0, 0])): + renorm_prob[v0, 0] = cumsum_sorted[v0, v1 + T.int64(1)] + + @T.prim_func(private=True) + def index(var_layer_norm355: T.handle, index: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16")): + T.func_attr({"target": T.target({"arch": "sm_89", "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + seq_len = T.int64() + layer_norm355 = T.match_buffer(var_layer_norm355, (T.int64(1), seq_len, T.int64(1280)), "float16") + # with T.block("root"): + for ax0_fused_0 in T.thread_binding(T.int64(2), thread="blockIdx.x"): + for ax0_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("index"): + v0 = T.axis.spatial(T.int64(1280), ax0_fused_0 * T.int64(1024) + ax0_fused_1) + T.where(ax0_fused_0 * T.int64(1024) + ax0_fused_1 < T.int64(1280)) + T.reads(layer_norm355[T.int64(0), seq_len - T.int64(1), v0]) + T.writes(index[T.int64(0), T.int64(0), v0]) + index[T.int64(0), T.int64(0), v0] = layer_norm355[T.int64(0), seq_len - T.int64(1), v0] + + @T.prim_func(private=True) + def layer_norm(var_add578: T.handle, model_decoder_layers_0_self_attn_layer_norm_weight3: T.Buffer((T.int64(1280),), "float16"), model_decoder_layers_0_self_attn_layer_norm_bias3: T.Buffer((T.int64(1280),), "float16"), var_T_layer_norm: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + batch_size = T.int64() + add578 = T.match_buffer(var_add578, (batch_size, T.int64(1), T.int64(1280)), "float16") + T_layer_norm = T.match_buffer(var_T_layer_norm, (batch_size, T.int64(1), T.int64(1280)), "float16") + # with T.block("root"): + add578_red_temp_v0_shared = T.alloc_buffer((batch_size, T.int64(1)), scope="shared") + add578_red_temp_v1_shared = T.alloc_buffer((batch_size, T.int64(1)), scope="shared") + for ax0_fused in T.thread_binding(batch_size, thread="blockIdx.x"): + for ax0 in range(T.int64(1)): + for ax1_fused_1 in T.thread_binding(T.int64(256), thread="threadIdx.x"): + for ax1_fused_0 in T.serial(T.int64(5), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): + with T.block("add578_red_temp"): + v0 = T.axis.spatial(batch_size, ax0_fused + ax0) + v1 = T.axis.reduce(T.int64(1280), ax1_fused_0 * T.int64(256) + ax1_fused_1) + T.reads(add578[v0, T.int64(0), v1]) + T.writes(add578_red_temp_v0_shared[v0, T.int64(0)], add578_red_temp_v1_shared[v0, T.int64(0)]) + with T.init(): + add578_red_temp_v0_shared[v0, T.int64(0)] = T.float32(0) + add578_red_temp_v1_shared[v0, T.int64(0)] = T.float32(0) + v_add578_red_temp_v0: T.float32 = add578_red_temp_v0_shared[v0, T.int64(0)] + T.Cast("float32", add578[v0, T.int64(0), v1]) + v_add578_red_temp_v1: T.float32 = add578_red_temp_v1_shared[v0, T.int64(0)] + T.Cast("float32", add578[v0, T.int64(0), v1]) * T.Cast("float32", add578[v0, T.int64(0), v1]) + add578_red_temp_v0_shared[v0, T.int64(0)] = v_add578_red_temp_v0 + add578_red_temp_v1_shared[v0, T.int64(0)] = v_add578_red_temp_v1 + for ax1_1 in T.thread_binding(T.int64(256), thread="threadIdx.x"): + for ax1_0 in T.serial(T.int64(5), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): + with T.block("T_layer_norm"): + v0 = T.axis.spatial(batch_size, ax0_fused) + v1 = T.axis.spatial(T.int64(1280), ax1_0 * T.int64(256) + ax1_1) + T.reads(add578[v0, T.int64(0), v1], add578_red_temp_v0_shared[v0, T.int64(0)], add578_red_temp_v1_shared[v0, T.int64(0)], model_decoder_layers_0_self_attn_layer_norm_weight3[v1], model_decoder_layers_0_self_attn_layer_norm_bias3[v1]) + T.writes(T_layer_norm[v0, T.int64(0), v1]) + T_layer_norm[v0, T.int64(0), v1] = T.Cast("float16", (T.Cast("float32", add578[v0, T.int64(0), v1]) - add578_red_temp_v0_shared[v0, T.int64(0)] * T.float32(0.00078125000000000004)) * T.rsqrt(add578_red_temp_v1_shared[v0, T.int64(0)] * T.float32(0.00078125000000000004) - add578_red_temp_v0_shared[v0, T.int64(0)] * T.float32(0.00078125000000000004) * (add578_red_temp_v0_shared[v0, T.int64(0)] * T.float32(0.00078125000000000004)) + T.float32(1.0000000000000001e-05))) * model_decoder_layers_0_self_attn_layer_norm_weight3[v1] + model_decoder_layers_0_self_attn_layer_norm_bias3[v1] + + @T.prim_func(private=True) + def layer_norm1(var_add: T.handle, model_encoder_layers_0_self_attn_layer_norm_weight: T.Buffer((T.int64(1280),), "float16"), model_encoder_layers_0_self_attn_layer_norm_bias: T.Buffer((T.int64(1280),), "float16"), var_T_layer_norm: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + batch_size = T.int64() + add = T.match_buffer(var_add, (batch_size, T.int64(1500), T.int64(1280)), "float16") + T_layer_norm = T.match_buffer(var_T_layer_norm, (batch_size, T.int64(1500), T.int64(1280)), "float16") + # with T.block("root"): + add_red_temp_v0_shared = T.alloc_buffer((batch_size, T.int64(1500)), scope="shared") + add_red_temp_v1_shared = T.alloc_buffer((batch_size, T.int64(1500)), scope="shared") + for ax0_ax1_fused in T.thread_binding(batch_size * T.int64(1500), thread="blockIdx.x"): + for ax0, ax1 in T.grid(T.int64(1), T.int64(1)): + for ax2_fused_1 in T.thread_binding(T.int64(256), thread="threadIdx.x"): + for ax2_fused_0 in T.serial(T.int64(5), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): + with T.block("add_red_temp"): + v0 = T.axis.spatial(batch_size, ax0_ax1_fused // T.int64(1500) + ax0) + v1 = T.axis.spatial(T.int64(1500), ax0_ax1_fused % T.int64(1500) + ax1) + v2 = T.axis.reduce(T.int64(1280), ax2_fused_0 * T.int64(256) + ax2_fused_1) + T.reads(add[v0, v1, v2]) + T.writes(add_red_temp_v0_shared[v0, v1], add_red_temp_v1_shared[v0, v1]) + with T.init(): + add_red_temp_v0_shared[v0, v1] = T.float32(0) + add_red_temp_v1_shared[v0, v1] = T.float32(0) + v_add_red_temp_v0: T.float32 = add_red_temp_v0_shared[v0, v1] + T.Cast("float32", add[v0, v1, v2]) + v_add_red_temp_v1: T.float32 = add_red_temp_v1_shared[v0, v1] + T.Cast("float32", add[v0, v1, v2]) * T.Cast("float32", add[v0, v1, v2]) + add_red_temp_v0_shared[v0, v1] = v_add_red_temp_v0 + add_red_temp_v1_shared[v0, v1] = v_add_red_temp_v1 + for ax2_1 in T.thread_binding(T.int64(256), thread="threadIdx.x"): + for ax2_0 in T.serial(T.int64(5), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): + with T.block("T_layer_norm"): + v0 = T.axis.spatial(batch_size, ax0_ax1_fused // T.int64(1500)) + v1 = T.axis.spatial(T.int64(1500), ax0_ax1_fused % T.int64(1500)) + v2 = T.axis.spatial(T.int64(1280), ax2_0 * T.int64(256) + ax2_1) + T.reads(add[v0, v1, v2], add_red_temp_v0_shared[v0, v1], add_red_temp_v1_shared[v0, v1], model_encoder_layers_0_self_attn_layer_norm_weight[v2], model_encoder_layers_0_self_attn_layer_norm_bias[v2]) + T.writes(T_layer_norm[v0, v1, v2]) + T_layer_norm[v0, v1, v2] = T.Cast("float16", (T.Cast("float32", add[v0, v1, v2]) - add_red_temp_v0_shared[v0, v1] * T.float32(0.00078125000000000004)) * T.rsqrt(add_red_temp_v1_shared[v0, v1] * T.float32(0.00078125000000000004) - add_red_temp_v0_shared[v0, v1] * T.float32(0.00078125000000000004) * (add_red_temp_v0_shared[v0, v1] * T.float32(0.00078125000000000004)) + T.float32(1.0000000000000001e-05))) * model_encoder_layers_0_self_attn_layer_norm_weight[v2] + model_encoder_layers_0_self_attn_layer_norm_bias[v2] + + @T.prim_func(private=True) + def layer_norm2(var_add257: T.handle, model_decoder_layers_0_self_attn_layer_norm_weight2: T.Buffer((T.int64(1280),), "float16"), model_decoder_layers_0_self_attn_layer_norm_bias2: T.Buffer((T.int64(1280),), "float16"), var_T_layer_norm: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + seq_len = T.int64() + add257 = T.match_buffer(var_add257, (T.int64(1), seq_len, T.int64(1280)), "float16") + T_layer_norm = T.match_buffer(var_T_layer_norm, (T.int64(1), seq_len, T.int64(1280)), "float16") + # with T.block("root"): + add257_red_temp_v0_shared = T.alloc_buffer((T.int64(1), seq_len), scope="shared") + add257_red_temp_v1_shared = T.alloc_buffer((T.int64(1), seq_len), scope="shared") + for ax0_fused in T.thread_binding(seq_len, thread="blockIdx.x"): + for ax0 in range(T.int64(1)): + for ax1_fused_1 in T.thread_binding(T.int64(256), thread="threadIdx.x"): + for ax1_fused_0 in T.serial(T.int64(5), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): + with T.block("add257_red_temp"): + v0 = T.axis.spatial(seq_len, ax0_fused + ax0) + v1 = T.axis.reduce(T.int64(1280), ax1_fused_0 * T.int64(256) + ax1_fused_1) + T.reads(add257[T.int64(0), v0, v1]) + T.writes(add257_red_temp_v0_shared[T.int64(0), v0], add257_red_temp_v1_shared[T.int64(0), v0]) + with T.init(): + add257_red_temp_v0_shared[T.int64(0), v0] = T.float32(0) + add257_red_temp_v1_shared[T.int64(0), v0] = T.float32(0) + v_add257_red_temp_v0: T.float32 = add257_red_temp_v0_shared[T.int64(0), v0] + T.Cast("float32", add257[T.int64(0), v0, v1]) + v_add257_red_temp_v1: T.float32 = add257_red_temp_v1_shared[T.int64(0), v0] + T.Cast("float32", add257[T.int64(0), v0, v1]) * T.Cast("float32", add257[T.int64(0), v0, v1]) + add257_red_temp_v0_shared[T.int64(0), v0] = v_add257_red_temp_v0 + add257_red_temp_v1_shared[T.int64(0), v0] = v_add257_red_temp_v1 + for ax1_1 in T.thread_binding(T.int64(256), thread="threadIdx.x"): + for ax1_0 in T.serial(T.int64(5), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): + with T.block("T_layer_norm"): + v0 = T.axis.spatial(seq_len, ax0_fused) + v1 = T.axis.spatial(T.int64(1280), ax1_0 * T.int64(256) + ax1_1) + T.reads(add257[T.int64(0), v0, v1], add257_red_temp_v0_shared[T.int64(0), v0], add257_red_temp_v1_shared[T.int64(0), v0], model_decoder_layers_0_self_attn_layer_norm_weight2[v1], model_decoder_layers_0_self_attn_layer_norm_bias2[v1]) + T.writes(T_layer_norm[T.int64(0), v0, v1]) + T_layer_norm[T.int64(0), v0, v1] = T.Cast("float16", (T.Cast("float32", add257[T.int64(0), v0, v1]) - add257_red_temp_v0_shared[T.int64(0), v0] * T.float32(0.00078125000000000004)) * T.rsqrt(add257_red_temp_v1_shared[T.int64(0), v0] * T.float32(0.00078125000000000004) - add257_red_temp_v0_shared[T.int64(0), v0] * T.float32(0.00078125000000000004) * (add257_red_temp_v0_shared[T.int64(0), v0] * T.float32(0.00078125000000000004)) + T.float32(1.0000000000000001e-05))) * model_decoder_layers_0_self_attn_layer_norm_weight2[v1] + model_decoder_layers_0_self_attn_layer_norm_bias2[v1] + + @T.prim_func(private=True) + def layer_norm3(add1220: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16"), model_decoder_layers_0_self_attn_layer_norm_weight5: T.Buffer((T.int64(1280),), "float16"), model_decoder_layers_0_self_attn_layer_norm_bias5: T.Buffer((T.int64(1280),), "float16"), T_layer_norm: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16")): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + # with T.block("root"): + add1220_red_temp_v0_shared = T.alloc_buffer((T.int64(1), T.int64(1)), scope="shared") + add1220_red_temp_v1_shared = T.alloc_buffer((T.int64(1), T.int64(1)), scope="shared") + for ax0_fused in T.thread_binding(T.int64(1), thread="blockIdx.x"): + for ax0 in range(T.int64(1)): + for ax1_fused_1 in T.thread_binding(T.int64(256), thread="threadIdx.x"): + for ax1_fused_0 in T.serial(T.int64(5), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): + with T.block("add1220_red_temp"): + v0 = T.axis.spatial(T.int64(1), ax0) + v1 = T.axis.reduce(T.int64(1280), ax1_fused_0 * T.int64(256) + ax1_fused_1) + T.reads(add1220[T.int64(0), T.int64(0), v1]) + T.writes(add1220_red_temp_v0_shared[T.int64(0), T.int64(0)], add1220_red_temp_v1_shared[T.int64(0), T.int64(0)]) + with T.init(): + add1220_red_temp_v0_shared[T.int64(0), T.int64(0)] = T.float32(0) + add1220_red_temp_v1_shared[T.int64(0), T.int64(0)] = T.float32(0) + v_add1220_red_temp_v0: T.float32 = add1220_red_temp_v0_shared[T.int64(0), T.int64(0)] + T.Cast("float32", add1220[T.int64(0), T.int64(0), v1]) + v_add1220_red_temp_v1: T.float32 = add1220_red_temp_v1_shared[T.int64(0), T.int64(0)] + T.Cast("float32", add1220[T.int64(0), T.int64(0), v1]) * T.Cast("float32", add1220[T.int64(0), T.int64(0), v1]) + add1220_red_temp_v0_shared[T.int64(0), T.int64(0)] = v_add1220_red_temp_v0 + add1220_red_temp_v1_shared[T.int64(0), T.int64(0)] = v_add1220_red_temp_v1 + for ax1_1 in T.thread_binding(T.int64(256), thread="threadIdx.x"): + for ax1_0 in T.serial(T.int64(5), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): + with T.block("T_layer_norm"): + v0 = T.axis.spatial(T.int64(1), T.int64(0)) + v1 = T.axis.spatial(T.int64(1280), ax1_0 * T.int64(256) + ax1_1) + T.reads(add1220[T.int64(0), T.int64(0), v1], add1220_red_temp_v0_shared[T.int64(0), T.int64(0)], add1220_red_temp_v1_shared[T.int64(0), T.int64(0)], model_decoder_layers_0_self_attn_layer_norm_weight5[v1], model_decoder_layers_0_self_attn_layer_norm_bias5[v1]) + T.writes(T_layer_norm[T.int64(0), T.int64(0), v1]) + T_layer_norm[T.int64(0), T.int64(0), v1] = T.Cast("float16", (T.Cast("float32", add1220[T.int64(0), T.int64(0), v1]) - add1220_red_temp_v0_shared[T.int64(0), T.int64(0)] * T.float32(0.00078125000000000004)) * T.rsqrt(add1220_red_temp_v1_shared[T.int64(0), T.int64(0)] * T.float32(0.00078125000000000004) - add1220_red_temp_v0_shared[T.int64(0), T.int64(0)] * T.float32(0.00078125000000000004) * (add1220_red_temp_v0_shared[T.int64(0), T.int64(0)] * T.float32(0.00078125000000000004)) + T.float32(1.0000000000000001e-05))) * model_decoder_layers_0_self_attn_layer_norm_weight5[v1] + model_decoder_layers_0_self_attn_layer_norm_bias5[v1] + + @T.prim_func + def merge_state_inplace(v: T.handle, s: T.handle, v_other: T.handle, s_other: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) + N, H, D = T.int32(is_size_var=True), T.int32(is_size_var=True), T.int32(is_size_var=True) + V = T.match_buffer(v, (N, H, D), "float16") + S = T.match_buffer(s, (N, H)) + V_other = T.match_buffer(v_other, (N, H, D), "float16") + S_other = T.match_buffer(s_other, (N, H)) + # with T.block("root"): + for bx in T.thread_binding(N, thread="blockIdx.x"): + for by in T.thread_binding(1, thread="blockIdx.y"): + for ty in T.thread_binding(20, thread="threadIdx.y"): + for tx in T.thread_binding(16, thread="threadIdx.x"): + with T.block("merge"): + T.reads(S[bx, ty + by * 20], S_other[bx, ty + by * 20], V[bx, ty + by * 20, tx * 4:tx * 4 + 4], V_other[bx, ty + by * 20, tx * 4:tx * 4 + 4]) + T.writes(V[bx, ty + by * 20, tx * 4:tx * 4 + 4], S[bx, ty + by * 20]) + s_val = T.alloc_buffer((1,), scope="local") + s_other_val = T.alloc_buffer((1,), scope="local") + s_max = T.alloc_buffer((1,), scope="local") + scale = T.alloc_buffer((1,), scope="local") + other_scale = T.alloc_buffer((1,), scope="local") + v_vec = T.alloc_buffer((4,), "float16", scope="local") + v_other_vec = T.alloc_buffer((4,), "float16", scope="local") + s_val[0] = S[bx, ty + by * 20] + s_other_val[0] = S_other[bx, ty + by * 20] + s_max[0] = T.max(s_val[0], s_other_val[0]) + s_val[0] = T.exp2(s_val[0] - s_max[0]) + s_other_val[0] = T.exp2(s_other_val[0] - s_max[0]) + scale[0] = s_val[0] / (s_val[0] + s_other_val[0]) + other_scale[0] = s_other_val[0] / (s_val[0] + s_other_val[0]) + for vec in T.vectorized(4): + v_vec[vec] = V[bx, ty + by * 20, tx * 4 + vec] + for vec in T.vectorized(4): + v_other_vec[vec] = V_other[bx, ty + by * 20, tx * 4 + vec] + for vec in range(4): + v_vec[vec] = T.Cast("float16", T.Cast("float32", v_vec[vec]) * scale[0] + T.Cast("float32", v_other_vec[vec]) * other_scale[0]) + for vec in T.vectorized(4): + V[bx, ty + by * 20, tx * 4 + vec] = v_vec[vec] + S[bx, ty + by * 20] = T.log2(s_val[0] + s_other_val[0]) + s_max[0] + + @T.prim_func + def parallel_sampling_from_prob(var_prob: T.handle, var_uniform_samples: T.handle, var_row_indices: T.handle, var_sampled_token_ids: T.handle): + T.func_attr({"tir.is_scheduled": 1}) + n, vocab_size = T.int64(), T.int64() + prob = T.match_buffer(var_prob, (n, vocab_size)) + batch_size = T.int64() + uniform_samples = T.match_buffer(var_uniform_samples, (batch_size, 1)) + row_indices = T.match_buffer(var_row_indices, (batch_size, 1), "int32") + token_ids = T.match_buffer(var_sampled_token_ids, (batch_size, 1), "int32") + # with T.block("root"): + aggregate = T.alloc_buffer((), scope="local") + sample_id_local = T.alloc_buffer((), "int32", scope="local") + step_iter = T.alloc_buffer((), "int32", scope="local") + for bx in T.thread_binding(batch_size, thread="blockIdx.x"): + row_idx: T.int32 = row_indices[bx, 0] + for ty in T.thread_binding(T.int64(4), thread="threadIdx.y"): + for tx in T.thread_binding(T.int64(32), thread="threadIdx.x"): + u: T.float32 = uniform_samples[bx, 0] + aggregate[()] = T.Cast("float32", 0) + step_iter[()] = 0 + while T.tvm_thread_invariant((step_iter[()] == 0 or aggregate[()] < u - T.float32(9.9999999999999995e-07)) and T.Cast("int64", step_iter[()]) < (vocab_size + T.int64(512) - T.int64(1)) // T.int64(512)): + with T.block(""): + T.reads(step_iter[()], prob[row_idx, T.Cast("int64", step_iter[()]) * T.int64(512) + ty * T.int64(128) + tx * T.int64(4):T.Cast("int64", step_iter[()]) * T.int64(512) + ty * T.int64(128) + tx * T.int64(4) + T.int64(4)], aggregate[()]) + T.writes(sample_id_local[()], aggregate[()]) + prob_gt_threshold = T.alloc_buffer((T.int64(4),), scope="local") + cumsum = T.alloc_buffer((T.int64(512),), scope="shared") + greater_than_u = T.alloc_buffer((T.int64(4),), "bool", scope="local") + mask = T.alloc_buffer((T.int64(4),), "bool", scope="local") + valid = T.alloc_buffer((T.int64(4),), "bool", scope="local") + indices = T.alloc_buffer((T.int64(4),), "int32", scope="local") + step_aggregate = T.alloc_buffer((), scope="local") + for v in T.unroll(T.int64(4)): + idx: T.int64 = T.Cast("int64", step_iter[()]) * T.int64(512) + ty * T.int64(128) + tx * T.int64(4) + v + prob_local: T.float32 = T.if_then_else(idx < vocab_size, prob[row_idx, idx], T.Cast("float32", 0)) + prob_gt_threshold[v] = T.if_then_else(prob_local > T.float32(0), prob_local, T.Cast("float32", 0)) + valid[v] = prob_local > T.float32(0) and idx < vocab_size + with T.block(""): + T.reads(prob_gt_threshold[T.int64(0):T.int64(4)]) + T.writes(step_aggregate[()]) + local_sum = T.alloc_buffer((), scope="local") + shared_buf = T.alloc_buffer((T.int64(128),), scope="shared") + idx: T.int64 = ty * T.int64(32) + tx + local_sum[()] = T.Cast("float32", 0) + for i in T.unroll(T.int64(4)): + local_sum[()] = local_sum[()] + prob_gt_threshold[i] + shared_buf[idx] = local_sum[()] + for i in T.unroll(T.int64(7)): + if idx % T.shift_left(T.int64(1), i + T.int64(1)) == T.int64(0): + shared_buf[idx] = shared_buf[idx] + shared_buf[idx + T.shift_left(T.int64(1), i)] + step_aggregate[()] = shared_buf[0] + if T.tvm_thread_invariant(aggregate[()] + step_aggregate[()] >= u - T.float32(9.9999999999999995e-07)): + for i in T.unroll(T.int64(1), T.int64(4)): + prob_gt_threshold[i] = prob_gt_threshold[i] + prob_gt_threshold[i - T.int64(1)] + for i in T.vectorized(T.int64(4)): + cumsum[ty * T.int64(128) + tx * T.int64(4) + i] = prob_gt_threshold[i] + for i in T.unroll(T.int64(5)): + for j in T.vectorized(T.int64(4)): + idx: T.int64 = ty * T.int64(128) + tx * T.int64(4) + if tx >= T.shift_left(T.int64(1), i): + cumsum[idx + j] = cumsum[idx + j] + cumsum[idx - T.shift_left(T.int64(1), i) * T.int64(4) + T.int64(4) - T.int64(1)] + for i in T.unroll(T.int64(1), T.int64(4)): + for j in T.vectorized(T.int64(4)): + if ty == T.int64(0): + idx: T.int64 = i * T.int64(128) + tx * T.int64(4) + cumsum[idx + j] = cumsum[idx + j] + cumsum[i * T.int64(128) - T.int64(1)] + for v in T.unroll(T.int64(4)): + greater_than_u[v] = cumsum[ty * T.int64(128) + tx * T.int64(4) + v] + aggregate[()] >= u - T.float32(9.9999999999999995e-07) + with T.block(""): + T.reads(greater_than_u[T.int64(0):T.int64(4)]) + T.writes(mask[T.int64(0):T.int64(4)]) + shared_buf = T.alloc_buffer((T.int64(128),), "bool", scope="shared") + tx_idx: T.int64 = ty * T.int64(32) + tx + shared_buf[tx_idx] = greater_than_u[T.int64(3)] + mask[0] = T.if_then_else(tx_idx != T.int64(0), T.Cast("int8", greater_than_u[0]) != T.Cast("int8", shared_buf[tx_idx - T.int64(1)]), greater_than_u[0]) + for i in T.unroll(T.int64(1), T.int64(4)): + mask[i] = T.Cast("int8", greater_than_u[i]) != T.Cast("int8", greater_than_u[i - T.int64(1)]) + for v in T.unroll(T.int64(4)): + mask[v] = mask[v] and valid[v] + indices[v] = T.Cast("int32", T.Cast("int64", step_iter[()]) * T.int64(512) + ty * T.int64(128) + tx * T.int64(4) + v) + with T.block(""): + T.reads(mask[T.int64(0):T.int64(4)], indices[T.int64(0):T.int64(4)]) + T.writes(sample_id_local[()]) + local_sum = T.alloc_buffer((), "int32", scope="local") + shared_buf = T.alloc_buffer((T.int64(128),), "int32", scope="shared") + idx: T.int64 = ty * T.int64(32) + tx + local_sum[()] = T.Cast("int32", vocab_size - T.int64(1)) + for i in T.unroll(T.int64(4)): + if mask[i]: + local_sum[()] = T.min(local_sum[()], indices[i]) + shared_buf[idx] = local_sum[()] + for i in T.unroll(T.int64(7)): + if idx % T.shift_left(T.int64(1), i + T.int64(1)) == T.int64(0): + shared_buf[idx] = T.min(shared_buf[idx], shared_buf[idx + T.shift_left(T.int64(1), i)]) + sample_id_local[()] = shared_buf[0] + aggregate[()] = aggregate[()] + step_aggregate[()] + step_iter[()] = step_iter[()] + 1 + if tx == T.int64(0) and ty == T.int64(0): + token_ids[bx, 0] = sample_id_local[()] + + @T.prim_func(private=True) + def reshape(var_lv: T.handle, var_T_reshape: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + batch_size = T.int64() + lv = T.match_buffer(var_lv, (batch_size, T.int64(1500), T.int64(1280)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (batch_size, T.int64(1500), T.int64(20), T.int64(64)), "float16") + # with T.block("root"): + for ax0_ax1_ax2_ax3_fused_0 in T.thread_binding(batch_size * T.int64(1875), thread="blockIdx.x"): + for ax0_ax1_ax2_ax3_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_reshape"): + v0 = T.axis.spatial(batch_size, (ax0_ax1_ax2_ax3_fused_0 * T.int64(1024) + ax0_ax1_ax2_ax3_fused_1) // T.int64(1920000)) + v1 = T.axis.spatial(T.int64(1500), (ax0_ax1_ax2_ax3_fused_0 * T.int64(1024) + ax0_ax1_ax2_ax3_fused_1) % T.int64(1920000) // T.int64(1280)) + v2 = T.axis.spatial(T.int64(20), (ax0_ax1_ax2_ax3_fused_0 * T.int64(1024) + ax0_ax1_ax2_ax3_fused_1) % T.int64(1280) // T.int64(64)) + v3 = T.axis.spatial(T.int64(64), (ax0_ax1_ax2_ax3_fused_0 * T.int64(1024) + ax0_ax1_ax2_ax3_fused_1) % T.int64(64)) + T.reads(lv[v0, v1, v2 * T.int64(64) + v3]) + T.writes(T_reshape[v0, v1, v2, v3]) + T_reshape[v0, v1, v2, v3] = lv[v0, v1, v2 * T.int64(64) + v3] + + @T.prim_func(private=True) + def reshape1(var_reshape256: T.handle, var_T_reshape: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + batch_size = T.int64() + reshape256 = T.match_buffer(var_reshape256, (batch_size, T.int64(1500), T.int64(20), T.int64(64)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (batch_size * T.int64(1500), T.int64(20), T.int64(64)), "float16") + # with T.block("root"): + for ax0_ax1_ax2_fused_0 in T.thread_binding(batch_size * T.int64(1875), thread="blockIdx.x"): + for ax0_ax1_ax2_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_reshape"): + v0 = T.axis.spatial(batch_size * T.int64(1500), (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) // T.int64(1280)) + v1 = T.axis.spatial(T.int64(20), (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) % T.int64(1280) // T.int64(64)) + v2 = T.axis.spatial(T.int64(64), (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) % T.int64(64)) + T.reads(reshape256[v0 // T.int64(1500), v0 % T.int64(1500), v1, v2]) + T.writes(T_reshape[v0, v1, v2]) + T_reshape[v0, v1, v2] = reshape256[v0 // T.int64(1500), v0 % T.int64(1500), v1, v2] + + @T.prim_func(private=True) + def reshape10(var_lv4: T.handle, var_T_reshape: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + batch_size = T.int64() + lv4 = T.match_buffer(var_lv4, (batch_size * T.int64(1500), T.int64(20), T.int64(64)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (batch_size, T.int64(1500), T.int64(20), T.int64(64)), "float16") + # with T.block("root"): + for ax0_ax1_ax2_ax3_fused_0 in T.thread_binding(batch_size * T.int64(1875), thread="blockIdx.x"): + for ax0_ax1_ax2_ax3_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_reshape"): + v0 = T.axis.spatial(batch_size, (ax0_ax1_ax2_ax3_fused_0 * T.int64(1024) + ax0_ax1_ax2_ax3_fused_1) // T.int64(1920000)) + v1 = T.axis.spatial(T.int64(1500), (ax0_ax1_ax2_ax3_fused_0 * T.int64(1024) + ax0_ax1_ax2_ax3_fused_1) % T.int64(1920000) // T.int64(1280)) + v2 = T.axis.spatial(T.int64(20), (ax0_ax1_ax2_ax3_fused_0 * T.int64(1024) + ax0_ax1_ax2_ax3_fused_1) % T.int64(1280) // T.int64(64)) + v3 = T.axis.spatial(T.int64(64), (ax0_ax1_ax2_ax3_fused_0 * T.int64(1024) + ax0_ax1_ax2_ax3_fused_1) % T.int64(64)) + T.reads(lv4[v0 * T.int64(1500) + v1, v2, v3]) + T.writes(T_reshape[v0, v1, v2, v3]) + T_reshape[v0, v1, v2, v3] = lv4[v0 * T.int64(1500) + v1, v2, v3] + + @T.prim_func(private=True) + def reshape11(var_reshape6: T.handle, var_T_reshape: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + batch_size = T.int64() + reshape6 = T.match_buffer(var_reshape6, (batch_size, T.int64(1500), T.int64(20), T.int64(64)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (batch_size, T.int64(1500), T.int64(1280)), "float16") + # with T.block("root"): + for ax0_ax1_ax2_fused_0 in T.thread_binding(batch_size * T.int64(1875), thread="blockIdx.x"): + for ax0_ax1_ax2_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_reshape"): + v0 = T.axis.spatial(batch_size, (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) // T.int64(1920000)) + v1 = T.axis.spatial(T.int64(1500), (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) % T.int64(1920000) // T.int64(1280)) + v2 = T.axis.spatial(T.int64(1280), (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) % T.int64(1280)) + T.reads(reshape6[v0, v1, v2 // T.int64(64), v2 % T.int64(64)]) + T.writes(T_reshape[v0, v1, v2]) + T_reshape[v0, v1, v2] = reshape6[v0, v1, v2 // T.int64(64), v2 % T.int64(64)] + + @T.prim_func(private=True) + def reshape12(var_input_ids: T.handle, var_T_reshape: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + seq_len = T.int64() + input_ids = T.match_buffer(var_input_ids, (T.int64(1), seq_len), "int32") + T_reshape = T.match_buffer(var_T_reshape, (seq_len,), "int32") + # with T.block("root"): + for ax0_fused_0 in T.thread_binding((seq_len + T.int64(1023)) // T.int64(1024), thread="blockIdx.x"): + for ax0_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_reshape"): + v0 = T.axis.spatial(seq_len, ax0_fused_0 * T.int64(1024) + ax0_fused_1) + T.where(ax0_fused_0 * T.int64(1024) + ax0_fused_1 < seq_len) + T.reads(input_ids[T.int64(0), v0]) + T.writes(T_reshape[v0]) + T_reshape[v0] = input_ids[T.int64(0), v0] + + @T.prim_func(private=True) + def reshape13(var_take: T.handle, var_T_reshape: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + seq_len = T.int64() + take = T.match_buffer(var_take, (seq_len, T.int64(1280)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (T.int64(1), seq_len, T.int64(1280)), "float16") + # with T.block("root"): + for ax0_ax1_fused_0 in T.thread_binding((seq_len * T.int64(1280) + T.int64(1023)) // T.int64(1024), thread="blockIdx.x"): + for ax0_ax1_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_reshape"): + v0 = T.axis.spatial(seq_len, (ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1) // T.int64(1280)) + v1 = T.axis.spatial(T.int64(1280), (ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1) % T.int64(1280)) + T.where(ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1 < seq_len * T.int64(1280)) + T.reads(take[v0, v1]) + T.writes(T_reshape[T.int64(0), v0, v1]) + T_reshape[T.int64(0), v0, v1] = take[v0, v1] + + @T.prim_func(private=True) + def reshape14(var_lv416: T.handle, var_T_reshape: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + seq_len = T.int64() + lv416 = T.match_buffer(var_lv416, (T.int64(1), seq_len, T.int64(1280)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (T.int64(1), seq_len, T.int64(20), T.int64(64)), "float16") + # with T.block("root"): + for ax0_ax1_ax2_fused_0 in T.thread_binding((seq_len * T.int64(1280) + T.int64(1023)) // T.int64(1024), thread="blockIdx.x"): + for ax0_ax1_ax2_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_reshape"): + v0 = T.axis.spatial(seq_len, (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) // T.int64(1280)) + v1 = T.axis.spatial(T.int64(20), (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) % T.int64(1280) // T.int64(64)) + v2 = T.axis.spatial(T.int64(64), (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) % T.int64(64)) + T.where(ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1 < seq_len * T.int64(1280)) + T.reads(lv416[T.int64(0), v0, v1 * T.int64(64) + v2]) + T.writes(T_reshape[T.int64(0), v0, v1, v2]) + T_reshape[T.int64(0), v0, v1, v2] = lv416[T.int64(0), v0, v1 * T.int64(64) + v2] + + @T.prim_func(private=True) + def reshape15(var_concat: T.handle, var_T_reshape: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + seq_len = T.int64() + concat = T.match_buffer(var_concat, (T.int64(1), seq_len, T.int64(60), T.int64(64)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (seq_len, T.int64(60), T.int64(64)), "float16") + # with T.block("root"): + for ax0_ax1_ax2_fused_0 in T.thread_binding((seq_len * T.int64(3840) + T.int64(1023)) // T.int64(1024), thread="blockIdx.x"): + for ax0_ax1_ax2_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_reshape"): + v0 = T.axis.spatial(seq_len, (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) // T.int64(3840)) + v1 = T.axis.spatial(T.int64(60), (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) % T.int64(3840) // T.int64(64)) + v2 = T.axis.spatial(T.int64(64), (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) % T.int64(64)) + T.where(ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1 < seq_len * T.int64(3840)) + T.reads(concat[T.int64(0), v0, v1, v2]) + T.writes(T_reshape[v0, v1, v2]) + T_reshape[v0, v1, v2] = concat[T.int64(0), v0, v1, v2] + + @T.prim_func(private=True) + def reshape16(var_lv69: T.handle, var_T_reshape: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + seq_len = T.int64() + lv69 = T.match_buffer(var_lv69, (seq_len, T.int64(20), T.int64(64)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (T.int64(1), seq_len, T.int64(20), T.int64(64)), "float16") + # with T.block("root"): + for ax0_ax1_ax2_fused_0 in T.thread_binding((seq_len * T.int64(1280) + T.int64(1023)) // T.int64(1024), thread="blockIdx.x"): + for ax0_ax1_ax2_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_reshape"): + v0 = T.axis.spatial(seq_len, (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) // T.int64(1280)) + v1 = T.axis.spatial(T.int64(20), (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) % T.int64(1280) // T.int64(64)) + v2 = T.axis.spatial(T.int64(64), (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) % T.int64(64)) + T.where(ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1 < seq_len * T.int64(1280)) + T.reads(lv69[v0, v1, v2]) + T.writes(T_reshape[T.int64(0), v0, v1, v2]) + T_reshape[T.int64(0), v0, v1, v2] = lv69[v0, v1, v2] + + @T.prim_func(private=True) + def reshape17(var_reshape391: T.handle, var_T_reshape: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + seq_len = T.int64() + reshape391 = T.match_buffer(var_reshape391, (T.int64(1), seq_len, T.int64(20), T.int64(64)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (T.int64(1), seq_len, T.int64(1280)), "float16") + # with T.block("root"): + for ax0_ax1_fused_0 in T.thread_binding((seq_len * T.int64(1280) + T.int64(1023)) // T.int64(1024), thread="blockIdx.x"): + for ax0_ax1_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_reshape"): + v0 = T.axis.spatial(seq_len, (ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1) // T.int64(1280)) + v1 = T.axis.spatial(T.int64(1280), (ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1) % T.int64(1280)) + T.where(ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1 < seq_len * T.int64(1280)) + T.reads(reshape391[T.int64(0), v0, v1 // T.int64(64), v1 % T.int64(64)]) + T.writes(T_reshape[T.int64(0), v0, v1]) + T_reshape[T.int64(0), v0, v1] = reshape391[T.int64(0), v0, v1 // T.int64(64), v1 % T.int64(64)] + + @T.prim_func(private=True) + def reshape18(var_reshape393: T.handle, var_T_reshape: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + seq_len = T.int64() + reshape393 = T.match_buffer(var_reshape393, (T.int64(1), seq_len, T.int64(20), T.int64(64)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (seq_len, T.int64(20), T.int64(64)), "float16") + # with T.block("root"): + for ax0_ax1_ax2_fused_0 in T.thread_binding((seq_len * T.int64(1280) + T.int64(1023)) // T.int64(1024), thread="blockIdx.x"): + for ax0_ax1_ax2_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_reshape"): + v0 = T.axis.spatial(seq_len, (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) // T.int64(1280)) + v1 = T.axis.spatial(T.int64(20), (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) % T.int64(1280) // T.int64(64)) + v2 = T.axis.spatial(T.int64(64), (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) % T.int64(64)) + T.where(ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1 < seq_len * T.int64(1280)) + T.reads(reshape393[T.int64(0), v0, v1, v2]) + T.writes(T_reshape[v0, v1, v2]) + T_reshape[v0, v1, v2] = reshape393[T.int64(0), v0, v1, v2] + + @T.prim_func(private=True) + def reshape19(input_ids: T.Buffer((T.int64(1), T.int64(1)), "int32"), T_reshape: T.Buffer((T.int64(1),), "int32")): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + # with T.block("root"): + for ax0_fused_0 in T.thread_binding(T.int64(1), thread="blockIdx.x"): + for ax0_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_reshape"): + v0 = T.axis.spatial(T.int64(1), T.int64(0)) + T.where(ax0_fused_0 * T.int64(1024) + ax0_fused_1 < T.int64(1)) + T.reads(input_ids[T.int64(0), T.int64(0)]) + T.writes(T_reshape[T.int64(0)]) + T_reshape[T.int64(0)] = input_ids[T.int64(0), T.int64(0)] + + @T.prim_func(private=True) + def reshape2(var_input_ids: T.handle, var_T_reshape: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + batch_size = T.int64() + input_ids = T.match_buffer(var_input_ids, (batch_size, T.int64(1)), "int32") + T_reshape = T.match_buffer(var_T_reshape, (batch_size,), "int32") + # with T.block("root"): + for ax0_fused_0 in T.thread_binding((batch_size + T.int64(1023)) // T.int64(1024), thread="blockIdx.x"): + for ax0_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_reshape"): + v0 = T.axis.spatial(batch_size, ax0_fused_0 * T.int64(1024) + ax0_fused_1) + T.where(ax0_fused_0 * T.int64(1024) + ax0_fused_1 < batch_size) + T.reads(input_ids[v0, T.int64(0)]) + T.writes(T_reshape[v0]) + T_reshape[v0] = input_ids[v0, T.int64(0)] + + @T.prim_func(private=True) + def reshape3(var_take3: T.handle, var_T_reshape: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + batch_size = T.int64() + take3 = T.match_buffer(var_take3, (batch_size, T.int64(1280)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (batch_size, T.int64(1), T.int64(1280)), "float16") + # with T.block("root"): + for ax0_ax1_fused_0 in T.thread_binding((batch_size * T.int64(1280) + T.int64(1023)) // T.int64(1024), thread="blockIdx.x"): + for ax0_ax1_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_reshape"): + v0 = T.axis.spatial(batch_size, (ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1) // T.int64(1280)) + v1 = T.axis.spatial(T.int64(1280), (ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1) % T.int64(1280)) + T.where(ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1 < batch_size * T.int64(1280)) + T.reads(take3[v0, v1]) + T.writes(T_reshape[v0, T.int64(0), v1]) + T_reshape[v0, T.int64(0), v1] = take3[v0, v1] + + @T.prim_func(private=True) + def reshape4(var_lv224: T.handle, var_T_reshape: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + batch_size = T.int64() + lv224 = T.match_buffer(var_lv224, (batch_size, T.int64(1), T.int64(1280)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (batch_size, T.int64(1), T.int64(20), T.int64(64)), "float16") + # with T.block("root"): + for ax0_ax1_ax2_fused_0 in T.thread_binding((batch_size * T.int64(1280) + T.int64(1023)) // T.int64(1024), thread="blockIdx.x"): + for ax0_ax1_ax2_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_reshape"): + v0 = T.axis.spatial(batch_size, (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) // T.int64(1280)) + v1 = T.axis.spatial(T.int64(20), (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) % T.int64(1280) // T.int64(64)) + v2 = T.axis.spatial(T.int64(64), (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) % T.int64(64)) + T.where(ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1 < batch_size * T.int64(1280)) + T.reads(lv224[v0, T.int64(0), v1 * T.int64(64) + v2]) + T.writes(T_reshape[v0, T.int64(0), v1, v2]) + T_reshape[v0, T.int64(0), v1, v2] = lv224[v0, T.int64(0), v1 * T.int64(64) + v2] + + @T.prim_func(private=True) + def reshape5(var_concat32: T.handle, var_T_reshape: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + batch_size = T.int64() + concat32 = T.match_buffer(var_concat32, (batch_size, T.int64(1), T.int64(60), T.int64(64)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (batch_size, T.int64(60), T.int64(64)), "float16") + # with T.block("root"): + for ax0_ax1_ax2_fused_0 in T.thread_binding((batch_size * T.int64(3840) + T.int64(1023)) // T.int64(1024), thread="blockIdx.x"): + for ax0_ax1_ax2_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_reshape"): + v0 = T.axis.spatial(batch_size, (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) // T.int64(3840)) + v1 = T.axis.spatial(T.int64(60), (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) % T.int64(3840) // T.int64(64)) + v2 = T.axis.spatial(T.int64(64), (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) % T.int64(64)) + T.where(ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1 < batch_size * T.int64(3840)) + T.reads(concat32[v0, T.int64(0), v1, v2]) + T.writes(T_reshape[v0, v1, v2]) + T_reshape[v0, v1, v2] = concat32[v0, T.int64(0), v1, v2] + + @T.prim_func(private=True) + def reshape6(var_lv134: T.handle, var_T_reshape: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + batch_size = T.int64() + lv134 = T.match_buffer(var_lv134, (batch_size, T.int64(20), T.int64(64)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (batch_size, T.int64(1), T.int64(20), T.int64(64)), "float16") + # with T.block("root"): + for ax0_ax1_ax2_fused_0 in T.thread_binding((batch_size * T.int64(1280) + T.int64(1023)) // T.int64(1024), thread="blockIdx.x"): + for ax0_ax1_ax2_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_reshape"): + v0 = T.axis.spatial(batch_size, (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) // T.int64(1280)) + v1 = T.axis.spatial(T.int64(20), (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) % T.int64(1280) // T.int64(64)) + v2 = T.axis.spatial(T.int64(64), (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) % T.int64(64)) + T.where(ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1 < batch_size * T.int64(1280)) + T.reads(lv134[v0, v1, v2]) + T.writes(T_reshape[v0, T.int64(0), v1, v2]) + T_reshape[v0, T.int64(0), v1, v2] = lv134[v0, v1, v2] + + @T.prim_func(private=True) + def reshape7(var_reshape714: T.handle, var_T_reshape: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + batch_size = T.int64() + reshape714 = T.match_buffer(var_reshape714, (batch_size, T.int64(1), T.int64(20), T.int64(64)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (batch_size, T.int64(1), T.int64(1280)), "float16") + # with T.block("root"): + for ax0_ax1_fused_0 in T.thread_binding((batch_size * T.int64(1280) + T.int64(1023)) // T.int64(1024), thread="blockIdx.x"): + for ax0_ax1_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_reshape"): + v0 = T.axis.spatial(batch_size, (ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1) // T.int64(1280)) + v1 = T.axis.spatial(T.int64(1280), (ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1) % T.int64(1280)) + T.where(ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1 < batch_size * T.int64(1280)) + T.reads(reshape714[v0, T.int64(0), v1 // T.int64(64), v1 % T.int64(64)]) + T.writes(T_reshape[v0, T.int64(0), v1]) + T_reshape[v0, T.int64(0), v1] = reshape714[v0, T.int64(0), v1 // T.int64(64), v1 % T.int64(64)] + + @T.prim_func(private=True) + def reshape8(var_reshape716: T.handle, var_T_reshape: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + batch_size = T.int64() + reshape716 = T.match_buffer(var_reshape716, (batch_size, T.int64(1), T.int64(20), T.int64(64)), "float16") + T_reshape = T.match_buffer(var_T_reshape, (batch_size, T.int64(20), T.int64(64)), "float16") + # with T.block("root"): + for ax0_ax1_ax2_fused_0 in T.thread_binding((batch_size * T.int64(1280) + T.int64(1023)) // T.int64(1024), thread="blockIdx.x"): + for ax0_ax1_ax2_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_reshape"): + v0 = T.axis.spatial(batch_size, (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) // T.int64(1280)) + v1 = T.axis.spatial(T.int64(20), (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) % T.int64(1280) // T.int64(64)) + v2 = T.axis.spatial(T.int64(64), (ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1) % T.int64(64)) + T.where(ax0_ax1_ax2_fused_0 * T.int64(1024) + ax0_ax1_ax2_fused_1 < batch_size * T.int64(1280)) + T.reads(reshape716[v0, T.int64(0), v1, v2]) + T.writes(T_reshape[v0, v1, v2]) + T_reshape[v0, v1, v2] = reshape716[v0, T.int64(0), v1, v2] + + @T.prim_func + def sampler_take_probs_tir(var_unsorted_probs: T.handle, var_sorted_indices: T.handle, var_sample_indices: T.handle, var_sampling_results: T.handle, var_top_prob_offsets: T.handle, var_sampled_values: T.handle, var_top_prob_probs: T.handle, var_top_prob_indices: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) + batch_size, vocab_size = T.int32(is_size_var=True), T.int32(is_size_var=True) + unsorted_probs = T.match_buffer(var_unsorted_probs, (batch_size, vocab_size)) + sorted_indices = T.match_buffer(var_sorted_indices, (batch_size, vocab_size), "int32") + num_samples = T.int32(is_size_var=True) + sample_indices = T.match_buffer(var_sample_indices, (num_samples,), "int32") + sampling_results = T.match_buffer(var_sampling_results, (num_samples,), "int32") + num_positions = T.int32(is_size_var=True) + top_prob_offsets = T.match_buffer(var_top_prob_offsets, (num_positions,), "int32") + sampled_values = T.match_buffer(var_sampled_values, (num_samples,)) + top_prob_probs = T.match_buffer(var_top_prob_probs, (num_positions,)) + top_prob_indices = T.match_buffer(var_top_prob_indices, (num_positions,), "int32") + # with T.block("root"): + for ax0_fused_0 in T.thread_binding((num_positions + num_samples + 1023) // 1024, thread="blockIdx.x"): + for ax0_fused_1 in T.thread_binding(1024, thread="threadIdx.x"): + with T.block("block"): + v0 = T.axis.spatial(num_positions + num_samples, ax0_fused_0 * 1024 + ax0_fused_1) + T.where(ax0_fused_0 * 1024 + ax0_fused_1 < num_positions + num_samples) + T.reads(top_prob_offsets[v0], sorted_indices[top_prob_offsets[v0] // vocab_size, top_prob_offsets[v0] % vocab_size], unsorted_probs[T.min(top_prob_offsets[v0] // vocab_size, sample_indices[v0 + (0 - num_positions)]):T.min(top_prob_offsets[v0] // vocab_size, sample_indices[v0 + (0 - num_positions)]) + (T.max(top_prob_offsets[v0] // vocab_size, sample_indices[v0 - num_positions]) + 1 - T.min(top_prob_offsets[v0] // vocab_size, sample_indices[v0 - num_positions])), T.min(sorted_indices[top_prob_offsets[v0] // vocab_size, top_prob_offsets[v0] % vocab_size], sampling_results[v0 + (0 - num_positions)]):T.min(sorted_indices[top_prob_offsets[v0] // vocab_size, top_prob_offsets[v0] % vocab_size], sampling_results[v0 + (0 - num_positions)]) + (T.max(sorted_indices[top_prob_offsets[v0] // vocab_size, top_prob_offsets[v0] % vocab_size], sampling_results[v0 - num_positions]) + 1 - T.min(sorted_indices[top_prob_offsets[v0] // vocab_size, top_prob_offsets[v0] % vocab_size], sampling_results[v0 - num_positions]))], sample_indices[v0 + (0 - num_positions)], sampling_results[v0 + (0 - num_positions)]) + T.writes(top_prob_indices[v0], top_prob_probs[v0], sampled_values[v0 + (0 - num_positions)]) + if v0 < num_positions: + row: T.int32 = top_prob_offsets[v0] // vocab_size + col: T.int32 = top_prob_offsets[v0] % vocab_size + top_prob_indices[v0] = sorted_indices[row, col] + top_prob_probs[v0] = unsorted_probs[row, sorted_indices[row, col]] + else: + vj: T.int32 = v0 - num_positions + sampled_values[vj] = unsorted_probs[sample_indices[vj], sampling_results[vj]] + + @T.prim_func + def scatter_probs(var_src: T.handle, var_indices: T.handle, var_dst: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + batch_size, n = T.int32(is_size_var=True), T.int32(is_size_var=True) + src = T.match_buffer(var_src, (batch_size, n)) + indices = T.match_buffer(var_indices, (batch_size,), "int32") + m = T.int32(is_size_var=True) + dst = T.match_buffer(var_dst, (m, n)) + # with T.block("root"): + for ax0_ax1_fused_0 in T.thread_binding((batch_size * n + 1023) // 1024, thread="blockIdx.x"): + for ax0_ax1_fused_1 in T.thread_binding(1024, thread="threadIdx.x"): + with T.block("scatter_2d"): + v0 = T.axis.spatial(batch_size, (ax0_ax1_fused_0 * 1024 + ax0_ax1_fused_1) % (n * batch_size) // n) + v1 = T.axis.spatial(n, (ax0_ax1_fused_0 * 1024 + ax0_ax1_fused_1) % n) + T.where(ax0_ax1_fused_0 * 1024 + ax0_ax1_fused_1 < batch_size * n) + T.reads(src[v0, v1], indices[v0]) + T.writes(dst[indices[v0], v1]) + dst[indices[v0], v1] = src[v0, v1] + + @T.prim_func + def softmax_with_chunked_sum(var_A: T.handle, var_temperature: T.handle, var_chunked_sum: T.handle, var_chunked_max: T.handle, var_softmax: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + batch_size, vocab_size = T.int64(is_size_var=True), T.int64(is_size_var=True) + A = T.match_buffer(var_A, (batch_size, vocab_size)) + temperature = T.match_buffer(var_temperature, (batch_size,)) + num_chunks = T.int64(is_size_var=True) + chunked_sum = T.match_buffer(var_chunked_sum, (batch_size, num_chunks)) + chunked_max = T.match_buffer(var_chunked_max, (batch_size, num_chunks)) + softmax = T.match_buffer(var_softmax, (batch_size, vocab_size)) + # with T.block("root"): + temp_max_shared = T.alloc_buffer((batch_size,), scope="shared") + temp_sum_shared = T.alloc_buffer((batch_size,), scope="shared") + for l0_l1_fused in T.thread_binding(batch_size * num_chunks, thread="blockIdx.x"): + for ax0_1 in T.thread_binding(T.int64(32), thread="threadIdx.x"): + for ax0_0 in T.serial((num_chunks + T.int64(31)) // T.int64(32), annotations={"pragma_auto_unroll_max_step": 64, "pragma_unroll_explicit": 1}): + with T.block("max"): + v0 = T.axis.spatial(batch_size, l0_l1_fused % (num_chunks * batch_size) // num_chunks) + v1 = T.axis.reduce(num_chunks, ax0_0 * T.int64(32) + ax0_1) + T.where(ax0_0 * T.int64(32) + ax0_1 < num_chunks) + T.reads(chunked_max[v0, v1]) + T.writes(temp_max_shared[v0]) + with T.init(): + temp_max_shared[v0] = T.float32(-3.4028234663852886e+38) + temp_max_shared[v0] = T.max(temp_max_shared[v0], chunked_max[v0, v1]) + for ax0_1 in T.thread_binding(T.int64(32), thread="threadIdx.x"): + for ax0_0 in T.serial((num_chunks + T.int64(31)) // T.int64(32), annotations={"pragma_auto_unroll_max_step": 64, "pragma_unroll_explicit": 1}): + with T.block("sum_exp"): + v0 = T.axis.spatial(batch_size, l0_l1_fused % (num_chunks * batch_size) // num_chunks) + v1 = T.axis.reduce(num_chunks, ax0_0 * T.int64(32) + ax0_1) + T.where(ax0_0 * T.int64(32) + ax0_1 < num_chunks) + T.reads(temperature[v0], chunked_sum[v0, v1], chunked_max[v0, v1], temp_max_shared[v0]) + T.writes(temp_sum_shared[v0]) + with T.init(): + temp_sum_shared[v0] = T.float32(0) + temp_sum_shared[v0] = temp_sum_shared[v0] + T.Select(temperature[v0] > T.float32(1.0000000000000001e-05), T.exp(chunked_sum[v0, v1] + chunked_max[v0, v1] - temp_max_shared[v0]), T.Cast("float32", chunked_max[v0, v1] == temp_max_shared[v0]) * chunked_sum[v0, v1]) + for l2_0 in T.serial(T.int64(4), annotations={"pragma_auto_unroll_max_step": 64, "pragma_unroll_explicit": 1}): + for l2_1 in T.thread_binding(T.int64(32), thread="threadIdx.y"): + for l2_2 in T.thread_binding(T.int64(32), thread="threadIdx.x"): + with T.block("log_pad"): + v0 = T.axis.spatial(batch_size, l0_l1_fused % (num_chunks * batch_size) // num_chunks) + v1 = T.axis.spatial(num_chunks, l0_l1_fused % num_chunks) + v2 = T.axis.spatial(T.int64(4096), l2_0 * T.int64(1024) + l2_1 * T.int64(32) + l2_2) + T.reads(temperature[v0], A[v0, v1 * T.int64(4096) + v2], temp_sum_shared[v0], temp_max_shared[v0]) + T.writes(softmax[v0, v1 * T.int64(4096) + v2]) + if v1 * T.int64(4096) + v2 < vocab_size: + softmax[v0, v1 * T.int64(4096) + v2] = T.if_then_else(temperature[v0] > T.float32(1.0000000000000001e-05), T.exp(A[v0, v1 * T.int64(4096) + v2] / temperature[v0] - (T.log(temp_sum_shared[v0]) + temp_max_shared[v0])), T.Cast("float32", A[v0, v1 * T.int64(4096) + v2] == temp_max_shared[v0]) / temp_sum_shared[v0]) + + @T.prim_func(private=True) + def take(model_decoder_embed_tokens_weight3: T.Buffer((T.int64(51866), T.int64(1280)), "float16"), var_reshape707: T.handle, var_T_take: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + batch_size = T.int64() + reshape707 = T.match_buffer(var_reshape707, (batch_size,), "int32") + T_take = T.match_buffer(var_T_take, (batch_size, T.int64(1280)), "float16") + # with T.block("root"): + for ax0_ax1_fused_0 in T.thread_binding((batch_size * T.int64(1280) + T.int64(1023)) // T.int64(1024), thread="blockIdx.x"): + for ax0_ax1_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_take"): + v0 = T.axis.spatial(batch_size, (ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1) // T.int64(1280)) + v1 = T.axis.spatial(T.int64(1280), (ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1) % T.int64(1280)) + T.where(ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1 < batch_size * T.int64(1280)) + T.reads(model_decoder_embed_tokens_weight3[reshape707[v0], v1], reshape707[v0]) + T.writes(T_take[v0, v1]) + T_take[v0, v1] = model_decoder_embed_tokens_weight3[reshape707[v0], v1] + + @T.prim_func(private=True) + def take1(model_decoder_embed_positions_weight3: T.Buffer((T.int64(448), T.int64(1280)), "float16"), var_lv133: T.handle, var_T_take: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + batch_size = T.int64() + lv133 = T.match_buffer(var_lv133, (batch_size,), "int32") + T_take = T.match_buffer(var_T_take, (batch_size, T.int64(1280)), "float16") + # with T.block("root"): + for ax0_ax1_fused_0 in T.thread_binding((batch_size * T.int64(1280) + T.int64(1023)) // T.int64(1024), thread="blockIdx.x"): + for ax0_ax1_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_take"): + v0 = T.axis.spatial(batch_size, (ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1) // T.int64(1280)) + v1 = T.axis.spatial(T.int64(1280), (ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1) % T.int64(1280)) + T.where(ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1 < batch_size * T.int64(1280)) + T.reads(model_decoder_embed_positions_weight3[lv133[v0], v1], lv133[v0]) + T.writes(T_take[v0, v1]) + T_take[v0, v1] = model_decoder_embed_positions_weight3[lv133[v0], v1] + + @T.prim_func(private=True) + def take2(var_layer_norm161: T.handle, var_logit_positions: T.handle, var_T_take: T.handle): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + seq_len = T.int64() + layer_norm161 = T.match_buffer(var_layer_norm161, (T.int64(1), seq_len, T.int64(1280)), "float16") + batch_size = T.int64() + logit_positions = T.match_buffer(var_logit_positions, (batch_size,), "int32") + T_take = T.match_buffer(var_T_take, (T.int64(1), batch_size, T.int64(1280)), "float16") + # with T.block("root"): + for ax0_ax1_fused_0 in T.thread_binding((batch_size * T.int64(1280) + T.int64(1023)) // T.int64(1024), thread="blockIdx.x"): + for ax0_ax1_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_take"): + v0 = T.axis.spatial(batch_size, (ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1) // T.int64(1280)) + v1 = T.axis.spatial(T.int64(1280), (ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1) % T.int64(1280)) + T.where(ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1 < batch_size * T.int64(1280)) + T.reads(layer_norm161[T.int64(0), logit_positions[v0], v1], logit_positions[v0]) + T.writes(T_take[T.int64(0), v0, v1]) + T_take[T.int64(0), v0, v1] = layer_norm161[T.int64(0), logit_positions[v0], v1] + + @T.prim_func(private=True) + def take3(model_decoder_embed_tokens_weight5: T.Buffer((T.int64(51866), T.int64(1280)), "float16"), reshape1353: T.Buffer((T.int64(1),), "int32"), T_take: T.Buffer((T.int64(1), T.int64(1280)), "float16")): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + # with T.block("root"): + for ax0_fused_0 in T.thread_binding(T.int64(2), thread="blockIdx.x"): + for ax0_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_take"): + v0 = T.axis.spatial(T.int64(1280), ax0_fused_0 * T.int64(1024) + ax0_fused_1) + T.where(ax0_fused_0 * T.int64(1024) + ax0_fused_1 < T.int64(1280)) + T.reads(model_decoder_embed_tokens_weight5[reshape1353[T.int64(0)], v0], reshape1353[T.int64(0)]) + T.writes(T_take[T.int64(0), v0]) + T_take[T.int64(0), v0] = model_decoder_embed_tokens_weight5[reshape1353[T.int64(0)], v0] + + @T.prim_func(private=True) + def take4(model_decoder_embed_positions_weight5: T.Buffer((T.int64(448), T.int64(1280)), "float16"), lv264: T.Buffer((T.int64(1),), "int32"), T_take: T.Buffer((T.int64(1), T.int64(1280)), "float16")): + T.func_attr({"tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + # with T.block("root"): + for ax0_fused_0 in T.thread_binding(T.int64(2), thread="blockIdx.x"): + for ax0_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("T_take"): + v0 = T.axis.spatial(T.int64(1280), ax0_fused_0 * T.int64(1024) + ax0_fused_1) + T.where(ax0_fused_0 * T.int64(1024) + ax0_fused_1 < T.int64(1280)) + T.reads(model_decoder_embed_positions_weight5[lv264[T.int64(0)], v0], lv264[T.int64(0)]) + T.writes(T_take[T.int64(0), v0]) + T_take[T.int64(0), v0] = model_decoder_embed_positions_weight5[lv264[T.int64(0)], v0] + + @T.prim_func(private=True) + def take_sorted_probs(var_probs: T.handle, var_lv1: T.handle, var_take_sorted_probs: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + batch_size, vocab_size = T.int64(), T.int64() + probs = T.match_buffer(var_probs, (batch_size, vocab_size)) + lv1 = T.match_buffer(var_lv1, (batch_size, vocab_size), "int32") + batch_size_1, vocab_size_1 = T.int64(), T.int64() + take_sorted_probs = T.match_buffer(var_take_sorted_probs, (batch_size_1, vocab_size_1)) + # with T.block("root"): + for ax0_ax1_fused_0 in T.thread_binding((batch_size_1 * vocab_size_1 + T.int64(1023)) // T.int64(1024), thread="blockIdx.x"): + for ax0_ax1_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("take_sorted_probs"): + v0 = T.axis.spatial(batch_size_1, (ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1) % (vocab_size_1 * batch_size_1) // vocab_size_1) + v1 = T.axis.spatial(vocab_size_1, (ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1) % vocab_size_1) + T.where(ax0_ax1_fused_0 * T.int64(1024) + ax0_ax1_fused_1 < batch_size_1 * vocab_size_1) + T.reads(probs[v0, lv1[v0, v1]], lv1[v0, v1]) + T.writes(take_sorted_probs[v0, v1]) + take_sorted_probs[v0, v1] = probs[v0, lv1[v0, v1]] + + @T.prim_func + def tir_kv_cache_debug_get_kv(var_pages: T.handle, var_position_map: T.handle, var_k_data: T.handle, var_v_data: T.handle, layer_id: T.int64): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + num_pages, page_size = T.int64(), T.int64(is_size_var=True) + pages = T.match_buffer(var_pages, (num_pages, 2, 20, page_size, 64), "float16") + seqlen = T.int64(is_size_var=True) + position_map = T.match_buffer(var_position_map, (seqlen,), "int32", offset_factor=1) + k_data = T.match_buffer(var_k_data, (32, seqlen, 20, 64), "float16") + v_data = T.match_buffer(var_v_data, (32, seqlen, 20, 64), "float16") + # with T.block("root"): + for p_h_d_fused_0 in T.thread_binding((seqlen * T.int64(1280) + T.int64(1023)) // T.int64(1024), thread="blockIdx.x"): + for p_h_d_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + with T.block("copy0"): + vp = T.axis.spatial(seqlen, (p_h_d_fused_0 * T.int64(1024) + p_h_d_fused_1) // T.int64(1280)) + vh = T.axis.spatial(20, T.Cast("int32", (p_h_d_fused_0 * T.int64(1024) + p_h_d_fused_1) % T.int64(1280) // T.int64(64))) + vd = T.axis.spatial(64, T.Cast("int32", (p_h_d_fused_0 * T.int64(1024) + p_h_d_fused_1) % T.int64(64))) + T.where(p_h_d_fused_0 * T.int64(1024) + p_h_d_fused_1 < seqlen * T.int64(1280)) + T.reads(position_map[vp], pages[T.Cast("int64", position_map[vp]) // page_size, 0:2, vh, T.Cast("int64", position_map[vp]) % page_size, vd]) + T.writes(k_data[layer_id, vp, vh, vd], v_data[layer_id, vp, vh, vd]) + position: T.int32 = position_map[vp] + k_data[layer_id, vp, vh, vd] = pages[T.Cast("int64", position) // page_size, 0, vh, T.Cast("int64", position) % page_size, vd] + v_data[layer_id, vp, vh, vd] = pages[T.Cast("int64", position) // page_size, 1, vh, T.Cast("int64", position) % page_size, vd] + + @T.prim_func + def tir_kv_cache_transpose_append(var_pages: T.handle, var_k_data: T.handle, var_v_data: T.handle, var_position_map: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + num_pages = T.int64() + pages = T.match_buffer(var_pages, (num_pages, 2, 20, 16, 64), "float16") + ntoken = T.int64(is_size_var=True) + k_data = T.match_buffer(var_k_data, (ntoken, 20, 64), "float16") + v_data = T.match_buffer(var_v_data, (ntoken, 20, 64), "float16") + position_map = T.match_buffer(var_position_map, (ntoken,), "int32", offset_factor=1) + # with T.block("root"): + for global_pos_h_f_fused_0 in T.thread_binding((ntoken * T.int64(1280) + T.int64(1023)) // T.int64(1024), thread="blockIdx.x"): + for global_pos_h_f_fused_1 in T.thread_binding(T.int64(1024), thread="threadIdx.x"): + if position_map[(global_pos_h_f_fused_0 * T.int64(1024) + global_pos_h_f_fused_1) // T.int64(1280)] != -1: + with T.block("k_transpose_append"): + vgpos = T.axis.spatial(ntoken, (global_pos_h_f_fused_0 * T.int64(1024) + global_pos_h_f_fused_1) // T.int64(1280)) + vh = T.axis.spatial(20, T.Cast("int32", (global_pos_h_f_fused_0 * T.int64(1024) + global_pos_h_f_fused_1) % T.int64(1280) // T.int64(64))) + vf = T.axis.spatial(64, T.Cast("int32", (global_pos_h_f_fused_0 * T.int64(1024) + global_pos_h_f_fused_1) % T.int64(64))) + T.where(global_pos_h_f_fused_0 * T.int64(1024) + global_pos_h_f_fused_1 < ntoken * T.int64(1280)) + T.reads(position_map[vgpos], k_data[vgpos, vh, vf]) + T.writes(pages[position_map[vgpos] // 16, 0, vh, position_map[vgpos] % 16, vf]) + position: T.int32 = position_map[vgpos] + pages[position // 16, 0, vh, position % 16, vf] = k_data[vgpos, vh, vf] + with T.block("v_transpose_append"): + vgpos = T.axis.spatial(ntoken, (global_pos_h_f_fused_0 * T.int64(1024) + global_pos_h_f_fused_1) // T.int64(1280)) + vh = T.axis.spatial(20, T.Cast("int32", (global_pos_h_f_fused_0 * T.int64(1024) + global_pos_h_f_fused_1) % T.int64(1280) // T.int64(64))) + vf = T.axis.spatial(64, T.Cast("int32", (global_pos_h_f_fused_0 * T.int64(1024) + global_pos_h_f_fused_1) % T.int64(64))) + T.where(global_pos_h_f_fused_0 * T.int64(1024) + global_pos_h_f_fused_1 < ntoken * T.int64(1280)) + T.reads(position_map[vgpos], v_data[vgpos, vh, vf]) + T.writes(pages[position_map[vgpos] // 16, 1, vh, position_map[vgpos] % 16, vf]) + position: T.int32 = position_map[vgpos] + pages[position // 16, 1, vh, position % 16, vf] = v_data[vgpos, vh, vf] + + @T.prim_func(private=True) + def top_p_pivot_cutoff(var_prob: T.handle, var_top_p_arr: T.handle, var_init_pivots: T.handle, var_final_pivot: T.handle, var_final_lsum: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + B, N = T.int32(), T.int32() + prob = T.match_buffer(var_prob, (B, N)) + top_p_arr = T.match_buffer(var_top_p_arr, (B,)) + init_pivots = T.match_buffer(var_init_pivots, (B, 3)) + final_pivot = T.match_buffer(var_final_pivot, (B,)) + final_lsum = T.match_buffer(var_final_lsum, (B,)) + # with T.block("root"): + pivot = T.alloc_buffer((3,), scope="local") + top_p = T.alloc_buffer((1,), scope="local") + L = T.alloc_buffer((1,), scope="shared") + R_1 = T.alloc_buffer((1,), scope="shared") + L_local = T.alloc_buffer((1,), scope="local") + R_local = T.alloc_buffer((1,), scope="local") + q = T.alloc_buffer((1,), scope="local") + lsum = T.alloc_buffer((3,), scope="local") + lmin_broadcast = T.alloc_buffer((1,), scope="shared") + lmin_broadcast_local = T.alloc_buffer((1,), scope="local") + lmin = T.alloc_buffer((3,), scope="local") + cmin = T.alloc_buffer((3,), "int32", scope="local") + total_sum = T.alloc_buffer((1,), scope="local") + it = T.alloc_buffer((1,), "int32", scope="local") + es_local = T.alloc_buffer((1,), "bool", scope="local") + es = T.alloc_buffer((1,), "bool", scope="shared") + find_pivot_local = T.alloc_buffer((1,), "bool", scope="local") + find_pivot = T.alloc_buffer((1,), "bool", scope="shared") + total_sum_reduce = T.alloc_buffer((1,), scope="local") + lsum_reduce = T.alloc_buffer((1,), scope="local") + lmin_reduce = T.alloc_buffer((1,), scope="local") + cmin_reduce = T.alloc_buffer((1,), "int32", scope="local") + for _bx in T.thread_binding(B, thread="blockIdx.x"): + for _tx in T.thread_binding(1024, thread="threadIdx.x"): + with T.block("CTA"): + b, tx = T.axis.remap("SS", [_bx, _tx]) + T.reads(top_p_arr[b], top_p[0], L[0], R_1[0], init_pivots[b, 0:3], L_local[0], R_local[0], find_pivot_local[0], it[0], es_local[0], prob[b, it[0] * 1024 + tx], total_sum[0], q[0], pivot[T.min(0, it[0]):T.min(0, it[0]) + (T.max(2, it[0]) + 1 - T.min(0, it[0]))], lsum[T.min(0, it[0]):T.min(0, it[0]) + (T.max(2, it[0]) + 1 - T.min(0, it[0]))], lmin[T.min(0, it[0]):T.min(0, it[0]) + (T.max(2, it[0]) + 1 - T.min(0, it[0]))], cmin[T.min(0, it[0]):T.min(0, it[0]) + (T.max(2, it[0]) + 1 - T.min(0, it[0]))], total_sum_reduce[0], es[0], lmin_reduce[0], lmin_broadcast[0], lmin_broadcast_local[0], lsum_reduce[0], cmin_reduce[0], find_pivot[0]) + T.writes(top_p[0], L[0], R_1[0], find_pivot[0], L_local[0], R_local[0], pivot[0:3], find_pivot_local[0], final_lsum[b], final_pivot[b], lsum[0:3], lmin[0:3], cmin[0:3], total_sum[0], it[0], es_local[0], q[0], total_sum_reduce[0], es[0], lsum_reduce[0], lmin_reduce[0], lmin_broadcast[0], lmin_broadcast_local[0], cmin_reduce[0]) + top_p[0] = top_p_arr[b] + if tx == 0: + L[0] = T.float32(1) - top_p[0] + R_1[0] = T.float32(9.9999999999999995e-08) + find_pivot[0] = T.bool(False) + T.tvm_storage_sync("shared") + L_local[0] = L[0] + R_local[0] = R_1[0] + for i in T.unroll(3): + pivot[i] = init_pivots[b, i] + find_pivot_local[0] = T.bool(False) + if L_local[0] - R_local[0] <= T.float32(9.9999999999999995e-08): + if tx == 0: + final_lsum[b] = T.float32(1) + final_pivot[b] = T.float32(0) + find_pivot_local[0] = T.bool(True) + while T.tvm_thread_invariant(L_local[0] - R_local[0] > T.float32(9.9999999999999995e-08) and not find_pivot_local[0]): + T.tvm_storage_sync("shared") + for pidx in T.unroll(3): + lsum[pidx] = T.float32(0) + lmin[pidx] = T.float32(3.4028234663852886e+38) + cmin[pidx] = 0 + total_sum[0] = T.float32(0) + it[0] = 0 + es_local[0] = T.bool(False) + while it[0] < (N + 1024 - 1) // 1024 and not es_local[0]: + q[0] = T.if_then_else(it[0] * 1024 + tx < N, prob[b, it[0] * 1024 + tx], T.float32(0)) + total_sum[0] = total_sum[0] + q[0] + for pidx in T.unroll(3): + if q[0] >= pivot[pidx]: + lsum[pidx] = lsum[pidx] + q[0] + if lmin[pidx] > q[0]: + lmin[pidx] = q[0] + cmin[pidx] = 1 + else: + if lmin[pidx] == q[0]: + cmin[pidx] = cmin[pidx] + 1 + it[0] = it[0] + 1 + if it[0] % 32 == 0: + with T.block("block_cross_thread"): + T.reads(total_sum[0]) + T.writes(total_sum_reduce[0]) + T.attr(T.comm_reducer(lambda x0, y0: x0 + y0, [T.float32(0)]), "reduce_scope", T.reinterpret("handle", T.uint64(0))) + T.tvm_thread_allreduce(T.uint32(1), total_sum[0], T.bool(True), total_sum_reduce[0], tx) + if tx == 0: + es[0] = T.float32(1) - total_sum_reduce[0] < pivot[2] + T.tvm_storage_sync("shared") + es_local[0] = es[0] + T.tvm_storage_sync("shared") + for pidx in range(3): + with T.block("block_cross_thread"): + T.reads(lsum[pidx]) + T.writes(lsum_reduce[0]) + T.attr(T.comm_reducer(lambda x0, y0: x0 + y0, [T.float32(0)]), "reduce_scope", T.reinterpret("handle", T.uint64(0))) + T.tvm_thread_allreduce(T.uint32(1), lsum[pidx], T.bool(True), lsum_reduce[0], tx) + with T.block("block_cross_thread"): + T.reads(lmin[pidx]) + T.writes(lmin_reduce[0]) + T.attr(T.comm_reducer(lambda x0, y0: T.min(x0, y0), [T.float32(0)]), "reduce_scope", T.reinterpret("handle", T.uint64(0))) + T.tvm_thread_allreduce(T.uint32(1), lmin[pidx], T.bool(True), lmin_reduce[0], tx) + if tx == 0: + lmin_broadcast[0] = lmin_reduce[0] + T.tvm_storage_sync("shared") + lmin_broadcast_local[0] = lmin_broadcast[0] + if lmin[pidx] > lmin_broadcast_local[0]: + cmin[pidx] = 0 + if tx == 0: + lsum[pidx] = lsum_reduce[0] + lmin[pidx] = lmin_reduce[0] + with T.block("block_cross_thread"): + T.reads(cmin[pidx]) + T.writes(cmin_reduce[0]) + T.attr(T.comm_reducer(lambda x0, y0: x0 + y0, [0]), "reduce_scope", T.reinterpret("handle", T.uint64(0))) + T.tvm_thread_allreduce(T.uint32(1), cmin[pidx], T.bool(True), cmin_reduce[0], tx) + if tx == 0: + cmin[pidx] = cmin_reduce[0] + T.tvm_storage_sync("shared") + if tx == 0: + it[0] = 0 + while it[0] < 3 and not find_pivot_local[0]: + if lsum[it[0]] >= top_p[0] and top_p[0] > lsum[it[0]] - T.Cast("float32", cmin[it[0]]) * lmin[it[0]]: + find_pivot[0] = T.bool(True) + find_pivot_local[0] = T.bool(True) + final_pivot[b] = pivot[it[0]] + final_lsum[b] = lsum[it[0]] + else: + if lsum[it[0]] - lmin[it[0]] * T.Cast("float32", cmin[it[0]]) >= top_p[0]: + R_1[0] = pivot[it[0]] + final_lsum[b] = lsum[it[0]] + else: + if lsum[it[0]] < top_p[0]: + L[0] = pivot[it[0]] + it[0] = it[0] + 1 + T.tvm_storage_sync("shared") + L_local[0] = L[0] + R_local[0] = R_1[0] + find_pivot_local[0] = find_pivot[0] + for pidx in T.unroll(3): + pivot[pidx] = L[0] - T.Cast("float32", pidx + 1) * (L_local[0] - R_local[0]) / T.float32(4) + if tx == 0: + if not find_pivot_local[0]: + final_pivot[b] = R_local[0] + if R_local[0] == T.float32(9.9999999999999995e-08): + final_lsum[b] = lsum[2] + + @T.prim_func(private=True) + def top_p_renorm_after_cutoff(var_prob: T.handle, var_final_pivot: T.handle, var_final_lsum: T.handle, var_renorm_prob: T.handle): + T.func_attr({"target": T.target({"arch": "sm_89", "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + B, N = T.int32(), T.int32() + prob = T.match_buffer(var_prob, (B, N)) + final_pivot = T.match_buffer(var_final_pivot, (B,)) + final_lsum = T.match_buffer(var_final_lsum, (B,)) + renorm_prob = T.match_buffer(var_renorm_prob, (B, N)) + # with T.block("root"): + pivot = T.alloc_buffer((1,), scope="local") + lsum = T.alloc_buffer((1,), scope="local") + for _by in T.thread_binding(B, thread="blockIdx.y"): + for _bx in T.thread_binding((B + 511) // B, thread="blockIdx.x"): + for _tx in T.thread_binding(1024, thread="threadIdx.x"): + with T.block("CTA"): + by, bx, tx = T.axis.remap("SSS", [_by, _bx, _tx]) + T.reads(final_pivot[by], final_lsum[by], prob[by, T.Select(0 <= (B + 511) // B, 0, (((B + 511) // B * 1024 + N - 1) // ((B + 511) // B * 1024) - 1) * ((B + 511) // B)) * 1024 + bx * 1024 + tx:T.Select(0 <= (B + 511) // B, 0, (((B + 511) // B * 1024 + N - 1) // ((B + 511) // B * 1024) - 1) * ((B + 511) // B)) * 1024 + bx * 1024 + tx + (T.Select(0 <= (B + 511) // B, (N - 1) // ((B + 511) // B * 1024) * ((B + 511) // B), 0 - (((B + 511) // B * 1024 + N - 1) // ((B + 511) // B * 1024) - 1) * ((B + 511) // B)) * 1024 + 1)], pivot[0], lsum[0]) + T.writes(pivot[0], lsum[0], renorm_prob[by, T.Select(0 <= (B + 511) // B, 0, (((B + 511) // B * 1024 + N - 1) // ((B + 511) // B * 1024) - 1) * ((B + 511) // B)) * 1024 + bx * 1024 + tx:T.Select(0 <= (B + 511) // B, 0, (((B + 511) // B * 1024 + N - 1) // ((B + 511) // B * 1024) - 1) * ((B + 511) // B)) * 1024 + bx * 1024 + tx + (T.Select(0 <= (B + 511) // B, (N - 1) // ((B + 511) // B * 1024) * ((B + 511) // B), 0 - (((B + 511) // B * 1024 + N - 1) // ((B + 511) // B * 1024) - 1) * ((B + 511) // B)) * 1024 + 1)]) + pivot[0] = final_pivot[by] + lsum[0] = final_lsum[by] + for i in range(((B + 511) // B * 1024 + N - 1) // ((B + 511) // B * 1024)): + if i * ((512 + B - 1) // B) * 1024 + bx * 1024 + tx < N: + renorm_prob[by, i * ((512 + B - 1) // B) * 1024 + bx * 1024 + tx] = T.if_then_else(prob[by, i * ((512 + B - 1) // B) * 1024 + bx * 1024 + tx] >= pivot[0], prob[by, i * ((512 + B - 1) // B) * 1024 + bx * 1024 + tx] / lsum[0], T.float32(0)) + + @R.function + def argsort_probs(probs: R.Tensor(("batch_size", "vocab_size"), dtype="float32")) -> R.Tuple(R.Tensor(("batch_size", "vocab_size"), dtype="float32"), R.Tensor(("batch_size", "vocab_size"), dtype="int32")): + batch_size = T.int64() + vocab_size = T.int64() + R.func_attr({"relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "num_positions": 48, "num_samples": 8}}) + cls = Module + with R.dataflow(): + lv: R.Tensor((8 * (batch_size * vocab_size * 4) + 8388608 + batch_size * vocab_size * 12,), dtype="uint8") = R.builtin.alloc_tensor(R.shape([8 * (batch_size * vocab_size * 4) + 8388608 + batch_size * vocab_size * 12]), R.dtype("uint8"), R.prim_value(0), R.str("global")) + lv1 = R.call_tir(cls.argsort_thrust, (probs, lv), out_sinfo=R.Tensor((batch_size, vocab_size), dtype="int32")) + lv2 = R.call_tir(cls.take_sorted_probs, (probs, lv1), out_sinfo=R.Tensor((batch_size, vocab_size), dtype="float32")) + gv1: R.Tuple(R.Tensor((batch_size, vocab_size), dtype="float32"), R.Tensor((batch_size, vocab_size), dtype="int32")) = lv2, lv1 + R.output(gv1) + return gv1 + + @R.function + def batch_compute_cross_attn_kv(encoder_hidden_states: R.Tensor(("batch_size", 1500, 1280), dtype="float16"), paged_kv_cache: R.Object, packed_params: R.Tuple(R.Tensor((1280, 128, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1500, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), 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R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"))) -> R.Object: + batch_size = T.int64() + R.func_attr({"num_input": 2, "relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "seq_len": 15000, "total_seq_len": 1500}}) + cls = Module + with R.dataflow(): + model_decoder_layers_0_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[498] + model_decoder_layers_0_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[499] + model_decoder_layers_0_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[500] + model_decoder_layers_1_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[522] + model_decoder_layers_1_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[523] + model_decoder_layers_1_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[524] + model_decoder_layers_2_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[546] + model_decoder_layers_2_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[547] + model_decoder_layers_2_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[548] + model_decoder_layers_3_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[570] + model_decoder_layers_3_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[571] + model_decoder_layers_3_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[572] + model_decoder_layers_4_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[594] + model_decoder_layers_4_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[595] + model_decoder_layers_4_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[596] + model_decoder_layers_5_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[618] + model_decoder_layers_5_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[619] + model_decoder_layers_5_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[620] + model_decoder_layers_6_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[642] + model_decoder_layers_6_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[643] + model_decoder_layers_6_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[644] + model_decoder_layers_7_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[666] + model_decoder_layers_7_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[667] + model_decoder_layers_7_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[668] + model_decoder_layers_8_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[690] + model_decoder_layers_8_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[691] + model_decoder_layers_8_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[692] + model_decoder_layers_9_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[714] + model_decoder_layers_9_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[715] + model_decoder_layers_9_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[716] + model_decoder_layers_10_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[738] + model_decoder_layers_10_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[739] + model_decoder_layers_10_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[740] + model_decoder_layers_11_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[762] + model_decoder_layers_11_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[763] + model_decoder_layers_11_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[764] + model_decoder_layers_12_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[786] + model_decoder_layers_12_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[787] + model_decoder_layers_12_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[788] + model_decoder_layers_13_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[810] + model_decoder_layers_13_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[811] + model_decoder_layers_13_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[812] + model_decoder_layers_14_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[834] + model_decoder_layers_14_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[835] + model_decoder_layers_14_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[836] + model_decoder_layers_15_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[858] + model_decoder_layers_15_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[859] + model_decoder_layers_15_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[860] + model_decoder_layers_16_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[882] + model_decoder_layers_16_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[883] + model_decoder_layers_16_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[884] + model_decoder_layers_17_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[906] + model_decoder_layers_17_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[907] + model_decoder_layers_17_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[908] + model_decoder_layers_18_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[930] + model_decoder_layers_18_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[931] + model_decoder_layers_18_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[932] + model_decoder_layers_19_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[954] + model_decoder_layers_19_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[955] + model_decoder_layers_19_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[956] + model_decoder_layers_20_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[978] + model_decoder_layers_20_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[979] + model_decoder_layers_20_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[980] + model_decoder_layers_21_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1002] + model_decoder_layers_21_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1003] + model_decoder_layers_21_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1004] + model_decoder_layers_22_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1026] + model_decoder_layers_22_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1027] + model_decoder_layers_22_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1028] + model_decoder_layers_23_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1050] + model_decoder_layers_23_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1051] + model_decoder_layers_23_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1052] + model_decoder_layers_24_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1074] + model_decoder_layers_24_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1075] + model_decoder_layers_24_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1076] + model_decoder_layers_25_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1098] + model_decoder_layers_25_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1099] + model_decoder_layers_25_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1100] + model_decoder_layers_26_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1122] + model_decoder_layers_26_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1123] + model_decoder_layers_26_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1124] + model_decoder_layers_27_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1146] + model_decoder_layers_27_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1147] + model_decoder_layers_27_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1148] + model_decoder_layers_28_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1170] + model_decoder_layers_28_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1171] + model_decoder_layers_28_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1172] + model_decoder_layers_29_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1194] + model_decoder_layers_29_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1195] + model_decoder_layers_29_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1196] + model_decoder_layers_30_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1218] + model_decoder_layers_30_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1219] + model_decoder_layers_30_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1220] + model_decoder_layers_31_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1242] + model_decoder_layers_31_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1243] + model_decoder_layers_31_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1244] + lv = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_0_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape256 = R.call_tir(cls.reshape, (lv,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_0_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_0_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape257 = R.call_tir(cls.reshape, (lv_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape258 = R.call_tir(cls.reshape1, (reshape256,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape259 = R.call_tir(cls.reshape1, (reshape257,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv36: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", paged_kv_cache, R.prim_value(0), reshape258, reshape259, sinfo_args=(R.Object,)) + lv1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_1_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape260 = R.call_tir(cls.reshape, (lv1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv1_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_1_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_1_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape261 = R.call_tir(cls.reshape, (lv1_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape262 = R.call_tir(cls.reshape1, (reshape260,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape263 = R.call_tir(cls.reshape1, (reshape261,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv37: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv36, R.prim_value(1), reshape262, reshape263, sinfo_args=(R.Object,)) + lv2 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_2_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape264 = R.call_tir(cls.reshape, (lv2,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv2_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_2_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_2_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape265 = R.call_tir(cls.reshape, (lv2_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape266 = R.call_tir(cls.reshape1, (reshape264,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape267 = R.call_tir(cls.reshape1, (reshape265,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv38: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv37, R.prim_value(2), reshape266, reshape267, sinfo_args=(R.Object,)) + lv3 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_3_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape268 = R.call_tir(cls.reshape, (lv3,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv3_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_3_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_3_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape269 = R.call_tir(cls.reshape, (lv3_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape270 = R.call_tir(cls.reshape1, (reshape268,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape271 = R.call_tir(cls.reshape1, (reshape269,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv39: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv38, R.prim_value(3), reshape270, reshape271, sinfo_args=(R.Object,)) + lv4 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_4_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape272 = R.call_tir(cls.reshape, (lv4,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv4_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_4_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_4_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape273 = R.call_tir(cls.reshape, (lv4_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape274 = R.call_tir(cls.reshape1, (reshape272,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape275 = R.call_tir(cls.reshape1, (reshape273,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv40: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv39, R.prim_value(4), reshape274, reshape275, sinfo_args=(R.Object,)) + lv5 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_5_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape276 = R.call_tir(cls.reshape, (lv5,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv5_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_5_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_5_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape277 = R.call_tir(cls.reshape, (lv5_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape278 = R.call_tir(cls.reshape1, (reshape276,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape279 = R.call_tir(cls.reshape1, (reshape277,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv41: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv40, R.prim_value(5), reshape278, reshape279, sinfo_args=(R.Object,)) + lv6 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_6_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape280 = R.call_tir(cls.reshape, (lv6,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv6_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_6_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_6_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape281 = R.call_tir(cls.reshape, (lv6_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape282 = R.call_tir(cls.reshape1, (reshape280,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape283 = R.call_tir(cls.reshape1, (reshape281,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv42: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv41, R.prim_value(6), reshape282, reshape283, sinfo_args=(R.Object,)) + lv7 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_7_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape284 = R.call_tir(cls.reshape, (lv7,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv7_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_7_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_7_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape285 = R.call_tir(cls.reshape, (lv7_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape286 = R.call_tir(cls.reshape1, (reshape284,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape287 = R.call_tir(cls.reshape1, (reshape285,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv43: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv42, R.prim_value(7), reshape286, reshape287, sinfo_args=(R.Object,)) + lv8 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_8_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape288 = R.call_tir(cls.reshape, (lv8,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv8_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_8_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_8_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape289 = R.call_tir(cls.reshape, (lv8_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape290 = R.call_tir(cls.reshape1, (reshape288,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape291 = R.call_tir(cls.reshape1, (reshape289,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv44: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv43, R.prim_value(8), reshape290, reshape291, sinfo_args=(R.Object,)) + lv9 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_9_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape292 = R.call_tir(cls.reshape, (lv9,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv9_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_9_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_9_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape293 = R.call_tir(cls.reshape, (lv9_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape294 = R.call_tir(cls.reshape1, (reshape292,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape295 = R.call_tir(cls.reshape1, (reshape293,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv45: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv44, R.prim_value(9), reshape294, reshape295, sinfo_args=(R.Object,)) + lv10 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_10_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape296 = R.call_tir(cls.reshape, (lv10,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv10_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_10_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_10_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape297 = R.call_tir(cls.reshape, (lv10_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape298 = R.call_tir(cls.reshape1, (reshape296,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape299 = R.call_tir(cls.reshape1, (reshape297,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv46: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv45, R.prim_value(10), reshape298, reshape299, sinfo_args=(R.Object,)) + lv11 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_11_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape300 = R.call_tir(cls.reshape, (lv11,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv11_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_11_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_11_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape301 = R.call_tir(cls.reshape, (lv11_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape302 = R.call_tir(cls.reshape1, (reshape300,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape303 = R.call_tir(cls.reshape1, (reshape301,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv47: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv46, R.prim_value(11), reshape302, reshape303, sinfo_args=(R.Object,)) + lv12 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_12_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape304 = R.call_tir(cls.reshape, (lv12,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv12_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_12_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_12_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape305 = R.call_tir(cls.reshape, (lv12_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape306 = R.call_tir(cls.reshape1, (reshape304,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape307 = R.call_tir(cls.reshape1, (reshape305,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv48: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv47, R.prim_value(12), reshape306, reshape307, sinfo_args=(R.Object,)) + lv13 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_13_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape308 = R.call_tir(cls.reshape, (lv13,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv13_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_13_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_13_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape309 = R.call_tir(cls.reshape, (lv13_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape310 = R.call_tir(cls.reshape1, (reshape308,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape311 = R.call_tir(cls.reshape1, (reshape309,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv49: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv48, R.prim_value(13), reshape310, reshape311, sinfo_args=(R.Object,)) + lv14 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_14_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape312 = R.call_tir(cls.reshape, (lv14,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv14_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_14_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_14_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape313 = R.call_tir(cls.reshape, (lv14_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape314 = R.call_tir(cls.reshape1, (reshape312,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape315 = R.call_tir(cls.reshape1, (reshape313,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv50: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv49, R.prim_value(14), reshape314, reshape315, sinfo_args=(R.Object,)) + lv15 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_15_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape316 = R.call_tir(cls.reshape, (lv15,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv15_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_15_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_15_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape317 = R.call_tir(cls.reshape, (lv15_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape318 = R.call_tir(cls.reshape1, (reshape316,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape319 = R.call_tir(cls.reshape1, (reshape317,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv51: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv50, R.prim_value(15), reshape318, reshape319, sinfo_args=(R.Object,)) + lv16 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_16_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape320 = R.call_tir(cls.reshape, (lv16,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv16_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_16_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_16_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape321 = R.call_tir(cls.reshape, (lv16_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape322 = R.call_tir(cls.reshape1, (reshape320,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape323 = R.call_tir(cls.reshape1, (reshape321,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv52: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv51, R.prim_value(16), reshape322, reshape323, sinfo_args=(R.Object,)) + lv17 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_17_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape324 = R.call_tir(cls.reshape, (lv17,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv17_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_17_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_17_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape325 = R.call_tir(cls.reshape, (lv17_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape326 = R.call_tir(cls.reshape1, (reshape324,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape327 = R.call_tir(cls.reshape1, (reshape325,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv53: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv52, R.prim_value(17), reshape326, reshape327, sinfo_args=(R.Object,)) + lv18 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_18_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape328 = R.call_tir(cls.reshape, (lv18,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv18_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_18_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_18_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape329 = R.call_tir(cls.reshape, (lv18_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape330 = R.call_tir(cls.reshape1, (reshape328,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape331 = R.call_tir(cls.reshape1, (reshape329,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv54: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv53, R.prim_value(18), reshape330, reshape331, sinfo_args=(R.Object,)) + lv19 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_19_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape332 = R.call_tir(cls.reshape, (lv19,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv19_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_19_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_19_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape333 = R.call_tir(cls.reshape, (lv19_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape334 = R.call_tir(cls.reshape1, (reshape332,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape335 = R.call_tir(cls.reshape1, (reshape333,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv55: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv54, R.prim_value(19), reshape334, reshape335, sinfo_args=(R.Object,)) + lv20 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_20_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape336 = R.call_tir(cls.reshape, (lv20,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv20_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_20_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_20_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape337 = R.call_tir(cls.reshape, (lv20_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape338 = R.call_tir(cls.reshape1, (reshape336,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape339 = R.call_tir(cls.reshape1, (reshape337,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv56: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv55, R.prim_value(20), reshape338, reshape339, sinfo_args=(R.Object,)) + lv21 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_21_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape340 = R.call_tir(cls.reshape, (lv21,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv21_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_21_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_21_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape341 = R.call_tir(cls.reshape, (lv21_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape342 = R.call_tir(cls.reshape1, (reshape340,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape343 = R.call_tir(cls.reshape1, (reshape341,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv57: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv56, R.prim_value(21), reshape342, reshape343, sinfo_args=(R.Object,)) + lv22 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_22_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape344 = R.call_tir(cls.reshape, (lv22,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv22_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_22_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_22_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape345 = R.call_tir(cls.reshape, (lv22_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape346 = R.call_tir(cls.reshape1, (reshape344,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape347 = R.call_tir(cls.reshape1, (reshape345,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv58: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv57, R.prim_value(22), reshape346, reshape347, sinfo_args=(R.Object,)) + lv23 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_23_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape348 = R.call_tir(cls.reshape, (lv23,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv23_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_23_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_23_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape349 = R.call_tir(cls.reshape, (lv23_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape350 = R.call_tir(cls.reshape1, (reshape348,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape351 = R.call_tir(cls.reshape1, (reshape349,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv59: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv58, R.prim_value(23), reshape350, reshape351, sinfo_args=(R.Object,)) + lv24 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_24_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape352 = R.call_tir(cls.reshape, (lv24,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv24_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_24_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_24_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape353 = R.call_tir(cls.reshape, (lv24_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape354 = R.call_tir(cls.reshape1, (reshape352,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape355 = R.call_tir(cls.reshape1, (reshape353,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv60: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv59, R.prim_value(24), reshape354, reshape355, sinfo_args=(R.Object,)) + lv25 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_25_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape356 = R.call_tir(cls.reshape, (lv25,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv25_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_25_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_25_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape357 = R.call_tir(cls.reshape, (lv25_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape358 = R.call_tir(cls.reshape1, (reshape356,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape359 = R.call_tir(cls.reshape1, (reshape357,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv61: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv60, R.prim_value(25), reshape358, reshape359, sinfo_args=(R.Object,)) + lv26 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_26_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape360 = R.call_tir(cls.reshape, (lv26,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv26_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_26_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_26_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape361 = R.call_tir(cls.reshape, (lv26_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape362 = R.call_tir(cls.reshape1, (reshape360,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape363 = R.call_tir(cls.reshape1, (reshape361,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv62: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv61, R.prim_value(26), reshape362, reshape363, sinfo_args=(R.Object,)) + lv27 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_27_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape364 = R.call_tir(cls.reshape, (lv27,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv27_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_27_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_27_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape365 = R.call_tir(cls.reshape, (lv27_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape366 = R.call_tir(cls.reshape1, (reshape364,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape367 = R.call_tir(cls.reshape1, (reshape365,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv63: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv62, R.prim_value(27), reshape366, reshape367, sinfo_args=(R.Object,)) + lv28 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_28_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape368 = R.call_tir(cls.reshape, (lv28,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv28_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_28_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_28_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape369 = R.call_tir(cls.reshape, (lv28_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape370 = R.call_tir(cls.reshape1, (reshape368,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape371 = R.call_tir(cls.reshape1, (reshape369,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv64: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv63, R.prim_value(28), reshape370, reshape371, sinfo_args=(R.Object,)) + lv29 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_29_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape372 = R.call_tir(cls.reshape, (lv29,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv29_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_29_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_29_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape373 = R.call_tir(cls.reshape, (lv29_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape374 = R.call_tir(cls.reshape1, (reshape372,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape375 = R.call_tir(cls.reshape1, (reshape373,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv65: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv64, R.prim_value(29), reshape374, reshape375, sinfo_args=(R.Object,)) + lv30 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_30_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape376 = R.call_tir(cls.reshape, (lv30,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv30_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_30_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_30_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape377 = R.call_tir(cls.reshape, (lv30_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape378 = R.call_tir(cls.reshape1, (reshape376,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape379 = R.call_tir(cls.reshape1, (reshape377,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv66: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv65, R.prim_value(30), reshape378, reshape379, sinfo_args=(R.Object,)) + lv31 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_31_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape380 = R.call_tir(cls.reshape, (lv31,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv31_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_31_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_31_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape381 = R.call_tir(cls.reshape, (lv31_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape382 = R.call_tir(cls.reshape1, (reshape380,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape383 = R.call_tir(cls.reshape1, (reshape381,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + gv1: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv66, R.prim_value(31), reshape382, reshape383, sinfo_args=(R.Object,)) + R.output(gv1) + return gv1 + + @R.function + def batch_decode(input_ids: R.Tensor(("batch_size", 1), dtype="int32"), paged_kv_cache: R.Object, packed_params: R.Tuple(R.Tensor((1280, 128, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1500, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), 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R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"))) -> R.Tensor(("batch_size", 1, 51866), dtype="float32"): + batch_size = T.int64() + R.func_attr({"num_input": 2, "relax.memory_plan_dynamic_func_output": 1, "relax.rewrite_cuda_graph.capture_symbolic_vars": ["batch_size"], "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "seq_len": 15000, "total_seq_len": 1500}}) + cls = Module + with R.dataflow(): + model_decoder_embed_tokens_weight3: R.Tensor((51866, 1280), dtype="float16") = packed_params[487] + model_decoder_embed_positions_weight3: R.Tensor((448, 1280), dtype="float16") = packed_params[488] + model_decoder_layers_0_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[489] + model_decoder_layers_0_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[490] + model_decoder_layers_0_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[491] + model_decoder_layers_0_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[492] + model_decoder_layers_0_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[493] + model_decoder_layers_0_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[494] + model_decoder_layers_0_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[495] + model_decoder_layers_0_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[496] + model_decoder_layers_0_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[497] + model_decoder_layers_0_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[501] + model_decoder_layers_0_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[502] + model_decoder_layers_0_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[503] + model_decoder_layers_0_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[504] + model_decoder_layers_0_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[505] + model_decoder_layers_0_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[506] + model_decoder_layers_0_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[507] + model_decoder_layers_0_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[508] + model_decoder_layers_0_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[509] + model_decoder_layers_0_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[510] + model_decoder_layers_0_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[511] + model_decoder_layers_0_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[512] + model_decoder_layers_1_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[513] + model_decoder_layers_1_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[514] + model_decoder_layers_1_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[515] + model_decoder_layers_1_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[516] + model_decoder_layers_1_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[517] + model_decoder_layers_1_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[518] + model_decoder_layers_1_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[519] + model_decoder_layers_1_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[520] + model_decoder_layers_1_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[521] + model_decoder_layers_1_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[525] + model_decoder_layers_1_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[526] + model_decoder_layers_1_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[527] + model_decoder_layers_1_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[528] + model_decoder_layers_1_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[529] + model_decoder_layers_1_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[530] + model_decoder_layers_1_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[531] + model_decoder_layers_1_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[532] + model_decoder_layers_1_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[533] + model_decoder_layers_1_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[534] + model_decoder_layers_1_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[535] + model_decoder_layers_1_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[536] + model_decoder_layers_2_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[537] + model_decoder_layers_2_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[538] + model_decoder_layers_2_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[539] + model_decoder_layers_2_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[540] + model_decoder_layers_2_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[541] + model_decoder_layers_2_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[542] + model_decoder_layers_2_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[543] + model_decoder_layers_2_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[544] + model_decoder_layers_2_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[545] + model_decoder_layers_2_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[549] + model_decoder_layers_2_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[550] + model_decoder_layers_2_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[551] + model_decoder_layers_2_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[552] + model_decoder_layers_2_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[553] + model_decoder_layers_2_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[554] + model_decoder_layers_2_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[555] + model_decoder_layers_2_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[556] + model_decoder_layers_2_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[557] + model_decoder_layers_2_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[558] + model_decoder_layers_2_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[559] + model_decoder_layers_2_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[560] + model_decoder_layers_3_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[561] + model_decoder_layers_3_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[562] + model_decoder_layers_3_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[563] + model_decoder_layers_3_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[564] + model_decoder_layers_3_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[565] + model_decoder_layers_3_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[566] + model_decoder_layers_3_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[567] + model_decoder_layers_3_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[568] + model_decoder_layers_3_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[569] + model_decoder_layers_3_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[573] + model_decoder_layers_3_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[574] + model_decoder_layers_3_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[575] + model_decoder_layers_3_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[576] + model_decoder_layers_3_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[577] + model_decoder_layers_3_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[578] + model_decoder_layers_3_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[579] + model_decoder_layers_3_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[580] + model_decoder_layers_3_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[581] + model_decoder_layers_3_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[582] + model_decoder_layers_3_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[583] + model_decoder_layers_3_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[584] + model_decoder_layers_4_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[585] + model_decoder_layers_4_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[586] + model_decoder_layers_4_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[587] + model_decoder_layers_4_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[588] + model_decoder_layers_4_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[589] + model_decoder_layers_4_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[590] + model_decoder_layers_4_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[591] + model_decoder_layers_4_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[592] + model_decoder_layers_4_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[593] + model_decoder_layers_4_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[597] + model_decoder_layers_4_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[598] + model_decoder_layers_4_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[599] + model_decoder_layers_4_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[600] + model_decoder_layers_4_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[601] + model_decoder_layers_4_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[602] + model_decoder_layers_4_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[603] + model_decoder_layers_4_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[604] + model_decoder_layers_4_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[605] + model_decoder_layers_4_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[606] + model_decoder_layers_4_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[607] + model_decoder_layers_4_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[608] + model_decoder_layers_5_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[609] + model_decoder_layers_5_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[610] + model_decoder_layers_5_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[611] + model_decoder_layers_5_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[612] + model_decoder_layers_5_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[613] + model_decoder_layers_5_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[614] + model_decoder_layers_5_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[615] + model_decoder_layers_5_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[616] + model_decoder_layers_5_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[617] + model_decoder_layers_5_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[621] + model_decoder_layers_5_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[622] + model_decoder_layers_5_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[623] + model_decoder_layers_5_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[624] + model_decoder_layers_5_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[625] + model_decoder_layers_5_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[626] + model_decoder_layers_5_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[627] + model_decoder_layers_5_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[628] + model_decoder_layers_5_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[629] + model_decoder_layers_5_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[630] + model_decoder_layers_5_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[631] + model_decoder_layers_5_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[632] + model_decoder_layers_6_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[633] + model_decoder_layers_6_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[634] + model_decoder_layers_6_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[635] + model_decoder_layers_6_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[636] + model_decoder_layers_6_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[637] + model_decoder_layers_6_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[638] + model_decoder_layers_6_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[639] + model_decoder_layers_6_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[640] + model_decoder_layers_6_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[641] + model_decoder_layers_6_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[645] + model_decoder_layers_6_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[646] + model_decoder_layers_6_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[647] + model_decoder_layers_6_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[648] + model_decoder_layers_6_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[649] + model_decoder_layers_6_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[650] + model_decoder_layers_6_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[651] + model_decoder_layers_6_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[652] + model_decoder_layers_6_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[653] + model_decoder_layers_6_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[654] + model_decoder_layers_6_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[655] + model_decoder_layers_6_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[656] + model_decoder_layers_7_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[657] + model_decoder_layers_7_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[658] + model_decoder_layers_7_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[659] + model_decoder_layers_7_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[660] + model_decoder_layers_7_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[661] + model_decoder_layers_7_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[662] + model_decoder_layers_7_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[663] + model_decoder_layers_7_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[664] + model_decoder_layers_7_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[665] + model_decoder_layers_7_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[669] + model_decoder_layers_7_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[670] + model_decoder_layers_7_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[671] + model_decoder_layers_7_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[672] + model_decoder_layers_7_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[673] + model_decoder_layers_7_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[674] + model_decoder_layers_7_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[675] + model_decoder_layers_7_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[676] + model_decoder_layers_7_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[677] + model_decoder_layers_7_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[678] + model_decoder_layers_7_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[679] + model_decoder_layers_7_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[680] + model_decoder_layers_8_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[681] + model_decoder_layers_8_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[682] + model_decoder_layers_8_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[683] + model_decoder_layers_8_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[684] + model_decoder_layers_8_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[685] + model_decoder_layers_8_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[686] + model_decoder_layers_8_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[687] + model_decoder_layers_8_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[688] + model_decoder_layers_8_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[689] + model_decoder_layers_8_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[693] + model_decoder_layers_8_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[694] + model_decoder_layers_8_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[695] + model_decoder_layers_8_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[696] + model_decoder_layers_8_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[697] + model_decoder_layers_8_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[698] + model_decoder_layers_8_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[699] + model_decoder_layers_8_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[700] + model_decoder_layers_8_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[701] + model_decoder_layers_8_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[702] + model_decoder_layers_8_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[703] + model_decoder_layers_8_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[704] + model_decoder_layers_9_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[705] + model_decoder_layers_9_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[706] + model_decoder_layers_9_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[707] + model_decoder_layers_9_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[708] + model_decoder_layers_9_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[709] + model_decoder_layers_9_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[710] + model_decoder_layers_9_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[711] + model_decoder_layers_9_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[712] + model_decoder_layers_9_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[713] + model_decoder_layers_9_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[717] + model_decoder_layers_9_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[718] + model_decoder_layers_9_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[719] + model_decoder_layers_9_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[720] + model_decoder_layers_9_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[721] + model_decoder_layers_9_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[722] + model_decoder_layers_9_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[723] + model_decoder_layers_9_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[724] + model_decoder_layers_9_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[725] + model_decoder_layers_9_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[726] + model_decoder_layers_9_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[727] + model_decoder_layers_9_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[728] + model_decoder_layers_10_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[729] + model_decoder_layers_10_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[730] + model_decoder_layers_10_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[731] + model_decoder_layers_10_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[732] + model_decoder_layers_10_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[733] + model_decoder_layers_10_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[734] + model_decoder_layers_10_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[735] + model_decoder_layers_10_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[736] + model_decoder_layers_10_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[737] + model_decoder_layers_10_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[741] + model_decoder_layers_10_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[742] + model_decoder_layers_10_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[743] + model_decoder_layers_10_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[744] + model_decoder_layers_10_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[745] + model_decoder_layers_10_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[746] + model_decoder_layers_10_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[747] + model_decoder_layers_10_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[748] + model_decoder_layers_10_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[749] + model_decoder_layers_10_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[750] + model_decoder_layers_10_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[751] + model_decoder_layers_10_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[752] + model_decoder_layers_11_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[753] + model_decoder_layers_11_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[754] + model_decoder_layers_11_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[755] + model_decoder_layers_11_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[756] + model_decoder_layers_11_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[757] + model_decoder_layers_11_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[758] + model_decoder_layers_11_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[759] + model_decoder_layers_11_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[760] + model_decoder_layers_11_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[761] + model_decoder_layers_11_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[765] + model_decoder_layers_11_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[766] + model_decoder_layers_11_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[767] + model_decoder_layers_11_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[768] + model_decoder_layers_11_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[769] + model_decoder_layers_11_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[770] + model_decoder_layers_11_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[771] + model_decoder_layers_11_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[772] + model_decoder_layers_11_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[773] + model_decoder_layers_11_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[774] + model_decoder_layers_11_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[775] + model_decoder_layers_11_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[776] + model_decoder_layers_12_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[777] + model_decoder_layers_12_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[778] + model_decoder_layers_12_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[779] + model_decoder_layers_12_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[780] + model_decoder_layers_12_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[781] + model_decoder_layers_12_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[782] + model_decoder_layers_12_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[783] + model_decoder_layers_12_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[784] + model_decoder_layers_12_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[785] + model_decoder_layers_12_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[789] + model_decoder_layers_12_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[790] + model_decoder_layers_12_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[791] + model_decoder_layers_12_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[792] + model_decoder_layers_12_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[793] + model_decoder_layers_12_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[794] + model_decoder_layers_12_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[795] + model_decoder_layers_12_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[796] + model_decoder_layers_12_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[797] + model_decoder_layers_12_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[798] + model_decoder_layers_12_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[799] + model_decoder_layers_12_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[800] + model_decoder_layers_13_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[801] + model_decoder_layers_13_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[802] + model_decoder_layers_13_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[803] + model_decoder_layers_13_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[804] + model_decoder_layers_13_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[805] + model_decoder_layers_13_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[806] + model_decoder_layers_13_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[807] + model_decoder_layers_13_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[808] + model_decoder_layers_13_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[809] + model_decoder_layers_13_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[813] + model_decoder_layers_13_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[814] + model_decoder_layers_13_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[815] + model_decoder_layers_13_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[816] + model_decoder_layers_13_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[817] + model_decoder_layers_13_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[818] + model_decoder_layers_13_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[819] + model_decoder_layers_13_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[820] + model_decoder_layers_13_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[821] + model_decoder_layers_13_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[822] + model_decoder_layers_13_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[823] + model_decoder_layers_13_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[824] + model_decoder_layers_14_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[825] + model_decoder_layers_14_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[826] + model_decoder_layers_14_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[827] + model_decoder_layers_14_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[828] + model_decoder_layers_14_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[829] + model_decoder_layers_14_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[830] + model_decoder_layers_14_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[831] + model_decoder_layers_14_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[832] + model_decoder_layers_14_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[833] + model_decoder_layers_14_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[837] + model_decoder_layers_14_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[838] + model_decoder_layers_14_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[839] + model_decoder_layers_14_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[840] + model_decoder_layers_14_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[841] + model_decoder_layers_14_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[842] + model_decoder_layers_14_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[843] + model_decoder_layers_14_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[844] + model_decoder_layers_14_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[845] + model_decoder_layers_14_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[846] + model_decoder_layers_14_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[847] + model_decoder_layers_14_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[848] + model_decoder_layers_15_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[849] + model_decoder_layers_15_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[850] + model_decoder_layers_15_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[851] + model_decoder_layers_15_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[852] + model_decoder_layers_15_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[853] + model_decoder_layers_15_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[854] + model_decoder_layers_15_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[855] + model_decoder_layers_15_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[856] + model_decoder_layers_15_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[857] + model_decoder_layers_15_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[861] + model_decoder_layers_15_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[862] + model_decoder_layers_15_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[863] + model_decoder_layers_15_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[864] + model_decoder_layers_15_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[865] + model_decoder_layers_15_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[866] + model_decoder_layers_15_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[867] + model_decoder_layers_15_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[868] + model_decoder_layers_15_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[869] + model_decoder_layers_15_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[870] + model_decoder_layers_15_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[871] + model_decoder_layers_15_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[872] + model_decoder_layers_16_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[873] + model_decoder_layers_16_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[874] + model_decoder_layers_16_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[875] + model_decoder_layers_16_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[876] + model_decoder_layers_16_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[877] + model_decoder_layers_16_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[878] + model_decoder_layers_16_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[879] + model_decoder_layers_16_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[880] + model_decoder_layers_16_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[881] + model_decoder_layers_16_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[885] + model_decoder_layers_16_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[886] + model_decoder_layers_16_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[887] + model_decoder_layers_16_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[888] + model_decoder_layers_16_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[889] + model_decoder_layers_16_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[890] + model_decoder_layers_16_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[891] + model_decoder_layers_16_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[892] + model_decoder_layers_16_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[893] + model_decoder_layers_16_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[894] + model_decoder_layers_16_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[895] + model_decoder_layers_16_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[896] + model_decoder_layers_17_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[897] + model_decoder_layers_17_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[898] + model_decoder_layers_17_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[899] + model_decoder_layers_17_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[900] + model_decoder_layers_17_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[901] + model_decoder_layers_17_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[902] + model_decoder_layers_17_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[903] + model_decoder_layers_17_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[904] + model_decoder_layers_17_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[905] + model_decoder_layers_17_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[909] + model_decoder_layers_17_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[910] + model_decoder_layers_17_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[911] + model_decoder_layers_17_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[912] + model_decoder_layers_17_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[913] + model_decoder_layers_17_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[914] + model_decoder_layers_17_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[915] + model_decoder_layers_17_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[916] + model_decoder_layers_17_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[917] + model_decoder_layers_17_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[918] + model_decoder_layers_17_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[919] + model_decoder_layers_17_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[920] + model_decoder_layers_18_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[921] + model_decoder_layers_18_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[922] + model_decoder_layers_18_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[923] + model_decoder_layers_18_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[924] + model_decoder_layers_18_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[925] + model_decoder_layers_18_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[926] + model_decoder_layers_18_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[927] + model_decoder_layers_18_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[928] + model_decoder_layers_18_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[929] + model_decoder_layers_18_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[933] + model_decoder_layers_18_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[934] + model_decoder_layers_18_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[935] + model_decoder_layers_18_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[936] + model_decoder_layers_18_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[937] + model_decoder_layers_18_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[938] + model_decoder_layers_18_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[939] + model_decoder_layers_18_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[940] + model_decoder_layers_18_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[941] + model_decoder_layers_18_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[942] + model_decoder_layers_18_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[943] + model_decoder_layers_18_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[944] + model_decoder_layers_19_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[945] + model_decoder_layers_19_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[946] + model_decoder_layers_19_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[947] + model_decoder_layers_19_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[948] + model_decoder_layers_19_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[949] + model_decoder_layers_19_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[950] + model_decoder_layers_19_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[951] + model_decoder_layers_19_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[952] + model_decoder_layers_19_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[953] + model_decoder_layers_19_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[957] + model_decoder_layers_19_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[958] + model_decoder_layers_19_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[959] + model_decoder_layers_19_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[960] + model_decoder_layers_19_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[961] + model_decoder_layers_19_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[962] + model_decoder_layers_19_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[963] + model_decoder_layers_19_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[964] + model_decoder_layers_19_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[965] + model_decoder_layers_19_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[966] + model_decoder_layers_19_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[967] + model_decoder_layers_19_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[968] + model_decoder_layers_20_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[969] + model_decoder_layers_20_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[970] + model_decoder_layers_20_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[971] + model_decoder_layers_20_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[972] + model_decoder_layers_20_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[973] + model_decoder_layers_20_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[974] + model_decoder_layers_20_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[975] + model_decoder_layers_20_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[976] + model_decoder_layers_20_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[977] + model_decoder_layers_20_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[981] + model_decoder_layers_20_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[982] + model_decoder_layers_20_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[983] + model_decoder_layers_20_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[984] + model_decoder_layers_20_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[985] + model_decoder_layers_20_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[986] + model_decoder_layers_20_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[987] + model_decoder_layers_20_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[988] + model_decoder_layers_20_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[989] + model_decoder_layers_20_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[990] + model_decoder_layers_20_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[991] + model_decoder_layers_20_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[992] + model_decoder_layers_21_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[993] + model_decoder_layers_21_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[994] + model_decoder_layers_21_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[995] + model_decoder_layers_21_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[996] + model_decoder_layers_21_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[997] + model_decoder_layers_21_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[998] + model_decoder_layers_21_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[999] + model_decoder_layers_21_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1000] + model_decoder_layers_21_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1001] + model_decoder_layers_21_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1005] + model_decoder_layers_21_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1006] + model_decoder_layers_21_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1007] + model_decoder_layers_21_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1008] + model_decoder_layers_21_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1009] + model_decoder_layers_21_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1010] + model_decoder_layers_21_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1011] + model_decoder_layers_21_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1012] + model_decoder_layers_21_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1013] + model_decoder_layers_21_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1014] + model_decoder_layers_21_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1015] + model_decoder_layers_21_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1016] + model_decoder_layers_22_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1017] + model_decoder_layers_22_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1018] + model_decoder_layers_22_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1019] + model_decoder_layers_22_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1020] + model_decoder_layers_22_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1021] + model_decoder_layers_22_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1022] + model_decoder_layers_22_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1023] + model_decoder_layers_22_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1024] + model_decoder_layers_22_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1025] + model_decoder_layers_22_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1029] + model_decoder_layers_22_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1030] + model_decoder_layers_22_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1031] + model_decoder_layers_22_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1032] + model_decoder_layers_22_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1033] + model_decoder_layers_22_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1034] + model_decoder_layers_22_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1035] + model_decoder_layers_22_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1036] + model_decoder_layers_22_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1037] + model_decoder_layers_22_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1038] + model_decoder_layers_22_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1039] + model_decoder_layers_22_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1040] + model_decoder_layers_23_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1041] + model_decoder_layers_23_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1042] + model_decoder_layers_23_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1043] + model_decoder_layers_23_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1044] + model_decoder_layers_23_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1045] + model_decoder_layers_23_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1046] + model_decoder_layers_23_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1047] + model_decoder_layers_23_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1048] + model_decoder_layers_23_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1049] + model_decoder_layers_23_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1053] + model_decoder_layers_23_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1054] + model_decoder_layers_23_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1055] + model_decoder_layers_23_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1056] + model_decoder_layers_23_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1057] + model_decoder_layers_23_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1058] + model_decoder_layers_23_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1059] + model_decoder_layers_23_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1060] + model_decoder_layers_23_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1061] + model_decoder_layers_23_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1062] + model_decoder_layers_23_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1063] + model_decoder_layers_23_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1064] + model_decoder_layers_24_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1065] + model_decoder_layers_24_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1066] + model_decoder_layers_24_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1067] + model_decoder_layers_24_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1068] + model_decoder_layers_24_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1069] + model_decoder_layers_24_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1070] + model_decoder_layers_24_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1071] + model_decoder_layers_24_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1072] + model_decoder_layers_24_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1073] + model_decoder_layers_24_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1077] + model_decoder_layers_24_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1078] + model_decoder_layers_24_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1079] + model_decoder_layers_24_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1080] + model_decoder_layers_24_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1081] + model_decoder_layers_24_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1082] + model_decoder_layers_24_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1083] + model_decoder_layers_24_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1084] + model_decoder_layers_24_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1085] + model_decoder_layers_24_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1086] + model_decoder_layers_24_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1087] + model_decoder_layers_24_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1088] + model_decoder_layers_25_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1089] + model_decoder_layers_25_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1090] + model_decoder_layers_25_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1091] + model_decoder_layers_25_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1092] + model_decoder_layers_25_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1093] + model_decoder_layers_25_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1094] + model_decoder_layers_25_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1095] + model_decoder_layers_25_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1096] + model_decoder_layers_25_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1097] + model_decoder_layers_25_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1101] + model_decoder_layers_25_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1102] + model_decoder_layers_25_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1103] + model_decoder_layers_25_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1104] + model_decoder_layers_25_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1105] + model_decoder_layers_25_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1106] + model_decoder_layers_25_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1107] + model_decoder_layers_25_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1108] + model_decoder_layers_25_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1109] + model_decoder_layers_25_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1110] + model_decoder_layers_25_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1111] + model_decoder_layers_25_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1112] + model_decoder_layers_26_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1113] + model_decoder_layers_26_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1114] + model_decoder_layers_26_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1115] + model_decoder_layers_26_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1116] + model_decoder_layers_26_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1117] + model_decoder_layers_26_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1118] + model_decoder_layers_26_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1119] + model_decoder_layers_26_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1120] + model_decoder_layers_26_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1121] + model_decoder_layers_26_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1125] + model_decoder_layers_26_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1126] + model_decoder_layers_26_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1127] + model_decoder_layers_26_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1128] + model_decoder_layers_26_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1129] + model_decoder_layers_26_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1130] + model_decoder_layers_26_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1131] + model_decoder_layers_26_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1132] + model_decoder_layers_26_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1133] + model_decoder_layers_26_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1134] + model_decoder_layers_26_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1135] + model_decoder_layers_26_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1136] + model_decoder_layers_27_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1137] + model_decoder_layers_27_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1138] + model_decoder_layers_27_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1139] + model_decoder_layers_27_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1140] + model_decoder_layers_27_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1141] + model_decoder_layers_27_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1142] + model_decoder_layers_27_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1143] + model_decoder_layers_27_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1144] + model_decoder_layers_27_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1145] + model_decoder_layers_27_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1149] + model_decoder_layers_27_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1150] + model_decoder_layers_27_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1151] + model_decoder_layers_27_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1152] + model_decoder_layers_27_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1153] + model_decoder_layers_27_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1154] + model_decoder_layers_27_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1155] + model_decoder_layers_27_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1156] + model_decoder_layers_27_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1157] + model_decoder_layers_27_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1158] + model_decoder_layers_27_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1159] + model_decoder_layers_27_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1160] + model_decoder_layers_28_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1161] + model_decoder_layers_28_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1162] + model_decoder_layers_28_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1163] + model_decoder_layers_28_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1164] + model_decoder_layers_28_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1165] + model_decoder_layers_28_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1166] + model_decoder_layers_28_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1167] + model_decoder_layers_28_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1168] + model_decoder_layers_28_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1169] + model_decoder_layers_28_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1173] + model_decoder_layers_28_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1174] + model_decoder_layers_28_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1175] + model_decoder_layers_28_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1176] + model_decoder_layers_28_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1177] + model_decoder_layers_28_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1178] + model_decoder_layers_28_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1179] + model_decoder_layers_28_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1180] + model_decoder_layers_28_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1181] + model_decoder_layers_28_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1182] + model_decoder_layers_28_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1183] + model_decoder_layers_28_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1184] + model_decoder_layers_29_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1185] + model_decoder_layers_29_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1186] + model_decoder_layers_29_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1187] + model_decoder_layers_29_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1188] + model_decoder_layers_29_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1189] + model_decoder_layers_29_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1190] + model_decoder_layers_29_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1191] + model_decoder_layers_29_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1192] + model_decoder_layers_29_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1193] + model_decoder_layers_29_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1197] + model_decoder_layers_29_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1198] + model_decoder_layers_29_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1199] + model_decoder_layers_29_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1200] + model_decoder_layers_29_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1201] + model_decoder_layers_29_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1202] + model_decoder_layers_29_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1203] + model_decoder_layers_29_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1204] + model_decoder_layers_29_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1205] + model_decoder_layers_29_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1206] + model_decoder_layers_29_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1207] + model_decoder_layers_29_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1208] + model_decoder_layers_30_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1209] + model_decoder_layers_30_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1210] + model_decoder_layers_30_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1211] + model_decoder_layers_30_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1212] + model_decoder_layers_30_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1213] + model_decoder_layers_30_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1214] + model_decoder_layers_30_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1215] + model_decoder_layers_30_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1216] + model_decoder_layers_30_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1217] + model_decoder_layers_30_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1221] + model_decoder_layers_30_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1222] + model_decoder_layers_30_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1223] + model_decoder_layers_30_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1224] + model_decoder_layers_30_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1225] + model_decoder_layers_30_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1226] + model_decoder_layers_30_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1227] + model_decoder_layers_30_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1228] + model_decoder_layers_30_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1229] + model_decoder_layers_30_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1230] + model_decoder_layers_30_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1231] + model_decoder_layers_30_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1232] + model_decoder_layers_31_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1233] + model_decoder_layers_31_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1234] + model_decoder_layers_31_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1235] + model_decoder_layers_31_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1236] + model_decoder_layers_31_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1237] + model_decoder_layers_31_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1238] + model_decoder_layers_31_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1239] + model_decoder_layers_31_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1240] + model_decoder_layers_31_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1241] + model_decoder_layers_31_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1245] + model_decoder_layers_31_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1246] + model_decoder_layers_31_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1247] + model_decoder_layers_31_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1248] + model_decoder_layers_31_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1249] + model_decoder_layers_31_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1250] + model_decoder_layers_31_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1251] + model_decoder_layers_31_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1252] + model_decoder_layers_31_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1253] + model_decoder_layers_31_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1254] + model_decoder_layers_31_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1255] + model_decoder_layers_31_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1256] + model_decoder_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1257] + model_decoder_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1258] + reshape707 = R.call_tir(cls.reshape2, (input_ids,), out_sinfo=R.Tensor((batch_size,), dtype="int32")) + take3 = R.call_tir(cls.take, (model_decoder_embed_tokens_weight3, reshape707), out_sinfo=R.Tensor((batch_size, 1280), dtype="float16")) + reshape708 = R.call_tir(cls.reshape3, (take3,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv133: R.Tensor((batch_size,), dtype="int32") = R.call_pure_packed("vm.builtin.attention_kv_cache_get_query_positions", paged_kv_cache, sinfo_args=(R.Tensor((batch_size,), dtype="int32"),)) + take4 = R.call_tir(cls.take1, (model_decoder_embed_positions_weight3, lv133), out_sinfo=R.Tensor((batch_size, 1280), dtype="float16")) + reshape709 = R.call_tir(cls.reshape3, (take4,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add578 = R.call_tir(cls.add, (reshape708, reshape709), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm162 = R.call_tir(cls.layer_norm, (add578, model_decoder_layers_0_self_attn_layer_norm_weight3, model_decoder_layers_0_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv224 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_0_self_attn_q_proj_weight3, layer_norm162, model_decoder_layers_0_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape710 = R.call_tir(cls.reshape4, (lv224,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv65 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_0_self_attn_k_proj_weight3, layer_norm162), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape711 = R.call_tir(cls.reshape4, (lv65,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv225 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_0_self_attn_v_proj_weight3, layer_norm162, model_decoder_layers_0_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape712 = R.call_tir(cls.reshape4, (lv225,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat32 = R.call_tir(cls.concatenate, (reshape710, reshape711, reshape712), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape713 = R.call_tir(cls.reshape5, (concat32,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv134 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), reshape713), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape714 = R.call_tir(cls.reshape6, (lv134,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape715 = R.call_tir(cls.reshape7, (reshape714,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv226 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_0_self_attn_out_proj_weight3, reshape715, model_decoder_layers_0_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add582 = R.call_tir(cls.add, (add578, lv226), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm163 = R.call_tir(cls.layer_norm, (add582, model_decoder_layers_0_encoder_attn_layer_norm_weight3, model_decoder_layers_0_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv227 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_0_encoder_attn_q_proj_weight3, layer_norm163, model_decoder_layers_0_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape716 = R.call_tir(cls.reshape4, (lv227,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape717 = R.call_tir(cls.reshape8, (reshape716,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv135 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), reshape717), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape718 = R.call_tir(cls.reshape6, (lv135,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape719 = R.call_tir(cls.reshape7, (reshape718,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv228 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_0_encoder_attn_out_proj_weight3, reshape719, model_decoder_layers_0_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add585 = R.call_tir(cls.add, (add582, lv228), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm164 = R.call_tir(cls.layer_norm, (add585, model_decoder_layers_0_final_layer_norm_weight3, model_decoder_layers_0_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv32 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_0_fc1_weight3, layer_norm164, model_decoder_layers_0_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv229 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_0_fc2_weight3, lv32, model_decoder_layers_0_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add588 = R.call_tir(cls.add, (add585, lv229), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm165 = R.call_tir(cls.layer_norm, (add588, model_decoder_layers_1_self_attn_layer_norm_weight3, model_decoder_layers_1_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv230 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_1_self_attn_q_proj_weight3, layer_norm165, model_decoder_layers_1_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape720 = R.call_tir(cls.reshape4, (lv230,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv66 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_1_self_attn_k_proj_weight3, layer_norm165), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape721 = R.call_tir(cls.reshape4, (lv66,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv231 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_1_self_attn_v_proj_weight3, layer_norm165, model_decoder_layers_1_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape722 = R.call_tir(cls.reshape4, (lv231,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat33 = R.call_tir(cls.concatenate, (reshape720, reshape721, reshape722), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape723 = R.call_tir(cls.reshape5, (concat33,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv136 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), reshape723), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape724 = R.call_tir(cls.reshape6, (lv136,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape725 = R.call_tir(cls.reshape7, (reshape724,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv232 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_1_self_attn_out_proj_weight3, reshape725, model_decoder_layers_1_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add592 = R.call_tir(cls.add, (add588, lv232), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm166 = R.call_tir(cls.layer_norm, (add592, model_decoder_layers_1_encoder_attn_layer_norm_weight3, model_decoder_layers_1_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv233 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_1_encoder_attn_q_proj_weight3, layer_norm166, model_decoder_layers_1_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape726 = R.call_tir(cls.reshape4, (lv233,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape727 = R.call_tir(cls.reshape8, (reshape726,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv137 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), reshape727), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape728 = R.call_tir(cls.reshape6, (lv137,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape729 = R.call_tir(cls.reshape7, (reshape728,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv234 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_1_encoder_attn_out_proj_weight3, reshape729, model_decoder_layers_1_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add595 = R.call_tir(cls.add, (add592, lv234), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm167 = R.call_tir(cls.layer_norm, (add595, model_decoder_layers_1_final_layer_norm_weight3, model_decoder_layers_1_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv33 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_1_fc1_weight3, layer_norm167, model_decoder_layers_1_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv235 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_1_fc2_weight3, lv33, model_decoder_layers_1_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add598 = R.call_tir(cls.add, (add595, lv235), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm168 = R.call_tir(cls.layer_norm, (add598, model_decoder_layers_2_self_attn_layer_norm_weight3, model_decoder_layers_2_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv236 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_2_self_attn_q_proj_weight3, layer_norm168, model_decoder_layers_2_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape730 = R.call_tir(cls.reshape4, (lv236,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv67 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_2_self_attn_k_proj_weight3, layer_norm168), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape731 = R.call_tir(cls.reshape4, (lv67,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv237 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_2_self_attn_v_proj_weight3, layer_norm168, model_decoder_layers_2_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape732 = R.call_tir(cls.reshape4, (lv237,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat34 = R.call_tir(cls.concatenate, (reshape730, reshape731, reshape732), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape733 = R.call_tir(cls.reshape5, (concat34,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv138 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), reshape733), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape734 = R.call_tir(cls.reshape6, (lv138,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape735 = R.call_tir(cls.reshape7, (reshape734,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv238 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_2_self_attn_out_proj_weight3, reshape735, model_decoder_layers_2_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add602 = R.call_tir(cls.add, (add598, lv238), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm169 = R.call_tir(cls.layer_norm, (add602, model_decoder_layers_2_encoder_attn_layer_norm_weight3, model_decoder_layers_2_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv239 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_2_encoder_attn_q_proj_weight3, layer_norm169, model_decoder_layers_2_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape736 = R.call_tir(cls.reshape4, (lv239,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape737 = R.call_tir(cls.reshape8, (reshape736,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv139 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), reshape737), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape738 = R.call_tir(cls.reshape6, (lv139,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape739 = R.call_tir(cls.reshape7, (reshape738,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv240 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_2_encoder_attn_out_proj_weight3, reshape739, model_decoder_layers_2_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add605 = R.call_tir(cls.add, (add602, lv240), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm170 = R.call_tir(cls.layer_norm, (add605, model_decoder_layers_2_final_layer_norm_weight3, model_decoder_layers_2_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv34 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_2_fc1_weight3, layer_norm170, model_decoder_layers_2_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv241 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_2_fc2_weight3, lv34, model_decoder_layers_2_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add608 = R.call_tir(cls.add, (add605, lv241), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm171 = R.call_tir(cls.layer_norm, (add608, model_decoder_layers_3_self_attn_layer_norm_weight3, model_decoder_layers_3_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv242 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_3_self_attn_q_proj_weight3, layer_norm171, model_decoder_layers_3_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape740 = R.call_tir(cls.reshape4, (lv242,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv68 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_3_self_attn_k_proj_weight3, layer_norm171), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape741 = R.call_tir(cls.reshape4, (lv68,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv243 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_3_self_attn_v_proj_weight3, layer_norm171, model_decoder_layers_3_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape742 = R.call_tir(cls.reshape4, (lv243,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat35 = R.call_tir(cls.concatenate, (reshape740, reshape741, reshape742), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape743 = R.call_tir(cls.reshape5, (concat35,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv140 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), reshape743), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape744 = R.call_tir(cls.reshape6, (lv140,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape745 = R.call_tir(cls.reshape7, (reshape744,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv244 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_3_self_attn_out_proj_weight3, reshape745, model_decoder_layers_3_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add612 = R.call_tir(cls.add, (add608, lv244), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm172 = R.call_tir(cls.layer_norm, (add612, model_decoder_layers_3_encoder_attn_layer_norm_weight3, model_decoder_layers_3_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv245 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_3_encoder_attn_q_proj_weight3, layer_norm172, model_decoder_layers_3_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape746 = R.call_tir(cls.reshape4, (lv245,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape747 = R.call_tir(cls.reshape8, (reshape746,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv141 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), reshape747), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape748 = R.call_tir(cls.reshape6, (lv141,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape749 = R.call_tir(cls.reshape7, (reshape748,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv246 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_3_encoder_attn_out_proj_weight3, reshape749, model_decoder_layers_3_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add615 = R.call_tir(cls.add, (add612, lv246), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm173 = R.call_tir(cls.layer_norm, (add615, model_decoder_layers_3_final_layer_norm_weight3, model_decoder_layers_3_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv35 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_3_fc1_weight3, layer_norm173, model_decoder_layers_3_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv247 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_3_fc2_weight3, lv35, model_decoder_layers_3_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add618 = R.call_tir(cls.add, (add615, lv247), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm174 = R.call_tir(cls.layer_norm, (add618, model_decoder_layers_4_self_attn_layer_norm_weight3, model_decoder_layers_4_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv248 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_4_self_attn_q_proj_weight3, layer_norm174, model_decoder_layers_4_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape750 = R.call_tir(cls.reshape4, (lv248,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv69 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_4_self_attn_k_proj_weight3, layer_norm174), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape751 = R.call_tir(cls.reshape4, (lv69,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv249 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_4_self_attn_v_proj_weight3, layer_norm174, model_decoder_layers_4_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape752 = R.call_tir(cls.reshape4, (lv249,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat36 = R.call_tir(cls.concatenate, (reshape750, reshape751, reshape752), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape753 = R.call_tir(cls.reshape5, (concat36,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv142 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), reshape753), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape754 = R.call_tir(cls.reshape6, (lv142,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape755 = R.call_tir(cls.reshape7, (reshape754,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv250 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_4_self_attn_out_proj_weight3, reshape755, model_decoder_layers_4_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add622 = R.call_tir(cls.add, (add618, lv250), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm175 = R.call_tir(cls.layer_norm, (add622, model_decoder_layers_4_encoder_attn_layer_norm_weight3, model_decoder_layers_4_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv251 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_4_encoder_attn_q_proj_weight3, layer_norm175, model_decoder_layers_4_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape756 = R.call_tir(cls.reshape4, (lv251,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape757 = R.call_tir(cls.reshape8, (reshape756,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv143 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), reshape757), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape758 = R.call_tir(cls.reshape6, (lv143,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape759 = R.call_tir(cls.reshape7, (reshape758,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv252 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_4_encoder_attn_out_proj_weight3, reshape759, model_decoder_layers_4_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add625 = R.call_tir(cls.add, (add622, lv252), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm176 = R.call_tir(cls.layer_norm, (add625, model_decoder_layers_4_final_layer_norm_weight3, model_decoder_layers_4_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv36 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_4_fc1_weight3, layer_norm176, model_decoder_layers_4_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv253 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_4_fc2_weight3, lv36, model_decoder_layers_4_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add628 = R.call_tir(cls.add, (add625, lv253), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm177 = R.call_tir(cls.layer_norm, (add628, model_decoder_layers_5_self_attn_layer_norm_weight3, model_decoder_layers_5_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv254 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_5_self_attn_q_proj_weight3, layer_norm177, model_decoder_layers_5_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape760 = R.call_tir(cls.reshape4, (lv254,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv70 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_5_self_attn_k_proj_weight3, layer_norm177), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape761 = R.call_tir(cls.reshape4, (lv70,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv255 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_5_self_attn_v_proj_weight3, layer_norm177, model_decoder_layers_5_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape762 = R.call_tir(cls.reshape4, (lv255,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat37 = R.call_tir(cls.concatenate, (reshape760, reshape761, reshape762), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape763 = R.call_tir(cls.reshape5, (concat37,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv144 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), reshape763), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape764 = R.call_tir(cls.reshape6, (lv144,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape765 = R.call_tir(cls.reshape7, (reshape764,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv256 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_5_self_attn_out_proj_weight3, reshape765, model_decoder_layers_5_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add632 = R.call_tir(cls.add, (add628, lv256), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm178 = R.call_tir(cls.layer_norm, (add632, model_decoder_layers_5_encoder_attn_layer_norm_weight3, model_decoder_layers_5_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv257 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_5_encoder_attn_q_proj_weight3, layer_norm178, model_decoder_layers_5_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape766 = R.call_tir(cls.reshape4, (lv257,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape767 = R.call_tir(cls.reshape8, (reshape766,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv145 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), reshape767), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape768 = R.call_tir(cls.reshape6, (lv145,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape769 = R.call_tir(cls.reshape7, (reshape768,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv258 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_5_encoder_attn_out_proj_weight3, reshape769, model_decoder_layers_5_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add635 = R.call_tir(cls.add, (add632, lv258), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm179 = R.call_tir(cls.layer_norm, (add635, model_decoder_layers_5_final_layer_norm_weight3, model_decoder_layers_5_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv37 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_5_fc1_weight3, layer_norm179, model_decoder_layers_5_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv259 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_5_fc2_weight3, lv37, model_decoder_layers_5_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add638 = R.call_tir(cls.add, (add635, lv259), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm180 = R.call_tir(cls.layer_norm, (add638, model_decoder_layers_6_self_attn_layer_norm_weight3, model_decoder_layers_6_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv260 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_6_self_attn_q_proj_weight3, layer_norm180, model_decoder_layers_6_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape770 = R.call_tir(cls.reshape4, (lv260,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv71 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_6_self_attn_k_proj_weight3, layer_norm180), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape771 = R.call_tir(cls.reshape4, (lv71,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv261 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_6_self_attn_v_proj_weight3, layer_norm180, model_decoder_layers_6_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape772 = R.call_tir(cls.reshape4, (lv261,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat38 = R.call_tir(cls.concatenate, (reshape770, reshape771, reshape772), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape773 = R.call_tir(cls.reshape5, (concat38,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv146 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), reshape773), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape774 = R.call_tir(cls.reshape6, (lv146,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape775 = R.call_tir(cls.reshape7, (reshape774,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv262 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_6_self_attn_out_proj_weight3, reshape775, model_decoder_layers_6_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add642 = R.call_tir(cls.add, (add638, lv262), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm181 = R.call_tir(cls.layer_norm, (add642, model_decoder_layers_6_encoder_attn_layer_norm_weight3, model_decoder_layers_6_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv263 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_6_encoder_attn_q_proj_weight3, layer_norm181, model_decoder_layers_6_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape776 = R.call_tir(cls.reshape4, (lv263,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape777 = R.call_tir(cls.reshape8, (reshape776,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv147 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), reshape777), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape778 = R.call_tir(cls.reshape6, (lv147,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape779 = R.call_tir(cls.reshape7, (reshape778,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv264 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_6_encoder_attn_out_proj_weight3, reshape779, model_decoder_layers_6_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add645 = R.call_tir(cls.add, (add642, lv264), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm182 = R.call_tir(cls.layer_norm, (add645, model_decoder_layers_6_final_layer_norm_weight3, model_decoder_layers_6_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv38 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_6_fc1_weight3, layer_norm182, model_decoder_layers_6_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv265 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_6_fc2_weight3, lv38, model_decoder_layers_6_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add648 = R.call_tir(cls.add, (add645, lv265), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm183 = R.call_tir(cls.layer_norm, (add648, model_decoder_layers_7_self_attn_layer_norm_weight3, model_decoder_layers_7_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv266 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_7_self_attn_q_proj_weight3, layer_norm183, model_decoder_layers_7_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape780 = R.call_tir(cls.reshape4, (lv266,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv72 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_7_self_attn_k_proj_weight3, layer_norm183), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape781 = R.call_tir(cls.reshape4, (lv72,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv267 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_7_self_attn_v_proj_weight3, layer_norm183, model_decoder_layers_7_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape782 = R.call_tir(cls.reshape4, (lv267,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat39 = R.call_tir(cls.concatenate, (reshape780, reshape781, reshape782), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape783 = R.call_tir(cls.reshape5, (concat39,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv148 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), reshape783), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape784 = R.call_tir(cls.reshape6, (lv148,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape785 = R.call_tir(cls.reshape7, (reshape784,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv268 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_7_self_attn_out_proj_weight3, reshape785, model_decoder_layers_7_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add652 = R.call_tir(cls.add, (add648, lv268), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm184 = R.call_tir(cls.layer_norm, (add652, model_decoder_layers_7_encoder_attn_layer_norm_weight3, model_decoder_layers_7_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv269 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_7_encoder_attn_q_proj_weight3, layer_norm184, model_decoder_layers_7_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape786 = R.call_tir(cls.reshape4, (lv269,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape787 = R.call_tir(cls.reshape8, (reshape786,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv149 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), reshape787), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape788 = R.call_tir(cls.reshape6, (lv149,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape789 = R.call_tir(cls.reshape7, (reshape788,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv270 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_7_encoder_attn_out_proj_weight3, reshape789, model_decoder_layers_7_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add655 = R.call_tir(cls.add, (add652, lv270), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm185 = R.call_tir(cls.layer_norm, (add655, model_decoder_layers_7_final_layer_norm_weight3, model_decoder_layers_7_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv39 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_7_fc1_weight3, layer_norm185, model_decoder_layers_7_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv271 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_7_fc2_weight3, lv39, model_decoder_layers_7_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add658 = R.call_tir(cls.add, (add655, lv271), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm186 = R.call_tir(cls.layer_norm, (add658, model_decoder_layers_8_self_attn_layer_norm_weight3, model_decoder_layers_8_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv272 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_8_self_attn_q_proj_weight3, layer_norm186, model_decoder_layers_8_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape790 = R.call_tir(cls.reshape4, (lv272,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv73 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_8_self_attn_k_proj_weight3, layer_norm186), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape791 = R.call_tir(cls.reshape4, (lv73,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv273 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_8_self_attn_v_proj_weight3, layer_norm186, model_decoder_layers_8_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape792 = R.call_tir(cls.reshape4, (lv273,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat40 = R.call_tir(cls.concatenate, (reshape790, reshape791, reshape792), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape793 = R.call_tir(cls.reshape5, (concat40,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv150 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), reshape793), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape794 = R.call_tir(cls.reshape6, (lv150,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape795 = R.call_tir(cls.reshape7, (reshape794,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv274 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_8_self_attn_out_proj_weight3, reshape795, model_decoder_layers_8_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add662 = R.call_tir(cls.add, (add658, lv274), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm187 = R.call_tir(cls.layer_norm, (add662, model_decoder_layers_8_encoder_attn_layer_norm_weight3, model_decoder_layers_8_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv275 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_8_encoder_attn_q_proj_weight3, layer_norm187, model_decoder_layers_8_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape796 = R.call_tir(cls.reshape4, (lv275,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape797 = R.call_tir(cls.reshape8, (reshape796,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv151 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), reshape797), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape798 = R.call_tir(cls.reshape6, (lv151,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape799 = R.call_tir(cls.reshape7, (reshape798,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv276 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_8_encoder_attn_out_proj_weight3, reshape799, model_decoder_layers_8_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add665 = R.call_tir(cls.add, (add662, lv276), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm188 = R.call_tir(cls.layer_norm, (add665, model_decoder_layers_8_final_layer_norm_weight3, model_decoder_layers_8_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv40 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_8_fc1_weight3, layer_norm188, model_decoder_layers_8_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv277 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_8_fc2_weight3, lv40, model_decoder_layers_8_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add668 = R.call_tir(cls.add, (add665, lv277), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm189 = R.call_tir(cls.layer_norm, (add668, model_decoder_layers_9_self_attn_layer_norm_weight3, model_decoder_layers_9_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv278 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_9_self_attn_q_proj_weight3, layer_norm189, model_decoder_layers_9_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape800 = R.call_tir(cls.reshape4, (lv278,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv74 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_9_self_attn_k_proj_weight3, layer_norm189), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape801 = R.call_tir(cls.reshape4, (lv74,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv279 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_9_self_attn_v_proj_weight3, layer_norm189, model_decoder_layers_9_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape802 = R.call_tir(cls.reshape4, (lv279,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat41 = R.call_tir(cls.concatenate, (reshape800, reshape801, reshape802), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape803 = R.call_tir(cls.reshape5, (concat41,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv152 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), reshape803), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape804 = R.call_tir(cls.reshape6, (lv152,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape805 = R.call_tir(cls.reshape7, (reshape804,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv280 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_9_self_attn_out_proj_weight3, reshape805, model_decoder_layers_9_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add672 = R.call_tir(cls.add, (add668, lv280), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm190 = R.call_tir(cls.layer_norm, (add672, model_decoder_layers_9_encoder_attn_layer_norm_weight3, model_decoder_layers_9_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv281 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_9_encoder_attn_q_proj_weight3, layer_norm190, model_decoder_layers_9_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape806 = R.call_tir(cls.reshape4, (lv281,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape807 = R.call_tir(cls.reshape8, (reshape806,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv153 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), reshape807), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape808 = R.call_tir(cls.reshape6, (lv153,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape809 = R.call_tir(cls.reshape7, (reshape808,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv282 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_9_encoder_attn_out_proj_weight3, reshape809, model_decoder_layers_9_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add675 = R.call_tir(cls.add, (add672, lv282), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm191 = R.call_tir(cls.layer_norm, (add675, model_decoder_layers_9_final_layer_norm_weight3, model_decoder_layers_9_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv41 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_9_fc1_weight3, layer_norm191, model_decoder_layers_9_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv283 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_9_fc2_weight3, lv41, model_decoder_layers_9_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add678 = R.call_tir(cls.add, (add675, lv283), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm192 = R.call_tir(cls.layer_norm, (add678, model_decoder_layers_10_self_attn_layer_norm_weight3, model_decoder_layers_10_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv284 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_10_self_attn_q_proj_weight3, layer_norm192, model_decoder_layers_10_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape810 = R.call_tir(cls.reshape4, (lv284,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv75 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_10_self_attn_k_proj_weight3, layer_norm192), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape811 = R.call_tir(cls.reshape4, (lv75,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv285 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_10_self_attn_v_proj_weight3, layer_norm192, model_decoder_layers_10_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape812 = R.call_tir(cls.reshape4, (lv285,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat42 = R.call_tir(cls.concatenate, (reshape810, reshape811, reshape812), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape813 = R.call_tir(cls.reshape5, (concat42,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv154 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), reshape813), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape814 = R.call_tir(cls.reshape6, (lv154,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape815 = R.call_tir(cls.reshape7, (reshape814,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv286 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_10_self_attn_out_proj_weight3, reshape815, model_decoder_layers_10_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add682 = R.call_tir(cls.add, (add678, lv286), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm193 = R.call_tir(cls.layer_norm, (add682, model_decoder_layers_10_encoder_attn_layer_norm_weight3, model_decoder_layers_10_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv287 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_10_encoder_attn_q_proj_weight3, layer_norm193, model_decoder_layers_10_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape816 = R.call_tir(cls.reshape4, (lv287,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape817 = R.call_tir(cls.reshape8, (reshape816,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv155 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), reshape817), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape818 = R.call_tir(cls.reshape6, (lv155,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape819 = R.call_tir(cls.reshape7, (reshape818,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv288 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_10_encoder_attn_out_proj_weight3, reshape819, model_decoder_layers_10_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add685 = R.call_tir(cls.add, (add682, lv288), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm194 = R.call_tir(cls.layer_norm, (add685, model_decoder_layers_10_final_layer_norm_weight3, model_decoder_layers_10_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv42 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_10_fc1_weight3, layer_norm194, model_decoder_layers_10_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv289 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_10_fc2_weight3, lv42, model_decoder_layers_10_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add688 = R.call_tir(cls.add, (add685, lv289), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm195 = R.call_tir(cls.layer_norm, (add688, model_decoder_layers_11_self_attn_layer_norm_weight3, model_decoder_layers_11_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv290 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_11_self_attn_q_proj_weight3, layer_norm195, model_decoder_layers_11_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape820 = R.call_tir(cls.reshape4, (lv290,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv76 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_11_self_attn_k_proj_weight3, layer_norm195), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape821 = R.call_tir(cls.reshape4, (lv76,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv291 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_11_self_attn_v_proj_weight3, layer_norm195, model_decoder_layers_11_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape822 = R.call_tir(cls.reshape4, (lv291,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat43 = R.call_tir(cls.concatenate, (reshape820, reshape821, reshape822), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape823 = R.call_tir(cls.reshape5, (concat43,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv156 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), reshape823), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape824 = R.call_tir(cls.reshape6, (lv156,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape825 = R.call_tir(cls.reshape7, (reshape824,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv292 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_11_self_attn_out_proj_weight3, reshape825, model_decoder_layers_11_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add692 = R.call_tir(cls.add, (add688, lv292), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm196 = R.call_tir(cls.layer_norm, (add692, model_decoder_layers_11_encoder_attn_layer_norm_weight3, model_decoder_layers_11_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv293 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_11_encoder_attn_q_proj_weight3, layer_norm196, model_decoder_layers_11_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape826 = R.call_tir(cls.reshape4, (lv293,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape827 = R.call_tir(cls.reshape8, (reshape826,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv157 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), reshape827), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape828 = R.call_tir(cls.reshape6, (lv157,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape829 = R.call_tir(cls.reshape7, (reshape828,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv294 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_11_encoder_attn_out_proj_weight3, reshape829, model_decoder_layers_11_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add695 = R.call_tir(cls.add, (add692, lv294), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm197 = R.call_tir(cls.layer_norm, (add695, model_decoder_layers_11_final_layer_norm_weight3, model_decoder_layers_11_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv43 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_11_fc1_weight3, layer_norm197, model_decoder_layers_11_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv295 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_11_fc2_weight3, lv43, model_decoder_layers_11_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add698 = R.call_tir(cls.add, (add695, lv295), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm198 = R.call_tir(cls.layer_norm, (add698, model_decoder_layers_12_self_attn_layer_norm_weight3, model_decoder_layers_12_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv296 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_12_self_attn_q_proj_weight3, layer_norm198, model_decoder_layers_12_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape830 = R.call_tir(cls.reshape4, (lv296,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv77 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_12_self_attn_k_proj_weight3, layer_norm198), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape831 = R.call_tir(cls.reshape4, (lv77,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv297 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_12_self_attn_v_proj_weight3, layer_norm198, model_decoder_layers_12_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape832 = R.call_tir(cls.reshape4, (lv297,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat44 = R.call_tir(cls.concatenate, (reshape830, reshape831, reshape832), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape833 = R.call_tir(cls.reshape5, (concat44,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv158 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), reshape833), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape834 = R.call_tir(cls.reshape6, (lv158,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape835 = R.call_tir(cls.reshape7, (reshape834,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv298 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_12_self_attn_out_proj_weight3, reshape835, model_decoder_layers_12_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add702 = R.call_tir(cls.add, (add698, lv298), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm199 = R.call_tir(cls.layer_norm, (add702, model_decoder_layers_12_encoder_attn_layer_norm_weight3, model_decoder_layers_12_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv299 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_12_encoder_attn_q_proj_weight3, layer_norm199, model_decoder_layers_12_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape836 = R.call_tir(cls.reshape4, (lv299,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape837 = R.call_tir(cls.reshape8, (reshape836,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv159 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), reshape837), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape838 = R.call_tir(cls.reshape6, (lv159,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape839 = R.call_tir(cls.reshape7, (reshape838,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv300 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_12_encoder_attn_out_proj_weight3, reshape839, model_decoder_layers_12_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add705 = R.call_tir(cls.add, (add702, lv300), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm200 = R.call_tir(cls.layer_norm, (add705, model_decoder_layers_12_final_layer_norm_weight3, model_decoder_layers_12_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv44 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_12_fc1_weight3, layer_norm200, model_decoder_layers_12_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv301 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_12_fc2_weight3, lv44, model_decoder_layers_12_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add708 = R.call_tir(cls.add, (add705, lv301), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm201 = R.call_tir(cls.layer_norm, (add708, model_decoder_layers_13_self_attn_layer_norm_weight3, model_decoder_layers_13_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv302 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_13_self_attn_q_proj_weight3, layer_norm201, model_decoder_layers_13_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape840 = R.call_tir(cls.reshape4, (lv302,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv78 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_13_self_attn_k_proj_weight3, layer_norm201), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape841 = R.call_tir(cls.reshape4, (lv78,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv303 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_13_self_attn_v_proj_weight3, layer_norm201, model_decoder_layers_13_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape842 = R.call_tir(cls.reshape4, (lv303,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat45 = R.call_tir(cls.concatenate, (reshape840, reshape841, reshape842), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape843 = R.call_tir(cls.reshape5, (concat45,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv160 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), reshape843), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape844 = R.call_tir(cls.reshape6, (lv160,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape845 = R.call_tir(cls.reshape7, (reshape844,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv304 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_13_self_attn_out_proj_weight3, reshape845, model_decoder_layers_13_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add712 = R.call_tir(cls.add, (add708, lv304), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm202 = R.call_tir(cls.layer_norm, (add712, model_decoder_layers_13_encoder_attn_layer_norm_weight3, model_decoder_layers_13_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv305 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_13_encoder_attn_q_proj_weight3, layer_norm202, model_decoder_layers_13_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape846 = R.call_tir(cls.reshape4, (lv305,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape847 = R.call_tir(cls.reshape8, (reshape846,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv161 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), reshape847), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape848 = R.call_tir(cls.reshape6, (lv161,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape849 = R.call_tir(cls.reshape7, (reshape848,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv306 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_13_encoder_attn_out_proj_weight3, reshape849, model_decoder_layers_13_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add715 = R.call_tir(cls.add, (add712, lv306), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm203 = R.call_tir(cls.layer_norm, (add715, model_decoder_layers_13_final_layer_norm_weight3, model_decoder_layers_13_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv45 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_13_fc1_weight3, layer_norm203, model_decoder_layers_13_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv307 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_13_fc2_weight3, lv45, model_decoder_layers_13_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add718 = R.call_tir(cls.add, (add715, lv307), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm204 = R.call_tir(cls.layer_norm, (add718, model_decoder_layers_14_self_attn_layer_norm_weight3, model_decoder_layers_14_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv308 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_14_self_attn_q_proj_weight3, layer_norm204, model_decoder_layers_14_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape850 = R.call_tir(cls.reshape4, (lv308,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv79 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_14_self_attn_k_proj_weight3, layer_norm204), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape851 = R.call_tir(cls.reshape4, (lv79,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv309 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_14_self_attn_v_proj_weight3, layer_norm204, model_decoder_layers_14_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape852 = R.call_tir(cls.reshape4, (lv309,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat46 = R.call_tir(cls.concatenate, (reshape850, reshape851, reshape852), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape853 = R.call_tir(cls.reshape5, (concat46,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv162 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), reshape853), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape854 = R.call_tir(cls.reshape6, (lv162,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape855 = R.call_tir(cls.reshape7, (reshape854,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv310 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_14_self_attn_out_proj_weight3, reshape855, model_decoder_layers_14_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add722 = R.call_tir(cls.add, (add718, lv310), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm205 = R.call_tir(cls.layer_norm, (add722, model_decoder_layers_14_encoder_attn_layer_norm_weight3, model_decoder_layers_14_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv311 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_14_encoder_attn_q_proj_weight3, layer_norm205, model_decoder_layers_14_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape856 = R.call_tir(cls.reshape4, (lv311,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape857 = R.call_tir(cls.reshape8, (reshape856,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv163 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), reshape857), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape858 = R.call_tir(cls.reshape6, (lv163,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape859 = R.call_tir(cls.reshape7, (reshape858,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv312 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_14_encoder_attn_out_proj_weight3, reshape859, model_decoder_layers_14_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add725 = R.call_tir(cls.add, (add722, lv312), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm206 = R.call_tir(cls.layer_norm, (add725, model_decoder_layers_14_final_layer_norm_weight3, model_decoder_layers_14_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv46 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_14_fc1_weight3, layer_norm206, model_decoder_layers_14_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv313 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_14_fc2_weight3, lv46, model_decoder_layers_14_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add728 = R.call_tir(cls.add, (add725, lv313), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm207 = R.call_tir(cls.layer_norm, (add728, model_decoder_layers_15_self_attn_layer_norm_weight3, model_decoder_layers_15_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv314 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_15_self_attn_q_proj_weight3, layer_norm207, model_decoder_layers_15_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape860 = R.call_tir(cls.reshape4, (lv314,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv80 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_15_self_attn_k_proj_weight3, layer_norm207), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape861 = R.call_tir(cls.reshape4, (lv80,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv315 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_15_self_attn_v_proj_weight3, layer_norm207, model_decoder_layers_15_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape862 = R.call_tir(cls.reshape4, (lv315,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat47 = R.call_tir(cls.concatenate, (reshape860, reshape861, reshape862), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape863 = R.call_tir(cls.reshape5, (concat47,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv164 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), reshape863), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape864 = R.call_tir(cls.reshape6, (lv164,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape865 = R.call_tir(cls.reshape7, (reshape864,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv316 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_15_self_attn_out_proj_weight3, reshape865, model_decoder_layers_15_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add732 = R.call_tir(cls.add, (add728, lv316), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm208 = R.call_tir(cls.layer_norm, (add732, model_decoder_layers_15_encoder_attn_layer_norm_weight3, model_decoder_layers_15_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv317 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_15_encoder_attn_q_proj_weight3, layer_norm208, model_decoder_layers_15_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape866 = R.call_tir(cls.reshape4, (lv317,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape867 = R.call_tir(cls.reshape8, (reshape866,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv165 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), reshape867), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape868 = R.call_tir(cls.reshape6, (lv165,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape869 = R.call_tir(cls.reshape7, (reshape868,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv318 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_15_encoder_attn_out_proj_weight3, reshape869, model_decoder_layers_15_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add735 = R.call_tir(cls.add, (add732, lv318), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm209 = R.call_tir(cls.layer_norm, (add735, model_decoder_layers_15_final_layer_norm_weight3, model_decoder_layers_15_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv47 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_15_fc1_weight3, layer_norm209, model_decoder_layers_15_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv319 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_15_fc2_weight3, lv47, model_decoder_layers_15_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add738 = R.call_tir(cls.add, (add735, lv319), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm210 = R.call_tir(cls.layer_norm, (add738, model_decoder_layers_16_self_attn_layer_norm_weight3, model_decoder_layers_16_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv320 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_16_self_attn_q_proj_weight3, layer_norm210, model_decoder_layers_16_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape870 = R.call_tir(cls.reshape4, (lv320,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv81 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_16_self_attn_k_proj_weight3, layer_norm210), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape871 = R.call_tir(cls.reshape4, (lv81,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv321 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_16_self_attn_v_proj_weight3, layer_norm210, model_decoder_layers_16_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape872 = R.call_tir(cls.reshape4, (lv321,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat48 = R.call_tir(cls.concatenate, (reshape870, reshape871, reshape872), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape873 = R.call_tir(cls.reshape5, (concat48,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv166 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), reshape873), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape874 = R.call_tir(cls.reshape6, (lv166,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape875 = R.call_tir(cls.reshape7, (reshape874,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv322 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_16_self_attn_out_proj_weight3, reshape875, model_decoder_layers_16_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add742 = R.call_tir(cls.add, (add738, lv322), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm211 = R.call_tir(cls.layer_norm, (add742, model_decoder_layers_16_encoder_attn_layer_norm_weight3, model_decoder_layers_16_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv323 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_16_encoder_attn_q_proj_weight3, layer_norm211, model_decoder_layers_16_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape876 = R.call_tir(cls.reshape4, (lv323,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape877 = R.call_tir(cls.reshape8, (reshape876,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv167 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), reshape877), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape878 = R.call_tir(cls.reshape6, (lv167,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape879 = R.call_tir(cls.reshape7, (reshape878,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv324 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_16_encoder_attn_out_proj_weight3, reshape879, model_decoder_layers_16_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add745 = R.call_tir(cls.add, (add742, lv324), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm212 = R.call_tir(cls.layer_norm, (add745, model_decoder_layers_16_final_layer_norm_weight3, model_decoder_layers_16_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv48 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_16_fc1_weight3, layer_norm212, model_decoder_layers_16_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv325 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_16_fc2_weight3, lv48, model_decoder_layers_16_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add748 = R.call_tir(cls.add, (add745, lv325), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm213 = R.call_tir(cls.layer_norm, (add748, model_decoder_layers_17_self_attn_layer_norm_weight3, model_decoder_layers_17_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv326 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_17_self_attn_q_proj_weight3, layer_norm213, model_decoder_layers_17_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape880 = R.call_tir(cls.reshape4, (lv326,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv82 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_17_self_attn_k_proj_weight3, layer_norm213), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape881 = R.call_tir(cls.reshape4, (lv82,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv327 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_17_self_attn_v_proj_weight3, layer_norm213, model_decoder_layers_17_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape882 = R.call_tir(cls.reshape4, (lv327,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat49 = R.call_tir(cls.concatenate, (reshape880, reshape881, reshape882), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape883 = R.call_tir(cls.reshape5, (concat49,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv168 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), reshape883), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape884 = R.call_tir(cls.reshape6, (lv168,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape885 = R.call_tir(cls.reshape7, (reshape884,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv328 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_17_self_attn_out_proj_weight3, reshape885, model_decoder_layers_17_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add752 = R.call_tir(cls.add, (add748, lv328), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm214 = R.call_tir(cls.layer_norm, (add752, model_decoder_layers_17_encoder_attn_layer_norm_weight3, model_decoder_layers_17_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv329 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_17_encoder_attn_q_proj_weight3, layer_norm214, model_decoder_layers_17_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape886 = R.call_tir(cls.reshape4, (lv329,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape887 = R.call_tir(cls.reshape8, (reshape886,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv169 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), reshape887), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape888 = R.call_tir(cls.reshape6, (lv169,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape889 = R.call_tir(cls.reshape7, (reshape888,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv330 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_17_encoder_attn_out_proj_weight3, reshape889, model_decoder_layers_17_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add755 = R.call_tir(cls.add, (add752, lv330), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm215 = R.call_tir(cls.layer_norm, (add755, model_decoder_layers_17_final_layer_norm_weight3, model_decoder_layers_17_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv49 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_17_fc1_weight3, layer_norm215, model_decoder_layers_17_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv331 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_17_fc2_weight3, lv49, model_decoder_layers_17_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add758 = R.call_tir(cls.add, (add755, lv331), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm216 = R.call_tir(cls.layer_norm, (add758, model_decoder_layers_18_self_attn_layer_norm_weight3, model_decoder_layers_18_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv332 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_18_self_attn_q_proj_weight3, layer_norm216, model_decoder_layers_18_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape890 = R.call_tir(cls.reshape4, (lv332,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv83 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_18_self_attn_k_proj_weight3, layer_norm216), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape891 = R.call_tir(cls.reshape4, (lv83,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv333 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_18_self_attn_v_proj_weight3, layer_norm216, model_decoder_layers_18_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape892 = R.call_tir(cls.reshape4, (lv333,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat50 = R.call_tir(cls.concatenate, (reshape890, reshape891, reshape892), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape893 = R.call_tir(cls.reshape5, (concat50,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv170 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), reshape893), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape894 = R.call_tir(cls.reshape6, (lv170,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape895 = R.call_tir(cls.reshape7, (reshape894,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv334 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_18_self_attn_out_proj_weight3, reshape895, model_decoder_layers_18_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add762 = R.call_tir(cls.add, (add758, lv334), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm217 = R.call_tir(cls.layer_norm, (add762, model_decoder_layers_18_encoder_attn_layer_norm_weight3, model_decoder_layers_18_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv335 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_18_encoder_attn_q_proj_weight3, layer_norm217, model_decoder_layers_18_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape896 = R.call_tir(cls.reshape4, (lv335,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape897 = R.call_tir(cls.reshape8, (reshape896,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv171 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), reshape897), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape898 = R.call_tir(cls.reshape6, (lv171,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape899 = R.call_tir(cls.reshape7, (reshape898,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv336 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_18_encoder_attn_out_proj_weight3, reshape899, model_decoder_layers_18_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add765 = R.call_tir(cls.add, (add762, lv336), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm218 = R.call_tir(cls.layer_norm, (add765, model_decoder_layers_18_final_layer_norm_weight3, model_decoder_layers_18_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv50 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_18_fc1_weight3, layer_norm218, model_decoder_layers_18_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv337 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_18_fc2_weight3, lv50, model_decoder_layers_18_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add768 = R.call_tir(cls.add, (add765, lv337), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm219 = R.call_tir(cls.layer_norm, (add768, model_decoder_layers_19_self_attn_layer_norm_weight3, model_decoder_layers_19_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv338 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_19_self_attn_q_proj_weight3, layer_norm219, model_decoder_layers_19_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape900 = R.call_tir(cls.reshape4, (lv338,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv84 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_19_self_attn_k_proj_weight3, layer_norm219), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape901 = R.call_tir(cls.reshape4, (lv84,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv339 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_19_self_attn_v_proj_weight3, layer_norm219, model_decoder_layers_19_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape902 = R.call_tir(cls.reshape4, (lv339,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat51 = R.call_tir(cls.concatenate, (reshape900, reshape901, reshape902), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape903 = R.call_tir(cls.reshape5, (concat51,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv172 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), reshape903), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape904 = R.call_tir(cls.reshape6, (lv172,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape905 = R.call_tir(cls.reshape7, (reshape904,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv340 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_19_self_attn_out_proj_weight3, reshape905, model_decoder_layers_19_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add772 = R.call_tir(cls.add, (add768, lv340), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm220 = R.call_tir(cls.layer_norm, (add772, model_decoder_layers_19_encoder_attn_layer_norm_weight3, model_decoder_layers_19_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv341 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_19_encoder_attn_q_proj_weight3, layer_norm220, model_decoder_layers_19_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape906 = R.call_tir(cls.reshape4, (lv341,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape907 = R.call_tir(cls.reshape8, (reshape906,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv173 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), reshape907), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape908 = R.call_tir(cls.reshape6, (lv173,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape909 = R.call_tir(cls.reshape7, (reshape908,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv342 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_19_encoder_attn_out_proj_weight3, reshape909, model_decoder_layers_19_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add775 = R.call_tir(cls.add, (add772, lv342), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm221 = R.call_tir(cls.layer_norm, (add775, model_decoder_layers_19_final_layer_norm_weight3, model_decoder_layers_19_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv51 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_19_fc1_weight3, layer_norm221, model_decoder_layers_19_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv343 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_19_fc2_weight3, lv51, model_decoder_layers_19_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add778 = R.call_tir(cls.add, (add775, lv343), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm222 = R.call_tir(cls.layer_norm, (add778, model_decoder_layers_20_self_attn_layer_norm_weight3, model_decoder_layers_20_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv344 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_20_self_attn_q_proj_weight3, layer_norm222, model_decoder_layers_20_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape910 = R.call_tir(cls.reshape4, (lv344,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv85 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_20_self_attn_k_proj_weight3, layer_norm222), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape911 = R.call_tir(cls.reshape4, (lv85,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv345 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_20_self_attn_v_proj_weight3, layer_norm222, model_decoder_layers_20_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape912 = R.call_tir(cls.reshape4, (lv345,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat52 = R.call_tir(cls.concatenate, (reshape910, reshape911, reshape912), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape913 = R.call_tir(cls.reshape5, (concat52,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv174 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), reshape913), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape914 = R.call_tir(cls.reshape6, (lv174,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape915 = R.call_tir(cls.reshape7, (reshape914,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv346 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_20_self_attn_out_proj_weight3, reshape915, model_decoder_layers_20_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add782 = R.call_tir(cls.add, (add778, lv346), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm223 = R.call_tir(cls.layer_norm, (add782, model_decoder_layers_20_encoder_attn_layer_norm_weight3, model_decoder_layers_20_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv347 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_20_encoder_attn_q_proj_weight3, layer_norm223, model_decoder_layers_20_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape916 = R.call_tir(cls.reshape4, (lv347,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape917 = R.call_tir(cls.reshape8, (reshape916,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv175 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), reshape917), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape918 = R.call_tir(cls.reshape6, (lv175,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape919 = R.call_tir(cls.reshape7, (reshape918,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv348 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_20_encoder_attn_out_proj_weight3, reshape919, model_decoder_layers_20_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add785 = R.call_tir(cls.add, (add782, lv348), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm224 = R.call_tir(cls.layer_norm, (add785, model_decoder_layers_20_final_layer_norm_weight3, model_decoder_layers_20_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv52 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_20_fc1_weight3, layer_norm224, model_decoder_layers_20_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv349 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_20_fc2_weight3, lv52, model_decoder_layers_20_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add788 = R.call_tir(cls.add, (add785, lv349), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm225 = R.call_tir(cls.layer_norm, (add788, model_decoder_layers_21_self_attn_layer_norm_weight3, model_decoder_layers_21_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv350 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_21_self_attn_q_proj_weight3, layer_norm225, model_decoder_layers_21_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape920 = R.call_tir(cls.reshape4, (lv350,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv86 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_21_self_attn_k_proj_weight3, layer_norm225), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape921 = R.call_tir(cls.reshape4, (lv86,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv351 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_21_self_attn_v_proj_weight3, layer_norm225, model_decoder_layers_21_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape922 = R.call_tir(cls.reshape4, (lv351,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat53 = R.call_tir(cls.concatenate, (reshape920, reshape921, reshape922), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape923 = R.call_tir(cls.reshape5, (concat53,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv176 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), reshape923), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape924 = R.call_tir(cls.reshape6, (lv176,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape925 = R.call_tir(cls.reshape7, (reshape924,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv352 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_21_self_attn_out_proj_weight3, reshape925, model_decoder_layers_21_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add792 = R.call_tir(cls.add, (add788, lv352), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm226 = R.call_tir(cls.layer_norm, (add792, model_decoder_layers_21_encoder_attn_layer_norm_weight3, model_decoder_layers_21_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv353 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_21_encoder_attn_q_proj_weight3, layer_norm226, model_decoder_layers_21_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape926 = R.call_tir(cls.reshape4, (lv353,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape927 = R.call_tir(cls.reshape8, (reshape926,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv177 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), reshape927), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape928 = R.call_tir(cls.reshape6, (lv177,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape929 = R.call_tir(cls.reshape7, (reshape928,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv354 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_21_encoder_attn_out_proj_weight3, reshape929, model_decoder_layers_21_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add795 = R.call_tir(cls.add, (add792, lv354), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm227 = R.call_tir(cls.layer_norm, (add795, model_decoder_layers_21_final_layer_norm_weight3, model_decoder_layers_21_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv53 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_21_fc1_weight3, layer_norm227, model_decoder_layers_21_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv355 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_21_fc2_weight3, lv53, model_decoder_layers_21_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add798 = R.call_tir(cls.add, (add795, lv355), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm228 = R.call_tir(cls.layer_norm, (add798, model_decoder_layers_22_self_attn_layer_norm_weight3, model_decoder_layers_22_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv356 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_22_self_attn_q_proj_weight3, layer_norm228, model_decoder_layers_22_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape930 = R.call_tir(cls.reshape4, (lv356,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv87 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_22_self_attn_k_proj_weight3, layer_norm228), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape931 = R.call_tir(cls.reshape4, (lv87,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv357 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_22_self_attn_v_proj_weight3, layer_norm228, model_decoder_layers_22_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape932 = R.call_tir(cls.reshape4, (lv357,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat54 = R.call_tir(cls.concatenate, (reshape930, reshape931, reshape932), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape933 = R.call_tir(cls.reshape5, (concat54,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv178 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), reshape933), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape934 = R.call_tir(cls.reshape6, (lv178,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape935 = R.call_tir(cls.reshape7, (reshape934,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv358 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_22_self_attn_out_proj_weight3, reshape935, model_decoder_layers_22_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add802 = R.call_tir(cls.add, (add798, lv358), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm229 = R.call_tir(cls.layer_norm, (add802, model_decoder_layers_22_encoder_attn_layer_norm_weight3, model_decoder_layers_22_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv359 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_22_encoder_attn_q_proj_weight3, layer_norm229, model_decoder_layers_22_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape936 = R.call_tir(cls.reshape4, (lv359,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape937 = R.call_tir(cls.reshape8, (reshape936,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv179 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), reshape937), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape938 = R.call_tir(cls.reshape6, (lv179,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape939 = R.call_tir(cls.reshape7, (reshape938,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv360 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_22_encoder_attn_out_proj_weight3, reshape939, model_decoder_layers_22_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add805 = R.call_tir(cls.add, (add802, lv360), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm230 = R.call_tir(cls.layer_norm, (add805, model_decoder_layers_22_final_layer_norm_weight3, model_decoder_layers_22_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv54 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_22_fc1_weight3, layer_norm230, model_decoder_layers_22_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv361 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_22_fc2_weight3, lv54, model_decoder_layers_22_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add808 = R.call_tir(cls.add, (add805, lv361), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm231 = R.call_tir(cls.layer_norm, (add808, model_decoder_layers_23_self_attn_layer_norm_weight3, model_decoder_layers_23_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv362 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_23_self_attn_q_proj_weight3, layer_norm231, model_decoder_layers_23_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape940 = R.call_tir(cls.reshape4, (lv362,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv88 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_23_self_attn_k_proj_weight3, layer_norm231), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape941 = R.call_tir(cls.reshape4, (lv88,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv363 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_23_self_attn_v_proj_weight3, layer_norm231, model_decoder_layers_23_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape942 = R.call_tir(cls.reshape4, (lv363,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat55 = R.call_tir(cls.concatenate, (reshape940, reshape941, reshape942), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape943 = R.call_tir(cls.reshape5, (concat55,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv180 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), reshape943), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape944 = R.call_tir(cls.reshape6, (lv180,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape945 = R.call_tir(cls.reshape7, (reshape944,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv364 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_23_self_attn_out_proj_weight3, reshape945, model_decoder_layers_23_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add812 = R.call_tir(cls.add, (add808, lv364), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm232 = R.call_tir(cls.layer_norm, (add812, model_decoder_layers_23_encoder_attn_layer_norm_weight3, model_decoder_layers_23_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv365 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_23_encoder_attn_q_proj_weight3, layer_norm232, model_decoder_layers_23_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape946 = R.call_tir(cls.reshape4, (lv365,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape947 = R.call_tir(cls.reshape8, (reshape946,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv181 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), reshape947), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape948 = R.call_tir(cls.reshape6, (lv181,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape949 = R.call_tir(cls.reshape7, (reshape948,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv366 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_23_encoder_attn_out_proj_weight3, reshape949, model_decoder_layers_23_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add815 = R.call_tir(cls.add, (add812, lv366), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm233 = R.call_tir(cls.layer_norm, (add815, model_decoder_layers_23_final_layer_norm_weight3, model_decoder_layers_23_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv55 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_23_fc1_weight3, layer_norm233, model_decoder_layers_23_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv367 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_23_fc2_weight3, lv55, model_decoder_layers_23_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add818 = R.call_tir(cls.add, (add815, lv367), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm234 = R.call_tir(cls.layer_norm, (add818, model_decoder_layers_24_self_attn_layer_norm_weight3, model_decoder_layers_24_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv368 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_24_self_attn_q_proj_weight3, layer_norm234, model_decoder_layers_24_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape950 = R.call_tir(cls.reshape4, (lv368,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv89 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_24_self_attn_k_proj_weight3, layer_norm234), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape951 = R.call_tir(cls.reshape4, (lv89,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv369 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_24_self_attn_v_proj_weight3, layer_norm234, model_decoder_layers_24_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape952 = R.call_tir(cls.reshape4, (lv369,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat56 = R.call_tir(cls.concatenate, (reshape950, reshape951, reshape952), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape953 = R.call_tir(cls.reshape5, (concat56,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv182 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), reshape953), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape954 = R.call_tir(cls.reshape6, (lv182,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape955 = R.call_tir(cls.reshape7, (reshape954,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv370 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_24_self_attn_out_proj_weight3, reshape955, model_decoder_layers_24_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add822 = R.call_tir(cls.add, (add818, lv370), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm235 = R.call_tir(cls.layer_norm, (add822, model_decoder_layers_24_encoder_attn_layer_norm_weight3, model_decoder_layers_24_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv371 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_24_encoder_attn_q_proj_weight3, layer_norm235, model_decoder_layers_24_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape956 = R.call_tir(cls.reshape4, (lv371,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape957 = R.call_tir(cls.reshape8, (reshape956,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv183 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), reshape957), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape958 = R.call_tir(cls.reshape6, (lv183,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape959 = R.call_tir(cls.reshape7, (reshape958,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv372 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_24_encoder_attn_out_proj_weight3, reshape959, model_decoder_layers_24_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add825 = R.call_tir(cls.add, (add822, lv372), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm236 = R.call_tir(cls.layer_norm, (add825, model_decoder_layers_24_final_layer_norm_weight3, model_decoder_layers_24_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv56 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_24_fc1_weight3, layer_norm236, model_decoder_layers_24_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv373 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_24_fc2_weight3, lv56, model_decoder_layers_24_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add828 = R.call_tir(cls.add, (add825, lv373), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm237 = R.call_tir(cls.layer_norm, (add828, model_decoder_layers_25_self_attn_layer_norm_weight3, model_decoder_layers_25_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv374 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_25_self_attn_q_proj_weight3, layer_norm237, model_decoder_layers_25_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape960 = R.call_tir(cls.reshape4, (lv374,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv90 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_25_self_attn_k_proj_weight3, layer_norm237), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape961 = R.call_tir(cls.reshape4, (lv90,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv375 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_25_self_attn_v_proj_weight3, layer_norm237, model_decoder_layers_25_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape962 = R.call_tir(cls.reshape4, (lv375,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat57 = R.call_tir(cls.concatenate, (reshape960, reshape961, reshape962), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape963 = R.call_tir(cls.reshape5, (concat57,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv184 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), reshape963), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape964 = R.call_tir(cls.reshape6, (lv184,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape965 = R.call_tir(cls.reshape7, (reshape964,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv376 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_25_self_attn_out_proj_weight3, reshape965, model_decoder_layers_25_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add832 = R.call_tir(cls.add, (add828, lv376), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm238 = R.call_tir(cls.layer_norm, (add832, model_decoder_layers_25_encoder_attn_layer_norm_weight3, model_decoder_layers_25_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv377 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_25_encoder_attn_q_proj_weight3, layer_norm238, model_decoder_layers_25_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape966 = R.call_tir(cls.reshape4, (lv377,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape967 = R.call_tir(cls.reshape8, (reshape966,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv185 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), reshape967), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape968 = R.call_tir(cls.reshape6, (lv185,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape969 = R.call_tir(cls.reshape7, (reshape968,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv378 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_25_encoder_attn_out_proj_weight3, reshape969, model_decoder_layers_25_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add835 = R.call_tir(cls.add, (add832, lv378), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm239 = R.call_tir(cls.layer_norm, (add835, model_decoder_layers_25_final_layer_norm_weight3, model_decoder_layers_25_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv57 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_25_fc1_weight3, layer_norm239, model_decoder_layers_25_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv379 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_25_fc2_weight3, lv57, model_decoder_layers_25_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add838 = R.call_tir(cls.add, (add835, lv379), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm240 = R.call_tir(cls.layer_norm, (add838, model_decoder_layers_26_self_attn_layer_norm_weight3, model_decoder_layers_26_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv380 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_26_self_attn_q_proj_weight3, layer_norm240, model_decoder_layers_26_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape970 = R.call_tir(cls.reshape4, (lv380,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv91 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_26_self_attn_k_proj_weight3, layer_norm240), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape971 = R.call_tir(cls.reshape4, (lv91,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv381 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_26_self_attn_v_proj_weight3, layer_norm240, model_decoder_layers_26_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape972 = R.call_tir(cls.reshape4, (lv381,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat58 = R.call_tir(cls.concatenate, (reshape970, reshape971, reshape972), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape973 = R.call_tir(cls.reshape5, (concat58,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv186 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), reshape973), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape974 = R.call_tir(cls.reshape6, (lv186,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape975 = R.call_tir(cls.reshape7, (reshape974,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv382 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_26_self_attn_out_proj_weight3, reshape975, model_decoder_layers_26_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add842 = R.call_tir(cls.add, (add838, lv382), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm241 = R.call_tir(cls.layer_norm, (add842, model_decoder_layers_26_encoder_attn_layer_norm_weight3, model_decoder_layers_26_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv383 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_26_encoder_attn_q_proj_weight3, layer_norm241, model_decoder_layers_26_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape976 = R.call_tir(cls.reshape4, (lv383,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape977 = R.call_tir(cls.reshape8, (reshape976,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv187 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), reshape977), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape978 = R.call_tir(cls.reshape6, (lv187,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape979 = R.call_tir(cls.reshape7, (reshape978,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv384 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_26_encoder_attn_out_proj_weight3, reshape979, model_decoder_layers_26_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add845 = R.call_tir(cls.add, (add842, lv384), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm242 = R.call_tir(cls.layer_norm, (add845, model_decoder_layers_26_final_layer_norm_weight3, model_decoder_layers_26_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv58 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_26_fc1_weight3, layer_norm242, model_decoder_layers_26_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv385 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_26_fc2_weight3, lv58, model_decoder_layers_26_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add848 = R.call_tir(cls.add, (add845, lv385), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm243 = R.call_tir(cls.layer_norm, (add848, model_decoder_layers_27_self_attn_layer_norm_weight3, model_decoder_layers_27_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv386 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_27_self_attn_q_proj_weight3, layer_norm243, model_decoder_layers_27_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape980 = R.call_tir(cls.reshape4, (lv386,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv92 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_27_self_attn_k_proj_weight3, layer_norm243), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape981 = R.call_tir(cls.reshape4, (lv92,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv387 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_27_self_attn_v_proj_weight3, layer_norm243, model_decoder_layers_27_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape982 = R.call_tir(cls.reshape4, (lv387,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat59 = R.call_tir(cls.concatenate, (reshape980, reshape981, reshape982), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape983 = R.call_tir(cls.reshape5, (concat59,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv188 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), reshape983), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape984 = R.call_tir(cls.reshape6, (lv188,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape985 = R.call_tir(cls.reshape7, (reshape984,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv388 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_27_self_attn_out_proj_weight3, reshape985, model_decoder_layers_27_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add852 = R.call_tir(cls.add, (add848, lv388), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm244 = R.call_tir(cls.layer_norm, (add852, model_decoder_layers_27_encoder_attn_layer_norm_weight3, model_decoder_layers_27_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv389 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_27_encoder_attn_q_proj_weight3, layer_norm244, model_decoder_layers_27_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape986 = R.call_tir(cls.reshape4, (lv389,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape987 = R.call_tir(cls.reshape8, (reshape986,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv189 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), reshape987), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape988 = R.call_tir(cls.reshape6, (lv189,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape989 = R.call_tir(cls.reshape7, (reshape988,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv390 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_27_encoder_attn_out_proj_weight3, reshape989, model_decoder_layers_27_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add855 = R.call_tir(cls.add, (add852, lv390), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm245 = R.call_tir(cls.layer_norm, (add855, model_decoder_layers_27_final_layer_norm_weight3, model_decoder_layers_27_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv59 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_27_fc1_weight3, layer_norm245, model_decoder_layers_27_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv391 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_27_fc2_weight3, lv59, model_decoder_layers_27_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add858 = R.call_tir(cls.add, (add855, lv391), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm246 = R.call_tir(cls.layer_norm, (add858, model_decoder_layers_28_self_attn_layer_norm_weight3, model_decoder_layers_28_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv392 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_28_self_attn_q_proj_weight3, layer_norm246, model_decoder_layers_28_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape990 = R.call_tir(cls.reshape4, (lv392,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv93 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_28_self_attn_k_proj_weight3, layer_norm246), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape991 = R.call_tir(cls.reshape4, (lv93,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv393 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_28_self_attn_v_proj_weight3, layer_norm246, model_decoder_layers_28_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape992 = R.call_tir(cls.reshape4, (lv393,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat60 = R.call_tir(cls.concatenate, (reshape990, reshape991, reshape992), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape993 = R.call_tir(cls.reshape5, (concat60,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv190 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), reshape993), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape994 = R.call_tir(cls.reshape6, (lv190,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape995 = R.call_tir(cls.reshape7, (reshape994,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv394 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_28_self_attn_out_proj_weight3, reshape995, model_decoder_layers_28_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add862 = R.call_tir(cls.add, (add858, lv394), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm247 = R.call_tir(cls.layer_norm, (add862, model_decoder_layers_28_encoder_attn_layer_norm_weight3, model_decoder_layers_28_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv395 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_28_encoder_attn_q_proj_weight3, layer_norm247, model_decoder_layers_28_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape996 = R.call_tir(cls.reshape4, (lv395,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape997 = R.call_tir(cls.reshape8, (reshape996,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv191 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), reshape997), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape998 = R.call_tir(cls.reshape6, (lv191,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape999 = R.call_tir(cls.reshape7, (reshape998,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv396 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_28_encoder_attn_out_proj_weight3, reshape999, model_decoder_layers_28_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add865 = R.call_tir(cls.add, (add862, lv396), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm248 = R.call_tir(cls.layer_norm, (add865, model_decoder_layers_28_final_layer_norm_weight3, model_decoder_layers_28_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv60 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_28_fc1_weight3, layer_norm248, model_decoder_layers_28_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv397 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_28_fc2_weight3, lv60, model_decoder_layers_28_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add868 = R.call_tir(cls.add, (add865, lv397), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm249 = R.call_tir(cls.layer_norm, (add868, model_decoder_layers_29_self_attn_layer_norm_weight3, model_decoder_layers_29_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv398 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_29_self_attn_q_proj_weight3, layer_norm249, model_decoder_layers_29_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape1000 = R.call_tir(cls.reshape4, (lv398,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv94 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_29_self_attn_k_proj_weight3, layer_norm249), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape1001 = R.call_tir(cls.reshape4, (lv94,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv399 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_29_self_attn_v_proj_weight3, layer_norm249, model_decoder_layers_29_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape1002 = R.call_tir(cls.reshape4, (lv399,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat61 = R.call_tir(cls.concatenate, (reshape1000, reshape1001, reshape1002), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape1003 = R.call_tir(cls.reshape5, (concat61,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv192 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), reshape1003), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape1004 = R.call_tir(cls.reshape6, (lv192,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape1005 = R.call_tir(cls.reshape7, (reshape1004,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv400 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_29_self_attn_out_proj_weight3, reshape1005, model_decoder_layers_29_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add872 = R.call_tir(cls.add, (add868, lv400), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm250 = R.call_tir(cls.layer_norm, (add872, model_decoder_layers_29_encoder_attn_layer_norm_weight3, model_decoder_layers_29_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv401 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_29_encoder_attn_q_proj_weight3, layer_norm250, model_decoder_layers_29_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape1006 = R.call_tir(cls.reshape4, (lv401,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape1007 = R.call_tir(cls.reshape8, (reshape1006,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv193 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), reshape1007), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape1008 = R.call_tir(cls.reshape6, (lv193,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape1009 = R.call_tir(cls.reshape7, (reshape1008,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv402 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_29_encoder_attn_out_proj_weight3, reshape1009, model_decoder_layers_29_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add875 = R.call_tir(cls.add, (add872, lv402), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm251 = R.call_tir(cls.layer_norm, (add875, model_decoder_layers_29_final_layer_norm_weight3, model_decoder_layers_29_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv61 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_29_fc1_weight3, layer_norm251, model_decoder_layers_29_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv403 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_29_fc2_weight3, lv61, model_decoder_layers_29_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add878 = R.call_tir(cls.add, (add875, lv403), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm252 = R.call_tir(cls.layer_norm, (add878, model_decoder_layers_30_self_attn_layer_norm_weight3, model_decoder_layers_30_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv404 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_30_self_attn_q_proj_weight3, layer_norm252, model_decoder_layers_30_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape1010 = R.call_tir(cls.reshape4, (lv404,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv95 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_30_self_attn_k_proj_weight3, layer_norm252), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape1011 = R.call_tir(cls.reshape4, (lv95,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv405 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_30_self_attn_v_proj_weight3, layer_norm252, model_decoder_layers_30_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape1012 = R.call_tir(cls.reshape4, (lv405,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat62 = R.call_tir(cls.concatenate, (reshape1010, reshape1011, reshape1012), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape1013 = R.call_tir(cls.reshape5, (concat62,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv194 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), reshape1013), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape1014 = R.call_tir(cls.reshape6, (lv194,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape1015 = R.call_tir(cls.reshape7, (reshape1014,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv406 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_30_self_attn_out_proj_weight3, reshape1015, model_decoder_layers_30_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add882 = R.call_tir(cls.add, (add878, lv406), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm253 = R.call_tir(cls.layer_norm, (add882, model_decoder_layers_30_encoder_attn_layer_norm_weight3, model_decoder_layers_30_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv407 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_30_encoder_attn_q_proj_weight3, layer_norm253, model_decoder_layers_30_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape1016 = R.call_tir(cls.reshape4, (lv407,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape1017 = R.call_tir(cls.reshape8, (reshape1016,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv195 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), reshape1017), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape1018 = R.call_tir(cls.reshape6, (lv195,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape1019 = R.call_tir(cls.reshape7, (reshape1018,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv408 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_30_encoder_attn_out_proj_weight3, reshape1019, model_decoder_layers_30_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add885 = R.call_tir(cls.add, (add882, lv408), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm254 = R.call_tir(cls.layer_norm, (add885, model_decoder_layers_30_final_layer_norm_weight3, model_decoder_layers_30_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv62 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_30_fc1_weight3, layer_norm254, model_decoder_layers_30_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv409 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_30_fc2_weight3, lv62, model_decoder_layers_30_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add888 = R.call_tir(cls.add, (add885, lv409), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm255 = R.call_tir(cls.layer_norm, (add888, model_decoder_layers_31_self_attn_layer_norm_weight3, model_decoder_layers_31_self_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv410 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_31_self_attn_q_proj_weight3, layer_norm255, model_decoder_layers_31_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape1020 = R.call_tir(cls.reshape4, (lv410,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv96 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_31_self_attn_k_proj_weight3, layer_norm255), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape1021 = R.call_tir(cls.reshape4, (lv96,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + lv411 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_31_self_attn_v_proj_weight3, layer_norm255, model_decoder_layers_31_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape1022 = R.call_tir(cls.reshape4, (lv411,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + concat63 = R.call_tir(cls.concatenate, (reshape1020, reshape1021, reshape1022), out_sinfo=R.Tensor((batch_size, 1, 60, 64), dtype="float16")) + reshape1023 = R.call_tir(cls.reshape5, (concat63,), out_sinfo=R.Tensor((batch_size, 60, 64), dtype="float16")) + lv196 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), reshape1023), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape1024 = R.call_tir(cls.reshape6, (lv196,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape1025 = R.call_tir(cls.reshape7, (reshape1024,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv412 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_31_self_attn_out_proj_weight3, reshape1025, model_decoder_layers_31_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add892 = R.call_tir(cls.add, (add888, lv412), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm256 = R.call_tir(cls.layer_norm, (add892, model_decoder_layers_31_encoder_attn_layer_norm_weight3, model_decoder_layers_31_encoder_attn_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv413 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_31_encoder_attn_q_proj_weight3, layer_norm256, model_decoder_layers_31_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + reshape1026 = R.call_tir(cls.reshape4, (lv413,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape1027 = R.call_tir(cls.reshape8, (reshape1026,), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + lv197 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), reshape1027), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) + reshape1028 = R.call_tir(cls.reshape6, (lv197,), out_sinfo=R.Tensor((batch_size, 1, 20, 64), dtype="float16")) + reshape1029 = R.call_tir(cls.reshape7, (reshape1028,), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv414 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_31_encoder_attn_out_proj_weight3, reshape1029, model_decoder_layers_31_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add895 = R.call_tir(cls.add, (add892, lv414), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm257 = R.call_tir(cls.layer_norm, (add895, model_decoder_layers_31_final_layer_norm_weight3, model_decoder_layers_31_final_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + lv63 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_31_fc1_weight3, layer_norm257, model_decoder_layers_31_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) + lv415 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_31_fc2_weight3, lv63, model_decoder_layers_31_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + add898 = R.call_tir(cls.add, (add895, lv415), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + layer_norm258 = R.call_tir(cls.layer_norm, (add898, model_decoder_layer_norm_weight3, model_decoder_layer_norm_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) + gv3 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul4_cublas", (model_decoder_embed_tokens_weight3, layer_norm258), out_sinfo=R.Tensor((batch_size, 1, 51866), dtype="float32")) + R.output(gv3) + return gv3 + + @R.function + def batch_encode(input_features: R.Tensor(("batch_size", 128, 3000), dtype="float16"), paged_kv_cache: R.Object, packed_params: R.Tuple(R.Tensor((1280, 128, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1500, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), 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dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"))) -> R.Tensor(("batch_size", 1500, 1280), dtype="float16"): + batch_size = T.int64() + R.func_attr({"num_input": 2, "relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "seq_len": 15000, "total_seq_len": 1500}}) + cls = Module + with R.dataflow(): + model_encoder_conv1_weight: R.Tensor((1280, 128, 3), dtype="float16") = packed_params[0] + lv: R.Tensor((1280,), dtype="float16") = packed_params[1] + lv1 = R.call_tir(cls.fused_reshape9, (lv,), out_sinfo=R.Tensor((1, 1280, 1), dtype="float16")) + model_encoder_conv2_weight: R.Tensor((1280, 1280, 3), dtype="float16") = packed_params[2] + lv2: R.Tensor((1280,), dtype="float16") = packed_params[3] + lv3 = R.call_tir(cls.fused_reshape9, (lv2,), out_sinfo=R.Tensor((1, 1280, 1), dtype="float16")) + lv4 = R.call_tir(cls.fused_conv1d_add1_gelu, (input_features, model_encoder_conv1_weight, lv1), out_sinfo=R.Tensor((batch_size, 1280, 3000), dtype="float16")) + lv5 = R.call_tir(cls.fused_conv1d1_add2_gelu1, (lv4, model_encoder_conv2_weight, lv3), out_sinfo=R.Tensor((batch_size, 1280, 1500), dtype="float16")) + lv6: R.Tensor((1500, 1280), dtype="float16") = packed_params[4] + lv7 = R.call_tir(cls.fused_transpose_add3, (lv6, lv5), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + model_encoder_layers_0_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[5] + model_encoder_layers_0_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[6] + model_encoder_layers_0_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[7] + model_encoder_layers_0_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[8] + model_encoder_layers_0_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[9] + model_encoder_layers_0_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[10] + model_encoder_layers_0_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[11] + model_encoder_layers_0_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[12] + model_encoder_layers_0_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[13] + model_encoder_layers_0_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[14] + model_encoder_layers_0_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[15] + model_encoder_layers_0_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[16] + model_encoder_layers_0_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[17] + model_encoder_layers_0_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[18] + model_encoder_layers_0_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[19] + model_encoder_layers_1_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[20] + model_encoder_layers_1_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[21] + model_encoder_layers_1_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[22] + model_encoder_layers_1_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[23] + model_encoder_layers_1_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[24] + model_encoder_layers_1_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[25] + model_encoder_layers_1_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[26] + model_encoder_layers_1_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[27] + model_encoder_layers_1_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[28] + model_encoder_layers_1_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[29] + model_encoder_layers_1_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[30] + model_encoder_layers_1_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[31] + model_encoder_layers_1_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[32] + model_encoder_layers_1_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[33] + model_encoder_layers_1_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[34] + model_encoder_layers_2_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[35] + model_encoder_layers_2_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[36] + model_encoder_layers_2_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[37] + model_encoder_layers_2_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[38] + model_encoder_layers_2_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[39] + model_encoder_layers_2_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[40] + model_encoder_layers_2_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[41] + model_encoder_layers_2_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[42] + model_encoder_layers_2_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[43] + model_encoder_layers_2_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[44] + model_encoder_layers_2_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[45] + model_encoder_layers_2_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[46] + model_encoder_layers_2_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[47] + model_encoder_layers_2_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[48] + model_encoder_layers_2_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[49] + model_encoder_layers_3_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[50] + model_encoder_layers_3_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[51] + model_encoder_layers_3_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[52] + model_encoder_layers_3_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[53] + model_encoder_layers_3_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[54] + model_encoder_layers_3_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[55] + model_encoder_layers_3_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[56] + model_encoder_layers_3_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[57] + model_encoder_layers_3_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[58] + model_encoder_layers_3_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[59] + model_encoder_layers_3_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[60] + model_encoder_layers_3_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[61] + model_encoder_layers_3_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[62] + model_encoder_layers_3_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[63] + model_encoder_layers_3_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[64] + model_encoder_layers_4_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[65] + model_encoder_layers_4_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[66] + model_encoder_layers_4_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[67] + model_encoder_layers_4_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[68] + model_encoder_layers_4_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[69] + model_encoder_layers_4_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[70] + model_encoder_layers_4_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[71] + model_encoder_layers_4_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[72] + model_encoder_layers_4_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[73] + model_encoder_layers_4_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[74] + model_encoder_layers_4_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[75] + model_encoder_layers_4_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[76] + model_encoder_layers_4_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[77] + model_encoder_layers_4_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[78] + model_encoder_layers_4_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[79] + model_encoder_layers_5_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[80] + model_encoder_layers_5_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[81] + model_encoder_layers_5_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[82] + model_encoder_layers_5_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[83] + model_encoder_layers_5_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[84] + model_encoder_layers_5_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[85] + model_encoder_layers_5_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[86] + model_encoder_layers_5_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[87] + model_encoder_layers_5_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[88] + model_encoder_layers_5_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[89] + model_encoder_layers_5_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[90] + model_encoder_layers_5_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[91] + model_encoder_layers_5_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[92] + model_encoder_layers_5_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[93] + model_encoder_layers_5_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[94] + model_encoder_layers_6_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[95] + model_encoder_layers_6_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[96] + model_encoder_layers_6_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[97] + model_encoder_layers_6_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[98] + model_encoder_layers_6_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[99] + model_encoder_layers_6_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[100] + model_encoder_layers_6_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[101] + model_encoder_layers_6_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[102] + model_encoder_layers_6_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[103] + model_encoder_layers_6_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[104] + model_encoder_layers_6_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[105] + model_encoder_layers_6_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[106] + model_encoder_layers_6_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[107] + model_encoder_layers_6_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[108] + model_encoder_layers_6_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[109] + model_encoder_layers_7_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[110] + model_encoder_layers_7_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[111] + model_encoder_layers_7_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[112] + model_encoder_layers_7_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[113] + model_encoder_layers_7_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[114] + model_encoder_layers_7_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[115] + model_encoder_layers_7_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[116] + model_encoder_layers_7_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[117] + model_encoder_layers_7_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[118] + model_encoder_layers_7_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[119] + model_encoder_layers_7_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[120] + model_encoder_layers_7_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[121] + model_encoder_layers_7_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[122] + model_encoder_layers_7_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[123] + model_encoder_layers_7_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[124] + model_encoder_layers_8_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[125] + model_encoder_layers_8_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[126] + model_encoder_layers_8_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[127] + model_encoder_layers_8_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[128] + model_encoder_layers_8_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[129] + model_encoder_layers_8_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[130] + model_encoder_layers_8_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[131] + model_encoder_layers_8_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[132] + model_encoder_layers_8_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[133] + model_encoder_layers_8_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[134] + model_encoder_layers_8_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[135] + model_encoder_layers_8_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[136] + model_encoder_layers_8_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[137] + model_encoder_layers_8_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[138] + model_encoder_layers_8_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[139] + model_encoder_layers_9_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[140] + model_encoder_layers_9_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[141] + model_encoder_layers_9_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[142] + model_encoder_layers_9_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[143] + model_encoder_layers_9_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[144] + model_encoder_layers_9_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[145] + model_encoder_layers_9_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[146] + model_encoder_layers_9_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[147] + model_encoder_layers_9_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[148] + model_encoder_layers_9_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[149] + model_encoder_layers_9_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[150] + model_encoder_layers_9_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[151] + model_encoder_layers_9_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[152] + model_encoder_layers_9_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[153] + model_encoder_layers_9_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[154] + model_encoder_layers_10_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[155] + model_encoder_layers_10_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[156] + model_encoder_layers_10_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[157] + model_encoder_layers_10_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[158] + model_encoder_layers_10_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[159] + model_encoder_layers_10_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[160] + model_encoder_layers_10_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[161] + model_encoder_layers_10_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[162] + model_encoder_layers_10_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[163] + model_encoder_layers_10_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[164] + model_encoder_layers_10_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[165] + model_encoder_layers_10_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[166] + model_encoder_layers_10_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[167] + model_encoder_layers_10_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[168] + model_encoder_layers_10_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[169] + model_encoder_layers_11_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[170] + model_encoder_layers_11_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[171] + model_encoder_layers_11_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[172] + model_encoder_layers_11_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[173] + model_encoder_layers_11_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[174] + model_encoder_layers_11_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[175] + model_encoder_layers_11_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[176] + model_encoder_layers_11_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[177] + model_encoder_layers_11_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[178] + model_encoder_layers_11_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[179] + model_encoder_layers_11_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[180] + model_encoder_layers_11_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[181] + model_encoder_layers_11_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[182] + model_encoder_layers_11_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[183] + model_encoder_layers_11_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[184] + model_encoder_layers_12_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[185] + model_encoder_layers_12_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[186] + model_encoder_layers_12_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[187] + model_encoder_layers_12_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[188] + model_encoder_layers_12_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[189] + model_encoder_layers_12_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[190] + model_encoder_layers_12_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[191] + model_encoder_layers_12_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[192] + model_encoder_layers_12_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[193] + model_encoder_layers_12_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[194] + model_encoder_layers_12_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[195] + model_encoder_layers_12_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[196] + model_encoder_layers_12_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[197] + model_encoder_layers_12_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[198] + model_encoder_layers_12_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[199] + model_encoder_layers_13_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[200] + model_encoder_layers_13_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[201] + model_encoder_layers_13_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[202] + model_encoder_layers_13_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[203] + model_encoder_layers_13_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[204] + model_encoder_layers_13_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[205] + model_encoder_layers_13_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[206] + model_encoder_layers_13_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[207] + model_encoder_layers_13_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[208] + model_encoder_layers_13_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[209] + model_encoder_layers_13_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[210] + model_encoder_layers_13_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[211] + model_encoder_layers_13_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[212] + model_encoder_layers_13_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[213] + model_encoder_layers_13_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[214] + model_encoder_layers_14_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[215] + model_encoder_layers_14_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[216] + model_encoder_layers_14_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[217] + model_encoder_layers_14_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[218] + model_encoder_layers_14_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[219] + model_encoder_layers_14_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[220] + model_encoder_layers_14_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[221] + model_encoder_layers_14_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[222] + model_encoder_layers_14_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[223] + model_encoder_layers_14_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[224] + model_encoder_layers_14_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[225] + model_encoder_layers_14_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[226] + model_encoder_layers_14_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[227] + model_encoder_layers_14_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[228] + model_encoder_layers_14_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[229] + model_encoder_layers_15_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[230] + model_encoder_layers_15_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[231] + model_encoder_layers_15_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[232] + model_encoder_layers_15_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[233] + model_encoder_layers_15_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[234] + model_encoder_layers_15_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[235] + model_encoder_layers_15_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[236] + model_encoder_layers_15_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[237] + model_encoder_layers_15_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[238] + model_encoder_layers_15_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[239] + model_encoder_layers_15_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[240] + model_encoder_layers_15_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[241] + model_encoder_layers_15_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[242] + model_encoder_layers_15_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[243] + model_encoder_layers_15_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[244] + model_encoder_layers_16_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[245] + model_encoder_layers_16_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[246] + model_encoder_layers_16_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[247] + model_encoder_layers_16_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[248] + model_encoder_layers_16_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[249] + model_encoder_layers_16_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[250] + model_encoder_layers_16_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[251] + model_encoder_layers_16_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[252] + model_encoder_layers_16_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[253] + model_encoder_layers_16_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[254] + model_encoder_layers_16_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[255] + model_encoder_layers_16_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[256] + model_encoder_layers_16_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[257] + model_encoder_layers_16_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[258] + model_encoder_layers_16_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[259] + model_encoder_layers_17_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[260] + model_encoder_layers_17_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[261] + model_encoder_layers_17_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[262] + model_encoder_layers_17_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[263] + model_encoder_layers_17_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[264] + model_encoder_layers_17_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[265] + model_encoder_layers_17_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[266] + model_encoder_layers_17_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[267] + model_encoder_layers_17_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[268] + model_encoder_layers_17_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[269] + model_encoder_layers_17_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[270] + model_encoder_layers_17_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[271] + model_encoder_layers_17_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[272] + model_encoder_layers_17_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[273] + model_encoder_layers_17_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[274] + model_encoder_layers_18_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[275] + model_encoder_layers_18_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[276] + model_encoder_layers_18_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[277] + model_encoder_layers_18_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[278] + model_encoder_layers_18_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[279] + model_encoder_layers_18_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[280] + model_encoder_layers_18_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[281] + model_encoder_layers_18_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[282] + model_encoder_layers_18_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[283] + model_encoder_layers_18_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[284] + model_encoder_layers_18_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[285] + model_encoder_layers_18_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[286] + model_encoder_layers_18_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[287] + model_encoder_layers_18_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[288] + model_encoder_layers_18_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[289] + model_encoder_layers_19_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[290] + model_encoder_layers_19_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[291] + model_encoder_layers_19_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[292] + model_encoder_layers_19_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[293] + model_encoder_layers_19_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[294] + model_encoder_layers_19_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[295] + model_encoder_layers_19_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[296] + model_encoder_layers_19_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[297] + model_encoder_layers_19_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[298] + model_encoder_layers_19_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[299] + model_encoder_layers_19_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[300] + model_encoder_layers_19_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[301] + model_encoder_layers_19_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[302] + model_encoder_layers_19_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[303] + model_encoder_layers_19_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[304] + model_encoder_layers_20_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[305] + model_encoder_layers_20_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[306] + model_encoder_layers_20_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[307] + model_encoder_layers_20_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[308] + model_encoder_layers_20_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[309] + model_encoder_layers_20_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[310] + model_encoder_layers_20_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[311] + model_encoder_layers_20_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[312] + model_encoder_layers_20_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[313] + model_encoder_layers_20_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[314] + model_encoder_layers_20_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[315] + model_encoder_layers_20_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[316] + model_encoder_layers_20_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[317] + model_encoder_layers_20_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[318] + model_encoder_layers_20_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[319] + model_encoder_layers_21_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[320] + model_encoder_layers_21_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[321] + model_encoder_layers_21_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[322] + model_encoder_layers_21_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[323] + model_encoder_layers_21_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[324] + model_encoder_layers_21_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[325] + model_encoder_layers_21_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[326] + model_encoder_layers_21_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[327] + model_encoder_layers_21_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[328] + model_encoder_layers_21_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[329] + model_encoder_layers_21_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[330] + model_encoder_layers_21_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[331] + model_encoder_layers_21_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[332] + model_encoder_layers_21_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[333] + model_encoder_layers_21_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[334] + model_encoder_layers_22_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[335] + model_encoder_layers_22_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[336] + model_encoder_layers_22_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[337] + model_encoder_layers_22_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[338] + model_encoder_layers_22_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[339] + model_encoder_layers_22_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[340] + model_encoder_layers_22_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[341] + model_encoder_layers_22_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[342] + model_encoder_layers_22_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[343] + model_encoder_layers_22_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[344] + model_encoder_layers_22_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[345] + model_encoder_layers_22_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[346] + model_encoder_layers_22_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[347] + model_encoder_layers_22_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[348] + model_encoder_layers_22_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[349] + model_encoder_layers_23_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[350] + model_encoder_layers_23_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[351] + model_encoder_layers_23_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[352] + model_encoder_layers_23_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[353] + model_encoder_layers_23_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[354] + model_encoder_layers_23_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[355] + model_encoder_layers_23_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[356] + model_encoder_layers_23_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[357] + model_encoder_layers_23_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[358] + model_encoder_layers_23_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[359] + model_encoder_layers_23_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[360] + model_encoder_layers_23_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[361] + model_encoder_layers_23_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[362] + model_encoder_layers_23_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[363] + model_encoder_layers_23_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[364] + model_encoder_layers_24_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[365] + model_encoder_layers_24_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[366] + model_encoder_layers_24_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[367] + model_encoder_layers_24_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[368] + model_encoder_layers_24_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[369] + model_encoder_layers_24_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[370] + model_encoder_layers_24_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[371] + model_encoder_layers_24_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[372] + model_encoder_layers_24_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[373] + model_encoder_layers_24_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[374] + model_encoder_layers_24_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[375] + model_encoder_layers_24_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[376] + model_encoder_layers_24_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[377] + model_encoder_layers_24_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[378] + model_encoder_layers_24_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[379] + model_encoder_layers_25_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[380] + model_encoder_layers_25_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[381] + model_encoder_layers_25_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[382] + model_encoder_layers_25_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[383] + model_encoder_layers_25_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[384] + model_encoder_layers_25_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[385] + model_encoder_layers_25_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[386] + model_encoder_layers_25_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[387] + model_encoder_layers_25_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[388] + model_encoder_layers_25_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[389] + model_encoder_layers_25_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[390] + model_encoder_layers_25_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[391] + model_encoder_layers_25_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[392] + model_encoder_layers_25_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[393] + model_encoder_layers_25_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[394] + model_encoder_layers_26_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[395] + model_encoder_layers_26_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[396] + model_encoder_layers_26_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[397] + model_encoder_layers_26_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[398] + model_encoder_layers_26_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[399] + model_encoder_layers_26_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[400] + model_encoder_layers_26_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[401] + model_encoder_layers_26_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[402] + model_encoder_layers_26_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[403] + model_encoder_layers_26_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[404] + model_encoder_layers_26_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[405] + model_encoder_layers_26_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[406] + model_encoder_layers_26_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[407] + model_encoder_layers_26_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[408] + model_encoder_layers_26_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[409] + model_encoder_layers_27_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[410] + model_encoder_layers_27_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[411] + model_encoder_layers_27_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[412] + model_encoder_layers_27_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[413] + model_encoder_layers_27_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[414] + model_encoder_layers_27_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[415] + model_encoder_layers_27_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[416] + model_encoder_layers_27_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[417] + model_encoder_layers_27_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[418] + model_encoder_layers_27_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[419] + model_encoder_layers_27_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[420] + model_encoder_layers_27_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[421] + model_encoder_layers_27_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[422] + model_encoder_layers_27_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[423] + model_encoder_layers_27_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[424] + model_encoder_layers_28_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[425] + model_encoder_layers_28_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[426] + model_encoder_layers_28_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[427] + model_encoder_layers_28_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[428] + model_encoder_layers_28_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[429] + model_encoder_layers_28_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[430] + model_encoder_layers_28_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[431] + model_encoder_layers_28_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[432] + model_encoder_layers_28_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[433] + model_encoder_layers_28_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[434] + model_encoder_layers_28_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[435] + model_encoder_layers_28_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[436] + model_encoder_layers_28_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[437] + model_encoder_layers_28_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[438] + model_encoder_layers_28_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[439] + model_encoder_layers_29_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[440] + model_encoder_layers_29_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[441] + model_encoder_layers_29_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[442] + model_encoder_layers_29_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[443] + model_encoder_layers_29_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[444] + model_encoder_layers_29_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[445] + model_encoder_layers_29_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[446] + model_encoder_layers_29_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[447] + model_encoder_layers_29_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[448] + model_encoder_layers_29_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[449] + model_encoder_layers_29_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[450] + model_encoder_layers_29_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[451] + model_encoder_layers_29_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[452] + model_encoder_layers_29_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[453] + model_encoder_layers_29_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[454] + model_encoder_layers_30_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[455] + model_encoder_layers_30_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[456] + model_encoder_layers_30_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[457] + model_encoder_layers_30_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[458] + model_encoder_layers_30_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[459] + model_encoder_layers_30_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[460] + model_encoder_layers_30_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[461] + model_encoder_layers_30_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[462] + model_encoder_layers_30_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[463] + model_encoder_layers_30_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[464] + model_encoder_layers_30_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[465] + model_encoder_layers_30_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[466] + model_encoder_layers_30_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[467] + model_encoder_layers_30_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[468] + model_encoder_layers_30_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[469] + model_encoder_layers_31_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[470] + model_encoder_layers_31_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[471] + model_encoder_layers_31_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[472] + model_encoder_layers_31_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[473] + model_encoder_layers_31_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[474] + model_encoder_layers_31_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[475] + model_encoder_layers_31_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[476] + model_encoder_layers_31_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[477] + model_encoder_layers_31_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[478] + model_encoder_layers_31_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[479] + model_encoder_layers_31_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[480] + model_encoder_layers_31_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[481] + model_encoder_layers_31_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[482] + model_encoder_layers_31_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[483] + model_encoder_layers_31_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[484] + model_encoder_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[485] + model_encoder_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[486] + layer_norm = R.call_tir(cls.layer_norm1, (lv7, model_encoder_layers_0_self_attn_layer_norm_weight, model_encoder_layers_0_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv608 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_0_self_attn_q_proj_weight, layer_norm, model_encoder_layers_0_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape = R.call_tir(cls.reshape, (lv608,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv131 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_0_self_attn_k_proj_weight, layer_norm), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape1 = R.call_tir(cls.reshape, (lv131,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv609 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_0_self_attn_v_proj_weight, layer_norm, model_encoder_layers_0_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape2 = R.call_tir(cls.reshape, (lv609,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape3 = R.call_tir(cls.reshape1, (reshape,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape4 = R.call_tir(cls.reshape1, (reshape1,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape5 = R.call_tir(cls.reshape1, (reshape2,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv4_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), reshape3, reshape4, reshape5), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape6 = R.call_tir(cls.reshape10, (lv4_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape7 = R.call_tir(cls.reshape11, (reshape6,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv610 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_0_self_attn_out_proj_weight, reshape7, model_encoder_layers_0_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add4 = R.call_tir(cls.add4, (lv7, lv610), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm1 = R.call_tir(cls.layer_norm1, (add4, model_encoder_layers_0_final_layer_norm_weight, model_encoder_layers_0_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv96 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_0_fc1_weight, layer_norm1, model_encoder_layers_0_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv611 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_0_fc2_weight, lv96, model_encoder_layers_0_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv8 = R.call_tir(cls.fused_add4_maximum_minimum, (add4, lv611), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm2 = R.call_tir(cls.layer_norm1, (lv8, model_encoder_layers_1_self_attn_layer_norm_weight, model_encoder_layers_1_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv612 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_1_self_attn_q_proj_weight, layer_norm2, model_encoder_layers_1_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape8 = R.call_tir(cls.reshape, (lv612,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv132 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_1_self_attn_k_proj_weight, layer_norm2), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape9 = R.call_tir(cls.reshape, (lv132,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv613 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_1_self_attn_v_proj_weight, layer_norm2, model_encoder_layers_1_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape10 = R.call_tir(cls.reshape, (lv613,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape11 = R.call_tir(cls.reshape1, (reshape8,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape12 = R.call_tir(cls.reshape1, (reshape9,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape13 = R.call_tir(cls.reshape1, (reshape10,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv5_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), reshape11, reshape12, reshape13), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape14 = R.call_tir(cls.reshape10, (lv5_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape15 = R.call_tir(cls.reshape11, (reshape14,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv614 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_1_self_attn_out_proj_weight, reshape15, model_encoder_layers_1_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add11 = R.call_tir(cls.add4, (lv8, lv614), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm3 = R.call_tir(cls.layer_norm1, (add11, model_encoder_layers_1_final_layer_norm_weight, model_encoder_layers_1_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv97 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_1_fc1_weight, layer_norm3, model_encoder_layers_1_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv615 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_1_fc2_weight, lv97, model_encoder_layers_1_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv9 = R.call_tir(cls.fused_add4_maximum_minimum, (add11, lv615), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm4 = R.call_tir(cls.layer_norm1, (lv9, model_encoder_layers_2_self_attn_layer_norm_weight, model_encoder_layers_2_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv616 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_2_self_attn_q_proj_weight, layer_norm4, model_encoder_layers_2_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape16 = R.call_tir(cls.reshape, (lv616,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv133 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_2_self_attn_k_proj_weight, layer_norm4), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape17 = R.call_tir(cls.reshape, (lv133,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv617 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_2_self_attn_v_proj_weight, layer_norm4, model_encoder_layers_2_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape18 = R.call_tir(cls.reshape, (lv617,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape19 = R.call_tir(cls.reshape1, (reshape16,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape20 = R.call_tir(cls.reshape1, (reshape17,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape21 = R.call_tir(cls.reshape1, (reshape18,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv6_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), reshape19, reshape20, reshape21), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape22 = R.call_tir(cls.reshape10, (lv6_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape23 = R.call_tir(cls.reshape11, (reshape22,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv618 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_2_self_attn_out_proj_weight, reshape23, model_encoder_layers_2_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add18 = R.call_tir(cls.add4, (lv9, lv618), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm5 = R.call_tir(cls.layer_norm1, (add18, model_encoder_layers_2_final_layer_norm_weight, model_encoder_layers_2_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv98 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_2_fc1_weight, layer_norm5, model_encoder_layers_2_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv619 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_2_fc2_weight, lv98, model_encoder_layers_2_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv10 = R.call_tir(cls.fused_add4_maximum_minimum, (add18, lv619), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm6 = R.call_tir(cls.layer_norm1, (lv10, model_encoder_layers_3_self_attn_layer_norm_weight, model_encoder_layers_3_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv620 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_3_self_attn_q_proj_weight, layer_norm6, model_encoder_layers_3_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape24 = R.call_tir(cls.reshape, (lv620,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv134 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_3_self_attn_k_proj_weight, layer_norm6), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape25 = R.call_tir(cls.reshape, (lv134,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv621 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_3_self_attn_v_proj_weight, layer_norm6, model_encoder_layers_3_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape26 = R.call_tir(cls.reshape, (lv621,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape27 = R.call_tir(cls.reshape1, (reshape24,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape28 = R.call_tir(cls.reshape1, (reshape25,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape29 = R.call_tir(cls.reshape1, (reshape26,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv7_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), reshape27, reshape28, reshape29), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape30 = R.call_tir(cls.reshape10, (lv7_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape31 = R.call_tir(cls.reshape11, (reshape30,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv622 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_3_self_attn_out_proj_weight, reshape31, model_encoder_layers_3_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add25 = R.call_tir(cls.add4, (lv10, lv622), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm7 = R.call_tir(cls.layer_norm1, (add25, model_encoder_layers_3_final_layer_norm_weight, model_encoder_layers_3_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv99 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_3_fc1_weight, layer_norm7, model_encoder_layers_3_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv623 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_3_fc2_weight, lv99, model_encoder_layers_3_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv11 = R.call_tir(cls.fused_add4_maximum_minimum, (add25, lv623), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm8 = R.call_tir(cls.layer_norm1, (lv11, model_encoder_layers_4_self_attn_layer_norm_weight, model_encoder_layers_4_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv624 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_4_self_attn_q_proj_weight, layer_norm8, model_encoder_layers_4_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape32 = R.call_tir(cls.reshape, (lv624,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv135 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_4_self_attn_k_proj_weight, layer_norm8), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape33 = R.call_tir(cls.reshape, (lv135,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv625 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_4_self_attn_v_proj_weight, layer_norm8, model_encoder_layers_4_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape34 = R.call_tir(cls.reshape, (lv625,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape35 = R.call_tir(cls.reshape1, (reshape32,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape36 = R.call_tir(cls.reshape1, (reshape33,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape37 = R.call_tir(cls.reshape1, (reshape34,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv8_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), reshape35, reshape36, reshape37), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape38 = R.call_tir(cls.reshape10, (lv8_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape39 = R.call_tir(cls.reshape11, (reshape38,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv626 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_4_self_attn_out_proj_weight, reshape39, model_encoder_layers_4_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add32 = R.call_tir(cls.add4, (lv11, lv626), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm9 = R.call_tir(cls.layer_norm1, (add32, model_encoder_layers_4_final_layer_norm_weight, model_encoder_layers_4_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv100 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_4_fc1_weight, layer_norm9, model_encoder_layers_4_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv627 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_4_fc2_weight, lv100, model_encoder_layers_4_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv12 = R.call_tir(cls.fused_add4_maximum_minimum, (add32, lv627), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm10 = R.call_tir(cls.layer_norm1, (lv12, model_encoder_layers_5_self_attn_layer_norm_weight, model_encoder_layers_5_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv628 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_5_self_attn_q_proj_weight, layer_norm10, model_encoder_layers_5_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape40 = R.call_tir(cls.reshape, (lv628,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv136 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_5_self_attn_k_proj_weight, layer_norm10), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape41 = R.call_tir(cls.reshape, (lv136,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv629 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_5_self_attn_v_proj_weight, layer_norm10, model_encoder_layers_5_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape42 = R.call_tir(cls.reshape, (lv629,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape43 = R.call_tir(cls.reshape1, (reshape40,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape44 = R.call_tir(cls.reshape1, (reshape41,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape45 = R.call_tir(cls.reshape1, (reshape42,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv9_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), reshape43, reshape44, reshape45), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape46 = R.call_tir(cls.reshape10, (lv9_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape47 = R.call_tir(cls.reshape11, (reshape46,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv630 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_5_self_attn_out_proj_weight, reshape47, model_encoder_layers_5_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add39 = R.call_tir(cls.add4, (lv12, lv630), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm11 = R.call_tir(cls.layer_norm1, (add39, model_encoder_layers_5_final_layer_norm_weight, model_encoder_layers_5_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv101 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_5_fc1_weight, layer_norm11, model_encoder_layers_5_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv631 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_5_fc2_weight, lv101, model_encoder_layers_5_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv13 = R.call_tir(cls.fused_add4_maximum_minimum, (add39, lv631), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm12 = R.call_tir(cls.layer_norm1, (lv13, model_encoder_layers_6_self_attn_layer_norm_weight, model_encoder_layers_6_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv632 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_6_self_attn_q_proj_weight, layer_norm12, model_encoder_layers_6_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape48 = R.call_tir(cls.reshape, (lv632,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv137 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_6_self_attn_k_proj_weight, layer_norm12), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape49 = R.call_tir(cls.reshape, (lv137,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv633 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_6_self_attn_v_proj_weight, layer_norm12, model_encoder_layers_6_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape50 = R.call_tir(cls.reshape, (lv633,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape51 = R.call_tir(cls.reshape1, (reshape48,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape52 = R.call_tir(cls.reshape1, (reshape49,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape53 = R.call_tir(cls.reshape1, (reshape50,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv10_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), reshape51, reshape52, reshape53), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape54 = R.call_tir(cls.reshape10, (lv10_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape55 = R.call_tir(cls.reshape11, (reshape54,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv634 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_6_self_attn_out_proj_weight, reshape55, model_encoder_layers_6_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add46 = R.call_tir(cls.add4, (lv13, lv634), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm13 = R.call_tir(cls.layer_norm1, (add46, model_encoder_layers_6_final_layer_norm_weight, model_encoder_layers_6_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv102 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_6_fc1_weight, layer_norm13, model_encoder_layers_6_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv635 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_6_fc2_weight, lv102, model_encoder_layers_6_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv14 = R.call_tir(cls.fused_add4_maximum_minimum, (add46, lv635), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm14 = R.call_tir(cls.layer_norm1, (lv14, model_encoder_layers_7_self_attn_layer_norm_weight, model_encoder_layers_7_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv636 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_7_self_attn_q_proj_weight, layer_norm14, model_encoder_layers_7_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape56 = R.call_tir(cls.reshape, (lv636,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv138 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_7_self_attn_k_proj_weight, layer_norm14), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape57 = R.call_tir(cls.reshape, (lv138,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv637 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_7_self_attn_v_proj_weight, layer_norm14, model_encoder_layers_7_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape58 = R.call_tir(cls.reshape, (lv637,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape59 = R.call_tir(cls.reshape1, (reshape56,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape60 = R.call_tir(cls.reshape1, (reshape57,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape61 = R.call_tir(cls.reshape1, (reshape58,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv11_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), reshape59, reshape60, reshape61), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape62 = R.call_tir(cls.reshape10, (lv11_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape63 = R.call_tir(cls.reshape11, (reshape62,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv638 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_7_self_attn_out_proj_weight, reshape63, model_encoder_layers_7_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add53 = R.call_tir(cls.add4, (lv14, lv638), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm15 = R.call_tir(cls.layer_norm1, (add53, model_encoder_layers_7_final_layer_norm_weight, model_encoder_layers_7_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv103 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_7_fc1_weight, layer_norm15, model_encoder_layers_7_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv639 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_7_fc2_weight, lv103, model_encoder_layers_7_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv15 = R.call_tir(cls.fused_add4_maximum_minimum, (add53, lv639), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm16 = R.call_tir(cls.layer_norm1, (lv15, model_encoder_layers_8_self_attn_layer_norm_weight, model_encoder_layers_8_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv640 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_8_self_attn_q_proj_weight, layer_norm16, model_encoder_layers_8_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape64 = R.call_tir(cls.reshape, (lv640,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv139 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_8_self_attn_k_proj_weight, layer_norm16), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape65 = R.call_tir(cls.reshape, (lv139,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv641 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_8_self_attn_v_proj_weight, layer_norm16, model_encoder_layers_8_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape66 = R.call_tir(cls.reshape, (lv641,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape67 = R.call_tir(cls.reshape1, (reshape64,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape68 = R.call_tir(cls.reshape1, (reshape65,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape69 = R.call_tir(cls.reshape1, (reshape66,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv12_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), reshape67, reshape68, reshape69), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape70 = R.call_tir(cls.reshape10, (lv12_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape71 = R.call_tir(cls.reshape11, (reshape70,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv642 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_8_self_attn_out_proj_weight, reshape71, model_encoder_layers_8_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add60 = R.call_tir(cls.add4, (lv15, lv642), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm17 = R.call_tir(cls.layer_norm1, (add60, model_encoder_layers_8_final_layer_norm_weight, model_encoder_layers_8_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv104 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_8_fc1_weight, layer_norm17, model_encoder_layers_8_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv643 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_8_fc2_weight, lv104, model_encoder_layers_8_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv16 = R.call_tir(cls.fused_add4_maximum_minimum, (add60, lv643), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm18 = R.call_tir(cls.layer_norm1, (lv16, model_encoder_layers_9_self_attn_layer_norm_weight, model_encoder_layers_9_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv644 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_9_self_attn_q_proj_weight, layer_norm18, model_encoder_layers_9_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape72 = R.call_tir(cls.reshape, (lv644,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv140 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_9_self_attn_k_proj_weight, layer_norm18), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape73 = R.call_tir(cls.reshape, (lv140,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv645 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_9_self_attn_v_proj_weight, layer_norm18, model_encoder_layers_9_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape74 = R.call_tir(cls.reshape, (lv645,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape75 = R.call_tir(cls.reshape1, (reshape72,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape76 = R.call_tir(cls.reshape1, (reshape73,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape77 = R.call_tir(cls.reshape1, (reshape74,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv13_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), reshape75, reshape76, reshape77), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape78 = R.call_tir(cls.reshape10, (lv13_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape79 = R.call_tir(cls.reshape11, (reshape78,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv646 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_9_self_attn_out_proj_weight, reshape79, model_encoder_layers_9_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add67 = R.call_tir(cls.add4, (lv16, lv646), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm19 = R.call_tir(cls.layer_norm1, (add67, model_encoder_layers_9_final_layer_norm_weight, model_encoder_layers_9_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv105 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_9_fc1_weight, layer_norm19, model_encoder_layers_9_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv647 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_9_fc2_weight, lv105, model_encoder_layers_9_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv17 = R.call_tir(cls.fused_add4_maximum_minimum, (add67, lv647), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm20 = R.call_tir(cls.layer_norm1, (lv17, model_encoder_layers_10_self_attn_layer_norm_weight, model_encoder_layers_10_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv648 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_10_self_attn_q_proj_weight, layer_norm20, model_encoder_layers_10_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape80 = R.call_tir(cls.reshape, (lv648,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv141 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_10_self_attn_k_proj_weight, layer_norm20), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape81 = R.call_tir(cls.reshape, (lv141,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv649 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_10_self_attn_v_proj_weight, layer_norm20, model_encoder_layers_10_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape82 = R.call_tir(cls.reshape, (lv649,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape83 = R.call_tir(cls.reshape1, (reshape80,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape84 = R.call_tir(cls.reshape1, (reshape81,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape85 = R.call_tir(cls.reshape1, (reshape82,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv14_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), reshape83, reshape84, reshape85), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape86 = R.call_tir(cls.reshape10, (lv14_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape87 = R.call_tir(cls.reshape11, (reshape86,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv650 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_10_self_attn_out_proj_weight, reshape87, model_encoder_layers_10_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add74 = R.call_tir(cls.add4, (lv17, lv650), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm21 = R.call_tir(cls.layer_norm1, (add74, model_encoder_layers_10_final_layer_norm_weight, model_encoder_layers_10_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv106 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_10_fc1_weight, layer_norm21, model_encoder_layers_10_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv651 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_10_fc2_weight, lv106, model_encoder_layers_10_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv18 = R.call_tir(cls.fused_add4_maximum_minimum, (add74, lv651), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm22 = R.call_tir(cls.layer_norm1, (lv18, model_encoder_layers_11_self_attn_layer_norm_weight, model_encoder_layers_11_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv652 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_11_self_attn_q_proj_weight, layer_norm22, model_encoder_layers_11_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape88 = R.call_tir(cls.reshape, (lv652,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv142 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_11_self_attn_k_proj_weight, layer_norm22), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape89 = R.call_tir(cls.reshape, (lv142,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv653 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_11_self_attn_v_proj_weight, layer_norm22, model_encoder_layers_11_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape90 = R.call_tir(cls.reshape, (lv653,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape91 = R.call_tir(cls.reshape1, (reshape88,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape92 = R.call_tir(cls.reshape1, (reshape89,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape93 = R.call_tir(cls.reshape1, (reshape90,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv15_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), reshape91, reshape92, reshape93), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape94 = R.call_tir(cls.reshape10, (lv15_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape95 = R.call_tir(cls.reshape11, (reshape94,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv654 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_11_self_attn_out_proj_weight, reshape95, model_encoder_layers_11_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add81 = R.call_tir(cls.add4, (lv18, lv654), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm23 = R.call_tir(cls.layer_norm1, (add81, model_encoder_layers_11_final_layer_norm_weight, model_encoder_layers_11_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv107 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_11_fc1_weight, layer_norm23, model_encoder_layers_11_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv655 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_11_fc2_weight, lv107, model_encoder_layers_11_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv19 = R.call_tir(cls.fused_add4_maximum_minimum, (add81, lv655), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm24 = R.call_tir(cls.layer_norm1, (lv19, model_encoder_layers_12_self_attn_layer_norm_weight, model_encoder_layers_12_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv656 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_12_self_attn_q_proj_weight, layer_norm24, model_encoder_layers_12_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape96 = R.call_tir(cls.reshape, (lv656,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv143 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_12_self_attn_k_proj_weight, layer_norm24), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape97 = R.call_tir(cls.reshape, (lv143,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv657 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_12_self_attn_v_proj_weight, layer_norm24, model_encoder_layers_12_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape98 = R.call_tir(cls.reshape, (lv657,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape99 = R.call_tir(cls.reshape1, (reshape96,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape100 = R.call_tir(cls.reshape1, (reshape97,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape101 = R.call_tir(cls.reshape1, (reshape98,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv16_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), reshape99, reshape100, reshape101), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape102 = R.call_tir(cls.reshape10, (lv16_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape103 = R.call_tir(cls.reshape11, (reshape102,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv658 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_12_self_attn_out_proj_weight, reshape103, model_encoder_layers_12_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add88 = R.call_tir(cls.add4, (lv19, lv658), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm25 = R.call_tir(cls.layer_norm1, (add88, model_encoder_layers_12_final_layer_norm_weight, model_encoder_layers_12_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv108 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_12_fc1_weight, layer_norm25, model_encoder_layers_12_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv659 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_12_fc2_weight, lv108, model_encoder_layers_12_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv20 = R.call_tir(cls.fused_add4_maximum_minimum, (add88, lv659), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm26 = R.call_tir(cls.layer_norm1, (lv20, model_encoder_layers_13_self_attn_layer_norm_weight, model_encoder_layers_13_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv660 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_13_self_attn_q_proj_weight, layer_norm26, model_encoder_layers_13_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape104 = R.call_tir(cls.reshape, (lv660,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv144 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_13_self_attn_k_proj_weight, layer_norm26), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape105 = R.call_tir(cls.reshape, (lv144,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv661 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_13_self_attn_v_proj_weight, layer_norm26, model_encoder_layers_13_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape106 = R.call_tir(cls.reshape, (lv661,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape107 = R.call_tir(cls.reshape1, (reshape104,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape108 = R.call_tir(cls.reshape1, (reshape105,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape109 = R.call_tir(cls.reshape1, (reshape106,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv17_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), reshape107, reshape108, reshape109), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape110 = R.call_tir(cls.reshape10, (lv17_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape111 = R.call_tir(cls.reshape11, (reshape110,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv662 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_13_self_attn_out_proj_weight, reshape111, model_encoder_layers_13_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add95 = R.call_tir(cls.add4, (lv20, lv662), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm27 = R.call_tir(cls.layer_norm1, (add95, model_encoder_layers_13_final_layer_norm_weight, model_encoder_layers_13_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv109 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_13_fc1_weight, layer_norm27, model_encoder_layers_13_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv663 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_13_fc2_weight, lv109, model_encoder_layers_13_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv21 = R.call_tir(cls.fused_add4_maximum_minimum, (add95, lv663), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm28 = R.call_tir(cls.layer_norm1, (lv21, model_encoder_layers_14_self_attn_layer_norm_weight, model_encoder_layers_14_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv664 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_14_self_attn_q_proj_weight, layer_norm28, model_encoder_layers_14_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape112 = R.call_tir(cls.reshape, (lv664,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv145 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_14_self_attn_k_proj_weight, layer_norm28), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape113 = R.call_tir(cls.reshape, (lv145,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv665 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_14_self_attn_v_proj_weight, layer_norm28, model_encoder_layers_14_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape114 = R.call_tir(cls.reshape, (lv665,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape115 = R.call_tir(cls.reshape1, (reshape112,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape116 = R.call_tir(cls.reshape1, (reshape113,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape117 = R.call_tir(cls.reshape1, (reshape114,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv18_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), reshape115, reshape116, reshape117), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape118 = R.call_tir(cls.reshape10, (lv18_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape119 = R.call_tir(cls.reshape11, (reshape118,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv666 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_14_self_attn_out_proj_weight, reshape119, model_encoder_layers_14_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add102 = R.call_tir(cls.add4, (lv21, lv666), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm29 = R.call_tir(cls.layer_norm1, (add102, model_encoder_layers_14_final_layer_norm_weight, model_encoder_layers_14_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv110 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_14_fc1_weight, layer_norm29, model_encoder_layers_14_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv667 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_14_fc2_weight, lv110, model_encoder_layers_14_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv22 = R.call_tir(cls.fused_add4_maximum_minimum, (add102, lv667), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm30 = R.call_tir(cls.layer_norm1, (lv22, model_encoder_layers_15_self_attn_layer_norm_weight, model_encoder_layers_15_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv668 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_15_self_attn_q_proj_weight, layer_norm30, model_encoder_layers_15_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape120 = R.call_tir(cls.reshape, (lv668,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv146 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_15_self_attn_k_proj_weight, layer_norm30), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape121 = R.call_tir(cls.reshape, (lv146,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv669 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_15_self_attn_v_proj_weight, layer_norm30, model_encoder_layers_15_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape122 = R.call_tir(cls.reshape, (lv669,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape123 = R.call_tir(cls.reshape1, (reshape120,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape124 = R.call_tir(cls.reshape1, (reshape121,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape125 = R.call_tir(cls.reshape1, (reshape122,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv19_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), reshape123, reshape124, reshape125), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape126 = R.call_tir(cls.reshape10, (lv19_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape127 = R.call_tir(cls.reshape11, (reshape126,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv670 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_15_self_attn_out_proj_weight, reshape127, model_encoder_layers_15_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add109 = R.call_tir(cls.add4, (lv22, lv670), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm31 = R.call_tir(cls.layer_norm1, (add109, model_encoder_layers_15_final_layer_norm_weight, model_encoder_layers_15_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv111 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_15_fc1_weight, layer_norm31, model_encoder_layers_15_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv671 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_15_fc2_weight, lv111, model_encoder_layers_15_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv23 = R.call_tir(cls.fused_add4_maximum_minimum, (add109, lv671), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm32 = R.call_tir(cls.layer_norm1, (lv23, model_encoder_layers_16_self_attn_layer_norm_weight, model_encoder_layers_16_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv672 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_16_self_attn_q_proj_weight, layer_norm32, model_encoder_layers_16_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape128 = R.call_tir(cls.reshape, (lv672,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv147 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_16_self_attn_k_proj_weight, layer_norm32), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape129 = R.call_tir(cls.reshape, (lv147,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv673 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_16_self_attn_v_proj_weight, layer_norm32, model_encoder_layers_16_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape130 = R.call_tir(cls.reshape, (lv673,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape131 = R.call_tir(cls.reshape1, (reshape128,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape132 = R.call_tir(cls.reshape1, (reshape129,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape133 = R.call_tir(cls.reshape1, (reshape130,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv20_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), reshape131, reshape132, reshape133), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape134 = R.call_tir(cls.reshape10, (lv20_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape135 = R.call_tir(cls.reshape11, (reshape134,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv674 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_16_self_attn_out_proj_weight, reshape135, model_encoder_layers_16_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add116 = R.call_tir(cls.add4, (lv23, lv674), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm33 = R.call_tir(cls.layer_norm1, (add116, model_encoder_layers_16_final_layer_norm_weight, model_encoder_layers_16_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv112 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_16_fc1_weight, layer_norm33, model_encoder_layers_16_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv675 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_16_fc2_weight, lv112, model_encoder_layers_16_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv24 = R.call_tir(cls.fused_add4_maximum_minimum, (add116, lv675), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm34 = R.call_tir(cls.layer_norm1, (lv24, model_encoder_layers_17_self_attn_layer_norm_weight, model_encoder_layers_17_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv676 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_17_self_attn_q_proj_weight, layer_norm34, model_encoder_layers_17_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape136 = R.call_tir(cls.reshape, (lv676,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv148 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_17_self_attn_k_proj_weight, layer_norm34), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape137 = R.call_tir(cls.reshape, (lv148,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv677 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_17_self_attn_v_proj_weight, layer_norm34, model_encoder_layers_17_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape138 = R.call_tir(cls.reshape, (lv677,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape139 = R.call_tir(cls.reshape1, (reshape136,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape140 = R.call_tir(cls.reshape1, (reshape137,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape141 = R.call_tir(cls.reshape1, (reshape138,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv21_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), reshape139, reshape140, reshape141), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape142 = R.call_tir(cls.reshape10, (lv21_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape143 = R.call_tir(cls.reshape11, (reshape142,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv678 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_17_self_attn_out_proj_weight, reshape143, model_encoder_layers_17_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add123 = R.call_tir(cls.add4, (lv24, lv678), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm35 = R.call_tir(cls.layer_norm1, (add123, model_encoder_layers_17_final_layer_norm_weight, model_encoder_layers_17_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv113 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_17_fc1_weight, layer_norm35, model_encoder_layers_17_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv679 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_17_fc2_weight, lv113, model_encoder_layers_17_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv25 = R.call_tir(cls.fused_add4_maximum_minimum, (add123, lv679), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm36 = R.call_tir(cls.layer_norm1, (lv25, model_encoder_layers_18_self_attn_layer_norm_weight, model_encoder_layers_18_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv680 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_18_self_attn_q_proj_weight, layer_norm36, model_encoder_layers_18_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape144 = R.call_tir(cls.reshape, (lv680,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv149 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_18_self_attn_k_proj_weight, layer_norm36), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape145 = R.call_tir(cls.reshape, (lv149,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv681 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_18_self_attn_v_proj_weight, layer_norm36, model_encoder_layers_18_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape146 = R.call_tir(cls.reshape, (lv681,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape147 = R.call_tir(cls.reshape1, (reshape144,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape148 = R.call_tir(cls.reshape1, (reshape145,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape149 = R.call_tir(cls.reshape1, (reshape146,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv22_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), reshape147, reshape148, reshape149), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape150 = R.call_tir(cls.reshape10, (lv22_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape151 = R.call_tir(cls.reshape11, (reshape150,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv682 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_18_self_attn_out_proj_weight, reshape151, model_encoder_layers_18_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add130 = R.call_tir(cls.add4, (lv25, lv682), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm37 = R.call_tir(cls.layer_norm1, (add130, model_encoder_layers_18_final_layer_norm_weight, model_encoder_layers_18_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv114 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_18_fc1_weight, layer_norm37, model_encoder_layers_18_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv683 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_18_fc2_weight, lv114, model_encoder_layers_18_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv26 = R.call_tir(cls.fused_add4_maximum_minimum, (add130, lv683), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm38 = R.call_tir(cls.layer_norm1, (lv26, model_encoder_layers_19_self_attn_layer_norm_weight, model_encoder_layers_19_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv684 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_19_self_attn_q_proj_weight, layer_norm38, model_encoder_layers_19_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape152 = R.call_tir(cls.reshape, (lv684,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv150 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_19_self_attn_k_proj_weight, layer_norm38), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape153 = R.call_tir(cls.reshape, (lv150,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv685 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_19_self_attn_v_proj_weight, layer_norm38, model_encoder_layers_19_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape154 = R.call_tir(cls.reshape, (lv685,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape155 = R.call_tir(cls.reshape1, (reshape152,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape156 = R.call_tir(cls.reshape1, (reshape153,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape157 = R.call_tir(cls.reshape1, (reshape154,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv23_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), reshape155, reshape156, reshape157), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape158 = R.call_tir(cls.reshape10, (lv23_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape159 = R.call_tir(cls.reshape11, (reshape158,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv686 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_19_self_attn_out_proj_weight, reshape159, model_encoder_layers_19_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add137 = R.call_tir(cls.add4, (lv26, lv686), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm39 = R.call_tir(cls.layer_norm1, (add137, model_encoder_layers_19_final_layer_norm_weight, model_encoder_layers_19_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv115 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_19_fc1_weight, layer_norm39, model_encoder_layers_19_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv687 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_19_fc2_weight, lv115, model_encoder_layers_19_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv27 = R.call_tir(cls.fused_add4_maximum_minimum, (add137, lv687), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm40 = R.call_tir(cls.layer_norm1, (lv27, model_encoder_layers_20_self_attn_layer_norm_weight, model_encoder_layers_20_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv688 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_20_self_attn_q_proj_weight, layer_norm40, model_encoder_layers_20_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape160 = R.call_tir(cls.reshape, (lv688,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv151 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_20_self_attn_k_proj_weight, layer_norm40), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape161 = R.call_tir(cls.reshape, (lv151,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv689 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_20_self_attn_v_proj_weight, layer_norm40, model_encoder_layers_20_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape162 = R.call_tir(cls.reshape, (lv689,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape163 = R.call_tir(cls.reshape1, (reshape160,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape164 = R.call_tir(cls.reshape1, (reshape161,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape165 = R.call_tir(cls.reshape1, (reshape162,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv24_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), reshape163, reshape164, reshape165), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape166 = R.call_tir(cls.reshape10, (lv24_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape167 = R.call_tir(cls.reshape11, (reshape166,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv690 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_20_self_attn_out_proj_weight, reshape167, model_encoder_layers_20_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add144 = R.call_tir(cls.add4, (lv27, lv690), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm41 = R.call_tir(cls.layer_norm1, (add144, model_encoder_layers_20_final_layer_norm_weight, model_encoder_layers_20_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv116 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_20_fc1_weight, layer_norm41, model_encoder_layers_20_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv691 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_20_fc2_weight, lv116, model_encoder_layers_20_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv28 = R.call_tir(cls.fused_add4_maximum_minimum, (add144, lv691), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm42 = R.call_tir(cls.layer_norm1, (lv28, model_encoder_layers_21_self_attn_layer_norm_weight, model_encoder_layers_21_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv692 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_21_self_attn_q_proj_weight, layer_norm42, model_encoder_layers_21_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape168 = R.call_tir(cls.reshape, (lv692,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv152 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_21_self_attn_k_proj_weight, layer_norm42), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape169 = R.call_tir(cls.reshape, (lv152,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv693 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_21_self_attn_v_proj_weight, layer_norm42, model_encoder_layers_21_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape170 = R.call_tir(cls.reshape, (lv693,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape171 = R.call_tir(cls.reshape1, (reshape168,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape172 = R.call_tir(cls.reshape1, (reshape169,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape173 = R.call_tir(cls.reshape1, (reshape170,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv25_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), reshape171, reshape172, reshape173), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape174 = R.call_tir(cls.reshape10, (lv25_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape175 = R.call_tir(cls.reshape11, (reshape174,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv694 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_21_self_attn_out_proj_weight, reshape175, model_encoder_layers_21_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add151 = R.call_tir(cls.add4, (lv28, lv694), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm43 = R.call_tir(cls.layer_norm1, (add151, model_encoder_layers_21_final_layer_norm_weight, model_encoder_layers_21_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv117 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_21_fc1_weight, layer_norm43, model_encoder_layers_21_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv695 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_21_fc2_weight, lv117, model_encoder_layers_21_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv29 = R.call_tir(cls.fused_add4_maximum_minimum, (add151, lv695), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm44 = R.call_tir(cls.layer_norm1, (lv29, model_encoder_layers_22_self_attn_layer_norm_weight, model_encoder_layers_22_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv696 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_22_self_attn_q_proj_weight, layer_norm44, model_encoder_layers_22_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape176 = R.call_tir(cls.reshape, (lv696,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv153 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_22_self_attn_k_proj_weight, layer_norm44), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape177 = R.call_tir(cls.reshape, (lv153,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv697 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_22_self_attn_v_proj_weight, layer_norm44, model_encoder_layers_22_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape178 = R.call_tir(cls.reshape, (lv697,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape179 = R.call_tir(cls.reshape1, (reshape176,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape180 = R.call_tir(cls.reshape1, (reshape177,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape181 = R.call_tir(cls.reshape1, (reshape178,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv26_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), reshape179, reshape180, reshape181), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape182 = R.call_tir(cls.reshape10, (lv26_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape183 = R.call_tir(cls.reshape11, (reshape182,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv698 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_22_self_attn_out_proj_weight, reshape183, model_encoder_layers_22_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add158 = R.call_tir(cls.add4, (lv29, lv698), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm45 = R.call_tir(cls.layer_norm1, (add158, model_encoder_layers_22_final_layer_norm_weight, model_encoder_layers_22_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv118 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_22_fc1_weight, layer_norm45, model_encoder_layers_22_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv699 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_22_fc2_weight, lv118, model_encoder_layers_22_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv30 = R.call_tir(cls.fused_add4_maximum_minimum, (add158, lv699), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm46 = R.call_tir(cls.layer_norm1, (lv30, model_encoder_layers_23_self_attn_layer_norm_weight, model_encoder_layers_23_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv700 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_23_self_attn_q_proj_weight, layer_norm46, model_encoder_layers_23_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape184 = R.call_tir(cls.reshape, (lv700,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv154 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_23_self_attn_k_proj_weight, layer_norm46), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape185 = R.call_tir(cls.reshape, (lv154,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv701 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_23_self_attn_v_proj_weight, layer_norm46, model_encoder_layers_23_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape186 = R.call_tir(cls.reshape, (lv701,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape187 = R.call_tir(cls.reshape1, (reshape184,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape188 = R.call_tir(cls.reshape1, (reshape185,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape189 = R.call_tir(cls.reshape1, (reshape186,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv27_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), reshape187, reshape188, reshape189), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape190 = R.call_tir(cls.reshape10, (lv27_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape191 = R.call_tir(cls.reshape11, (reshape190,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv702 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_23_self_attn_out_proj_weight, reshape191, model_encoder_layers_23_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add165 = R.call_tir(cls.add4, (lv30, lv702), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm47 = R.call_tir(cls.layer_norm1, (add165, model_encoder_layers_23_final_layer_norm_weight, model_encoder_layers_23_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv119 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_23_fc1_weight, layer_norm47, model_encoder_layers_23_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv703 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_23_fc2_weight, lv119, model_encoder_layers_23_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv31 = R.call_tir(cls.fused_add4_maximum_minimum, (add165, lv703), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm48 = R.call_tir(cls.layer_norm1, (lv31, model_encoder_layers_24_self_attn_layer_norm_weight, model_encoder_layers_24_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv704 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_24_self_attn_q_proj_weight, layer_norm48, model_encoder_layers_24_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape192 = R.call_tir(cls.reshape, (lv704,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv155 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_24_self_attn_k_proj_weight, layer_norm48), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape193 = R.call_tir(cls.reshape, (lv155,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv705 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_24_self_attn_v_proj_weight, layer_norm48, model_encoder_layers_24_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape194 = R.call_tir(cls.reshape, (lv705,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape195 = R.call_tir(cls.reshape1, (reshape192,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape196 = R.call_tir(cls.reshape1, (reshape193,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape197 = R.call_tir(cls.reshape1, (reshape194,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv28_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), reshape195, reshape196, reshape197), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape198 = R.call_tir(cls.reshape10, (lv28_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape199 = R.call_tir(cls.reshape11, (reshape198,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv706 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_24_self_attn_out_proj_weight, reshape199, model_encoder_layers_24_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add172 = R.call_tir(cls.add4, (lv31, lv706), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm49 = R.call_tir(cls.layer_norm1, (add172, model_encoder_layers_24_final_layer_norm_weight, model_encoder_layers_24_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv120 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_24_fc1_weight, layer_norm49, model_encoder_layers_24_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv707 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_24_fc2_weight, lv120, model_encoder_layers_24_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv32 = R.call_tir(cls.fused_add4_maximum_minimum, (add172, lv707), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm50 = R.call_tir(cls.layer_norm1, (lv32, model_encoder_layers_25_self_attn_layer_norm_weight, model_encoder_layers_25_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv708 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_25_self_attn_q_proj_weight, layer_norm50, model_encoder_layers_25_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape200 = R.call_tir(cls.reshape, (lv708,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv156 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_25_self_attn_k_proj_weight, layer_norm50), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape201 = R.call_tir(cls.reshape, (lv156,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv709 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_25_self_attn_v_proj_weight, layer_norm50, model_encoder_layers_25_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape202 = R.call_tir(cls.reshape, (lv709,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape203 = R.call_tir(cls.reshape1, (reshape200,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape204 = R.call_tir(cls.reshape1, (reshape201,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape205 = R.call_tir(cls.reshape1, (reshape202,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv29_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), reshape203, reshape204, reshape205), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape206 = R.call_tir(cls.reshape10, (lv29_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape207 = R.call_tir(cls.reshape11, (reshape206,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv710 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_25_self_attn_out_proj_weight, reshape207, model_encoder_layers_25_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add179 = R.call_tir(cls.add4, (lv32, lv710), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm51 = R.call_tir(cls.layer_norm1, (add179, model_encoder_layers_25_final_layer_norm_weight, model_encoder_layers_25_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv121 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_25_fc1_weight, layer_norm51, model_encoder_layers_25_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv711 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_25_fc2_weight, lv121, model_encoder_layers_25_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv33 = R.call_tir(cls.fused_add4_maximum_minimum, (add179, lv711), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm52 = R.call_tir(cls.layer_norm1, (lv33, model_encoder_layers_26_self_attn_layer_norm_weight, model_encoder_layers_26_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv712 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_26_self_attn_q_proj_weight, layer_norm52, model_encoder_layers_26_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape208 = R.call_tir(cls.reshape, (lv712,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv157 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_26_self_attn_k_proj_weight, layer_norm52), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape209 = R.call_tir(cls.reshape, (lv157,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv713 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_26_self_attn_v_proj_weight, layer_norm52, model_encoder_layers_26_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape210 = R.call_tir(cls.reshape, (lv713,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape211 = R.call_tir(cls.reshape1, (reshape208,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape212 = R.call_tir(cls.reshape1, (reshape209,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape213 = R.call_tir(cls.reshape1, (reshape210,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv30_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), reshape211, reshape212, reshape213), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape214 = R.call_tir(cls.reshape10, (lv30_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape215 = R.call_tir(cls.reshape11, (reshape214,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv714 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_26_self_attn_out_proj_weight, reshape215, model_encoder_layers_26_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add186 = R.call_tir(cls.add4, (lv33, lv714), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm53 = R.call_tir(cls.layer_norm1, (add186, model_encoder_layers_26_final_layer_norm_weight, model_encoder_layers_26_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv122 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_26_fc1_weight, layer_norm53, model_encoder_layers_26_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv715 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_26_fc2_weight, lv122, model_encoder_layers_26_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv34 = R.call_tir(cls.fused_add4_maximum_minimum, (add186, lv715), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm54 = R.call_tir(cls.layer_norm1, (lv34, model_encoder_layers_27_self_attn_layer_norm_weight, model_encoder_layers_27_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv716 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_27_self_attn_q_proj_weight, layer_norm54, model_encoder_layers_27_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape216 = R.call_tir(cls.reshape, (lv716,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv158 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_27_self_attn_k_proj_weight, layer_norm54), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape217 = R.call_tir(cls.reshape, (lv158,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv717 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_27_self_attn_v_proj_weight, layer_norm54, model_encoder_layers_27_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape218 = R.call_tir(cls.reshape, (lv717,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape219 = R.call_tir(cls.reshape1, (reshape216,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape220 = R.call_tir(cls.reshape1, (reshape217,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape221 = R.call_tir(cls.reshape1, (reshape218,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv31_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), reshape219, reshape220, reshape221), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape222 = R.call_tir(cls.reshape10, (lv31_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape223 = R.call_tir(cls.reshape11, (reshape222,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv718 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_27_self_attn_out_proj_weight, reshape223, model_encoder_layers_27_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add193 = R.call_tir(cls.add4, (lv34, lv718), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm55 = R.call_tir(cls.layer_norm1, (add193, model_encoder_layers_27_final_layer_norm_weight, model_encoder_layers_27_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv123 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_27_fc1_weight, layer_norm55, model_encoder_layers_27_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv719 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_27_fc2_weight, lv123, model_encoder_layers_27_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv35 = R.call_tir(cls.fused_add4_maximum_minimum, (add193, lv719), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm56 = R.call_tir(cls.layer_norm1, (lv35, model_encoder_layers_28_self_attn_layer_norm_weight, model_encoder_layers_28_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv720 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_28_self_attn_q_proj_weight, layer_norm56, model_encoder_layers_28_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape224 = R.call_tir(cls.reshape, (lv720,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv159 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_28_self_attn_k_proj_weight, layer_norm56), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape225 = R.call_tir(cls.reshape, (lv159,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv721 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_28_self_attn_v_proj_weight, layer_norm56, model_encoder_layers_28_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape226 = R.call_tir(cls.reshape, (lv721,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape227 = R.call_tir(cls.reshape1, (reshape224,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape228 = R.call_tir(cls.reshape1, (reshape225,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape229 = R.call_tir(cls.reshape1, (reshape226,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv32_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), reshape227, reshape228, reshape229), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape230 = R.call_tir(cls.reshape10, (lv32_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape231 = R.call_tir(cls.reshape11, (reshape230,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv722 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_28_self_attn_out_proj_weight, reshape231, model_encoder_layers_28_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add200 = R.call_tir(cls.add4, (lv35, lv722), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm57 = R.call_tir(cls.layer_norm1, (add200, model_encoder_layers_28_final_layer_norm_weight, model_encoder_layers_28_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv124 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_28_fc1_weight, layer_norm57, model_encoder_layers_28_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv723 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_28_fc2_weight, lv124, model_encoder_layers_28_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv36 = R.call_tir(cls.fused_add4_maximum_minimum, (add200, lv723), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm58 = R.call_tir(cls.layer_norm1, (lv36, model_encoder_layers_29_self_attn_layer_norm_weight, model_encoder_layers_29_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv724 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_29_self_attn_q_proj_weight, layer_norm58, model_encoder_layers_29_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape232 = R.call_tir(cls.reshape, (lv724,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv160 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_29_self_attn_k_proj_weight, layer_norm58), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape233 = R.call_tir(cls.reshape, (lv160,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv725 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_29_self_attn_v_proj_weight, layer_norm58, model_encoder_layers_29_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape234 = R.call_tir(cls.reshape, (lv725,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape235 = R.call_tir(cls.reshape1, (reshape232,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape236 = R.call_tir(cls.reshape1, (reshape233,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape237 = R.call_tir(cls.reshape1, (reshape234,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv33_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), reshape235, reshape236, reshape237), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape238 = R.call_tir(cls.reshape10, (lv33_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape239 = R.call_tir(cls.reshape11, (reshape238,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv726 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_29_self_attn_out_proj_weight, reshape239, model_encoder_layers_29_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add207 = R.call_tir(cls.add4, (lv36, lv726), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm59 = R.call_tir(cls.layer_norm1, (add207, model_encoder_layers_29_final_layer_norm_weight, model_encoder_layers_29_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv125 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_29_fc1_weight, layer_norm59, model_encoder_layers_29_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv727 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_29_fc2_weight, lv125, model_encoder_layers_29_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv37 = R.call_tir(cls.fused_add4_maximum_minimum, (add207, lv727), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm60 = R.call_tir(cls.layer_norm1, (lv37, model_encoder_layers_30_self_attn_layer_norm_weight, model_encoder_layers_30_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv728 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_30_self_attn_q_proj_weight, layer_norm60, model_encoder_layers_30_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape240 = R.call_tir(cls.reshape, (lv728,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv161 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_30_self_attn_k_proj_weight, layer_norm60), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape241 = R.call_tir(cls.reshape, (lv161,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv729 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_30_self_attn_v_proj_weight, layer_norm60, model_encoder_layers_30_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape242 = R.call_tir(cls.reshape, (lv729,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape243 = R.call_tir(cls.reshape1, (reshape240,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape244 = R.call_tir(cls.reshape1, (reshape241,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape245 = R.call_tir(cls.reshape1, (reshape242,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv34_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), reshape243, reshape244, reshape245), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape246 = R.call_tir(cls.reshape10, (lv34_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape247 = R.call_tir(cls.reshape11, (reshape246,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv730 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_30_self_attn_out_proj_weight, reshape247, model_encoder_layers_30_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add214 = R.call_tir(cls.add4, (lv37, lv730), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm61 = R.call_tir(cls.layer_norm1, (add214, model_encoder_layers_30_final_layer_norm_weight, model_encoder_layers_30_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv126 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_30_fc1_weight, layer_norm61, model_encoder_layers_30_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv731 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_30_fc2_weight, lv126, model_encoder_layers_30_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv38 = R.call_tir(cls.fused_add4_maximum_minimum, (add214, lv731), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm62 = R.call_tir(cls.layer_norm1, (lv38, model_encoder_layers_31_self_attn_layer_norm_weight, model_encoder_layers_31_self_attn_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv732 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_31_self_attn_q_proj_weight, layer_norm62, model_encoder_layers_31_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape248 = R.call_tir(cls.reshape, (lv732,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv162 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_31_self_attn_k_proj_weight, layer_norm62), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape249 = R.call_tir(cls.reshape, (lv162,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + lv733 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_31_self_attn_v_proj_weight, layer_norm62, model_encoder_layers_31_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + reshape250 = R.call_tir(cls.reshape, (lv733,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape251 = R.call_tir(cls.reshape1, (reshape248,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape252 = R.call_tir(cls.reshape1, (reshape249,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape253 = R.call_tir(cls.reshape1, (reshape250,), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + lv35_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), reshape251, reshape252, reshape253), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) + reshape254 = R.call_tir(cls.reshape10, (lv35_1,), out_sinfo=R.Tensor((batch_size, 1500, 20, 64), dtype="float16")) + reshape255 = R.call_tir(cls.reshape11, (reshape254,), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv734 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_31_self_attn_out_proj_weight, reshape255, model_encoder_layers_31_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + add221 = R.call_tir(cls.add4, (lv38, lv734), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + layer_norm63 = R.call_tir(cls.layer_norm1, (add221, model_encoder_layers_31_final_layer_norm_weight, model_encoder_layers_31_final_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv127 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_31_fc1_weight, layer_norm63, model_encoder_layers_31_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) + lv735 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_31_fc2_weight, lv127, model_encoder_layers_31_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + lv39 = R.call_tir(cls.fused_add4_maximum_minimum, (add221, lv735), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + gv = R.call_tir(cls.layer_norm1, (lv39, model_encoder_layer_norm_weight, model_encoder_layer_norm_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) + R.output(gv) + return gv + + @R.function + def batch_prefill(input_ids: R.Tensor((1, "seq_len"), dtype="int32"), logit_positions: R.Tensor(("batch_size",), dtype="int32"), paged_kv_cache: R.Object, packed_params: R.Tuple(R.Tensor((1280, 128, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1500, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), 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dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"))) -> R.Tensor((1, "batch_size", 51866), dtype="float32"): + batch_size = T.int64() + seq_len = T.int64() + R.func_attr({"num_input": 3, "relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "seq_len": 15000, "total_seq_len": 1500}}) + cls = Module + with R.dataflow(): + model_decoder_embed_tokens_weight2: R.Tensor((51866, 1280), dtype="float16") = packed_params[487] + model_decoder_embed_positions_weight2: R.Tensor((448, 1280), dtype="float16") = packed_params[488] + model_decoder_layers_0_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[489] + model_decoder_layers_0_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[490] + model_decoder_layers_0_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[491] + model_decoder_layers_0_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[492] + model_decoder_layers_0_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[493] + model_decoder_layers_0_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[494] + model_decoder_layers_0_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[495] + model_decoder_layers_0_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[496] + model_decoder_layers_0_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[497] + model_decoder_layers_0_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[501] + model_decoder_layers_0_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[502] + model_decoder_layers_0_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[503] + model_decoder_layers_0_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[504] + model_decoder_layers_0_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[505] + model_decoder_layers_0_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[506] + model_decoder_layers_0_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[507] + model_decoder_layers_0_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[508] + model_decoder_layers_0_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[509] + model_decoder_layers_0_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[510] + model_decoder_layers_0_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[511] + model_decoder_layers_0_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[512] + model_decoder_layers_1_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[513] + model_decoder_layers_1_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[514] + model_decoder_layers_1_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[515] + model_decoder_layers_1_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[516] + model_decoder_layers_1_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[517] + model_decoder_layers_1_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[518] + model_decoder_layers_1_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[519] + model_decoder_layers_1_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[520] + model_decoder_layers_1_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[521] + model_decoder_layers_1_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[525] + model_decoder_layers_1_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[526] + model_decoder_layers_1_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[527] + model_decoder_layers_1_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[528] + model_decoder_layers_1_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[529] + model_decoder_layers_1_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[530] + model_decoder_layers_1_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[531] + model_decoder_layers_1_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[532] + model_decoder_layers_1_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[533] + model_decoder_layers_1_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[534] + model_decoder_layers_1_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[535] + model_decoder_layers_1_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[536] + model_decoder_layers_2_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[537] + model_decoder_layers_2_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[538] + model_decoder_layers_2_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[539] + model_decoder_layers_2_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[540] + model_decoder_layers_2_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[541] + model_decoder_layers_2_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[542] + model_decoder_layers_2_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[543] + model_decoder_layers_2_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[544] + model_decoder_layers_2_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[545] + model_decoder_layers_2_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[549] + model_decoder_layers_2_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[550] + model_decoder_layers_2_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[551] + model_decoder_layers_2_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[552] + model_decoder_layers_2_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[553] + model_decoder_layers_2_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[554] + model_decoder_layers_2_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[555] + model_decoder_layers_2_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[556] + model_decoder_layers_2_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[557] + model_decoder_layers_2_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[558] + model_decoder_layers_2_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[559] + model_decoder_layers_2_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[560] + model_decoder_layers_3_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[561] + model_decoder_layers_3_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[562] + model_decoder_layers_3_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[563] + model_decoder_layers_3_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[564] + model_decoder_layers_3_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[565] + model_decoder_layers_3_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[566] + model_decoder_layers_3_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[567] + model_decoder_layers_3_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[568] + model_decoder_layers_3_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[569] + model_decoder_layers_3_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[573] + model_decoder_layers_3_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[574] + model_decoder_layers_3_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[575] + model_decoder_layers_3_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[576] + model_decoder_layers_3_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[577] + model_decoder_layers_3_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[578] + model_decoder_layers_3_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[579] + model_decoder_layers_3_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[580] + model_decoder_layers_3_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[581] + model_decoder_layers_3_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[582] + model_decoder_layers_3_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[583] + model_decoder_layers_3_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[584] + model_decoder_layers_4_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[585] + model_decoder_layers_4_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[586] + model_decoder_layers_4_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[587] + model_decoder_layers_4_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[588] + model_decoder_layers_4_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[589] + model_decoder_layers_4_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[590] + model_decoder_layers_4_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[591] + model_decoder_layers_4_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[592] + model_decoder_layers_4_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[593] + model_decoder_layers_4_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[597] + model_decoder_layers_4_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[598] + model_decoder_layers_4_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[599] + model_decoder_layers_4_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[600] + model_decoder_layers_4_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[601] + model_decoder_layers_4_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[602] + model_decoder_layers_4_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[603] + model_decoder_layers_4_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[604] + model_decoder_layers_4_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[605] + model_decoder_layers_4_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[606] + model_decoder_layers_4_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[607] + model_decoder_layers_4_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[608] + model_decoder_layers_5_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[609] + model_decoder_layers_5_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[610] + model_decoder_layers_5_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[611] + model_decoder_layers_5_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[612] + model_decoder_layers_5_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[613] + model_decoder_layers_5_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[614] + model_decoder_layers_5_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[615] + model_decoder_layers_5_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[616] + model_decoder_layers_5_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[617] + model_decoder_layers_5_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[621] + model_decoder_layers_5_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[622] + model_decoder_layers_5_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[623] + model_decoder_layers_5_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[624] + model_decoder_layers_5_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[625] + model_decoder_layers_5_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[626] + model_decoder_layers_5_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[627] + model_decoder_layers_5_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[628] + model_decoder_layers_5_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[629] + model_decoder_layers_5_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[630] + model_decoder_layers_5_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[631] + model_decoder_layers_5_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[632] + model_decoder_layers_6_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[633] + model_decoder_layers_6_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[634] + model_decoder_layers_6_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[635] + model_decoder_layers_6_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[636] + model_decoder_layers_6_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[637] + model_decoder_layers_6_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[638] + model_decoder_layers_6_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[639] + model_decoder_layers_6_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[640] + model_decoder_layers_6_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[641] + model_decoder_layers_6_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[645] + model_decoder_layers_6_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[646] + model_decoder_layers_6_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[647] + model_decoder_layers_6_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[648] + model_decoder_layers_6_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[649] + model_decoder_layers_6_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[650] + model_decoder_layers_6_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[651] + model_decoder_layers_6_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[652] + model_decoder_layers_6_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[653] + model_decoder_layers_6_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[654] + model_decoder_layers_6_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[655] + model_decoder_layers_6_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[656] + model_decoder_layers_7_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[657] + model_decoder_layers_7_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[658] + model_decoder_layers_7_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[659] + model_decoder_layers_7_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[660] + model_decoder_layers_7_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[661] + model_decoder_layers_7_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[662] + model_decoder_layers_7_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[663] + model_decoder_layers_7_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[664] + model_decoder_layers_7_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[665] + model_decoder_layers_7_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[669] + model_decoder_layers_7_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[670] + model_decoder_layers_7_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[671] + model_decoder_layers_7_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[672] + model_decoder_layers_7_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[673] + model_decoder_layers_7_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[674] + model_decoder_layers_7_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[675] + model_decoder_layers_7_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[676] + model_decoder_layers_7_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[677] + model_decoder_layers_7_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[678] + model_decoder_layers_7_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[679] + model_decoder_layers_7_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[680] + model_decoder_layers_8_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[681] + model_decoder_layers_8_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[682] + model_decoder_layers_8_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[683] + model_decoder_layers_8_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[684] + model_decoder_layers_8_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[685] + model_decoder_layers_8_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[686] + model_decoder_layers_8_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[687] + model_decoder_layers_8_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[688] + model_decoder_layers_8_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[689] + model_decoder_layers_8_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[693] + model_decoder_layers_8_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[694] + model_decoder_layers_8_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[695] + model_decoder_layers_8_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[696] + model_decoder_layers_8_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[697] + model_decoder_layers_8_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[698] + model_decoder_layers_8_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[699] + model_decoder_layers_8_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[700] + model_decoder_layers_8_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[701] + model_decoder_layers_8_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[702] + model_decoder_layers_8_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[703] + model_decoder_layers_8_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[704] + model_decoder_layers_9_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[705] + model_decoder_layers_9_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[706] + model_decoder_layers_9_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[707] + model_decoder_layers_9_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[708] + model_decoder_layers_9_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[709] + model_decoder_layers_9_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[710] + model_decoder_layers_9_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[711] + model_decoder_layers_9_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[712] + model_decoder_layers_9_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[713] + model_decoder_layers_9_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[717] + model_decoder_layers_9_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[718] + model_decoder_layers_9_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[719] + model_decoder_layers_9_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[720] + model_decoder_layers_9_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[721] + model_decoder_layers_9_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[722] + model_decoder_layers_9_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[723] + model_decoder_layers_9_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[724] + model_decoder_layers_9_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[725] + model_decoder_layers_9_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[726] + model_decoder_layers_9_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[727] + model_decoder_layers_9_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[728] + model_decoder_layers_10_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[729] + model_decoder_layers_10_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[730] + model_decoder_layers_10_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[731] + model_decoder_layers_10_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[732] + model_decoder_layers_10_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[733] + model_decoder_layers_10_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[734] + model_decoder_layers_10_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[735] + model_decoder_layers_10_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[736] + model_decoder_layers_10_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[737] + model_decoder_layers_10_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[741] + model_decoder_layers_10_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[742] + model_decoder_layers_10_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[743] + model_decoder_layers_10_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[744] + model_decoder_layers_10_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[745] + model_decoder_layers_10_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[746] + model_decoder_layers_10_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[747] + model_decoder_layers_10_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[748] + model_decoder_layers_10_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[749] + model_decoder_layers_10_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[750] + model_decoder_layers_10_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[751] + model_decoder_layers_10_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[752] + model_decoder_layers_11_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[753] + model_decoder_layers_11_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[754] + model_decoder_layers_11_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[755] + model_decoder_layers_11_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[756] + model_decoder_layers_11_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[757] + model_decoder_layers_11_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[758] + model_decoder_layers_11_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[759] + model_decoder_layers_11_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[760] + model_decoder_layers_11_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[761] + model_decoder_layers_11_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[765] + model_decoder_layers_11_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[766] + model_decoder_layers_11_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[767] + model_decoder_layers_11_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[768] + model_decoder_layers_11_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[769] + model_decoder_layers_11_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[770] + model_decoder_layers_11_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[771] + model_decoder_layers_11_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[772] + model_decoder_layers_11_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[773] + model_decoder_layers_11_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[774] + model_decoder_layers_11_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[775] + model_decoder_layers_11_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[776] + model_decoder_layers_12_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[777] + model_decoder_layers_12_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[778] + model_decoder_layers_12_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[779] + model_decoder_layers_12_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[780] + model_decoder_layers_12_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[781] + model_decoder_layers_12_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[782] + model_decoder_layers_12_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[783] + model_decoder_layers_12_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[784] + model_decoder_layers_12_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[785] + model_decoder_layers_12_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[789] + model_decoder_layers_12_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[790] + model_decoder_layers_12_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[791] + model_decoder_layers_12_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[792] + model_decoder_layers_12_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[793] + model_decoder_layers_12_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[794] + model_decoder_layers_12_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[795] + model_decoder_layers_12_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[796] + model_decoder_layers_12_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[797] + model_decoder_layers_12_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[798] + model_decoder_layers_12_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[799] + model_decoder_layers_12_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[800] + model_decoder_layers_13_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[801] + model_decoder_layers_13_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[802] + model_decoder_layers_13_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[803] + model_decoder_layers_13_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[804] + model_decoder_layers_13_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[805] + model_decoder_layers_13_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[806] + model_decoder_layers_13_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[807] + model_decoder_layers_13_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[808] + model_decoder_layers_13_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[809] + model_decoder_layers_13_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[813] + model_decoder_layers_13_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[814] + model_decoder_layers_13_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[815] + model_decoder_layers_13_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[816] + model_decoder_layers_13_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[817] + model_decoder_layers_13_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[818] + model_decoder_layers_13_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[819] + model_decoder_layers_13_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[820] + model_decoder_layers_13_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[821] + model_decoder_layers_13_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[822] + model_decoder_layers_13_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[823] + model_decoder_layers_13_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[824] + model_decoder_layers_14_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[825] + model_decoder_layers_14_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[826] + model_decoder_layers_14_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[827] + model_decoder_layers_14_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[828] + model_decoder_layers_14_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[829] + model_decoder_layers_14_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[830] + model_decoder_layers_14_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[831] + model_decoder_layers_14_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[832] + model_decoder_layers_14_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[833] + model_decoder_layers_14_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[837] + model_decoder_layers_14_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[838] + model_decoder_layers_14_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[839] + model_decoder_layers_14_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[840] + model_decoder_layers_14_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[841] + model_decoder_layers_14_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[842] + model_decoder_layers_14_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[843] + model_decoder_layers_14_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[844] + model_decoder_layers_14_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[845] + model_decoder_layers_14_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[846] + model_decoder_layers_14_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[847] + model_decoder_layers_14_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[848] + model_decoder_layers_15_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[849] + model_decoder_layers_15_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[850] + model_decoder_layers_15_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[851] + model_decoder_layers_15_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[852] + model_decoder_layers_15_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[853] + model_decoder_layers_15_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[854] + model_decoder_layers_15_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[855] + model_decoder_layers_15_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[856] + model_decoder_layers_15_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[857] + model_decoder_layers_15_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[861] + model_decoder_layers_15_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[862] + model_decoder_layers_15_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[863] + model_decoder_layers_15_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[864] + model_decoder_layers_15_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[865] + model_decoder_layers_15_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[866] + model_decoder_layers_15_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[867] + model_decoder_layers_15_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[868] + model_decoder_layers_15_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[869] + model_decoder_layers_15_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[870] + model_decoder_layers_15_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[871] + model_decoder_layers_15_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[872] + model_decoder_layers_16_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[873] + model_decoder_layers_16_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[874] + model_decoder_layers_16_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[875] + model_decoder_layers_16_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[876] + model_decoder_layers_16_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[877] + model_decoder_layers_16_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[878] + model_decoder_layers_16_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[879] + model_decoder_layers_16_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[880] + model_decoder_layers_16_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[881] + model_decoder_layers_16_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[885] + model_decoder_layers_16_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[886] + model_decoder_layers_16_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[887] + model_decoder_layers_16_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[888] + model_decoder_layers_16_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[889] + model_decoder_layers_16_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[890] + model_decoder_layers_16_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[891] + model_decoder_layers_16_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[892] + model_decoder_layers_16_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[893] + model_decoder_layers_16_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[894] + model_decoder_layers_16_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[895] + model_decoder_layers_16_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[896] + model_decoder_layers_17_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[897] + model_decoder_layers_17_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[898] + model_decoder_layers_17_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[899] + model_decoder_layers_17_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[900] + model_decoder_layers_17_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[901] + model_decoder_layers_17_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[902] + model_decoder_layers_17_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[903] + model_decoder_layers_17_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[904] + model_decoder_layers_17_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[905] + model_decoder_layers_17_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[909] + model_decoder_layers_17_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[910] + model_decoder_layers_17_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[911] + model_decoder_layers_17_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[912] + model_decoder_layers_17_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[913] + model_decoder_layers_17_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[914] + model_decoder_layers_17_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[915] + model_decoder_layers_17_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[916] + model_decoder_layers_17_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[917] + model_decoder_layers_17_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[918] + model_decoder_layers_17_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[919] + model_decoder_layers_17_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[920] + model_decoder_layers_18_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[921] + model_decoder_layers_18_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[922] + model_decoder_layers_18_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[923] + model_decoder_layers_18_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[924] + model_decoder_layers_18_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[925] + model_decoder_layers_18_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[926] + model_decoder_layers_18_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[927] + model_decoder_layers_18_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[928] + model_decoder_layers_18_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[929] + model_decoder_layers_18_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[933] + model_decoder_layers_18_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[934] + model_decoder_layers_18_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[935] + model_decoder_layers_18_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[936] + model_decoder_layers_18_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[937] + model_decoder_layers_18_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[938] + model_decoder_layers_18_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[939] + model_decoder_layers_18_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[940] + model_decoder_layers_18_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[941] + model_decoder_layers_18_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[942] + model_decoder_layers_18_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[943] + model_decoder_layers_18_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[944] + model_decoder_layers_19_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[945] + model_decoder_layers_19_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[946] + model_decoder_layers_19_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[947] + model_decoder_layers_19_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[948] + model_decoder_layers_19_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[949] + model_decoder_layers_19_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[950] + model_decoder_layers_19_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[951] + model_decoder_layers_19_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[952] + model_decoder_layers_19_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[953] + model_decoder_layers_19_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[957] + model_decoder_layers_19_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[958] + model_decoder_layers_19_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[959] + model_decoder_layers_19_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[960] + model_decoder_layers_19_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[961] + model_decoder_layers_19_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[962] + model_decoder_layers_19_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[963] + model_decoder_layers_19_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[964] + model_decoder_layers_19_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[965] + model_decoder_layers_19_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[966] + model_decoder_layers_19_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[967] + model_decoder_layers_19_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[968] + model_decoder_layers_20_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[969] + model_decoder_layers_20_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[970] + model_decoder_layers_20_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[971] + model_decoder_layers_20_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[972] + model_decoder_layers_20_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[973] + model_decoder_layers_20_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[974] + model_decoder_layers_20_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[975] + model_decoder_layers_20_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[976] + model_decoder_layers_20_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[977] + model_decoder_layers_20_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[981] + model_decoder_layers_20_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[982] + model_decoder_layers_20_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[983] + model_decoder_layers_20_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[984] + model_decoder_layers_20_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[985] + model_decoder_layers_20_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[986] + model_decoder_layers_20_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[987] + model_decoder_layers_20_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[988] + model_decoder_layers_20_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[989] + model_decoder_layers_20_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[990] + model_decoder_layers_20_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[991] + model_decoder_layers_20_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[992] + model_decoder_layers_21_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[993] + model_decoder_layers_21_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[994] + model_decoder_layers_21_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[995] + model_decoder_layers_21_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[996] + model_decoder_layers_21_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[997] + model_decoder_layers_21_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[998] + model_decoder_layers_21_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[999] + model_decoder_layers_21_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1000] + model_decoder_layers_21_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1001] + model_decoder_layers_21_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1005] + model_decoder_layers_21_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1006] + model_decoder_layers_21_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1007] + model_decoder_layers_21_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1008] + model_decoder_layers_21_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1009] + model_decoder_layers_21_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1010] + model_decoder_layers_21_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1011] + model_decoder_layers_21_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1012] + model_decoder_layers_21_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1013] + model_decoder_layers_21_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1014] + model_decoder_layers_21_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1015] + model_decoder_layers_21_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1016] + model_decoder_layers_22_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1017] + model_decoder_layers_22_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1018] + model_decoder_layers_22_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1019] + model_decoder_layers_22_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1020] + model_decoder_layers_22_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1021] + model_decoder_layers_22_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1022] + model_decoder_layers_22_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1023] + model_decoder_layers_22_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1024] + model_decoder_layers_22_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1025] + model_decoder_layers_22_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1029] + model_decoder_layers_22_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1030] + model_decoder_layers_22_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1031] + model_decoder_layers_22_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1032] + model_decoder_layers_22_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1033] + model_decoder_layers_22_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1034] + model_decoder_layers_22_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1035] + model_decoder_layers_22_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1036] + model_decoder_layers_22_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1037] + model_decoder_layers_22_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1038] + model_decoder_layers_22_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1039] + model_decoder_layers_22_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1040] + model_decoder_layers_23_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1041] + model_decoder_layers_23_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1042] + model_decoder_layers_23_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1043] + model_decoder_layers_23_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1044] + model_decoder_layers_23_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1045] + model_decoder_layers_23_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1046] + model_decoder_layers_23_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1047] + model_decoder_layers_23_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1048] + model_decoder_layers_23_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1049] + model_decoder_layers_23_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1053] + model_decoder_layers_23_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1054] + model_decoder_layers_23_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1055] + model_decoder_layers_23_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1056] + model_decoder_layers_23_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1057] + model_decoder_layers_23_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1058] + model_decoder_layers_23_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1059] + model_decoder_layers_23_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1060] + model_decoder_layers_23_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1061] + model_decoder_layers_23_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1062] + model_decoder_layers_23_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1063] + model_decoder_layers_23_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1064] + model_decoder_layers_24_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1065] + model_decoder_layers_24_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1066] + model_decoder_layers_24_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1067] + model_decoder_layers_24_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1068] + model_decoder_layers_24_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1069] + model_decoder_layers_24_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1070] + model_decoder_layers_24_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1071] + model_decoder_layers_24_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1072] + model_decoder_layers_24_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1073] + model_decoder_layers_24_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1077] + model_decoder_layers_24_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1078] + model_decoder_layers_24_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1079] + model_decoder_layers_24_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1080] + model_decoder_layers_24_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1081] + model_decoder_layers_24_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1082] + model_decoder_layers_24_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1083] + model_decoder_layers_24_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1084] + model_decoder_layers_24_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1085] + model_decoder_layers_24_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1086] + model_decoder_layers_24_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1087] + model_decoder_layers_24_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1088] + model_decoder_layers_25_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1089] + model_decoder_layers_25_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1090] + model_decoder_layers_25_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1091] + model_decoder_layers_25_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1092] + model_decoder_layers_25_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1093] + model_decoder_layers_25_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1094] + model_decoder_layers_25_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1095] + model_decoder_layers_25_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1096] + model_decoder_layers_25_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1097] + model_decoder_layers_25_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1101] + model_decoder_layers_25_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1102] + model_decoder_layers_25_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1103] + model_decoder_layers_25_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1104] + model_decoder_layers_25_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1105] + model_decoder_layers_25_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1106] + model_decoder_layers_25_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1107] + model_decoder_layers_25_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1108] + model_decoder_layers_25_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1109] + model_decoder_layers_25_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1110] + model_decoder_layers_25_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1111] + model_decoder_layers_25_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1112] + model_decoder_layers_26_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1113] + model_decoder_layers_26_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1114] + model_decoder_layers_26_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1115] + model_decoder_layers_26_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1116] + model_decoder_layers_26_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1117] + model_decoder_layers_26_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1118] + model_decoder_layers_26_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1119] + model_decoder_layers_26_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1120] + model_decoder_layers_26_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1121] + model_decoder_layers_26_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1125] + model_decoder_layers_26_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1126] + model_decoder_layers_26_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1127] + model_decoder_layers_26_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1128] + model_decoder_layers_26_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1129] + model_decoder_layers_26_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1130] + model_decoder_layers_26_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1131] + model_decoder_layers_26_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1132] + model_decoder_layers_26_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1133] + model_decoder_layers_26_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1134] + model_decoder_layers_26_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1135] + model_decoder_layers_26_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1136] + model_decoder_layers_27_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1137] + model_decoder_layers_27_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1138] + model_decoder_layers_27_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1139] + model_decoder_layers_27_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1140] + model_decoder_layers_27_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1141] + model_decoder_layers_27_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1142] + model_decoder_layers_27_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1143] + model_decoder_layers_27_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1144] + model_decoder_layers_27_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1145] + model_decoder_layers_27_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1149] + model_decoder_layers_27_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1150] + model_decoder_layers_27_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1151] + model_decoder_layers_27_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1152] + model_decoder_layers_27_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1153] + model_decoder_layers_27_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1154] + model_decoder_layers_27_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1155] + model_decoder_layers_27_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1156] + model_decoder_layers_27_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1157] + model_decoder_layers_27_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1158] + model_decoder_layers_27_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1159] + model_decoder_layers_27_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1160] + model_decoder_layers_28_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1161] + model_decoder_layers_28_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1162] + model_decoder_layers_28_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1163] + model_decoder_layers_28_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1164] + model_decoder_layers_28_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1165] + model_decoder_layers_28_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1166] + model_decoder_layers_28_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1167] + model_decoder_layers_28_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1168] + model_decoder_layers_28_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1169] + model_decoder_layers_28_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1173] + model_decoder_layers_28_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1174] + model_decoder_layers_28_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1175] + model_decoder_layers_28_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1176] + model_decoder_layers_28_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1177] + model_decoder_layers_28_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1178] + model_decoder_layers_28_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1179] + model_decoder_layers_28_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1180] + model_decoder_layers_28_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1181] + model_decoder_layers_28_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1182] + model_decoder_layers_28_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1183] + model_decoder_layers_28_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1184] + model_decoder_layers_29_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1185] + model_decoder_layers_29_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1186] + model_decoder_layers_29_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1187] + model_decoder_layers_29_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1188] + model_decoder_layers_29_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1189] + model_decoder_layers_29_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1190] + model_decoder_layers_29_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1191] + model_decoder_layers_29_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1192] + model_decoder_layers_29_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1193] + model_decoder_layers_29_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1197] + model_decoder_layers_29_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1198] + model_decoder_layers_29_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1199] + model_decoder_layers_29_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1200] + model_decoder_layers_29_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1201] + model_decoder_layers_29_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1202] + model_decoder_layers_29_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1203] + model_decoder_layers_29_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1204] + model_decoder_layers_29_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1205] + model_decoder_layers_29_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1206] + model_decoder_layers_29_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1207] + model_decoder_layers_29_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1208] + model_decoder_layers_30_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1209] + model_decoder_layers_30_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1210] + model_decoder_layers_30_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1211] + model_decoder_layers_30_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1212] + model_decoder_layers_30_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1213] + model_decoder_layers_30_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1214] + model_decoder_layers_30_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1215] + model_decoder_layers_30_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1216] + model_decoder_layers_30_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1217] + model_decoder_layers_30_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1221] + model_decoder_layers_30_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1222] + model_decoder_layers_30_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1223] + model_decoder_layers_30_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1224] + model_decoder_layers_30_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1225] + model_decoder_layers_30_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1226] + model_decoder_layers_30_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1227] + model_decoder_layers_30_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1228] + model_decoder_layers_30_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1229] + model_decoder_layers_30_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1230] + model_decoder_layers_30_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1231] + model_decoder_layers_30_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1232] + model_decoder_layers_31_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1233] + model_decoder_layers_31_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1234] + model_decoder_layers_31_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1235] + model_decoder_layers_31_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1236] + model_decoder_layers_31_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1237] + model_decoder_layers_31_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1238] + model_decoder_layers_31_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1239] + model_decoder_layers_31_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1240] + model_decoder_layers_31_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1241] + model_decoder_layers_31_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1245] + model_decoder_layers_31_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1246] + model_decoder_layers_31_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1247] + model_decoder_layers_31_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1248] + model_decoder_layers_31_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1249] + model_decoder_layers_31_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1250] + model_decoder_layers_31_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1251] + model_decoder_layers_31_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1252] + model_decoder_layers_31_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1253] + model_decoder_layers_31_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1254] + model_decoder_layers_31_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1255] + model_decoder_layers_31_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1256] + model_decoder_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1257] + model_decoder_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1258] + reshape384 = R.call_tir(cls.reshape12, (input_ids,), out_sinfo=R.Tensor((seq_len,), dtype="int32")) + take = R.call_tir(cls.take, (model_decoder_embed_tokens_weight2, reshape384), out_sinfo=R.Tensor((seq_len, 1280), dtype="float16")) + reshape385 = R.call_tir(cls.reshape13, (take,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv68: R.Tensor((seq_len,), dtype="int32") = R.call_pure_packed("vm.builtin.attention_kv_cache_get_query_positions", paged_kv_cache, sinfo_args=(R.Tensor((seq_len,), dtype="int32"),)) + take1 = R.call_tir(cls.take1, (model_decoder_embed_positions_weight2, lv68), out_sinfo=R.Tensor((seq_len, 1280), dtype="float16")) + reshape386 = R.call_tir(cls.reshape13, (take1,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add257 = R.call_tir(cls.add5, (reshape385, reshape386), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm65 = R.call_tir(cls.layer_norm2, (add257, model_decoder_layers_0_self_attn_layer_norm_weight2, model_decoder_layers_0_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv416 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_0_self_attn_q_proj_weight2, layer_norm65, model_decoder_layers_0_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape387 = R.call_tir(cls.reshape14, (lv416,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv98 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_0_self_attn_k_proj_weight2, layer_norm65), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape388 = R.call_tir(cls.reshape14, (lv98,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv417 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_0_self_attn_v_proj_weight2, layer_norm65, model_decoder_layers_0_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape389 = R.call_tir(cls.reshape14, (lv417,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat = R.call_tir(cls.concatenate1, (reshape387, reshape388, reshape389), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape390 = R.call_tir(cls.reshape15, (concat,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv69 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), reshape390), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape391 = R.call_tir(cls.reshape16, (lv69,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape392 = R.call_tir(cls.reshape17, (reshape391,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv418 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_0_self_attn_out_proj_weight2, reshape392, model_decoder_layers_0_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add261 = R.call_tir(cls.add5, (add257, lv418), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm66 = R.call_tir(cls.layer_norm2, (add261, model_decoder_layers_0_encoder_attn_layer_norm_weight2, model_decoder_layers_0_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv419 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_0_encoder_attn_q_proj_weight2, layer_norm66, model_decoder_layers_0_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape393 = R.call_tir(cls.reshape14, (lv419,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape394 = R.call_tir(cls.reshape18, (reshape393,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv70 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), reshape394), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape395 = R.call_tir(cls.reshape16, (lv70,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape396 = R.call_tir(cls.reshape17, (reshape395,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv420 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_0_encoder_attn_out_proj_weight2, reshape396, model_decoder_layers_0_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add264 = R.call_tir(cls.add5, (add261, lv420), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm67 = R.call_tir(cls.layer_norm2, (add264, model_decoder_layers_0_final_layer_norm_weight2, model_decoder_layers_0_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv64 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_0_fc1_weight2, layer_norm67, model_decoder_layers_0_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv421 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_0_fc2_weight2, lv64, model_decoder_layers_0_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add267 = R.call_tir(cls.add5, (add264, lv421), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm68 = R.call_tir(cls.layer_norm2, (add267, model_decoder_layers_1_self_attn_layer_norm_weight2, model_decoder_layers_1_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv422 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_1_self_attn_q_proj_weight2, layer_norm68, model_decoder_layers_1_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape397 = R.call_tir(cls.reshape14, (lv422,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv99 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_1_self_attn_k_proj_weight2, layer_norm68), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape398 = R.call_tir(cls.reshape14, (lv99,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv423 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_1_self_attn_v_proj_weight2, layer_norm68, model_decoder_layers_1_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape399 = R.call_tir(cls.reshape14, (lv423,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat1 = R.call_tir(cls.concatenate1, (reshape397, reshape398, reshape399), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape400 = R.call_tir(cls.reshape15, (concat1,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv71 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), reshape400), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape401 = R.call_tir(cls.reshape16, (lv71,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape402 = R.call_tir(cls.reshape17, (reshape401,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv424 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_1_self_attn_out_proj_weight2, reshape402, model_decoder_layers_1_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add271 = R.call_tir(cls.add5, (add267, lv424), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm69 = R.call_tir(cls.layer_norm2, (add271, model_decoder_layers_1_encoder_attn_layer_norm_weight2, model_decoder_layers_1_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv425 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_1_encoder_attn_q_proj_weight2, layer_norm69, model_decoder_layers_1_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape403 = R.call_tir(cls.reshape14, (lv425,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape404 = R.call_tir(cls.reshape18, (reshape403,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv72 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), reshape404), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape405 = R.call_tir(cls.reshape16, (lv72,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape406 = R.call_tir(cls.reshape17, (reshape405,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv426 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_1_encoder_attn_out_proj_weight2, reshape406, model_decoder_layers_1_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add274 = R.call_tir(cls.add5, (add271, lv426), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm70 = R.call_tir(cls.layer_norm2, (add274, model_decoder_layers_1_final_layer_norm_weight2, model_decoder_layers_1_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv65 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_1_fc1_weight2, layer_norm70, model_decoder_layers_1_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv427 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_1_fc2_weight2, lv65, model_decoder_layers_1_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add277 = R.call_tir(cls.add5, (add274, lv427), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm71 = R.call_tir(cls.layer_norm2, (add277, model_decoder_layers_2_self_attn_layer_norm_weight2, model_decoder_layers_2_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv428 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_2_self_attn_q_proj_weight2, layer_norm71, model_decoder_layers_2_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape407 = R.call_tir(cls.reshape14, (lv428,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv100 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_2_self_attn_k_proj_weight2, layer_norm71), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape408 = R.call_tir(cls.reshape14, (lv100,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv429 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_2_self_attn_v_proj_weight2, layer_norm71, model_decoder_layers_2_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape409 = R.call_tir(cls.reshape14, (lv429,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat2 = R.call_tir(cls.concatenate1, (reshape407, reshape408, reshape409), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape410 = R.call_tir(cls.reshape15, (concat2,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv73 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), reshape410), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape411 = R.call_tir(cls.reshape16, (lv73,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape412 = R.call_tir(cls.reshape17, (reshape411,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv430 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_2_self_attn_out_proj_weight2, reshape412, model_decoder_layers_2_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add281 = R.call_tir(cls.add5, (add277, lv430), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm72 = R.call_tir(cls.layer_norm2, (add281, model_decoder_layers_2_encoder_attn_layer_norm_weight2, model_decoder_layers_2_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv431 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_2_encoder_attn_q_proj_weight2, layer_norm72, model_decoder_layers_2_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape413 = R.call_tir(cls.reshape14, (lv431,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape414 = R.call_tir(cls.reshape18, (reshape413,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv74 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), reshape414), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape415 = R.call_tir(cls.reshape16, (lv74,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape416 = R.call_tir(cls.reshape17, (reshape415,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv432 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_2_encoder_attn_out_proj_weight2, reshape416, model_decoder_layers_2_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add284 = R.call_tir(cls.add5, (add281, lv432), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm73 = R.call_tir(cls.layer_norm2, (add284, model_decoder_layers_2_final_layer_norm_weight2, model_decoder_layers_2_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv66 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_2_fc1_weight2, layer_norm73, model_decoder_layers_2_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv433 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_2_fc2_weight2, lv66, model_decoder_layers_2_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add287 = R.call_tir(cls.add5, (add284, lv433), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm74 = R.call_tir(cls.layer_norm2, (add287, model_decoder_layers_3_self_attn_layer_norm_weight2, model_decoder_layers_3_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv434 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_3_self_attn_q_proj_weight2, layer_norm74, model_decoder_layers_3_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape417 = R.call_tir(cls.reshape14, (lv434,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv101 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_3_self_attn_k_proj_weight2, layer_norm74), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape418 = R.call_tir(cls.reshape14, (lv101,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv435 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_3_self_attn_v_proj_weight2, layer_norm74, model_decoder_layers_3_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape419 = R.call_tir(cls.reshape14, (lv435,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat3 = R.call_tir(cls.concatenate1, (reshape417, reshape418, reshape419), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape420 = R.call_tir(cls.reshape15, (concat3,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv75 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), reshape420), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape421 = R.call_tir(cls.reshape16, (lv75,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape422 = R.call_tir(cls.reshape17, (reshape421,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv436 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_3_self_attn_out_proj_weight2, reshape422, model_decoder_layers_3_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add291 = R.call_tir(cls.add5, (add287, lv436), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm75 = R.call_tir(cls.layer_norm2, (add291, model_decoder_layers_3_encoder_attn_layer_norm_weight2, model_decoder_layers_3_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv437 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_3_encoder_attn_q_proj_weight2, layer_norm75, model_decoder_layers_3_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape423 = R.call_tir(cls.reshape14, (lv437,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape424 = R.call_tir(cls.reshape18, (reshape423,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv76 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), reshape424), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape425 = R.call_tir(cls.reshape16, (lv76,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape426 = R.call_tir(cls.reshape17, (reshape425,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv438 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_3_encoder_attn_out_proj_weight2, reshape426, model_decoder_layers_3_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add294 = R.call_tir(cls.add5, (add291, lv438), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm76 = R.call_tir(cls.layer_norm2, (add294, model_decoder_layers_3_final_layer_norm_weight2, model_decoder_layers_3_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv67 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_3_fc1_weight2, layer_norm76, model_decoder_layers_3_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv439 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_3_fc2_weight2, lv67, model_decoder_layers_3_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add297 = R.call_tir(cls.add5, (add294, lv439), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm77 = R.call_tir(cls.layer_norm2, (add297, model_decoder_layers_4_self_attn_layer_norm_weight2, model_decoder_layers_4_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv440 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_4_self_attn_q_proj_weight2, layer_norm77, model_decoder_layers_4_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape427 = R.call_tir(cls.reshape14, (lv440,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv102 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_4_self_attn_k_proj_weight2, layer_norm77), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape428 = R.call_tir(cls.reshape14, (lv102,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv441 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_4_self_attn_v_proj_weight2, layer_norm77, model_decoder_layers_4_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape429 = R.call_tir(cls.reshape14, (lv441,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat4 = R.call_tir(cls.concatenate1, (reshape427, reshape428, reshape429), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape430 = R.call_tir(cls.reshape15, (concat4,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv77 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), reshape430), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape431 = R.call_tir(cls.reshape16, (lv77,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape432 = R.call_tir(cls.reshape17, (reshape431,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv442 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_4_self_attn_out_proj_weight2, reshape432, model_decoder_layers_4_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add301 = R.call_tir(cls.add5, (add297, lv442), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm78 = R.call_tir(cls.layer_norm2, (add301, model_decoder_layers_4_encoder_attn_layer_norm_weight2, model_decoder_layers_4_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv443 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_4_encoder_attn_q_proj_weight2, layer_norm78, model_decoder_layers_4_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape433 = R.call_tir(cls.reshape14, (lv443,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape434 = R.call_tir(cls.reshape18, (reshape433,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv78 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), reshape434), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape435 = R.call_tir(cls.reshape16, (lv78,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape436 = R.call_tir(cls.reshape17, (reshape435,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv444 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_4_encoder_attn_out_proj_weight2, reshape436, model_decoder_layers_4_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add304 = R.call_tir(cls.add5, (add301, lv444), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm79 = R.call_tir(cls.layer_norm2, (add304, model_decoder_layers_4_final_layer_norm_weight2, model_decoder_layers_4_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv68_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_4_fc1_weight2, layer_norm79, model_decoder_layers_4_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv445 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_4_fc2_weight2, lv68_1, model_decoder_layers_4_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add307 = R.call_tir(cls.add5, (add304, lv445), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm80 = R.call_tir(cls.layer_norm2, (add307, model_decoder_layers_5_self_attn_layer_norm_weight2, model_decoder_layers_5_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv446 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_5_self_attn_q_proj_weight2, layer_norm80, model_decoder_layers_5_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape437 = R.call_tir(cls.reshape14, (lv446,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv103 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_5_self_attn_k_proj_weight2, layer_norm80), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape438 = R.call_tir(cls.reshape14, (lv103,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv447 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_5_self_attn_v_proj_weight2, layer_norm80, model_decoder_layers_5_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape439 = R.call_tir(cls.reshape14, (lv447,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat5 = R.call_tir(cls.concatenate1, (reshape437, reshape438, reshape439), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape440 = R.call_tir(cls.reshape15, (concat5,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv79 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), reshape440), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape441 = R.call_tir(cls.reshape16, (lv79,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape442 = R.call_tir(cls.reshape17, (reshape441,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv448 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_5_self_attn_out_proj_weight2, reshape442, model_decoder_layers_5_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add311 = R.call_tir(cls.add5, (add307, lv448), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm81 = R.call_tir(cls.layer_norm2, (add311, model_decoder_layers_5_encoder_attn_layer_norm_weight2, model_decoder_layers_5_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv449 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_5_encoder_attn_q_proj_weight2, layer_norm81, model_decoder_layers_5_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape443 = R.call_tir(cls.reshape14, (lv449,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape444 = R.call_tir(cls.reshape18, (reshape443,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv80 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), reshape444), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape445 = R.call_tir(cls.reshape16, (lv80,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape446 = R.call_tir(cls.reshape17, (reshape445,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv450 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_5_encoder_attn_out_proj_weight2, reshape446, model_decoder_layers_5_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add314 = R.call_tir(cls.add5, (add311, lv450), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm82 = R.call_tir(cls.layer_norm2, (add314, model_decoder_layers_5_final_layer_norm_weight2, model_decoder_layers_5_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv69_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_5_fc1_weight2, layer_norm82, model_decoder_layers_5_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv451 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_5_fc2_weight2, lv69_1, model_decoder_layers_5_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add317 = R.call_tir(cls.add5, (add314, lv451), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm83 = R.call_tir(cls.layer_norm2, (add317, model_decoder_layers_6_self_attn_layer_norm_weight2, model_decoder_layers_6_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv452 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_6_self_attn_q_proj_weight2, layer_norm83, model_decoder_layers_6_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape447 = R.call_tir(cls.reshape14, (lv452,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv104 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_6_self_attn_k_proj_weight2, layer_norm83), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape448 = R.call_tir(cls.reshape14, (lv104,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv453 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_6_self_attn_v_proj_weight2, layer_norm83, model_decoder_layers_6_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape449 = R.call_tir(cls.reshape14, (lv453,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat6 = R.call_tir(cls.concatenate1, (reshape447, reshape448, reshape449), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape450 = R.call_tir(cls.reshape15, (concat6,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv81 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), reshape450), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape451 = R.call_tir(cls.reshape16, (lv81,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape452 = R.call_tir(cls.reshape17, (reshape451,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv454 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_6_self_attn_out_proj_weight2, reshape452, model_decoder_layers_6_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add321 = R.call_tir(cls.add5, (add317, lv454), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm84 = R.call_tir(cls.layer_norm2, (add321, model_decoder_layers_6_encoder_attn_layer_norm_weight2, model_decoder_layers_6_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv455 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_6_encoder_attn_q_proj_weight2, layer_norm84, model_decoder_layers_6_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape453 = R.call_tir(cls.reshape14, (lv455,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape454 = R.call_tir(cls.reshape18, (reshape453,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv82 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), reshape454), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape455 = R.call_tir(cls.reshape16, (lv82,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape456 = R.call_tir(cls.reshape17, (reshape455,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv456 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_6_encoder_attn_out_proj_weight2, reshape456, model_decoder_layers_6_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add324 = R.call_tir(cls.add5, (add321, lv456), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm85 = R.call_tir(cls.layer_norm2, (add324, model_decoder_layers_6_final_layer_norm_weight2, model_decoder_layers_6_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv70_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_6_fc1_weight2, layer_norm85, model_decoder_layers_6_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv457 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_6_fc2_weight2, lv70_1, model_decoder_layers_6_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add327 = R.call_tir(cls.add5, (add324, lv457), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm86 = R.call_tir(cls.layer_norm2, (add327, model_decoder_layers_7_self_attn_layer_norm_weight2, model_decoder_layers_7_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv458 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_7_self_attn_q_proj_weight2, layer_norm86, model_decoder_layers_7_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape457 = R.call_tir(cls.reshape14, (lv458,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv105 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_7_self_attn_k_proj_weight2, layer_norm86), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape458 = R.call_tir(cls.reshape14, (lv105,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv459 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_7_self_attn_v_proj_weight2, layer_norm86, model_decoder_layers_7_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape459 = R.call_tir(cls.reshape14, (lv459,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat7 = R.call_tir(cls.concatenate1, (reshape457, reshape458, reshape459), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape460 = R.call_tir(cls.reshape15, (concat7,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv83 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), reshape460), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape461 = R.call_tir(cls.reshape16, (lv83,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape462 = R.call_tir(cls.reshape17, (reshape461,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv460 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_7_self_attn_out_proj_weight2, reshape462, model_decoder_layers_7_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add331 = R.call_tir(cls.add5, (add327, lv460), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm87 = R.call_tir(cls.layer_norm2, (add331, model_decoder_layers_7_encoder_attn_layer_norm_weight2, model_decoder_layers_7_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv461 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_7_encoder_attn_q_proj_weight2, layer_norm87, model_decoder_layers_7_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape463 = R.call_tir(cls.reshape14, (lv461,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape464 = R.call_tir(cls.reshape18, (reshape463,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv84 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), reshape464), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape465 = R.call_tir(cls.reshape16, (lv84,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape466 = R.call_tir(cls.reshape17, (reshape465,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv462 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_7_encoder_attn_out_proj_weight2, reshape466, model_decoder_layers_7_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add334 = R.call_tir(cls.add5, (add331, lv462), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm88 = R.call_tir(cls.layer_norm2, (add334, model_decoder_layers_7_final_layer_norm_weight2, model_decoder_layers_7_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv71_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_7_fc1_weight2, layer_norm88, model_decoder_layers_7_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv463 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_7_fc2_weight2, lv71_1, model_decoder_layers_7_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add337 = R.call_tir(cls.add5, (add334, lv463), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm89 = R.call_tir(cls.layer_norm2, (add337, model_decoder_layers_8_self_attn_layer_norm_weight2, model_decoder_layers_8_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv464 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_8_self_attn_q_proj_weight2, layer_norm89, model_decoder_layers_8_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape467 = R.call_tir(cls.reshape14, (lv464,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv106 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_8_self_attn_k_proj_weight2, layer_norm89), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape468 = R.call_tir(cls.reshape14, (lv106,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv465 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_8_self_attn_v_proj_weight2, layer_norm89, model_decoder_layers_8_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape469 = R.call_tir(cls.reshape14, (lv465,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat8 = R.call_tir(cls.concatenate1, (reshape467, reshape468, reshape469), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape470 = R.call_tir(cls.reshape15, (concat8,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv85 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), reshape470), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape471 = R.call_tir(cls.reshape16, (lv85,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape472 = R.call_tir(cls.reshape17, (reshape471,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv466 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_8_self_attn_out_proj_weight2, reshape472, model_decoder_layers_8_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add341 = R.call_tir(cls.add5, (add337, lv466), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm90 = R.call_tir(cls.layer_norm2, (add341, model_decoder_layers_8_encoder_attn_layer_norm_weight2, model_decoder_layers_8_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv467 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_8_encoder_attn_q_proj_weight2, layer_norm90, model_decoder_layers_8_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape473 = R.call_tir(cls.reshape14, (lv467,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape474 = R.call_tir(cls.reshape18, (reshape473,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv86 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), reshape474), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape475 = R.call_tir(cls.reshape16, (lv86,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape476 = R.call_tir(cls.reshape17, (reshape475,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv468 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_8_encoder_attn_out_proj_weight2, reshape476, model_decoder_layers_8_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add344 = R.call_tir(cls.add5, (add341, lv468), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm91 = R.call_tir(cls.layer_norm2, (add344, model_decoder_layers_8_final_layer_norm_weight2, model_decoder_layers_8_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv72_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_8_fc1_weight2, layer_norm91, model_decoder_layers_8_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv469 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_8_fc2_weight2, lv72_1, model_decoder_layers_8_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add347 = R.call_tir(cls.add5, (add344, lv469), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm92 = R.call_tir(cls.layer_norm2, (add347, model_decoder_layers_9_self_attn_layer_norm_weight2, model_decoder_layers_9_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv470 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_9_self_attn_q_proj_weight2, layer_norm92, model_decoder_layers_9_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape477 = R.call_tir(cls.reshape14, (lv470,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv107 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_9_self_attn_k_proj_weight2, layer_norm92), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape478 = R.call_tir(cls.reshape14, (lv107,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv471 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_9_self_attn_v_proj_weight2, layer_norm92, model_decoder_layers_9_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape479 = R.call_tir(cls.reshape14, (lv471,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat9 = R.call_tir(cls.concatenate1, (reshape477, reshape478, reshape479), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape480 = R.call_tir(cls.reshape15, (concat9,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv87 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), reshape480), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape481 = R.call_tir(cls.reshape16, (lv87,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape482 = R.call_tir(cls.reshape17, (reshape481,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv472 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_9_self_attn_out_proj_weight2, reshape482, model_decoder_layers_9_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add351 = R.call_tir(cls.add5, (add347, lv472), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm93 = R.call_tir(cls.layer_norm2, (add351, model_decoder_layers_9_encoder_attn_layer_norm_weight2, model_decoder_layers_9_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv473 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_9_encoder_attn_q_proj_weight2, layer_norm93, model_decoder_layers_9_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape483 = R.call_tir(cls.reshape14, (lv473,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape484 = R.call_tir(cls.reshape18, (reshape483,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv88 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), reshape484), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape485 = R.call_tir(cls.reshape16, (lv88,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape486 = R.call_tir(cls.reshape17, (reshape485,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv474 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_9_encoder_attn_out_proj_weight2, reshape486, model_decoder_layers_9_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add354 = R.call_tir(cls.add5, (add351, lv474), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm94 = R.call_tir(cls.layer_norm2, (add354, model_decoder_layers_9_final_layer_norm_weight2, model_decoder_layers_9_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv73_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_9_fc1_weight2, layer_norm94, model_decoder_layers_9_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv475 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_9_fc2_weight2, lv73_1, model_decoder_layers_9_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add357 = R.call_tir(cls.add5, (add354, lv475), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm95 = R.call_tir(cls.layer_norm2, (add357, model_decoder_layers_10_self_attn_layer_norm_weight2, model_decoder_layers_10_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv476 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_10_self_attn_q_proj_weight2, layer_norm95, model_decoder_layers_10_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape487 = R.call_tir(cls.reshape14, (lv476,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv108 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_10_self_attn_k_proj_weight2, layer_norm95), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape488 = R.call_tir(cls.reshape14, (lv108,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv477 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_10_self_attn_v_proj_weight2, layer_norm95, model_decoder_layers_10_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape489 = R.call_tir(cls.reshape14, (lv477,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat10 = R.call_tir(cls.concatenate1, (reshape487, reshape488, reshape489), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape490 = R.call_tir(cls.reshape15, (concat10,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv89 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), reshape490), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape491 = R.call_tir(cls.reshape16, (lv89,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape492 = R.call_tir(cls.reshape17, (reshape491,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv478 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_10_self_attn_out_proj_weight2, reshape492, model_decoder_layers_10_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add361 = R.call_tir(cls.add5, (add357, lv478), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm96 = R.call_tir(cls.layer_norm2, (add361, model_decoder_layers_10_encoder_attn_layer_norm_weight2, model_decoder_layers_10_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv479 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_10_encoder_attn_q_proj_weight2, layer_norm96, model_decoder_layers_10_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape493 = R.call_tir(cls.reshape14, (lv479,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape494 = R.call_tir(cls.reshape18, (reshape493,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv90 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), reshape494), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape495 = R.call_tir(cls.reshape16, (lv90,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape496 = R.call_tir(cls.reshape17, (reshape495,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv480 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_10_encoder_attn_out_proj_weight2, reshape496, model_decoder_layers_10_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add364 = R.call_tir(cls.add5, (add361, lv480), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm97 = R.call_tir(cls.layer_norm2, (add364, model_decoder_layers_10_final_layer_norm_weight2, model_decoder_layers_10_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv74_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_10_fc1_weight2, layer_norm97, model_decoder_layers_10_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv481 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_10_fc2_weight2, lv74_1, model_decoder_layers_10_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add367 = R.call_tir(cls.add5, (add364, lv481), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm98 = R.call_tir(cls.layer_norm2, (add367, model_decoder_layers_11_self_attn_layer_norm_weight2, model_decoder_layers_11_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv482 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_11_self_attn_q_proj_weight2, layer_norm98, model_decoder_layers_11_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape497 = R.call_tir(cls.reshape14, (lv482,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv109 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_11_self_attn_k_proj_weight2, layer_norm98), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape498 = R.call_tir(cls.reshape14, (lv109,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv483 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_11_self_attn_v_proj_weight2, layer_norm98, model_decoder_layers_11_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape499 = R.call_tir(cls.reshape14, (lv483,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat11 = R.call_tir(cls.concatenate1, (reshape497, reshape498, reshape499), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape500 = R.call_tir(cls.reshape15, (concat11,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv91 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), reshape500), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape501 = R.call_tir(cls.reshape16, (lv91,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape502 = R.call_tir(cls.reshape17, (reshape501,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv484 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_11_self_attn_out_proj_weight2, reshape502, model_decoder_layers_11_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add371 = R.call_tir(cls.add5, (add367, lv484), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm99 = R.call_tir(cls.layer_norm2, (add371, model_decoder_layers_11_encoder_attn_layer_norm_weight2, model_decoder_layers_11_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv485 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_11_encoder_attn_q_proj_weight2, layer_norm99, model_decoder_layers_11_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape503 = R.call_tir(cls.reshape14, (lv485,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape504 = R.call_tir(cls.reshape18, (reshape503,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv92 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), reshape504), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape505 = R.call_tir(cls.reshape16, (lv92,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape506 = R.call_tir(cls.reshape17, (reshape505,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv486 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_11_encoder_attn_out_proj_weight2, reshape506, model_decoder_layers_11_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add374 = R.call_tir(cls.add5, (add371, lv486), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm100 = R.call_tir(cls.layer_norm2, (add374, model_decoder_layers_11_final_layer_norm_weight2, model_decoder_layers_11_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv75_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_11_fc1_weight2, layer_norm100, model_decoder_layers_11_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv487 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_11_fc2_weight2, lv75_1, model_decoder_layers_11_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add377 = R.call_tir(cls.add5, (add374, lv487), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm101 = R.call_tir(cls.layer_norm2, (add377, model_decoder_layers_12_self_attn_layer_norm_weight2, model_decoder_layers_12_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv488 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_12_self_attn_q_proj_weight2, layer_norm101, model_decoder_layers_12_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape507 = R.call_tir(cls.reshape14, (lv488,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv110 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_12_self_attn_k_proj_weight2, layer_norm101), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape508 = R.call_tir(cls.reshape14, (lv110,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv489 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_12_self_attn_v_proj_weight2, layer_norm101, model_decoder_layers_12_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape509 = R.call_tir(cls.reshape14, (lv489,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat12 = R.call_tir(cls.concatenate1, (reshape507, reshape508, reshape509), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape510 = R.call_tir(cls.reshape15, (concat12,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv93 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), reshape510), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape511 = R.call_tir(cls.reshape16, (lv93,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape512 = R.call_tir(cls.reshape17, (reshape511,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv490 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_12_self_attn_out_proj_weight2, reshape512, model_decoder_layers_12_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add381 = R.call_tir(cls.add5, (add377, lv490), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm102 = R.call_tir(cls.layer_norm2, (add381, model_decoder_layers_12_encoder_attn_layer_norm_weight2, model_decoder_layers_12_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv491 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_12_encoder_attn_q_proj_weight2, layer_norm102, model_decoder_layers_12_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape513 = R.call_tir(cls.reshape14, (lv491,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape514 = R.call_tir(cls.reshape18, (reshape513,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv94 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), reshape514), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape515 = R.call_tir(cls.reshape16, (lv94,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape516 = R.call_tir(cls.reshape17, (reshape515,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv492 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_12_encoder_attn_out_proj_weight2, reshape516, model_decoder_layers_12_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add384 = R.call_tir(cls.add5, (add381, lv492), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm103 = R.call_tir(cls.layer_norm2, (add384, model_decoder_layers_12_final_layer_norm_weight2, model_decoder_layers_12_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv76_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_12_fc1_weight2, layer_norm103, model_decoder_layers_12_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv493 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_12_fc2_weight2, lv76_1, model_decoder_layers_12_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add387 = R.call_tir(cls.add5, (add384, lv493), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm104 = R.call_tir(cls.layer_norm2, (add387, model_decoder_layers_13_self_attn_layer_norm_weight2, model_decoder_layers_13_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv494 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_13_self_attn_q_proj_weight2, layer_norm104, model_decoder_layers_13_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape517 = R.call_tir(cls.reshape14, (lv494,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv111 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_13_self_attn_k_proj_weight2, layer_norm104), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape518 = R.call_tir(cls.reshape14, (lv111,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv495 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_13_self_attn_v_proj_weight2, layer_norm104, model_decoder_layers_13_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape519 = R.call_tir(cls.reshape14, (lv495,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat13 = R.call_tir(cls.concatenate1, (reshape517, reshape518, reshape519), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape520 = R.call_tir(cls.reshape15, (concat13,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv95 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), reshape520), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape521 = R.call_tir(cls.reshape16, (lv95,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape522 = R.call_tir(cls.reshape17, (reshape521,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv496 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_13_self_attn_out_proj_weight2, reshape522, model_decoder_layers_13_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add391 = R.call_tir(cls.add5, (add387, lv496), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm105 = R.call_tir(cls.layer_norm2, (add391, model_decoder_layers_13_encoder_attn_layer_norm_weight2, model_decoder_layers_13_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv497 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_13_encoder_attn_q_proj_weight2, layer_norm105, model_decoder_layers_13_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape523 = R.call_tir(cls.reshape14, (lv497,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape524 = R.call_tir(cls.reshape18, (reshape523,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv96 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), reshape524), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape525 = R.call_tir(cls.reshape16, (lv96,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape526 = R.call_tir(cls.reshape17, (reshape525,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv498 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_13_encoder_attn_out_proj_weight2, reshape526, model_decoder_layers_13_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add394 = R.call_tir(cls.add5, (add391, lv498), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm106 = R.call_tir(cls.layer_norm2, (add394, model_decoder_layers_13_final_layer_norm_weight2, model_decoder_layers_13_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv77_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_13_fc1_weight2, layer_norm106, model_decoder_layers_13_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv499 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_13_fc2_weight2, lv77_1, model_decoder_layers_13_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add397 = R.call_tir(cls.add5, (add394, lv499), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm107 = R.call_tir(cls.layer_norm2, (add397, model_decoder_layers_14_self_attn_layer_norm_weight2, model_decoder_layers_14_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv500 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_14_self_attn_q_proj_weight2, layer_norm107, model_decoder_layers_14_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape527 = R.call_tir(cls.reshape14, (lv500,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv112 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_14_self_attn_k_proj_weight2, layer_norm107), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape528 = R.call_tir(cls.reshape14, (lv112,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv501 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_14_self_attn_v_proj_weight2, layer_norm107, model_decoder_layers_14_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape529 = R.call_tir(cls.reshape14, (lv501,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat14 = R.call_tir(cls.concatenate1, (reshape527, reshape528, reshape529), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape530 = R.call_tir(cls.reshape15, (concat14,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv97 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), reshape530), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape531 = R.call_tir(cls.reshape16, (lv97,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape532 = R.call_tir(cls.reshape17, (reshape531,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv502 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_14_self_attn_out_proj_weight2, reshape532, model_decoder_layers_14_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add401 = R.call_tir(cls.add5, (add397, lv502), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm108 = R.call_tir(cls.layer_norm2, (add401, model_decoder_layers_14_encoder_attn_layer_norm_weight2, model_decoder_layers_14_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv503 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_14_encoder_attn_q_proj_weight2, layer_norm108, model_decoder_layers_14_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape533 = R.call_tir(cls.reshape14, (lv503,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape534 = R.call_tir(cls.reshape18, (reshape533,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv98_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), reshape534), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape535 = R.call_tir(cls.reshape16, (lv98_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape536 = R.call_tir(cls.reshape17, (reshape535,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv504 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_14_encoder_attn_out_proj_weight2, reshape536, model_decoder_layers_14_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add404 = R.call_tir(cls.add5, (add401, lv504), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm109 = R.call_tir(cls.layer_norm2, (add404, model_decoder_layers_14_final_layer_norm_weight2, model_decoder_layers_14_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv78_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_14_fc1_weight2, layer_norm109, model_decoder_layers_14_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv505 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_14_fc2_weight2, lv78_1, model_decoder_layers_14_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add407 = R.call_tir(cls.add5, (add404, lv505), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm110 = R.call_tir(cls.layer_norm2, (add407, model_decoder_layers_15_self_attn_layer_norm_weight2, model_decoder_layers_15_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv506 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_15_self_attn_q_proj_weight2, layer_norm110, model_decoder_layers_15_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape537 = R.call_tir(cls.reshape14, (lv506,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv113 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_15_self_attn_k_proj_weight2, layer_norm110), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape538 = R.call_tir(cls.reshape14, (lv113,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv507 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_15_self_attn_v_proj_weight2, layer_norm110, model_decoder_layers_15_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape539 = R.call_tir(cls.reshape14, (lv507,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat15 = R.call_tir(cls.concatenate1, (reshape537, reshape538, reshape539), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape540 = R.call_tir(cls.reshape15, (concat15,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv99_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), reshape540), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape541 = R.call_tir(cls.reshape16, (lv99_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape542 = R.call_tir(cls.reshape17, (reshape541,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv508 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_15_self_attn_out_proj_weight2, reshape542, model_decoder_layers_15_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add411 = R.call_tir(cls.add5, (add407, lv508), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm111 = R.call_tir(cls.layer_norm2, (add411, model_decoder_layers_15_encoder_attn_layer_norm_weight2, model_decoder_layers_15_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv509 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_15_encoder_attn_q_proj_weight2, layer_norm111, model_decoder_layers_15_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape543 = R.call_tir(cls.reshape14, (lv509,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape544 = R.call_tir(cls.reshape18, (reshape543,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv100_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), reshape544), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape545 = R.call_tir(cls.reshape16, (lv100_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape546 = R.call_tir(cls.reshape17, (reshape545,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv510 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_15_encoder_attn_out_proj_weight2, reshape546, model_decoder_layers_15_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add414 = R.call_tir(cls.add5, (add411, lv510), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm112 = R.call_tir(cls.layer_norm2, (add414, model_decoder_layers_15_final_layer_norm_weight2, model_decoder_layers_15_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv79_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_15_fc1_weight2, layer_norm112, model_decoder_layers_15_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv511 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_15_fc2_weight2, lv79_1, model_decoder_layers_15_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add417 = R.call_tir(cls.add5, (add414, lv511), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm113 = R.call_tir(cls.layer_norm2, (add417, model_decoder_layers_16_self_attn_layer_norm_weight2, model_decoder_layers_16_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv512 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_16_self_attn_q_proj_weight2, layer_norm113, model_decoder_layers_16_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape547 = R.call_tir(cls.reshape14, (lv512,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv114 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_16_self_attn_k_proj_weight2, layer_norm113), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape548 = R.call_tir(cls.reshape14, (lv114,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv513 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_16_self_attn_v_proj_weight2, layer_norm113, model_decoder_layers_16_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape549 = R.call_tir(cls.reshape14, (lv513,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat16 = R.call_tir(cls.concatenate1, (reshape547, reshape548, reshape549), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape550 = R.call_tir(cls.reshape15, (concat16,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv101_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), reshape550), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape551 = R.call_tir(cls.reshape16, (lv101_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape552 = R.call_tir(cls.reshape17, (reshape551,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv514 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_16_self_attn_out_proj_weight2, reshape552, model_decoder_layers_16_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add421 = R.call_tir(cls.add5, (add417, lv514), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm114 = R.call_tir(cls.layer_norm2, (add421, model_decoder_layers_16_encoder_attn_layer_norm_weight2, model_decoder_layers_16_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv515 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_16_encoder_attn_q_proj_weight2, layer_norm114, model_decoder_layers_16_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape553 = R.call_tir(cls.reshape14, (lv515,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape554 = R.call_tir(cls.reshape18, (reshape553,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv102_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), reshape554), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape555 = R.call_tir(cls.reshape16, (lv102_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape556 = R.call_tir(cls.reshape17, (reshape555,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv516 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_16_encoder_attn_out_proj_weight2, reshape556, model_decoder_layers_16_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add424 = R.call_tir(cls.add5, (add421, lv516), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm115 = R.call_tir(cls.layer_norm2, (add424, model_decoder_layers_16_final_layer_norm_weight2, model_decoder_layers_16_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv80_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_16_fc1_weight2, layer_norm115, model_decoder_layers_16_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv517 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_16_fc2_weight2, lv80_1, model_decoder_layers_16_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add427 = R.call_tir(cls.add5, (add424, lv517), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm116 = R.call_tir(cls.layer_norm2, (add427, model_decoder_layers_17_self_attn_layer_norm_weight2, model_decoder_layers_17_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv518 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_17_self_attn_q_proj_weight2, layer_norm116, model_decoder_layers_17_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape557 = R.call_tir(cls.reshape14, (lv518,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv115 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_17_self_attn_k_proj_weight2, layer_norm116), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape558 = R.call_tir(cls.reshape14, (lv115,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv519 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_17_self_attn_v_proj_weight2, layer_norm116, model_decoder_layers_17_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape559 = R.call_tir(cls.reshape14, (lv519,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat17 = R.call_tir(cls.concatenate1, (reshape557, reshape558, reshape559), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape560 = R.call_tir(cls.reshape15, (concat17,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv103_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), reshape560), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape561 = R.call_tir(cls.reshape16, (lv103_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape562 = R.call_tir(cls.reshape17, (reshape561,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv520 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_17_self_attn_out_proj_weight2, reshape562, model_decoder_layers_17_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add431 = R.call_tir(cls.add5, (add427, lv520), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm117 = R.call_tir(cls.layer_norm2, (add431, model_decoder_layers_17_encoder_attn_layer_norm_weight2, model_decoder_layers_17_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv521 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_17_encoder_attn_q_proj_weight2, layer_norm117, model_decoder_layers_17_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape563 = R.call_tir(cls.reshape14, (lv521,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape564 = R.call_tir(cls.reshape18, (reshape563,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv104_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), reshape564), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape565 = R.call_tir(cls.reshape16, (lv104_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape566 = R.call_tir(cls.reshape17, (reshape565,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv522 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_17_encoder_attn_out_proj_weight2, reshape566, model_decoder_layers_17_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add434 = R.call_tir(cls.add5, (add431, lv522), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm118 = R.call_tir(cls.layer_norm2, (add434, model_decoder_layers_17_final_layer_norm_weight2, model_decoder_layers_17_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv81_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_17_fc1_weight2, layer_norm118, model_decoder_layers_17_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv523 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_17_fc2_weight2, lv81_1, model_decoder_layers_17_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add437 = R.call_tir(cls.add5, (add434, lv523), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm119 = R.call_tir(cls.layer_norm2, (add437, model_decoder_layers_18_self_attn_layer_norm_weight2, model_decoder_layers_18_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv524 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_18_self_attn_q_proj_weight2, layer_norm119, model_decoder_layers_18_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape567 = R.call_tir(cls.reshape14, (lv524,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv116 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_18_self_attn_k_proj_weight2, layer_norm119), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape568 = R.call_tir(cls.reshape14, (lv116,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv525 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_18_self_attn_v_proj_weight2, layer_norm119, model_decoder_layers_18_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape569 = R.call_tir(cls.reshape14, (lv525,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat18 = R.call_tir(cls.concatenate1, (reshape567, reshape568, reshape569), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape570 = R.call_tir(cls.reshape15, (concat18,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv105_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), reshape570), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape571 = R.call_tir(cls.reshape16, (lv105_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape572 = R.call_tir(cls.reshape17, (reshape571,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv526 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_18_self_attn_out_proj_weight2, reshape572, model_decoder_layers_18_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add441 = R.call_tir(cls.add5, (add437, lv526), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm120 = R.call_tir(cls.layer_norm2, (add441, model_decoder_layers_18_encoder_attn_layer_norm_weight2, model_decoder_layers_18_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv527 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_18_encoder_attn_q_proj_weight2, layer_norm120, model_decoder_layers_18_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape573 = R.call_tir(cls.reshape14, (lv527,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape574 = R.call_tir(cls.reshape18, (reshape573,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv106_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), reshape574), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape575 = R.call_tir(cls.reshape16, (lv106_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape576 = R.call_tir(cls.reshape17, (reshape575,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv528 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_18_encoder_attn_out_proj_weight2, reshape576, model_decoder_layers_18_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add444 = R.call_tir(cls.add5, (add441, lv528), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm121 = R.call_tir(cls.layer_norm2, (add444, model_decoder_layers_18_final_layer_norm_weight2, model_decoder_layers_18_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv82_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_18_fc1_weight2, layer_norm121, model_decoder_layers_18_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv529 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_18_fc2_weight2, lv82_1, model_decoder_layers_18_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add447 = R.call_tir(cls.add5, (add444, lv529), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm122 = R.call_tir(cls.layer_norm2, (add447, model_decoder_layers_19_self_attn_layer_norm_weight2, model_decoder_layers_19_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv530 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_19_self_attn_q_proj_weight2, layer_norm122, model_decoder_layers_19_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape577 = R.call_tir(cls.reshape14, (lv530,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv117 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_19_self_attn_k_proj_weight2, layer_norm122), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape578 = R.call_tir(cls.reshape14, (lv117,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv531 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_19_self_attn_v_proj_weight2, layer_norm122, model_decoder_layers_19_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape579 = R.call_tir(cls.reshape14, (lv531,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat19 = R.call_tir(cls.concatenate1, (reshape577, reshape578, reshape579), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape580 = R.call_tir(cls.reshape15, (concat19,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv107_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), reshape580), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape581 = R.call_tir(cls.reshape16, (lv107_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape582 = R.call_tir(cls.reshape17, (reshape581,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv532 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_19_self_attn_out_proj_weight2, reshape582, model_decoder_layers_19_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add451 = R.call_tir(cls.add5, (add447, lv532), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm123 = R.call_tir(cls.layer_norm2, (add451, model_decoder_layers_19_encoder_attn_layer_norm_weight2, model_decoder_layers_19_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv533 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_19_encoder_attn_q_proj_weight2, layer_norm123, model_decoder_layers_19_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape583 = R.call_tir(cls.reshape14, (lv533,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape584 = R.call_tir(cls.reshape18, (reshape583,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv108_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), reshape584), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape585 = R.call_tir(cls.reshape16, (lv108_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape586 = R.call_tir(cls.reshape17, (reshape585,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv534 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_19_encoder_attn_out_proj_weight2, reshape586, model_decoder_layers_19_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add454 = R.call_tir(cls.add5, (add451, lv534), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm124 = R.call_tir(cls.layer_norm2, (add454, model_decoder_layers_19_final_layer_norm_weight2, model_decoder_layers_19_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv83_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_19_fc1_weight2, layer_norm124, model_decoder_layers_19_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv535 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_19_fc2_weight2, lv83_1, model_decoder_layers_19_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add457 = R.call_tir(cls.add5, (add454, lv535), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm125 = R.call_tir(cls.layer_norm2, (add457, model_decoder_layers_20_self_attn_layer_norm_weight2, model_decoder_layers_20_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv536 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_20_self_attn_q_proj_weight2, layer_norm125, model_decoder_layers_20_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape587 = R.call_tir(cls.reshape14, (lv536,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv118 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_20_self_attn_k_proj_weight2, layer_norm125), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape588 = R.call_tir(cls.reshape14, (lv118,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv537 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_20_self_attn_v_proj_weight2, layer_norm125, model_decoder_layers_20_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape589 = R.call_tir(cls.reshape14, (lv537,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat20 = R.call_tir(cls.concatenate1, (reshape587, reshape588, reshape589), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape590 = R.call_tir(cls.reshape15, (concat20,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv109_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), reshape590), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape591 = R.call_tir(cls.reshape16, (lv109_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape592 = R.call_tir(cls.reshape17, (reshape591,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv538 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_20_self_attn_out_proj_weight2, reshape592, model_decoder_layers_20_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add461 = R.call_tir(cls.add5, (add457, lv538), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm126 = R.call_tir(cls.layer_norm2, (add461, model_decoder_layers_20_encoder_attn_layer_norm_weight2, model_decoder_layers_20_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv539 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_20_encoder_attn_q_proj_weight2, layer_norm126, model_decoder_layers_20_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape593 = R.call_tir(cls.reshape14, (lv539,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape594 = R.call_tir(cls.reshape18, (reshape593,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv110_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), reshape594), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape595 = R.call_tir(cls.reshape16, (lv110_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape596 = R.call_tir(cls.reshape17, (reshape595,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv540 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_20_encoder_attn_out_proj_weight2, reshape596, model_decoder_layers_20_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add464 = R.call_tir(cls.add5, (add461, lv540), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm127 = R.call_tir(cls.layer_norm2, (add464, model_decoder_layers_20_final_layer_norm_weight2, model_decoder_layers_20_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv84_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_20_fc1_weight2, layer_norm127, model_decoder_layers_20_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv541 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_20_fc2_weight2, lv84_1, model_decoder_layers_20_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add467 = R.call_tir(cls.add5, (add464, lv541), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm128 = R.call_tir(cls.layer_norm2, (add467, model_decoder_layers_21_self_attn_layer_norm_weight2, model_decoder_layers_21_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv542 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_21_self_attn_q_proj_weight2, layer_norm128, model_decoder_layers_21_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape597 = R.call_tir(cls.reshape14, (lv542,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv119 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_21_self_attn_k_proj_weight2, layer_norm128), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape598 = R.call_tir(cls.reshape14, (lv119,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv543 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_21_self_attn_v_proj_weight2, layer_norm128, model_decoder_layers_21_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape599 = R.call_tir(cls.reshape14, (lv543,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat21 = R.call_tir(cls.concatenate1, (reshape597, reshape598, reshape599), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape600 = R.call_tir(cls.reshape15, (concat21,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv111_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), reshape600), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape601 = R.call_tir(cls.reshape16, (lv111_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape602 = R.call_tir(cls.reshape17, (reshape601,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv544 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_21_self_attn_out_proj_weight2, reshape602, model_decoder_layers_21_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add471 = R.call_tir(cls.add5, (add467, lv544), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm129 = R.call_tir(cls.layer_norm2, (add471, model_decoder_layers_21_encoder_attn_layer_norm_weight2, model_decoder_layers_21_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv545 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_21_encoder_attn_q_proj_weight2, layer_norm129, model_decoder_layers_21_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape603 = R.call_tir(cls.reshape14, (lv545,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape604 = R.call_tir(cls.reshape18, (reshape603,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv112_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), reshape604), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape605 = R.call_tir(cls.reshape16, (lv112_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape606 = R.call_tir(cls.reshape17, (reshape605,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv546 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_21_encoder_attn_out_proj_weight2, reshape606, model_decoder_layers_21_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add474 = R.call_tir(cls.add5, (add471, lv546), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm130 = R.call_tir(cls.layer_norm2, (add474, model_decoder_layers_21_final_layer_norm_weight2, model_decoder_layers_21_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv85_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_21_fc1_weight2, layer_norm130, model_decoder_layers_21_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv547 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_21_fc2_weight2, lv85_1, model_decoder_layers_21_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add477 = R.call_tir(cls.add5, (add474, lv547), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm131 = R.call_tir(cls.layer_norm2, (add477, model_decoder_layers_22_self_attn_layer_norm_weight2, model_decoder_layers_22_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv548 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_22_self_attn_q_proj_weight2, layer_norm131, model_decoder_layers_22_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape607 = R.call_tir(cls.reshape14, (lv548,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv120 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_22_self_attn_k_proj_weight2, layer_norm131), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape608 = R.call_tir(cls.reshape14, (lv120,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv549 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_22_self_attn_v_proj_weight2, layer_norm131, model_decoder_layers_22_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape609 = R.call_tir(cls.reshape14, (lv549,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat22 = R.call_tir(cls.concatenate1, (reshape607, reshape608, reshape609), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape610 = R.call_tir(cls.reshape15, (concat22,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv113_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), reshape610), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape611 = R.call_tir(cls.reshape16, (lv113_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape612 = R.call_tir(cls.reshape17, (reshape611,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv550 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_22_self_attn_out_proj_weight2, reshape612, model_decoder_layers_22_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add481 = R.call_tir(cls.add5, (add477, lv550), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm132 = R.call_tir(cls.layer_norm2, (add481, model_decoder_layers_22_encoder_attn_layer_norm_weight2, model_decoder_layers_22_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv551 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_22_encoder_attn_q_proj_weight2, layer_norm132, model_decoder_layers_22_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape613 = R.call_tir(cls.reshape14, (lv551,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape614 = R.call_tir(cls.reshape18, (reshape613,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv114_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), reshape614), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape615 = R.call_tir(cls.reshape16, (lv114_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape616 = R.call_tir(cls.reshape17, (reshape615,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv552 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_22_encoder_attn_out_proj_weight2, reshape616, model_decoder_layers_22_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add484 = R.call_tir(cls.add5, (add481, lv552), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm133 = R.call_tir(cls.layer_norm2, (add484, model_decoder_layers_22_final_layer_norm_weight2, model_decoder_layers_22_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv86_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_22_fc1_weight2, layer_norm133, model_decoder_layers_22_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv553 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_22_fc2_weight2, lv86_1, model_decoder_layers_22_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add487 = R.call_tir(cls.add5, (add484, lv553), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm134 = R.call_tir(cls.layer_norm2, (add487, model_decoder_layers_23_self_attn_layer_norm_weight2, model_decoder_layers_23_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv554 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_23_self_attn_q_proj_weight2, layer_norm134, model_decoder_layers_23_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape617 = R.call_tir(cls.reshape14, (lv554,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv121 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_23_self_attn_k_proj_weight2, layer_norm134), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape618 = R.call_tir(cls.reshape14, (lv121,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv555 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_23_self_attn_v_proj_weight2, layer_norm134, model_decoder_layers_23_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape619 = R.call_tir(cls.reshape14, (lv555,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat23 = R.call_tir(cls.concatenate1, (reshape617, reshape618, reshape619), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape620 = R.call_tir(cls.reshape15, (concat23,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv115_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), reshape620), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape621 = R.call_tir(cls.reshape16, (lv115_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape622 = R.call_tir(cls.reshape17, (reshape621,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv556 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_23_self_attn_out_proj_weight2, reshape622, model_decoder_layers_23_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add491 = R.call_tir(cls.add5, (add487, lv556), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm135 = R.call_tir(cls.layer_norm2, (add491, model_decoder_layers_23_encoder_attn_layer_norm_weight2, model_decoder_layers_23_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv557 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_23_encoder_attn_q_proj_weight2, layer_norm135, model_decoder_layers_23_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape623 = R.call_tir(cls.reshape14, (lv557,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape624 = R.call_tir(cls.reshape18, (reshape623,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv116_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), reshape624), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape625 = R.call_tir(cls.reshape16, (lv116_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape626 = R.call_tir(cls.reshape17, (reshape625,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv558 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_23_encoder_attn_out_proj_weight2, reshape626, model_decoder_layers_23_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add494 = R.call_tir(cls.add5, (add491, lv558), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm136 = R.call_tir(cls.layer_norm2, (add494, model_decoder_layers_23_final_layer_norm_weight2, model_decoder_layers_23_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv87_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_23_fc1_weight2, layer_norm136, model_decoder_layers_23_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv559 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_23_fc2_weight2, lv87_1, model_decoder_layers_23_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add497 = R.call_tir(cls.add5, (add494, lv559), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm137 = R.call_tir(cls.layer_norm2, (add497, model_decoder_layers_24_self_attn_layer_norm_weight2, model_decoder_layers_24_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv560 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_24_self_attn_q_proj_weight2, layer_norm137, model_decoder_layers_24_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape627 = R.call_tir(cls.reshape14, (lv560,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv122 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_24_self_attn_k_proj_weight2, layer_norm137), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape628 = R.call_tir(cls.reshape14, (lv122,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv561 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_24_self_attn_v_proj_weight2, layer_norm137, model_decoder_layers_24_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape629 = R.call_tir(cls.reshape14, (lv561,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat24 = R.call_tir(cls.concatenate1, (reshape627, reshape628, reshape629), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape630 = R.call_tir(cls.reshape15, (concat24,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv117_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), reshape630), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape631 = R.call_tir(cls.reshape16, (lv117_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape632 = R.call_tir(cls.reshape17, (reshape631,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv562 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_24_self_attn_out_proj_weight2, reshape632, model_decoder_layers_24_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add501 = R.call_tir(cls.add5, (add497, lv562), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm138 = R.call_tir(cls.layer_norm2, (add501, model_decoder_layers_24_encoder_attn_layer_norm_weight2, model_decoder_layers_24_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv563 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_24_encoder_attn_q_proj_weight2, layer_norm138, model_decoder_layers_24_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape633 = R.call_tir(cls.reshape14, (lv563,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape634 = R.call_tir(cls.reshape18, (reshape633,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv118_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), reshape634), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape635 = R.call_tir(cls.reshape16, (lv118_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape636 = R.call_tir(cls.reshape17, (reshape635,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv564 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_24_encoder_attn_out_proj_weight2, reshape636, model_decoder_layers_24_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add504 = R.call_tir(cls.add5, (add501, lv564), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm139 = R.call_tir(cls.layer_norm2, (add504, model_decoder_layers_24_final_layer_norm_weight2, model_decoder_layers_24_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv88_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_24_fc1_weight2, layer_norm139, model_decoder_layers_24_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv565 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_24_fc2_weight2, lv88_1, model_decoder_layers_24_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add507 = R.call_tir(cls.add5, (add504, lv565), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm140 = R.call_tir(cls.layer_norm2, (add507, model_decoder_layers_25_self_attn_layer_norm_weight2, model_decoder_layers_25_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv566 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_25_self_attn_q_proj_weight2, layer_norm140, model_decoder_layers_25_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape637 = R.call_tir(cls.reshape14, (lv566,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv123 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_25_self_attn_k_proj_weight2, layer_norm140), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape638 = R.call_tir(cls.reshape14, (lv123,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv567 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_25_self_attn_v_proj_weight2, layer_norm140, model_decoder_layers_25_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape639 = R.call_tir(cls.reshape14, (lv567,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat25 = R.call_tir(cls.concatenate1, (reshape637, reshape638, reshape639), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape640 = R.call_tir(cls.reshape15, (concat25,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv119_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), reshape640), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape641 = R.call_tir(cls.reshape16, (lv119_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape642 = R.call_tir(cls.reshape17, (reshape641,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv568 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_25_self_attn_out_proj_weight2, reshape642, model_decoder_layers_25_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add511 = R.call_tir(cls.add5, (add507, lv568), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm141 = R.call_tir(cls.layer_norm2, (add511, model_decoder_layers_25_encoder_attn_layer_norm_weight2, model_decoder_layers_25_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv569 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_25_encoder_attn_q_proj_weight2, layer_norm141, model_decoder_layers_25_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape643 = R.call_tir(cls.reshape14, (lv569,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape644 = R.call_tir(cls.reshape18, (reshape643,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv120_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), reshape644), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape645 = R.call_tir(cls.reshape16, (lv120_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape646 = R.call_tir(cls.reshape17, (reshape645,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv570 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_25_encoder_attn_out_proj_weight2, reshape646, model_decoder_layers_25_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add514 = R.call_tir(cls.add5, (add511, lv570), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm142 = R.call_tir(cls.layer_norm2, (add514, model_decoder_layers_25_final_layer_norm_weight2, model_decoder_layers_25_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv89_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_25_fc1_weight2, layer_norm142, model_decoder_layers_25_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv571 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_25_fc2_weight2, lv89_1, model_decoder_layers_25_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add517 = R.call_tir(cls.add5, (add514, lv571), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm143 = R.call_tir(cls.layer_norm2, (add517, model_decoder_layers_26_self_attn_layer_norm_weight2, model_decoder_layers_26_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv572 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_26_self_attn_q_proj_weight2, layer_norm143, model_decoder_layers_26_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape647 = R.call_tir(cls.reshape14, (lv572,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv124 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_26_self_attn_k_proj_weight2, layer_norm143), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape648 = R.call_tir(cls.reshape14, (lv124,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv573 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_26_self_attn_v_proj_weight2, layer_norm143, model_decoder_layers_26_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape649 = R.call_tir(cls.reshape14, (lv573,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat26 = R.call_tir(cls.concatenate1, (reshape647, reshape648, reshape649), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape650 = R.call_tir(cls.reshape15, (concat26,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv121_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), reshape650), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape651 = R.call_tir(cls.reshape16, (lv121_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape652 = R.call_tir(cls.reshape17, (reshape651,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv574 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_26_self_attn_out_proj_weight2, reshape652, model_decoder_layers_26_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add521 = R.call_tir(cls.add5, (add517, lv574), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm144 = R.call_tir(cls.layer_norm2, (add521, model_decoder_layers_26_encoder_attn_layer_norm_weight2, model_decoder_layers_26_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv575 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_26_encoder_attn_q_proj_weight2, layer_norm144, model_decoder_layers_26_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape653 = R.call_tir(cls.reshape14, (lv575,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape654 = R.call_tir(cls.reshape18, (reshape653,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv122_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), reshape654), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape655 = R.call_tir(cls.reshape16, (lv122_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape656 = R.call_tir(cls.reshape17, (reshape655,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv576 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_26_encoder_attn_out_proj_weight2, reshape656, model_decoder_layers_26_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add524 = R.call_tir(cls.add5, (add521, lv576), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm145 = R.call_tir(cls.layer_norm2, (add524, model_decoder_layers_26_final_layer_norm_weight2, model_decoder_layers_26_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv90_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_26_fc1_weight2, layer_norm145, model_decoder_layers_26_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv577 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_26_fc2_weight2, lv90_1, model_decoder_layers_26_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add527 = R.call_tir(cls.add5, (add524, lv577), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm146 = R.call_tir(cls.layer_norm2, (add527, model_decoder_layers_27_self_attn_layer_norm_weight2, model_decoder_layers_27_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv578 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_27_self_attn_q_proj_weight2, layer_norm146, model_decoder_layers_27_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape657 = R.call_tir(cls.reshape14, (lv578,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv125 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_27_self_attn_k_proj_weight2, layer_norm146), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape658 = R.call_tir(cls.reshape14, (lv125,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv579 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_27_self_attn_v_proj_weight2, layer_norm146, model_decoder_layers_27_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape659 = R.call_tir(cls.reshape14, (lv579,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat27 = R.call_tir(cls.concatenate1, (reshape657, reshape658, reshape659), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape660 = R.call_tir(cls.reshape15, (concat27,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv123_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), reshape660), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape661 = R.call_tir(cls.reshape16, (lv123_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape662 = R.call_tir(cls.reshape17, (reshape661,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv580 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_27_self_attn_out_proj_weight2, reshape662, model_decoder_layers_27_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add531 = R.call_tir(cls.add5, (add527, lv580), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm147 = R.call_tir(cls.layer_norm2, (add531, model_decoder_layers_27_encoder_attn_layer_norm_weight2, model_decoder_layers_27_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv581 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_27_encoder_attn_q_proj_weight2, layer_norm147, model_decoder_layers_27_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape663 = R.call_tir(cls.reshape14, (lv581,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape664 = R.call_tir(cls.reshape18, (reshape663,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv124_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), reshape664), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape665 = R.call_tir(cls.reshape16, (lv124_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape666 = R.call_tir(cls.reshape17, (reshape665,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv582 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_27_encoder_attn_out_proj_weight2, reshape666, model_decoder_layers_27_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add534 = R.call_tir(cls.add5, (add531, lv582), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm148 = R.call_tir(cls.layer_norm2, (add534, model_decoder_layers_27_final_layer_norm_weight2, model_decoder_layers_27_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv91_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_27_fc1_weight2, layer_norm148, model_decoder_layers_27_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv583 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_27_fc2_weight2, lv91_1, model_decoder_layers_27_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add537 = R.call_tir(cls.add5, (add534, lv583), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm149 = R.call_tir(cls.layer_norm2, (add537, model_decoder_layers_28_self_attn_layer_norm_weight2, model_decoder_layers_28_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv584 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_28_self_attn_q_proj_weight2, layer_norm149, model_decoder_layers_28_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape667 = R.call_tir(cls.reshape14, (lv584,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv126 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_28_self_attn_k_proj_weight2, layer_norm149), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape668 = R.call_tir(cls.reshape14, (lv126,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv585 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_28_self_attn_v_proj_weight2, layer_norm149, model_decoder_layers_28_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape669 = R.call_tir(cls.reshape14, (lv585,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat28 = R.call_tir(cls.concatenate1, (reshape667, reshape668, reshape669), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape670 = R.call_tir(cls.reshape15, (concat28,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv125_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), reshape670), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape671 = R.call_tir(cls.reshape16, (lv125_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape672 = R.call_tir(cls.reshape17, (reshape671,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv586 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_28_self_attn_out_proj_weight2, reshape672, model_decoder_layers_28_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add541 = R.call_tir(cls.add5, (add537, lv586), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm150 = R.call_tir(cls.layer_norm2, (add541, model_decoder_layers_28_encoder_attn_layer_norm_weight2, model_decoder_layers_28_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv587 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_28_encoder_attn_q_proj_weight2, layer_norm150, model_decoder_layers_28_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape673 = R.call_tir(cls.reshape14, (lv587,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape674 = R.call_tir(cls.reshape18, (reshape673,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv126_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), reshape674), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape675 = R.call_tir(cls.reshape16, (lv126_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape676 = R.call_tir(cls.reshape17, (reshape675,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv588 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_28_encoder_attn_out_proj_weight2, reshape676, model_decoder_layers_28_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add544 = R.call_tir(cls.add5, (add541, lv588), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm151 = R.call_tir(cls.layer_norm2, (add544, model_decoder_layers_28_final_layer_norm_weight2, model_decoder_layers_28_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv92_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_28_fc1_weight2, layer_norm151, model_decoder_layers_28_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv589 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_28_fc2_weight2, lv92_1, model_decoder_layers_28_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add547 = R.call_tir(cls.add5, (add544, lv589), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm152 = R.call_tir(cls.layer_norm2, (add547, model_decoder_layers_29_self_attn_layer_norm_weight2, model_decoder_layers_29_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv590 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_29_self_attn_q_proj_weight2, layer_norm152, model_decoder_layers_29_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape677 = R.call_tir(cls.reshape14, (lv590,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv127 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_29_self_attn_k_proj_weight2, layer_norm152), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape678 = R.call_tir(cls.reshape14, (lv127,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv591 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_29_self_attn_v_proj_weight2, layer_norm152, model_decoder_layers_29_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape679 = R.call_tir(cls.reshape14, (lv591,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat29 = R.call_tir(cls.concatenate1, (reshape677, reshape678, reshape679), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape680 = R.call_tir(cls.reshape15, (concat29,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv127_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), reshape680), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape681 = R.call_tir(cls.reshape16, (lv127_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape682 = R.call_tir(cls.reshape17, (reshape681,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv592 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_29_self_attn_out_proj_weight2, reshape682, model_decoder_layers_29_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add551 = R.call_tir(cls.add5, (add547, lv592), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm153 = R.call_tir(cls.layer_norm2, (add551, model_decoder_layers_29_encoder_attn_layer_norm_weight2, model_decoder_layers_29_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv593 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_29_encoder_attn_q_proj_weight2, layer_norm153, model_decoder_layers_29_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape683 = R.call_tir(cls.reshape14, (lv593,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape684 = R.call_tir(cls.reshape18, (reshape683,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv128 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), reshape684), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape685 = R.call_tir(cls.reshape16, (lv128,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape686 = R.call_tir(cls.reshape17, (reshape685,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv594 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_29_encoder_attn_out_proj_weight2, reshape686, model_decoder_layers_29_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add554 = R.call_tir(cls.add5, (add551, lv594), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm154 = R.call_tir(cls.layer_norm2, (add554, model_decoder_layers_29_final_layer_norm_weight2, model_decoder_layers_29_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv93_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_29_fc1_weight2, layer_norm154, model_decoder_layers_29_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv595 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_29_fc2_weight2, lv93_1, model_decoder_layers_29_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add557 = R.call_tir(cls.add5, (add554, lv595), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm155 = R.call_tir(cls.layer_norm2, (add557, model_decoder_layers_30_self_attn_layer_norm_weight2, model_decoder_layers_30_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv596 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_30_self_attn_q_proj_weight2, layer_norm155, model_decoder_layers_30_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape687 = R.call_tir(cls.reshape14, (lv596,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv128_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_30_self_attn_k_proj_weight2, layer_norm155), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape688 = R.call_tir(cls.reshape14, (lv128_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv597 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_30_self_attn_v_proj_weight2, layer_norm155, model_decoder_layers_30_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape689 = R.call_tir(cls.reshape14, (lv597,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat30 = R.call_tir(cls.concatenate1, (reshape687, reshape688, reshape689), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape690 = R.call_tir(cls.reshape15, (concat30,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv129 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), reshape690), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape691 = R.call_tir(cls.reshape16, (lv129,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape692 = R.call_tir(cls.reshape17, (reshape691,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv598 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_30_self_attn_out_proj_weight2, reshape692, model_decoder_layers_30_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add561 = R.call_tir(cls.add5, (add557, lv598), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm156 = R.call_tir(cls.layer_norm2, (add561, model_decoder_layers_30_encoder_attn_layer_norm_weight2, model_decoder_layers_30_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv599 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_30_encoder_attn_q_proj_weight2, layer_norm156, model_decoder_layers_30_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape693 = R.call_tir(cls.reshape14, (lv599,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape694 = R.call_tir(cls.reshape18, (reshape693,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv130 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), reshape694), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape695 = R.call_tir(cls.reshape16, (lv130,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape696 = R.call_tir(cls.reshape17, (reshape695,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv600 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_30_encoder_attn_out_proj_weight2, reshape696, model_decoder_layers_30_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add564 = R.call_tir(cls.add5, (add561, lv600), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm157 = R.call_tir(cls.layer_norm2, (add564, model_decoder_layers_30_final_layer_norm_weight2, model_decoder_layers_30_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv94_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_30_fc1_weight2, layer_norm157, model_decoder_layers_30_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv601 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_30_fc2_weight2, lv94_1, model_decoder_layers_30_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add567 = R.call_tir(cls.add5, (add564, lv601), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm158 = R.call_tir(cls.layer_norm2, (add567, model_decoder_layers_31_self_attn_layer_norm_weight2, model_decoder_layers_31_self_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv602 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_31_self_attn_q_proj_weight2, layer_norm158, model_decoder_layers_31_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape697 = R.call_tir(cls.reshape14, (lv602,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv129_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_31_self_attn_k_proj_weight2, layer_norm158), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape698 = R.call_tir(cls.reshape14, (lv129_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv603 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_31_self_attn_v_proj_weight2, layer_norm158, model_decoder_layers_31_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape699 = R.call_tir(cls.reshape14, (lv603,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat31 = R.call_tir(cls.concatenate1, (reshape697, reshape698, reshape699), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape700 = R.call_tir(cls.reshape15, (concat31,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv131 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), reshape700), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape701 = R.call_tir(cls.reshape16, (lv131,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape702 = R.call_tir(cls.reshape17, (reshape701,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv604 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_31_self_attn_out_proj_weight2, reshape702, model_decoder_layers_31_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add571 = R.call_tir(cls.add5, (add567, lv604), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm159 = R.call_tir(cls.layer_norm2, (add571, model_decoder_layers_31_encoder_attn_layer_norm_weight2, model_decoder_layers_31_encoder_attn_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv605 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_31_encoder_attn_q_proj_weight2, layer_norm159, model_decoder_layers_31_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape703 = R.call_tir(cls.reshape14, (lv605,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape704 = R.call_tir(cls.reshape18, (reshape703,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv132 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), reshape704), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape705 = R.call_tir(cls.reshape16, (lv132,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape706 = R.call_tir(cls.reshape17, (reshape705,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv606 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_31_encoder_attn_out_proj_weight2, reshape706, model_decoder_layers_31_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add574 = R.call_tir(cls.add5, (add571, lv606), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm160 = R.call_tir(cls.layer_norm2, (add574, model_decoder_layers_31_final_layer_norm_weight2, model_decoder_layers_31_final_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv95_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_31_fc1_weight2, layer_norm160, model_decoder_layers_31_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv607 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_31_fc2_weight2, lv95_1, model_decoder_layers_31_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add577 = R.call_tir(cls.add5, (add574, lv607), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm161 = R.call_tir(cls.layer_norm2, (add577, model_decoder_layer_norm_weight2, model_decoder_layer_norm_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + take2 = R.call_tir(cls.take2, (layer_norm161, logit_positions), out_sinfo=R.Tensor((1, batch_size, 1280), dtype="float16")) + gv2 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul5_cublas", (model_decoder_embed_tokens_weight2, take2), out_sinfo=R.Tensor((1, batch_size, 51866), dtype="float32")) + R.output(gv2) + return gv2 + + @R.function + def create_tir_paged_kv_cache(max_batch_size_: R.Shape(["max_batch_size"]), max_total_seq_len_: R.Shape(["max_total_seq_len"]), prefill_chunk_size_: R.Shape(["prefill_chunk_size"]), page_size_: R.Shape(["page_size"]), support_sliding_window_: R.Shape(["support_sliding_window"])) -> R.Object: + max_batch_size = T.int64() + max_total_seq_len = T.int64() + prefill_chunk_size = T.int64() + page_size = T.int64() + support_sliding_window = T.int64() + R.func_attr({"relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "seq_len": 15000, "total_seq_len": 1500}}) + cls = Module + paged_kv_cache: R.Object = R.call_pure_packed("vm.builtin.paged_attention_kv_cache_create_reduced", R.shape([max_batch_size, max_total_seq_len, prefill_chunk_size, page_size, support_sliding_window]), R.prim_value(32), R.prim_value(20), R.prim_value(20), R.prim_value(64), R.prim_value(0), R.prim_value(1), R.prim_value(1), R.const(0, "float16"), cls.tir_kv_cache_transpose_append, cls.batch_prefill_paged_kv, cls.batch_decode_paged_kv, cls.batch_prefill_paged_kv_sliding_window, cls.batch_decode_paged_kv_sliding_window, cls.batch_prefill_ragged_kv, cls.merge_state_inplace, cls.fused_rope, cls.copy_single_page, cls.tir_kv_cache_debug_get_kv, cls.compact_kv_copy, cls.batch_tree_attn, sinfo_args=(R.Object,)) + return paged_kv_cache + + @R.function + def decode(input_ids: R.Tensor((1, 1), dtype="int32"), paged_kv_cache: R.Object, packed_params: R.Tuple(R.Tensor((1280, 128, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1500, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), 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R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"))) -> R.Tensor((1, 1, 51866), dtype="float32"): + R.func_attr({"num_input": 2, "relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "seq_len": 15000, "total_seq_len": 1500}}) + cls = Module + with R.dataflow(): + model_decoder_embed_tokens_weight5: R.Tensor((51866, 1280), dtype="float16") = packed_params[487] + model_decoder_embed_positions_weight5: R.Tensor((448, 1280), dtype="float16") = packed_params[488] + model_decoder_layers_0_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[489] + model_decoder_layers_0_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[490] + model_decoder_layers_0_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[491] + model_decoder_layers_0_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[492] + model_decoder_layers_0_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[493] + model_decoder_layers_0_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[494] + model_decoder_layers_0_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[495] + model_decoder_layers_0_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[496] + model_decoder_layers_0_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[497] + model_decoder_layers_0_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[501] + model_decoder_layers_0_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[502] + model_decoder_layers_0_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[503] + model_decoder_layers_0_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[504] + model_decoder_layers_0_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[505] + model_decoder_layers_0_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[506] + model_decoder_layers_0_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[507] + model_decoder_layers_0_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[508] + model_decoder_layers_0_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[509] + model_decoder_layers_0_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[510] + model_decoder_layers_0_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[511] + model_decoder_layers_0_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[512] + model_decoder_layers_1_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[513] + model_decoder_layers_1_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[514] + model_decoder_layers_1_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[515] + model_decoder_layers_1_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[516] + model_decoder_layers_1_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[517] + model_decoder_layers_1_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[518] + model_decoder_layers_1_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[519] + model_decoder_layers_1_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[520] + model_decoder_layers_1_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[521] + model_decoder_layers_1_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[525] + model_decoder_layers_1_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[526] + model_decoder_layers_1_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[527] + model_decoder_layers_1_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[528] + model_decoder_layers_1_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[529] + model_decoder_layers_1_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[530] + model_decoder_layers_1_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[531] + model_decoder_layers_1_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[532] + model_decoder_layers_1_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[533] + model_decoder_layers_1_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[534] + model_decoder_layers_1_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[535] + model_decoder_layers_1_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[536] + model_decoder_layers_2_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[537] + model_decoder_layers_2_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[538] + model_decoder_layers_2_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[539] + model_decoder_layers_2_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[540] + model_decoder_layers_2_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[541] + model_decoder_layers_2_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[542] + model_decoder_layers_2_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[543] + model_decoder_layers_2_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[544] + model_decoder_layers_2_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[545] + model_decoder_layers_2_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[549] + model_decoder_layers_2_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[550] + model_decoder_layers_2_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[551] + model_decoder_layers_2_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[552] + model_decoder_layers_2_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[553] + model_decoder_layers_2_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[554] + model_decoder_layers_2_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[555] + model_decoder_layers_2_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[556] + model_decoder_layers_2_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[557] + model_decoder_layers_2_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[558] + model_decoder_layers_2_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[559] + model_decoder_layers_2_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[560] + model_decoder_layers_3_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[561] + model_decoder_layers_3_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[562] + model_decoder_layers_3_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[563] + model_decoder_layers_3_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[564] + model_decoder_layers_3_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[565] + model_decoder_layers_3_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[566] + model_decoder_layers_3_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[567] + model_decoder_layers_3_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[568] + model_decoder_layers_3_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[569] + model_decoder_layers_3_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[573] + model_decoder_layers_3_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[574] + model_decoder_layers_3_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[575] + model_decoder_layers_3_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[576] + model_decoder_layers_3_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[577] + model_decoder_layers_3_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[578] + model_decoder_layers_3_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[579] + model_decoder_layers_3_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[580] + model_decoder_layers_3_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[581] + model_decoder_layers_3_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[582] + model_decoder_layers_3_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[583] + model_decoder_layers_3_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[584] + model_decoder_layers_4_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[585] + model_decoder_layers_4_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[586] + model_decoder_layers_4_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[587] + model_decoder_layers_4_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[588] + model_decoder_layers_4_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[589] + model_decoder_layers_4_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[590] + model_decoder_layers_4_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[591] + model_decoder_layers_4_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[592] + model_decoder_layers_4_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[593] + model_decoder_layers_4_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[597] + model_decoder_layers_4_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[598] + model_decoder_layers_4_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[599] + model_decoder_layers_4_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[600] + model_decoder_layers_4_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[601] + model_decoder_layers_4_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[602] + model_decoder_layers_4_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[603] + model_decoder_layers_4_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[604] + model_decoder_layers_4_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[605] + model_decoder_layers_4_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[606] + model_decoder_layers_4_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[607] + model_decoder_layers_4_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[608] + model_decoder_layers_5_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[609] + model_decoder_layers_5_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[610] + model_decoder_layers_5_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[611] + model_decoder_layers_5_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[612] + model_decoder_layers_5_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[613] + model_decoder_layers_5_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[614] + model_decoder_layers_5_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[615] + model_decoder_layers_5_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[616] + model_decoder_layers_5_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[617] + model_decoder_layers_5_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[621] + model_decoder_layers_5_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[622] + model_decoder_layers_5_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[623] + model_decoder_layers_5_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[624] + model_decoder_layers_5_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[625] + model_decoder_layers_5_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[626] + model_decoder_layers_5_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[627] + model_decoder_layers_5_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[628] + model_decoder_layers_5_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[629] + model_decoder_layers_5_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[630] + model_decoder_layers_5_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[631] + model_decoder_layers_5_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[632] + model_decoder_layers_6_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[633] + model_decoder_layers_6_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[634] + model_decoder_layers_6_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[635] + model_decoder_layers_6_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[636] + model_decoder_layers_6_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[637] + model_decoder_layers_6_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[638] + model_decoder_layers_6_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[639] + model_decoder_layers_6_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[640] + model_decoder_layers_6_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[641] + model_decoder_layers_6_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[645] + model_decoder_layers_6_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[646] + model_decoder_layers_6_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[647] + model_decoder_layers_6_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[648] + model_decoder_layers_6_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[649] + model_decoder_layers_6_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[650] + model_decoder_layers_6_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[651] + model_decoder_layers_6_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[652] + model_decoder_layers_6_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[653] + model_decoder_layers_6_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[654] + model_decoder_layers_6_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[655] + model_decoder_layers_6_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[656] + model_decoder_layers_7_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[657] + model_decoder_layers_7_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[658] + model_decoder_layers_7_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[659] + model_decoder_layers_7_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[660] + model_decoder_layers_7_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[661] + model_decoder_layers_7_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[662] + model_decoder_layers_7_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[663] + model_decoder_layers_7_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[664] + model_decoder_layers_7_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[665] + model_decoder_layers_7_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[669] + model_decoder_layers_7_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[670] + model_decoder_layers_7_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[671] + model_decoder_layers_7_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[672] + model_decoder_layers_7_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[673] + model_decoder_layers_7_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[674] + model_decoder_layers_7_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[675] + model_decoder_layers_7_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[676] + model_decoder_layers_7_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[677] + model_decoder_layers_7_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[678] + model_decoder_layers_7_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[679] + model_decoder_layers_7_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[680] + model_decoder_layers_8_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[681] + model_decoder_layers_8_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[682] + model_decoder_layers_8_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[683] + model_decoder_layers_8_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[684] + model_decoder_layers_8_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[685] + model_decoder_layers_8_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[686] + model_decoder_layers_8_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[687] + model_decoder_layers_8_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[688] + model_decoder_layers_8_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[689] + model_decoder_layers_8_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[693] + model_decoder_layers_8_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[694] + model_decoder_layers_8_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[695] + model_decoder_layers_8_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[696] + model_decoder_layers_8_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[697] + model_decoder_layers_8_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[698] + model_decoder_layers_8_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[699] + model_decoder_layers_8_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[700] + model_decoder_layers_8_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[701] + model_decoder_layers_8_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[702] + model_decoder_layers_8_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[703] + model_decoder_layers_8_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[704] + model_decoder_layers_9_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[705] + model_decoder_layers_9_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[706] + model_decoder_layers_9_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[707] + model_decoder_layers_9_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[708] + model_decoder_layers_9_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[709] + model_decoder_layers_9_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[710] + model_decoder_layers_9_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[711] + model_decoder_layers_9_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[712] + model_decoder_layers_9_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[713] + model_decoder_layers_9_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[717] + model_decoder_layers_9_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[718] + model_decoder_layers_9_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[719] + model_decoder_layers_9_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[720] + model_decoder_layers_9_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[721] + model_decoder_layers_9_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[722] + model_decoder_layers_9_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[723] + model_decoder_layers_9_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[724] + model_decoder_layers_9_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[725] + model_decoder_layers_9_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[726] + model_decoder_layers_9_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[727] + model_decoder_layers_9_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[728] + model_decoder_layers_10_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[729] + model_decoder_layers_10_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[730] + model_decoder_layers_10_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[731] + model_decoder_layers_10_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[732] + model_decoder_layers_10_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[733] + model_decoder_layers_10_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[734] + model_decoder_layers_10_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[735] + model_decoder_layers_10_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[736] + model_decoder_layers_10_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[737] + model_decoder_layers_10_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[741] + model_decoder_layers_10_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[742] + model_decoder_layers_10_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[743] + model_decoder_layers_10_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[744] + model_decoder_layers_10_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[745] + model_decoder_layers_10_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[746] + model_decoder_layers_10_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[747] + model_decoder_layers_10_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[748] + model_decoder_layers_10_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[749] + model_decoder_layers_10_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[750] + model_decoder_layers_10_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[751] + model_decoder_layers_10_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[752] + model_decoder_layers_11_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[753] + model_decoder_layers_11_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[754] + model_decoder_layers_11_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[755] + model_decoder_layers_11_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[756] + model_decoder_layers_11_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[757] + model_decoder_layers_11_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[758] + model_decoder_layers_11_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[759] + model_decoder_layers_11_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[760] + model_decoder_layers_11_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[761] + model_decoder_layers_11_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[765] + model_decoder_layers_11_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[766] + model_decoder_layers_11_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[767] + model_decoder_layers_11_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[768] + model_decoder_layers_11_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[769] + model_decoder_layers_11_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[770] + model_decoder_layers_11_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[771] + model_decoder_layers_11_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[772] + model_decoder_layers_11_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[773] + model_decoder_layers_11_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[774] + model_decoder_layers_11_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[775] + model_decoder_layers_11_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[776] + model_decoder_layers_12_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[777] + model_decoder_layers_12_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[778] + model_decoder_layers_12_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[779] + model_decoder_layers_12_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[780] + model_decoder_layers_12_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[781] + model_decoder_layers_12_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[782] + model_decoder_layers_12_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[783] + model_decoder_layers_12_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[784] + model_decoder_layers_12_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[785] + model_decoder_layers_12_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[789] + model_decoder_layers_12_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[790] + model_decoder_layers_12_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[791] + model_decoder_layers_12_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[792] + model_decoder_layers_12_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[793] + model_decoder_layers_12_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[794] + model_decoder_layers_12_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[795] + model_decoder_layers_12_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[796] + model_decoder_layers_12_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[797] + model_decoder_layers_12_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[798] + model_decoder_layers_12_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[799] + model_decoder_layers_12_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[800] + model_decoder_layers_13_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[801] + model_decoder_layers_13_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[802] + model_decoder_layers_13_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[803] + model_decoder_layers_13_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[804] + model_decoder_layers_13_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[805] + model_decoder_layers_13_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[806] + model_decoder_layers_13_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[807] + model_decoder_layers_13_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[808] + model_decoder_layers_13_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[809] + model_decoder_layers_13_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[813] + model_decoder_layers_13_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[814] + model_decoder_layers_13_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[815] + model_decoder_layers_13_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[816] + model_decoder_layers_13_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[817] + model_decoder_layers_13_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[818] + model_decoder_layers_13_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[819] + model_decoder_layers_13_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[820] + model_decoder_layers_13_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[821] + model_decoder_layers_13_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[822] + model_decoder_layers_13_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[823] + model_decoder_layers_13_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[824] + model_decoder_layers_14_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[825] + model_decoder_layers_14_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[826] + model_decoder_layers_14_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[827] + model_decoder_layers_14_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[828] + model_decoder_layers_14_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[829] + model_decoder_layers_14_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[830] + model_decoder_layers_14_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[831] + model_decoder_layers_14_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[832] + model_decoder_layers_14_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[833] + model_decoder_layers_14_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[837] + model_decoder_layers_14_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[838] + model_decoder_layers_14_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[839] + model_decoder_layers_14_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[840] + model_decoder_layers_14_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[841] + model_decoder_layers_14_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[842] + model_decoder_layers_14_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[843] + model_decoder_layers_14_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[844] + model_decoder_layers_14_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[845] + model_decoder_layers_14_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[846] + model_decoder_layers_14_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[847] + model_decoder_layers_14_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[848] + model_decoder_layers_15_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[849] + model_decoder_layers_15_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[850] + model_decoder_layers_15_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[851] + model_decoder_layers_15_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[852] + model_decoder_layers_15_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[853] + model_decoder_layers_15_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[854] + model_decoder_layers_15_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[855] + model_decoder_layers_15_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[856] + model_decoder_layers_15_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[857] + model_decoder_layers_15_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[861] + model_decoder_layers_15_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[862] + model_decoder_layers_15_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[863] + model_decoder_layers_15_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[864] + model_decoder_layers_15_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[865] + model_decoder_layers_15_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[866] + model_decoder_layers_15_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[867] + model_decoder_layers_15_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[868] + model_decoder_layers_15_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[869] + model_decoder_layers_15_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[870] + model_decoder_layers_15_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[871] + model_decoder_layers_15_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[872] + model_decoder_layers_16_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[873] + model_decoder_layers_16_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[874] + model_decoder_layers_16_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[875] + model_decoder_layers_16_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[876] + model_decoder_layers_16_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[877] + model_decoder_layers_16_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[878] + model_decoder_layers_16_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[879] + model_decoder_layers_16_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[880] + model_decoder_layers_16_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[881] + model_decoder_layers_16_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[885] + model_decoder_layers_16_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[886] + model_decoder_layers_16_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[887] + model_decoder_layers_16_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[888] + model_decoder_layers_16_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[889] + model_decoder_layers_16_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[890] + model_decoder_layers_16_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[891] + model_decoder_layers_16_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[892] + model_decoder_layers_16_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[893] + model_decoder_layers_16_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[894] + model_decoder_layers_16_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[895] + model_decoder_layers_16_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[896] + model_decoder_layers_17_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[897] + model_decoder_layers_17_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[898] + model_decoder_layers_17_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[899] + model_decoder_layers_17_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[900] + model_decoder_layers_17_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[901] + model_decoder_layers_17_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[902] + model_decoder_layers_17_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[903] + model_decoder_layers_17_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[904] + model_decoder_layers_17_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[905] + model_decoder_layers_17_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[909] + model_decoder_layers_17_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[910] + model_decoder_layers_17_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[911] + model_decoder_layers_17_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[912] + model_decoder_layers_17_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[913] + model_decoder_layers_17_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[914] + model_decoder_layers_17_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[915] + model_decoder_layers_17_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[916] + model_decoder_layers_17_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[917] + model_decoder_layers_17_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[918] + model_decoder_layers_17_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[919] + model_decoder_layers_17_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[920] + model_decoder_layers_18_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[921] + model_decoder_layers_18_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[922] + model_decoder_layers_18_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[923] + model_decoder_layers_18_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[924] + model_decoder_layers_18_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[925] + model_decoder_layers_18_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[926] + model_decoder_layers_18_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[927] + model_decoder_layers_18_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[928] + model_decoder_layers_18_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[929] + model_decoder_layers_18_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[933] + model_decoder_layers_18_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[934] + model_decoder_layers_18_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[935] + model_decoder_layers_18_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[936] + model_decoder_layers_18_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[937] + model_decoder_layers_18_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[938] + model_decoder_layers_18_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[939] + model_decoder_layers_18_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[940] + model_decoder_layers_18_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[941] + model_decoder_layers_18_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[942] + model_decoder_layers_18_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[943] + model_decoder_layers_18_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[944] + model_decoder_layers_19_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[945] + model_decoder_layers_19_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[946] + model_decoder_layers_19_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[947] + model_decoder_layers_19_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[948] + model_decoder_layers_19_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[949] + model_decoder_layers_19_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[950] + model_decoder_layers_19_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[951] + model_decoder_layers_19_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[952] + model_decoder_layers_19_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[953] + model_decoder_layers_19_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[957] + model_decoder_layers_19_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[958] + model_decoder_layers_19_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[959] + model_decoder_layers_19_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[960] + model_decoder_layers_19_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[961] + model_decoder_layers_19_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[962] + model_decoder_layers_19_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[963] + model_decoder_layers_19_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[964] + model_decoder_layers_19_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[965] + model_decoder_layers_19_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[966] + model_decoder_layers_19_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[967] + model_decoder_layers_19_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[968] + model_decoder_layers_20_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[969] + model_decoder_layers_20_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[970] + model_decoder_layers_20_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[971] + model_decoder_layers_20_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[972] + model_decoder_layers_20_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[973] + model_decoder_layers_20_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[974] + model_decoder_layers_20_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[975] + model_decoder_layers_20_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[976] + model_decoder_layers_20_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[977] + model_decoder_layers_20_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[981] + model_decoder_layers_20_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[982] + model_decoder_layers_20_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[983] + model_decoder_layers_20_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[984] + model_decoder_layers_20_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[985] + model_decoder_layers_20_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[986] + model_decoder_layers_20_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[987] + model_decoder_layers_20_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[988] + model_decoder_layers_20_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[989] + model_decoder_layers_20_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[990] + model_decoder_layers_20_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[991] + model_decoder_layers_20_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[992] + model_decoder_layers_21_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[993] + model_decoder_layers_21_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[994] + model_decoder_layers_21_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[995] + model_decoder_layers_21_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[996] + model_decoder_layers_21_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[997] + model_decoder_layers_21_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[998] + model_decoder_layers_21_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[999] + model_decoder_layers_21_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1000] + model_decoder_layers_21_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1001] + model_decoder_layers_21_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1005] + model_decoder_layers_21_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1006] + model_decoder_layers_21_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1007] + model_decoder_layers_21_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1008] + model_decoder_layers_21_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1009] + model_decoder_layers_21_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1010] + model_decoder_layers_21_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1011] + model_decoder_layers_21_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1012] + model_decoder_layers_21_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1013] + model_decoder_layers_21_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1014] + model_decoder_layers_21_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1015] + model_decoder_layers_21_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1016] + model_decoder_layers_22_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1017] + model_decoder_layers_22_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1018] + model_decoder_layers_22_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1019] + model_decoder_layers_22_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1020] + model_decoder_layers_22_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1021] + model_decoder_layers_22_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1022] + model_decoder_layers_22_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1023] + model_decoder_layers_22_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1024] + model_decoder_layers_22_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1025] + model_decoder_layers_22_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1029] + model_decoder_layers_22_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1030] + model_decoder_layers_22_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1031] + model_decoder_layers_22_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1032] + model_decoder_layers_22_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1033] + model_decoder_layers_22_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1034] + model_decoder_layers_22_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1035] + model_decoder_layers_22_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1036] + model_decoder_layers_22_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1037] + model_decoder_layers_22_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1038] + model_decoder_layers_22_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1039] + model_decoder_layers_22_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1040] + model_decoder_layers_23_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1041] + model_decoder_layers_23_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1042] + model_decoder_layers_23_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1043] + model_decoder_layers_23_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1044] + model_decoder_layers_23_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1045] + model_decoder_layers_23_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1046] + model_decoder_layers_23_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1047] + model_decoder_layers_23_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1048] + model_decoder_layers_23_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1049] + model_decoder_layers_23_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1053] + model_decoder_layers_23_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1054] + model_decoder_layers_23_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1055] + model_decoder_layers_23_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1056] + model_decoder_layers_23_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1057] + model_decoder_layers_23_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1058] + model_decoder_layers_23_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1059] + model_decoder_layers_23_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1060] + model_decoder_layers_23_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1061] + model_decoder_layers_23_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1062] + model_decoder_layers_23_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1063] + model_decoder_layers_23_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1064] + model_decoder_layers_24_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1065] + model_decoder_layers_24_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1066] + model_decoder_layers_24_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1067] + model_decoder_layers_24_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1068] + model_decoder_layers_24_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1069] + model_decoder_layers_24_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1070] + model_decoder_layers_24_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1071] + model_decoder_layers_24_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1072] + model_decoder_layers_24_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1073] + model_decoder_layers_24_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1077] + model_decoder_layers_24_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1078] + model_decoder_layers_24_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1079] + model_decoder_layers_24_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1080] + model_decoder_layers_24_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1081] + model_decoder_layers_24_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1082] + model_decoder_layers_24_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1083] + model_decoder_layers_24_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1084] + model_decoder_layers_24_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1085] + model_decoder_layers_24_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1086] + model_decoder_layers_24_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1087] + model_decoder_layers_24_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1088] + model_decoder_layers_25_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1089] + model_decoder_layers_25_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1090] + model_decoder_layers_25_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1091] + model_decoder_layers_25_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1092] + model_decoder_layers_25_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1093] + model_decoder_layers_25_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1094] + model_decoder_layers_25_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1095] + model_decoder_layers_25_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1096] + model_decoder_layers_25_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1097] + model_decoder_layers_25_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1101] + model_decoder_layers_25_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1102] + model_decoder_layers_25_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1103] + model_decoder_layers_25_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1104] + model_decoder_layers_25_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1105] + model_decoder_layers_25_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1106] + model_decoder_layers_25_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1107] + model_decoder_layers_25_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1108] + model_decoder_layers_25_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1109] + model_decoder_layers_25_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1110] + model_decoder_layers_25_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1111] + model_decoder_layers_25_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1112] + model_decoder_layers_26_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1113] + model_decoder_layers_26_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1114] + model_decoder_layers_26_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1115] + model_decoder_layers_26_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1116] + model_decoder_layers_26_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1117] + model_decoder_layers_26_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1118] + model_decoder_layers_26_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1119] + model_decoder_layers_26_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1120] + model_decoder_layers_26_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1121] + model_decoder_layers_26_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1125] + model_decoder_layers_26_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1126] + model_decoder_layers_26_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1127] + model_decoder_layers_26_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1128] + model_decoder_layers_26_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1129] + model_decoder_layers_26_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1130] + model_decoder_layers_26_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1131] + model_decoder_layers_26_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1132] + model_decoder_layers_26_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1133] + model_decoder_layers_26_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1134] + model_decoder_layers_26_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1135] + model_decoder_layers_26_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1136] + model_decoder_layers_27_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1137] + model_decoder_layers_27_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1138] + model_decoder_layers_27_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1139] + model_decoder_layers_27_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1140] + model_decoder_layers_27_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1141] + model_decoder_layers_27_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1142] + model_decoder_layers_27_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1143] + model_decoder_layers_27_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1144] + model_decoder_layers_27_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1145] + model_decoder_layers_27_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1149] + model_decoder_layers_27_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1150] + model_decoder_layers_27_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1151] + model_decoder_layers_27_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1152] + model_decoder_layers_27_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1153] + model_decoder_layers_27_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1154] + model_decoder_layers_27_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1155] + model_decoder_layers_27_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1156] + model_decoder_layers_27_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1157] + model_decoder_layers_27_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1158] + model_decoder_layers_27_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1159] + model_decoder_layers_27_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1160] + model_decoder_layers_28_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1161] + model_decoder_layers_28_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1162] + model_decoder_layers_28_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1163] + model_decoder_layers_28_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1164] + model_decoder_layers_28_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1165] + model_decoder_layers_28_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1166] + model_decoder_layers_28_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1167] + model_decoder_layers_28_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1168] + model_decoder_layers_28_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1169] + model_decoder_layers_28_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1173] + model_decoder_layers_28_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1174] + model_decoder_layers_28_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1175] + model_decoder_layers_28_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1176] + model_decoder_layers_28_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1177] + model_decoder_layers_28_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1178] + model_decoder_layers_28_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1179] + model_decoder_layers_28_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1180] + model_decoder_layers_28_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1181] + model_decoder_layers_28_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1182] + model_decoder_layers_28_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1183] + model_decoder_layers_28_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1184] + model_decoder_layers_29_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1185] + model_decoder_layers_29_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1186] + model_decoder_layers_29_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1187] + model_decoder_layers_29_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1188] + model_decoder_layers_29_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1189] + model_decoder_layers_29_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1190] + model_decoder_layers_29_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1191] + model_decoder_layers_29_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1192] + model_decoder_layers_29_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1193] + model_decoder_layers_29_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1197] + model_decoder_layers_29_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1198] + model_decoder_layers_29_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1199] + model_decoder_layers_29_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1200] + model_decoder_layers_29_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1201] + model_decoder_layers_29_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1202] + model_decoder_layers_29_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1203] + model_decoder_layers_29_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1204] + model_decoder_layers_29_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1205] + model_decoder_layers_29_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1206] + model_decoder_layers_29_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1207] + model_decoder_layers_29_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1208] + model_decoder_layers_30_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1209] + model_decoder_layers_30_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1210] + model_decoder_layers_30_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1211] + model_decoder_layers_30_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1212] + model_decoder_layers_30_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1213] + model_decoder_layers_30_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1214] + model_decoder_layers_30_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1215] + model_decoder_layers_30_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1216] + model_decoder_layers_30_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1217] + model_decoder_layers_30_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1221] + model_decoder_layers_30_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1222] + model_decoder_layers_30_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1223] + model_decoder_layers_30_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1224] + model_decoder_layers_30_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1225] + model_decoder_layers_30_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1226] + model_decoder_layers_30_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1227] + model_decoder_layers_30_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1228] + model_decoder_layers_30_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1229] + model_decoder_layers_30_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1230] + model_decoder_layers_30_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1231] + model_decoder_layers_30_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1232] + model_decoder_layers_31_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1233] + model_decoder_layers_31_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1234] + model_decoder_layers_31_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1235] + model_decoder_layers_31_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1236] + model_decoder_layers_31_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1237] + model_decoder_layers_31_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1238] + model_decoder_layers_31_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1239] + model_decoder_layers_31_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1240] + model_decoder_layers_31_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1241] + model_decoder_layers_31_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1245] + model_decoder_layers_31_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1246] + model_decoder_layers_31_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1247] + model_decoder_layers_31_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1248] + model_decoder_layers_31_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1249] + model_decoder_layers_31_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1250] + model_decoder_layers_31_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1251] + model_decoder_layers_31_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1252] + model_decoder_layers_31_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1253] + model_decoder_layers_31_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1254] + model_decoder_layers_31_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1255] + model_decoder_layers_31_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1256] + model_decoder_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1257] + model_decoder_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1258] + reshape1353 = R.call_tir(cls.reshape19, (input_ids,), out_sinfo=R.Tensor((1,), dtype="int32")) + take7 = R.call_tir(cls.take3, (model_decoder_embed_tokens_weight5, reshape1353), out_sinfo=R.Tensor((1, 1280), dtype="float16")) + lv264: R.Tensor((1,), dtype="int32") = R.call_pure_packed("vm.builtin.attention_kv_cache_get_query_positions", paged_kv_cache, sinfo_args=(R.Tensor((1,), dtype="int32"),)) + take8 = R.call_tir(cls.take4, (model_decoder_embed_positions_weight5, lv264), out_sinfo=R.Tensor((1, 1280), dtype="float16")) + lv40 = R.call_tir(cls.fused_reshape20_reshape20_add6, (take7, take8), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm356 = R.call_tir(cls.layer_norm3, (lv40, model_decoder_layers_0_self_attn_layer_norm_weight5, model_decoder_layers_0_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv41 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm356, model_decoder_layers_0_self_attn_q_proj_weight5, model_decoder_layers_0_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv1 = R.call_tir(cls.NT_matmul, (layer_norm356, model_decoder_layers_0_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv42 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm356, model_decoder_layers_0_self_attn_v_proj_weight5, model_decoder_layers_0_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv43 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv41, lv1, lv42), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv265 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), lv43), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv44 = R.call_tir(cls.fused_reshape23_reshape24, (lv265,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv45 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv44, model_decoder_layers_0_self_attn_out_proj_weight5, model_decoder_layers_0_self_attn_out_proj_bias5, lv40), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm357 = R.call_tir(cls.layer_norm3, (lv45, model_decoder_layers_0_encoder_attn_layer_norm_weight5, model_decoder_layers_0_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv46 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm357, model_decoder_layers_0_encoder_attn_q_proj_weight5, model_decoder_layers_0_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv47 = R.call_tir(cls.fused_reshape21_reshape25, (lv46,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv266 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), lv47), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv48 = R.call_tir(cls.fused_reshape23_reshape24, (lv266,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv49 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv48, model_decoder_layers_0_encoder_attn_out_proj_weight5, model_decoder_layers_0_encoder_attn_out_proj_bias5, lv45), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm358 = R.call_tir(cls.layer_norm3, (lv49, model_decoder_layers_0_final_layer_norm_weight5, model_decoder_layers_0_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv50 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm358, model_decoder_layers_0_fc1_weight5, model_decoder_layers_0_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv51 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv50, model_decoder_layers_0_fc2_weight5, model_decoder_layers_0_fc2_bias5, lv49), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm359 = R.call_tir(cls.layer_norm3, (lv51, model_decoder_layers_1_self_attn_layer_norm_weight5, model_decoder_layers_1_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv52 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm359, model_decoder_layers_1_self_attn_q_proj_weight5, model_decoder_layers_1_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv9 = R.call_tir(cls.NT_matmul, (layer_norm359, model_decoder_layers_1_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv53 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm359, model_decoder_layers_1_self_attn_v_proj_weight5, model_decoder_layers_1_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv54 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv52, lv9, lv53), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv267 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), lv54), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv55 = R.call_tir(cls.fused_reshape23_reshape24, (lv267,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv56 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv55, model_decoder_layers_1_self_attn_out_proj_weight5, model_decoder_layers_1_self_attn_out_proj_bias5, lv51), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm360 = R.call_tir(cls.layer_norm3, (lv56, model_decoder_layers_1_encoder_attn_layer_norm_weight5, model_decoder_layers_1_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv57 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm360, model_decoder_layers_1_encoder_attn_q_proj_weight5, model_decoder_layers_1_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv58 = R.call_tir(cls.fused_reshape21_reshape25, (lv57,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv268 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), lv58), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv59 = R.call_tir(cls.fused_reshape23_reshape24, (lv268,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv60 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv59, model_decoder_layers_1_encoder_attn_out_proj_weight5, model_decoder_layers_1_encoder_attn_out_proj_bias5, lv56), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm361 = R.call_tir(cls.layer_norm3, (lv60, model_decoder_layers_1_final_layer_norm_weight5, model_decoder_layers_1_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv61 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm361, model_decoder_layers_1_fc1_weight5, model_decoder_layers_1_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv62 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv61, model_decoder_layers_1_fc2_weight5, model_decoder_layers_1_fc2_bias5, lv60), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm362 = R.call_tir(cls.layer_norm3, (lv62, model_decoder_layers_2_self_attn_layer_norm_weight5, model_decoder_layers_2_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv63 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm362, model_decoder_layers_2_self_attn_q_proj_weight5, model_decoder_layers_2_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv17 = R.call_tir(cls.NT_matmul, (layer_norm362, model_decoder_layers_2_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv64 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm362, model_decoder_layers_2_self_attn_v_proj_weight5, model_decoder_layers_2_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv65 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv63, lv17, lv64), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv269 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), lv65), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv66 = R.call_tir(cls.fused_reshape23_reshape24, (lv269,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv67 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv66, model_decoder_layers_2_self_attn_out_proj_weight5, model_decoder_layers_2_self_attn_out_proj_bias5, lv62), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm363 = R.call_tir(cls.layer_norm3, (lv67, model_decoder_layers_2_encoder_attn_layer_norm_weight5, model_decoder_layers_2_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv68 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm363, model_decoder_layers_2_encoder_attn_q_proj_weight5, model_decoder_layers_2_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv69 = R.call_tir(cls.fused_reshape21_reshape25, (lv68,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv270 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), lv69), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv70 = R.call_tir(cls.fused_reshape23_reshape24, (lv270,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv71 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv70, model_decoder_layers_2_encoder_attn_out_proj_weight5, model_decoder_layers_2_encoder_attn_out_proj_bias5, lv67), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm364 = R.call_tir(cls.layer_norm3, (lv71, model_decoder_layers_2_final_layer_norm_weight5, model_decoder_layers_2_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv72 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm364, model_decoder_layers_2_fc1_weight5, model_decoder_layers_2_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv73 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv72, model_decoder_layers_2_fc2_weight5, model_decoder_layers_2_fc2_bias5, lv71), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm365 = R.call_tir(cls.layer_norm3, (lv73, model_decoder_layers_3_self_attn_layer_norm_weight5, model_decoder_layers_3_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv74 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm365, model_decoder_layers_3_self_attn_q_proj_weight5, model_decoder_layers_3_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv25 = R.call_tir(cls.NT_matmul, (layer_norm365, model_decoder_layers_3_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv75 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm365, model_decoder_layers_3_self_attn_v_proj_weight5, model_decoder_layers_3_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv76 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv74, lv25, lv75), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv271 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), lv76), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv77 = R.call_tir(cls.fused_reshape23_reshape24, (lv271,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv78 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv77, model_decoder_layers_3_self_attn_out_proj_weight5, model_decoder_layers_3_self_attn_out_proj_bias5, lv73), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm366 = R.call_tir(cls.layer_norm3, (lv78, model_decoder_layers_3_encoder_attn_layer_norm_weight5, model_decoder_layers_3_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv79 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm366, model_decoder_layers_3_encoder_attn_q_proj_weight5, model_decoder_layers_3_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv80 = R.call_tir(cls.fused_reshape21_reshape25, (lv79,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv272 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), lv80), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv81 = R.call_tir(cls.fused_reshape23_reshape24, (lv272,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv82 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv81, model_decoder_layers_3_encoder_attn_out_proj_weight5, model_decoder_layers_3_encoder_attn_out_proj_bias5, lv78), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm367 = R.call_tir(cls.layer_norm3, (lv82, model_decoder_layers_3_final_layer_norm_weight5, model_decoder_layers_3_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv83 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm367, model_decoder_layers_3_fc1_weight5, model_decoder_layers_3_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv84 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv83, model_decoder_layers_3_fc2_weight5, model_decoder_layers_3_fc2_bias5, lv82), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm368 = R.call_tir(cls.layer_norm3, (lv84, model_decoder_layers_4_self_attn_layer_norm_weight5, model_decoder_layers_4_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv85 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm368, model_decoder_layers_4_self_attn_q_proj_weight5, model_decoder_layers_4_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv33 = R.call_tir(cls.NT_matmul, (layer_norm368, model_decoder_layers_4_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv86 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm368, model_decoder_layers_4_self_attn_v_proj_weight5, model_decoder_layers_4_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv87 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv85, lv33, lv86), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv273 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), lv87), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv88 = R.call_tir(cls.fused_reshape23_reshape24, (lv273,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv89 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv88, model_decoder_layers_4_self_attn_out_proj_weight5, model_decoder_layers_4_self_attn_out_proj_bias5, lv84), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm369 = R.call_tir(cls.layer_norm3, (lv89, model_decoder_layers_4_encoder_attn_layer_norm_weight5, model_decoder_layers_4_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv90 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm369, model_decoder_layers_4_encoder_attn_q_proj_weight5, model_decoder_layers_4_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv91 = R.call_tir(cls.fused_reshape21_reshape25, (lv90,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv274 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), lv91), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv92 = R.call_tir(cls.fused_reshape23_reshape24, (lv274,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv93 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv92, model_decoder_layers_4_encoder_attn_out_proj_weight5, model_decoder_layers_4_encoder_attn_out_proj_bias5, lv89), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm370 = R.call_tir(cls.layer_norm3, (lv93, model_decoder_layers_4_final_layer_norm_weight5, model_decoder_layers_4_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv94 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm370, model_decoder_layers_4_fc1_weight5, model_decoder_layers_4_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv95 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv94, model_decoder_layers_4_fc2_weight5, model_decoder_layers_4_fc2_bias5, lv93), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm371 = R.call_tir(cls.layer_norm3, (lv95, model_decoder_layers_5_self_attn_layer_norm_weight5, model_decoder_layers_5_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv96 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm371, model_decoder_layers_5_self_attn_q_proj_weight5, model_decoder_layers_5_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv41_1 = R.call_tir(cls.NT_matmul, (layer_norm371, model_decoder_layers_5_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv97 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm371, model_decoder_layers_5_self_attn_v_proj_weight5, model_decoder_layers_5_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv98 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv96, lv41_1, lv97), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv275 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), lv98), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv99 = R.call_tir(cls.fused_reshape23_reshape24, (lv275,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv100 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv99, model_decoder_layers_5_self_attn_out_proj_weight5, model_decoder_layers_5_self_attn_out_proj_bias5, lv95), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm372 = R.call_tir(cls.layer_norm3, (lv100, model_decoder_layers_5_encoder_attn_layer_norm_weight5, model_decoder_layers_5_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv101 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm372, model_decoder_layers_5_encoder_attn_q_proj_weight5, model_decoder_layers_5_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv102 = R.call_tir(cls.fused_reshape21_reshape25, (lv101,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv276 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), lv102), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv103 = R.call_tir(cls.fused_reshape23_reshape24, (lv276,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv104 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv103, model_decoder_layers_5_encoder_attn_out_proj_weight5, model_decoder_layers_5_encoder_attn_out_proj_bias5, lv100), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm373 = R.call_tir(cls.layer_norm3, (lv104, model_decoder_layers_5_final_layer_norm_weight5, model_decoder_layers_5_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv105 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm373, model_decoder_layers_5_fc1_weight5, model_decoder_layers_5_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv106 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv105, model_decoder_layers_5_fc2_weight5, model_decoder_layers_5_fc2_bias5, lv104), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm374 = R.call_tir(cls.layer_norm3, (lv106, model_decoder_layers_6_self_attn_layer_norm_weight5, model_decoder_layers_6_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv107 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm374, model_decoder_layers_6_self_attn_q_proj_weight5, model_decoder_layers_6_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv49_1 = R.call_tir(cls.NT_matmul, (layer_norm374, model_decoder_layers_6_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv108 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm374, model_decoder_layers_6_self_attn_v_proj_weight5, model_decoder_layers_6_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv109 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv107, lv49_1, lv108), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv277 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), lv109), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv110 = R.call_tir(cls.fused_reshape23_reshape24, (lv277,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv111 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv110, model_decoder_layers_6_self_attn_out_proj_weight5, model_decoder_layers_6_self_attn_out_proj_bias5, lv106), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm375 = R.call_tir(cls.layer_norm3, (lv111, model_decoder_layers_6_encoder_attn_layer_norm_weight5, model_decoder_layers_6_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv112 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm375, model_decoder_layers_6_encoder_attn_q_proj_weight5, model_decoder_layers_6_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv113 = R.call_tir(cls.fused_reshape21_reshape25, (lv112,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv278 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), lv113), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv114 = R.call_tir(cls.fused_reshape23_reshape24, (lv278,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv115 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv114, model_decoder_layers_6_encoder_attn_out_proj_weight5, model_decoder_layers_6_encoder_attn_out_proj_bias5, lv111), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm376 = R.call_tir(cls.layer_norm3, (lv115, model_decoder_layers_6_final_layer_norm_weight5, model_decoder_layers_6_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv116 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm376, model_decoder_layers_6_fc1_weight5, model_decoder_layers_6_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv117 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv116, model_decoder_layers_6_fc2_weight5, model_decoder_layers_6_fc2_bias5, lv115), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm377 = R.call_tir(cls.layer_norm3, (lv117, model_decoder_layers_7_self_attn_layer_norm_weight5, model_decoder_layers_7_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv118 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm377, model_decoder_layers_7_self_attn_q_proj_weight5, model_decoder_layers_7_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv57_1 = R.call_tir(cls.NT_matmul, (layer_norm377, model_decoder_layers_7_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv119 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm377, model_decoder_layers_7_self_attn_v_proj_weight5, model_decoder_layers_7_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv120 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv118, lv57_1, lv119), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv279 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), lv120), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv121 = R.call_tir(cls.fused_reshape23_reshape24, (lv279,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv122 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv121, model_decoder_layers_7_self_attn_out_proj_weight5, model_decoder_layers_7_self_attn_out_proj_bias5, lv117), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm378 = R.call_tir(cls.layer_norm3, (lv122, model_decoder_layers_7_encoder_attn_layer_norm_weight5, model_decoder_layers_7_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv123 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm378, model_decoder_layers_7_encoder_attn_q_proj_weight5, model_decoder_layers_7_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv124 = R.call_tir(cls.fused_reshape21_reshape25, (lv123,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv280 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), lv124), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv125 = R.call_tir(cls.fused_reshape23_reshape24, (lv280,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv126 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv125, model_decoder_layers_7_encoder_attn_out_proj_weight5, model_decoder_layers_7_encoder_attn_out_proj_bias5, lv122), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm379 = R.call_tir(cls.layer_norm3, (lv126, model_decoder_layers_7_final_layer_norm_weight5, model_decoder_layers_7_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv127 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm379, model_decoder_layers_7_fc1_weight5, model_decoder_layers_7_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv128 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv127, model_decoder_layers_7_fc2_weight5, model_decoder_layers_7_fc2_bias5, lv126), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm380 = R.call_tir(cls.layer_norm3, (lv128, model_decoder_layers_8_self_attn_layer_norm_weight5, model_decoder_layers_8_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv129 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm380, model_decoder_layers_8_self_attn_q_proj_weight5, model_decoder_layers_8_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv65_1 = R.call_tir(cls.NT_matmul, (layer_norm380, model_decoder_layers_8_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv130 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm380, model_decoder_layers_8_self_attn_v_proj_weight5, model_decoder_layers_8_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv131 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv129, lv65_1, lv130), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv281 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), lv131), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv132 = R.call_tir(cls.fused_reshape23_reshape24, (lv281,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv133 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv132, model_decoder_layers_8_self_attn_out_proj_weight5, model_decoder_layers_8_self_attn_out_proj_bias5, lv128), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm381 = R.call_tir(cls.layer_norm3, (lv133, model_decoder_layers_8_encoder_attn_layer_norm_weight5, model_decoder_layers_8_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv134 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm381, model_decoder_layers_8_encoder_attn_q_proj_weight5, model_decoder_layers_8_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv135 = R.call_tir(cls.fused_reshape21_reshape25, (lv134,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv282 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), lv135), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv136 = R.call_tir(cls.fused_reshape23_reshape24, (lv282,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv137 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv136, model_decoder_layers_8_encoder_attn_out_proj_weight5, model_decoder_layers_8_encoder_attn_out_proj_bias5, lv133), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm382 = R.call_tir(cls.layer_norm3, (lv137, model_decoder_layers_8_final_layer_norm_weight5, model_decoder_layers_8_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv138 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm382, model_decoder_layers_8_fc1_weight5, model_decoder_layers_8_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv139 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv138, model_decoder_layers_8_fc2_weight5, model_decoder_layers_8_fc2_bias5, lv137), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm383 = R.call_tir(cls.layer_norm3, (lv139, model_decoder_layers_9_self_attn_layer_norm_weight5, model_decoder_layers_9_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv140 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm383, model_decoder_layers_9_self_attn_q_proj_weight5, model_decoder_layers_9_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv73_1 = R.call_tir(cls.NT_matmul, (layer_norm383, model_decoder_layers_9_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv141 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm383, model_decoder_layers_9_self_attn_v_proj_weight5, model_decoder_layers_9_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv142 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv140, lv73_1, lv141), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv283 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), lv142), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv143 = R.call_tir(cls.fused_reshape23_reshape24, (lv283,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv144 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv143, model_decoder_layers_9_self_attn_out_proj_weight5, model_decoder_layers_9_self_attn_out_proj_bias5, lv139), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm384 = R.call_tir(cls.layer_norm3, (lv144, model_decoder_layers_9_encoder_attn_layer_norm_weight5, model_decoder_layers_9_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv145 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm384, model_decoder_layers_9_encoder_attn_q_proj_weight5, model_decoder_layers_9_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv146 = R.call_tir(cls.fused_reshape21_reshape25, (lv145,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv284 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), lv146), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv147 = R.call_tir(cls.fused_reshape23_reshape24, (lv284,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv148 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv147, model_decoder_layers_9_encoder_attn_out_proj_weight5, model_decoder_layers_9_encoder_attn_out_proj_bias5, lv144), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm385 = R.call_tir(cls.layer_norm3, (lv148, model_decoder_layers_9_final_layer_norm_weight5, model_decoder_layers_9_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv149 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm385, model_decoder_layers_9_fc1_weight5, model_decoder_layers_9_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv150 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv149, model_decoder_layers_9_fc2_weight5, model_decoder_layers_9_fc2_bias5, lv148), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm386 = R.call_tir(cls.layer_norm3, (lv150, model_decoder_layers_10_self_attn_layer_norm_weight5, model_decoder_layers_10_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv151 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm386, model_decoder_layers_10_self_attn_q_proj_weight5, model_decoder_layers_10_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv81_1 = R.call_tir(cls.NT_matmul, (layer_norm386, model_decoder_layers_10_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv152 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm386, model_decoder_layers_10_self_attn_v_proj_weight5, model_decoder_layers_10_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv153 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv151, lv81_1, lv152), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv285 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), lv153), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv154 = R.call_tir(cls.fused_reshape23_reshape24, (lv285,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv155 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv154, model_decoder_layers_10_self_attn_out_proj_weight5, model_decoder_layers_10_self_attn_out_proj_bias5, lv150), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm387 = R.call_tir(cls.layer_norm3, (lv155, model_decoder_layers_10_encoder_attn_layer_norm_weight5, model_decoder_layers_10_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv156 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm387, model_decoder_layers_10_encoder_attn_q_proj_weight5, model_decoder_layers_10_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv157 = R.call_tir(cls.fused_reshape21_reshape25, (lv156,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv286 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), lv157), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv158 = R.call_tir(cls.fused_reshape23_reshape24, (lv286,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv159 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv158, model_decoder_layers_10_encoder_attn_out_proj_weight5, model_decoder_layers_10_encoder_attn_out_proj_bias5, lv155), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm388 = R.call_tir(cls.layer_norm3, (lv159, model_decoder_layers_10_final_layer_norm_weight5, model_decoder_layers_10_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv160 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm388, model_decoder_layers_10_fc1_weight5, model_decoder_layers_10_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv161 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv160, model_decoder_layers_10_fc2_weight5, model_decoder_layers_10_fc2_bias5, lv159), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm389 = R.call_tir(cls.layer_norm3, (lv161, model_decoder_layers_11_self_attn_layer_norm_weight5, model_decoder_layers_11_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv162 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm389, model_decoder_layers_11_self_attn_q_proj_weight5, model_decoder_layers_11_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv89_1 = R.call_tir(cls.NT_matmul, (layer_norm389, model_decoder_layers_11_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv163 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm389, model_decoder_layers_11_self_attn_v_proj_weight5, model_decoder_layers_11_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv164 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv162, lv89_1, lv163), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv287 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), lv164), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv165 = R.call_tir(cls.fused_reshape23_reshape24, (lv287,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv166 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv165, model_decoder_layers_11_self_attn_out_proj_weight5, model_decoder_layers_11_self_attn_out_proj_bias5, lv161), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm390 = R.call_tir(cls.layer_norm3, (lv166, model_decoder_layers_11_encoder_attn_layer_norm_weight5, model_decoder_layers_11_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv167 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm390, model_decoder_layers_11_encoder_attn_q_proj_weight5, model_decoder_layers_11_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv168 = R.call_tir(cls.fused_reshape21_reshape25, (lv167,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv288 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), lv168), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv169 = R.call_tir(cls.fused_reshape23_reshape24, (lv288,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv170 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv169, model_decoder_layers_11_encoder_attn_out_proj_weight5, model_decoder_layers_11_encoder_attn_out_proj_bias5, lv166), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm391 = R.call_tir(cls.layer_norm3, (lv170, model_decoder_layers_11_final_layer_norm_weight5, model_decoder_layers_11_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv171 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm391, model_decoder_layers_11_fc1_weight5, model_decoder_layers_11_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv172 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv171, model_decoder_layers_11_fc2_weight5, model_decoder_layers_11_fc2_bias5, lv170), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm392 = R.call_tir(cls.layer_norm3, (lv172, model_decoder_layers_12_self_attn_layer_norm_weight5, model_decoder_layers_12_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv173 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm392, model_decoder_layers_12_self_attn_q_proj_weight5, model_decoder_layers_12_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv97_1 = R.call_tir(cls.NT_matmul, (layer_norm392, model_decoder_layers_12_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv174 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm392, model_decoder_layers_12_self_attn_v_proj_weight5, model_decoder_layers_12_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv175 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv173, lv97_1, lv174), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv289 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), lv175), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv176 = R.call_tir(cls.fused_reshape23_reshape24, (lv289,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv177 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv176, model_decoder_layers_12_self_attn_out_proj_weight5, model_decoder_layers_12_self_attn_out_proj_bias5, lv172), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm393 = R.call_tir(cls.layer_norm3, (lv177, model_decoder_layers_12_encoder_attn_layer_norm_weight5, model_decoder_layers_12_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv178 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm393, model_decoder_layers_12_encoder_attn_q_proj_weight5, model_decoder_layers_12_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv179 = R.call_tir(cls.fused_reshape21_reshape25, (lv178,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv290 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), lv179), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv180 = R.call_tir(cls.fused_reshape23_reshape24, (lv290,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv181 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv180, model_decoder_layers_12_encoder_attn_out_proj_weight5, model_decoder_layers_12_encoder_attn_out_proj_bias5, lv177), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm394 = R.call_tir(cls.layer_norm3, (lv181, model_decoder_layers_12_final_layer_norm_weight5, model_decoder_layers_12_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv182 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm394, model_decoder_layers_12_fc1_weight5, model_decoder_layers_12_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv183 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv182, model_decoder_layers_12_fc2_weight5, model_decoder_layers_12_fc2_bias5, lv181), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm395 = R.call_tir(cls.layer_norm3, (lv183, model_decoder_layers_13_self_attn_layer_norm_weight5, model_decoder_layers_13_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv184 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm395, model_decoder_layers_13_self_attn_q_proj_weight5, model_decoder_layers_13_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv105_1 = R.call_tir(cls.NT_matmul, (layer_norm395, model_decoder_layers_13_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv185 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm395, model_decoder_layers_13_self_attn_v_proj_weight5, model_decoder_layers_13_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv186 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv184, lv105_1, lv185), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv291 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), lv186), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv187 = R.call_tir(cls.fused_reshape23_reshape24, (lv291,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv188 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv187, model_decoder_layers_13_self_attn_out_proj_weight5, model_decoder_layers_13_self_attn_out_proj_bias5, lv183), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm396 = R.call_tir(cls.layer_norm3, (lv188, model_decoder_layers_13_encoder_attn_layer_norm_weight5, model_decoder_layers_13_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv189 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm396, model_decoder_layers_13_encoder_attn_q_proj_weight5, model_decoder_layers_13_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv190 = R.call_tir(cls.fused_reshape21_reshape25, (lv189,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv292 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), lv190), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv191 = R.call_tir(cls.fused_reshape23_reshape24, (lv292,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv192 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv191, model_decoder_layers_13_encoder_attn_out_proj_weight5, model_decoder_layers_13_encoder_attn_out_proj_bias5, lv188), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm397 = R.call_tir(cls.layer_norm3, (lv192, model_decoder_layers_13_final_layer_norm_weight5, model_decoder_layers_13_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv193 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm397, model_decoder_layers_13_fc1_weight5, model_decoder_layers_13_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv194 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv193, model_decoder_layers_13_fc2_weight5, model_decoder_layers_13_fc2_bias5, lv192), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm398 = R.call_tir(cls.layer_norm3, (lv194, model_decoder_layers_14_self_attn_layer_norm_weight5, model_decoder_layers_14_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv195 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm398, model_decoder_layers_14_self_attn_q_proj_weight5, model_decoder_layers_14_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv113_1 = R.call_tir(cls.NT_matmul, (layer_norm398, model_decoder_layers_14_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv196 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm398, model_decoder_layers_14_self_attn_v_proj_weight5, model_decoder_layers_14_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv197 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv195, lv113_1, lv196), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv293 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), lv197), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv198 = R.call_tir(cls.fused_reshape23_reshape24, (lv293,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv199 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv198, model_decoder_layers_14_self_attn_out_proj_weight5, model_decoder_layers_14_self_attn_out_proj_bias5, lv194), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm399 = R.call_tir(cls.layer_norm3, (lv199, model_decoder_layers_14_encoder_attn_layer_norm_weight5, model_decoder_layers_14_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv200 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm399, model_decoder_layers_14_encoder_attn_q_proj_weight5, model_decoder_layers_14_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv201 = R.call_tir(cls.fused_reshape21_reshape25, (lv200,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv294 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), lv201), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv202 = R.call_tir(cls.fused_reshape23_reshape24, (lv294,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv203 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv202, model_decoder_layers_14_encoder_attn_out_proj_weight5, model_decoder_layers_14_encoder_attn_out_proj_bias5, lv199), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm400 = R.call_tir(cls.layer_norm3, (lv203, model_decoder_layers_14_final_layer_norm_weight5, model_decoder_layers_14_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv204 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm400, model_decoder_layers_14_fc1_weight5, model_decoder_layers_14_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv205 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv204, model_decoder_layers_14_fc2_weight5, model_decoder_layers_14_fc2_bias5, lv203), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm401 = R.call_tir(cls.layer_norm3, (lv205, model_decoder_layers_15_self_attn_layer_norm_weight5, model_decoder_layers_15_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv206 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm401, model_decoder_layers_15_self_attn_q_proj_weight5, model_decoder_layers_15_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv121_1 = R.call_tir(cls.NT_matmul, (layer_norm401, model_decoder_layers_15_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv207 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm401, model_decoder_layers_15_self_attn_v_proj_weight5, model_decoder_layers_15_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv208 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv206, lv121_1, lv207), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv295 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), lv208), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv209 = R.call_tir(cls.fused_reshape23_reshape24, (lv295,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv210 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv209, model_decoder_layers_15_self_attn_out_proj_weight5, model_decoder_layers_15_self_attn_out_proj_bias5, lv205), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm402 = R.call_tir(cls.layer_norm3, (lv210, model_decoder_layers_15_encoder_attn_layer_norm_weight5, model_decoder_layers_15_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv211 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm402, model_decoder_layers_15_encoder_attn_q_proj_weight5, model_decoder_layers_15_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv212 = R.call_tir(cls.fused_reshape21_reshape25, (lv211,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv296 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), lv212), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv213 = R.call_tir(cls.fused_reshape23_reshape24, (lv296,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv214 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv213, model_decoder_layers_15_encoder_attn_out_proj_weight5, model_decoder_layers_15_encoder_attn_out_proj_bias5, lv210), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm403 = R.call_tir(cls.layer_norm3, (lv214, model_decoder_layers_15_final_layer_norm_weight5, model_decoder_layers_15_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv215 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm403, model_decoder_layers_15_fc1_weight5, model_decoder_layers_15_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv216 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv215, model_decoder_layers_15_fc2_weight5, model_decoder_layers_15_fc2_bias5, lv214), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm404 = R.call_tir(cls.layer_norm3, (lv216, model_decoder_layers_16_self_attn_layer_norm_weight5, model_decoder_layers_16_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv217 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm404, model_decoder_layers_16_self_attn_q_proj_weight5, model_decoder_layers_16_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv129_1 = R.call_tir(cls.NT_matmul, (layer_norm404, model_decoder_layers_16_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv218 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm404, model_decoder_layers_16_self_attn_v_proj_weight5, model_decoder_layers_16_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv219 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv217, lv129_1, lv218), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv297 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), lv219), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv220 = R.call_tir(cls.fused_reshape23_reshape24, (lv297,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv221 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv220, model_decoder_layers_16_self_attn_out_proj_weight5, model_decoder_layers_16_self_attn_out_proj_bias5, lv216), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm405 = R.call_tir(cls.layer_norm3, (lv221, model_decoder_layers_16_encoder_attn_layer_norm_weight5, model_decoder_layers_16_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv222 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm405, model_decoder_layers_16_encoder_attn_q_proj_weight5, model_decoder_layers_16_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv223 = R.call_tir(cls.fused_reshape21_reshape25, (lv222,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv298 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), lv223), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv224 = R.call_tir(cls.fused_reshape23_reshape24, (lv298,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv225 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv224, model_decoder_layers_16_encoder_attn_out_proj_weight5, model_decoder_layers_16_encoder_attn_out_proj_bias5, lv221), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm406 = R.call_tir(cls.layer_norm3, (lv225, model_decoder_layers_16_final_layer_norm_weight5, model_decoder_layers_16_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv226 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm406, model_decoder_layers_16_fc1_weight5, model_decoder_layers_16_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv227 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv226, model_decoder_layers_16_fc2_weight5, model_decoder_layers_16_fc2_bias5, lv225), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm407 = R.call_tir(cls.layer_norm3, (lv227, model_decoder_layers_17_self_attn_layer_norm_weight5, model_decoder_layers_17_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv228 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm407, model_decoder_layers_17_self_attn_q_proj_weight5, model_decoder_layers_17_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv137_1 = R.call_tir(cls.NT_matmul, (layer_norm407, model_decoder_layers_17_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv229 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm407, model_decoder_layers_17_self_attn_v_proj_weight5, model_decoder_layers_17_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv230 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv228, lv137_1, lv229), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv299 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), lv230), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv231 = R.call_tir(cls.fused_reshape23_reshape24, (lv299,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv232 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv231, model_decoder_layers_17_self_attn_out_proj_weight5, model_decoder_layers_17_self_attn_out_proj_bias5, lv227), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm408 = R.call_tir(cls.layer_norm3, (lv232, model_decoder_layers_17_encoder_attn_layer_norm_weight5, model_decoder_layers_17_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv233 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm408, model_decoder_layers_17_encoder_attn_q_proj_weight5, model_decoder_layers_17_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv234 = R.call_tir(cls.fused_reshape21_reshape25, (lv233,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv300 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), lv234), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv235 = R.call_tir(cls.fused_reshape23_reshape24, (lv300,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv236 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv235, model_decoder_layers_17_encoder_attn_out_proj_weight5, model_decoder_layers_17_encoder_attn_out_proj_bias5, lv232), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm409 = R.call_tir(cls.layer_norm3, (lv236, model_decoder_layers_17_final_layer_norm_weight5, model_decoder_layers_17_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv237 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm409, model_decoder_layers_17_fc1_weight5, model_decoder_layers_17_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv238 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv237, model_decoder_layers_17_fc2_weight5, model_decoder_layers_17_fc2_bias5, lv236), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm410 = R.call_tir(cls.layer_norm3, (lv238, model_decoder_layers_18_self_attn_layer_norm_weight5, model_decoder_layers_18_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv239 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm410, model_decoder_layers_18_self_attn_q_proj_weight5, model_decoder_layers_18_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv145_1 = R.call_tir(cls.NT_matmul, (layer_norm410, model_decoder_layers_18_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv240 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm410, model_decoder_layers_18_self_attn_v_proj_weight5, model_decoder_layers_18_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv241 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv239, lv145_1, lv240), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv301 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), lv241), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv242 = R.call_tir(cls.fused_reshape23_reshape24, (lv301,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv243 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv242, model_decoder_layers_18_self_attn_out_proj_weight5, model_decoder_layers_18_self_attn_out_proj_bias5, lv238), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm411 = R.call_tir(cls.layer_norm3, (lv243, model_decoder_layers_18_encoder_attn_layer_norm_weight5, model_decoder_layers_18_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv244 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm411, model_decoder_layers_18_encoder_attn_q_proj_weight5, model_decoder_layers_18_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv245 = R.call_tir(cls.fused_reshape21_reshape25, (lv244,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv302 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), lv245), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv246 = R.call_tir(cls.fused_reshape23_reshape24, (lv302,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv247 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv246, model_decoder_layers_18_encoder_attn_out_proj_weight5, model_decoder_layers_18_encoder_attn_out_proj_bias5, lv243), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm412 = R.call_tir(cls.layer_norm3, (lv247, model_decoder_layers_18_final_layer_norm_weight5, model_decoder_layers_18_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv248 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm412, model_decoder_layers_18_fc1_weight5, model_decoder_layers_18_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv249 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv248, model_decoder_layers_18_fc2_weight5, model_decoder_layers_18_fc2_bias5, lv247), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm413 = R.call_tir(cls.layer_norm3, (lv249, model_decoder_layers_19_self_attn_layer_norm_weight5, model_decoder_layers_19_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv250 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm413, model_decoder_layers_19_self_attn_q_proj_weight5, model_decoder_layers_19_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv153_1 = R.call_tir(cls.NT_matmul, (layer_norm413, model_decoder_layers_19_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv251 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm413, model_decoder_layers_19_self_attn_v_proj_weight5, model_decoder_layers_19_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv252 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv250, lv153_1, lv251), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv303 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), lv252), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv253 = R.call_tir(cls.fused_reshape23_reshape24, (lv303,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv254 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv253, model_decoder_layers_19_self_attn_out_proj_weight5, model_decoder_layers_19_self_attn_out_proj_bias5, lv249), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm414 = R.call_tir(cls.layer_norm3, (lv254, model_decoder_layers_19_encoder_attn_layer_norm_weight5, model_decoder_layers_19_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv255 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm414, model_decoder_layers_19_encoder_attn_q_proj_weight5, model_decoder_layers_19_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv256 = R.call_tir(cls.fused_reshape21_reshape25, (lv255,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv304 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), lv256), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv257 = R.call_tir(cls.fused_reshape23_reshape24, (lv304,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv258 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv257, model_decoder_layers_19_encoder_attn_out_proj_weight5, model_decoder_layers_19_encoder_attn_out_proj_bias5, lv254), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm415 = R.call_tir(cls.layer_norm3, (lv258, model_decoder_layers_19_final_layer_norm_weight5, model_decoder_layers_19_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv259 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm415, model_decoder_layers_19_fc1_weight5, model_decoder_layers_19_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv260 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv259, model_decoder_layers_19_fc2_weight5, model_decoder_layers_19_fc2_bias5, lv258), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm416 = R.call_tir(cls.layer_norm3, (lv260, model_decoder_layers_20_self_attn_layer_norm_weight5, model_decoder_layers_20_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv261 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm416, model_decoder_layers_20_self_attn_q_proj_weight5, model_decoder_layers_20_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv161_1 = R.call_tir(cls.NT_matmul, (layer_norm416, model_decoder_layers_20_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv262 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm416, model_decoder_layers_20_self_attn_v_proj_weight5, model_decoder_layers_20_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv263 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv261, lv161_1, lv262), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv305 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), lv263), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv264_1 = R.call_tir(cls.fused_reshape23_reshape24, (lv305,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv265_1 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv264_1, model_decoder_layers_20_self_attn_out_proj_weight5, model_decoder_layers_20_self_attn_out_proj_bias5, lv260), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm417 = R.call_tir(cls.layer_norm3, (lv265_1, model_decoder_layers_20_encoder_attn_layer_norm_weight5, model_decoder_layers_20_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv266_1 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm417, model_decoder_layers_20_encoder_attn_q_proj_weight5, model_decoder_layers_20_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv267_1 = R.call_tir(cls.fused_reshape21_reshape25, (lv266_1,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv306 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), lv267_1), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv268_1 = R.call_tir(cls.fused_reshape23_reshape24, (lv306,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv269_1 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv268_1, model_decoder_layers_20_encoder_attn_out_proj_weight5, model_decoder_layers_20_encoder_attn_out_proj_bias5, lv265_1), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm418 = R.call_tir(cls.layer_norm3, (lv269_1, model_decoder_layers_20_final_layer_norm_weight5, model_decoder_layers_20_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv270_1 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm418, model_decoder_layers_20_fc1_weight5, model_decoder_layers_20_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv271_1 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv270_1, model_decoder_layers_20_fc2_weight5, model_decoder_layers_20_fc2_bias5, lv269_1), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm419 = R.call_tir(cls.layer_norm3, (lv271_1, model_decoder_layers_21_self_attn_layer_norm_weight5, model_decoder_layers_21_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv272_1 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm419, model_decoder_layers_21_self_attn_q_proj_weight5, model_decoder_layers_21_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv169_1 = R.call_tir(cls.NT_matmul, (layer_norm419, model_decoder_layers_21_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv273_1 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm419, model_decoder_layers_21_self_attn_v_proj_weight5, model_decoder_layers_21_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv274_1 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv272_1, lv169_1, lv273_1), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv307 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), lv274_1), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv275_1 = R.call_tir(cls.fused_reshape23_reshape24, (lv307,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv276_1 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv275_1, model_decoder_layers_21_self_attn_out_proj_weight5, model_decoder_layers_21_self_attn_out_proj_bias5, lv271_1), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm420 = R.call_tir(cls.layer_norm3, (lv276_1, model_decoder_layers_21_encoder_attn_layer_norm_weight5, model_decoder_layers_21_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv277_1 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm420, model_decoder_layers_21_encoder_attn_q_proj_weight5, model_decoder_layers_21_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv278_1 = R.call_tir(cls.fused_reshape21_reshape25, (lv277_1,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv308 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), lv278_1), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv279_1 = R.call_tir(cls.fused_reshape23_reshape24, (lv308,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv280_1 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv279_1, model_decoder_layers_21_encoder_attn_out_proj_weight5, model_decoder_layers_21_encoder_attn_out_proj_bias5, lv276_1), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm421 = R.call_tir(cls.layer_norm3, (lv280_1, model_decoder_layers_21_final_layer_norm_weight5, model_decoder_layers_21_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv281_1 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm421, model_decoder_layers_21_fc1_weight5, model_decoder_layers_21_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv282_1 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv281_1, model_decoder_layers_21_fc2_weight5, model_decoder_layers_21_fc2_bias5, lv280_1), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm422 = R.call_tir(cls.layer_norm3, (lv282_1, model_decoder_layers_22_self_attn_layer_norm_weight5, model_decoder_layers_22_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv283_1 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm422, model_decoder_layers_22_self_attn_q_proj_weight5, model_decoder_layers_22_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv177_1 = R.call_tir(cls.NT_matmul, (layer_norm422, model_decoder_layers_22_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv284_1 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm422, model_decoder_layers_22_self_attn_v_proj_weight5, model_decoder_layers_22_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv285_1 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv283_1, lv177_1, lv284_1), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv309 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), lv285_1), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv286_1 = R.call_tir(cls.fused_reshape23_reshape24, (lv309,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv287_1 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv286_1, model_decoder_layers_22_self_attn_out_proj_weight5, model_decoder_layers_22_self_attn_out_proj_bias5, lv282_1), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm423 = R.call_tir(cls.layer_norm3, (lv287_1, model_decoder_layers_22_encoder_attn_layer_norm_weight5, model_decoder_layers_22_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv288_1 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm423, model_decoder_layers_22_encoder_attn_q_proj_weight5, model_decoder_layers_22_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv289_1 = R.call_tir(cls.fused_reshape21_reshape25, (lv288_1,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv310 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), lv289_1), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv290_1 = R.call_tir(cls.fused_reshape23_reshape24, (lv310,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv291_1 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv290_1, model_decoder_layers_22_encoder_attn_out_proj_weight5, model_decoder_layers_22_encoder_attn_out_proj_bias5, lv287_1), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm424 = R.call_tir(cls.layer_norm3, (lv291_1, model_decoder_layers_22_final_layer_norm_weight5, model_decoder_layers_22_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv292_1 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm424, model_decoder_layers_22_fc1_weight5, model_decoder_layers_22_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv293_1 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv292_1, model_decoder_layers_22_fc2_weight5, model_decoder_layers_22_fc2_bias5, lv291_1), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm425 = R.call_tir(cls.layer_norm3, (lv293_1, model_decoder_layers_23_self_attn_layer_norm_weight5, model_decoder_layers_23_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv294_1 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm425, model_decoder_layers_23_self_attn_q_proj_weight5, model_decoder_layers_23_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv185_1 = R.call_tir(cls.NT_matmul, (layer_norm425, model_decoder_layers_23_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv295_1 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm425, model_decoder_layers_23_self_attn_v_proj_weight5, model_decoder_layers_23_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv296_1 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv294_1, lv185_1, lv295_1), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv311 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), lv296_1), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv297_1 = R.call_tir(cls.fused_reshape23_reshape24, (lv311,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv298_1 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv297_1, model_decoder_layers_23_self_attn_out_proj_weight5, model_decoder_layers_23_self_attn_out_proj_bias5, lv293_1), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm426 = R.call_tir(cls.layer_norm3, (lv298_1, model_decoder_layers_23_encoder_attn_layer_norm_weight5, model_decoder_layers_23_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv299_1 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm426, model_decoder_layers_23_encoder_attn_q_proj_weight5, model_decoder_layers_23_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv300_1 = R.call_tir(cls.fused_reshape21_reshape25, (lv299_1,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv312 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), lv300_1), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv301_1 = R.call_tir(cls.fused_reshape23_reshape24, (lv312,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv302_1 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv301_1, model_decoder_layers_23_encoder_attn_out_proj_weight5, model_decoder_layers_23_encoder_attn_out_proj_bias5, lv298_1), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm427 = R.call_tir(cls.layer_norm3, (lv302_1, model_decoder_layers_23_final_layer_norm_weight5, model_decoder_layers_23_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv303_1 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm427, model_decoder_layers_23_fc1_weight5, model_decoder_layers_23_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv304_1 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv303_1, model_decoder_layers_23_fc2_weight5, model_decoder_layers_23_fc2_bias5, lv302_1), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm428 = R.call_tir(cls.layer_norm3, (lv304_1, model_decoder_layers_24_self_attn_layer_norm_weight5, model_decoder_layers_24_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv305_1 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm428, model_decoder_layers_24_self_attn_q_proj_weight5, model_decoder_layers_24_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv193_1 = R.call_tir(cls.NT_matmul, (layer_norm428, model_decoder_layers_24_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv306_1 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm428, model_decoder_layers_24_self_attn_v_proj_weight5, model_decoder_layers_24_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv307_1 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv305_1, lv193_1, lv306_1), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv313 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), lv307_1), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv308_1 = R.call_tir(cls.fused_reshape23_reshape24, (lv313,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv309_1 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv308_1, model_decoder_layers_24_self_attn_out_proj_weight5, model_decoder_layers_24_self_attn_out_proj_bias5, lv304_1), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm429 = R.call_tir(cls.layer_norm3, (lv309_1, model_decoder_layers_24_encoder_attn_layer_norm_weight5, model_decoder_layers_24_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv310_1 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm429, model_decoder_layers_24_encoder_attn_q_proj_weight5, model_decoder_layers_24_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv311_1 = R.call_tir(cls.fused_reshape21_reshape25, (lv310_1,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv314 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), lv311_1), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv312_1 = R.call_tir(cls.fused_reshape23_reshape24, (lv314,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv313_1 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv312_1, model_decoder_layers_24_encoder_attn_out_proj_weight5, model_decoder_layers_24_encoder_attn_out_proj_bias5, lv309_1), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm430 = R.call_tir(cls.layer_norm3, (lv313_1, model_decoder_layers_24_final_layer_norm_weight5, model_decoder_layers_24_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv314_1 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm430, model_decoder_layers_24_fc1_weight5, model_decoder_layers_24_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv315 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv314_1, model_decoder_layers_24_fc2_weight5, model_decoder_layers_24_fc2_bias5, lv313_1), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm431 = R.call_tir(cls.layer_norm3, (lv315, model_decoder_layers_25_self_attn_layer_norm_weight5, model_decoder_layers_25_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv316 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm431, model_decoder_layers_25_self_attn_q_proj_weight5, model_decoder_layers_25_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv201_1 = R.call_tir(cls.NT_matmul, (layer_norm431, model_decoder_layers_25_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv317 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm431, model_decoder_layers_25_self_attn_v_proj_weight5, model_decoder_layers_25_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv318 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv316, lv201_1, lv317), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv315_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), lv318), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv319 = R.call_tir(cls.fused_reshape23_reshape24, (lv315_1,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv320 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv319, model_decoder_layers_25_self_attn_out_proj_weight5, model_decoder_layers_25_self_attn_out_proj_bias5, lv315), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm432 = R.call_tir(cls.layer_norm3, (lv320, model_decoder_layers_25_encoder_attn_layer_norm_weight5, model_decoder_layers_25_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv321 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm432, model_decoder_layers_25_encoder_attn_q_proj_weight5, model_decoder_layers_25_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv322 = R.call_tir(cls.fused_reshape21_reshape25, (lv321,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv316_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), lv322), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv323 = R.call_tir(cls.fused_reshape23_reshape24, (lv316_1,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv324 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv323, model_decoder_layers_25_encoder_attn_out_proj_weight5, model_decoder_layers_25_encoder_attn_out_proj_bias5, lv320), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm433 = R.call_tir(cls.layer_norm3, (lv324, model_decoder_layers_25_final_layer_norm_weight5, model_decoder_layers_25_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv325 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm433, model_decoder_layers_25_fc1_weight5, model_decoder_layers_25_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv326 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv325, model_decoder_layers_25_fc2_weight5, model_decoder_layers_25_fc2_bias5, lv324), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm434 = R.call_tir(cls.layer_norm3, (lv326, model_decoder_layers_26_self_attn_layer_norm_weight5, model_decoder_layers_26_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv327 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm434, model_decoder_layers_26_self_attn_q_proj_weight5, model_decoder_layers_26_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv209_1 = R.call_tir(cls.NT_matmul, (layer_norm434, model_decoder_layers_26_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv328 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm434, model_decoder_layers_26_self_attn_v_proj_weight5, model_decoder_layers_26_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv329 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv327, lv209_1, lv328), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv317_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), lv329), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv330 = R.call_tir(cls.fused_reshape23_reshape24, (lv317_1,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv331 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv330, model_decoder_layers_26_self_attn_out_proj_weight5, model_decoder_layers_26_self_attn_out_proj_bias5, lv326), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm435 = R.call_tir(cls.layer_norm3, (lv331, model_decoder_layers_26_encoder_attn_layer_norm_weight5, model_decoder_layers_26_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv332 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm435, model_decoder_layers_26_encoder_attn_q_proj_weight5, model_decoder_layers_26_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv333 = R.call_tir(cls.fused_reshape21_reshape25, (lv332,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv318_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), lv333), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv334 = R.call_tir(cls.fused_reshape23_reshape24, (lv318_1,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv335 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv334, model_decoder_layers_26_encoder_attn_out_proj_weight5, model_decoder_layers_26_encoder_attn_out_proj_bias5, lv331), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm436 = R.call_tir(cls.layer_norm3, (lv335, model_decoder_layers_26_final_layer_norm_weight5, model_decoder_layers_26_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv336 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm436, model_decoder_layers_26_fc1_weight5, model_decoder_layers_26_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv337 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv336, model_decoder_layers_26_fc2_weight5, model_decoder_layers_26_fc2_bias5, lv335), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm437 = R.call_tir(cls.layer_norm3, (lv337, model_decoder_layers_27_self_attn_layer_norm_weight5, model_decoder_layers_27_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv338 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm437, model_decoder_layers_27_self_attn_q_proj_weight5, model_decoder_layers_27_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv217_1 = R.call_tir(cls.NT_matmul, (layer_norm437, model_decoder_layers_27_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv339 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm437, model_decoder_layers_27_self_attn_v_proj_weight5, model_decoder_layers_27_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv340 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv338, lv217_1, lv339), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv319_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), lv340), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv341 = R.call_tir(cls.fused_reshape23_reshape24, (lv319_1,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv342 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv341, model_decoder_layers_27_self_attn_out_proj_weight5, model_decoder_layers_27_self_attn_out_proj_bias5, lv337), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm438 = R.call_tir(cls.layer_norm3, (lv342, model_decoder_layers_27_encoder_attn_layer_norm_weight5, model_decoder_layers_27_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv343 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm438, model_decoder_layers_27_encoder_attn_q_proj_weight5, model_decoder_layers_27_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv344 = R.call_tir(cls.fused_reshape21_reshape25, (lv343,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv320_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), lv344), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv345 = R.call_tir(cls.fused_reshape23_reshape24, (lv320_1,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv346 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv345, model_decoder_layers_27_encoder_attn_out_proj_weight5, model_decoder_layers_27_encoder_attn_out_proj_bias5, lv342), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm439 = R.call_tir(cls.layer_norm3, (lv346, model_decoder_layers_27_final_layer_norm_weight5, model_decoder_layers_27_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv347 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm439, model_decoder_layers_27_fc1_weight5, model_decoder_layers_27_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv348 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv347, model_decoder_layers_27_fc2_weight5, model_decoder_layers_27_fc2_bias5, lv346), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm440 = R.call_tir(cls.layer_norm3, (lv348, model_decoder_layers_28_self_attn_layer_norm_weight5, model_decoder_layers_28_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv349 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm440, model_decoder_layers_28_self_attn_q_proj_weight5, model_decoder_layers_28_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv225_1 = R.call_tir(cls.NT_matmul, (layer_norm440, model_decoder_layers_28_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv350 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm440, model_decoder_layers_28_self_attn_v_proj_weight5, model_decoder_layers_28_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv351 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv349, lv225_1, lv350), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv321_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), lv351), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv352 = R.call_tir(cls.fused_reshape23_reshape24, (lv321_1,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv353 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv352, model_decoder_layers_28_self_attn_out_proj_weight5, model_decoder_layers_28_self_attn_out_proj_bias5, lv348), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm441 = R.call_tir(cls.layer_norm3, (lv353, model_decoder_layers_28_encoder_attn_layer_norm_weight5, model_decoder_layers_28_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv354 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm441, model_decoder_layers_28_encoder_attn_q_proj_weight5, model_decoder_layers_28_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv355 = R.call_tir(cls.fused_reshape21_reshape25, (lv354,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv322_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), lv355), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv356 = R.call_tir(cls.fused_reshape23_reshape24, (lv322_1,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv357 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv356, model_decoder_layers_28_encoder_attn_out_proj_weight5, model_decoder_layers_28_encoder_attn_out_proj_bias5, lv353), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm442 = R.call_tir(cls.layer_norm3, (lv357, model_decoder_layers_28_final_layer_norm_weight5, model_decoder_layers_28_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv358 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm442, model_decoder_layers_28_fc1_weight5, model_decoder_layers_28_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv359 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv358, model_decoder_layers_28_fc2_weight5, model_decoder_layers_28_fc2_bias5, lv357), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm443 = R.call_tir(cls.layer_norm3, (lv359, model_decoder_layers_29_self_attn_layer_norm_weight5, model_decoder_layers_29_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv360 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm443, model_decoder_layers_29_self_attn_q_proj_weight5, model_decoder_layers_29_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv233_1 = R.call_tir(cls.NT_matmul, (layer_norm443, model_decoder_layers_29_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv361 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm443, model_decoder_layers_29_self_attn_v_proj_weight5, model_decoder_layers_29_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv362 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv360, lv233_1, lv361), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv323_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), lv362), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv363 = R.call_tir(cls.fused_reshape23_reshape24, (lv323_1,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv364 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv363, model_decoder_layers_29_self_attn_out_proj_weight5, model_decoder_layers_29_self_attn_out_proj_bias5, lv359), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm444 = R.call_tir(cls.layer_norm3, (lv364, model_decoder_layers_29_encoder_attn_layer_norm_weight5, model_decoder_layers_29_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv365 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm444, model_decoder_layers_29_encoder_attn_q_proj_weight5, model_decoder_layers_29_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv366 = R.call_tir(cls.fused_reshape21_reshape25, (lv365,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv324_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), lv366), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv367 = R.call_tir(cls.fused_reshape23_reshape24, (lv324_1,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv368 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv367, model_decoder_layers_29_encoder_attn_out_proj_weight5, model_decoder_layers_29_encoder_attn_out_proj_bias5, lv364), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm445 = R.call_tir(cls.layer_norm3, (lv368, model_decoder_layers_29_final_layer_norm_weight5, model_decoder_layers_29_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv369 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm445, model_decoder_layers_29_fc1_weight5, model_decoder_layers_29_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv370 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv369, model_decoder_layers_29_fc2_weight5, model_decoder_layers_29_fc2_bias5, lv368), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm446 = R.call_tir(cls.layer_norm3, (lv370, model_decoder_layers_30_self_attn_layer_norm_weight5, model_decoder_layers_30_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv371 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm446, model_decoder_layers_30_self_attn_q_proj_weight5, model_decoder_layers_30_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv241_1 = R.call_tir(cls.NT_matmul, (layer_norm446, model_decoder_layers_30_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv372 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm446, model_decoder_layers_30_self_attn_v_proj_weight5, model_decoder_layers_30_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv373 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv371, lv241_1, lv372), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv325_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), lv373), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv374 = R.call_tir(cls.fused_reshape23_reshape24, (lv325_1,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv375 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv374, model_decoder_layers_30_self_attn_out_proj_weight5, model_decoder_layers_30_self_attn_out_proj_bias5, lv370), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm447 = R.call_tir(cls.layer_norm3, (lv375, model_decoder_layers_30_encoder_attn_layer_norm_weight5, model_decoder_layers_30_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv376 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm447, model_decoder_layers_30_encoder_attn_q_proj_weight5, model_decoder_layers_30_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv377 = R.call_tir(cls.fused_reshape21_reshape25, (lv376,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv326_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), lv377), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv378 = R.call_tir(cls.fused_reshape23_reshape24, (lv326_1,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv379 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv378, model_decoder_layers_30_encoder_attn_out_proj_weight5, model_decoder_layers_30_encoder_attn_out_proj_bias5, lv375), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm448 = R.call_tir(cls.layer_norm3, (lv379, model_decoder_layers_30_final_layer_norm_weight5, model_decoder_layers_30_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv380 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm448, model_decoder_layers_30_fc1_weight5, model_decoder_layers_30_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv381 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv380, model_decoder_layers_30_fc2_weight5, model_decoder_layers_30_fc2_bias5, lv379), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm449 = R.call_tir(cls.layer_norm3, (lv381, model_decoder_layers_31_self_attn_layer_norm_weight5, model_decoder_layers_31_self_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv382 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm449, model_decoder_layers_31_self_attn_q_proj_weight5, model_decoder_layers_31_self_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv249_1 = R.call_tir(cls.NT_matmul, (layer_norm449, model_decoder_layers_31_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv383 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm449, model_decoder_layers_31_self_attn_v_proj_weight5, model_decoder_layers_31_self_attn_v_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv384 = R.call_tir(cls.fused_reshape21_reshape21_reshape21_concatenate2_reshape22, (lv382, lv249_1, lv383), out_sinfo=R.Tensor((1, 60, 64), dtype="float16")) + lv327_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), lv384), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv385 = R.call_tir(cls.fused_reshape23_reshape24, (lv327_1,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv386 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv385, model_decoder_layers_31_self_attn_out_proj_weight5, model_decoder_layers_31_self_attn_out_proj_bias5, lv381), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm450 = R.call_tir(cls.layer_norm3, (lv386, model_decoder_layers_31_encoder_attn_layer_norm_weight5, model_decoder_layers_31_encoder_attn_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv387 = R.call_tir(cls.fused_NT_matmul_add7, (layer_norm450, model_decoder_layers_31_encoder_attn_q_proj_weight5, model_decoder_layers_31_encoder_attn_q_proj_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv388 = R.call_tir(cls.fused_reshape21_reshape25, (lv387,), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv328_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), lv388), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) + lv389 = R.call_tir(cls.fused_reshape23_reshape24, (lv328_1,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv390 = R.call_tir(cls.fused_NT_matmul_add7_add6, (lv389, model_decoder_layers_31_encoder_attn_out_proj_weight5, model_decoder_layers_31_encoder_attn_out_proj_bias5, lv386), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm451 = R.call_tir(cls.layer_norm3, (lv390, model_decoder_layers_31_final_layer_norm_weight5, model_decoder_layers_31_final_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + lv391 = R.call_tir(cls.fused_NT_matmul1_add8_gelu2, (layer_norm451, model_decoder_layers_31_fc1_weight5, model_decoder_layers_31_fc1_bias5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) + lv392 = R.call_tir(cls.fused_NT_matmul2_add7_add6, (lv391, model_decoder_layers_31_fc2_weight5, model_decoder_layers_31_fc2_bias5, lv390), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + layer_norm452 = R.call_tir(cls.layer_norm3, (lv392, model_decoder_layer_norm_weight5, model_decoder_layer_norm_bias5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + gv5 = R.call_tir(cls.NT_matmul3, (layer_norm452, model_decoder_embed_tokens_weight5), out_sinfo=R.Tensor((1, 1, 51866), dtype="float32")) + R.output(gv5) + return gv5 + + @R.function + def multinomial_from_uniform(probs: R.Tensor(("batch_size", "vocab_size"), dtype="float32"), uniform_samples: R.Tensor(("num_samples",), dtype="float32"), sample_indices: R.Tensor(("num_samples",), dtype="int32")) -> R.Tensor(("num_samples",), dtype="int32"): + num_samples = T.int64() + batch_size = T.int64() + vocab_size = T.int64() + R.func_attr({"relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "num_positions": 48, "num_samples": 8}}) + cls = Module + with R.dataflow(): + uniform_samples_1: R.Tensor((num_samples, 1), dtype="float32") = R.call_pure_packed("vm.builtin.reshape", uniform_samples, R.shape([num_samples, 1]), sinfo_args=(R.Tensor((num_samples, 1), dtype="float32"),)) + sample_indices_1: R.Tensor((num_samples, 1), dtype="int32") = R.call_pure_packed("vm.builtin.reshape", sample_indices, R.shape([num_samples, 1]), sinfo_args=(R.Tensor((num_samples, 1), dtype="int32"),)) + nn_multinomial_from_uniform = R.call_tir(cls.parallel_sampling_from_prob, (probs, uniform_samples_1, sample_indices_1), out_sinfo=R.Tensor((num_samples, 1), dtype="int32")) + gv: R.Tensor((num_samples,), dtype="int32") = R.call_pure_packed("vm.builtin.reshape", nn_multinomial_from_uniform, R.shape([num_samples]), sinfo_args=(R.Tensor((num_samples,), dtype="int32"),)) + R.output(gv) + return gv + + @R.function + def prefill(input_ids: R.Tensor((1, "seq_len"), dtype="int32"), paged_kv_cache: R.Object, packed_params: R.Tuple(R.Tensor((1280, 128, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1500, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), 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R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"))) -> R.Tensor((1, 1, 51866), dtype="float32"): + seq_len = T.int64() + R.func_attr({"num_input": 2, "relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "seq_len": 15000, "total_seq_len": 1500}}) + cls = Module + with R.dataflow(): + model_decoder_embed_tokens_weight4: R.Tensor((51866, 1280), dtype="float16") = packed_params[487] + model_decoder_embed_positions_weight4: R.Tensor((448, 1280), dtype="float16") = packed_params[488] + model_decoder_layers_0_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[489] + model_decoder_layers_0_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[490] + model_decoder_layers_0_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[491] + model_decoder_layers_0_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[492] + model_decoder_layers_0_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[493] + model_decoder_layers_0_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[494] + model_decoder_layers_0_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[495] + model_decoder_layers_0_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[496] + model_decoder_layers_0_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[497] + model_decoder_layers_0_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[501] + model_decoder_layers_0_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[502] + model_decoder_layers_0_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[503] + model_decoder_layers_0_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[504] + model_decoder_layers_0_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[505] + model_decoder_layers_0_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[506] + model_decoder_layers_0_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[507] + model_decoder_layers_0_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[508] + model_decoder_layers_0_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[509] + model_decoder_layers_0_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[510] + model_decoder_layers_0_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[511] + model_decoder_layers_0_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[512] + model_decoder_layers_1_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[513] + model_decoder_layers_1_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[514] + model_decoder_layers_1_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[515] + model_decoder_layers_1_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[516] + model_decoder_layers_1_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[517] + model_decoder_layers_1_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[518] + model_decoder_layers_1_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[519] + model_decoder_layers_1_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[520] + model_decoder_layers_1_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[521] + model_decoder_layers_1_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[525] + model_decoder_layers_1_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[526] + model_decoder_layers_1_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[527] + model_decoder_layers_1_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[528] + model_decoder_layers_1_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[529] + model_decoder_layers_1_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[530] + model_decoder_layers_1_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[531] + model_decoder_layers_1_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[532] + model_decoder_layers_1_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[533] + model_decoder_layers_1_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[534] + model_decoder_layers_1_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[535] + model_decoder_layers_1_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[536] + model_decoder_layers_2_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[537] + model_decoder_layers_2_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[538] + model_decoder_layers_2_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[539] + model_decoder_layers_2_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[540] + model_decoder_layers_2_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[541] + model_decoder_layers_2_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[542] + model_decoder_layers_2_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[543] + model_decoder_layers_2_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[544] + model_decoder_layers_2_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[545] + model_decoder_layers_2_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[549] + model_decoder_layers_2_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[550] + model_decoder_layers_2_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[551] + model_decoder_layers_2_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[552] + model_decoder_layers_2_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[553] + model_decoder_layers_2_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[554] + model_decoder_layers_2_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[555] + model_decoder_layers_2_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[556] + model_decoder_layers_2_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[557] + model_decoder_layers_2_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[558] + model_decoder_layers_2_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[559] + model_decoder_layers_2_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[560] + model_decoder_layers_3_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[561] + model_decoder_layers_3_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[562] + model_decoder_layers_3_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[563] + model_decoder_layers_3_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[564] + model_decoder_layers_3_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[565] + model_decoder_layers_3_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[566] + model_decoder_layers_3_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[567] + model_decoder_layers_3_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[568] + model_decoder_layers_3_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[569] + model_decoder_layers_3_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[573] + model_decoder_layers_3_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[574] + model_decoder_layers_3_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[575] + model_decoder_layers_3_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[576] + model_decoder_layers_3_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[577] + model_decoder_layers_3_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[578] + model_decoder_layers_3_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[579] + model_decoder_layers_3_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[580] + model_decoder_layers_3_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[581] + model_decoder_layers_3_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[582] + model_decoder_layers_3_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[583] + model_decoder_layers_3_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[584] + model_decoder_layers_4_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[585] + model_decoder_layers_4_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[586] + model_decoder_layers_4_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[587] + model_decoder_layers_4_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[588] + model_decoder_layers_4_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[589] + model_decoder_layers_4_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[590] + model_decoder_layers_4_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[591] + model_decoder_layers_4_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[592] + model_decoder_layers_4_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[593] + model_decoder_layers_4_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[597] + model_decoder_layers_4_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[598] + model_decoder_layers_4_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[599] + model_decoder_layers_4_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[600] + model_decoder_layers_4_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[601] + model_decoder_layers_4_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[602] + model_decoder_layers_4_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[603] + model_decoder_layers_4_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[604] + model_decoder_layers_4_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[605] + model_decoder_layers_4_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[606] + model_decoder_layers_4_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[607] + model_decoder_layers_4_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[608] + model_decoder_layers_5_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[609] + model_decoder_layers_5_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[610] + model_decoder_layers_5_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[611] + model_decoder_layers_5_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[612] + model_decoder_layers_5_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[613] + model_decoder_layers_5_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[614] + model_decoder_layers_5_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[615] + model_decoder_layers_5_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[616] + model_decoder_layers_5_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[617] + model_decoder_layers_5_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[621] + model_decoder_layers_5_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[622] + model_decoder_layers_5_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[623] + model_decoder_layers_5_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[624] + model_decoder_layers_5_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[625] + model_decoder_layers_5_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[626] + model_decoder_layers_5_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[627] + model_decoder_layers_5_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[628] + model_decoder_layers_5_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[629] + model_decoder_layers_5_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[630] + model_decoder_layers_5_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[631] + model_decoder_layers_5_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[632] + model_decoder_layers_6_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[633] + model_decoder_layers_6_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[634] + model_decoder_layers_6_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[635] + model_decoder_layers_6_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[636] + model_decoder_layers_6_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[637] + model_decoder_layers_6_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[638] + model_decoder_layers_6_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[639] + model_decoder_layers_6_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[640] + model_decoder_layers_6_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[641] + model_decoder_layers_6_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[645] + model_decoder_layers_6_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[646] + model_decoder_layers_6_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[647] + model_decoder_layers_6_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[648] + model_decoder_layers_6_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[649] + model_decoder_layers_6_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[650] + model_decoder_layers_6_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[651] + model_decoder_layers_6_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[652] + model_decoder_layers_6_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[653] + model_decoder_layers_6_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[654] + model_decoder_layers_6_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[655] + model_decoder_layers_6_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[656] + model_decoder_layers_7_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[657] + model_decoder_layers_7_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[658] + model_decoder_layers_7_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[659] + model_decoder_layers_7_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[660] + model_decoder_layers_7_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[661] + model_decoder_layers_7_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[662] + model_decoder_layers_7_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[663] + model_decoder_layers_7_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[664] + model_decoder_layers_7_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[665] + model_decoder_layers_7_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[669] + model_decoder_layers_7_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[670] + model_decoder_layers_7_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[671] + model_decoder_layers_7_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[672] + model_decoder_layers_7_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[673] + model_decoder_layers_7_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[674] + model_decoder_layers_7_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[675] + model_decoder_layers_7_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[676] + model_decoder_layers_7_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[677] + model_decoder_layers_7_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[678] + model_decoder_layers_7_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[679] + model_decoder_layers_7_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[680] + model_decoder_layers_8_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[681] + model_decoder_layers_8_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[682] + model_decoder_layers_8_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[683] + model_decoder_layers_8_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[684] + model_decoder_layers_8_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[685] + model_decoder_layers_8_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[686] + model_decoder_layers_8_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[687] + model_decoder_layers_8_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[688] + model_decoder_layers_8_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[689] + model_decoder_layers_8_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[693] + model_decoder_layers_8_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[694] + model_decoder_layers_8_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[695] + model_decoder_layers_8_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[696] + model_decoder_layers_8_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[697] + model_decoder_layers_8_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[698] + model_decoder_layers_8_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[699] + model_decoder_layers_8_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[700] + model_decoder_layers_8_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[701] + model_decoder_layers_8_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[702] + model_decoder_layers_8_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[703] + model_decoder_layers_8_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[704] + model_decoder_layers_9_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[705] + model_decoder_layers_9_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[706] + model_decoder_layers_9_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[707] + model_decoder_layers_9_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[708] + model_decoder_layers_9_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[709] + model_decoder_layers_9_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[710] + model_decoder_layers_9_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[711] + model_decoder_layers_9_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[712] + model_decoder_layers_9_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[713] + model_decoder_layers_9_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[717] + model_decoder_layers_9_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[718] + model_decoder_layers_9_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[719] + model_decoder_layers_9_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[720] + model_decoder_layers_9_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[721] + model_decoder_layers_9_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[722] + model_decoder_layers_9_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[723] + model_decoder_layers_9_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[724] + model_decoder_layers_9_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[725] + model_decoder_layers_9_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[726] + model_decoder_layers_9_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[727] + model_decoder_layers_9_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[728] + model_decoder_layers_10_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[729] + model_decoder_layers_10_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[730] + model_decoder_layers_10_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[731] + model_decoder_layers_10_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[732] + model_decoder_layers_10_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[733] + model_decoder_layers_10_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[734] + model_decoder_layers_10_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[735] + model_decoder_layers_10_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[736] + model_decoder_layers_10_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[737] + model_decoder_layers_10_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[741] + model_decoder_layers_10_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[742] + model_decoder_layers_10_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[743] + model_decoder_layers_10_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[744] + model_decoder_layers_10_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[745] + model_decoder_layers_10_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[746] + model_decoder_layers_10_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[747] + model_decoder_layers_10_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[748] + model_decoder_layers_10_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[749] + model_decoder_layers_10_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[750] + model_decoder_layers_10_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[751] + model_decoder_layers_10_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[752] + model_decoder_layers_11_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[753] + model_decoder_layers_11_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[754] + model_decoder_layers_11_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[755] + model_decoder_layers_11_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[756] + model_decoder_layers_11_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[757] + model_decoder_layers_11_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[758] + model_decoder_layers_11_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[759] + model_decoder_layers_11_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[760] + model_decoder_layers_11_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[761] + model_decoder_layers_11_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[765] + model_decoder_layers_11_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[766] + model_decoder_layers_11_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[767] + model_decoder_layers_11_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[768] + model_decoder_layers_11_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[769] + model_decoder_layers_11_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[770] + model_decoder_layers_11_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[771] + model_decoder_layers_11_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[772] + model_decoder_layers_11_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[773] + model_decoder_layers_11_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[774] + model_decoder_layers_11_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[775] + model_decoder_layers_11_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[776] + model_decoder_layers_12_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[777] + model_decoder_layers_12_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[778] + model_decoder_layers_12_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[779] + model_decoder_layers_12_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[780] + model_decoder_layers_12_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[781] + model_decoder_layers_12_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[782] + model_decoder_layers_12_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[783] + model_decoder_layers_12_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[784] + model_decoder_layers_12_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[785] + model_decoder_layers_12_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[789] + model_decoder_layers_12_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[790] + model_decoder_layers_12_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[791] + model_decoder_layers_12_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[792] + model_decoder_layers_12_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[793] + model_decoder_layers_12_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[794] + model_decoder_layers_12_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[795] + model_decoder_layers_12_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[796] + model_decoder_layers_12_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[797] + model_decoder_layers_12_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[798] + model_decoder_layers_12_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[799] + model_decoder_layers_12_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[800] + model_decoder_layers_13_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[801] + model_decoder_layers_13_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[802] + model_decoder_layers_13_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[803] + model_decoder_layers_13_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[804] + model_decoder_layers_13_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[805] + model_decoder_layers_13_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[806] + model_decoder_layers_13_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[807] + model_decoder_layers_13_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[808] + model_decoder_layers_13_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[809] + model_decoder_layers_13_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[813] + model_decoder_layers_13_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[814] + model_decoder_layers_13_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[815] + model_decoder_layers_13_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[816] + model_decoder_layers_13_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[817] + model_decoder_layers_13_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[818] + model_decoder_layers_13_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[819] + model_decoder_layers_13_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[820] + model_decoder_layers_13_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[821] + model_decoder_layers_13_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[822] + model_decoder_layers_13_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[823] + model_decoder_layers_13_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[824] + model_decoder_layers_14_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[825] + model_decoder_layers_14_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[826] + model_decoder_layers_14_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[827] + model_decoder_layers_14_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[828] + model_decoder_layers_14_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[829] + model_decoder_layers_14_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[830] + model_decoder_layers_14_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[831] + model_decoder_layers_14_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[832] + model_decoder_layers_14_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[833] + model_decoder_layers_14_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[837] + model_decoder_layers_14_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[838] + model_decoder_layers_14_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[839] + model_decoder_layers_14_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[840] + model_decoder_layers_14_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[841] + model_decoder_layers_14_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[842] + model_decoder_layers_14_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[843] + model_decoder_layers_14_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[844] + model_decoder_layers_14_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[845] + model_decoder_layers_14_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[846] + model_decoder_layers_14_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[847] + model_decoder_layers_14_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[848] + model_decoder_layers_15_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[849] + model_decoder_layers_15_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[850] + model_decoder_layers_15_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[851] + model_decoder_layers_15_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[852] + model_decoder_layers_15_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[853] + model_decoder_layers_15_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[854] + model_decoder_layers_15_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[855] + model_decoder_layers_15_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[856] + model_decoder_layers_15_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[857] + model_decoder_layers_15_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[861] + model_decoder_layers_15_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[862] + model_decoder_layers_15_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[863] + model_decoder_layers_15_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[864] + model_decoder_layers_15_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[865] + model_decoder_layers_15_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[866] + model_decoder_layers_15_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[867] + model_decoder_layers_15_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[868] + model_decoder_layers_15_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[869] + model_decoder_layers_15_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[870] + model_decoder_layers_15_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[871] + model_decoder_layers_15_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[872] + model_decoder_layers_16_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[873] + model_decoder_layers_16_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[874] + model_decoder_layers_16_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[875] + model_decoder_layers_16_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[876] + model_decoder_layers_16_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[877] + model_decoder_layers_16_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[878] + model_decoder_layers_16_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[879] + model_decoder_layers_16_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[880] + model_decoder_layers_16_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[881] + model_decoder_layers_16_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[885] + model_decoder_layers_16_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[886] + model_decoder_layers_16_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[887] + model_decoder_layers_16_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[888] + model_decoder_layers_16_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[889] + model_decoder_layers_16_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[890] + model_decoder_layers_16_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[891] + model_decoder_layers_16_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[892] + model_decoder_layers_16_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[893] + model_decoder_layers_16_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[894] + model_decoder_layers_16_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[895] + model_decoder_layers_16_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[896] + model_decoder_layers_17_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[897] + model_decoder_layers_17_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[898] + model_decoder_layers_17_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[899] + model_decoder_layers_17_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[900] + model_decoder_layers_17_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[901] + model_decoder_layers_17_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[902] + model_decoder_layers_17_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[903] + model_decoder_layers_17_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[904] + model_decoder_layers_17_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[905] + model_decoder_layers_17_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[909] + model_decoder_layers_17_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[910] + model_decoder_layers_17_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[911] + model_decoder_layers_17_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[912] + model_decoder_layers_17_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[913] + model_decoder_layers_17_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[914] + model_decoder_layers_17_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[915] + model_decoder_layers_17_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[916] + model_decoder_layers_17_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[917] + model_decoder_layers_17_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[918] + model_decoder_layers_17_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[919] + model_decoder_layers_17_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[920] + model_decoder_layers_18_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[921] + model_decoder_layers_18_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[922] + model_decoder_layers_18_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[923] + model_decoder_layers_18_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[924] + model_decoder_layers_18_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[925] + model_decoder_layers_18_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[926] + model_decoder_layers_18_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[927] + model_decoder_layers_18_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[928] + model_decoder_layers_18_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[929] + model_decoder_layers_18_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[933] + model_decoder_layers_18_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[934] + model_decoder_layers_18_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[935] + model_decoder_layers_18_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[936] + model_decoder_layers_18_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[937] + model_decoder_layers_18_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[938] + model_decoder_layers_18_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[939] + model_decoder_layers_18_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[940] + model_decoder_layers_18_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[941] + model_decoder_layers_18_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[942] + model_decoder_layers_18_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[943] + model_decoder_layers_18_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[944] + model_decoder_layers_19_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[945] + model_decoder_layers_19_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[946] + model_decoder_layers_19_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[947] + model_decoder_layers_19_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[948] + model_decoder_layers_19_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[949] + model_decoder_layers_19_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[950] + model_decoder_layers_19_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[951] + model_decoder_layers_19_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[952] + model_decoder_layers_19_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[953] + model_decoder_layers_19_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[957] + model_decoder_layers_19_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[958] + model_decoder_layers_19_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[959] + model_decoder_layers_19_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[960] + model_decoder_layers_19_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[961] + model_decoder_layers_19_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[962] + model_decoder_layers_19_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[963] + model_decoder_layers_19_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[964] + model_decoder_layers_19_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[965] + model_decoder_layers_19_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[966] + model_decoder_layers_19_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[967] + model_decoder_layers_19_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[968] + model_decoder_layers_20_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[969] + model_decoder_layers_20_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[970] + model_decoder_layers_20_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[971] + model_decoder_layers_20_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[972] + model_decoder_layers_20_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[973] + model_decoder_layers_20_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[974] + model_decoder_layers_20_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[975] + model_decoder_layers_20_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[976] + model_decoder_layers_20_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[977] + model_decoder_layers_20_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[981] + model_decoder_layers_20_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[982] + model_decoder_layers_20_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[983] + model_decoder_layers_20_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[984] + model_decoder_layers_20_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[985] + model_decoder_layers_20_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[986] + model_decoder_layers_20_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[987] + model_decoder_layers_20_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[988] + model_decoder_layers_20_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[989] + model_decoder_layers_20_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[990] + model_decoder_layers_20_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[991] + model_decoder_layers_20_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[992] + model_decoder_layers_21_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[993] + model_decoder_layers_21_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[994] + model_decoder_layers_21_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[995] + model_decoder_layers_21_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[996] + model_decoder_layers_21_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[997] + model_decoder_layers_21_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[998] + model_decoder_layers_21_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[999] + model_decoder_layers_21_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1000] + model_decoder_layers_21_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1001] + model_decoder_layers_21_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1005] + model_decoder_layers_21_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1006] + model_decoder_layers_21_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1007] + model_decoder_layers_21_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1008] + model_decoder_layers_21_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1009] + model_decoder_layers_21_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1010] + model_decoder_layers_21_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1011] + model_decoder_layers_21_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1012] + model_decoder_layers_21_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1013] + model_decoder_layers_21_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1014] + model_decoder_layers_21_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1015] + model_decoder_layers_21_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1016] + model_decoder_layers_22_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1017] + model_decoder_layers_22_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1018] + model_decoder_layers_22_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1019] + model_decoder_layers_22_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1020] + model_decoder_layers_22_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1021] + model_decoder_layers_22_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1022] + model_decoder_layers_22_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1023] + model_decoder_layers_22_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1024] + model_decoder_layers_22_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1025] + model_decoder_layers_22_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1029] + model_decoder_layers_22_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1030] + model_decoder_layers_22_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1031] + model_decoder_layers_22_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1032] + model_decoder_layers_22_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1033] + model_decoder_layers_22_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1034] + model_decoder_layers_22_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1035] + model_decoder_layers_22_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1036] + model_decoder_layers_22_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1037] + model_decoder_layers_22_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1038] + model_decoder_layers_22_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1039] + model_decoder_layers_22_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1040] + model_decoder_layers_23_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1041] + model_decoder_layers_23_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1042] + model_decoder_layers_23_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1043] + model_decoder_layers_23_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1044] + model_decoder_layers_23_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1045] + model_decoder_layers_23_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1046] + model_decoder_layers_23_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1047] + model_decoder_layers_23_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1048] + model_decoder_layers_23_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1049] + model_decoder_layers_23_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1053] + model_decoder_layers_23_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1054] + model_decoder_layers_23_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1055] + model_decoder_layers_23_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1056] + model_decoder_layers_23_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1057] + model_decoder_layers_23_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1058] + model_decoder_layers_23_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1059] + model_decoder_layers_23_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1060] + model_decoder_layers_23_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1061] + model_decoder_layers_23_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1062] + model_decoder_layers_23_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1063] + model_decoder_layers_23_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1064] + model_decoder_layers_24_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1065] + model_decoder_layers_24_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1066] + model_decoder_layers_24_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1067] + model_decoder_layers_24_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1068] + model_decoder_layers_24_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1069] + model_decoder_layers_24_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1070] + model_decoder_layers_24_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1071] + model_decoder_layers_24_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1072] + model_decoder_layers_24_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1073] + model_decoder_layers_24_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1077] + model_decoder_layers_24_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1078] + model_decoder_layers_24_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1079] + model_decoder_layers_24_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1080] + model_decoder_layers_24_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1081] + model_decoder_layers_24_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1082] + model_decoder_layers_24_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1083] + model_decoder_layers_24_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1084] + model_decoder_layers_24_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1085] + model_decoder_layers_24_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1086] + model_decoder_layers_24_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1087] + model_decoder_layers_24_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1088] + model_decoder_layers_25_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1089] + model_decoder_layers_25_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1090] + model_decoder_layers_25_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1091] + model_decoder_layers_25_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1092] + model_decoder_layers_25_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1093] + model_decoder_layers_25_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1094] + model_decoder_layers_25_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1095] + model_decoder_layers_25_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1096] + model_decoder_layers_25_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1097] + model_decoder_layers_25_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1101] + model_decoder_layers_25_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1102] + model_decoder_layers_25_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1103] + model_decoder_layers_25_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1104] + model_decoder_layers_25_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1105] + model_decoder_layers_25_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1106] + model_decoder_layers_25_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1107] + model_decoder_layers_25_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1108] + model_decoder_layers_25_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1109] + model_decoder_layers_25_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1110] + model_decoder_layers_25_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1111] + model_decoder_layers_25_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1112] + model_decoder_layers_26_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1113] + model_decoder_layers_26_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1114] + model_decoder_layers_26_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1115] + model_decoder_layers_26_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1116] + model_decoder_layers_26_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1117] + model_decoder_layers_26_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1118] + model_decoder_layers_26_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1119] + model_decoder_layers_26_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1120] + model_decoder_layers_26_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1121] + model_decoder_layers_26_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1125] + model_decoder_layers_26_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1126] + model_decoder_layers_26_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1127] + model_decoder_layers_26_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1128] + model_decoder_layers_26_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1129] + model_decoder_layers_26_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1130] + model_decoder_layers_26_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1131] + model_decoder_layers_26_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1132] + model_decoder_layers_26_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1133] + model_decoder_layers_26_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1134] + model_decoder_layers_26_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1135] + model_decoder_layers_26_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1136] + model_decoder_layers_27_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1137] + model_decoder_layers_27_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1138] + model_decoder_layers_27_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1139] + model_decoder_layers_27_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1140] + model_decoder_layers_27_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1141] + model_decoder_layers_27_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1142] + model_decoder_layers_27_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1143] + model_decoder_layers_27_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1144] + model_decoder_layers_27_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1145] + model_decoder_layers_27_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1149] + model_decoder_layers_27_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1150] + model_decoder_layers_27_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1151] + model_decoder_layers_27_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1152] + model_decoder_layers_27_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1153] + model_decoder_layers_27_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1154] + model_decoder_layers_27_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1155] + model_decoder_layers_27_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1156] + model_decoder_layers_27_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1157] + model_decoder_layers_27_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1158] + model_decoder_layers_27_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1159] + model_decoder_layers_27_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1160] + model_decoder_layers_28_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1161] + model_decoder_layers_28_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1162] + model_decoder_layers_28_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1163] + model_decoder_layers_28_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1164] + model_decoder_layers_28_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1165] + model_decoder_layers_28_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1166] + model_decoder_layers_28_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1167] + model_decoder_layers_28_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1168] + model_decoder_layers_28_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1169] + model_decoder_layers_28_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1173] + model_decoder_layers_28_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1174] + model_decoder_layers_28_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1175] + model_decoder_layers_28_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1176] + model_decoder_layers_28_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1177] + model_decoder_layers_28_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1178] + model_decoder_layers_28_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1179] + model_decoder_layers_28_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1180] + model_decoder_layers_28_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1181] + model_decoder_layers_28_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1182] + model_decoder_layers_28_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1183] + model_decoder_layers_28_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1184] + model_decoder_layers_29_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1185] + model_decoder_layers_29_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1186] + model_decoder_layers_29_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1187] + model_decoder_layers_29_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1188] + model_decoder_layers_29_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1189] + model_decoder_layers_29_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1190] + model_decoder_layers_29_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1191] + model_decoder_layers_29_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1192] + model_decoder_layers_29_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1193] + model_decoder_layers_29_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1197] + model_decoder_layers_29_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1198] + model_decoder_layers_29_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1199] + model_decoder_layers_29_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1200] + model_decoder_layers_29_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1201] + model_decoder_layers_29_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1202] + model_decoder_layers_29_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1203] + model_decoder_layers_29_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1204] + model_decoder_layers_29_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1205] + model_decoder_layers_29_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1206] + model_decoder_layers_29_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1207] + model_decoder_layers_29_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1208] + model_decoder_layers_30_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1209] + model_decoder_layers_30_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1210] + model_decoder_layers_30_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1211] + model_decoder_layers_30_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1212] + model_decoder_layers_30_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1213] + model_decoder_layers_30_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1214] + model_decoder_layers_30_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1215] + model_decoder_layers_30_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1216] + model_decoder_layers_30_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1217] + model_decoder_layers_30_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1221] + model_decoder_layers_30_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1222] + model_decoder_layers_30_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1223] + model_decoder_layers_30_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1224] + model_decoder_layers_30_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1225] + model_decoder_layers_30_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1226] + model_decoder_layers_30_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1227] + model_decoder_layers_30_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1228] + model_decoder_layers_30_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1229] + model_decoder_layers_30_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1230] + model_decoder_layers_30_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1231] + model_decoder_layers_30_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1232] + model_decoder_layers_31_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1233] + model_decoder_layers_31_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1234] + model_decoder_layers_31_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1235] + model_decoder_layers_31_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1236] + model_decoder_layers_31_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1237] + model_decoder_layers_31_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1238] + model_decoder_layers_31_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1239] + model_decoder_layers_31_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1240] + model_decoder_layers_31_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1241] + model_decoder_layers_31_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1245] + model_decoder_layers_31_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1246] + model_decoder_layers_31_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1247] + model_decoder_layers_31_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1248] + model_decoder_layers_31_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1249] + model_decoder_layers_31_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1250] + model_decoder_layers_31_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1251] + model_decoder_layers_31_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1252] + model_decoder_layers_31_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1253] + model_decoder_layers_31_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1254] + model_decoder_layers_31_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1255] + model_decoder_layers_31_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1256] + model_decoder_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1257] + model_decoder_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1258] + reshape1030 = R.call_tir(cls.reshape12, (input_ids,), out_sinfo=R.Tensor((seq_len,), dtype="int32")) + take5 = R.call_tir(cls.take, (model_decoder_embed_tokens_weight4, reshape1030), out_sinfo=R.Tensor((seq_len, 1280), dtype="float16")) + reshape1031 = R.call_tir(cls.reshape13, (take5,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv198: R.Tensor((seq_len,), dtype="int32") = R.call_pure_packed("vm.builtin.attention_kv_cache_get_query_positions", paged_kv_cache, sinfo_args=(R.Tensor((seq_len,), dtype="int32"),)) + take6 = R.call_tir(cls.take1, (model_decoder_embed_positions_weight4, lv198), out_sinfo=R.Tensor((seq_len, 1280), dtype="float16")) + reshape1032 = R.call_tir(cls.reshape13, (take6,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add899 = R.call_tir(cls.add5, (reshape1031, reshape1032), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm259 = R.call_tir(cls.layer_norm2, (add899, model_decoder_layers_0_self_attn_layer_norm_weight4, model_decoder_layers_0_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv32 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_0_self_attn_q_proj_weight4, layer_norm259, model_decoder_layers_0_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1033 = R.call_tir(cls.reshape14, (lv32,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv32_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_0_self_attn_k_proj_weight4, layer_norm259), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1034 = R.call_tir(cls.reshape14, (lv32_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv33 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_0_self_attn_v_proj_weight4, layer_norm259, model_decoder_layers_0_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1035 = R.call_tir(cls.reshape14, (lv33,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat64 = R.call_tir(cls.concatenate1, (reshape1033, reshape1034, reshape1035), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1036 = R.call_tir(cls.reshape15, (concat64,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv199 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), reshape1036), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1037 = R.call_tir(cls.reshape16, (lv199,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1038 = R.call_tir(cls.reshape17, (reshape1037,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv34 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_0_self_attn_out_proj_weight4, reshape1038, model_decoder_layers_0_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add903 = R.call_tir(cls.add5, (add899, lv34), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm260 = R.call_tir(cls.layer_norm2, (add903, model_decoder_layers_0_encoder_attn_layer_norm_weight4, model_decoder_layers_0_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv35 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_0_encoder_attn_q_proj_weight4, layer_norm260, model_decoder_layers_0_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1039 = R.call_tir(cls.reshape14, (lv35,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1040 = R.call_tir(cls.reshape18, (reshape1039,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv200 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), reshape1040), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1041 = R.call_tir(cls.reshape16, (lv200,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1042 = R.call_tir(cls.reshape17, (reshape1041,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv36 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_0_encoder_attn_out_proj_weight4, reshape1042, model_decoder_layers_0_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add906 = R.call_tir(cls.add5, (add903, lv36), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm261 = R.call_tir(cls.layer_norm2, (add906, model_decoder_layers_0_final_layer_norm_weight4, model_decoder_layers_0_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_0_fc1_weight4, layer_norm261, model_decoder_layers_0_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv37 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_0_fc2_weight4, lv, model_decoder_layers_0_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add909 = R.call_tir(cls.add5, (add906, lv37), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm262 = R.call_tir(cls.layer_norm2, (add909, model_decoder_layers_1_self_attn_layer_norm_weight4, model_decoder_layers_1_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv38 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_1_self_attn_q_proj_weight4, layer_norm262, model_decoder_layers_1_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1043 = R.call_tir(cls.reshape14, (lv38,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv33_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_1_self_attn_k_proj_weight4, layer_norm262), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1044 = R.call_tir(cls.reshape14, (lv33_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv39 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_1_self_attn_v_proj_weight4, layer_norm262, model_decoder_layers_1_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1045 = R.call_tir(cls.reshape14, (lv39,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat65 = R.call_tir(cls.concatenate1, (reshape1043, reshape1044, reshape1045), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1046 = R.call_tir(cls.reshape15, (concat65,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv201 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), reshape1046), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1047 = R.call_tir(cls.reshape16, (lv201,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1048 = R.call_tir(cls.reshape17, (reshape1047,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv40 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_1_self_attn_out_proj_weight4, reshape1048, model_decoder_layers_1_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add913 = R.call_tir(cls.add5, (add909, lv40), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm263 = R.call_tir(cls.layer_norm2, (add913, model_decoder_layers_1_encoder_attn_layer_norm_weight4, model_decoder_layers_1_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv41 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_1_encoder_attn_q_proj_weight4, layer_norm263, model_decoder_layers_1_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1049 = R.call_tir(cls.reshape14, (lv41,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1050 = R.call_tir(cls.reshape18, (reshape1049,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv202 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), reshape1050), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1051 = R.call_tir(cls.reshape16, (lv202,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1052 = R.call_tir(cls.reshape17, (reshape1051,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv42 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_1_encoder_attn_out_proj_weight4, reshape1052, model_decoder_layers_1_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add916 = R.call_tir(cls.add5, (add913, lv42), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm264 = R.call_tir(cls.layer_norm2, (add916, model_decoder_layers_1_final_layer_norm_weight4, model_decoder_layers_1_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_1_fc1_weight4, layer_norm264, model_decoder_layers_1_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv43 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_1_fc2_weight4, lv1, model_decoder_layers_1_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add919 = R.call_tir(cls.add5, (add916, lv43), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm265 = R.call_tir(cls.layer_norm2, (add919, model_decoder_layers_2_self_attn_layer_norm_weight4, model_decoder_layers_2_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv44 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_2_self_attn_q_proj_weight4, layer_norm265, model_decoder_layers_2_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1053 = R.call_tir(cls.reshape14, (lv44,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv34_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_2_self_attn_k_proj_weight4, layer_norm265), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1054 = R.call_tir(cls.reshape14, (lv34_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv45 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_2_self_attn_v_proj_weight4, layer_norm265, model_decoder_layers_2_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1055 = R.call_tir(cls.reshape14, (lv45,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat66 = R.call_tir(cls.concatenate1, (reshape1053, reshape1054, reshape1055), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1056 = R.call_tir(cls.reshape15, (concat66,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv203 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), reshape1056), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1057 = R.call_tir(cls.reshape16, (lv203,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1058 = R.call_tir(cls.reshape17, (reshape1057,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv46 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_2_self_attn_out_proj_weight4, reshape1058, model_decoder_layers_2_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add923 = R.call_tir(cls.add5, (add919, lv46), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm266 = R.call_tir(cls.layer_norm2, (add923, model_decoder_layers_2_encoder_attn_layer_norm_weight4, model_decoder_layers_2_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv47 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_2_encoder_attn_q_proj_weight4, layer_norm266, model_decoder_layers_2_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1059 = R.call_tir(cls.reshape14, (lv47,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1060 = R.call_tir(cls.reshape18, (reshape1059,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv204 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), reshape1060), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1061 = R.call_tir(cls.reshape16, (lv204,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1062 = R.call_tir(cls.reshape17, (reshape1061,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv48 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_2_encoder_attn_out_proj_weight4, reshape1062, model_decoder_layers_2_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add926 = R.call_tir(cls.add5, (add923, lv48), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm267 = R.call_tir(cls.layer_norm2, (add926, model_decoder_layers_2_final_layer_norm_weight4, model_decoder_layers_2_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv2 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_2_fc1_weight4, layer_norm267, model_decoder_layers_2_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv49 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_2_fc2_weight4, lv2, model_decoder_layers_2_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add929 = R.call_tir(cls.add5, (add926, lv49), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm268 = R.call_tir(cls.layer_norm2, (add929, model_decoder_layers_3_self_attn_layer_norm_weight4, model_decoder_layers_3_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv50 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_3_self_attn_q_proj_weight4, layer_norm268, model_decoder_layers_3_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1063 = R.call_tir(cls.reshape14, (lv50,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv35_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_3_self_attn_k_proj_weight4, layer_norm268), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1064 = R.call_tir(cls.reshape14, (lv35_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv51 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_3_self_attn_v_proj_weight4, layer_norm268, model_decoder_layers_3_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1065 = R.call_tir(cls.reshape14, (lv51,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat67 = R.call_tir(cls.concatenate1, (reshape1063, reshape1064, reshape1065), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1066 = R.call_tir(cls.reshape15, (concat67,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv205 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), reshape1066), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1067 = R.call_tir(cls.reshape16, (lv205,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1068 = R.call_tir(cls.reshape17, (reshape1067,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv52 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_3_self_attn_out_proj_weight4, reshape1068, model_decoder_layers_3_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add933 = R.call_tir(cls.add5, (add929, lv52), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm269 = R.call_tir(cls.layer_norm2, (add933, model_decoder_layers_3_encoder_attn_layer_norm_weight4, model_decoder_layers_3_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv53 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_3_encoder_attn_q_proj_weight4, layer_norm269, model_decoder_layers_3_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1069 = R.call_tir(cls.reshape14, (lv53,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1070 = R.call_tir(cls.reshape18, (reshape1069,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv206 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), reshape1070), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1071 = R.call_tir(cls.reshape16, (lv206,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1072 = R.call_tir(cls.reshape17, (reshape1071,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv54 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_3_encoder_attn_out_proj_weight4, reshape1072, model_decoder_layers_3_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add936 = R.call_tir(cls.add5, (add933, lv54), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm270 = R.call_tir(cls.layer_norm2, (add936, model_decoder_layers_3_final_layer_norm_weight4, model_decoder_layers_3_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv3 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_3_fc1_weight4, layer_norm270, model_decoder_layers_3_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv55 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_3_fc2_weight4, lv3, model_decoder_layers_3_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add939 = R.call_tir(cls.add5, (add936, lv55), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm271 = R.call_tir(cls.layer_norm2, (add939, model_decoder_layers_4_self_attn_layer_norm_weight4, model_decoder_layers_4_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv56 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_4_self_attn_q_proj_weight4, layer_norm271, model_decoder_layers_4_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1073 = R.call_tir(cls.reshape14, (lv56,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv36_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_4_self_attn_k_proj_weight4, layer_norm271), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1074 = R.call_tir(cls.reshape14, (lv36_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv57 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_4_self_attn_v_proj_weight4, layer_norm271, model_decoder_layers_4_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1075 = R.call_tir(cls.reshape14, (lv57,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat68 = R.call_tir(cls.concatenate1, (reshape1073, reshape1074, reshape1075), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1076 = R.call_tir(cls.reshape15, (concat68,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv207 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), reshape1076), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1077 = R.call_tir(cls.reshape16, (lv207,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1078 = R.call_tir(cls.reshape17, (reshape1077,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv58 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_4_self_attn_out_proj_weight4, reshape1078, model_decoder_layers_4_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add943 = R.call_tir(cls.add5, (add939, lv58), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm272 = R.call_tir(cls.layer_norm2, (add943, model_decoder_layers_4_encoder_attn_layer_norm_weight4, model_decoder_layers_4_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv59 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_4_encoder_attn_q_proj_weight4, layer_norm272, model_decoder_layers_4_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1079 = R.call_tir(cls.reshape14, (lv59,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1080 = R.call_tir(cls.reshape18, (reshape1079,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv208 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), reshape1080), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1081 = R.call_tir(cls.reshape16, (lv208,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1082 = R.call_tir(cls.reshape17, (reshape1081,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv60 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_4_encoder_attn_out_proj_weight4, reshape1082, model_decoder_layers_4_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add946 = R.call_tir(cls.add5, (add943, lv60), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm273 = R.call_tir(cls.layer_norm2, (add946, model_decoder_layers_4_final_layer_norm_weight4, model_decoder_layers_4_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv4 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_4_fc1_weight4, layer_norm273, model_decoder_layers_4_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv61 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_4_fc2_weight4, lv4, model_decoder_layers_4_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add949 = R.call_tir(cls.add5, (add946, lv61), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm274 = R.call_tir(cls.layer_norm2, (add949, model_decoder_layers_5_self_attn_layer_norm_weight4, model_decoder_layers_5_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv62 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_5_self_attn_q_proj_weight4, layer_norm274, model_decoder_layers_5_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1083 = R.call_tir(cls.reshape14, (lv62,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv37_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_5_self_attn_k_proj_weight4, layer_norm274), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1084 = R.call_tir(cls.reshape14, (lv37_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv63 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_5_self_attn_v_proj_weight4, layer_norm274, model_decoder_layers_5_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1085 = R.call_tir(cls.reshape14, (lv63,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat69 = R.call_tir(cls.concatenate1, (reshape1083, reshape1084, reshape1085), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1086 = R.call_tir(cls.reshape15, (concat69,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv209 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), reshape1086), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1087 = R.call_tir(cls.reshape16, (lv209,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1088 = R.call_tir(cls.reshape17, (reshape1087,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv64 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_5_self_attn_out_proj_weight4, reshape1088, model_decoder_layers_5_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add953 = R.call_tir(cls.add5, (add949, lv64), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm275 = R.call_tir(cls.layer_norm2, (add953, model_decoder_layers_5_encoder_attn_layer_norm_weight4, model_decoder_layers_5_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv65 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_5_encoder_attn_q_proj_weight4, layer_norm275, model_decoder_layers_5_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1089 = R.call_tir(cls.reshape14, (lv65,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1090 = R.call_tir(cls.reshape18, (reshape1089,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv210 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), reshape1090), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1091 = R.call_tir(cls.reshape16, (lv210,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1092 = R.call_tir(cls.reshape17, (reshape1091,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv66 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_5_encoder_attn_out_proj_weight4, reshape1092, model_decoder_layers_5_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add956 = R.call_tir(cls.add5, (add953, lv66), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm276 = R.call_tir(cls.layer_norm2, (add956, model_decoder_layers_5_final_layer_norm_weight4, model_decoder_layers_5_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv5 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_5_fc1_weight4, layer_norm276, model_decoder_layers_5_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv67 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_5_fc2_weight4, lv5, model_decoder_layers_5_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add959 = R.call_tir(cls.add5, (add956, lv67), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm277 = R.call_tir(cls.layer_norm2, (add959, model_decoder_layers_6_self_attn_layer_norm_weight4, model_decoder_layers_6_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv68 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_6_self_attn_q_proj_weight4, layer_norm277, model_decoder_layers_6_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1093 = R.call_tir(cls.reshape14, (lv68,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv38_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_6_self_attn_k_proj_weight4, layer_norm277), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1094 = R.call_tir(cls.reshape14, (lv38_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv69 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_6_self_attn_v_proj_weight4, layer_norm277, model_decoder_layers_6_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1095 = R.call_tir(cls.reshape14, (lv69,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat70 = R.call_tir(cls.concatenate1, (reshape1093, reshape1094, reshape1095), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1096 = R.call_tir(cls.reshape15, (concat70,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv211 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), reshape1096), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1097 = R.call_tir(cls.reshape16, (lv211,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1098 = R.call_tir(cls.reshape17, (reshape1097,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv70 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_6_self_attn_out_proj_weight4, reshape1098, model_decoder_layers_6_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add963 = R.call_tir(cls.add5, (add959, lv70), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm278 = R.call_tir(cls.layer_norm2, (add963, model_decoder_layers_6_encoder_attn_layer_norm_weight4, model_decoder_layers_6_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv71 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_6_encoder_attn_q_proj_weight4, layer_norm278, model_decoder_layers_6_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1099 = R.call_tir(cls.reshape14, (lv71,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1100 = R.call_tir(cls.reshape18, (reshape1099,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv212 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), reshape1100), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1101 = R.call_tir(cls.reshape16, (lv212,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1102 = R.call_tir(cls.reshape17, (reshape1101,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv72 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_6_encoder_attn_out_proj_weight4, reshape1102, model_decoder_layers_6_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add966 = R.call_tir(cls.add5, (add963, lv72), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm279 = R.call_tir(cls.layer_norm2, (add966, model_decoder_layers_6_final_layer_norm_weight4, model_decoder_layers_6_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv6 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_6_fc1_weight4, layer_norm279, model_decoder_layers_6_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv73 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_6_fc2_weight4, lv6, model_decoder_layers_6_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add969 = R.call_tir(cls.add5, (add966, lv73), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm280 = R.call_tir(cls.layer_norm2, (add969, model_decoder_layers_7_self_attn_layer_norm_weight4, model_decoder_layers_7_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv74 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_7_self_attn_q_proj_weight4, layer_norm280, model_decoder_layers_7_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1103 = R.call_tir(cls.reshape14, (lv74,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv39_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_7_self_attn_k_proj_weight4, layer_norm280), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1104 = R.call_tir(cls.reshape14, (lv39_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv75 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_7_self_attn_v_proj_weight4, layer_norm280, model_decoder_layers_7_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1105 = R.call_tir(cls.reshape14, (lv75,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat71 = R.call_tir(cls.concatenate1, (reshape1103, reshape1104, reshape1105), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1106 = R.call_tir(cls.reshape15, (concat71,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv213 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), reshape1106), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1107 = R.call_tir(cls.reshape16, (lv213,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1108 = R.call_tir(cls.reshape17, (reshape1107,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv76 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_7_self_attn_out_proj_weight4, reshape1108, model_decoder_layers_7_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add973 = R.call_tir(cls.add5, (add969, lv76), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm281 = R.call_tir(cls.layer_norm2, (add973, model_decoder_layers_7_encoder_attn_layer_norm_weight4, model_decoder_layers_7_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv77 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_7_encoder_attn_q_proj_weight4, layer_norm281, model_decoder_layers_7_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1109 = R.call_tir(cls.reshape14, (lv77,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1110 = R.call_tir(cls.reshape18, (reshape1109,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv214 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), reshape1110), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1111 = R.call_tir(cls.reshape16, (lv214,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1112 = R.call_tir(cls.reshape17, (reshape1111,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv78 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_7_encoder_attn_out_proj_weight4, reshape1112, model_decoder_layers_7_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add976 = R.call_tir(cls.add5, (add973, lv78), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm282 = R.call_tir(cls.layer_norm2, (add976, model_decoder_layers_7_final_layer_norm_weight4, model_decoder_layers_7_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv7 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_7_fc1_weight4, layer_norm282, model_decoder_layers_7_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv79 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_7_fc2_weight4, lv7, model_decoder_layers_7_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add979 = R.call_tir(cls.add5, (add976, lv79), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm283 = R.call_tir(cls.layer_norm2, (add979, model_decoder_layers_8_self_attn_layer_norm_weight4, model_decoder_layers_8_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv80 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_8_self_attn_q_proj_weight4, layer_norm283, model_decoder_layers_8_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1113 = R.call_tir(cls.reshape14, (lv80,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv40_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_8_self_attn_k_proj_weight4, layer_norm283), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1114 = R.call_tir(cls.reshape14, (lv40_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv81 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_8_self_attn_v_proj_weight4, layer_norm283, model_decoder_layers_8_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1115 = R.call_tir(cls.reshape14, (lv81,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat72 = R.call_tir(cls.concatenate1, (reshape1113, reshape1114, reshape1115), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1116 = R.call_tir(cls.reshape15, (concat72,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv215 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), reshape1116), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1117 = R.call_tir(cls.reshape16, (lv215,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1118 = R.call_tir(cls.reshape17, (reshape1117,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv82 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_8_self_attn_out_proj_weight4, reshape1118, model_decoder_layers_8_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add983 = R.call_tir(cls.add5, (add979, lv82), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm284 = R.call_tir(cls.layer_norm2, (add983, model_decoder_layers_8_encoder_attn_layer_norm_weight4, model_decoder_layers_8_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv83 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_8_encoder_attn_q_proj_weight4, layer_norm284, model_decoder_layers_8_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1119 = R.call_tir(cls.reshape14, (lv83,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1120 = R.call_tir(cls.reshape18, (reshape1119,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv216 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), reshape1120), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1121 = R.call_tir(cls.reshape16, (lv216,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1122 = R.call_tir(cls.reshape17, (reshape1121,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv84 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_8_encoder_attn_out_proj_weight4, reshape1122, model_decoder_layers_8_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add986 = R.call_tir(cls.add5, (add983, lv84), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm285 = R.call_tir(cls.layer_norm2, (add986, model_decoder_layers_8_final_layer_norm_weight4, model_decoder_layers_8_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv8 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_8_fc1_weight4, layer_norm285, model_decoder_layers_8_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv85 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_8_fc2_weight4, lv8, model_decoder_layers_8_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add989 = R.call_tir(cls.add5, (add986, lv85), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm286 = R.call_tir(cls.layer_norm2, (add989, model_decoder_layers_9_self_attn_layer_norm_weight4, model_decoder_layers_9_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv86 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_9_self_attn_q_proj_weight4, layer_norm286, model_decoder_layers_9_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1123 = R.call_tir(cls.reshape14, (lv86,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv41_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_9_self_attn_k_proj_weight4, layer_norm286), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1124 = R.call_tir(cls.reshape14, (lv41_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv87 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_9_self_attn_v_proj_weight4, layer_norm286, model_decoder_layers_9_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1125 = R.call_tir(cls.reshape14, (lv87,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat73 = R.call_tir(cls.concatenate1, (reshape1123, reshape1124, reshape1125), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1126 = R.call_tir(cls.reshape15, (concat73,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv217 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), reshape1126), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1127 = R.call_tir(cls.reshape16, (lv217,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1128 = R.call_tir(cls.reshape17, (reshape1127,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv88 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_9_self_attn_out_proj_weight4, reshape1128, model_decoder_layers_9_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add993 = R.call_tir(cls.add5, (add989, lv88), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm287 = R.call_tir(cls.layer_norm2, (add993, model_decoder_layers_9_encoder_attn_layer_norm_weight4, model_decoder_layers_9_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv89 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_9_encoder_attn_q_proj_weight4, layer_norm287, model_decoder_layers_9_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1129 = R.call_tir(cls.reshape14, (lv89,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1130 = R.call_tir(cls.reshape18, (reshape1129,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv218 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), reshape1130), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1131 = R.call_tir(cls.reshape16, (lv218,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1132 = R.call_tir(cls.reshape17, (reshape1131,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv90 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_9_encoder_attn_out_proj_weight4, reshape1132, model_decoder_layers_9_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add996 = R.call_tir(cls.add5, (add993, lv90), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm288 = R.call_tir(cls.layer_norm2, (add996, model_decoder_layers_9_final_layer_norm_weight4, model_decoder_layers_9_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv9 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_9_fc1_weight4, layer_norm288, model_decoder_layers_9_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv91 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_9_fc2_weight4, lv9, model_decoder_layers_9_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add999 = R.call_tir(cls.add5, (add996, lv91), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm289 = R.call_tir(cls.layer_norm2, (add999, model_decoder_layers_10_self_attn_layer_norm_weight4, model_decoder_layers_10_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv92 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_10_self_attn_q_proj_weight4, layer_norm289, model_decoder_layers_10_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1133 = R.call_tir(cls.reshape14, (lv92,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv42_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_10_self_attn_k_proj_weight4, layer_norm289), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1134 = R.call_tir(cls.reshape14, (lv42_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv93 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_10_self_attn_v_proj_weight4, layer_norm289, model_decoder_layers_10_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1135 = R.call_tir(cls.reshape14, (lv93,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat74 = R.call_tir(cls.concatenate1, (reshape1133, reshape1134, reshape1135), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1136 = R.call_tir(cls.reshape15, (concat74,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv219 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), reshape1136), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1137 = R.call_tir(cls.reshape16, (lv219,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1138 = R.call_tir(cls.reshape17, (reshape1137,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv94 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_10_self_attn_out_proj_weight4, reshape1138, model_decoder_layers_10_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1003 = R.call_tir(cls.add5, (add999, lv94), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm290 = R.call_tir(cls.layer_norm2, (add1003, model_decoder_layers_10_encoder_attn_layer_norm_weight4, model_decoder_layers_10_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv95 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_10_encoder_attn_q_proj_weight4, layer_norm290, model_decoder_layers_10_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1139 = R.call_tir(cls.reshape14, (lv95,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1140 = R.call_tir(cls.reshape18, (reshape1139,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv220 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), reshape1140), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1141 = R.call_tir(cls.reshape16, (lv220,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1142 = R.call_tir(cls.reshape17, (reshape1141,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv96 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_10_encoder_attn_out_proj_weight4, reshape1142, model_decoder_layers_10_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1006 = R.call_tir(cls.add5, (add1003, lv96), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm291 = R.call_tir(cls.layer_norm2, (add1006, model_decoder_layers_10_final_layer_norm_weight4, model_decoder_layers_10_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv10 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_10_fc1_weight4, layer_norm291, model_decoder_layers_10_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv97 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_10_fc2_weight4, lv10, model_decoder_layers_10_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1009 = R.call_tir(cls.add5, (add1006, lv97), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm292 = R.call_tir(cls.layer_norm2, (add1009, model_decoder_layers_11_self_attn_layer_norm_weight4, model_decoder_layers_11_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv98 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_11_self_attn_q_proj_weight4, layer_norm292, model_decoder_layers_11_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1143 = R.call_tir(cls.reshape14, (lv98,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv43_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_11_self_attn_k_proj_weight4, layer_norm292), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1144 = R.call_tir(cls.reshape14, (lv43_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv99 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_11_self_attn_v_proj_weight4, layer_norm292, model_decoder_layers_11_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1145 = R.call_tir(cls.reshape14, (lv99,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat75 = R.call_tir(cls.concatenate1, (reshape1143, reshape1144, reshape1145), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1146 = R.call_tir(cls.reshape15, (concat75,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv221 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), reshape1146), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1147 = R.call_tir(cls.reshape16, (lv221,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1148 = R.call_tir(cls.reshape17, (reshape1147,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv100 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_11_self_attn_out_proj_weight4, reshape1148, model_decoder_layers_11_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1013 = R.call_tir(cls.add5, (add1009, lv100), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm293 = R.call_tir(cls.layer_norm2, (add1013, model_decoder_layers_11_encoder_attn_layer_norm_weight4, model_decoder_layers_11_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv101 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_11_encoder_attn_q_proj_weight4, layer_norm293, model_decoder_layers_11_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1149 = R.call_tir(cls.reshape14, (lv101,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1150 = R.call_tir(cls.reshape18, (reshape1149,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv222 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), reshape1150), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1151 = R.call_tir(cls.reshape16, (lv222,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1152 = R.call_tir(cls.reshape17, (reshape1151,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv102 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_11_encoder_attn_out_proj_weight4, reshape1152, model_decoder_layers_11_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1016 = R.call_tir(cls.add5, (add1013, lv102), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm294 = R.call_tir(cls.layer_norm2, (add1016, model_decoder_layers_11_final_layer_norm_weight4, model_decoder_layers_11_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv11 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_11_fc1_weight4, layer_norm294, model_decoder_layers_11_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv103 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_11_fc2_weight4, lv11, model_decoder_layers_11_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1019 = R.call_tir(cls.add5, (add1016, lv103), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm295 = R.call_tir(cls.layer_norm2, (add1019, model_decoder_layers_12_self_attn_layer_norm_weight4, model_decoder_layers_12_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv104 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_12_self_attn_q_proj_weight4, layer_norm295, model_decoder_layers_12_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1153 = R.call_tir(cls.reshape14, (lv104,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv44_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_12_self_attn_k_proj_weight4, layer_norm295), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1154 = R.call_tir(cls.reshape14, (lv44_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv105 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_12_self_attn_v_proj_weight4, layer_norm295, model_decoder_layers_12_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1155 = R.call_tir(cls.reshape14, (lv105,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat76 = R.call_tir(cls.concatenate1, (reshape1153, reshape1154, reshape1155), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1156 = R.call_tir(cls.reshape15, (concat76,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv223 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), reshape1156), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1157 = R.call_tir(cls.reshape16, (lv223,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1158 = R.call_tir(cls.reshape17, (reshape1157,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv106 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_12_self_attn_out_proj_weight4, reshape1158, model_decoder_layers_12_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1023 = R.call_tir(cls.add5, (add1019, lv106), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm296 = R.call_tir(cls.layer_norm2, (add1023, model_decoder_layers_12_encoder_attn_layer_norm_weight4, model_decoder_layers_12_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv107 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_12_encoder_attn_q_proj_weight4, layer_norm296, model_decoder_layers_12_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1159 = R.call_tir(cls.reshape14, (lv107,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1160 = R.call_tir(cls.reshape18, (reshape1159,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv224 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), reshape1160), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1161 = R.call_tir(cls.reshape16, (lv224,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1162 = R.call_tir(cls.reshape17, (reshape1161,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv108 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_12_encoder_attn_out_proj_weight4, reshape1162, model_decoder_layers_12_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1026 = R.call_tir(cls.add5, (add1023, lv108), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm297 = R.call_tir(cls.layer_norm2, (add1026, model_decoder_layers_12_final_layer_norm_weight4, model_decoder_layers_12_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv12 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_12_fc1_weight4, layer_norm297, model_decoder_layers_12_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv109 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_12_fc2_weight4, lv12, model_decoder_layers_12_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1029 = R.call_tir(cls.add5, (add1026, lv109), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm298 = R.call_tir(cls.layer_norm2, (add1029, model_decoder_layers_13_self_attn_layer_norm_weight4, model_decoder_layers_13_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv110 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_13_self_attn_q_proj_weight4, layer_norm298, model_decoder_layers_13_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1163 = R.call_tir(cls.reshape14, (lv110,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv45_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_13_self_attn_k_proj_weight4, layer_norm298), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1164 = R.call_tir(cls.reshape14, (lv45_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv111 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_13_self_attn_v_proj_weight4, layer_norm298, model_decoder_layers_13_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1165 = R.call_tir(cls.reshape14, (lv111,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat77 = R.call_tir(cls.concatenate1, (reshape1163, reshape1164, reshape1165), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1166 = R.call_tir(cls.reshape15, (concat77,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv225 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), reshape1166), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1167 = R.call_tir(cls.reshape16, (lv225,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1168 = R.call_tir(cls.reshape17, (reshape1167,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv112 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_13_self_attn_out_proj_weight4, reshape1168, model_decoder_layers_13_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1033 = R.call_tir(cls.add5, (add1029, lv112), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm299 = R.call_tir(cls.layer_norm2, (add1033, model_decoder_layers_13_encoder_attn_layer_norm_weight4, model_decoder_layers_13_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv113 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_13_encoder_attn_q_proj_weight4, layer_norm299, model_decoder_layers_13_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1169 = R.call_tir(cls.reshape14, (lv113,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1170 = R.call_tir(cls.reshape18, (reshape1169,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv226 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), reshape1170), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1171 = R.call_tir(cls.reshape16, (lv226,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1172 = R.call_tir(cls.reshape17, (reshape1171,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv114 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_13_encoder_attn_out_proj_weight4, reshape1172, model_decoder_layers_13_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1036 = R.call_tir(cls.add5, (add1033, lv114), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm300 = R.call_tir(cls.layer_norm2, (add1036, model_decoder_layers_13_final_layer_norm_weight4, model_decoder_layers_13_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv13 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_13_fc1_weight4, layer_norm300, model_decoder_layers_13_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv115 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_13_fc2_weight4, lv13, model_decoder_layers_13_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1039 = R.call_tir(cls.add5, (add1036, lv115), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm301 = R.call_tir(cls.layer_norm2, (add1039, model_decoder_layers_14_self_attn_layer_norm_weight4, model_decoder_layers_14_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv116 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_14_self_attn_q_proj_weight4, layer_norm301, model_decoder_layers_14_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1173 = R.call_tir(cls.reshape14, (lv116,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv46_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_14_self_attn_k_proj_weight4, layer_norm301), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1174 = R.call_tir(cls.reshape14, (lv46_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv117 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_14_self_attn_v_proj_weight4, layer_norm301, model_decoder_layers_14_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1175 = R.call_tir(cls.reshape14, (lv117,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat78 = R.call_tir(cls.concatenate1, (reshape1173, reshape1174, reshape1175), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1176 = R.call_tir(cls.reshape15, (concat78,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv227 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), reshape1176), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1177 = R.call_tir(cls.reshape16, (lv227,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1178 = R.call_tir(cls.reshape17, (reshape1177,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv118 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_14_self_attn_out_proj_weight4, reshape1178, model_decoder_layers_14_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1043 = R.call_tir(cls.add5, (add1039, lv118), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm302 = R.call_tir(cls.layer_norm2, (add1043, model_decoder_layers_14_encoder_attn_layer_norm_weight4, model_decoder_layers_14_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv119 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_14_encoder_attn_q_proj_weight4, layer_norm302, model_decoder_layers_14_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1179 = R.call_tir(cls.reshape14, (lv119,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1180 = R.call_tir(cls.reshape18, (reshape1179,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv228 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), reshape1180), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1181 = R.call_tir(cls.reshape16, (lv228,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1182 = R.call_tir(cls.reshape17, (reshape1181,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv120 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_14_encoder_attn_out_proj_weight4, reshape1182, model_decoder_layers_14_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1046 = R.call_tir(cls.add5, (add1043, lv120), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm303 = R.call_tir(cls.layer_norm2, (add1046, model_decoder_layers_14_final_layer_norm_weight4, model_decoder_layers_14_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv14 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_14_fc1_weight4, layer_norm303, model_decoder_layers_14_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv121 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_14_fc2_weight4, lv14, model_decoder_layers_14_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1049 = R.call_tir(cls.add5, (add1046, lv121), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm304 = R.call_tir(cls.layer_norm2, (add1049, model_decoder_layers_15_self_attn_layer_norm_weight4, model_decoder_layers_15_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv122 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_15_self_attn_q_proj_weight4, layer_norm304, model_decoder_layers_15_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1183 = R.call_tir(cls.reshape14, (lv122,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv47_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_15_self_attn_k_proj_weight4, layer_norm304), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1184 = R.call_tir(cls.reshape14, (lv47_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv123 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_15_self_attn_v_proj_weight4, layer_norm304, model_decoder_layers_15_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1185 = R.call_tir(cls.reshape14, (lv123,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat79 = R.call_tir(cls.concatenate1, (reshape1183, reshape1184, reshape1185), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1186 = R.call_tir(cls.reshape15, (concat79,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv229 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), reshape1186), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1187 = R.call_tir(cls.reshape16, (lv229,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1188 = R.call_tir(cls.reshape17, (reshape1187,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv124 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_15_self_attn_out_proj_weight4, reshape1188, model_decoder_layers_15_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1053 = R.call_tir(cls.add5, (add1049, lv124), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm305 = R.call_tir(cls.layer_norm2, (add1053, model_decoder_layers_15_encoder_attn_layer_norm_weight4, model_decoder_layers_15_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv125 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_15_encoder_attn_q_proj_weight4, layer_norm305, model_decoder_layers_15_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1189 = R.call_tir(cls.reshape14, (lv125,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1190 = R.call_tir(cls.reshape18, (reshape1189,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv230 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), reshape1190), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1191 = R.call_tir(cls.reshape16, (lv230,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1192 = R.call_tir(cls.reshape17, (reshape1191,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv126 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_15_encoder_attn_out_proj_weight4, reshape1192, model_decoder_layers_15_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1056 = R.call_tir(cls.add5, (add1053, lv126), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm306 = R.call_tir(cls.layer_norm2, (add1056, model_decoder_layers_15_final_layer_norm_weight4, model_decoder_layers_15_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv15 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_15_fc1_weight4, layer_norm306, model_decoder_layers_15_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv127 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_15_fc2_weight4, lv15, model_decoder_layers_15_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1059 = R.call_tir(cls.add5, (add1056, lv127), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm307 = R.call_tir(cls.layer_norm2, (add1059, model_decoder_layers_16_self_attn_layer_norm_weight4, model_decoder_layers_16_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv128 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_16_self_attn_q_proj_weight4, layer_norm307, model_decoder_layers_16_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1193 = R.call_tir(cls.reshape14, (lv128,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv48_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_16_self_attn_k_proj_weight4, layer_norm307), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1194 = R.call_tir(cls.reshape14, (lv48_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv129 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_16_self_attn_v_proj_weight4, layer_norm307, model_decoder_layers_16_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1195 = R.call_tir(cls.reshape14, (lv129,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat80 = R.call_tir(cls.concatenate1, (reshape1193, reshape1194, reshape1195), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1196 = R.call_tir(cls.reshape15, (concat80,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv231 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), reshape1196), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1197 = R.call_tir(cls.reshape16, (lv231,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1198 = R.call_tir(cls.reshape17, (reshape1197,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv130 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_16_self_attn_out_proj_weight4, reshape1198, model_decoder_layers_16_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1063 = R.call_tir(cls.add5, (add1059, lv130), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm308 = R.call_tir(cls.layer_norm2, (add1063, model_decoder_layers_16_encoder_attn_layer_norm_weight4, model_decoder_layers_16_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv131 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_16_encoder_attn_q_proj_weight4, layer_norm308, model_decoder_layers_16_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1199 = R.call_tir(cls.reshape14, (lv131,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1200 = R.call_tir(cls.reshape18, (reshape1199,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv232 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), reshape1200), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1201 = R.call_tir(cls.reshape16, (lv232,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1202 = R.call_tir(cls.reshape17, (reshape1201,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv132 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_16_encoder_attn_out_proj_weight4, reshape1202, model_decoder_layers_16_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1066 = R.call_tir(cls.add5, (add1063, lv132), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm309 = R.call_tir(cls.layer_norm2, (add1066, model_decoder_layers_16_final_layer_norm_weight4, model_decoder_layers_16_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv16 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_16_fc1_weight4, layer_norm309, model_decoder_layers_16_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv133 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_16_fc2_weight4, lv16, model_decoder_layers_16_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1069 = R.call_tir(cls.add5, (add1066, lv133), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm310 = R.call_tir(cls.layer_norm2, (add1069, model_decoder_layers_17_self_attn_layer_norm_weight4, model_decoder_layers_17_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv134 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_17_self_attn_q_proj_weight4, layer_norm310, model_decoder_layers_17_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1203 = R.call_tir(cls.reshape14, (lv134,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv49_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_17_self_attn_k_proj_weight4, layer_norm310), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1204 = R.call_tir(cls.reshape14, (lv49_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv135 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_17_self_attn_v_proj_weight4, layer_norm310, model_decoder_layers_17_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1205 = R.call_tir(cls.reshape14, (lv135,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat81 = R.call_tir(cls.concatenate1, (reshape1203, reshape1204, reshape1205), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1206 = R.call_tir(cls.reshape15, (concat81,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv233 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), reshape1206), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1207 = R.call_tir(cls.reshape16, (lv233,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1208 = R.call_tir(cls.reshape17, (reshape1207,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv136 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_17_self_attn_out_proj_weight4, reshape1208, model_decoder_layers_17_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1073 = R.call_tir(cls.add5, (add1069, lv136), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm311 = R.call_tir(cls.layer_norm2, (add1073, model_decoder_layers_17_encoder_attn_layer_norm_weight4, model_decoder_layers_17_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv137 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_17_encoder_attn_q_proj_weight4, layer_norm311, model_decoder_layers_17_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1209 = R.call_tir(cls.reshape14, (lv137,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1210 = R.call_tir(cls.reshape18, (reshape1209,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv234 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), reshape1210), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1211 = R.call_tir(cls.reshape16, (lv234,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1212 = R.call_tir(cls.reshape17, (reshape1211,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv138 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_17_encoder_attn_out_proj_weight4, reshape1212, model_decoder_layers_17_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1076 = R.call_tir(cls.add5, (add1073, lv138), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm312 = R.call_tir(cls.layer_norm2, (add1076, model_decoder_layers_17_final_layer_norm_weight4, model_decoder_layers_17_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv17 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_17_fc1_weight4, layer_norm312, model_decoder_layers_17_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv139 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_17_fc2_weight4, lv17, model_decoder_layers_17_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1079 = R.call_tir(cls.add5, (add1076, lv139), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm313 = R.call_tir(cls.layer_norm2, (add1079, model_decoder_layers_18_self_attn_layer_norm_weight4, model_decoder_layers_18_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv140 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_18_self_attn_q_proj_weight4, layer_norm313, model_decoder_layers_18_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1213 = R.call_tir(cls.reshape14, (lv140,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv50_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_18_self_attn_k_proj_weight4, layer_norm313), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1214 = R.call_tir(cls.reshape14, (lv50_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv141 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_18_self_attn_v_proj_weight4, layer_norm313, model_decoder_layers_18_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1215 = R.call_tir(cls.reshape14, (lv141,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat82 = R.call_tir(cls.concatenate1, (reshape1213, reshape1214, reshape1215), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1216 = R.call_tir(cls.reshape15, (concat82,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv235 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), reshape1216), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1217 = R.call_tir(cls.reshape16, (lv235,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1218 = R.call_tir(cls.reshape17, (reshape1217,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv142 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_18_self_attn_out_proj_weight4, reshape1218, model_decoder_layers_18_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1083 = R.call_tir(cls.add5, (add1079, lv142), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm314 = R.call_tir(cls.layer_norm2, (add1083, model_decoder_layers_18_encoder_attn_layer_norm_weight4, model_decoder_layers_18_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv143 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_18_encoder_attn_q_proj_weight4, layer_norm314, model_decoder_layers_18_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1219 = R.call_tir(cls.reshape14, (lv143,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1220 = R.call_tir(cls.reshape18, (reshape1219,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv236 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), reshape1220), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1221 = R.call_tir(cls.reshape16, (lv236,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1222 = R.call_tir(cls.reshape17, (reshape1221,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv144 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_18_encoder_attn_out_proj_weight4, reshape1222, model_decoder_layers_18_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1086 = R.call_tir(cls.add5, (add1083, lv144), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm315 = R.call_tir(cls.layer_norm2, (add1086, model_decoder_layers_18_final_layer_norm_weight4, model_decoder_layers_18_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv18 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_18_fc1_weight4, layer_norm315, model_decoder_layers_18_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv145 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_18_fc2_weight4, lv18, model_decoder_layers_18_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1089 = R.call_tir(cls.add5, (add1086, lv145), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm316 = R.call_tir(cls.layer_norm2, (add1089, model_decoder_layers_19_self_attn_layer_norm_weight4, model_decoder_layers_19_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv146 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_19_self_attn_q_proj_weight4, layer_norm316, model_decoder_layers_19_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1223 = R.call_tir(cls.reshape14, (lv146,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv51_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_19_self_attn_k_proj_weight4, layer_norm316), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1224 = R.call_tir(cls.reshape14, (lv51_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv147 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_19_self_attn_v_proj_weight4, layer_norm316, model_decoder_layers_19_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1225 = R.call_tir(cls.reshape14, (lv147,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat83 = R.call_tir(cls.concatenate1, (reshape1223, reshape1224, reshape1225), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1226 = R.call_tir(cls.reshape15, (concat83,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv237 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), reshape1226), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1227 = R.call_tir(cls.reshape16, (lv237,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1228 = R.call_tir(cls.reshape17, (reshape1227,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv148 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_19_self_attn_out_proj_weight4, reshape1228, model_decoder_layers_19_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1093 = R.call_tir(cls.add5, (add1089, lv148), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm317 = R.call_tir(cls.layer_norm2, (add1093, model_decoder_layers_19_encoder_attn_layer_norm_weight4, model_decoder_layers_19_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv149 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_19_encoder_attn_q_proj_weight4, layer_norm317, model_decoder_layers_19_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1229 = R.call_tir(cls.reshape14, (lv149,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1230 = R.call_tir(cls.reshape18, (reshape1229,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv238 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), reshape1230), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1231 = R.call_tir(cls.reshape16, (lv238,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1232 = R.call_tir(cls.reshape17, (reshape1231,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv150 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_19_encoder_attn_out_proj_weight4, reshape1232, model_decoder_layers_19_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1096 = R.call_tir(cls.add5, (add1093, lv150), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm318 = R.call_tir(cls.layer_norm2, (add1096, model_decoder_layers_19_final_layer_norm_weight4, model_decoder_layers_19_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv19 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_19_fc1_weight4, layer_norm318, model_decoder_layers_19_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv151 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_19_fc2_weight4, lv19, model_decoder_layers_19_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1099 = R.call_tir(cls.add5, (add1096, lv151), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm319 = R.call_tir(cls.layer_norm2, (add1099, model_decoder_layers_20_self_attn_layer_norm_weight4, model_decoder_layers_20_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv152 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_20_self_attn_q_proj_weight4, layer_norm319, model_decoder_layers_20_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1233 = R.call_tir(cls.reshape14, (lv152,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv52_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_20_self_attn_k_proj_weight4, layer_norm319), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1234 = R.call_tir(cls.reshape14, (lv52_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv153 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_20_self_attn_v_proj_weight4, layer_norm319, model_decoder_layers_20_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1235 = R.call_tir(cls.reshape14, (lv153,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat84 = R.call_tir(cls.concatenate1, (reshape1233, reshape1234, reshape1235), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1236 = R.call_tir(cls.reshape15, (concat84,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv239 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), reshape1236), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1237 = R.call_tir(cls.reshape16, (lv239,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1238 = R.call_tir(cls.reshape17, (reshape1237,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv154 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_20_self_attn_out_proj_weight4, reshape1238, model_decoder_layers_20_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1103 = R.call_tir(cls.add5, (add1099, lv154), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm320 = R.call_tir(cls.layer_norm2, (add1103, model_decoder_layers_20_encoder_attn_layer_norm_weight4, model_decoder_layers_20_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv155 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_20_encoder_attn_q_proj_weight4, layer_norm320, model_decoder_layers_20_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1239 = R.call_tir(cls.reshape14, (lv155,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1240 = R.call_tir(cls.reshape18, (reshape1239,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv240 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), reshape1240), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1241 = R.call_tir(cls.reshape16, (lv240,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1242 = R.call_tir(cls.reshape17, (reshape1241,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv156 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_20_encoder_attn_out_proj_weight4, reshape1242, model_decoder_layers_20_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1106 = R.call_tir(cls.add5, (add1103, lv156), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm321 = R.call_tir(cls.layer_norm2, (add1106, model_decoder_layers_20_final_layer_norm_weight4, model_decoder_layers_20_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv20 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_20_fc1_weight4, layer_norm321, model_decoder_layers_20_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv157 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_20_fc2_weight4, lv20, model_decoder_layers_20_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1109 = R.call_tir(cls.add5, (add1106, lv157), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm322 = R.call_tir(cls.layer_norm2, (add1109, model_decoder_layers_21_self_attn_layer_norm_weight4, model_decoder_layers_21_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv158 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_21_self_attn_q_proj_weight4, layer_norm322, model_decoder_layers_21_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1243 = R.call_tir(cls.reshape14, (lv158,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv53_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_21_self_attn_k_proj_weight4, layer_norm322), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1244 = R.call_tir(cls.reshape14, (lv53_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv159 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_21_self_attn_v_proj_weight4, layer_norm322, model_decoder_layers_21_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1245 = R.call_tir(cls.reshape14, (lv159,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat85 = R.call_tir(cls.concatenate1, (reshape1243, reshape1244, reshape1245), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1246 = R.call_tir(cls.reshape15, (concat85,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv241 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), reshape1246), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1247 = R.call_tir(cls.reshape16, (lv241,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1248 = R.call_tir(cls.reshape17, (reshape1247,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv160 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_21_self_attn_out_proj_weight4, reshape1248, model_decoder_layers_21_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1113 = R.call_tir(cls.add5, (add1109, lv160), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm323 = R.call_tir(cls.layer_norm2, (add1113, model_decoder_layers_21_encoder_attn_layer_norm_weight4, model_decoder_layers_21_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv161 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_21_encoder_attn_q_proj_weight4, layer_norm323, model_decoder_layers_21_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1249 = R.call_tir(cls.reshape14, (lv161,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1250 = R.call_tir(cls.reshape18, (reshape1249,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv242 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), reshape1250), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1251 = R.call_tir(cls.reshape16, (lv242,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1252 = R.call_tir(cls.reshape17, (reshape1251,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv162 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_21_encoder_attn_out_proj_weight4, reshape1252, model_decoder_layers_21_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1116 = R.call_tir(cls.add5, (add1113, lv162), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm324 = R.call_tir(cls.layer_norm2, (add1116, model_decoder_layers_21_final_layer_norm_weight4, model_decoder_layers_21_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv21 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_21_fc1_weight4, layer_norm324, model_decoder_layers_21_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv163 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_21_fc2_weight4, lv21, model_decoder_layers_21_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1119 = R.call_tir(cls.add5, (add1116, lv163), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm325 = R.call_tir(cls.layer_norm2, (add1119, model_decoder_layers_22_self_attn_layer_norm_weight4, model_decoder_layers_22_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv164 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_22_self_attn_q_proj_weight4, layer_norm325, model_decoder_layers_22_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1253 = R.call_tir(cls.reshape14, (lv164,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv54_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_22_self_attn_k_proj_weight4, layer_norm325), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1254 = R.call_tir(cls.reshape14, (lv54_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv165 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_22_self_attn_v_proj_weight4, layer_norm325, model_decoder_layers_22_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1255 = R.call_tir(cls.reshape14, (lv165,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat86 = R.call_tir(cls.concatenate1, (reshape1253, reshape1254, reshape1255), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1256 = R.call_tir(cls.reshape15, (concat86,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv243 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), reshape1256), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1257 = R.call_tir(cls.reshape16, (lv243,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1258 = R.call_tir(cls.reshape17, (reshape1257,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv166 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_22_self_attn_out_proj_weight4, reshape1258, model_decoder_layers_22_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1123 = R.call_tir(cls.add5, (add1119, lv166), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm326 = R.call_tir(cls.layer_norm2, (add1123, model_decoder_layers_22_encoder_attn_layer_norm_weight4, model_decoder_layers_22_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv167 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_22_encoder_attn_q_proj_weight4, layer_norm326, model_decoder_layers_22_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1259 = R.call_tir(cls.reshape14, (lv167,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1260 = R.call_tir(cls.reshape18, (reshape1259,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv244 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), reshape1260), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1261 = R.call_tir(cls.reshape16, (lv244,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1262 = R.call_tir(cls.reshape17, (reshape1261,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv168 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_22_encoder_attn_out_proj_weight4, reshape1262, model_decoder_layers_22_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1126 = R.call_tir(cls.add5, (add1123, lv168), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm327 = R.call_tir(cls.layer_norm2, (add1126, model_decoder_layers_22_final_layer_norm_weight4, model_decoder_layers_22_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv22 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_22_fc1_weight4, layer_norm327, model_decoder_layers_22_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv169 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_22_fc2_weight4, lv22, model_decoder_layers_22_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1129 = R.call_tir(cls.add5, (add1126, lv169), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm328 = R.call_tir(cls.layer_norm2, (add1129, model_decoder_layers_23_self_attn_layer_norm_weight4, model_decoder_layers_23_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv170 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_23_self_attn_q_proj_weight4, layer_norm328, model_decoder_layers_23_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1263 = R.call_tir(cls.reshape14, (lv170,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv55_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_23_self_attn_k_proj_weight4, layer_norm328), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1264 = R.call_tir(cls.reshape14, (lv55_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv171 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_23_self_attn_v_proj_weight4, layer_norm328, model_decoder_layers_23_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1265 = R.call_tir(cls.reshape14, (lv171,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat87 = R.call_tir(cls.concatenate1, (reshape1263, reshape1264, reshape1265), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1266 = R.call_tir(cls.reshape15, (concat87,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv245 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), reshape1266), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1267 = R.call_tir(cls.reshape16, (lv245,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1268 = R.call_tir(cls.reshape17, (reshape1267,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv172 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_23_self_attn_out_proj_weight4, reshape1268, model_decoder_layers_23_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1133 = R.call_tir(cls.add5, (add1129, lv172), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm329 = R.call_tir(cls.layer_norm2, (add1133, model_decoder_layers_23_encoder_attn_layer_norm_weight4, model_decoder_layers_23_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv173 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_23_encoder_attn_q_proj_weight4, layer_norm329, model_decoder_layers_23_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1269 = R.call_tir(cls.reshape14, (lv173,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1270 = R.call_tir(cls.reshape18, (reshape1269,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv246 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), reshape1270), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1271 = R.call_tir(cls.reshape16, (lv246,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1272 = R.call_tir(cls.reshape17, (reshape1271,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv174 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_23_encoder_attn_out_proj_weight4, reshape1272, model_decoder_layers_23_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1136 = R.call_tir(cls.add5, (add1133, lv174), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm330 = R.call_tir(cls.layer_norm2, (add1136, model_decoder_layers_23_final_layer_norm_weight4, model_decoder_layers_23_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv23 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_23_fc1_weight4, layer_norm330, model_decoder_layers_23_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv175 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_23_fc2_weight4, lv23, model_decoder_layers_23_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1139 = R.call_tir(cls.add5, (add1136, lv175), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm331 = R.call_tir(cls.layer_norm2, (add1139, model_decoder_layers_24_self_attn_layer_norm_weight4, model_decoder_layers_24_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv176 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_24_self_attn_q_proj_weight4, layer_norm331, model_decoder_layers_24_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1273 = R.call_tir(cls.reshape14, (lv176,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv56_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_24_self_attn_k_proj_weight4, layer_norm331), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1274 = R.call_tir(cls.reshape14, (lv56_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv177 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_24_self_attn_v_proj_weight4, layer_norm331, model_decoder_layers_24_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1275 = R.call_tir(cls.reshape14, (lv177,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat88 = R.call_tir(cls.concatenate1, (reshape1273, reshape1274, reshape1275), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1276 = R.call_tir(cls.reshape15, (concat88,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv247 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), reshape1276), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1277 = R.call_tir(cls.reshape16, (lv247,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1278 = R.call_tir(cls.reshape17, (reshape1277,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv178 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_24_self_attn_out_proj_weight4, reshape1278, model_decoder_layers_24_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1143 = R.call_tir(cls.add5, (add1139, lv178), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm332 = R.call_tir(cls.layer_norm2, (add1143, model_decoder_layers_24_encoder_attn_layer_norm_weight4, model_decoder_layers_24_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv179 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_24_encoder_attn_q_proj_weight4, layer_norm332, model_decoder_layers_24_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1279 = R.call_tir(cls.reshape14, (lv179,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1280 = R.call_tir(cls.reshape18, (reshape1279,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv248 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), reshape1280), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1281 = R.call_tir(cls.reshape16, (lv248,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1282 = R.call_tir(cls.reshape17, (reshape1281,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv180 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_24_encoder_attn_out_proj_weight4, reshape1282, model_decoder_layers_24_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1146 = R.call_tir(cls.add5, (add1143, lv180), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm333 = R.call_tir(cls.layer_norm2, (add1146, model_decoder_layers_24_final_layer_norm_weight4, model_decoder_layers_24_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv24 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_24_fc1_weight4, layer_norm333, model_decoder_layers_24_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv181 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_24_fc2_weight4, lv24, model_decoder_layers_24_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1149 = R.call_tir(cls.add5, (add1146, lv181), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm334 = R.call_tir(cls.layer_norm2, (add1149, model_decoder_layers_25_self_attn_layer_norm_weight4, model_decoder_layers_25_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv182 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_25_self_attn_q_proj_weight4, layer_norm334, model_decoder_layers_25_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1283 = R.call_tir(cls.reshape14, (lv182,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv57_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_25_self_attn_k_proj_weight4, layer_norm334), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1284 = R.call_tir(cls.reshape14, (lv57_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv183 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_25_self_attn_v_proj_weight4, layer_norm334, model_decoder_layers_25_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1285 = R.call_tir(cls.reshape14, (lv183,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat89 = R.call_tir(cls.concatenate1, (reshape1283, reshape1284, reshape1285), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1286 = R.call_tir(cls.reshape15, (concat89,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv249 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), reshape1286), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1287 = R.call_tir(cls.reshape16, (lv249,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1288 = R.call_tir(cls.reshape17, (reshape1287,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv184 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_25_self_attn_out_proj_weight4, reshape1288, model_decoder_layers_25_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1153 = R.call_tir(cls.add5, (add1149, lv184), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm335 = R.call_tir(cls.layer_norm2, (add1153, model_decoder_layers_25_encoder_attn_layer_norm_weight4, model_decoder_layers_25_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv185 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_25_encoder_attn_q_proj_weight4, layer_norm335, model_decoder_layers_25_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1289 = R.call_tir(cls.reshape14, (lv185,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1290 = R.call_tir(cls.reshape18, (reshape1289,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv250 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), reshape1290), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1291 = R.call_tir(cls.reshape16, (lv250,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1292 = R.call_tir(cls.reshape17, (reshape1291,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv186 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_25_encoder_attn_out_proj_weight4, reshape1292, model_decoder_layers_25_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1156 = R.call_tir(cls.add5, (add1153, lv186), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm336 = R.call_tir(cls.layer_norm2, (add1156, model_decoder_layers_25_final_layer_norm_weight4, model_decoder_layers_25_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv25 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_25_fc1_weight4, layer_norm336, model_decoder_layers_25_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv187 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_25_fc2_weight4, lv25, model_decoder_layers_25_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1159 = R.call_tir(cls.add5, (add1156, lv187), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm337 = R.call_tir(cls.layer_norm2, (add1159, model_decoder_layers_26_self_attn_layer_norm_weight4, model_decoder_layers_26_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv188 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_26_self_attn_q_proj_weight4, layer_norm337, model_decoder_layers_26_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1293 = R.call_tir(cls.reshape14, (lv188,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv58_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_26_self_attn_k_proj_weight4, layer_norm337), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1294 = R.call_tir(cls.reshape14, (lv58_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv189 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_26_self_attn_v_proj_weight4, layer_norm337, model_decoder_layers_26_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1295 = R.call_tir(cls.reshape14, (lv189,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat90 = R.call_tir(cls.concatenate1, (reshape1293, reshape1294, reshape1295), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1296 = R.call_tir(cls.reshape15, (concat90,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv251 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), reshape1296), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1297 = R.call_tir(cls.reshape16, (lv251,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1298 = R.call_tir(cls.reshape17, (reshape1297,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv190 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_26_self_attn_out_proj_weight4, reshape1298, model_decoder_layers_26_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1163 = R.call_tir(cls.add5, (add1159, lv190), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm338 = R.call_tir(cls.layer_norm2, (add1163, model_decoder_layers_26_encoder_attn_layer_norm_weight4, model_decoder_layers_26_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv191 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_26_encoder_attn_q_proj_weight4, layer_norm338, model_decoder_layers_26_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1299 = R.call_tir(cls.reshape14, (lv191,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1300 = R.call_tir(cls.reshape18, (reshape1299,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv252 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), reshape1300), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1301 = R.call_tir(cls.reshape16, (lv252,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1302 = R.call_tir(cls.reshape17, (reshape1301,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv192 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_26_encoder_attn_out_proj_weight4, reshape1302, model_decoder_layers_26_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1166 = R.call_tir(cls.add5, (add1163, lv192), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm339 = R.call_tir(cls.layer_norm2, (add1166, model_decoder_layers_26_final_layer_norm_weight4, model_decoder_layers_26_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv26 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_26_fc1_weight4, layer_norm339, model_decoder_layers_26_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv193 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_26_fc2_weight4, lv26, model_decoder_layers_26_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1169 = R.call_tir(cls.add5, (add1166, lv193), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm340 = R.call_tir(cls.layer_norm2, (add1169, model_decoder_layers_27_self_attn_layer_norm_weight4, model_decoder_layers_27_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv194 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_27_self_attn_q_proj_weight4, layer_norm340, model_decoder_layers_27_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1303 = R.call_tir(cls.reshape14, (lv194,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv59_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_27_self_attn_k_proj_weight4, layer_norm340), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1304 = R.call_tir(cls.reshape14, (lv59_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv195 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_27_self_attn_v_proj_weight4, layer_norm340, model_decoder_layers_27_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1305 = R.call_tir(cls.reshape14, (lv195,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat91 = R.call_tir(cls.concatenate1, (reshape1303, reshape1304, reshape1305), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1306 = R.call_tir(cls.reshape15, (concat91,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv253 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), reshape1306), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1307 = R.call_tir(cls.reshape16, (lv253,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1308 = R.call_tir(cls.reshape17, (reshape1307,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv196 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_27_self_attn_out_proj_weight4, reshape1308, model_decoder_layers_27_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1173 = R.call_tir(cls.add5, (add1169, lv196), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm341 = R.call_tir(cls.layer_norm2, (add1173, model_decoder_layers_27_encoder_attn_layer_norm_weight4, model_decoder_layers_27_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv197 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_27_encoder_attn_q_proj_weight4, layer_norm341, model_decoder_layers_27_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1309 = R.call_tir(cls.reshape14, (lv197,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1310 = R.call_tir(cls.reshape18, (reshape1309,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv254 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), reshape1310), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1311 = R.call_tir(cls.reshape16, (lv254,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1312 = R.call_tir(cls.reshape17, (reshape1311,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv198_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_27_encoder_attn_out_proj_weight4, reshape1312, model_decoder_layers_27_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1176 = R.call_tir(cls.add5, (add1173, lv198_1), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm342 = R.call_tir(cls.layer_norm2, (add1176, model_decoder_layers_27_final_layer_norm_weight4, model_decoder_layers_27_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv27 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_27_fc1_weight4, layer_norm342, model_decoder_layers_27_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv199_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_27_fc2_weight4, lv27, model_decoder_layers_27_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1179 = R.call_tir(cls.add5, (add1176, lv199_1), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm343 = R.call_tir(cls.layer_norm2, (add1179, model_decoder_layers_28_self_attn_layer_norm_weight4, model_decoder_layers_28_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv200_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_28_self_attn_q_proj_weight4, layer_norm343, model_decoder_layers_28_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1313 = R.call_tir(cls.reshape14, (lv200_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv60_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_28_self_attn_k_proj_weight4, layer_norm343), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1314 = R.call_tir(cls.reshape14, (lv60_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv201_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_28_self_attn_v_proj_weight4, layer_norm343, model_decoder_layers_28_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1315 = R.call_tir(cls.reshape14, (lv201_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat92 = R.call_tir(cls.concatenate1, (reshape1313, reshape1314, reshape1315), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1316 = R.call_tir(cls.reshape15, (concat92,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv255 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), reshape1316), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1317 = R.call_tir(cls.reshape16, (lv255,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1318 = R.call_tir(cls.reshape17, (reshape1317,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv202_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_28_self_attn_out_proj_weight4, reshape1318, model_decoder_layers_28_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1183 = R.call_tir(cls.add5, (add1179, lv202_1), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm344 = R.call_tir(cls.layer_norm2, (add1183, model_decoder_layers_28_encoder_attn_layer_norm_weight4, model_decoder_layers_28_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv203_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_28_encoder_attn_q_proj_weight4, layer_norm344, model_decoder_layers_28_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1319 = R.call_tir(cls.reshape14, (lv203_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1320 = R.call_tir(cls.reshape18, (reshape1319,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv256 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), reshape1320), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1321 = R.call_tir(cls.reshape16, (lv256,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1322 = R.call_tir(cls.reshape17, (reshape1321,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv204_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_28_encoder_attn_out_proj_weight4, reshape1322, model_decoder_layers_28_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1186 = R.call_tir(cls.add5, (add1183, lv204_1), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm345 = R.call_tir(cls.layer_norm2, (add1186, model_decoder_layers_28_final_layer_norm_weight4, model_decoder_layers_28_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv28 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_28_fc1_weight4, layer_norm345, model_decoder_layers_28_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv205_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_28_fc2_weight4, lv28, model_decoder_layers_28_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1189 = R.call_tir(cls.add5, (add1186, lv205_1), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm346 = R.call_tir(cls.layer_norm2, (add1189, model_decoder_layers_29_self_attn_layer_norm_weight4, model_decoder_layers_29_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv206_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_29_self_attn_q_proj_weight4, layer_norm346, model_decoder_layers_29_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1323 = R.call_tir(cls.reshape14, (lv206_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv61_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_29_self_attn_k_proj_weight4, layer_norm346), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1324 = R.call_tir(cls.reshape14, (lv61_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv207_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_29_self_attn_v_proj_weight4, layer_norm346, model_decoder_layers_29_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1325 = R.call_tir(cls.reshape14, (lv207_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat93 = R.call_tir(cls.concatenate1, (reshape1323, reshape1324, reshape1325), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1326 = R.call_tir(cls.reshape15, (concat93,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv257 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), reshape1326), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1327 = R.call_tir(cls.reshape16, (lv257,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1328 = R.call_tir(cls.reshape17, (reshape1327,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv208_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_29_self_attn_out_proj_weight4, reshape1328, model_decoder_layers_29_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1193 = R.call_tir(cls.add5, (add1189, lv208_1), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm347 = R.call_tir(cls.layer_norm2, (add1193, model_decoder_layers_29_encoder_attn_layer_norm_weight4, model_decoder_layers_29_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv209_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_29_encoder_attn_q_proj_weight4, layer_norm347, model_decoder_layers_29_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1329 = R.call_tir(cls.reshape14, (lv209_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1330 = R.call_tir(cls.reshape18, (reshape1329,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv258 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), reshape1330), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1331 = R.call_tir(cls.reshape16, (lv258,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1332 = R.call_tir(cls.reshape17, (reshape1331,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv210_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_29_encoder_attn_out_proj_weight4, reshape1332, model_decoder_layers_29_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1196 = R.call_tir(cls.add5, (add1193, lv210_1), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm348 = R.call_tir(cls.layer_norm2, (add1196, model_decoder_layers_29_final_layer_norm_weight4, model_decoder_layers_29_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv29 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_29_fc1_weight4, layer_norm348, model_decoder_layers_29_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv211_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_29_fc2_weight4, lv29, model_decoder_layers_29_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1199 = R.call_tir(cls.add5, (add1196, lv211_1), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm349 = R.call_tir(cls.layer_norm2, (add1199, model_decoder_layers_30_self_attn_layer_norm_weight4, model_decoder_layers_30_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv212_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_30_self_attn_q_proj_weight4, layer_norm349, model_decoder_layers_30_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1333 = R.call_tir(cls.reshape14, (lv212_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv62_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_30_self_attn_k_proj_weight4, layer_norm349), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1334 = R.call_tir(cls.reshape14, (lv62_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv213_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_30_self_attn_v_proj_weight4, layer_norm349, model_decoder_layers_30_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1335 = R.call_tir(cls.reshape14, (lv213_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat94 = R.call_tir(cls.concatenate1, (reshape1333, reshape1334, reshape1335), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1336 = R.call_tir(cls.reshape15, (concat94,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv259 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), reshape1336), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1337 = R.call_tir(cls.reshape16, (lv259,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1338 = R.call_tir(cls.reshape17, (reshape1337,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv214_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_30_self_attn_out_proj_weight4, reshape1338, model_decoder_layers_30_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1203 = R.call_tir(cls.add5, (add1199, lv214_1), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm350 = R.call_tir(cls.layer_norm2, (add1203, model_decoder_layers_30_encoder_attn_layer_norm_weight4, model_decoder_layers_30_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv215_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_30_encoder_attn_q_proj_weight4, layer_norm350, model_decoder_layers_30_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1339 = R.call_tir(cls.reshape14, (lv215_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1340 = R.call_tir(cls.reshape18, (reshape1339,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv260 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), reshape1340), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1341 = R.call_tir(cls.reshape16, (lv260,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1342 = R.call_tir(cls.reshape17, (reshape1341,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv216_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_30_encoder_attn_out_proj_weight4, reshape1342, model_decoder_layers_30_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1206 = R.call_tir(cls.add5, (add1203, lv216_1), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm351 = R.call_tir(cls.layer_norm2, (add1206, model_decoder_layers_30_final_layer_norm_weight4, model_decoder_layers_30_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv30 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_30_fc1_weight4, layer_norm351, model_decoder_layers_30_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv217_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_30_fc2_weight4, lv30, model_decoder_layers_30_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1209 = R.call_tir(cls.add5, (add1206, lv217_1), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm352 = R.call_tir(cls.layer_norm2, (add1209, model_decoder_layers_31_self_attn_layer_norm_weight4, model_decoder_layers_31_self_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv218_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_31_self_attn_q_proj_weight4, layer_norm352, model_decoder_layers_31_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1343 = R.call_tir(cls.reshape14, (lv218_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv63_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_31_self_attn_k_proj_weight4, layer_norm352), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1344 = R.call_tir(cls.reshape14, (lv63_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + lv219_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_31_self_attn_v_proj_weight4, layer_norm352, model_decoder_layers_31_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1345 = R.call_tir(cls.reshape14, (lv219_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + concat95 = R.call_tir(cls.concatenate1, (reshape1343, reshape1344, reshape1345), out_sinfo=R.Tensor((1, seq_len, 60, 64), dtype="float16")) + reshape1346 = R.call_tir(cls.reshape15, (concat95,), out_sinfo=R.Tensor((seq_len, 60, 64), dtype="float16")) + lv261 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), reshape1346), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1347 = R.call_tir(cls.reshape16, (lv261,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1348 = R.call_tir(cls.reshape17, (reshape1347,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv220_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_31_self_attn_out_proj_weight4, reshape1348, model_decoder_layers_31_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1213 = R.call_tir(cls.add5, (add1209, lv220_1), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm353 = R.call_tir(cls.layer_norm2, (add1213, model_decoder_layers_31_encoder_attn_layer_norm_weight4, model_decoder_layers_31_encoder_attn_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv221_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_31_encoder_attn_q_proj_weight4, layer_norm353, model_decoder_layers_31_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + reshape1349 = R.call_tir(cls.reshape14, (lv221_1,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1350 = R.call_tir(cls.reshape18, (reshape1349,), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + lv262 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), reshape1350), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) + reshape1351 = R.call_tir(cls.reshape16, (lv262,), out_sinfo=R.Tensor((1, seq_len, 20, 64), dtype="float16")) + reshape1352 = R.call_tir(cls.reshape17, (reshape1351,), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv222_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_31_encoder_attn_out_proj_weight4, reshape1352, model_decoder_layers_31_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1216 = R.call_tir(cls.add5, (add1213, lv222_1), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm354 = R.call_tir(cls.layer_norm2, (add1216, model_decoder_layers_31_final_layer_norm_weight4, model_decoder_layers_31_final_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv31 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_31_fc1_weight4, layer_norm354, model_decoder_layers_31_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) + lv223_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_31_fc2_weight4, lv31, model_decoder_layers_31_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + add1219 = R.call_tir(cls.add5, (add1216, lv223_1), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + layer_norm355 = R.call_tir(cls.layer_norm2, (add1219, model_decoder_layer_norm_weight4, model_decoder_layer_norm_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) + lv263 = R.call_tir(cls.index, (layer_norm355,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) + gv4 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul2_cublas", (model_decoder_embed_tokens_weight4, lv263), out_sinfo=R.Tensor((1, 1, 51866), dtype="float32")) + R.output(gv4) + return gv4 + + @R.function + def renormalize_by_top_p(probs: R.Tensor(("batch_size", "vocab_size"), dtype="float32"), top_p: R.Tensor(("batch_size",), dtype="float32"), init_pivots: R.Tensor(("batch_size", 3), dtype="float32")) -> R.Tensor(("batch_size", "vocab_size"), dtype="float32"): + batch_size = T.int64() + vocab_size = T.int64() + R.func_attr({"relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "num_positions": 48, "num_samples": 8}}) + cls = Module + with R.dataflow(): + lv6 = R.call_tir(cls.top_p_pivot_cutoff, (probs, top_p, init_pivots), out_sinfo=[R.Tensor((batch_size,), dtype="float32"), R.Tensor((batch_size,), dtype="float32")]) + lv7: R.Tensor((batch_size,), dtype="float32") = lv6[0] + lv8: R.Tensor((batch_size,), dtype="float32") = lv6[1] + gv5 = R.call_tir(cls.top_p_renorm_after_cutoff, (probs, lv7, lv8), out_sinfo=R.Tensor((batch_size, vocab_size), dtype="float32")) + R.output(gv5) + return gv5 + + @R.function + def sample_with_top_p(sorted_probs: R.Tensor(("batch_size", "vocab_size"), dtype="float32"), sorted_indices: R.Tensor(("batch_size", "vocab_size"), dtype="int32"), uniform_samples: R.Tensor(("num_samples",), dtype="float32"), sample_indices: R.Tensor(("num_samples",), dtype="int32"), top_p: R.Tensor(("batch_size",), dtype="float32")) -> R.Tensor(("num_samples",), dtype="int32"): + num_samples = T.int64() + batch_size = T.int64() + vocab_size = T.int64() + R.func_attr({"relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "num_positions": 48, "num_samples": 8}}) + cls = Module + with R.dataflow(): + uniform_samples1: R.Tensor((num_samples, 1), dtype="float32") = R.call_pure_packed("vm.builtin.reshape", uniform_samples, R.shape([num_samples, 1]), sinfo_args=(R.Tensor((num_samples, 1), dtype="float32"),)) + sample_indices1: R.Tensor((num_samples, 1), dtype="int32") = R.call_pure_packed("vm.builtin.reshape", sample_indices, R.shape([num_samples, 1]), sinfo_args=(R.Tensor((num_samples, 1), dtype="int32"),)) + sample_indices2: R.Tensor((batch_size, 1), dtype="float32") = R.call_pure_packed("vm.builtin.reshape", top_p, R.shape([batch_size, 1]), sinfo_args=(R.Tensor((batch_size, 1), dtype="float32"),)) + lv3 = R.call_tir(cls.full, R.tuple(), out_sinfo=R.Tensor((batch_size, 1), dtype="int32"), tir_vars=R.shape([vocab_size])) + lv1: R.Tensor((8 * (batch_size * vocab_size * 4) + 8388608 + batch_size * vocab_size * 12,), dtype="uint8") = R.builtin.alloc_tensor(R.shape([8 * (batch_size * vocab_size * 4) + 8388608 + batch_size * vocab_size * 12]), R.dtype("uint8"), R.prim_value(0), R.str("global")) + cumsum = R.call_tir(cls.cumsum, (sorted_probs, lv1), out_sinfo=R.Tensor((batch_size, vocab_size), dtype="float32")) + lv4 = R.call_tir(cls.get_renorm_prob, (cumsum, sample_indices2, lv3), out_sinfo=R.Tensor((batch_size, 1), dtype="float32")) + lv5 = R.call_tir(cls.get_index_from_sorted, (cumsum, sorted_indices, lv4, uniform_samples1, sample_indices1), out_sinfo=R.Tensor((num_samples, 1), dtype="int32")) + gv2: R.Tensor((num_samples,), dtype="int32") = R.call_pure_packed("vm.builtin.reshape", lv5, R.shape([num_samples]), sinfo_args=(R.Tensor((num_samples,), dtype="int32"),)) + R.output(gv2) + return gv2 + + @R.function + def sampler_take_probs(unsorted_probs: R.Tensor(("batch_size", "vocab_size"), dtype="float32"), sorted_indices: R.Tensor(("batch_size", "vocab_size"), dtype="int32"), sample_indices: R.Tensor(("num_samples",), dtype="int32"), sampling_result: R.Tensor(("num_samples",), dtype="int32"), lobprob_offsets: R.Tensor(("num_positions",), dtype="int32")) -> R.Tuple(R.Tensor(("num_samples",), dtype="float32"), R.Tensor(("num_positions",), dtype="float32"), R.Tensor(("num_positions",), dtype="int32")): + num_samples = T.int64() + num_positions = T.int64() + batch_size = T.int64() + vocab_size = T.int64() + R.func_attr({"relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "num_positions": 48, "num_samples": 8}}) + cls = Module + with R.dataflow(): + gv3 = R.call_tir(cls.sampler_take_probs_tir, (unsorted_probs, sorted_indices, sample_indices, sampling_result, lobprob_offsets), out_sinfo=[R.Tensor((num_samples,), dtype="float32"), R.Tensor((num_positions,), dtype="float32"), R.Tensor((num_positions,), dtype="int32")]) + R.output(gv3) + return gv3 + + @R.function + def sampler_verify_draft_tokens(draft_probs: R.Tensor(("num_nodes", "vocab_size"), dtype="float32"), draft_tokens: R.Tensor(("num_nodes",), dtype="int32"), model_probs: R.Tensor(("num_nodes", "vocab_size"), dtype="float32"), token_tree_first_child: R.Tensor(("num_nodes",), dtype="int32"), token_tree_next_sibling: R.Tensor(("num_nodes",), dtype="int32"), uniform_samples: R.Tensor(("num_nodes",), dtype="float32"), token_tree_parent_ptr: R.Tensor(("nbatch",), dtype="int32")) -> R.Tuple(R.Tensor(("num_nodes", "vocab_size"), dtype="float32"), R.Tensor(("nbatch",), dtype="int32")): + num_nodes = T.int64() + vocab_size = T.int64() + nbatch = T.int64() + R.func_attr({"relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "num_positions": 48, "num_samples": 8}}) + cls = Module + with R.dataflow(): + gv4: R.Tuple(R.Tensor((num_nodes, vocab_size), dtype="float32"), R.Tensor((nbatch,), dtype="int32")) = R.call_tir_inplace(cls.batch_verify_on_gpu_single_kernel, (draft_probs, draft_tokens, model_probs, token_tree_first_child, token_tree_next_sibling, uniform_samples, token_tree_parent_ptr), out_sinfo=[R.Tensor((num_nodes, vocab_size), dtype="float32"), R.Tensor((nbatch,), dtype="int32")], inplace_indices=[2, 6]) + R.output(gv4) + return gv4 + + @R.function + def softmax_with_temperature(logits: R.Tensor(("batch_size", 1, "vocab_size"), dtype="float32"), temperature: R.Tensor(("batch_size",), dtype="float32")) -> R.Tensor(("batch_size", 1, "vocab_size"), dtype="float32"): + batch_size = T.int64() + vocab_size = T.int64() + R.func_attr({"relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "seq_len": 15000, "total_seq_len": 1500}}) + cls = Module + with R.dataflow(): + lv: R.Tensor((batch_size, vocab_size), dtype="float32") = R.call_pure_packed("vm.builtin.reshape", logits, R.shape([batch_size, vocab_size]), sinfo_args=(R.Tensor((batch_size, vocab_size), dtype="float32"),)) + lv1 = R.call_tir(cls.chunk_lse, (lv, temperature), out_sinfo=[R.Tensor((batch_size, (vocab_size + 4096 - 1) // 4096), dtype="float32"), R.Tensor((batch_size, (vocab_size + 4096 - 1) // 4096), dtype="float32")]) + lv2: R.Tensor((batch_size, (vocab_size + 4096 - 1) // 4096), dtype="float32") = lv1[0] + lv3: R.Tensor((batch_size, (vocab_size + 4096 - 1) // 4096), dtype="float32") = lv1[1] + lv4 = R.call_tir(cls.softmax_with_chunked_sum, (lv, temperature, lv2, lv3), out_sinfo=R.Tensor((batch_size, vocab_size), dtype="float32")) + gv: R.Tensor((batch_size, 1, vocab_size), dtype="float32") = R.call_pure_packed("vm.builtin.reshape", lv4, R.shape([batch_size, 1, vocab_size]), sinfo_args=(R.Tensor((batch_size, 1, vocab_size), dtype="float32"),)) + R.output(gv) + return gv + +# Metadata omitted. Use show_meta=True in script() method to show it. \ No newline at end of file diff --git a/merges.txt b/merges.txt new file mode 100644 index 0000000000000000000000000000000000000000..6038932a2a1f09a66991b1c2adae0d14066fa29e --- /dev/null +++ b/merges.txt @@ -0,0 +1,50001 @@ +#version: 0.2 +Ġ t +Ġ a +Ġt h +i n +e r +Ġ w +Ġ s +o u +Ġth e +r e +o n +a t +e n +Ġ c +i t +i s +Ġ b +n d +Ġ d +Ġ m +Ġ h +Ġ o +in g +e s +Ġ p +Ġt o +a n +Ġ f +o r +l l +Ġ I +Ġ l +Ġ y +a r +Ġ g +Ġy ou +e d +Ġa nd +Ġ in +Ġo f +a s +Ġ n +o m +i c +Ġth at +u s +e t +v e +a l +o w +l e +Ġ is +Ġ e +Ġ it +o t +' s +Ġb e +i on +Ġ T +Ġw h +Ġ A +en t +Ġ S +Ġ re +a y +Ġw e +Ġ on +er e +Ġh a +u t +a c +i d +i g +o s +k e +v er +i m +Ġ Ð +ĠT h +a m +a ll +Ġf or +e l +c h +r o +Ġth is +Ġs t +Ġ W +Ġ u +a d +ou t +i r +l d +c t +Ġ k +i f +Ġg o +. . +Ð ¾ +it h +l y +h t +q u +Ġ - +Ġd o +Ġ j +Ġha ve +Ġ B +Ġa n +Ġw ith +Ġa re +Ġ r +Ġd e +Ġs e +Ġs o +Ġ v +s t +i ll +u r +Ġl i +Ġ M +es t +o d +all y +' t +us t +Ġa s +Ġ C +c e +Ġm e +Ð ° +Ð µ +i l +Ġ H +Ġw as +t er +t h +Ġc an +an t +Ġc om +ou r +ig ht +Ġ Y +at ion +ĠA nd +o l +Ġs h +Ñ Ĥ +o p +s e +Ġn ot +ĠS o +Ġn e +u n +Ġa b +Ġli ke +Ġa t +Ġ D +i e +Ġh e +Ġc on +Ġc h +o re +Ġa l +Ġo r +Ġ qu +Ġ O +om e +r a +u l +Ġ N +p p +Ġyou r +ou ld +Ġ P +Ġf r +g e +er s +' re +Ð ¸ +Ġthe y +Ġwh at +us e +Ġa ll +ĠTh e +Ġ L +es s +e m +Ġk n +Ġj ust +ar t +Ġp ro +ver y +u m +Ġl o +Ġ ì +Ġm y +o k +Ġe x +a b +Ġth ere +Ġb ut +Ġkn ow +Ġs u +Ġ G +Ñ ģ +Ġ E +Ġm a +о Ð +Ġ en +Ġab out +ĠI t +is t +Ġw or +r i +in d +Ġon e +at e +a nd +in k +Ġl e +or t +' m +Ġ F +ic h +Ñ Ģ +id e +Ġg et +Ġ out +.. . +Ġw ill +ã ģ +i ve +Ð ½ +Ġfr om +a in +ĠW e +Ġu p +p e +re s +c a +Ġ R +Ġ if +Ġp l +Ġd on +ac k +Ġ 1 +Ġ " +Ġt r +Ġ us +ĠW h +it y +Ġ J +ĠY ou +Ġh ere +h er +Ġs ome +ou g +a k +ar d +Ġgo ing +Ġu n +m ent +Ġth ink +Ġp e +en d +Ġ ( +ca use +Ġt im +as t +à © +Ġ our +Ġw ant +am e +i es +Ġ ë +u d +in e +Ġre ally +Ġt e +Ġse e +c i +Ġb y +s o +u re +os e +Ġ [ +a re +Ġm ore +a h +on e +c k +op le +а Ð +Ġthe n +Ġth ing +Ġthe m +v en +ou nd +os t +on g +e ct +Ġr ight +a g +Ġin t +Ġpe ople +Ġwh en +ou s +p l +Ġtim e +Ġ im +Ġwh o +Ġ 2 +a p +Ġbe cause +h ing +Ġn o +ic e +Ġlo ok +Ġh as +Ġw ould +Ġh ow +ac t +Ġf e +n t +oug h +Ġp r +ĠB ut +Ġs ay +Ñ ĥ +Ġn ow +Ġm an +Ġ very +Ġwor k +i z +Ġ K +i v +it t +Ġa r +e p +Ġc l +Ġwh ich +Ġc o +an s +' ve +Ġs a +f f +' ll +Ġan y +Ġa ct +Ġy e +b er +ac h +a ge +p er +Ġal so +f er +Ġthe se +Ġa d +е Ð +th er +ac e +ic k +a ke +re at +i re +u e +Ġa g +Ġ U +u ch +ion s +r y +0 0 +n a +Ġd id +Ġqu e +Ġha d +Ġe very +ĠH e +Ġl a +Ġw ay +Ġs p +b le +ĠTh is +as s +Ġthe ir +it e +Ġne ed +Ġp art +Ġw ere +Ġb ack +i p +ow n +om et +b e +as e +Ġma ke +ir st +i a +en ce +an g +an k +Ġg ot +Ġp re +Ġcon t +Ġo ther +p t +ĠTh at +o g +Ġgo od +Ġint o +al k +Ġbe en +Ġa m +Ġo ver +u ally +Ġ â +ì Ŀ +Ġu nd +h e +w ay +Ġg r +Ñ Į +Ġd if +Ġp er +Ñ ı +ĠI n +Ġt w +on d +ar s +in t +or m +Ġl ot +Ġwh ere +Ġ à +Ġ V +Ġs omet +Ð » +en s +Ġg u +Ġa c +u g +Ñ ĭ +Ä ± +Ġf irst +re e +Ġh is +itt le +Ġim p +Ġm o +a v +Ġl ittle +ĠWh at +Ġm uch +Ġ z +Ġ ê +ab le +ĠÐ ¿ +Ġp o +Ġcom p +n e +Ġd is +Ġl et +an ce +Ġh er +Ġthing s +Ġst art +ul t +Ġa pp +Ġre s +Ġf o +Ġc ould +Ġin ter +Ġth ose +Ġd es +Ġwe ll +Ġtw o +Ġk ind +x t +res s +el y +à ¤ +Ġb r +Ġth r +ĠÐ ² +Ġ i +is h +Ġdif fer +Ġ ro +ĠS t +Ġsomet hing +Ġt ake +Ġb o +y s +Ġsh e +Ġt alk +l o +Ñ ĩ +Ġe ven +Ð º +ã Ģ +ĠÐ ½ +Ġb u +ĠI f +Ġd own +ĠC h +ad e +ation s +Ġ use +or d +Ġof f +Ġact ually +Ġs pe +d u +at ed +at er +os s +n ing +à ¼ +Ġdo es +Ġ Ñģ +Ġne w +Ġb et +ve l +c ess +p le +Ġha pp +t ing +on na +Ġ es +Ġd ay +Ġon ly +ig n +k ay +s el +ent s +ou nt +i ld +i le +Ġs c +Ġh im +Ġag ain +v ing +Ġg onna +Ġcom m +Ġh el +ot her +Ġ ke +ic al +Ġ 3 +Ġe l +Ġthr ough +Ġcom e +ar k +d ay +i er +à ³ +Ġth an +ĠThe y +Ġm ay +Ġs er +í ķ +Ġc all +Ġdiffer ent +Ġsh ould +ĠTh ere +ar y +ĠN ow +ã Ĥ +th ing +w e +or y +f ter +Ġp ut +or s +i al +ë ĭ +Ġund er +Ġin c +ĠY e +u b +f orm +Ġv ide +à ¸ +ver s +Ġfe el +à ¡ +od y +f t +f ore +Ġe m +g et +Ġsa id +it ion +Ġre c +i ous +at ch +Ġtr y +Ġhel p +Ġsh ow +Ð ´ +Ġb it +u ll +Ð ² +ÑĤ о +g r +Ġpl ay +if e +a il +ĠYe ah +Ġqu est +Ġman y +Ġp ers +Ġg reat +Ã Ń +Ġ est +n g +Ġâ Ļ +t y +l a +ĠO h +Ġ × +à ® +ĠB e +ad y +Ġm ost +ct ion +ĠN o +Ġdo ing +Ġbe ing +Ġto o +c es +Ġb l +. " +Ġre m +is s +on s +> > +r u +w n +on t +i b +e ll +Ġs m +ot h +u al +Ġ >> +Ġp h +l es +o c +f ul +Ġse c +is e +Ġad d +ig h +er t +Ġs ame +â Ģ +Ġme an +Ġf ind +e k +Ġen d +- - +Ð ¼ +Ġst ill +a z +Ġ ' +Ġm in +Ġye ars +ur n +Ġar ound +sel f +Ġw r +b s +oug ht +ĠâĻ ª +Ġf l +an ge +Ġa fter +Ġpo int +m er +v ed +Ġl ong +o y +ä ¸ +Ġc r +way s +Ġs y +Ġt ra +Ġ2 0 +a ve +Ġch e +Ġ ent +Ġbe fore +p h +Ġat t +i an +i ly +Ġpers on +Ġb ig +Ġs ch +Ġre al +Ġne xt +Ġlo ve +Ġvide o +ĠL et +Ġf in +Ġma k +i ble +Ġto day +er m +ĠA l +ow er +an n +i x +Ġp ar +Ġst ud +à ¶ +Ġimp ort +t e +Ġg ive +v es +Ġd ie +Ġde c +Ġte ll +ĠÐ º +Ñģ ÑĤ +Ġwh y +ic ally +ic t +re d +Ġb as +Ġsu re +Ġbe l +at ing +Ġt ak +Ġs et +Ġl ife +Ġdid n +Ø § +o b +u nd +at h +Ġo p +ĠÐ ¾ +a it +Ġwor ld +Ġsu pp +i o +Ġc our +ĠÐ ¸ +w ard +е н +Ġal ways +u p +Ġha nd +ĠH ow +ci al +Ġcon s +Ġ Ñ +Ġin d +Ġ 4 +ĠA s +Ġf un +j ect +Ġimport ant +Ġs ur +e w +at es +Ġ 5 +Ġd i +Ġm ade +Ġin s +Ġas k +Ġ et +Ġn um +Ġc ar +ĠO kay +Ġs im +i k +Ġl ast +ĠG o +Ġm us +Ġre l +ul ar +´ ì +ĠWe ll +pe ct +ĠTh ank +Ġth ree +à £ +ã ĥ +Ġin v +Ġg en +l ic +Ġhapp en +ë Ĭ +i en +e ver +оР² +Ġst r +ĠA ll +Ġin st +Ġâ Ģ +Ġde f +Ġs l +Ġm ight +un g +Ġye ar +Ġo wn +Ġke ep +b ody +d er +Ġ ÑĤ +ĠÐ ´ +Ġan other +Ġm od +Ġe v +Ġgu ys +Ġab le +ã o +qu e +id ent +ĠY es +Ġit s +Ġpl ace +Ġpro du +ar n +ĠÐ ¼ +Ġre p +Ġex per +Ġf am +it ies +if ic +Ġh igh +i ed +o ol +ie w +е ÑĤ +re n +Ġdon e +Ġ ... +ëĬ Ķ +st em +ĠS e +Ġbet ter +c ome +Ġd el +Ġt y +Ġu m +Ġh o +ĠA n +Ġm on +ing s +Ġs k +Ġo b +c om +ble m +op e +st and +' d +ment s +Ġe le +ĠI s +Ġd a +Ġre g +le ase +i ke +al s +iz e +ê ° +Ġc are +Ġne ver +ìĿ ´ +es e +Ġm et +ol og +ĠWh en +u ck +е ÑĢ +Ġ é +Ġd at +à § +Ġex am +il ity +Ġd et +c ri +Ġus ed +ĠD o +Ġtr ans +e g +t en +Ñ İ +c us +Ġsec ond +Ġb est +Ġh ard +Ġ ide +Ġpro blem +ê ³ +ĠU n +Ñ ħ +Ġ Î +Ġw atch +ĠS h +at ter +Ġpre t +Ġd er +Ġcour se +Å Ł +at ive +ic s +Ġquest ion +ut e +ì Ĺ +ĠF or +at her +Ġc ol +i end +Ġ í +Ġ Z +Ġdoes n +ar ch +Ġinter est +Ġp ol +Ġc or +i ence +Ġp res +Ġe ach +Ġsy stem +Ġf act +i el +ab ly +Ġ er +Ġr un +Ġì Ŀ +Ġto p +n er +Ġth ought +Ġe as +i ent +Ġc re +Ñ Ī +Ġcomm un +y e +re ady +ll ow +Ġevery thing +om m +Ġm ed +ļ Ķ +Ġc ount +it s +Ġcom pl +h ip +Ù Ħ +o ok +Ġto get +Ġtoget her +am p +Ġg ame +Ġal ready +аР» +Ġcall ed +al e +Å Ĥ +ĠM y +Ġunder stand +Ġd r +Ġm om +it ed +оР» +Ġus ing +z y +Ġnum ber +ãĢ ģ +c ed +Ġc le +н о +ëĭ ¤ +in ce +Ġlook ing +Ġpret ty +Ġpro b +ĠS he +Ġ ve +Ġget ting +Ġwe ek +Ġe ff +u ff +a ir +u es +er n +Ġ Q +ou p +ent ion +Ġs ide +оР¼ +Ġfor m +Ġb us +Ġas s +Ġ ed +as on +we en +âĢ ¦ +Ġt urn +Ġc ur +Ġco ll +Ġd ire +ĠG od +Ġ1 0 +Ġe qu +ĠÐ ± +Ġop en +Ġsu ch +ir d +аРº +Ġe ar +Ä Ļ +g an +Ġpart ic +Ġfr iend +Ġex p +Ġex t +Ġh ome +Ġw ater +ĠO n +ÑĤ ÑĮ +or k +Ġп ÑĢ +Ġmo ve +n ess +en se +h o +Ġch ar +c o +in s +Ġb oth +Ġ1 9 +Ġg ra +Ġbet ween +á » +Ġì ķ +as h +ĠR e +a i +al th +u res +em ber +Ġa v +Ġ ver +à ª +one y +Ġth ank +Ġmay be +u c +im e +ê³ ł +Ġa way +Ġn ame +ou se +Ġac c +Ġmus ic +Ġch ange +Ġp ass +g er +Ġbu ild +Ġv al +in ess +an y +Ġfe w +´ ë +t a +Ġl ist +à ¥ +Ġo ld +Ġì ŀ +Ġs ort +Ġme m +Ġc a +ce pt +Ġgen er +Ġye ah +Ġwh ile +Ġany thing +r ic +gr am +Ġe in +c y +ur ing +ĠD e +Ġp ower +Ġcom ing +Ġwor d +Ġ- - +Ġbel ie +Ġf ound +t o +Ð ¿ +Ġme ans +Ġin form +Ġ Ø +Ġ Ñĩ +Ġsm all +00 0 +Ġc ame +Ġ íķ +w h +Ġwork ing +Ġexam ple +Ġp os +Ġde p +ê ² +ä º +ot e +Ġde m +ì § +t s +Ġv ar +a ut +Ġt ri +ch n +Ġhe ad +Ġwho le +× Ļ +z e +Ġtry ing +Ġt em +Ġc ou +et s +Ġ 6 +Ġf il +vel op +Ġc ase +à ¯ +Ġprob ably +Ġo kay +Ġpl an +Ġs it +Ġsch ool +ĠTh en +¸ ë +m e +Ġpro cess +Ġf ar +Ġre ad +Ġp oss +Ġb re +Ġso l +ic ht +Ġsupp ort +ĠT o +ert ain +Ġstart ed +Ġc ap +Ġle ft +Ġdat a +Ġtim es +еР» +Ġwant ed +а н +Ġtalk ing +Ġis t +Ġha ving +um p +Ġcont in +Ġsu b +ĠÐ · +p r +ëĭ Ī +in a +Å ¼ +Ġc reat +od e +× ķ +æ ĺ +! 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13630, + "arter": 13631, + "obile": 13632, + "ĠYan": 13633, + "ĠAdv": 13634, + "Ġdiscipline": 13635, + "ĠìłķëıĦ": 13636, + "ĠPlace": 13637, + "ĠSelect": 13638, + "TE": 13639, + "ĠбÑĭла": 13640, + "Ġwhis": 13641, + "Ġbay": 13642, + "ĠDor": 13643, + "encing": 13644, + "Ġrepet": 13645, + "Ġficar": 13646, + "pad": 13647, + "Ġfog": 13648, + "uyor": 13649, + "Ġsnap": 13650, + "ibt": 13651, + "Ġsobie": 13652, + "Ġappointment": 13653, + "ĠRy": 13654, + "Ġceiling": 13655, + "ourse": 13656, + "Ġwrites": 13657, + "ĠAfghanistan": 13658, + "Ġmos": 13659, + "aze": 13660, + "Ġpenal": 13661, + "Ġcrystal": 13662, + "ICE": 13663, + "ê°IJ": 13664, + "éŁ": 13665, + "ĠTesla": 13666, + "Ġtheories": 13667, + "Ġappeal": 13668, + "Ġnewspaper": 13669, + "Ġcookies": 13670, + "æ©": 13671, + "ĠاÙĦÙĦ": 13672, + "Ġmaj": 13673, + "ĠGetting": 13674, + "kommen": 13675, + "ĠHeaven": 13676, + "ells": 13677, + "Ġdivine": 13678, + "Ä«": 13679, + "Ġakt": 13680, + "Ġhopes": 13681, + "ĠChen": 13682, + "wegen": 13683, + "***": 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"ĠBor": 13739, + "ç©": 13740, + "400": 13741, + "ĠSociety": 13742, + "Ġsubtle": 13743, + "Ġmissions": 13744, + "Ġremembered": 13745, + "ĠEither": 13746, + "Ġdafür": 13747, + "ORD": 13748, + "Ġintensity": 13749, + "ESIN": 13750, + "ĠCup": 13751, + "Ġrarely": 13752, + "Ġtoys": 13753, + "ĠCharlie": 13754, + "ợ": 13755, + "Ġglaube": 13756, + "Ġrounds": 13757, + "TIN": 13758, + "Ġcapability": 13759, + "Ġderivative": 13760, + "Ġreferring": 13761, + "ĠdÃ¥": 13762, + "ĠTALI": 13763, + "Ġcotton": 13764, + "Ġconfer": 13765, + "Ġcolumns": 13766, + "Ġliberal": 13767, + "Ġnunca": 13768, + "Ġμε": 13769, + "Ġindo": 13770, + "iben": 13771, + "ĠBeispiel": 13772, + "Ġê·¸ëłĩ": 13773, + "ĠÑĥÑĩ": 13774, + "Ġhoy": 13775, + "Ġfry": 13776, + "ĠScottish": 13777, + "èĬ": 13778, + "Ġciv": 13779, + "Ġconservative": 13780, + "Ġairpl": 13781, + "Ġsar": 13782, + "rus": 13783, + "Ġinvestments": 13784, + "Ġinfinite": 13785, + "Ġà®ķ": 13786, + "ĠTALIESIN": 13787, + "ĠGary": 13788, + "uell": 13789, + "Ġак": 13790, + "ĠCir": 13791, + "Ġritual": 13792, + "Ġ>>>": 13793, + "Ġtempt": 13794, + "ĠTech": 13795, + "ĠPokemon": 13796, + "Ġimprovements": 13797, + "Ġspare": 13798, + "Ġtranslate": 13799, + "Ġsonra": 13800, + "ĠFilm": 13801, + "wort": 13802, + "Ġми": 13803, + "Ġperiods": 13804, + "Ġjealous": 13805, + "ãģĦãģĦ": 13806, + "Ġtir": 13807, + "MI": 13808, + "Ġconducted": 13809, + "ĠìķĪëħķ": 13810, + "09": 13811, + "ĠPolit": 13812, + "ĠWhereas": 13813, + "Ġmoisture": 13814, + "Ġsins": 13815, + "Ġkap": 13816, + "ĠÑįк": 13817, + "Ġbenim": 13818, + "Ġeliminate": 13819, + "Ġathletes": 13820, + "ĠManager": 13821, + "Ġfeatured": 13822, + "apore": 13823, + "äºĽ": 13824, + "Ġë°ľ": 13825, + "Ġperf": 13826, + "ĠThus": 13827, + "Ġdebut": 13828, + "обÑĢ": 13829, + "Ġseñ": 13830, + "Ġmysterious": 13831, + "words": 13832, + "Ķê°Ģ": 13833, + "Ġchecks": 13834, + "Ġvolunteer": 13835, + "Ġwashing": 13836, + "ĠMarvel": 13837, + "ĠAB": 13838, + "issors": 13839, + "!'": 13840, + "ĠFull": 13841, + "yeon": 13842, + "Ġweigh": 13843, + "ĠJOHN": 13844, + "Ġvos": 13845, + "Ġprocedures": 13846, + "Ġaddressed": 13847, + "ĠBerlin": 13848, + "puter": 13849, + "ĠBan": 13850, + "Ġmedication": 13851, + "Ġdrone": 13852, + "ĠÑĥб": 13853, + "ĠJean": 13854, + "Ġcaps": 13855, + "Ġdisappointed": 13856, + "Ġwore": 13857, + "ĠêµŃ": 13858, + "Ġorganize": 13859, + "ĠHalloween": 13860, + "Ġfantasy": 13861, + "yard": 13862, + "Ġnosotros": 13863, + "Ġjumped": 13864, + "Ġphotography": 13865, + "ĠName": 13866, + "rec": 13867, + "AB": 13868, + "Ġblessing": 13869, + "ĠShut": 13870, + "Ġbitter": 13871, + "pop": 13872, + "ãģĿãĤĮ": 13873, + "Ġdei": 13874, + "Ġfulfill": 13875, + "çIJĨ": 13876, + "Ġdengan": 13877, + "Ġbelo": 13878, + "ĠMeanwhile": 13879, + "Ġdepois": 13880, + "Ġdiabetes": 13881, + "Ġbund": 13882, + "ĠZealand": 13883, + "Ġdigest": 13884, + "Ġtires": 13885, + "Ġdod": 13886, + "agne": 13887, + "ết": 13888, + "Ġpeel": 13889, + "Ġзаб": 13890, + "Ġnodes": 13891, + "Ġtrends": 13892, + "ĠSwitch": 13893, + "ĠAward": 13894, + 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+ "Ġved": 14267, + "Ú¾": 14268, + "Ġbeam": 14269, + "Ġidiot": 14270, + "ëĵ¤": 14271, + "наÑĩ": 14272, + "idd": 14273, + "Ġski": 14274, + "itative": 14275, + "Ġhypothes": 14276, + "ãģ§ãģĻãģŃ": 14277, + "enter": 14278, + "ĠìķĦëĭĪë": 14279, + "Ġihre": 14280, + "Ġpreview": 14281, + "angel": 14282, + "Ġdemon": 14283, + "Ġdus": 14284, + "Ġdic": 14285, + "ĠKom": 14286, + "LEY": 14287, + "...!": 14288, + "Ġsieht": 14289, + "ĠSonic": 14290, + "Ġtenho": 14291, + "anas": 14292, + "Ġdigit": 14293, + "ĠMaar": 14294, + "Ġundergrad": 14295, + "ouncer": 14296, + "uffy": 14297, + "Ġconversion": 14298, + "Ġdisconnect": 14299, + "Ġecho": 14300, + "omer": 14301, + "Ġcurriculum": 14302, + "Ġperché": 14303, + "Ġwand": 14304, + "..?": 14305, + "Ġrolled": 14306, + "Ġentrepreneur": 14307, + "Ġtheoret": 14308, + "ĠÑīо": 14309, + "Ġinsights": 14310, + "Ġzusammen": 14311, + "oin": 14312, + "rett": 14313, + "produ": 14314, + "Ġvisitors": 14315, + "eous": 14316, + "Ġgrandmother": 14317, + "Ġhumor": 14318, + "ĠниÑħ": 14319, + "zenia": 14320, + "inson": 14321, + "Ġreset": 14322, + "Ġbaseball": 14323, + "Ġmatching": 14324, + "ëĭ¤ê°Ģ": 14325, + "Ġpunto": 14326, + "ì¡": 14327, + "Ġrede": 14328, + "Ġaddressing": 14329, + "Ġforecast": 14330, + "ĠBol": 14331, + "Ġcolored": 14332, + "Ġdocumentation": 14333, + "Ġexpectation": 14334, + "ĠNorthern": 14335, + "Ġcreo": 14336, + "Ġà®ļ": 14337, + "fon": 14338, + "Ġunsere": 14339, + "UM": 14340, + "Ġcopies": 14341, + "Ġexpanded": 14342, + "Ġveterans": 14343, + "ĠAlm": 14344, + "ĠвообÑīе": 14345, + "Ġpsychological": 14346, + "Ġnosso": 14347, + "Ġpayments": 14348, + "imeters": 14349, + "Ġ-->": 14350, + "ĠJennifer": 14351, + "Ġvolunteers": 14352, + "osse": 14353, + "orious": 14354, + "ĠбÑĭли": 14355, + "èĤ": 14356, + "ĠEss": 14357, + "ws": 14358, + "ĠBC": 14359, + "ĠIC": 14360, + "Woman": 14361, + "Ġvont": 14362, + "Ġethnic": 14363, + "ENN": 14364, + "имо": 14365, + "Ġlob": 14366, + "Ġoui": 14367, + "cs": 14368, + "Ġrehe": 14369, + "Ġìłģ": 14370, + "Ġchick": 14371, + "úsica": 14372, + "Ġkont": 14373, + "ĠDistrict": 14374, + "Ġpile": 14375, + "Ġав": 14376, + "ейÑģÑĤв": 14377, + "Ġ£": 14378, + "Ġissued": 14379, + "Ġкомп": 14380, + "Ġprosper": 14381, + "Ġprofound": 14382, + "ĠDear": 14383, + "Ġãģĵ": 14384, + "Ġfunded": 14385, + "Ġbisa": 14386, + "ŀĺë": 14387, + "ף": 14388, + "ĠìĿĺ": 14389, + "Ġtwelve": 14390, + "ĠChampions": 14391, + "éĿŀ常": 14392, + "Ñģл": 14393, + "Ġ2005": 14394, + "pm": 14395, + "Ġonde": 14396, + "Ġdiffé": 14397, + "ĠChall": 14398, + "Ġdifficulties": 14399, + "Ġgarage": 14400, + "Ġdá": 14401, + "ünk": 14402, + "Ġ물": 14403, + "Ġtran": 14404, + "Ġsubmitted": 14405, + "zw": 14406, + "ÙĪا": 14407, + "Ġark": 14408, + "ĠìĦ±": 14409, + "Ġgrocery": 14410, + "она": 14411, + "iere": 14412, + "Ġaest": 14413, + "Ġexhibition": 14414, + "Ġrés": 14415, + "Ġconsistency": 14416, + "Ġcookie": 14417, + "ней": 14418, + "Ġreplacement": 14419, + "æ²¹": 14420, + "ĠSem": 14421, + "ĠìĤ¬ìļ©": 14422, + "800": 14423, + "Ġgenes": 14424, + "Ġtransaction": 14425, + "ĠEL": 14426, + "Ġdurante": 14427, + "ibles": 14428, + "ĠEat": 14429, + "tail": 14430, + "issance": 14431, + "Ġtoss": 14432, + "Ġsurvived": 14433, + "Ġoffices": 14434, + "Ġsupportive": 14435, + "Where": 14436, + "Ġtoutes": 14437, + "Ġë§ī": 14438, + "Ġjokes": 14439, + "ieron": 14440, + "apers": 14441, + "Ġmature": 14442, + "ĠMarsh": 14443, + "Ġsido": 14444, + "kind": 14445, + "Ġrealmente": 14446, + "ĠChef": 14447, + "Ġquelque": 14448, + "Ġjudges": 14449, + "eft": 14450, + "ERS": 14451, + "Ġjet": 14452, + "Ġpersons": 14453, + "è»": 14454, + "izations": 14455, + "rik": 14456, + "Ġshops": 14457, + "ĠWy": 14458, + "Ġeleg": 14459, + "què": 14460, + "quoi": 14461, + "Ġjuga": 14462, + "Ġíķľë²Ī": 14463, + "ĠQuestion": 14464, + "ĠGlobal": 14465, + "Ġìķ½ê°Ħ": 14466, + "ĠStation": 14467, + "æİ¥": 14468, + "ĠOhio": 14469, + "Ġsticky": 14470, + "Ġstressed": 14471, + "Ġgün": 14472, + "ĠíĿ": 14473, + "ÑģÑĤÑĥп": 14474, + "é¡Į": 14475, + "ĠPhD": 14476, + "immer": 14477, + "Ġmentor": 14478, + "Ġinvented": 14479, + "Ġreun": 14480, + "Ġinevit": 14481, + "ĠpolÃŃt": 14482, + "Ġexecute": 14483, + "ĠStory": 14484, + "Ġoutstanding": 14485, + "Ġguer": 14486, + "ĠRain": 14487, + "Ġchoses": 14488, + "ĠTit": 14489, + "ĠÑģеÑĢ": 14490, + "ĠSingapore": 14491, + "ĠNone": 14492, + "Ġchronic": 14493, + "°ëį°": 14494, + "Ġego": 14495, + "æł·": 14496, + "EST": 14497, + "ãģĤãĤĬ": 14498, + "ĠWang": 14499, + "ĠNAT": 14500, + "Ġaug": 14501, + "Ġdesktop": 14502, + "Ġeternal": 14503, + "ĠìĤ¬ìĭ¤": 14504, + "ĠConstitution": 14505, + "ìĤ¬ë": 14506, + "×Ļ׾": 14507, + "pres": 14508, + "ĠТÑĭ": 14509, + "Ġinterf": 14510, + "Ġlists": 14511, + "Ġfights": 14512, + "ften": 14513, + "ĠIowa": 14514, + "Ġmotivated": 14515, + "ĠHosp": 14516, + "Ġelsewhere": 14517, + "Ġpaths": 14518, + "Ġinstances": 14519, + "Bl": 14520, + "range": 14521, + "á»±": 14522, + "ĠSit": 14523, + "mana": 14524, + "Ġìĭľìŀij": 14525, + "Ġmình": 14526, + "ansas": 14527, + "Ġsna": 14528, + "Ġphilosoph": 14529, + "Ġpasse": 14530, + "Æ°á»Ŀi": 14531, + "akh": 14532, + "ental": 14533, + "Ġihn": 14534, + "ructor": 14535, + "ĠваÑĪ": 14536, + "Ġgenerous": 14537, + "Ġpivot": 14538, + "пол": 14539, + "Ġjamais": 14540, + "Ġcoment": 14541, + "ĠLew": 14542, + "odzi": 14543, + "ĠXbox": 14544, + "Ġвод": 14545, + "Ġconsent": 14546, + "īìŀ¥": 14547, + "Ġdispar": 14548, + "lass": 14549, + "ĠGovernor": 14550, + "Beifall": 14551, + "Ġê°ľ": 14552, + "Ġbeloved": 14553, + "׳×ķ": 14554, + "sell": 14555, + "Ġhonored": 14556, + "leh": 14557, + "Ġwäre": 14558, + "unting": 14559, + "Ġfraud": 14560, + "ĠRAM": 14561, + "걸": 14562, + "Ġkills": 14563, + "Ġeconomics": 14564, + "04": 14565, + "пеÑĢ": 14566, + "Ġcoisas": 14567, + "ĠигÑĢ": 14568, + "ÃŃm": 14569, + "Ġmöchte": 14570, + "Ġìµľ": 14571, + "Ġstimul": 14572, + "Ġfastest": 14573, + "lv": 14574, + "Ġgén": 14575, + "ĠSounds": 14576, + "Ġ1970": 14577, + "Ġhomework": 14578, + "speaking": 14579, + "Ġencouraging": 14580, + "Ġquery": 14581, + "Ġrevers": 14582, + "profit": 14583, + "Ġdy": 14584, + "Ġìŀij": 14585, + "ëĬĶëį°ìļĶ": 14586, + "Ġsoap": 14587, + "ĠGall": 14588, + "ĠCN": 14589, + "ĠAns": 14590, + "Ġfic": 14591, + "anks": 14592, + "Ġdessert": 14593, + "ĠìłĢíĿ¬": 14594, + "ĠMaking": 14595, + "Ġcomeç": 14596, + "ê³Ħ": 14597, + "Ġassociation": 14598, + "Dad": 14599, + "hee": 14600, + "Ġhogy": 14601, + "Ġapro": 14602, + "Ġinvisible": 14603, + "American": 14604, + "íİ": 14605, + "Ġvibe": 14606, + "Ġemissions": 14607, + "Ġadvocate": 14608, + "Ġkicked": 14609, + "Ġvel": 14610, + "Ġsummar": 14611, + "Ġfreaking": 14612, + "chron": 14613, + "Ġpinch": 14614, + "Ġwszystk": 14615, + "iscal": 14616, + "Ġproved": 14617, + "Ġmindful": 14618, + "Ġtä": 14619, + "Ġnoises": 14620, + "Ġisolated": 14621, + "Ġcrossed": 14622, + "Ġê°ķ": 14623, + "ĠvoilÃł": 14624, + "Ġchore": 14625, + "ĠRA": 14626, + "Com": 14627, + "Ġrelaxed": 14628, + "atro": 14629, + "Ġprevention": 14630, + "Voiceover": 14631, + "OD": 14632, + "ĠCovid": 14633, + "Ġseparation": 14634, + "Ġ-[": 14635, + "иÑĩего": 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22793, + "Ġspite": 22794, + "azar": 22795, + "éĥ½æĺ¯": 22796, + "Ġcritically": 22797, + "Ġobra": 22798, + "owitz": 22799, + "ĠZone": 22800, + "ĠÑĢек": 22801, + "Ġsug": 22802, + "arded": 22803, + "Ġgì": 22804, + "ffentlich": 22805, + "anche": 22806, + "ØŁ": 22807, + "astically": 22808, + "ìĿ¼ë": 22809, + "лав": 22810, + "Ġsimplest": 22811, + "ĠFriend": 22812, + "Ġquello": 22813, + "Ġambition": 22814, + "Ġabbiamo": 22815, + "åºķ": 22816, + "ĠÑĦоÑĢм": 22817, + "ĠEssa": 22818, + "Ġeducators": 22819, + "Ġstatistical": 22820, + "éĢĻéĤĬ": 22821, + "Ġchanger": 22822, + "Ġatau": 22823, + "étais": 22824, + "ĠShakespeare": 22825, + "ëIJĺ": 22826, + "Ġtriggers": 22827, + "Ġrealiz": 22828, + "Ġcelui": 22829, + "wheel": 22830, + "Ġloyalty": 22831, + "Ġscreams": 22832, + "kehr": 22833, + "ĠMega": 22834, + "east": 22835, + "Ġtops": 22836, + "ĠTotally": 22837, + "ountain": 22838, + "lord": 22839, + "Ġviolation": 22840, + "ĠGA": 22841, + "Ġnicer": 22842, + "ĠFresh": 22843, + "ĠMelissa": 22844, + 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30922, + "ние": 30923, + "ĠitÃŃs": 30924, + "onnaise": 30925, + "sol": 30926, + "èı¯": 30927, + "algia": 30928, + "Ġrocking": 30929, + "Ġbesten": 30930, + "rites": 30931, + "^^": 30932, + "иной": 30933, + "Ġbaixo": 30934, + "Ġ기ìĸµ": 30935, + "оÑĤÑĢи": 30936, + "sim": 30937, + "Ġincarn": 30938, + "ëĭ¤ìĿĮ": 30939, + "Ġlick": 30940, + "sided": 30941, + "Ġ71": 30942, + "forder": 30943, + "Ġresonance": 30944, + "Ġtegen": 30945, + "Ġmetaph": 30946, + "owser": 30947, + "Ġ×IJ׳×Ĺ׳×ķ": 30948, + "?ãĢį": 30949, + "Ġspielen": 30950, + "Ġvolley": 30951, + "ĶìĿ´íģ¬ìĹħ": 30952, + "looked": 30953, + "Ġsentenced": 30954, + "Ġmultiplying": 30955, + "Ġideals": 30956, + "Ġwahrscheinlich": 30957, + "Ġdeposits": 30958, + "bilir": 30959, + "Ġeffet": 30960, + "illon": 30961, + "Īë§Į": 30962, + "Ġtestimon": 30963, + "Ġzawsze": 30964, + "ĠпÑĢоÑĨеÑģÑģ": 30965, + "ĠLav": 30966, + "ä¸įéĮ¯": 30967, + "Ġtravailler": 30968, + "Ġlaisse": 30969, + "ĠMountains": 30970, + "ĠÑĢоб": 30971, + "Ġexamined": 30972, + "itus": 30973, + "Was": 30974, + "лÑĭ": 30975, + "Ġattributed": 30976, + "ĠìĬ¹": 30977, + "ĠBaron": 30978, + "Ġgep": 30979, + "Ġattent": 30980, + "ĠCollection": 30981, + "Ġtheat": 30982, + "ĠCai": 30983, + "Ġwells": 30984, + "Ġhumano": 30985, + "çĹħ": 30986, + "ĠHast": 30987, + "ĠÑħоÑĤÑı": 30988, + "czas": 30989, + "Ġpermits": 30990, + "Ġlegg": 30991, + "Ġepo": 30992, + "ĠFen": 30993, + "Ġthi": 30994, + "ĠFoi": 30995, + "Ġélect": 30996, + "Ġ83": 30997, + "Ġoverth": 30998, + "Ġè¬Ŀè¬Ŀ": 30999, + "Ġtenant": 31000, + "è²·": 31001, + "Next": 31002, + "Ġpraised": 31003, + "security": 31004, + "ĠImpact": 31005, + "为ä»Ģä¹Ī": 31006, + "Ġvouch": 31007, + "Ġnegó": 31008, + "Ġunve": 31009, + "Ġcriticize": 31010, + "ĠKenya": 31011, + "Ġtactic": 31012, + "Ġlogr": 31013, + "Ġpois": 31014, + "Ġpapa": 31015, + "speaks": 31016, + "ðŁij": 31017, + "ispers": 31018, + "Ġsurplus": 31019, + "Ġcolder": 31020, + "åįĹ": 31021, + "åIJ¬": 31022, + "plets": 31023, + "ĠVienna": 31024, + "ĠLead": 31025, + "Ġaerial": 31026, + "ĠTah": 31027, + "енÑĤов": 31028, + "ĠGreeks": 31029, + "Cam": 31030, + "Ġmáxim": 31031, + "Ġkuin": 31032, + "chio": 31033, + "Ġdemonstrates": 31034, + "anos": 31035, + "ĠCert": 31036, + "ĠÑįн": 31037, + "Ġblogs": 31038, + "ĠìĦľìļ¸": 31039, + "Ġbeams": 31040, + "иков": 31041, + "Ġprompted": 31042, + "Ġfrightening": 31043, + "ĠPorsche": 31044, + "ãģĪãģ¦": 31045, + "larını": 31046, + "Ġchilling": 31047, + "isphere": 31048, + "Ġflashing": 31049, + "ĠKard": 31050, + "bread": 31051, + "Ġexh": 31052, + "Ġtycker": 31053, + "Ġecological": 31054, + "ĠMae": 31055, + "Ġ×ŀ×IJ×ķ×ĵ": 31056, + "ĠëĤĺëıĦ": 31057, + "лон": 31058, + "yss": 31059, + "Ġpergunt": 31060, + "Ġprix": 31061, + "izzard": 31062, + "Ġcancers": 31063, + "Ġ91": 31064, + "susp": 31065, + "ĠItem": 31066, + "ÅŁa": 31067, + "Ġpest": 31068, + "ĠtakÄħ": 31069, + "Ġlymph": 31070, + "ĠPatri": 31071, + "fill": 31072, + "Ġreconna": 31073, + "Ġoptimism": 31074, + "Ġmimic": 31075, + "Ġì²ľ": 31076, + "ĠMadame": 31077, + "ocy": 31078, + "lining": 31079, + "åijĬ訴": 31080, + "erme": 31081, + "Ġfolders": 31082, + "ĠczÅĤ": 31083, + "uchar": 31084, + "Ġcurso": 31085, + "Ġbreach": 31086, + "ниÑĤÑĮ": 31087, + "ĠpamiÄĻ": 31088, + "Ġelig": 31089, + "Ġautop": 31090, + "Flow": 31091, + "Ġprogrammed": 31092, + "ĠProcess": 31093, + "Ġfigur": 31094, + "ĠSF": 31095, + "ĠEles": 31096, + "Ġprogrammes": 31097, + "Ġdizzy": 31098, + "ìĭľê°Ħ": 31099, + "Ġлибо": 31100, + "Ġsniff": 31101, + "ĠSebastian": 31102, + "ĠHye": 31103, + "Ġ4000": 31104, + "Ġpermite": 31105, + "æ¢Ŀ": 31106, + "ĠзаÑī": 31107, + "Ġguit": 31108, + "ĠDais": 31109, + "Ġaccordance": 31110, + "Ġmodular": 31111, + "ogeneous": 31112, + "æĭį": 31113, + "Ġpouquinho": 31114, + "Ġartillery": 31115, + "Ġlubric": 31116, + "Ġvolcan": 31117, + "ĠNH": 31118, + "ðŁ¤": 31119, + "Ġdean": 31120, + "Rh": 31121, + "Ġministre": 31122, + "åĿIJ": 31123, + "ĠInv": 31124, + "ĠBulgar": 31125, + "ĠDaten": 31126, + "èİ": 31127, + "Im": 31128, + "Ġoriginated": 31129, + "ĠNixon": 31130, + "integr": 31131, + "Ġlacks": 31132, + "ĠNacht": 31133, + "ìĸ´ëĤĺ": 31134, + "camera": 31135, + "Ġradish": 31136, + "kiye": 31137, + "Ġanges": 31138, + "Ġpréf": 31139, + "juk": 31140, + "ĠBee": 31141, + "ĠBU": 31142, + "ĠвоÑģп": 31143, + "ĠBT": 31144, + "êmes": 31145, + "ĠStück": 31146, + "ĠInk": 31147, + "æĪĸèĢħ": 31148, + "ĠSergeant": 31149, + "ĠMultip": 31150, + "Ġhiçbir": 31151, + "ĠСам": 31152, + "ĠDé": 31153, + "olph": 31154, + "ìĸ¸": 31155, + "Ġimpat": 31156, + "ĠìķĬê³ł": 31157, + "ĠÑĤакого": 31158, + "ĠнавеÑĢное": 31159, + "Ġunpredictable": 31160, + "Ġmend": 31161, + "ĠìĹĨìĸ´ìļĶ": 31162, + "ĠjakieÅĽ": 31163, + "Ġanni": 31164, + "Ġdonné": 31165, + "ĠKirsty": 31166, + "Ġrectangular": 31167, + "Ġempezar": 31168, + "ĠExchange": 31169, + "ê°Ķ": 31170, + "Ġéconom": 31171, + "ãģĵãĤĵ": 31172, + "elin": 31173, + "reibt": 31174, + "Ġ×Ķפ": 31175, + "Ġcemetery": 31176, + "Ġespañol": 31177, + "olin": 31178, + "лÑİд": 31179, + "Ġgrâce": 31180, + "allen": 31181, + "ĠPhilos": 31182, + "ĠErst": 31183, + "ĠìĥĪ": 31184, + "ĠVid": 31185, + "Give": 31186, + "OH": 31187, + "μο": 31188, + "ĠPare": 31189, + "Ġmetabolism": 31190, + "Ġmaple": 31191, + "Ġaxle": 31192, + "ĠDy": 31193, + "Ġkomme": 31194, + "Ïİν": 31195, + "Ġgreatness": 31196, + "Ġverified": 31197, + "Ġspé": 31198, + "ĠFahrenheit": 31199, + "ĠBren": 31200, + "ĠConfeder": 31201, + "Ġhistoire": 31202, + "Ġeliminating": 31203, + "ĠAdding": 31204, + "ĠAbi": 31205, + "æĿİ": 31206, + "Ġhospitality": 31207, + "tim": 31208, + "Ġbonito": 31209, + "Ġpartes": 31210, + "ĠдÑĢÑĥгиÑħ": 31211, + "ĠShay": 31212, + "ĠSed": 31213, + "Ġregrets": 31214, + "Ñıми": 31215, + "Ġtenants": 31216, + "éĢŁ": 31217, + "ĠPTS": 31218, + "Ġdevi": 31219, + "ĠLate": 31220, + "uez": 31221, + "Ġsöyl": 31222, + "ãĤ»": 31223, + "Ġìŀ¬ë°Į": 31224, + "Ġtoggle": 31225, + "Ġmasking": 31226, + "алÑĮного": 31227, + "Ġpersön": 31228, + "Ġamerican": 31229, + "fik": 31230, + "ĠRGB": 31231, + "enson": 31232, + "ĠKA": 31233, + "wwww": 31234, + "ĠÑĢег": 31235, + "metics": 31236, + "Ġeducator": 31237, + "ãĤ·ãĥ«ãĤ¯": 31238, + "park": 31239, + "елÑĮзÑı": 31240, + "arus": 31241, + "ÑĢеÑĤ": 31242, + "Ġfeito": 31243, + "Ġchoir": 31244, + "Ġlargo": 31245, + "Ġeens": 31246, + "Ġwatts": 31247, + "ĠSingle": 31248, + "Ġsusceptible": 31249, + "icer": 31250, + "ĠвклÑİÑĩ": 31251, + "Ġpus": 31252, + "íĻĺ": 31253, + "Eng": 31254, + "Ġfantas": 31255, + "Ġspecification": 31256, + "Ġconfronted": 31257, + "ĠColumbus": 31258, + "ивеÑĤ": 31259, + "arım": 31260, + "Ġcaffeine": 31261, + "munition": 31262, + "Ġmigrants": 31263, + "lide": 31264, + "itations": 31265, + "ĠGeme": 31266, + "ẫ": 31267, + "Ġplanner": 31268, + "Ġstimulate": 31269, + "Ġaproxim": 31270, + "ceu": 31271, + "ĠNom": 31272, + "Ġvog": 31273, + "ĠÑĢаÑģÑĤ": 31274, + "Ġenseñ": 31275, + "Ġsellers": 31276, + "Ġguten": 31277, + "zd": 31278, + "Cal": 31279, + "Ġdescript": 31280, + "Ġreconciliation": 31281, + "zinho": 31282, + "á¹ĩa": 31283, + "ãģĺãĤĥãģĤ": 31284, + "acyj": 31285, + "ĠCOL": 31286, + "saw": 31287, + "ĠíĻķìĿ¸": 31288, + "Ġvarit": 31289, + "Ġpartnering": 31290, + "Ġdetention": 31291, + "Ġbombing": 31292, + "clapping": 31293, + "iencies": 31294, + "ondu": 31295, + "AME": 31296, + "Ġê°ĻìĬµëĭĪëĭ¤": 31297, + "cÃŃa": 31298, + "ĠпоÑģÑĤо": 31299, + "ĠASMR": 31300, + "Ġhomepage": 31301, + "Ġsiè": 31302, + "antha": 31303, + "ĠPoll": 31304, + "Ġigen": 31305, + "cych": 31306, + "Ġê°ijìŀIJ기": 31307, + "Ġconsiderably": 31308, + "ä»ĸçļĦ": 31309, + "ĠArist": 31310, + "Ġwithstand": 31311, + "Ġqualitative": 31312, + "ĠKraft": 31313, + "ĠÑįлекÑĤ": 31314, + "ĠBead": 31315, + "екÑĤив": 31316, + "Ġcrushing": 31317, + "ì³IJ": 31318, + "Ġnavy": 31319, + "ÙĪÚº": 31320, + "sho": 31321, + "Ġoak": 31322, + "ippers": 31323, + "Ġsoils": 31324, + "Ġpigment": 31325, + "Ġevitar": 31326, + "ãĥĩ": 31327, + "Ġfuse": 31328, + "ĠDale": 31329, + ":\"": 31330, + "Ġcomplètement": 31331, + "Ġkel": 31332, + "à¹Ĩ": 31333, + "Ġquatre": 31334, + "ĠUM": 31335, + "Ġë§IJë": 31336, + "æł¹": 31337, + "ÃŃr": 31338, + "Ġleisure": 31339, + "ĠHousing": 31340, + "Ġfolds": 31341, + "estion": 31342, + "ARS": 31343, + "Ġmash": 31344, + "urpose": 31345, + "Ġaccumulated": 31346, + "ĠStuff": 31347, + "èªŀ": 31348, + "Ġtapes": 31349, + "ĠÑģилÑĮно": 31350, + "ĠLOVE": 31351, + "Ġ1982": 31352, + "Ġscars": 31353, + "Ġcapitalist": 31354, + "ĠNed": 31355, + "Ġsoften": 31356, + "Ġnotably": 31357, + "Ġforcément": 31358, + "ĠRaum": 31359, + "ĠнеобÑħод": 31360, + "Ġtrademark": 31361, + "Ġfertig": 31362, + "Ġ?!": 31363, + "æĹł": 31364, + "Ġreinforced": 31365, + "Ġrecharge": 31366, + "ĠPutting": 31367, + "Ġvillains": 31368, + "Ġhandic": 31369, + "Ġadvertisement": 31370, + "تÙĬ": 31371, + "ĠÑģÑĥм": 31372, + "ĠRiley": 31373, + "×ķ×ij×": 31374, + "京": 31375, + "Os": 31376, + "از": 31377, + "Boy": 31378, + "Ġsquish": 31379, + "ocket": 31380, + "Ġtestify": 31381, + "æ¼Ķ": 31382, + "Ġ׾×ŀ×": 31383, + "ĠмаÑģÑģ": 31384, + "manuel": 31385, + "ĠArkansas": 31386, + "iffe": 31387, + "Ġanalysts": 31388, + "ĠDeaf": 31389, + "Ġjó": 31390, + "Ġgroceries": 31391, + "ĠWheel": 31392, + "ĠÑĢиÑģ": 31393, + "Ġcòn": 31394, + "ĠCob": 31395, + "Ġprisons": 31396, + "ève": 31397, + "ĠCabinet": 31398, + "Ġposed": 31399, + "Ġguerre": 31400, + "ĠLloyd": 31401, + "Ġclerk": 31402, + "Ġcrises": 31403, + "ĠSho": 31404, + "ĠOre": 31405, + "ĠFootball": 31406, + "ĠAdvis": 31407, + "ĠZheng": 31408, + "èį": 31409, + "ĠAMY": 31410, + "Ġunfor": 31411, + "Ġmonaster": 31412, + "Ġcompile": 31413, + "Ġimmortal": 31414, + "atable": 31415, + "Ġparano": 31416, + "Ġtiver": 31417, + "ĠSteph": 31418, + "ĠFuÃŁ": 31419, + "Ġdiscontin": 31420, + "Ġripe": 31421, + "Ġhacking": 31422, + "Ġsiendo": 31423, + "Ġseguro": 31424, + "altres": 31425, + "Ġanderes": 31426, + "Ġ리ë": 31427, + "Ġexports": 31428, + "æŃ¥": 31429, + "Ġtabii": 31430, + "Ġ기ëĭ¤ë": 31431, + "Ġbothering": 31432, + "Ġpickle": 31433, + "ĠBRIAN": 31434, + "Ġaltar": 31435, + "ĠпÑĢиб": 31436, + "Ġtransferring": 31437, + "ĠVors": 31438, + "ĠÙĩÙĪ": 31439, + "ĠZa": 31440, + "ĠFrances": 31441, + "Ġbrowse": 31442, + "emit": 31443, + "Ġchewing": 31444, + "ĠFreddy": 31445, + "Ġeditors": 31446, + "älle": 31447, + "ĠíĮĢ": 31448, + "ĠSque": 31449, + "ĠCultural": 31450, + "awk": 31451, + "ĠSache": 31452, + "ĠCarbon": 31453, + "ắt": 31454, + "FL": 31455, + "ĠNGO": 31456, + "peÅĤ": 31457, + "ĠSou": 31458, + "Ġhvor": 31459, + "unintelligible": 31460, + "Ġë²ķ": 31461, + "Ġ°": 31462, + "iin": 31463, + "Ġ×¢×Ŀ": 31464, + "Ġderrière": 31465, + "Ġczym": 31466, + "ĠApost": 31467, + "Ġregarder": 31468, + "Ġagrade": 31469, + "ĠCandy": 31470, + "Ġmare": 31471, + "Ġintroduces": 31472, + "birds": 31473, + "Ġuniquely": 31474, + "Ġmuk": 31475, + "Ġcooker": 31476, + "Ġcrews": 31477, + "Ġjeito": 31478, + "ERT": 31479, + "¶Ħë": 31480, + "nisse": 31481, + "Ġef": 31482, + "Ġcarte": 31483, + "ĠYak": 31484, + "ĠPAT": 31485, + "ино": 31486, + "bokki": 31487, + "Ġmates": 31488, + "Ġdistint": 31489, + "Ġì½Ķë¡ľëĤĺ": 31490, + "Ġyıl": 31491, + "Ġκάν": 31492, + "Ġconfigurations": 31493, + "enga": 31494, + "recht": 31495, + "Happy": 31496, + "ãĤĦãģ£ãģ¦": 31497, + "invest": 31498, + "Ġreconstruct": 31499, + "ĠÑįÑĤомÑĥ": 31500, + "Ġmosque": 31501, + "raum": 31502, + "Ġvoyez": 31503, + "ĠNBC": 31504, + "ĠìŀIJìĭł": 31505, + "Ġsturdy": 31506, + "Ġкап": 31507, + "Ġansch": 31508, + "alid": 31509, + "Ġmasih": 31510, + "ĠREP": 31511, + "Ġì½Ķë": 31512, + "Ġdeduct": 31513, + "Ġsalir": 31514, + "wurf": 31515, + "ilot": 31516, + "ĠMutter": 31517, + "olds": 31518, + "ĠFEMA": 31519, + "ĠBib": 31520, + "Ġneighboring": 31521, + "Ġbliss": 31522, + "Ġíĺ¼": 31523, + "лиÑģÑĮ": 31524, + "ĠÑĤÑĢеб": 31525, + "Ġå°±æĺ¯": 31526, + "Ġgrenade": 31527, + "Ġegal": 31528, + "Ġfinely": 31529, + "Ġpetals": 31530, + "Ġkeer": 31531, + "Ġchyba": 31532, + "Ġskipping": 31533, + "Ġthirteen": 31534, + "Ġgravy": 31535, + "ĠSAT": 31536, + "61": 31537, + "Ġног": 31538, + "Ġmins": 31539, + "ITE": 31540, + "Ġsozial": 31541, + "íķĺë©´ìĦľ": 31542, + "ruktur": 31543, + "Ġвозмож": 31544, + "ĠопÑıÑĤÑĮ": 31545, + "Ġarth": 31546, + "ĠCuban": 31547, + "Ġtreasures": 31548, + "Ġfertilizer": 31549, + "Ġawakening": 31550, + "Ġë°±ìĭł": 31551, + "Ġrall": 31552, + "Ġdepict": 31553, + "ĠPablo": 31554, + "Ġnineteen": 31555, + "Ġwatt": 31556, + "Ġentirety": 31557, + "KS": 31558, + "ĠWoods": 31559, + "Sch": 31560, + "ĠÚ©ÙĪ": 31561, + "ĠDry": 31562, + "ãģŀ": 31563, + "uve": 31564, + "Ġreconstruction": 31565, + "Ġanatomy": 31566, + "Ī를": 31567, + "Ġbaba": 31568, + "Ġlistener": 31569, + "Ġsharpen": 31570, + "ĠPeru": 31571, + "ĠвÑĭз": 31572, + "Ġrecreation": 31573, + "Ġinitiate": 31574, + "Ġcalor": 31575, + "ĠNaj": 31576, + "gee": 31577, + "ĠFeels": 31578, + "ĠSnapchat": 31579, + "ĠTet": 31580, + "ĠNest": 31581, + "ĠDaf": 31582, + "ĠFinish": 31583, + "ĠÑĤаким": 31584, + "úc": 31585, + "izens": 31586, + "Ġspins": 31587, + "Ġembry": 31588, + "Ġpassages": 31589, + "Ġcient": 31590, + "Ġjustification": 31591, + "ä»ĸ說": 31592, + "Ġolmaz": 31593, + "Ġflooded": 31594, + "Ġemoji": 31595, + "Ġembracing": 31596, + "Ġdiscard": 31597, + "ĠBasic": 31598, + "agog": 31599, + "ĠìľĦíķ´": 31600, + "Ġasylum": 31601, + "erin": 31602, + "Ġfim": 31603, + "Ġninja": 31604, + "Ġautomate": 31605, + "Ġallergic": 31606, + "ÿÿÿÿ": 31607, + "amam": 31608, + "ĠмаÑĢ": 31609, + "ĠOi": 31610, + "äus": 31611, + "Ġinduct": 31612, + "ĠBEN": 31613, + "ĠzÅĤ": 31614, + "Ġkażdy": 31615, + "ĠAMP": 31616, + "nÄĽ": 31617, + "Sure": 31618, + "Ġquil": 31619, + "Ġespec": 31620, + "rok": 31621, + "BSCRI": 31622, + "Ġliebe": 31623, + "pus": 31624, + "achsen": 31625, + "Ġcricket": 31626, + "ëĬIJ": 31627, + "ĠFrame": 31628, + "ekkür": 31629, + "arb": 31630, + "ĠpÅĻ": 31631, + "иÑģÑģ": 31632, + "Ġzeggen": 31633, + "Ġdoubles": 31634, + "ĠDre": 31635, + "test": 31636, + "insp": 31637, + "boys": 31638, + "Ġmão": 31639, + "ĠVerse": 31640, + "Ġmuscular": 31641, + "ĠMALE": 31642, + "Ġdulu": 31643, + "Ġoccasional": 31644, + "Lo": 31645, + "conomic": 31646, + "Ġvak": 31647, + "Ġremedy": 31648, + "å¤ł": 31649, + "ĠâĻªâĻªâĻª": 31650, + "vem": 31651, + "Ġönem": 31652, + "ĠkarÅŁÄ±": 31653, + "ĠSharp": 31654, + "hur": 31655, + "Ġë°©ë²ķ": 31656, + "Ġgrandson": 31657, + "Ġaktiv": 31658, + "ĠThrones": 31659, + "ĠìķĪìĹIJ": 31660, + "Ġtots": 31661, + "Ġsubd": 31662, + "ĠPaula": 31663, + "Ġgraves": 31664, + "ĠBrent": 31665, + "ĠникÑĤо": 31666, + "Ġsöz": 31667, + "Ġcrec": 31668, + "ĠVladimir": 31669, + "çĸ«": 31670, + "Ġпой": 31671, + "Ġ\"-": 31672, + "Ġpsy": 31673, + "atri": 31674, + "idan": 31675, + "Ġaún": 31676, + "Ġstandardized": 31677, + "ì¹ĺë": 31678, + "ĠкÑĢов": 31679, + "ĠZhu": 31680, + "something": 31681, + "Ġ750": 31682, + "Ġmujeres": 31683, + "Ġait": 31684, + "éĹ´": 31685, + "agu": 31686, + "Ġcorrected": 31687, + "ikka": 31688, + "eled": 31689, + "ĠCareer": 31690, + "owym": 31691, + "Ġroommate": 31692, + "Ġdescendants": 31693, + "ĠNapoleon": 31694, + "ĠÐĶо": 31695, + "íĸĪìĸ´ìļĶ": 31696, + "Ġbunun": 31697, + "ĠMicha": 31698, + "ç·ļ": 31699, + "Ġdescob": 31700, + "PI": 31701, + "Ġpalabra": 31702, + "Ġtracked": 31703, + "Ġdependence": 31704, + "ĠBarack": 31705, + "åģĩ": 31706, + "Ġfertility": 31707, + "ĠSouthwest": 31708, + "Ġincomplete": 31709, + "Ġcomunic": 31710, + "Ġcompris": 31711, + "ĠRestaur": 31712, + "Ġacron": 31713, + "κα": 31714, + "Ġapprentices": 31715, + "Ġmusst": 31716, + "ĠAbr": 31717, + "Ġpentru": 31718, + "ĠConsort": 31719, + "ĠAvec": 31720, + "Ġdumplings": 31721, + "LR": 31722, + "Ġwszystkie": 31723, + "Ġswamp": 31724, + "нев": 31725, + "uggle": 31726, + "Ġwatercolor": 31727, + "Ġproton": 31728, + "ĠEspaña": 31729, + "ocking": 31730, + "овал": 31731, + "Ġtakim": 31732, + "Very": 31733, + "Ġdementia": 31734, + "ĠÅŁeyi": 31735, + "Jac": 31736, + "ĠMacBook": 31737, + "ĠLiv": 31738, + "fficients": 31739, + "ĠHunt": 31740, + "Ġoverlay": 31741, + "æĦŁè¦º": 31742, + "ĠSkype": 31743, + "punkt": 31744, + "Ġconfined": 31745, + "ĠAdrian": 31746, + "رÙĥ": 31747, + "ĠJeep": 31748, + "Ġenquanto": 31749, + "Ġanest": 31750, + "оÑĤвеÑĤ": 31751, + "ĠменÑĮ": 31752, + "Ġirrigation": 31753, + "á»ijn": 31754, + "Ġeighteen": 31755, + "ĠPon": 31756, + "Ġrescued": 31757, + "Ġ1983": 31758, + "rü": 31759, + "jae": 31760, + "ĠJeong": 31761, + "Ġamazingly": 31762, + "ĠFDP": 31763, + "Ġbackstage": 31764, + "cue": 31765, + "ĠÏĥÏĦην": 31766, + "ĠاÙĦص": 31767, + "Ġlivestock": 31768, + "ĠWarner": 31769, + "Ġmajors": 31770, + "ãĥģãĥ£": 31771, + "Ġcooperative": 31772, + "ĠBrady": 31773, + "rained": 31774, + "rieb": 31775, + "Ġ×ij×ŀ×": 31776, + "ĠдоволÑĮно": 31777, + "ĠFE": 31778, + "Ġleaked": 31779, + "ĠMercury": 31780, + "Ġpersuade": 31781, + "Ġtransformer": 31782, + "ĠNorweg": 31783, + "ĠìŬ룬": 31784, + "ĠzrobiÄĩ": 31785, + "Ġcardiovascular": 31786, + "ĠCrash": 31787, + "Ġgossip": 31788, + "аÑģÑĤÑĮ": 31789, + "Ġ쪽": 31790, + "Ġswept": 31791, + "ĠHorn": 31792, + "ĠAté": 31793, + "Ġbukan": 31794, + "ĠKaw": 31795, + "KY": 31796, + "ĠStories": 31797, + "Gary": 31798, + "Ġgardening": 31799, + "ĠQuickly": 31800, + "ĠFalcon": 31801, + "Ġovat": 31802, + "cı": 31803, + "ĠComplet": 31804, + "ĠDate": 31805, + "ĠпÑĢим": 31806, + "Ġläuft": 31807, + "ĠAudrey": 31808, + "ĠWent": 31809, + "ĠpelÃŃcul": 31810, + "Ġcarriage": 31811, + "Ġunacceptable": 31812, + "nymi": 31813, + "ĠÑģлÑĭÑĪ": 31814, + "Ġterre": 31815, + "uellement": 31816, + "EEEE": 31817, + "Ġpharmac": 31818, + "hões": 31819, + "Ġzich": 31820, + "Ġmigrate": 31821, + "ĠFry": 31822, + "ñana": 31823, + "ĠMuito": 31824, + "EOVER": 31825, + "Ġfortress": 31826, + "ĠCompan": 31827, + "ĠJSON": 31828, + "ordnung": 31829, + "Ġwarto": 31830, + "Ġungef": 31831, + "ìħĶìĦľ": 31832, + "ĠÑĢок": 31833, + "Ġpaddle": 31834, + "Jared": 31835, + "Ġsubmitting": 31836, + "Ġlatch": 31837, + "Ġfug": 31838, + "ĠкоÑģ": 31839, + "ĠEf": 31840, + "Ġlaunches": 31841, + "Ġft": 31842, + "otechn": 31843, + "Ġtravelled": 31844, + "اÙģ": 31845, + "éģķ": 31846, + "Ġproch": 31847, + "Ġdedim": 31848, + "83": 31849, + "Ġrebound": 31850, + "ĠLU": 31851, + "path": 31852, + "ĠÑģпÑĢав": 31853, + "Ġöl": 31854, + "ĠíĤ¤": 31855, + "Ġprivat": 31856, + "Ġtractor": 31857, + "ĠAttention": 31858, + "Ser": 31859, + "Ġcoses": 31860, + "ária": 31861, + "pal": 31862, + "ĠìĿĢ": 31863, + "Ġsuccessor": 31864, + "Ġconnectors": 31865, + "ĠÑĥÑģÑĤанов": 31866, + "Ġgenocide": 31867, + "Ġsufficiently": 31868, + "ĠAixò": 31869, + "Ġstabilize": 31870, + "Ġcongest": 31871, + "Ġcarving": 31872, + "Ġzost": 31873, + "ĠбÑĭÑģÑĤÑĢо": 31874, + "Ġshortest": 31875, + "Ġlivel": 31876, + "Ġ89": 31877, + "éģĬ": 31878, + "Ġerk": 31879, + "Ġportraits": 31880, + "à¥Ģ": 31881, + "èĺ": 31882, + "boat": 31883, + "llah": 31884, + "ANC": 31885, + "Ġempirical": 31886, + "ĠEcho": 31887, + "ĠNederland": 31888, + "è¿Ļä¹Ī": 31889, + "Net": 31890, + "Ġcuidado": 31891, + "ĠRoma": 31892, + "Ġcalf": 31893, + "Ġgiants": 31894, + "ĠExplorer": 31895, + "ĠCollect": 31896, + "alition": 31897, + "ĠDestiny": 31898, + "Ġausge": 31899, + "ĠEdu": 31900, + "ĠClo": 31901, + "Ġearrings": 31902, + "ĠTrack": 31903, + "ĠROS": 31904, + "ĠBelle": 31905, + "çĻ¾": 31906, + "Ġpueda": 31907, + "Ġdaytime": 31908, + "Ġsupplier": 31909, + "ĠSV": 31910, + "ĠExhale": 31911, + "Ġgalera": 31912, + "course": 31913, + "Ġcentimeter": 31914, + "ĠBast": 31915, + "mud": 31916, + "Ġsangat": 31917, + "ĠPhysical": 31918, + "Ġprivately": 31919, + "Ġtrata": 31920, + "lynn": 31921, + "illi": 31922, + "Ġë©ĶìĿ´íģ¬ìĹħ": 31923, + "Ġcrystall": 31924, + "Ġpods": 31925, + "ản": 31926, + "inator": 31927, + "ĠRecords": 31928, + "å®ĺ": 31929, + "ÄŁimiz": 31930, + "issement": 31931, + "hare": 31932, + "hadow": 31933, + "ĠDK": 31934, + "ĠìķĮê³ł": 31935, + "Ġwyn": 31936, + "Ġrequesting": 31937, + "ĠDonna": 31938, + "ĠìĹ´ìĭ¬íŀĪ": 31939, + "inea": 31940, + "Ġexert": 31941, + "ĠDuncan": 31942, + "ĠвеÑĩ": 31943, + "ĠHah": 31944, + "à¤Ĥ": 31945, + "ĠLif": 31946, + "ĠFinding": 31947, + "ĠNov": 31948, + "Ġзнак": 31949, + "ĠоÑĦ": 31950, + "ĠQuè": 31951, + "Ġquarterback": 31952, + "ĠÑĦак": 31953, + "Ġbipartisan": 31954, + "ÄŁin": 31955, + "Ġnécess": 31956, + "Ġreferendum": 31957, + "Ġcompiler": 31958, + "Ġprobabil": 31959, + "еди": 31960, + "Ġtrader": 31961, + "æĺĵ": 31962, + "ĠRum": 31963, + "geme": 31964, + "Ġdio": 31965, + "ĠbÄĻdziemy": 31966, + "ĠÏĢά": 31967, + "꾸": 31968, + "×ķ×ĺ": 31969, + "Ġà¤ķ": 31970, + "Ġблаг": 31971, + "Ġscalp": 31972, + "ĠPause": 31973, + "Ġcaption": 31974, + "Ġendanger": 31975, + "Ġenlar": 31976, + "Ġrotten": 31977, + "ãĥĥãĥĪ": 31978, + "Ġwah": 31979, + "èĤī": 31980, + "Ġdzi": 31981, + "ĠInstall": 31982, + "Ay": 31983, + "Ġcrear": 31984, + "енÑĤа": 31985, + "Ġweighing": 31986, + "Ġbutterflies": 31987, + "ĠGast": 31988, + "äºķ": 31989, + "horn": 31990, + "warz": 31991, + "ICEOVER": 31992, + "ĠнайÑĤи": 31993, + "Ġcoefficients": 31994, + "ç°¡åĸ®": 31995, + "ĠSpencer": 31996, + "ĠHigher": 31997, + "Ġcowork": 31998, + "å¨ĺ": 31999, + "ĠкоÑĤоÑĢое": 32000, + "Ġmonit": 32001, + "Ġdysfunction": 32002, + "ĠÑģÑĤанов": 32003, + "Ġtournaments": 32004, + "Ġoyster": 32005, + "BN": 32006, + "Ġtrud": 32007, + "slow": 32008, + "ĠPenny": 32009, + "ĠOdys": 32010, + "ær": 32011, + "Ġfou": 32012, + "Ġenjoyment": 32013, + "аÑĤÑĭ": 32014, + "ĠwyglÄħda": 32015, + "алÑĮнаÑı": 32016, + "ĠProtect": 32017, + "Ġmoy": 32018, + "Ġclaw": 32019, + "Ġsuspicion": 32020, + "Ġsacrificed": 32021, + "Ġgosto": 32022, + "Big": 32023, + "Ġaggressively": 32024, + "Ġvorne": 32025, + "ãĥł": 32026, + "Ġblamed": 32027, + "ĠSehr": 32028, + "פר": 32029, + "cito": 32030, + "Ġseals": 32031, + "Ġmujer": 32032, + "ĠWeird": 32033, + "Ġforens": 32034, + "Ġcontributes": 32035, + "estra": 32036, + "Ġpog": 32037, + "LOL": 32038, + "Ġhacerlo": 32039, + "оÑĤÑĮ": 32040, + "fiction": 32041, + "79": 32042, + "λο": 32043, + "大æ¦Ĥ": 32044, + "声": 32045, + "ĠÑĤоб": 32046, + "ĠGS": 32047, + "ĠClara": 32048, + "itez": 32049, + "Ġadvocating": 32050, + "ĠíĶĦë": 32051, + "sung": 32052, + "Ġvertices": 32053, + "Ġnavigating": 32054, + "Ġeuropé": 32055, + "çļĨ": 32056, + "Ġslowed": 32057, + "Ġforeground": 32058, + "ĠIndustrial": 32059, + "Ġadore": 32060, + "ìĭŃ": 32061, + "Ġcréer": 32062, + "æŀĹ": 32063, + "chnitt": 32064, + "Ġunaware": 32065, + "Ġcurly": 32066, + "entar": 32067, + "Ġler": 32068, + "Ġprohibited": 32069, + "ĠHeroes": 32070, + "ĠReed": 32071, + "uca": 32072, + "Ġsmok": 32073, + "Ġkunna": 32074, + "zeitig": 32075, + "immen": 32076, + "ĠLun": 32077, + "ĠабÑģолÑİÑĤ": 32078, + "Ġdegli": 32079, + "Ġvillagers": 32080, + "Ġpreset": 32081, + "zept": 32082, + "uds": 32083, + "Ġemit": 32084, + "ä½łè¦ģ": 32085, + "Ġëī": 32086, + "ëĬĶì§Ģ": 32087, + "нако": 32088, + "Ġosób": 32089, + "Ġ1969": 32090, + "ĠÐIJÑĢ": 32091, + "Ġmanchmal": 32092, + "ĠBrock": 32093, + "Ġmantra": 32094, + "ĠWIL": 32095, + "bach": 32096, + "inä": 32097, + "elas": 32098, + "keln": 32099, + "Ġdisciple": 32100, + "Ġqualc": 32101, + "Ġdehyd": 32102, + "ìĿ´ëĿ¼ëĬĶ": 32103, + "Af": 32104, + "ìĦ±ìĿ´": 32105, + "Ryan": 32106, + "Ġpuppet": 32107, + "ĠдÑĢÑĥгие": 32108, + "Ġrud": 32109, + "Ġpending": 32110, + "Plus": 32111, + "ĠìķĬìĿĦ": 32112, + "Ġbá»ĭ": 32113, + "ĠSega": 32114, + "çe": 32115, + "Ġprogrammer": 32116, + "bli": 32117, + "Ġunl": 32118, + "Ġenslaved": 32119, + "Ġsociété": 32120, + "Äģh": 32121, + "Ġinheritance": 32122, + "ĠBangl": 32123, + "ermaid": 32124, + "Ġpractitioner": 32125, + "ĠStalin": 32126, + "ĠUser": 32127, + "cible": 32128, + "Ġcardiac": 32129, + "ĠKoreans": 32130, + "Ġdumped": 32131, + "Ġ×Ķ×Ļ×Ķ": 32132, + "áis": 32133, + "Ġhydraulic": 32134, + "oubtedly": 32135, + "ĠPit": 32136, + "Ġpicnic": 32137, + "Ġbehöver": 32138, + "ĠÑģмог": 32139, + "Ġbraking": 32140, + "é»ij": 32141, + "utar": 32142, + "ĠìĦ¸ë": 32143, + "ubl": 32144, + "Ġüz": 32145, + "Ġmajesty": 32146, + "Ġbers": 32147, + "utable": 32148, + "Ġhotter": 32149, + "çħ§": 32150, + "ÛĮÙĨ": 32151, + "Ġbiases": 32152, + "Ġsubjected": 32153, + "Ġnaughty": 32154, + "Ġcircus": 32155, + "ãģĹãģĭ": 32156, + "ĠImmedi": 32157, + "ĠStefan": 32158, + "ĠTriple": 32159, + "enk": 32160, + "Ġwit": 32161, + "Ġrecycle": 32162, + "emie": 32163, + "dated": 32164, + "Ġunload": 32165, + "Ġpopula": 32166, + "chin": 32167, + "Ġyields": 32168, + "Ġenglish": 32169, + "ĠBonnie": 32170, + "Ġspiders": 32171, + "Ãģ": 32172, + "Ġerosion": 32173, + "éĥ¨åĪĨ": 32174, + "ĠNICK": 32175, + "иÑıÑħ": 32176, + "Ġimpart": 32177, + "Ġкни": 32178, + "Ġresolutions": 32179, + "Ġlithium": 32180, + "Ġconvergence": 32181, + "ĠTara": 32182, + "Ġдве": 32183, + "ths": 32184, + "ĠCindy": 32185, + "æĪijè¦ģ": 32186, + "幫": 32187, + "ĠDIE": 32188, + "Ġassurance": 32189, + "ĠопиÑģ": 32190, + "Ġbuckets": 32191, + "Ġcues": 32192, + "ĠQuiet": 32193, + "Ġsimilarity": 32194, + "Ġfoundational": 32195, + "ĠMinist": 32196, + "滿": 32197, + "Ġpian": 32198, + "Ġcentr": 32199, + "Ġnumb": 32200, + "Ġmonks": 32201, + "ujourd": 32202, + "enzie": 32203, + "Ġskateboard": 32204, + "Ġdlatego": 32205, + "ĠÑģоÑĤ": 32206, + "ĠAE": 32207, + "Ġmasterpiece": 32208, + "ĠSolomon": 32209, + "ĠReddit": 32210, + "Ġriot": 32211, + "abl": 32212, + "ĠJazz": 32213, + "Ġelectromagnetic": 32214, + "Ġinsecure": 32215, + "ĠCompet": 32216, + "geries": 32217, + "обод": 32218, + "ł×ķ": 32219, + "ðŁĴ": 32220, + "Ġsenators": 32221, + "ĠBrisbane": 32222, + "ĠAlb": 32223, + "uttering": 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"ĠlÃ¥": 33939, + "ĠëijIJë": 33940, + "Ġtyped": 33941, + "ĠBana": 33942, + "ëĵľë": 33943, + "Ġsavory": 33944, + "ĠZomb": 33945, + "standen": 33946, + "Ġpedestrian": 33947, + "Ġdifférents": 33948, + "Ġìĭ¸": 33949, + "èī¯": 33950, + "Ġcomplained": 33951, + "ç¦ı": 33952, + "ĠÐļÑĤо": 33953, + "Ġ׾פ": 33954, + "aliÅĽmy": 33955, + "Ġmortar": 33956, + "Ġverdict": 33957, + "Ġsuficiente": 33958, + "ĠMillion": 33959, + "mittel": 33960, + "inals": 33961, + "ĠاÙĦØ®": 33962, + "аÑİÑģÑĮ": 33963, + "ĠmiÄĻdzy": 33964, + "ĠOle": 33965, + "Ġinvert": 33966, + "czyÄĩ": 33967, + "озможно": 33968, + "starter": 33969, + "Ġauditor": 33970, + "ĠScout": 33971, + "chien": 33972, + "ĠSverige": 33973, + "uffled": 33974, + "Ġzehn": 33975, + "ĠAuckland": 33976, + "Ġargent": 33977, + "Ġ1976": 33978, + "ĠHoe": 33979, + "Ġbothers": 33980, + "Ġsocialist": 33981, + "Ġpliers": 33982, + "Ġemergen": 33983, + "ĠXP": 33984, + "еÑĢов": 33985, + "More": 33986, + "ĠLevi": 33987, + "ĠAnders": 33988, + "ibilidad": 33989, + 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34093, + "ĠJohann": 34094, + "Ġaftermath": 34095, + "ÑĤоб": 34096, + "Ġuży": 34097, + "Ġsamp": 34098, + "360": 34099, + "ĠMei": 34100, + "Ġtaco": 34101, + "Ġreceptors": 34102, + "Ġpunches": 34103, + "ĠHoje": 34104, + "ĠÙĩÙĨا": 34105, + "=\"#": 34106, + "ĠAngular": 34107, + "Ġmusique": 34108, + "Ġrol": 34109, + "Ġñ": 34110, + "sterreich": 34111, + "Ġclam": 34112, + "ĠTreasury": 34113, + "chemical": 34114, + "Ġapar": 34115, + "Ġappend": 34116, + "Ġforbid": 34117, + "ĠHamburg": 34118, + "аков": 34119, + "Ġê¸Ī": 34120, + "ilda": 34121, + "Ġpreparations": 34122, + "ĠmogÄħ": 34123, + "Ġcamino": 34124, + "Eric": 34125, + "ĠBlind": 34126, + "èĪĩ": 34127, + "å¹´çļĦ": 34128, + "ĠDiscovery": 34129, + "ì¸ł": 34130, + "çĪ¶": 34131, + "Ġinterpreter": 34132, + "Ġbred": 34133, + "ĠPsalm": 34134, + "Ġdefended": 34135, + "ìī¬": 34136, + "ĠErfahr": 34137, + "ĠPeach": 34138, + "Ġmoons": 34139, + "ĠOst": 34140, + "Ġspécial": 34141, + "Ġarriver": 34142, + "ĠWis": 34143, + "uci": 34144, + "Ġrobotics": 34145, 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"Ġkunt": 34199, + "tering": 34200, + "Ġerect": 34201, + "ìŀ¥ìĿ´": 34202, + "ĠìĿĮìĭĿ": 34203, + "Ġspecimen": 34204, + "!...": 34205, + "æĪij說": 34206, + "Ġligne": 34207, + "Ġkonst": 34208, + "adequ": 34209, + "Ġìĥģíĥľ": 34210, + "Ġaccessed": 34211, + "ĠPole": 34212, + "kill": 34213, + "Ġë²Ħë": 34214, + "Ġauthenticity": 34215, + "Ġappelle": 34216, + "ulle": 34217, + "Ġrevision": 34218, + "Ġgoats": 34219, + "гли": 34220, + "Ġpau": 34221, + "ĠRanger": 34222, + "ĠImag": 34223, + "author": 34224, + "Ġeve": 34225, + "ĠMessenger": 34226, + "Ġnay": 34227, + "Ġwholes": 34228, + "ätte": 34229, + "Ġonwards": 34230, + "ĠDepois": 34231, + "ĠíijľíĺĦ": 34232, + "ĠSARS": 34233, + "Ġwszystkich": 34234, + "Ġdestru": 34235, + "umbing": 34236, + "Ġcompatibility": 34237, + "Ġmisinformation": 34238, + "odore": 34239, + "ĠFavor": 34240, + "eko": 34241, + "ıĮ": 34242, + "waukee": 34243, + "ĠTeaching": 34244, + "ĠKO": 34245, + "Ġbetting": 34246, + "Ġquests": 34247, + "Ġvivre": 34248, + "ĠмÑĥзÑĭ": 34249, + 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"ĠVu": 37703, + "ĠíĬ¹íŀĪ": 37704, + "ĠBROWN": 37705, + "ounded": 37706, + "\";": 37707, + "Ġtremb": 37708, + "Ġtiet": 37709, + "ĠÑĢежим": 37710, + "Ġnutshell": 37711, + "елиÑĩ": 37712, + "Ġlosers": 37713, + "ricting": 37714, + "Ġredeem": 37715, + "defined": 37716, + "Nice": 37717, + "Ġbroadband": 37718, + "KO": 37719, + "Ġteasing": 37720, + "Ġpartisan": 37721, + "ıma": 37722, + "Ġìŀ¬ë¯¸": 37723, + "ĠJourney": 37724, + "Ġslopes": 37725, + "uning": 37726, + "grunts": 37727, + "Ġtäll": 37728, + "Ġuncovered": 37729, + "ĠmyÅĽlÄĻ": 37730, + "ĠEsther": 37731, + "äºİ": 37732, + "ĠHealthy": 37733, + "Ġë°ij": 37734, + "rée": 37735, + "Ġpolarization": 37736, + "Ġflav": 37737, + "Ġcambiar": 37738, + "Ġyr": 37739, + "ĠRanch": 37740, + "Ġsplits": 37741, + "Ġtrouvé": 37742, + "åľĭ家": 37743, + "Ġrecorder": 37744, + "Ġdépart": 37745, + "ÙĪب": 37746, + "ĠKry": 37747, + "Ġinteressant": 37748, + "Ġederim": 37749, + "ÅĽwiad": 37750, + "ilateral": 37751, + "wright": 37752, + "Ġpourra": 37753, + "êter": 37754, + "Ġcamel": 37755, + "áŀ": 37756, + "Ġrapidement": 37757, + "Ġmej": 37758, + "Ġstiffness": 37759, + "ADAS": 37760, + "Ġdiffers": 37761, + "Ġalot": 37762, + "ĠSig": 37763, + "ÑıÑĤелÑĮ": 37764, + "Ġabstraction": 37765, + "åľĺ": 37766, + "Ġkeiner": 37767, + "grupp": 37768, + "ĠSherlock": 37769, + "íĺĶ": 37770, + "Ġcite": 37771, + "Ġoverflow": 37772, + "Ġtại": 37773, + "úcar": 37774, + "bula": 37775, + "Ġconjunto": 37776, + "ĠCI": 37777, + "Ġmoderator": 37778, + "Ġindirectly": 37779, + "Ġalleine": 37780, + "âĤ": 37781, + "ÑĪиб": 37782, + "Ġбаб": 37783, + "Ġdanach": 37784, + "Ġ1939": 37785, + "Ġpromet": 37786, + "Ġdestinations": 37787, + "ĠIllust": 37788, + "ικÏĮ": 37789, + "Ġsabes": 37790, + "Ġheh": 37791, + "ĠGesetzent": 37792, + "ĠMiz": 37793, + "енко": 37794, + "ĠMys": 37795, + "Ь": 37796, + "ĠJudaism": 37797, + "Ġmustache": 37798, + "Ġstimmt": 37799, + "ĠGaza": 37800, + "Ġvolte": 37801, + "Ġnuo": 37802, + "Ġmón": 37803, + "ĠComput": 37804, + "ูà¹Ī": 37805, + "ĠRadi": 37806, + "Ġexceptionally": 37807, + "Ġassumes": 37808, + "éĸĭå¿ĥ": 37809, + "ãģĪãģ°": 37810, + "inform": 37811, + "Ġshrine": 37812, + "æĵĬ": 37813, + "Ġimplication": 37814, + "ĠFitz": 37815, + "æ²ĴéĹľä¿Ĥ": 37816, + "!.": 37817, + "Ġlt": 37818, + "Ġalloy": 37819, + "Ġethic": 37820, + "Ġmonastery": 37821, + "ìĭľì£ł": 37822, + "icação": 37823, + "Ġcoordinating": 37824, + "ĠMoto": 37825, + "Ġoverlook": 37826, + "Ġchois": 37827, + "Ġantibiotic": 37828, + "ĠMinne": 37829, + "ĠBJ": 37830, + "ĠApa": 37831, + "orian": 37832, + "Ġspilled": 37833, + "Jam": 37834, + "Ġhusbands": 37835, + "Ġcreations": 37836, + "Ġañ": 37837, + "üssel": 37838, + "ĠìĿ´ìļ©": 37839, + "Ġanalyse": 37840, + "rose": 37841, + "Ġpunched": 37842, + "Ġpresque": 37843, + "Ġastronomy": 37844, + "Ġschwierig": 37845, + "ĠEbola": 37846, + "Ġcis": 37847, + "Ġacet": 37848, + "ĠFX": 37849, + "endre": 37850, + "ĠìĿĮìķħ": 37851, + "Ġwebpage": 37852, + "Ġfreaked": 37853, + "Ġlatte": 37854, + "Ġì¿ł": 37855, + "Ġ머ë": 37856, + "Never": 37857, + "Gra": 37858, + "íĻĶ를": 37859, + "eyed": 37860, + "Ġë°ľëĿ¼": 37861, + "Ġespera": 37862, + "Ġaparece": 37863, + "ração": 37864, + "Ġdisruptive": 37865, + "ĠJoint": 37866, + "urous": 37867, + "reas": 37868, + "ĠquerÃŃa": 37869, + "Ġdistributions": 37870, + "Ġexponent": 37871, + "ì¹ĺ를": 37872, + "Ġdl": 37873, + "zhou": 37874, + "ĠHearing": 37875, + "å·®ä¸įå¤ļ": 37876, + "ĠCraw": 37877, + "Ġfloats": 37878, + "ounced": 37879, + "Lab": 37880, + "World": 37881, + "Ġburdens": 37882, + "Ġauthoritarian": 37883, + "ĠBolt": 37884, + "ĠоднÑĥ": 37885, + "Ġpigeon": 37886, + "Ġdistractions": 37887, + "ĠHerausforder": 37888, + "Ġzest": 37889, + "esc": 37890, + "Ġshakes": 37891, + "atas": 37892, + "ĠÙħØ´": 37893, + "holes": 37894, + "Ġthinkers": 37895, + "alta": 37896, + "Ġarche": 37897, + "ĠSuk": 37898, + "anha": 37899, + "Ġtempting": 37900, + "Ġyoutuber": 37901, + "Ġvì": 37902, + "ĠdziaÅĤa": 37903, + "ĠVatican": 37904, + "Park": 37905, + "Ġsupers": 37906, + "ĠNikki": 37907, + "ëĬIJë": 37908, + "orang": 37909, + "ramient": 37910, + "鬼": 37911, + "Ġê°ĸê³ł": 37912, + "Ġdesserts": 37913, + "Ġavere": 37914, + "ĠGregory": 37915, + "Ġëĵ¤ìĸ´ìĺ": 37916, + "Ġcosting": 37917, + "ĠClinic": 37918, + "Ġrebels": 37919, + "ĠMob": 37920, + "Ġbunlar": 37921, + "ĠYours": 37922, + "ertime": 37923, + "Ġretali": 37924, + "mara": 37925, + "atus": 37926, + "alles": 37927, + "ĠдÑĢ": 37928, + "ĠдиÑģ": 37929, + "Ġdiscounts": 37930, + "ĠGUY": 37931, + "Ġкакое": 37932, + "ĠExperiment": 37933, + "rement": 37934, + "ĠXiang": 37935, + "Ġbate": 37936, + "WE": 37937, + "Ġspecialize": 37938, + "Ġdeity": 37939, + "ĠLoki": 37940, + "mag": 37941, + "ĠNit": 37942, + "West": 37943, + "Ġmaternal": 37944, + "Ġquis": 37945, + "åŁºæľ¬": 37946, + "broken": 37947, + "Ġlasers": 37948, + "Ġhakk": 37949, + "ĠAngels": 37950, + "Ġmastery": 37951, + "antis": 37952, + "Tiffany": 37953, + "eee": 37954, + "çij": 37955, + "orem": 37956, + "Ġinacc": 37957, + "Ġjurisdictions": 37958, + "ĠKardash": 37959, + "æľº": 37960, + "Il": 37961, + "ĠSinn": 37962, + "åĭķçĶ»": 37963, + "Ġathletics": 37964, + "cÄĻ": 37965, + "Ġloosely": 37966, + "Ġdieta": 37967, + "Ag": 37968, + "Ġ??": 37969, + "ĠëĮĢíijľ": 37970, + "Ġsuperv": 37971, + "Ġnutrit": 37972, + "Ġdrifting": 37973, + "ĠìĦłìĥĿëĭĺ": 37974, + "ĠпонÑıл": 37975, + "ĠVictory": 37976, + "ÙĦØ©": 37977, + "×ķ׳×Ķ": 37978, + "ĠпиÑĪ": 37979, + "Ġshaved": 37980, + "Ġmesure": 37981, + "onden": 37982, + "Ùĥر": 37983, + "Ġexile": 37984, + "ĠDesde": 37985, + "ĠPinterest": 37986, + "Ġattachments": 37987, + "Ġhombres": 37988, + "Ġfines": 37989, + "ĠìĦ¸ìĥģ": 37990, + "Ġsleeps": 37991, + "ĠTaco": 37992, + "ĠIRA": 37993, + "rios": 37994, + "Ġoll": 37995, + "etes": 37996, + "Ġunut": 37997, + "fashioned": 37998, + "Ġtreball": 37999, + "ĠNearly": 38000, + "ĠÑĢеалÑĮно": 38001, + "Ġchil": 38002, + "éĢ±": 38003, + "ÄŁa": 38004, + "ĠMEL": 38005, + "roscop": 38006, + "ĠCG": 38007, + "Ġvenge": 38008, + "Ġdishwasher": 38009, + "algic": 38010, + "Ġmodifier": 38011, + "Ġembassy": 38012, + "timer": 38013, + "emics": 38014, + "Ġintricate": 38015, + "Ġevet": 38016, + "ĠëĮĢë°ķ": 38017, + "Ġisot": 38018, + "ĠнаÑĥÑĩ": 38019, + "ĠQuiz": 38020, + "reso": 38021, + "δÏİ": 38022, + "Ġyelled": 38023, + "Ġfeder": 38024, + "ELLER": 38025, + "Ġexceeded": 38026, + "onas": 38027, + "icano": 38028, + "ĠживоÑĤ": 38029, + "ĠMao": 38030, + "ĠKazuto": 38031, + "Ġãħĭãħĭãħĭãħĭ": 38032, + "Ġfrontline": 38033, + "ĠHungarian": 38034, + "Ġüberall": 38035, + "awat": 38036, + "Ġgrips": 38037, + "ições": 38038, + "arnya": 38039, + "ĠÍ¡": 38040, + "Ġseid": 38041, + "Ġanak": 38042, + "Ġacabou": 38043, + "íķij": 38044, + "Ġnotorious": 38045, + "ĠGodzilla": 38046, + "Ġovercoming": 38047, + "ĠPend": 38048, + "Ġolabilir": 38049, + "ülme": 38050, + "Ġerhalten": 38051, + "ãĤīãģĦ": 38052, + "ê·¹": 38053, + "ĠMeter": 38054, + "Ġstaan": 38055, + "Ol": 38056, + "Ġchats": 38057, + "ĠBuenos": 38058, + "ÃŃve": 38059, + "aluable": 38060, + "Ġstrategically": 38061, + "Ġcomprised": 38062, + "ĠпеÑĢÑģонаж": 38063, + "Ġwann": 38064, + "ĠCen": 38065, + "ниÑĤе": 38066, + "Łģ": 38067, + "ĠÑĤобой": 38068, + "iad": 38069, + "ĠkardeÅŁim": 38070, + "ĠCongressman": 38071, + "reaming": 38072, + "homme": 38073, + "Ġcommunaut": 38074, + "Ġalcoholic": 38075, + "Ġpickled": 38076, + "Ġacord": 38077, + "position": 38078, + "egól": 38079, + "Ġtroubling": 38080, + "ĠMarcheg": 38081, + "Ġzumindest": 38082, + "Ġseamlessly": 38083, + "Ġolun": 38084, + "ĠTVs": 38085, + "ĠпÑĢакÑĤиÑĩеÑģки": 38086, + "Ġbackend": 38087, + "ãģĵãĤĵãģ«ãģ¡ãģ¯": 38088, + "idable": 38089, + "Ġgadget": 38090, + "Ġfaço": 38091, + "ĠMarchegiani": 38092, + "Ġë°¤": 38093, + "Ġaccidental": 38094, + "ĠLP": 38095, + "Ġeldest": 38096, + "ĠAdmiral": 38097, + "ĠnÄĥm": 38098, + "lever": 38099, + "Ġpastel": 38100, + "Ġfondo": 38101, + "Connie": 38102, + "Ġtercer": 38103, + "Ġpact": 38104, + "ĠMonte": 38105, + "Ġmeats": 38106, + "ĠSMS": 38107, + "ĠAustralians": 38108, + "ç¼": 38109, + "Rhett": 38110, + "Ġexactement": 38111, + "Ġë¹¼": 38112, + "ĠMOD": 38113, + "ç¡": 38114, + "ĠRapt": 38115, + "ĠNoch": 38116, + "Ġabort": 38117, + "ĠNaval": 38118, + "ĠFuji": 38119, + "INTER": 38120, + "ĠновÑĭй": 38121, + "Ġmiejsce": 38122, + "ĠICU": 38123, + "ĠGraduate": 38124, + "ĠGlen": 38125, + "ardi": 38126, + "ĠÈĺ": 38127, + "Ġsolder": 38128, + "Ġprofessions": 38129, + "Ġorthog": 38130, + "omn": 38131, + "introdu": 38132, + "ĠDenise": 38133, + "ìŀIJ를": 38134, + "Ġcorrespondence": 38135, + "AMA": 38136, + "Ġinflict": 38137, + "Ġfand": 38138, + "ĠGü": 38139, + "ĠÑĩеÑĤ": 38140, + "Ġtraced": 38141, + "Ġpatents": 38142, + "Ġambush": 38143, + "Ġlotta": 38144, + "ffer": 38145, + "ĠWagner": 38146, + "Ġimperson": 38147, + "Ġextrêmement": 38148, + "ÙĤت": 38149, + "conduct": 38150, + "Att": 38151, + "ĠMueller": 38152, + "ĠAlicia": 38153, + "Ġcyc": 38154, + "Ġhacker": 38155, + "Ġtys": 38156, + "Ġhail": 38157, + "ĠзаÑıв": 38158, + "Ġpasso": 38159, + "Ġì¶Ķê°Ģ": 38160, + "ĠÎĪ": 38161, + "Ġpackaged": 38162, + "ĠCynthia": 38163, + "heet": 38164, + "ä¸ŃåĽ½": 38165, + "ĠNissan": 38166, + "ĠQuesto": 38167, + "é¨": 38168, + "did": 38169, + "Ġμια": 38170, + "ĠEllis": 38171, + "ĠAnalysis": 38172, + "cemos": 38173, + "Ġaseg": 38174, + "ĠMyster": 38175, + "ĠCao": 38176, + "Ġtuv": 38177, + "ĠIndustry": 38178, + "ì£¼ê³ł": 38179, + "otal": 38180, + "Ġpequeño": 38181, + "bras": 38182, + "Ġcomprehend": 38183, + "ĠSimpson": 38184, + "ÑģÑĤвие": 38185, + "ocracy": 38186, + "иÑĩеÑģки": 38187, + "ĠMush": 38188, + "ĠLaurie": 38189, + "Ġtriangular": 38190, + "ĠPresents": 38191, + "ĠKunden": 38192, + "ç´¹": 38193, + "æѦ": 38194, + "ĠIss": 38195, + "ĠDeck": 38196, + "á»ĥn": 38197, + "ĠDarkness": 38198, + "Ġinflammatory": 38199, + "eremiah": 38200, + "Ġwarmed": 38201, + "veyard": 38202, + "ĠMemory": 38203, + "etty": 38204, + "Ġtaxpayers": 38205, + "à¸ĵ": 38206, + "Ø¡": 38207, + "Ġpractise": 38208, + "ëĭ¬ë": 38209, + "Ġdrilled": 38210, + "mÃ¼ÅŁ": 38211, + "logo": 38212, + "ĠFach": 38213, + "¤ë¡ľ": 38214, + "Ġübrigens": 38215, + "Ġkonnten": 38216, + "Ġnormalmente": 38217, + "Ġargues": 38218, + "ilingual": 38219, + "°ë¥¼": 38220, + "egal": 38221, + "Ġtravaill": 38222, + "ovy": 38223, + "аÑĤо": 38224, + "Ġruth": 38225, + "ĠLights": 38226, + "Ġconsisted": 38227, + "×ijר×Ļ×Ŀ": 38228, + "Ġstereotype": 38229, + "Ġpayer": 38230, + "ĠRee": 38231, + "ĠAirbnb": 38232, + "Ġdrowned": 38233, + "ĠZoe": 38234, + "Ġcanopy": 38235, + "Ġbarr": 38236, + "ĠноÑĩ": 38237, + "Ġpagan": 38238, + "Ġjars": 38239, + "Ġrê": 38240, + "erver": 38241, + "æĪ¿": 38242, + "ieben": 38243, + "Ġespect": 38244, + "ĠFi": 38245, + "Ġunwilling": 38246, + "Ġtechnician": 38247, + "ặt": 38248, + "member": 38249, + "ĠCanal": 38250, + "سÙħ": 38251, + "Ġlieber": 38252, + "Ġinference": 38253, + "Ġhonoring": 38254, + "åijµ": 38255, + "ĠCampaign": 38256, + "Ġlineage": 38257, + "ĠStress": 38258, + "Ġvictories": 38259, + "Ġdeja": 38260, + "×£": 38261, + "êtes": 38262, + "blick": 38263, + "Ġменее": 38264, + "oths": 38265, + "ĠCouple": 38266, + "Jason": 38267, + "ĠNicolas": 38268, + "екÑģ": 38269, + "lib": 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"ivering": 48940, + "ĠдеÑĤи": 48941, + "charger": 48942, + "Ġpurl": 48943, + "Ġacademically": 48944, + "ĠNurse": 48945, + "Ġdeleting": 48946, + "ayo": 48947, + "Ġrefusal": 48948, + "Ġdepicts": 48949, + "ĠDracula": 48950, + "Ġtoasted": 48951, + "ĠZombie": 48952, + "ĠSuperior": 48953, + "ĠBold": 48954, + "Ġquizzes": 48955, + "Ġgle": 48956, + "450": 48957, + "Ġcomeço": 48958, + "ynn": 48959, + "Ġverst": 48960, + "ĠOlaf": 48961, + "Ġpomoc": 48962, + "ĠSask": 48963, + "ëĺ": 48964, + "ĠTCP": 48965, + "ĠProperty": 48966, + "íķĺì£ł": 48967, + "à¸ľà¸¡": 48968, + "boom": 48969, + "aros": 48970, + "ĠÑĢоÑģÑģий": 48971, + "ĠбÑĭваеÑĤ": 48972, + "åĩºåİ»": 48973, + "ĠìĿ´ìķ¼ê¸°ë¥¼": 48974, + "Ġcombien": 48975, + "vacc": 48976, + "Ġebenfalls": 48977, + "para": 48978, + "Ġзм": 48979, + "Ġdesperation": 48980, + "ordre": 48981, + "Ġש׾×Ļ": 48982, + "Ġgenerously": 48983, + "ĠÐŀк": 48984, + "Ġorbiting": 48985, + "> >", + "r u", + "w n", + "on t", + "i b", + "e ll", + "Ġs m", + "ot h", + "u al", + "Ġ >>", + 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"Ø §", + "o b", + "u nd", + "at h", + "Ġo p", + "ĠÐ ¾", + "a it", + "Ġwor ld", + "Ġsu pp", + "i o", + "Ġc our", + "ĠÐ ¸", + "w ard", + "е н", + "Ġal ways", + "u p", + "Ġha nd", + "ĠH ow", + "ci al", + "Ġcon s", + "Ġ Ñ", + "Ġin d", + "Ġ 4", + "ĠA s", + "Ġf un", + "j ect", + "Ġimport ant", + "Ġs ur", + "e w", + "at es", + "Ġ 5", + "Ġd i", + "Ġm ade", + "Ġin s", + "Ġas k", + "Ġ et", + "Ġn um", + "Ġc ar", + "ĠO kay", + "Ġs im", + "i k", + "Ġl ast", + "ĠG o", + "Ġm us", + "Ġre l", + "ul ar", + "´ ì", + "ĠWe ll", + "pe ct", + "ĠTh ank", + "Ġth ree", + "à £", + "ã ĥ", + "Ġin v", + "Ġg en", + "l ic", + "Ġhapp en", + "ë Ĭ", + "i en", + "e ver", + "оР²", + "Ġst r", + "ĠA ll", + "Ġin st", + "Ġâ Ģ", + "Ġde f", + "Ġs l", + "Ġm ight", + "un g", + "Ġye ar", + "Ġo wn", + "Ġke ep", + "b ody", + "d er", + "Ġ ÑĤ", + "ĠÐ ´", + "Ġan other", + "Ġm od", + "Ġe v", + "Ġgu ys", + "Ġab le", + "ã o", + "qu e", + "id ent", + "ĠY es", + "Ġit s", + "Ġpl ace", + "Ġpro du", + "ar n", + "ĠÐ ¼", + "Ġre p", + "Ġex per", + "Ġf am", + "it ies", + "if ic", + "Ġh igh", + "i ed", + "o ol", + "ie w", + "е ÑĤ", + "re n", + "Ġdon e", + "Ġ ...", + "ëĬ Ķ", + "st em", + "ĠS e", + "Ġbet ter", + "c ome", + "Ġd el", + "Ġt y", + "Ġu m", + "Ġh o", + "ĠA n", + "Ġm on", + "ing s", + "Ġs k", + "Ġo b", + "c om", + "ble m", + "op e", + "st and", + "' d", + "ment s", + "Ġe le", + "ĠI s", + "Ġd a", + "Ġre g", + "le ase", + "i ke", + "al s", + "iz e", + "ê °", + "Ġc are", + "Ġne ver", + "ìĿ ´", + "es e", + "Ġm et", + "ol og", + "ĠWh en", + "u ck", + "е ÑĢ", + "Ġ é", + "Ġd at", + "à §", + "Ġex am", + "il ity", + "Ġd et", + "c ri", + "Ġus ed", + "ĠD o", + "Ġtr ans", + "e g", + "t en", + "Ñ İ", + "c us", + "Ġsec ond", + "Ġb est", + "Ġh ard", + "Ġ ide", + "Ġpro blem", + "ê ³", + "ĠU n", + "Ñ ħ", + "Ġ Î", + "Ġw atch", + "ĠS h", + "at ter", + "Ġpre t", + "Ġd er", + "Ġcour se", + "Å Ł", + "at ive", + "ic s", + "Ġquest ion", + "ut e", + "ì Ĺ", + "ĠF or", + "at her", + "Ġc ol", + "i end", + "Ġ í", + "Ġ Z", + "Ġdoes n", + "ar ch", + "Ġinter est", + "Ġp ol", + "Ġc or", + "i ence", + "Ġp res", + "Ġe ach", + "Ġsy stem", + "Ġf act", + "i el", + "ab ly", + "Ġ er", + "Ġr un", + "Ġì Ŀ", + "Ġto p", + "n er", + "Ġth ought", + "Ġe as", + "i ent", + "Ġc re", + "Ñ Ī", + "Ġcomm un", + "y e", + "re ady", + "ll ow", + "Ġevery thing", + "om m", + "Ġm ed", + "ļ Ķ", + "Ġc ount", + "it s", + "Ġcom pl", + "h ip", + "Ù Ħ", + "o ok", + "Ġto get", + "Ġtoget her", + "am p", + "Ġg ame", + "Ġal ready", + "аР»", + "Ġcall ed", + "al e", + "Å Ĥ", + "ĠM y", + "Ġunder stand", + "Ġd r", + "Ġm om", + "it ed", + "оР»", + "Ġus ing", + "z y", + "Ġnum ber", + "ãĢ ģ", + "c ed", + "Ġc le", + "н о", + "ëĭ ¤", + "in ce", + "Ġlook ing", + "Ġpret ty", + "Ġpro b", + "ĠS he", + "Ġ ve", + "Ġget ting", + "Ġwe ek", + "Ġe ff", + "u ff", + "a ir", + "u es", + "er n", + "Ġ Q", + "ou p", + "ent ion", + "Ġs ide", + "оР¼", + "Ġfor m", + "Ġb us", + "Ġas s", + "Ġ ed", + "as on", + "we en", + "âĢ ¦", + "Ġt urn", + "Ġc ur", + "Ġco ll", + "Ġd ire", + "ĠG od", + "Ġ1 0", + "Ġe qu", + "ĠÐ ±", + "Ġop en", + "Ġsu ch", + "ir d", + "аРº", + "Ġe ar", + "Ä Ļ", + "g an", + "Ġpart ic", + "Ġfr iend", + "Ġex p", + "Ġex t", + "Ġh ome", + "Ġw ater", + "ĠO n", + "ÑĤ ÑĮ", + "or k", + "Ġп ÑĢ", + "Ġmo ve", + "n ess", + "en se", + "h o", + "Ġch ar", + "c o", + "in s", + "Ġb oth", + "Ġ1 9", + "Ġg ra", + "Ġbet ween", + "á »", + "Ġì ķ", + "as h", + "ĠR e", + "a i", + "al th", + "u res", + "em ber", + "Ġa v", + "Ġ ver", + "à ª", + "one y", + "Ġth ank", + "Ġmay be", + "u c", + "im e", + "ê³ ł", + "Ġa way", + "Ġn ame", + "ou se", + "Ġac c", + "Ġmus ic", + "Ġch ange", + "Ġp ass", + "g er", + "Ġbu ild", + "Ġv al", + "in ess", + "an y", + "Ġfe w", + "´ ë", + "t a", + "Ġl ist", + "à ¥", + "Ġo ld", + "Ġì ŀ", + "Ġs ort", + "Ġme m", + "Ġc a", + "ce pt", + "Ġgen er", + "Ġye ah", + "Ġwh ile", + "Ġany thing", + "r ic", + "gr am", + "Ġe in", + "c y", + "ur ing", + "ĠD e", + "Ġp ower", + "Ġcom ing", + "Ġwor d", + "Ġ- -", + "Ġbel ie", + "Ġf ound", + "t o", + "Ð ¿", + "Ġme ans", + "Ġin form", + "Ġ Ø", + "Ġ Ñĩ", + "Ġsm all", + "00 0", + "Ġc ame", + "Ġ íķ", + "w h", + "Ġwork ing", + "Ġexam ple", + "Ġp os", + "Ġde p", + "ê ²", + "ä º", + "ot e", + "Ġde m", + "ì §", + "t s", + "Ġv ar", + "a ut", + "Ġt ri", + "ch n", + "Ġhe ad", + "Ġwho le", + "× Ļ", + "z e", + "Ġtry ing", + "Ġt em", + "Ġc ou", + "et s", + "Ġ 6", + "Ġf il", + "vel op", + "Ġc ase", + "à ¯", + "Ġprob ably", + "Ġo kay", + "Ġpl an", + "Ġs it", + "Ġsch ool", + "ĠTh en", + "¸ ë", + "m e", + "Ġpro cess", + "Ġf ar", + "Ġre ad", + "Ġp oss", + "Ġb re", + "Ġso l", + "ic ht", + "Ġsupp ort", + "ĠT o", + "ert ain", + "Ġstart ed", + "Ġc ap", + "Ġle ft", + "Ġdat a", + "Ġtim es", + "еР»", + "Ġwant ed", + "а н", + "Ġtalk ing", + "Ġis t", + "Ġha ving", + "um p", + "Ġcont in", + "Ġsu b", + "ĠÐ ·", + "p r", + "ëĭ Ī", + "in a", + "Å ¼", + "Ġc reat", + "od e", + "× ķ", + "æ ĺ", + "! !", + "Ġt erm", + "is m", + "оР´", + "ĠBe cause", + "Ġw ent", + "id er", + "Ġpro v", + "Ġch ild", + "Ġd en", + "Ġl ight", + "b r", + "³ о", + "o h", + "Ġbo ok", + "Ġ Ù", + "ut ion", + "ĠJ ust", + "en e", + "Ġf our", + "Ġv is", + "ê° Ģ", + "Ġh ope", + "Ġmak ing", + "ĠL e", + "ì ķ", + "Ġo pp", + "a u", + "Ġm oney", + "Ġpro gram", + "à ¨", + "Ġst and", + "I N", + "Ġs ign", + "Ġle arn", + "à ł", + "ĠD on", + "Ġte am", + "Ġн а", + "l ud", + "Ġre st", + "ic es", + "æ ľ", + "Ġ ÑĢ", + "Ġa ut", + "Ġle ad", + "ation al", + "d e", + "g y", + "Ġn ice", + "Ġd as", + "Ġd ist", + "Ġh um", + "ĠO ne", + "æ Ī", + "Ġcom es", + "Ġj o", + "Ġc ent", + "Ġex pl", + "Ġm ark", + "re en", + "l ed", + "g in", + "ì ļĶ", + "Ġle vel", + "Ġcon f", + "us h", + "Ġde velop", + "Ġt est", + "en g", + "v ious", + "at ure", + "еР¼", + "re t", + "Ġj e", + "Ġst uff", + "Ġcl ass", + "ow s", + "Ġê ·", + "Ġs i", + "Ġl es", + "ro p", + "ç ļ", + "Ġp or", + "Ġw ar", + "ìĹ IJ", + "Ġevery one", + "Ġg e", + "Ġche ck", + "ot t", + "Ġs ing", + "Ġar t", + "Ġfo llow", + "Ġ20 1", + "ĠF r", + "a is", + "ì ĸ", + "Î ±", + "å °", + "Ġà ł", + "im es", + "Ġre t", + "Ġch ang", + "Ġp ub", + "Ġin f", + "Ġte chn", + "ad a", + "iv es", + "Ġbe h", + "æĺ ¯", + "Ġlook s", + "ãĢ Ĥ", + "Ð ·", + "ĠWh y", + "çļ Ħ", + "Ġen ough", + "Ġb ra", + "it ch", + "ä »", + "Ġad v", + "Ð ±", + "Ġwith out", + "w er", + "mer ic", + "d en", + "Ġcompl et", + "Ġide a", + "ter s", + "o ck", + "Ġdef in", + "Ġe ver", + "Ġg l", + "Ġon ce", + "Ġbr ing", + "Ġsay ing", + "Ġan s", + "Ġhe ar", + "n ect", + "Ġl ess", + "g o", + "re am", + "ad o", + "ì ŀ", + "Ġm ind", + "ent e", + "Ġf ull", + "Ġb ad", + "Ġw om", + "Ġsome one", + "Ġd u", + "Ġw on", + "Ġcont ro", + "ort un", + "Ġhe alth", + "Ġch o", + "ĠA r", + "Ġcon c", + "Ġinform ation", + "Ġst op", + "at t", + "at ely", + "ä ½", + "Ġgr oup", + "Ġ Ñĥ", + "Ġqu ite", + "Ġres p", + "E R", + "ug ht", + "ê ¸", + "m an", + "iz ed", + "ĠB r", + "Ġrem ember", + "Ġfam ily", + "Ġbus iness", + "a w", + "Ġspe c", + "Ġa u", + "ĠO r", + "Ä ħ", + "Ġse en", + "Ġl ar", + "Ġ 7", + "g g", + "b ers", + "Ġd ra", + "Ġmon th", + "Ġsay s", + "Ġis s", + "Ġli ve", + "Ġl ine", + "Ġmom ent", + "Ġex c", + "el s", + "Ġs ound", + "Ġco ol", + "Ġlo c", + "Ġc ertain", + "Ġd ri", + "о ÑĤ", + "am es", + "Ġm ust", + "n y", + "и ÑĤ", + "Ġk id", + "Ġinc lud", + "ìĿ Ħ", + "at or", + "Ä Ł", + "h a", + "are d", + "Ġse em", + "Ð ¹", + "ì Ħ", + "Ġel se", + "Ġì ł", + "ir l", + "Ġ 8", + "Ġv o", + "Ġquest ions", + "in es", + "e e", + "æĪ ij", + "ü r", + "ĠA meric", + "Ġst ory", + "Ġser v", + "ver n", + "ag es", + "l and", + "ĠâĢ ĵ", + "er a", + "ĠC an", + "Ġp op", + "et her", + "Ġn a", + "Ġor der", + "Ġmak es", + "Ġs ince", + "c on", + "ct or", + "Ġth ough", + "Ġprodu ct", + "л и", + "Ġle g", + "Ġme et", + "al f", + "Ñģ Ñı", + "un ch", + "it er", + "o ve", + "×ķ ×", + "i et", + "аР¼", + "it al", + "Ġsu per", + "l ing", + "Ġp ay", + "Ġpar a", + "Ġj ob", + "ĠH ere", + "Ġs w", + "k s", + "pt ion", + "m a", + "Ġbelie ve", + "¬ ë", + "Ġw ait", + "оР¹", + "Ġun t", + "Ġqu ick", + "h r", + "ĠÑ į", + "ĠP ro", + "Ġm en", + "à ¹", + "Ġday s", + "Ġgo es", + "Ġspe ak", + "ĠA t", + "em ent", + "Ġm iss", + "Ġa w", + "Ġdes ign", + "Ġpro ject", + "о ÑĢ", + "i j", + "ant s", + "at s", + "ĠCh r", + "Ġ 9", + "Ġc ut", + "Ġre qu", + "Ġн е", + "ĠN ot", + "as ter", + "Ġm ill", + "Ġpartic ular", + "Ġp ie", + "Ġstud ents", + "Ġf ive", + "ou n", + "ĠN e", + "Ġg i", + "Ġp as", + "Ġf ree", + "ĠS p", + "l ich", + "Ġpro f", + "Ġen g", + "Ġpr ot", + "ĠL ike", + "os ed", + "Ġcon nect", + "a pp", + "Ġë §", + "it ing", + "Ġb lo", + "Ġl os", + "ist s", + "Ġexper ience", + "re nt", + "Ġst ay", + "Ġfo od", + "t on", + "ru ct", + "Ġh ist", + "v iew", + "in ing", + "m ost", + "i vers", + "b o", + "ãģ Ħ", + "ĠT r", + "g en", + "Ġp lease", + "Ġcommun ity", + "Ġc e", + "A N", + "n o", + "Ġb ody", + "Ġh our", + "Ġ vers", + "á º", + "c er", + "Ġê °", + "Ġre ason", + "ĠR ight", + "Ġl ater", + "Ï Ħ", + "Ġh ouse", + "Ġ X", + "оР½", + "Ġst ate", + "f ic", + "å ¤", + "Å Ľ", + "iel d", + "Ġp ri", + "Ġp ast", + "Ġw alk", + "olog y", + "er ing", + "an na", + "Ġt er", + "Ġho ld", + "Ġor gan", + "b en", + "Î ¿", + "ó n", + "Ġeff ect", + "Ġyour self", + "Ġpl us", + "a j", + "and o", + "ur al", + "Ġro om", + "le ct", + "ê² Į", + "? \"", + "s ide", + "Ġbe come", + "Ñ Ĩ", + "Ġ Â", + "o od", + "Ġcon st", + "Ġn ight", + "ut es", + "Ð ¶", + "Ġbre ak", + "Ġp ain", + "Ġst ep", + "ire d", + "Ġnot hing", + "Ġunt il", + "Ñ ĸ", + "аР²", + "Ù Ĭ", + "Ġd uring", + "ì§ Ģ", + "l ess", + "o ll", + "н Ñĭ", + "Î ¹", + "f ect", + "i ver", + "ı Ħ", + "ith er", + "y ing", + "Ġbe gin", + "×Ļ ×", + "iv id", + "Ġà §", + "Ġs al", + "Ġt a", + "Ġp ot", + "Ġ $", + "Ġm ar", + "Ġcle ar", + "Ġf ace", + "Ġgr ow", + "Ġ *", + "Ġins ide", + "Ġfriend s", + "Ġle ave", + "en n", + "Ġeas y", + "Ġare a", + "al ity", + "ou d", + "Ġe at", + "Ù Ĩ", + "Ġp ur", + "or n", + "Ġsa w", + "Ġans wer", + "Ġfr ont", + "Ġbe aut", + "¼ ë", + "Ġm atter", + "Ġs on", + "ĠN ew", + "Ġres ult", + "id es", + "ch e", + "Ġf ut", + "p s", + "Ġfo cus", + "Ġinterest ing", + "å ¥", + "Ġa p", + "\" .", + "Ġcre ate", + "о Ñģ", + "Ġp ress", + "r oss", + "Ġp ick", + "l ine", + "Ġto ok", + "ĠM ay", + "r ow", + "Ġ ich", + "ĺ ë", + "Ġre f", + "Ġm or", + "r act", + "are nt", + "A R", + "Ġex act", + "Ġsp ace", + "w ork", + "н и", + "Ġb ir", + "Ġde v", + "Ð ³", + "Ġto ld", + "Ġpub lic", + "ci ally", + "Ġv iew", + "ĠHe y", + "m ed", + "ll o", + "c c", + "Ġf ac", + "Ġcou ple", + "Ġhe art", + "l er", + "Ġre ady", + "Ġal most", + "ar ing", + "Ġh alf", + "ĠM e", + "av or", + "i que", + "Ġchar ac", + "Ġpr act", + "O N", + "an e", + "Ġ il", + "н а", + "Ġv i", + "l ish", + "he ad", + "Ġle ast", + "Ġbas ically", + "as ed", + "r ight", + "Ġy et", + "Ġtak ing", + "Ġcount ry", + "Ġw in", + "Ġis n", + "Ġposs ible", + "Ġc am", + "Ġinc re", + "Ġp at", + "Ġw anna", + "Ġcons ider", + "Ġab s", + "Ġwith in", + "Ġhum an", + "Ġthink ing", + "Ġo h", + "¡ ľ", + "Ġqu i", + "as es", + "Ġ 0", + "it ely", + "ä¸ į", + "Ġk ill", + "Ġm il", + "Ġinv est", + "is ter", + "Ġsu c", + "ion al", + "el f", + "Ġwh ether", + "Ġcontro l", + "Ġagain st", + "ot s", + "ëĭĪ ëĭ¤", + "i or", + "Ġpres ent", + "Ġ ا", + "Ġwatch ing", + "u be", + "er v", + "Ġn icht", + "Ġgo vern", + "ĠTh ese", + "Ġ :", + "u it", + "ug h", + "Ġwork s", + "o o", + "Ġw ir", + "Ġa ir", + "ĠT e", + "аР·", + "is ion", + "wh ere", + "Ġto t", + "j oy", + "ì ĭ", + "Ġv ol", + "ĠÐ µ", + "Ġcl ose", + "ĠA d", + "Ñ ī", + "in ed", + "Ġun a", + "Ġê· ¸ë", + "° ë", + "or ry", + "Ġb ro", + "Ġfil m", + "if t", + "2 0", + "Ġty pe", + "Ġhappen ed", + "ĠA m", + "Ġg irl", + "ĠA re", + "ward s", + "Ġp our", + "Ġcol or", + "el t", + "а Ñģ", + "Ġs ense", + "le x", + "ĠW ith", + "us s", + "ri b", + "Ġre se", + "Ġn orm", + "Ġfut ure", + "Ġde al", + "end ing", + "e y", + "Ġ x", + "er o", + "ĠC l", + "u k", + "Ġwhat ever", + "sel ves", + "Ġyou ng", + "ì Ĭ", + "ĠM ar", + "ĠChr ist", + "Ġgu ess", + "Ġper form", + "Ġen er", + "r on", + "Ġh it", + "Ġw ond", + "Ġdire ct", + "ĠE very", + "Ġof ten", + "Ġf a", + "Ġal ong", + "Ġcl ick", + "ĠL ook", + "Ġsit u", + "Ġhapp y", + "e ad", + "Ġag o", + "Ġen c", + "Ġmy self", + "Ġco ver", + "оР±", + "Ġm id", + "Ġc ost", + "Ġt en", + "ĠS ch", + "Ġex pect", + "Ġwas n", + "Ġstr ong", + "if ul", + "Ġopp ortun", + "in al", + "y le", + "Ġsh are", + "Ġtr ue", + "Ġapp ro", + "Ġch all", + "Ġmin utes", + "Ġch ann", + "Ġë Ĥ", + "Î µ", + "l i", + "Ġm ess", + "or ies", + "pe cially", + "Ġwr ong", + "Ġy es", + "Ġì Ĺ", + "ir on", + "Ġall ow", + "Ġsu bs", + "Ġf ore", + "Ġf ight", + "Ġso cial", + "Ġc ra", + "an a", + "Ġa ff", + "Ġ ess", + "Ġway s", + "Ġsh ort", + "Ġf all", + "Ġla w", + "ĠWh o", + "Ġen joy", + "Ġc al", + "Ġac cess", + "f e", + "Ġn on", + "Ġac ross", + "er y", + "vious ly", + "ĠE x", + "id ed", + "Ġl ink", + "ĠP r", + "Ġterm s", + "ac es", + "Ġl and", + "az ing", + "Ġ1 5", + "Ġm ult", + "Ġspe cial", + "å Ģ", + "iv ing", + "ìĿ Ģ", + "Ġty p", + "Ġst e", + "Ġ Ä", + "Ġfor ward", + "å ı", + "Ġf re", + "å¥ ½", + "Ġrese arch", + "௠į", + "а ÑĤ", + "Ġma in", + "Ġrec ord", + "Ġh u", + "Ġdefin itely", + "Ġe ither", + "Ġlist en", + "Ġke y", + "Ġmark et", + "ĠÑĩ ÑĤо", + "iz ation", + "Ġvide os", + "Ġgu y", + "Ġf ig", + "Ġst ra", + "ĠP l", + "ull y", + "am os", + "Ġm ention", + "Ġs ong", + "Ġinter n", + "r al", + "ur s", + "Ġh on", + "Ġval ue", + "Ġb ar", + "c le", + "оР¶", + "Ä ĩ", + "ľ ë", + "Ġz u", + "и м", + "ä½ ł", + "Ġsing le", + "Ġa uch", + "cus s", + "Ġget s", + "Ġsomet imes", + "å ¾", + "am b", + "m m", + "c ing", + "Ġper fect", + "ĠB l", + "out h", + "ì ł", + "Ġs ci", + "p ar", + "Ġre d", + "Ġp ost", + "Ġm ot", + "Ġele ct", + "ĠE u", + "it ive", + "ĠS ome", + "Ġdes cri", + "Ġcur rent", + "é s", + "Ġt re", + "ĠE n", + "Ġm it", + "E N", + "Ī ë", + "i um", + "Ġhe ard", + "Ġsim ple", + "l ar", + "Ġevery body", + "il ar", + "Ġneed s", + "Ġdif fic", + "ĠGo od", + "um ent", + "c ent", + "Ġo per", + "а ÑĤÑĮ", + "et y", + "Ġbl ack", + "Ġgi ven", + "on es", + "Ġwe l", + "é Ģ", + "Ġìķ Ħ", + "Ġ3 0", + "A T", + "Ġst at", + "ou ch", + "ĠM r", + "а ÑĢ", + "Ġsh o", + "Ġcon d", + "× Ķ", + "m y", + "Ġchild ren", + "Ġe u", + "еР´", + "ìķ Ħ", + "ter n", + "Ġu h", + "Ġh ar", + "Ġpr om", + "Ġp ull", + "re w", + "Ġcomp any", + "Ġbeaut iful", + "ust om", + "íķ ĺ", + "к и", + "Ġst re", + "Ġam azing", + "ri es", + "Ġsuc cess", + "Ġm ach", + "n ot", + "Ġdis cuss", + "Ġn at", + "¦ ¬", + "Ġun e", + "Ġdiffic ult", + "Ġr is", + "Î ½", + "Ġc amp", + "Ġbu y", + "ä¸ Ģ", + "Ġma g", + "p o", + "ĠY our", + "Ġbeh ind", + "ic a", + "ı n", + "ĠO K", + "Ġl ang", + "Ġwom en", + "Ġen v", + "Ġre ce", + "Ġchann el", + "i ally", + "u le", + "Ġ1 2", + "th ers", + "Ġb ott", + "Ġrep ort", + "ent ly", + "f ully", + "T he", + "Ġs ent", + "Ġev ent", + "Ġener gy", + "l t", + "Ġword s", + "ar r", + "d le", + "Ġa head", + "ard s", + "Ø ±", + "äº Ĩ", + "Ġto ol", + "con om", + "е Ñģ", + "Ġexact ly", + "Ġf avor", + "Ġl ow", + "Ġpro per", + "Ġìŀ Ī", + "Ġ !", + "Ġrel ations", + "Ġm as", + "Ġkid s", + "Ġent ire", + "ud e", + "Ù ħ", + "ĠWh ere", + "Ġon es", + "Ġc ity", + "ol ut", + "Ġs ix", + "ab ility", + "ö r", + "il i", + "ĠE s", + "Ġhapp ens", + "ain s", + "Ġmod el", + "Ġp ict", + "Ġes pecially", + "Ġ1 00", + "k t", + "Ġso on", + "b y", + "ro du", + "Ġan n", + "Ġsubs cri", + "ĠQ u", + "Ġav ail", + "im ent", + "Ġv oc", + "k a", + "Ġ2 00", + "ap er", + "ĠI nd", + "Ġì §", + "h or", + "į °", + "j or", + "и л", + "Ġs qu", + "A U", + "ar ning", + "ĠÐ ³", + "I S", + "ĠÐ »", + "еР¹", + "y es", + "å ħ", + "ĠÐ Ĵ", + "Ġor ig", + "оР³Ð¾", + "Ġask ed", + "il t", + "оР³", + "Ġcontin ue", + "Ġì ĺ", + "r am", + "Ġo thers", + "E S", + "oh n", + "Ġl ay", + "Ġbas ed", + "Ġp u", + "Ġapp e", + "Ġl im", + "Ġpro p", + "Ģ ë", + "m in", + "Ġh ot", + "ĠL a", + "Ġf ast", + "Ġprot ect", + "Ġam ount", + "Ġa qu", + "Ġf und", + "Ġc ustom", + "Ġc ult", + "Ġhand s", + "Ġha ven", + "Ġa ud", + "Ġout side", + "ĠA fter", + "ap s", + "Ġan im", + "pl oy", + "Ġh at", + "ĠF irst", + "Ġt reat", + "Ġe p", + "Ġm ater", + "Ġbuild ing", + "Ġë °", + "å IJ", + "ìĦ ľ", + "z a", + "ught er", + "ĠP e", + "ne y", + "et er", + "at ic", + "Ġed uc", + "ê¸ °", + "Ġmo v", + "ĵ ¤", + "am a", + "r ation", + "Ġs n", + "Ù Ī", + "Ġs um", + "Ġph ot", + "ĠÐ Ŀ", + "Ġ .", + "æľ ī", + "Ġfin ish", + "itt ing", + "å ®", + "Ġlar ge", + "Ġì ĸ", + "Ġwh ite", + "ar a", + "Ġma is", + "ĠH i", + "Ġd am", + "Ġا ÙĦ", + "Ġbo x", + "ĠHe llo", + "Ġs le", + "Ġo pt", + "ri ed", + "¥ ¼", + "Ġact iv", + "Ġn ão", + "ĠC om", + "Ġplay ing", + "T h", + "Ġavail able", + "Ġp ort", + "å Ī", + "ĠA h", + "Ġl as", + "Ġear ly", + "Ġwond er", + "± °", + "Ġ1 8", + "c ul", + "Ġfun ction", + "Ġmor ning", + "ll e", + "i ents", + "u x", + "Ġc ir", + "it ions", + "Ġde ep", + "Ġpol it", + "y or", + "m p", + "ak ing", + "Į ë", + "ĠM an", + "Ġmill ion", + "Ġ /", + "Ġind ivid", + "Ġp an", + "Ġgovern ment", + "Ġwr ite", + "ĠT od", + "am ent", + "Ġ Ï", + "Ġw ind", + "ĠE ng", + "ch en", + "W h", + "ì ľ", + "Ġ ident", + "ãģ §", + "v ent", + "ur ch", + "Ġh y", + "Ġy a", + "Ġtr ad", + "Ġrelations hip", + "à º", + "Ġd ou", + "O R", + "Ġs we", + "Ġne g", + "in ation", + "Ġte xt", + "i pp", + "Ġf ine", + "á s", + "ĠD r", + "ĠC ome", + "Ġmonth s", + ", \"", + "ен и", + "Ġhour s", + "Ġp od", + "ir t", + "Ġinv ol", + "Ġcoll ect", + "Ġau f", + "Ġp a", + "Ġhist ory", + "m b", + "if y", + "Ġ ?", + "Ġbel ow", + "as ure", + "ab y", + "Ġlang u", + "Ġan t", + "Ġcom b", + "at o", + "Ġex ist", + "Ġë ĭ", + "Ġtak es", + "Ġcharac ter", + "a ff", + "Ġf ield", + "Ġe conom", + "ie f", + "Ġpie ce", + "å ľ", + "Ġre ach", + "Ġê ²", + "on y", + "Ġmater ial", + "Ġd ig", + "Ġph ys", + "Ġimp ro", + "Ġsim ilar", + "I C", + "Ġn et", + "y n", + "Ġpos ition", + "à Ł", + "Ġb ene", + "re ad", + "Ġle arning", + "um e", + "Ġcle an", + "ÑĤо ÑĢ", + "Ġco ok", + "Ġseem s", + "Ġo l", + "ĠU S", + "ĠJ es", + "Ġ à®", + "ent ial", + "ivers ity", + "ac y", + "Ġ Ñı", + "olut ely", + "re ct", + "ĠP lease", + "Ġrep res", + "Ġt ouch", + "m en", + "ĠÐ °", + "i ón", + "ĠThank s", + "Ġan g", + "Ġma jor", + "Ġit self", + "ill s", + "\" ,", + "i ans", + "Ġsc reen", + "Ġh or", + "Ġknow n", + "Ġenv iron", + "Ġfin al", + "Ġfig ure", + "ĠT w", + "Ġe yes", + "Ġim ag", + "Ġsee ing", + "Ġha ir", + "re m", + "Ġapp lic", + "end s", + "p ut", + "Ġnew s", + "Ġcomplet ely", + "ugh s", + "Ġkn ew", + "if ied", + "ĠJ e", + "ĠD id", + "Ġsitu ation", + "Ġf lo", + "m s", + "Ġph one", + "Ġb all", + "d o", + "Ġp arent", + "Ġs orry", + "ur y", + "и н", + "ip s", + "аР´", + "Ġinst ead", + "Ġhu ge", + "Ġt u", + "Ġ ãģ", + "ĠG r", + "Ġdet ail", + "ĠÐ Ł", + "Ġindivid ual", + "Ġf ire", + "Ġcl os", + "Ġw er", + "un e", + "Ġrun ning", + "Ġcon vers", + "Ġrec omm", + "Ġcom o", + "Ġsome body", + "ĠJ ohn", + "ĠìĿ ´", + "ĠO ur", + "pl es", + "ĠP h", + "Ġan al", + "Ġ5 0", + "Ġof fer", + "Ġ <", + "ition al", + "g est", + "Ġv ous", + "l et", + "ic y", + "Ġfeel ing", + "L E", + "r os", + "Ġth ird", + "оРº", + "Ġser ies", + "ĠAn y", + "is ed", + "o ld", + "Ġdra w", + "Ġserv ice", + "Ġcan not", + "b al", + "ãģ Ĩ", + "Ġli ving", + "ı m", + "Ġdiffer ence", + "Ġopportun ity", + "Ġne ar", + "or th", + "k en", + "Ġloc al", + "Ø ª", + "ĠC on", + "Ġob ject", + "Ġd ass", + "ãģ Ļ", + "IJ ×", + "Ġquick ly", + "ra ph", + "Ġiss ues", + "éĢ Ļ", + "ĠAmeric an", + "Ġpre p", + "en ces", + "Ġprof ess", + "ll ing", + "o f", + "Ġfo ot", + "b re", + "Ġus ually", + "Ġgener al", + "d a", + "an ces", + "Ġd est", + "Ġo cc", + "Ġmem bers", + "Ġd ans", + "Ġequ al", + "z t", + "Ġbe com", + "Ġmo ving", + "Ġspec ific", + "ÃŃ a", + "Ġf ur", + "Ġne cess", + "Ġcomm on", + "Ġatt ack", + "ĠÑį ÑĤо", + "ĠTod ay", + "Ġun s", + "ĠG u", + "i od", + "Ġacc ount", + "Ġgra nd", + "Ġs elf", + "ĠE l", + "Ġt ast", + "Ġcont ent", + "Ġc u", + "Ħ ë", + "ĠMay be", + "ĠJes us", + "ore s", + "p ort", + "© ´", + "Ġg ives", + "Ġnorm al", + "ÑĢ Ñĥ", + "Ġimp act", + "ä r", + "Ġd ies", + "Ġl ab", + "s h", + "i os", + "ĠP res", + "ĠU nd", + "ĠO f", + "Ġfin ally", + "Ġdo ll", + "Ġvoc ê", + "p ly", + "ĠA g", + "Ġtak en", + "Ġgr ound", + "f ort", + "Ġg ave", + "ĠIn st", + "Ġl ost", + "Ġwork ed", + "Ġl iter", + "Ġiss ue", + "Ġind ust", + "Ġret urn", + "Ġhappen ing", + "Ġwant s", + "и в", + "Ġproblem s", + "ĠC ar", + "Ŀ ¼", + "ĠAl so", + "Ġs ize", + "Ġob viously", + "ĠS u", + "ĠS c", + "Ġrecomm end", + "our ces", + "ast ic", + ".. ..", + "Ġm i", + "l ier", + "ĠE ven", + "ci a", + "Ġh ur", + "v a", + "Ġm ass", + "Ġwould n", + "un t", + "ck s", + "Ġf elt", + "os p", + "l ight", + "ол ÑĮ", + "n ie", + "Ġbott om", + "Ġб Ñĭ", + "ore d", + "is on", + "Ġgr ad", + "Ġum a", + "Ġv a", + "Ġì Ĥ", + "ress ion", + "ul ation", + "I D", + "id ence", + "Ġb ur", + "Ġg one", + "l u", + "ìĸ ´ì", + "Ġre du", + "Ġj a", + "ìĿ ĺ", + "it a", + "Ġso ft", + "Ġç a", + "ic o", + "er al", + "à ±", + "a f", + "Ġpoint s", + "g u", + "Ġd é", + "ap t", + "a x", + "ĠAl right", + "Ġcam era", + "Ġa ch", + "Ġп о", + "Ġse ver", + "5 0", + "Ġs ie", + "Ï ģ", + "Ġm al", + "Ġcomp ut", + "Ġmid dle", + "Ġcould n", + "m ing", + "Ġì ĭ", + "ĠH is", + "Ġg ames", + "Ġint rodu", + "Ġc ell", + "p or", + "Ġsle ep", + "Ġë ³", + "id ing", + "Ġ ou", + "Ġde g", + "Ġdr ink", + "Ġenviron ment", + "ĠUn ited", + "Ġtalk ed", + "Ġcho ose", + "Ġj our", + "e ge", + "ĠM in", + "Ġint e", + "Ġr ather", + "Ġoff ic", + "к а", + "ac hing", + "Ġmention ed", + "Ġf ill", + "Ġtr ack", + "Ġn ie", + "Ġ ut", + "Ġв Ñĭ", + "ib ility", + "Ġv ac", + "Ġr ad", + "Ġp ack", + "Ġs end", + "ĠD as", + "ĠA b", + "Ġeng ine", + "ãģ Ĺ", + "Ġcomp et", + "à ´", + "Ġв Ñģ", + "Ġdo or", + "Ġlong er", + "å° į", + "Ġlangu age", + "Ġext ra", + "pl ay", + "Ġwe bs", + "um b", + "ro om", + "ç ľ", + "Ġbegin ning", + "Ġre fer", + "A M", + "n en", + "ig her", + "f ace", + "er c", + "Ġfor get", + "Ġcom ment", + "еРº", + "л Ñı", + "r or", + "ż e", + "ĠG e", + "Ġd ark", + "Ġany one", + "ant e", + "g es", + "ìĬ µ", + "Ñ ij", + "b ed", + "j e", + "ruct ure", + "Ġpr im", + "id a", + "è ¦", + "ãģ ¾", + "Ġm ix", + "Ġstart ing", + "ĠìĿ ´ë", + "Ġprov ide", + "act ion", + "Ġm other", + "Ġper iod", + "Ġst ick", + "ĠYou T", + "Ġtechn ology", + "ê ¹", + "Ġb ed", + "Ġg iving", + "Ġexpl ain", + "z en", + "im ate", + "Ġrepres ent", + "lo ad", + "ĠHow ever", + "Ġli ves", + "ut h", + "ir it", + "og n", + "Ġli k", + "Ġresp ons", + "Ġpri v", + "Ġto m", + "ç ão", + "i am", + "Ġexc ited", + "Ġc ard", + "gr ound", + "Ġ× Ķ", + "Ġs ens", + "Ġte ach", + "id o", + "h od", + "Ġep is", + "Ġwel come", + "Ġw all", + "ä ¹", + "Ġch ance", + "h en", + "ĠÐ ¡", + "ĠÄ ij", + "Ġsim ply", + "ĠÑĤ ак", + "r ing", + "j a", + "b ook", + "Ġsever al", + "st e", + "Ġcreat ed", + "Ġо ÑĤ", + "Ġp ush", + "= =", + "Ġh igher", + "u f", + "our ce", + "o ke", + "Ġon line", + "Ġre le", + "Ġt on", + "ens ive", + "Ġfavor ite", + "Ñĥ д", + "Ġlook ed", + "Ġv on", + "âĢ Ķ", + "Ġf ür", + "Ġbut ton", + "Ġb ill", + "Ġchang es", + "! \"", + "Ġsl ow", + "ab les", + "Ġde ath", + "and s", + "ate g", + "Ġthem selves", + "ãģ £", + "Ġc op", + "ãģ ®", + "Ġperson al", + "ug hing", + "Ġ1 1", + "g ar", + "ad es", + "Ġneed ed", + "Ġstud y", + "ag ed", + "ÑģÑĤ в", + "in o", + "Ġdis c", + "k i", + "Ġadd ress", + "× ¨", + "itt en", + "es ome", + "ĠÐ ¶", + "¤ ë", + "ur a", + "Ġm u", + "Ġcontin u", + "f or", + "Ġm atch", + "ãģ ¦", + "Ġstra ight", + "IJ ë", + "n ers", + "Ġdo g", + "Ġde b", + "ĠC O", + "Ġo s", + "g ed", + "c ame", + "Ġcor rect", + "et te", + "ĠSe e", + "Ġinclud ing", + "ĠEu ro", + "est er", + "Ġj ump", + "ĠWh ich", + "Ġк ак", + "s on", + "y a", + "IN G", + "Ġe ine", + "os h", + "en cy", + "Ġmed ia", + "Ġsubscri be", + "é Ĥ", + "Ġpr in", + "Ġha b", + "ĠP er", + "ĠW as", + "Ġp age", + "it or", + "Ġto wards", + "Ġtri ed", + "en ge", + "art ment", + "Ġvar i", + "Ġp aper", + "Ġpict ure", + "Ġvers ion", + "Ġbr ought", + "w are", + "ĠSt ates", + "Ġs ich", + "led ge", + "Ġper cent", + "Ġgo d", + "e c", + "ĠC omm", + "Ġdec ided", + "Ġse lect", + "íķ ľ", + ") .", + "ur ity", + "Ġfur ther", + "Ġcom ments", + "le ment", + "Ġd ream", + "Ġcent er", + "m i", + "Ġc as", + "Ġwom an", + "Ġro ad", + "Ġf ail", + "Ġbe came", + "l us", + "il ities", + "ãģ ¯", + "ĠC o", + "Ġman age", + "Ġrec ogn", + "Ġact ion", + "Ġbene f", + "Ġear lier", + "× ľ", + "Ġspe ed", + "Ġm ent", + "Ġso ci", + "Ġsho ot", + "u i", + "Ġà ¤", + "Ġapp ly", + "v o", + "x im", + "Ġca use", + "Ġsur pr", + "Ġha ben", + "D I", + "Ġf ather", + "ĠNe xt", + "ĠYouT ube", + "Ġc ode", + "Ġro le", + "g ress", + "Ġg reen", + "et t", + "Ġbu ilt", + "Ġfl ow", + "Ġb ase", + "Ġtra ining", + "Ġr ound", + "ĠW ill", + "Ġp ath", + "ĠR o", + "Ġinterest ed", + "ìĸ ´", + "Ġres pect", + "Ġchang ed", + "iss ion", + "Ġstud ent", + "og raph", + "Ġappro ach", + "Ġshow s", + "å° ±", + "Ġt ar", + "Ġcr it", + "Ġg lo", + "ìĬµ ëĭĪëĭ¤", + "Ġde ad", + "ĠPres ident", + "Ġth ous", + "Ġb al", + "st er", + "e x", + "Ġabs olutely", + "Ġm ic", + "Ġpract ice", + "Ġqu ality", + "Ġl ower", + "og le", + "Ġse par", + "b all", + "med i", + "Ġre view", + "ĠA pp", + "Ġo k", + "âĢ ĭ", + "Ġexper ien", + "Ġconc ern", + "ent ially", + "m ore", + "ĠJ o", + "ap an", + "ĠI ch", + "ist ic", + "Ġf air", + "Ġwebs ite", + "i res", + "ĠB y", + "Ġtra vel", + "Ġris k", + "Ġm ir", + "Ġbo ard", + "Ġs en", + "Ġparent s", + "ĠW ow", + "Ġfe ed", + "Ġsa ve", + "Ġser ious", + "Ġin it", + "E L", + "und red", + "A S", + "Ġv an", + "or row", + "Ġwor th", + "Ġse arch", + "Ġ1 6", + "Ġpart s", + "ÑģÑĤ ÑĮ", + "Ġcomp an", + "Ġmov ie", + "Ġmet hod", + "Ġ ill", + "Ġw ish", + "d y", + "Ġit em", + "Ġmin us", + "ang er", + "Ġvo ice", + "Ġsk in", + "Ġare as", + "Ġe ight", + "Ġo bs", + "Ġ ,", + "аР¹", + "Ġo il", + "Ġc y", + "Ġb aby", + "s y", + "Ġem ploy", + "ĠK e", + "Ġpl aces", + "Ġf ix", + "Ġest á", + "ãģ ¨", + "iv ed", + "Ġlot s", + "Ġse ason", + "un k", + "al t", + "Ġt able", + "ĠÐ ¢", + "à ¢", + "Ġatt ention", + "ãģ ª", + "ĠH er", + "Ġa ge", + "Ġp ra", + "b ack", + "c il", + "Ġnet work", + "r it", + "Ġdo c", + "Ġare n", + "ig en", + "Ġë Ħ", + "Ø ¯", + "end er", + "Ġtot al", + "Ġpr ice", + "Ġcra zy", + "ì ļ", + "i qu", + "th ough", + "Y ou", + "Ù ĩ", + "ãĤ ĵ", + "Ï ħ", + "Ġs at", + "Ġb i", + "ĠD ie", + "Ġsh a", + "Ġthank s", + "u h", + "Ġst age", + "аР¶", + "ĠF l", + "Ġle av", + "Ġbo y", + "Ġa f", + "ö n", + "ĠG et", + "Ġac cept", + "Ġent er", + "Ġt ur", + "Ġsi ÄĻ", + "Ġhon est", + "ãĢ Į", + "Ġs am", + "Ġre pl", + "g ing", + "Ġdevelop ment", + "ĠA ct", + "or a", + "ãĢ į", + "ä ¾", + "Ġknow s", + "Ġim age", + "ĠL ord", + "и ÑĤÑĮ", + "Ġweek s", + "Ġse x", + "Ķ ë", + "Ġh undred", + "Ġsound s", + "Ġlearn ed", + "Ġb ud", + "ĠÑģ ÑĤ", + "Ġinc red", + "â Ļ", + "Ġn os", + "Ġd rop", + "Ġb en", + "ĠÐ ĺ", + "Ġsa fe", + "at a", + "Ġf uck", + "so ci", + "Ġd an", + "Ġcr oss", + "1 0", + "m o", + "ver t", + "Ġ1 7", + "z ie", + "å ķ", + "Ġd om", + "ĠB o", + "Ġset ting", + "Ġinvol ved", + "ar ily", + "Ġs ind", + "Ġs us", + "Ġwor ry", + "et h", + "ê¹ Į", + "Ġs un", + "Ġh ier", + "Ġcertain ly", + "ou l", + "ort s", + "ĠE r", + "ĠU m", + "Ġca us", + "Ġnat ural", + "Ġà ¼", + "Ġc ry", + "ĠSe c", + "Ġs om", + "æ ²", + "Ġeduc ation", + "а еÑĤ", + "Ġmult ip", + "Ġal one", + "Ġe ye", + "Ġr ate", + "ĠEuro pe", + "è ¿", + "m on", + "Ġf it", + "iz ing", + "pp ed", + "Ġpress ure", + "th e", + "и Ñģ", + "it es", + "ĠA f", + "re ci", + "att le", + "Ġserv ices", + "ĠGo ogle", + "é ģ", + "Ġc ases", + "Ġdri ve", + "Ġchall eng", + "u z", + "ĠM o", + "ìľ ¼ë", + "v al", + "åĢ ĭ", + "Ġf ol", + "Ġì ¢", + "ff ic", + "Ġr a", + "Ġs in", + "Ġbl ue", + "Ġaff ect", + "Ġm is", + "Ġsh ot", + "Ġо б", + "as ing", + "Ġsign ific", + "ĠC he", + "Ġê ³", + "Ġpos itive", + "ì £", + "Ġw ie", + "Ġ4 0", + "ord ing", + "ĠFr om", + "ê µ", + "Ġbra nd", + "Ġtr ust", + "Ġp le", + "Ġcommun ic", + "Ġwe ight", + "Ġask ing", + "Ġta x", + "ĠJ apan", + "ãģ Ł", + "Ġíķ ĺ", + "op s", + "Ï Ĥ", + "Ġput ting", + "Ġro ll", + "ĠAmeric a", + "re g", + "ŀ ×", + "at ures", + "ens ion", + "ĠS omet", + "Ġorig inal", + "p ing", + "Ġ ÅŁ", + "Ġproduct s", + "ãĥ ¼", + "Ġcont act", + "ol ution", + "Ġgo al", + "Ġp ow", + "Ġperform ance", + "Ġblo od", + "at ors", + "ĠM ich", + "Ġtem per", + "ĠD an", + "Ġsu gg", + "ÑĤ и", + "Ġim m", + "Ġoff ice", + "Ġar ri", + "Ġcom fort", + "ĠÐ Ķ", + "Ġsugg est", + "Ġpl at", + "Ĥ ĺ", + "1 9", + "Ġo m", + "Ġse ven", + "ĠC ent", + "ill e", + "Ġcon cept", + "Ġb ag", + "ü n", + "ive ly", + "Ġd iv", + "m os", + "æ ī", + "Ġfeel s", + "Ġ ir", + "ak es", + "le y", + "Ġpartic ip", + "ĠÐ ļ", + "f l", + "j ust", + "Ġs il", + "ĠP a", + "A L", + "Ġgot ta", + "Ġf an", + "Ġchall enge", + "Ġcompan ies", + "ĠPe ople", + "< /", + "оР·", + "Ġp en", + "is ing", + "Ġa us", + "em ic", + "am ente", + "Ġmeet ing", + "Ġvis it", + "Ġsupp osed", + "ĠOn ce", + "д а", + "or ld", + "3 0", + "U S", + "Ġvi ol", + "Ġnot ice", + "ĠÐ IJ", + "h an", + "p ed", + "ì ĺ", + "h h", + "Ġtr ou", + "Ġmin ute", + "ĠP ar", + "r ay", + "Ġt it", + "Ġup d", + "Ġblo ck", + "Ġd ue", + "a ur", + "Ġfor ce", + "Ġcou n", + "ĠâĢ Ķ", + "Ġtyp es", + "ë §", + "Ġl ate", + "Ġimpro ve", + "Ġì Ī", + "Ġa ve", + "ul es", + "c l", + "am ed", + "Ġaw esome", + "ĠO k", + "Ġv ot", + "Ġmach ine", + "Ġfollow ing", + "Ġme asure", + "ac ión", + "u el", + "ch an", + "Ġab ility", + "Ġt out", + "Ġide as", + "Ġincre ase", + "Ġen s", + "ĠÑ ħ", + "Ġë ª", + "Ġj est", + "ĠÐ ľ", + "Ġtr uth", + "h y", + "Ġsp end", + "Ġsci ence", + "et e", + "Ġ1 4", + "Ġepis ode", + "Ġal g", + "end ed", + "ãģ ĵ", + "ar i", + "ll a", + "Ġf ish", + "Ġthr ow", + "m it", + "å ¹", + "Ġcir c", + "ĠC al", + "Ġt our", + "Ġdire ction", + "Ġno ch", + "еР²", + "é n", + "Ġcount ries", + "Ġindust ry", + "in y", + "ic le", + "Ġfe et", + "I t", + "Ġlead ers", + "et zt", + "Ġst aff", + "ç Ķ", + "Ġpur p", + "it o", + "? !", + "ĠJ a", + "Ġst ore", + "et ic", + "ĠCh ina", + "Ġë IJ", + "ĠUn iversity", + "Ġ #", + "Ġdec ision", + "Ġach ie", + "Ġact ual", + "u ly", + "Ġse ction", + "Ġresult s", + "Ġst ar", + "Ġm ist", + "ib ly", + "Ġd ad", + "Ġnum bers", + "om b", + "è ª", + "ĠS pe", + "Ġm er", + "Ġ2 5", + "Ġaut om", + "Ġco ld", + "Ø ¨", + "Ħ ľ", + "ag er", + "ĠT V", + "ĠS ie", + "ĠH ave", + "Ġ że", + "ug g", + "ain ed", + "Ġup on", + "Ġlo g", + "Ġcomplet e", + "Ġbra in", + "ag ing", + "ĠM us", + "o ver", + "Ġeas ier", + "Ġinte gr", + "Ġm ás", + "Ġturn ed", + "Ġst ri", + "iv al", + "Ġhe av", + "ĠT H", + "Ġwr iting", + "ÑĢ а", + "åľ ¨", + "å¤ §", + "Ġcl a", + "d ing", + "Ġtell ing", + "и д", + "ic ated", + "ä» ¥", + "ac ht", + "ãģ Ĥ", + "h aps", + "ĠSt e", + "Ġres ources", + "Ġd ann", + "Ġpart y", + "Ġ ÏĦ", + "Ġsa f", + "is es", + "t re", + "o int", + "Ġknow ledge", + "Ġany more", + "Ġf ly", + "Ġma int", + "и к", + "å ij", + "Ġse ll", + "la ughs", + "ĠY ork", + "Ġb ien", + "Ġo d", + "Ġeas ily", + "Ġr ange", + "Ġo ption", + "Ø ¹", + "Ġapp reci", + "oc r", + "Ġdet erm", + "Ñ Ħ", + "Ġmean ing", + "Ġs ite", + "Ġdis co", + "ver age", + "Ġl ose", + "Ġinst all", + "Ġem ot", + "ant ly", + "ä t", + "Ġt amb", + "ĠW ar", + "ĠH o", + "ĠG en", + "em y", + "еР·", + "ĠP ol", + "Ġmess age", + "Ġnot e", + "Į Ģ", + "Ġh et", + "Ġim medi", + "Ġav o", + "Ġbook s", + "Ġbecom es", + "res h", + "è s", + "as ons", + "Ġhim self", + "ut s", + "Ġj u", + "Ġaw are", + "Ġrequ ire", + "Ġsystem s", + "ĠH ar", + "Ġam ong", + "Ġh om", + "Ġb reat", + "Ġwe ird", + "Ġë ¶", + "Î »", + "Ø ©", + "if f", + "or ing", + "Ġplat form", + "ĠT ake", + "Ġhelp s", + "ut ions", + "Ġfor g", + "Ġl uck", + "ĠEng lish", + "Ġwe b", + "Ġneg ative", + "Ġt ut", + "Ġab ove", + "ng th", + "Ġê ±°", + "Ġst ories", + "Ġlo ad", + "Ġback ground", + "Ġsw itch", + "g a", + "Ġprin ci", + "Ġfin an", + "Ġvar ious", + "Ġl Ãł", + "Ġkind s", + "ain ing", + "Ġn ature", + "ĠÐ ŀ", + "c z", + "Ġpr ay", + "Ġg ar", + "ir m", + "Ġ &", + "Ġì ĥ", + "n s", + "ĠR ep", + "ĠF e", + "Ġre v", + "ra nd", + "Ġlike ly", + "Ġunderstand ing", + "ı r", + "ãģ ĭ", + "Ġf al", + "Ġ1 3", + "ÑĨ и", + "Ġsu d", + "Ġbr other", + "Ġpl ant", + "Ġthrough out", + "w ise", + "p re", + "Ġcult ure", + "ĠÙ ħ", + "Ġwonder ful", + "Ġa h", + "pp er", + "Ġso ld", + "Ġstart s", + "Ġwr itten", + "Î ¯", + "n i", + "Ġ×Ķ ×", + "ĠD av", + "Ġu lt", + "Ġar m", + "Ġro ck", + "Ġwe ar", + "ë į°", + "an o", + "ra g", + "Ġsqu are", + "ан и", + "c ast", + "le br", + "Ġliter ally", + "Ġplay ed", + "Ġhe at", + "on se", + "r ict", + "Ġins p", + "id s", + "Ġpop ular", + "ë ıĦ", + "Ġc atch", + "Ġm ount", + "Ġj ud", + "Wh at", + "еР±", + "R A", + "a ud", + "к о", + "Ġsur face", + "Ġcon v", + "Ġpie ces", + "O h", + "æ Ģ", + "Ġst yle", + "pp ing", + "Ġread ing", + "Ġconvers ation", + "оР¿", + "ä¾ Ĩ", + "ĠAg ain", + "Ġb ank", + "t ime", + "Ñĥ ÑĤ", + "er ve", + "ĠG reat", + "Ġcap t", + "аР±", + "ay s", + "ĠF in", + "ific ation", + "Ġä r", + "а Ñİ", + "Ġe gg", + "ĠW el", + "Ġtar get", + "ul a", + "ch es", + "an i", + "O O", + "ic ious", + "n ow", + "Ï ĥ", + "bo ard", + "Ġg ente", + "Ġd ro", + "ĠE t", + "Ġd in", + "Ġc os", + "Ġaut hor", + "Ø ³", + "Ġo ch", + "Ġem ail", + "Ġsp irit", + "Ġs itting", + "m as", + "Ġstre ngth", + "Ġbig ger", + "ĠW ait", + "Ġm at", + "Ġpol ice", + "ress ed", + "Ġwait ing", + "is hing", + "Ġdoll ars", + "ho od", + "s s", + "Ġimag ine", + "in i", + "Ġm es", + "Ġdis e", + "id ge", + "ab or", + "Ġp et", + "Ġh op", + "ĠK ing", + "Ġcomput er", + "Ġgo ld", + "Ġn u", + "Ġf ing", + ") ,", + "Ġsec urity", + "ru ction", + "Ġsol ution", + "e xt", + "Ġp atter", + "ick en", + "ure d", + "Ġstand ard", + "ìĭ ľ", + "Ġdou ble", + "Î ·", + "Ġw ife", + "is a", + "Ġdirect ly", + "ac ed", + "Ġb unch", + "Ġ ¿", + "ал ÑĮ", + "Ġreg ard", + "Ġswe et", + "Ġun ique", + "ĠâĻ «", + "Ġtra in", + "ĠG erm", + "Î ¬", + "R E", + "Ġbeh av", + "Ġpre d", + "ì ĥ", + "s et", + "Ġdescri ption", + "é e", + "Ġc at", + "å ĵ", + "Ġcoll ege", + "ì Ľ", + "Ġapplic ation", + "ĠS en", + "as k", + "Ġc red", + "ub lic", + "Ġmultip le", + "Ġn i", + "Ġpres ident", + "Ġadd ed", + "Ġro b", + "Ġaqu i", + "Ġh osp", + "Ġtool s", + "Ġg un", + "Ġbas ic", + "Ġl ines", + "Ġst ructure", + "ĠR uss", + "Ġtot ally", + "Ġbig gest", + "Ġe en", + "Ġar g", + "Ġ× ľ", + "Ġp ark", + "ĠD es", + "Ġce lebr", + "Ġf ait", + "ен ÑĮ", + "Ġsu ff", + "Ġreg ular", + "¨ ë", + "Ġm ine", + "ĠK ore", + "Ġpre vious", + "Ġp i", + "Ġse g", + "Ġpol icy", + "Ġк о", + "ĠTr ump", + "Ġvac c", + "ó w", + "ĠS y", + "и Ñĩ", + "it ter", + "Ġpolit ical", + "r as", + "Ġal s", + "ел ÑĮ", + "Ġsha pe", + "an z", + "Ġon to", + "Ġar ch", + "Ġam b", + "ag ram", + "ĠS m", + "ct ions", + "Ġjo in", + "b or", + "å Ľ", + "Ġfr ame", + "ł ĩ", + "Ġcho ice", + "௠ģ", + "Ñĥ Ñİ", + "ĠC or", + "ĠS w", + "I T", + "Ġt end", + "ĠE ar", + "Ġto r", + "Ġev ents", + "Ġcla im", + "ĠD a", + "ĠM ark", + "Ġgroup s", + "Ġe ating", + "ĠW orld", + "Ġrec ently", + "Ġtast e", + "Ġsur v", + "à ¤", + "Ġsk ills", + "Ġи з", + "itt ed", + "Ġsh op", + "ìĿ ´ì", + "Ġest ab", + "ĠëĤ ĺ", + "Ġsecond s", + "ĠTh ose", + "ĠE nt", + "Ġì Ħ", + "ers on", + "Ġto wn", + "Ġc and", + "Ġopt ions", + "Ġ ing", + "V ID", + "Ġenc our", + "Ġr é", + "âĻ ª", + "Ġent re", + "Ġmove ment", + "ĠB en", + "Ġbir th", + "Ġwh e", + "Ġh ang", + "ĠE m", + "ig e", + "ro ll", + "Ġun f", + "ì Ĥ", + "Ġr id", + "Ġsp read", + "Ġh ost", + "al d", + "ĠE d", + "Ġcons um", + "U N", + "Ġop in", + "it ar", + "ĠM ed", + "Ġsub ject", + "Ġp al", + "Ġcar ry", + "Ġag ree", + "ĠWh ile", + "Ġcare er", + "Ġsci ent", + "Ġsud den", + "Ġf ile", + "z i", + "Ġex cept", + "é º", + "Ġpot ential", + "ĠAn other", + "Ġcomp lex", + "ĠS im", + "end o", + "Ġr ais", + "Ġphys ical", + "Ġd ate", + "ak er", + "ĠC ol", + "Ġpower ful", + "Ġmem ber", + "ra p", + "Ġsp ot", + "Ġs ource", + "Ġf em", + "é m", + "Ġem p", + "j i", + "iet y", + "Ġinf lu", + "Ġd ry", + "Ġlo ck", + "Ġz ero", + "ĠU h", + "Ġr out", + "Ġpor que", + "Ġ2 4", + "Ġt al", + "Ġfol ks", + "Ġla unch", + "Ġcomp on", + "ĠWel come", + "Ġk ann", + "ä n", + "ĠÑį ÑĤ", + "e es", + "ĠÙ Ī", + "Ġany way", + "Ġaud ience", + "äº º", + "Ġsl ight", + "on a", + "Ġu r", + "Ġrel ig", + "Ġext rem", + "ı z", + "ĠM a", + "Î ¼", + "Ġà ¶", + "Ġall ows", + "Ġf at", + "ĠF ace", + "Ġn ational", + "Ġinter view", + "ĠM c", + "é t", + "Ġc ute", + "el a", + "Ġsec ret", + "ĠW est", + "ĠD ep", + "Ġex erc", + "Ġhist or", + "Ġpri or", + "Ġ6 0", + "av a", + "ac her", + "y ond", + "ĠH a", + "Ġest e", + "in ary", + "ĠN orth", + "on st", + "Ġsm art", + "am s", + "ал и", + "Ġd ar", + "er ed", + "Ġfun ny", + "ĠO b", + "ĠBl ack", + "Ġrel ated", + "ĠB u", + "Ġsome where", + "ĠR em", + "n es", + "ment e", + "ĠRe ally", + "Ġcreat ing", + "Ġfam il", + "Ġsoci ety", + "Ġg el", + "Ġtrans form", + "Ä ĥ", + "Ġinclud e", + "Ġh ol", + "l ike", + "k o", + "air s", + "Ġп од", + "Ġpers pect", + "Ġb es", + "Ġparticular ly", + "Ġshow ing", + "ĠP art", + "Ġqu al", + "lo ck", + "Ġreal ity", + "ho ld", + "ict ion", + "o on", + "Ġv ir", + "ãģ «", + "it ary", + "Ġdr ug", + "Ġfe ature", + "Ġre asons", + "Ġ× ©", + "Ġwr ote", + "Ġf ant", + "Ġb and", + "Ù ĥ", + "en a", + "ke y", + "Ġear th", + "d om", + "Ġfe atures", + "Ġflo or", + "Ġspeak ing", + "Ġt ip", + "ĠA ust", + "Ġst ock", + "Ġch urch", + "Ġr ac", + "ìľ¼ë ¡ľ", + "ภĻ", + "ãĤ Į", + "k y", + "Ġresp onse", + "Û Į", + "ul ations", + "Ġsl ide", + "Ġgrad u", + "ci ous", + "Ġme ant", + "Ġ ==", + "Ġ× IJ×", + "ã ħ", + "Ġkind a", + "Ġsc ene", + "Ġm uit", + "Ġê° Ģ", + "r ast", + "re st", + "Ġplay ers", + "w a", + "Ġbro ad", + "Ġtom orrow", + "oc ol", + "ĠÑģ в", + "ĠB ar", + "ı k", + "Ġse a", + "Ġrem ove", + "Ġrem ind", + "ом Ñĥ", + "ĠS ince", + "Ġave c", + "ce ll", + "и Ñħ", + "Ġdoc ument", + "Ġê·¸ë Ł", + "Ġne igh", + "be at", + "Ġp Ã¥", + "Ġas pect", + "Ġd ed", + "lish ed", + "il s", + "Ġour selves", + "u ce", + "Ġhe y", + "ĠпÑĢ о", + "ent y", + "Ġas soci", + "ad os", + "um ber", + "Ġ ]", + "éĤ £", + "no v", + "Ġì Ļ", + "Ñĥ Ñĩ", + "Ġcond ition", + "ëĬĶ ëį°", + "Ġval ues", + "Ġsc en", + "min ist", + "Ġc ast", + "Ġgrow ing", + "Ġus er", + "Ġresp ond", + "l im", + "é r", + "y m", + "çľ ĭ", + "os es", + "sy ch", + "ĠÑĢ аз", + "Ġappe ar", + "Ġpro gress", + "eng th", + "Ġj ak", + "ĠD is", + "Ġpat ients", + "ĠS er", + "Ġg as", + "è re", + "ìĸ´ì ļĶ", + "Ġre ci", + "ìĿ ¸", + "Ġs ca", + "ep end", + "Ñģ к", + "аР¿", + "Ġb atter", + "Ġve h", + "ð Ł", + "Ġac com", + "Ġbe at", + "Ġpain t", + "Ġcont rib", + "Ġs ad", + "Æ °", + "al es", + "Ġt ree", + "b a", + "Ġb orn", + "ic ed", + "à® ķ", + "b and", + "Ġme chan", + "ĠD et", + "Ġcap ital", + "Ġdel iver", + "Ġfe ar", + "ŀ ĺ", + "ĠS outh", + "Ġb ought", + "Ġst ress", + "Ġv or", + "? ?", + "i h", + "ìķ ¼", + "Ġer a", + "ìĿ´ ë", + "а Ñı", + "is ions", + "iv ity", + "Ġhelp ed", + "Ġass ist", + "Ġplay er", + "r an", + "Ġimmedi ately", + "Ġmo ved", + "c ie", + "ê ±", + "Ġann oun", + "å ¿", + "ìŀ IJ", + "Ġprodu ction", + "Ġsum mer", + "Ġt un", + "Ġprogram s", + "G H", + "al ing", + "ir a", + "el ess", + ". )", + "Ġa verage", + "è¦ ģ", + "Ġgl ass", + "om an", + "if ically", + "Ġëĭ ¤", + "ĠC ong", + "ĠV er", + "Ġtr ick", + "Ġbe gan", + "Ġv ill", + "ê ±°", + "h ow", + "æ Ń", + "Ġt ill", + "Ġ9 0", + "ber t", + "Ġê ¸", + "Ġtemper ature", + "à ²", + "๠Ī", + "Ġgra ph", + "Ġê· ¸", + "Ġr ot", + "Ġmo b", + "A Y", + "a el", + "Ġre pe", + "Ġdev ice", + "Ġ19 9", + "Ġte le", + "Ġke pt", + "p a", + "æ ĸ", + "ver se", + "Ġst ream", + "е Ñĩ", + "ess ion", + "Ġstr ugg", + "z z", + "Ġdeg ree", + "Ġhelp ing", + "Ġsm ell", + "Ġper haps", + "p ro", + "Ġcont ext", + "Ġi k", + "Ġп еÑĢ", + "Ġcal cul", + "éº ¼", + "b ing", + "Ġreal ize", + "l am", + "ĠCh ar", + "y t", + "ĠìĿ ´ì", + "Ġd anger", + "ĠI m", + "a a", + "Ġlo ved", + "Ġpurp ose", + "Ġfinish ed", + "Ġpe ace", + "Ġo t", + "Ġglo bal", + "Ï Ģ", + "Ġab er", + "ĸ Ī", + "Ġcharac ters", + "Ġn ur", + "Ġdam age", + "Ġem er", + "Ġpre c", + "ĠW ir", + "Ġinst it", + "ij ×", + "Ġallow ed", + "b on", + "Ġto d", + "еР³Ð¾", + "Ġj etzt", + "Ġmed ic", + "Ġsmall er", + "ce ed", + "Ġlevel s", + "Ġint ell", + "W e", + "Ġse m", + "Ġcurrent ly", + "Ġmod ern", + "Ġcont ract", + "Ġdetail s", + "ortun ately", + "O S", + "Ġst ates", + "Ġad just", + "ant age", + "e z", + "ĠV ery", + "Ġsc ale", + "Ġre lease", + "Ġf az", + "Ġ ic", + "it ude", + "A C", + "ĠP at", + "id en", + "Ń IJ", + "Ġpre fer", + "olog ical", + "ĠFace book", + "Ġê° Ļ", + "Ġ ..", + "ĠM ake", + "Ġко ÑĤоÑĢ", + "ĠDav id", + "ĠAf ric", + "Ġmod e", + "ĠC ity", + "Ġsh all", + "ĠÑ Ħ", + "im in", + "Ġз а", + "r om", + "u a", + "Ġbe yond", + "Ġdist rib", + "к Ñĥ", + "ĠDo es", + "Ġv ict", + "r ate", + "Ġv ai", + "Ġsuccess ful", + "Ġh ous", + "ah a", + "est s", + "ĠE st", + "Ġdisco ver", + "Ġthere fore", + "ch a", + "Ġc up", + "Ġpop ulation", + "ĠI l", + "s c", + "Ġsp ent", + "re l", + "Ġuse ful", + "Ġt ab", + "æ Ŀ", + "Ġ Å", + "Ġìł ľ", + "Ġcon se", + "Ġqu ant", + "ay a", + "Ġb on", + "åı ¯", + "ĠCh in", + "Ġê² ĥ", + "ound s", + "е ÑĪ", + "ell e", + "Ġ ice", + "2 1", + "Ġk ick", + "ä¸ ĭ", + "Ġstep s", + "Ġton ight", + "нÑĭ й", + "ren ch", + ". '", + "Ġgra b", + "Ġimp lement", + "ĠìĪ ĺ", + "Ġmiss ion", + "Ġclear ly", + "Ġappreci ate", + "è Ģ", + "Ġf resh", + "ar m", + "ĠTw o", + "Ġex ec", + "Ġproject s", + "Ġcommun ities", + "ri ble", + "Ġreg ion", + "Ġfre qu", + "ro y", + "Ġhow ever", + "Ġpart ners", + "an c", + "Ġmin im", + "Ġl at", + "Ġfamil ies", + "Ġev idence", + "Ġp un", + "ra ft", + "Ġl oss", + "Ġma p", + "Ġany body", + "Ġchang ing", + "Ġr ules", + "Ġorgan ization", + "Ġess entially", + "ĠR ed", + "Ġele ment", + "æ Ĺ", + "Ġv irt", + "r at", + "Ġpr int", + "and er", + "are n", + "em os", + "ο Ïħ", + "Ġcond itions", + "ab e", + "Ġd ance", + "и ÑĢ", + "Ġd os", + "о Ñĩ", + "ĠQ ue", + "Ġwalk ing", + "Ġt ro", + "Ġ id", + "Ġadd itional", + "Ġfull y", + "Ġf ans", + "Ġadd ition", + "Ġlik ed", + "Ġü ber", + "Ġb ow", + "d i", + "Ġm aster", + "o ff", + ") :", + "m ber", + "Ġë ¬", + "å ¯", + "åĪ °", + "la use", + "Ġo der", + "Ġsaf ety", + "Ġre act", + "à® ¿", + "b t", + "Ġdis app", + "Ġgirl s", + "S t", + "ĠA ng", + "Ġfa ith", + "Ġturn s", + "Ġt ight", + "Ġm outh", + "am i", + "z er", + "Ġwe ap", + "Ġб Ñĥд", + "Ġhosp ital", + "ra id", + "Ġmic ro", + "ĠSt ate", + "ĠM ost", + "ag n", + "Ġdec ide", + "Ġpat ient", + "Ġcor ner", + "Ġdi ed", + "N o", + "ĠSt ud", + "re nd", + "em pt", + "Ġli e", + "Ġl if", + "ĠBe fore", + "t ó", + "ĠSu per", + "Ġbe ll", + "6 0", + "Ġpriv ate", + "ĠPa ul", + "Ġg ib", + "Ġag re", + "´ì Ħľ", + "Ġs ig", + "Ġinvest ig", + "Ñı ÑĤ", + "en ing", + "Ġdist ance", + "Ġwar m", + "Ġdig ital", + "å¾ Ī", + "in er", + "Ġp and", + "ĠCO VID", + "Ð ³Ð¾", + "g n", + "Ġr ace", + "Ġpr oud", + "Ġte aching", + "Ġ ÑĤо", + "ìŀ ¥", + "ĠAll ah", + "I n", + "Ġw ood", + "Ġcol ors", + "Ġw ird", + "u j", + "id ad", + "Ġcustom ers", + "Ġconnect ed", + "Ġlay er", + "Ġachie ve", + "Ġperspect ive", + "ĠC oll", + "Ù Ĥ", + "Ġcl oud", + "!! !", + "Ġend ed", + "łĩ ê²Į", + "Ġmanage ment", + "Ġr ich", + "Ġsub st", + "Ġrem o", + "Ġser ve", + "Ġres ist", + "Ġthought s", + "Ġgrow th", + "ili ar", + "Ġright s", + "Ġchar ge", + "Ġcons ist", + "Ġwer den", + "Ġem b", + "and om", + "Ġhur t", + "Ġk an", + "i as", + "л о", + "Ġsh it", + "Ġbe g", + "Ġrece ived", + "it ation", + "Ġme at", + "Ġis so", + "ff ee", + "Ġfam ous", + "Ġcomfort able", + "I L", + "ĠB ye", + "èª ª", + "åĢ ij", + "oth es", + "Ġmed ical", + "Ġenjoy ed", + "Ġhealth y", + "Ġw y", + "c ies", + "Ġeff ort", + "Ġdo ctor", + "Ġmil itary", + "L AU", + "Ġg ro", + "Ġb attle", + "Ġf ed", + "Ġcap ac", + "Ġaf raid", + "iv il", + "ĠвÑģ е", + "Ġl ength", + "ys is", + "Ġbe i", + "¤ í", + "Ġorgan iz", + "or g", + "in c", + "Ġinter act", + "ĠChin ese", + "Ġacc ording", + "Ġincred ible", + "Ġkill ed", + "Ġda ughter", + "ĠÏ Ģ", + "Ñĭ в", + "Ġschool s", + "Ġ «", + "ll er", + "Ġshould n", + "n al", + "Ġcr is", + "Ġch icken", + "Ġf aster", + "Ġextrem ely", + "Ġopp os", + "Ġn ous", + "Ġ +", + "ri a", + "Ġfinan cial", + "Ġexc iting", + "Ġjour ney", + "×Ļ× Ŀ", + "ł ë", + "Ġdis play", + "Ġmem ory", + "Ġheav y", + "н е", + "Ġpass ed", + "ÑĢ и", + "il es", + "Ġp sych", + "Ġspec ifically", + "Ġeng age", + "Ġl ed", + "or ge", + "ĠD em", + "ord er", + "Ġ8 0", + "Ġcre am", + "ester day", + "Ġed ge", + "Ġп ол", + "Ġbu ll", + "Ġind ic", + "Ġk tó", + "Ġhope fully", + "um ents", + "ag en", + "н ого", + "Ġh ate", + "ch t", + "8 0", + "Ġeff ic", + "Ġì§ Ģ", + "Ġintern et", + "Ġbud get", + "Ġproper ty", + "id ay", + "Ġì ļ", + "Ġм ож", + "ol a", + "Ġshow ed", + "ĠM on", + "Ġthous and", + "A P", + "Ġpo or", + "us ed", + "ĠJ ack", + "Ġs Ã¥", + "ĥ ½", + "Ġes c", + "Ġsoft ware", + "Ġqu ar", + "ĠØ ¨", + "Ġnecess arily", + "om en", + "i y", + "Ġevent ually", + "ish ed", + "Ġbr ight", + "E D", + "Ġs pl", + "Ġdem and", + "Ġth reat", + "Ġs ir", + "Ġrele ased", + "ck et", + "ĠâĢ «", + "Ġrequ ired", + "Ġv ote", + "ì ¹", + "à® ¤", + "Ġdevelop ed", + "ĠìĤ ¬", + "at ory", + "Ġd ir", + "ca pe", + "Ġslight ly", + "à ¬", + "๠ī", + "re et", + "Ġdise ase", + "Ġcour t", + "Ġitem s", + "ĠEar th", + "ÑģÑĤ и", + "ж е", + "ì ²", + "Ġchalleng es", + "ĠBr it", + "Ġdesign ed", + "1 2", + "Ġhear ing", + "Ġlisten ing", + "z o", + "ĠÑģ л", + "ãģ§ ãģĻ", + "Ġper o", + "Ġwe aring", + "pl ic", + "Ġch em", + "Ġbal ance", + "Ġb a", + "Ġrece ive", + "im a", + "Ġsignific ant", + "Ġм Ñĭ", + "an ch", + "ĠC r", + "ĠC oun", + "ê¸ Ī", + "Ġjo bs", + "Ġoffic ial", + "Ġper m", + "om s", + "Ġopportun ities", + "Ġover all", + "Ġh us", + "od es", + "Ġn ation", + "ĠR eg", + "Ġor d", + "Ġrest aur", + "Ġì Ĩ", + "Ġm el", + "v in", + "Ġw enn", + "Ġk ön", + "æ ĥ", + "Ġopin ion", + "ãĤ Ĥ", + "è ¬", + "ĠSomet imes", + "ç Ĥ", + "Ñī е", + "as c", + "O U", + "Ġ20 20", + "Ġdel icious", + "ig er", + "Ġìķ Ī", + "o le", + "Ġhand le", + "Ġc it", + "Ġíķ ľ", + "Ġf ör", + "o oth", + "Ġnecess ary", + "Ġind epend", + "æ Ħ", + "ist en", + "h am", + "Ġé t", + "ãĥ ³", + "Ġmult i", + "Ï Į", + "? )", + "Ġcamp us", + "Ġtop ic", + "Ġr ain", + "Ġpan el", + "ĠS am", + "Ġlar ger", + "aud ience", + "Ġpa id", + "Ġeconom ic", + "ol t", + "Ġstre et", + "ĠC ont", + "Ġdri ving", + "Ġìł Ģ", + "Ġh ay", + "Ġprofess ional", + "ĠIn tern", + "å ¸", + "Ġin put", + "Ġc ateg", + "Ġc ro", + "Ġ ll", + "E T", + "Ñĭ й", + "* *", + "ĠZ e", + "B LE", + "Ġì ¤", + "re es", + "ĠÐ ¯", + "ed e", + "ier t", + "Ġfo ld", + "Ġd ur", + "ĠN ational", + "Ġìĸ ´ë", + "an ced", + "Ġfa ire", + "ut ed", + "Ġk ing", + "Ġw ild", + "o i", + "up beat", + "Ġpre vent", + "i us", + "Ġà ¨", + "Ġw ide", + "Ġr ing", + "Ġtit le", + "Ġstand ing", + "Ġal though", + "Ġh i", + "Ġsa uce", + "Ġs ides", + "Ġanim als", + "il ing", + "at ives", + "ìĹIJ ìĦľ", + "ĠO ver", + "Ġdes p", + "Ġconsider ed", + "ar ies", + "i ers", + "Ġein en", + "Ġs ister", + "Ġë ķ", + "ĠS ure", + "ãĤ ĭ", + "ri end", + "a ign", + "Ġsh own", + "Ġs ac", + "Ġs ont", + "Ġcent ury", + "Ġt ien", + "ĠÎ º", + "ĠS T", + "åķ Ĭ", + "Ġold er", + "ie m", + "Ġtr uly", + "ĠS i", + "Ġwind ow", + "iqu es", + "ar io", + "æ² Ĵ", + "Ġloc ation", + "Î º", + "Ġì ľ", + "v i", + "ag ue", + "ĠS orry", + "Ġdis p", + "Ġhe ll", + "Ġà ī", + "Ġtr ade", + "Ġcrit ical", + "Ġê ±", + "Ġn amed", + "Ġprep ared", + "ĠH ouse", + "al u", + "Ġt ough", + "Ġtri p", + "Ġs and", + "c el", + "ü z", + "ĠP ut", + "Ġap art", + "is f", + "v is", + "Ġli br", + "a ven", + "Ġv ie", + "Ġeffect ive", + "ภ²", + "Ġmag n", + "Ġmuit o", + "Ġê µ", + "h al", + "Ġlim it", + "Ġn ine", + "Ġwill ing", + "ı ÅŁ", + "s p", + "еР³", + "h i", + "Ġal t", + "ĠJ an", + "Ġorig in", + "ĠU s", + "Ġele ments", + "Ġus es", + "Ġhelp ful", + "Ġfl at", + "Ġfam iliar", + "ĠP ark", + "Ġc ore", + "Ġclos er", + "Ġact ive", + "Ġad minist", + "C E", + "нÑĭ е", + "ç Ħ", + "Ġrel ative", + "Ġment al", + "Ġr andom", + "Ġpart ner", + "Ġut il", + "ph one", + "Ġr ule", + "w w", + "Ġìł ķ", + "Ġsch on", + "Ġco ffee", + "H A", + "Ġconnect ion", + "Ġun it", + "la ughing", + "l og", + "Ġapp l", + "л а", + "us ic", + "ĠB ra", + "Ġany where", + "AU DI", + "Ġsepar ate", + "bo x", + "Ġd ivid", + "Ġtest ing", + "Ġs ick", + "Ġwer en", + "ä» ĸ", + "Ġ׾ ×", + "Ġadv antage", + "Ġtrans fer", + "' .", + "Ġë ¹", + "Ġfind ing", + "н ой", + "Ġì¢ ĭ", + "Ġfor t", + "Ġeconom y", + "Ġl ack", + "Ġleav ing", + "Ġd im", + "å İ", + "ĠR es", + "Ø Ń", + "Ġdiscuss ion", + "еР¿", + "Ġg es", + "du ct", + "Ġch ain", + "Ġus ers", + "e ch", + "ÅĤ a", + "Ġdis h", + "Ġcare ful", + "Ġte acher", + "Ġopt im", + "Ġfl u", + "at ically", + "Ġref lect", + "Ġtreat ment", + "e ed", + "i ÄĻ", + "à ¹", + "à® ¾", + "Ġequ ip", + "Ġplan ning", + "Ġsol ve", + "ãģ Ŀ", + "ĠT om", + "Ġavo id", + "Ġp ou", + "Ġgreat er", + "l in", + "O L", + "ĠL u", + "ĠM ore", + "Ġatt ract", + "ê n", + "un a", + "Ġphot o", + "er ation", + "Ġplan et", + "Ġcop y", + "Ġvis ual", + "ir ing", + "Ġintern ational", + "Ġla ughing", + "Ġth ick", + "Ġhold ing", + "Ġbring ing", + "Ġlet ter", + "Ġb urn", + "Ġeffect s", + "it é", + "our s", + "O T", + "ê me", + "ĠSch ool", + "×ķ× ª", + "rop ri", + "l ig", + "α ι", + "Ġad ult", + "Ġsu gar", + "Ġr ide", + "Ġhigh light", + "Ġno body", + "Ġ2 1", + "Ġch at", + "ĠпÑĢ и", + "Ġin nov", + "ung en", + "Ġatt ach", + "ed om", + "å Ĭ", + "y l", + "Ġleg al", + "Ġr ice", + "Ġcoll abor", + "k ing", + "d own", + "æ Ļ", + "ãĤ Ĭ", + "Ġi h", + "ĠA c", + "ous ly", + "Ġr ap", + "Ġsol id", + "Ġgener ally", + "Ġpatter n", + "al i", + "ภŃ", + "Ġtrans l", + "in ter", + "a ult", + "Ġë ¨", + "Ġexp ress", + "Ġexam ples", + "Ġch ose", + "Ġtell s", + "ÃŃ s", + "ain t", + "ĠT ell", + "ĠMich ael", + "æ ¨", + "ĠN umber", + "Ġt ap", + "Ġexper iment", + "Ġbenef it", + "Ġì °", + "Ġse qu", + "Ġexp ensive", + "Ġgener ation", + "ĠM any", + "Ġadd ing", + "Ġk il", + "Ġcamp aign", + "ĠA nt", + "ra w", + "omm en", + "Ġs oul", + "j o", + "ĠAct ually", + "am m", + "ê² ł", + "Ġma xim", + "Ġsal t", + "Ġc ru", + "Ġcall ing", + "ãģ Į", + "Ġbas is", + "b an", + "Ġkeep ing", + "ĠM or", + "ed s", + "ì Ĩ", + "Ġto do", + "ам и", + "н Ñı", + "Ġli ved", + "ĠD u", + "ãĤ ī", + "å® ¶", + "for ce", + "å¹ ´", + "fer ence", + "al a", + "Ġocc ur", + "s k", + "Ġrec ent", + "Ġc ars", + "Ġtrad itional", + "ent le", + "² Ī", + "Ġhel d", + "Ġn ach", + "ĠCent er", + "er en", + "Ġb in", + "Ù ģ", + "Ġcomm e", + "Ġre ve", + "Ġìĺ ¤", + "Ġexpect ed", + "ab il", + "Ġfocus ed", + "o v", + "Ġi P", + "or ial", + "i ro", + "Ġet c", + "am ing", + "ĠS on", + "Ġy esterday", + "Ġstr ate", + "ĠÑ Ĩ", + "Ġë ı", + "p es", + "Ġactiv ity", + "Ġadv ice", + "Ġopen ing", + "f in", + "Ġre la", + "é ĸ", + "Ġinst ance", + "ĠEvery one", + "b l", + "p en", + "Ġvis ion", + "ĠA lex", + "if orn", + "Ġt ick", + "H e", + "Ġstrate gy", + "Ġk om", + "P E", + "ĠG l", + "Ġelect ric", + "1 5", + "Ġda ily", + "Ġhus band", + "Ġst ation", + "Ġanal ysis", + "yn am", + "Ġatt empt", + "Ġbill ion", + "v ant", + "Ġfor th", + "Ġm ath", + "al y", + "Ġbehav ior", + "ĠM as", + "k an", + "ĠD ay", + "Ġbl ess", + "Ġg ut", + "ĠH igh", + "o x", + "Ġd ress", + "Ġj ed", + "è ¯", + "å ĸ", + "Ġexperien ces", + "ist a", + "Ġfight ing", + "å ·", + "ĠÑģ к", + "Ġmost ly", + "a use", + "Ġpict ures", + "ен ÑĤ", + "Ġm ad", + "Ġmod els", + "ÑĪ е", + "ĠC ount", + "Å Ħ", + "ÅĤ o", + "ep t", + "O M", + "ĠA N", + "Ġtrou ble", + "4 0", + "Ġb ird", + "ul ate", + "Ġm ur", + "Ġprodu ce", + "Ġmar ried", + "b it", + "Ġthe ory", + "í ĺ", + "Ġlead er", + "ĠL ast", + "A A", + "è µ", + "Ġim ages", + "Ġexp and", + "ĠP or", + "Ġpur ch", + "ĠS an", + "ĠChrist mas", + "ĠAust ral", + "Ġw id", + "ĠM iss", + "Ġknow ing", + "Ġz e", + "s hip", + "k u", + "Ñħ од", + "ĠInst agram", + "ĠInd ia", + "Ġest a", + "ĠCal iforn", + "Ġ7 0", + "Ġdra g", + "Ġbr ush", + "Ġn ames", + "A nd", + "Ġy o", + "ill a", + "Ġsch ed", + "Ġdest roy", + "ye ar", + "Ġv amos", + "Ġ ÙĦ", + "ç a", + "Ġforg ot", + "и е", + "Ġra ise", + "re me", + "íķ ´", + "ĠG ive", + "Ġcont ain", + "ra b", + "Ġg ift", + "ĠÑģ п", + "Ġrequ est", + "Ġsh ut", + "Ġdeg rees", + "Ġbenef its", + "Ñĭ е", + "Ġstud ies", + "Ġend s", + "Ġevery where", + "Ġher o", + "op h", + "er ry", + "Ġmaterial s", + "en ed", + "N A", + "å į", + "Ġmu y", + "Ġwor se", + "ä» Ģ", + "ĠM ad", + "Ġdec isions", + "ion e", + "Ġfore ign", + "la ughter", + "i ber", + "ени Ñı", + "ãħ ĭ", + "Ġreal ized", + "Ġ ign", + "Ġwe ak", + "ĠÎ ¼", + "Ġsca red", + "Ġass um", + "A K", + "ï ¿", + "ï¿ ½", + "Ġcover ed", + "ĠS at", + "Ġо н", + "Ġindividual s", + "Ġcomp ared", + "1 1", + "ĠAd d", + "ic les", + "Ġc ert", + "r ar", + "Ġbr ief", + "Ġactiv ities", + "Ġf ab", + "b ar", + "Ġa st", + "ĠO ther", + "Ġclass es", + "Ġo g", + "Ġmiss ing", + "ãģ ł", + "é Ŀ", + "w ers", + "× ©", + "Ġintrodu ce", + "Ġequ ation", + "ãģ¾ ãģĻ", + "Ġn om", + "Ġpain ting", + "us hing", + "ĠA P", + "Ġencour age", + "Ġsh ip", + "itt ee", + "iver se", + "ot a", + "n am", + "ãĥ »", + "Ġexerc ise", + "ĠÐ Ń", + "Ġn as", + "Ġthous ands", + "ĠCaliforn ia", + "Ġs es", + "Ġr ow", + "ŀ Ī", + "Ġpand emic", + "Ġsk ill", + "b el", + "Ġdire ctor", + "Ġmil k", + "Ġn ut", + "Ġmot ion", + "Ġcl osed", + "è ¨", + "Ġcred it", + "ah r", + "Ġche ese", + "Ġal tern", + "im ately", + "Ġs ust", + "ĠT ra", + "Ġgl ad", + "Ġhigh ly", + "Ġw a", + "Ġredu ce", + "Ġb le", + "ad or", + "in ated", + "ion es", + "ci ent", + "Ġdep ending", + "Ġsh aring", + "Ġca ught", + "ra el", + "Ġme hr", + "Ġpass ion", + "ç Ľ", + "Ġr u", + "Ġfar m", + "T I", + "av es", + "ĠR ob", + "ĠB ro", + "Ġmot iv", + "ret ch", + "ru pt", + "ĠB ig", + "Ġall e", + "Ġet t", + "ub s", + "ĠJapan ese", + "ĠH all", + "и ли", + "AUDI BLE", + "ç ¬", + "Ġcell s", + "ik a", + "el ine", + "il er", + "Ġì £", + "Ġsk y", + "IN AUDIBLE", + "end e", + "ap ter", + "Ġp in", + "Ġg ather", + "h ol", + "le ction", + "Ġsy n", + "Ġpl ug", + "r ound", + "Ġun iversity", + "h ib", + "Ġfant astic", + "k n", + "Ġho le", + "ĠRem ember", + "in ct", + "ak s", + "C H", + "Ġbro ken", + "Ġstr ateg", + "Ġal ive", + "Ġt ank", + "Ġc art", + "r ated", + "r ie", + "ĠSt ep", + "ĠEvery thing", + "Ġb ound", + "Ġso bre", + "Ġcustom er", + "¡ Į", + "ur g", + "ĠB ill", + "L a", + "wh at", + "Ġre action", + "Ġs ession", + "Ġpl ans", + "ĠìĿ´ë łĩê²Į", + "Ġdown load", + "ì Ļ", + "u er", + "Ġc ab", + "Ġinst r", + "if ying", + "ĠN ice", + "Ġteam s", + "ı l", + "Ġgo als", + "is ch", + "Ġtrans port", + "Ġanim al", + "Ġcost s", + "Ġcall s", + "Ġse hr", + "ì Ī", + "ri an", + "Ġd ial", + "Ġwe ather", + "๠Ģ", + "Ġв оÑĤ", + "ĠPl ay", + "Ġsh ared", + "Ġsm ooth", + "ab a", + "Ġleav es", + "à® ©", + "Ġconc ent", + "Ġsh ift", + "ĠëIJ ĺ", + "ĠGo vern", + "Ġdem onst", + "Ġbut ter", + "ĠìĹ ¬", + "Ġsat isf", + "Īë ¬", + "Ġrecogn ize", + "ĠF rench", + "Ġvol ume", + "ä nd", + "Ñĥ м", + "Ġì§ Ħ", + "ĠKe ep", + "ow a", + "ipp ed", + "ÑģÑĤ ÑĢ", + "Ġdet ect", + "ĠÏ ĥ", + "Ġl ift", + "Ġcl othes", + "ĠSt op", + "à µ", + "m et", + "Ġcl in", + "Ġar r", + "f riend", + "Ġst uck", + "Y e", + "h and", + "um a", + "Ġsc ri", + "Ġfuck ing", + "ct ors", + "× ª", + "Ġjo ining", + "Ġc ette", + "ĠØ £", + "ĠWh ite", + "Ġi hr", + "Î Ń", + "ãģ Ń", + "Ġinclud ed", + "ess o", + "Ġac ad", + "b um", + "Ġs ab", + "Ġд лÑı", + "è¿ Ļ", + "uf act", + "ĠRep ublic", + "r im", + "Ġye llow", + "Ġlim ited", + "T ER", + "ĠT y", + "Ġnot es", + "v est", + "и з", + "al ed", + "Ġph ase", + "and a", + "ĠM om", + "R I", + "Ġim mer", + "m al", + "Ġin j", + "Ġy ang", + "ud ible", + "аР³", + "Ġset t", + "Ġmag ic", + "Ġens ure", + "Ġsp ring", + "Ġsh ock", + "Ġwhe el", + "ог да", + "ãĤ Ī", + "Ġcan cer", + "Ġro ot", + "Ð IJ", + "gen cy", + "Ġë į", + "i i", + "Ġout put", + "Ġcomm it", + "Ġwork ers", + "ìķĦ ìļĶ", + "ĠÑģ ам", + "ve y", + "Ġpe u", + "Ġc ivil", + "is c", + "Ġbr ings", + "ÑĢ ав", + "an ia", + "Ä ģ", + "c raft", + "mb ol", + "Ġintell ig", + "b i", + "ac ing", + "y ou", + "Ġbecom ing", + "ĠD er", + "em a", + "å°± æĺ¯", + "Ġing red", + "Ġcomm and", + "Ġupd ate", + "Ġpre m", + "Ġopen ed", + "Ħ ¤", + "ени е", + "Ġg ard", + "Ġstat ement", + "Ġsc rew", + "Ġpr ote", + "Ġc ards", + "Ġt ask", + "Ġeven ing", + "Ġst itch", + "in en", + "ĠB er", + "m ark", + "ĠD ad", + "Ġе ÑģÑĤÑĮ", + "Ġ× ŀ×", + "ìĹ Ī", + "Ġb an", + "Ġcl im", + "Ġfre edom", + "Ġnorm ally", + "еÑģ ÑĮ", + "å ¦", + "Ġprov ided", + "Ġìŀ IJ", + "ĠìķĦ ëĭĪ", + "ĠK im", + "ied er", + "ìĿ Į", + "Ġcit iz", + "Ġb ike", + "Ġb ak", + "Ġno ise", + "Ġcl imate", + "iz es", + "å¾ Į", + "Ġincre asing", + "ĠTH E", + "Ġli qu", + "Ġperson ally", + "e f", + "res p", + "Ġleg s", + "ind er", + "Ġp ed", + "Ġë§ İ", + "Ġdep end", + "Ġvar iety", + "ĠIs rael", + "Ġwas h", + "å Ĩ", + "Ġqu iet", + "ĠJ ames", + "ĠJ ew", + "Ġfore ver", + "ĠI nt", + "Ġcoun ter", + "ur ance", + "ĠAny way", + "ca re", + "ĠOn ly", + "ci ón", + "ad i", + "ĠE v", + "ëĭĪ ê¹Į", + "ĠÎ ±", + "Ġslow ly", + "Ġо д", + "Ġnot iced", + "ier en", + "Ġfe ll", + "ĠÐ ij", + "Ġm ême", + "Ġwhen ever", + "! )", + "ĠH y", + "å ¼", + "ord s", + "us ion", + "ĠSt ar", + "Ġí ĺ", + "ĠM ac", + "ä¸ Ĭ", + "i ven", + "Ġìĭ ľ", + "ĠìĹ Ĩ", + "ĠT ur", + "Ġg er", + "r is", + "Ġve z", + "Ġл Ñİ", + "Ġvers us", + "ا Ø", + "ocol ate", + "Ġplan e", + "Ġz o", + "Ġsu it", + "Th is", + "Ġn erv", + "ĠA cc", + "Ñĥ ж", + "ìĤ ¬", + "n h", + "em e", + "Ġa uss", + "Ġme as", + "Ġtr ès", + "Ï ī", + "Ñģ ли", + "ĠAr t", + "ĠSec ond", + "олÑĮ ко", + "ch o", + "it ect", + "е ÑģÑĤ", + "Ġb oss", + "Ġinc ome", + "ł ¤", + "Ġsh ad", + "Ġapp ropri", + "ĠM al", + "op t", + "Ġart ist", + "Ġplay s", + "oth ers", + "ĠIn ter", + "Ġvir us", + "Ġh ung", + "Ġconst ant", + "Ġscri pt", + "Ġsn ow", + "ul f", + "k et", + "Ġdev ices", + "Ġmet al", + "ight s", + "ìĦ ¸", + "Ġsal es", + "Ġve get", + "Ġcollect ion", + "Ġv ia", + "k er", + "Ġgot ten", + "O W", + "i én", + "Ġacc ur", + "Ġw ave", + "ult y", + "ĠA ir", + "Ġlead ing", + "ic ing", + "Ġcent ral", + "ĠChrist ian", + "f r", + "ĠAl though", + "Ġsong s", + "Ġf if", + "нÑĭ Ñħ", + "Ġbel ong", + "oss ible", + "ì °", + "Ġphot os", + "is l", + "Ġrela x", + "s a", + "US IC", + "ê ·", + "Ġman ufact", + "ĠTw itter", + "Ġdanger ous", + "Ġhy d", + "le ar", + "i ant", + "ĠâĢ ¦", + "Ġsudden ly", + "Ġla ugh", + "Ġang le", + "ĠG ot", + "Ġwor ried", + "о е", + "Ġp ap", + "ĠM art", + "en o", + "Ġbatter y", + "Ġп оÑģ", + "Ġlight s", + "Ġar ms", + "ĠA bs", + "m es", + "âĢ ĵ", + "use um", + "Ġte a", + "ĠM ic", + "Ġfor mer", + "ograph y", + "Ġapplic ations", + "ĠD ire", + "çĦ ¶", + "Ġfeed back", + "itch en", + "yor um", + "u ed", + "ig t", + "Æ° á»", + "os ition", + "ĠD el", + "Ġíķ ĺë", + "ĠB ack", + "ad s", + "Ġpr ime", + "ì£ ¼", + "ì£ ł", + "× ij", + "Ġm ut", + "] .", + "ĠÐ Ĺ", + "lo c", + "k in", + "Ġexper t", + "Ġal right", + "ung s", + "Ġsupp ly", + "Ġleaders hip", + "ĠF ra", + "Ġtyp ically", + "Ġs el", + "Ġtre es", + "Ġ2 2", + "h ar", + "Ġwor st", + "Ġbus y", + "ant o", + "ĠU p", + "ĠB as", + "Ġpresent ation", + "Ġstr ange", + "Ġth in", + "ÑĤ е", + "Ġveh icle", + "Ġд о", + "cell ent", + "7 0", + "Ġt ired", + "Ġcris is", + "Ġt iny", + "as y", + "Ġr an", + "é ĩ", + "Ġfor ces", + "Ġо Ñĩ", + "Ġident ify", + "Ġass ess", + "иÑĤ е", + "S E", + "Ġcreat ive", + "ç Ł", + "Ġdep artment", + "Ġinit ial", + "æĪij åĢij", + "ĠD am", + "ak t", + "v ere", + "Ġinf ect", + "Ġp ump", + "Ạ¡", + "Ġv iel", + "Ġr are", + "Ġd ot", + "ash ion", + "em pl", + "Ġf lex", + "Ġk on", + "Ġtr uck", + "Ġle ct", + "Ġpl astic", + "la w", + "Ġlik es", + "Ġr ough", + "ĠM AT", + "í ŀĪ", + "Ġcomm er", + "Ġas se", + "Ġc ake", + "Ġact ions", + "Ġad m", + "Ġother wise", + "ĠHe alth", + "Ġcoll e", + "à¹Ģ à¸", + "Ġr ub", + "å¾ Ĺ", + "æ Ķ", + "Ġsc r", + "Ġz um", + "ĠH im", + "Ġch amp", + "Ġconcern ed", + "Ġ5 00", + "Ġpl ate", + "ĠO ut", + "Ġdon c", + "Ġequip ment", + "Ġta ught", + "ll ed", + "Ġí Ļ", + "iv a", + "Ġmot or", + " »", + "Ġgu ide", + "å ī", + "Ġstop ped", + "Ġr at", + "Ġlab or", + "Ġa im", + "Ġprep are", + "ĠÑ Ī", + "Ġshoot ing", + "ann ed", + "cri pt", + "Ġen emy", + "Ġdep ends", + "Ġn av", + "Ġb er", + "Ġland s", + "Ġun ivers", + "i u", + "Ġfact or", + "ok ing", + "Ġcar bon", + "b ut", + "ĠL ove", + "el d", + "ĠÎ µ", + "Ġg a", + "Ġé s", + "Ġbre ad", + "Ġvol t", + "í Ĭ", + "Ġwas te", + "Ġkeep s", + "æī Ģ", + "Ġst or", + "Ġhon or", + "Ġun less", + "Ġcol um", + "Ġë ĮĢ", + "Ġpl ants", + "Ye ah", + "Ġinclud es", + "ä¸ Ń", + "Ġo x", + "Ġpe ut", + "ë§ Į", + "ìĥ ģ", + "ist ry", + "ภ±", + "ĠDep artment", + "ant a", + "Ġfing er", + "Ġst retch", + "Ġsy mbol", + "Ġneigh bor", + "æ ¬", + "ê° Ħ", + "~ ~", + "ĠÑĤ Ñĭ", + "ĠA ber", + "k es", + "Ġmass ive", + "ĠC H", + "ĠS al", + "× ł", + "ãĤ Ĵ", + "Ġd ynam", + "ach e", + "ĠP re", + "Ġmon itor", + "ent ed", + "E O", + "Ġrais ed", + "ist ics", + "Ú ©", + "Ġv ou", + "it en", + "¡ °", + "Ġbusiness es", + "Ġe arn", + "Ġmob ile", + "id ade", + "Ġha be", + "y r", + "l ict", + "Ġcon duct", + "Ġfed eral", + "Ġw o", + "b u", + "Ġn one", + "Ġteach ers", + "ĠاÙĦ Ø", + "éģ ĵ", + "id ents", + "ا ÙĦ", + "Ġtre nd", + "еР¶", + "Ġal bum", + "Ġm ich", + "b ased", + "ภµ", + "Ġtrans ition", + "Ġн о", + "õ es", + "h ost", + "ed y", + "ĠPro f", + "p an", + "ij n", + "Ġcapac ity", + "und o", + "Ġ× ij×", + "Ġbreat h", + "Ġм ен", + "Ġm ü", + "í Ļ", + "ĠA ut", + "hing ton", + "Ġn or", + "Ġg ain", + "po int", + "Y es", + "ĠØ ª", + "ĠN a", + "Ã¥ r", + "Ġi ç", + "ĠM ary", + "Ġsp in", + "Ġant i", + "åIJ §", + "Ġsome how", + "Ġlaw s", + "Ġmom ents", + "Ġg re", + "Ġmo ves", + "ĠW ould", + "Ġpred ict", + "Ġv ra", + "Ġ201 9", + "¶ Ħ", + "Ġfund ament", + "2 5", + "Ġp ure", + "Ġw ow", + "Ġis land", + "Ġinvest ment", + "Ġb ath", + "ĠY a", + "Ġhard er", + "Ġt ips", + "å Ĺ", + "Ġelect ron", + "ĠB ob", + "Ġb ond", + "od ies", + "ĠA ug", + "Ġgib t", + "Ġch air", + "Ġtw ice", + "w ood", + "Ġcl ar", + "Ġmas k", + "Ġhonest ly", + "Ġ201 8", + "t ies", + "' ,", + "Ġp ens", + "Ġsurpr ised", + "Ġcommunic ation", + "ãģ£ ãģ¦", + "Ġsp r", + "Ġwh ose", + "Ġst ars", + "× IJ×", + "ĠâĢ ĭ", + "Ġproper ly", + "Ġg rew", + "os ing", + "Ġdi vers", + "A D", + "Ġem pt", + "Ġexp ression", + "Ạ¿", + "ĠP al", + "ãģ Ĭ", + "Ġjust ice", + "Ġp air", + "w o", + "Ġse at", + "or ter", + "Ġlink s", + "ĠM er", + "Ġre nd", + "но е", + "up id", + "ĠH el", + "ĠM arch", + "ĠL o", + "Ñģ ÑĮ", + "Ġhas n", + "Ġev alu", + "ãģ ı", + "å¤ ©", + "il os", + "Ġfund ing", + "Ġv en", + "u an", + "ĠM aster", + "ĠO l", + "ĠF re", + "Ġy ap", + "ĠS ir", + "s ch", + "Ġmist ake", + "am an", + "Ġdin ner", + "ĠWas hington", + "Ġorganiz ations", + "Ġж е", + "av ing", + "Ġv ÃŃ", + "Ġbirth day", + "Ġbe ar", + "ĠÙ ģ", + "Ġaff ord", + "Ġre ven", + "Ġrelationship s", + "r ough", + "ĠT ime", + "Ġt ag", + "ĠS un", + "u ary", + "ĠP o", + "c ar", + "ab ilities", + "Ġpr ison", + "Ġl ic", + "ìł ķ", + "id den", + "Ġspec ies", + "é »", + "Ġf irm", + "Ġsc ore", + "Ġd it", + "Ġspe ct", + "Ġp el", + "Ġcompl icated", + "æ¨ £", + "Ġr ank", + "Ġoppos ite", + "Ġpick ed", + "Ġк он", + "el er", + "Ġm ig", + "ĠS l", + "ĠN et", + "Ġne ck", + "ĠFr ance", + "Ġtechn ical", + "ภ¡", + "Ġmil es", + "Ġprim ary", + "Ġse in", + "s es", + "Ġla ughs", + "b ra", + "ÅĽ ci", + "ri age", + "Ġn ic", + "et ers", + "Ġà ª", + "olog ies", + "ĠI S", + "r ad", + "ud o", + "ı nd", + "m ar", + "Ġex ch", + "Ġcompet ition", + "Ġauss i", + "ĠS erv", + "Ġre nt", + "Ġch ocolate", + "Ġw ieder", + "Ġnear ly", + "Ġspe ech", + "Ġun c", + "Ġpar am", + "ĠBrit ish", + "Ġrem ain", + "ภģ", + "ur t", + "ĠØ ¹", + "Ġcr ack", + "ail s", + "Ġprom ise", + "Ġpay ing", + "i ÃŁ", + "Ġad apt", + "ал а", + "Ġmov ies", + "Ġw ire", + "Ł ¬", + "æľ ĥ", + "Ġter rible", + "Ġs ó", + "Ġperfect ly", + "åij ¢", + "ord in", + "Ġj á", + "Ġimp ossible", + "ĠTh ree", + "Ġn h", + "Ġtur ning", + "r um", + "ĠB el", + "ig g", + "Ġrespons ible", + "и й", + "Ġincred ibly", + "w i", + "ian o", + "Ġhum ans", + "Ġà ĩ", + "Ġsetting s", + "Ġj oy", + "o ot", + "Ġdeal ing", + "ill ed", + "Ġsur round", + "Ġfollow ed", + "Ġposs ibly", + "Ġinit i", + "st en", + "Ġpr os", + "Ġcand id", + "Ġass ign", + "Ġviol ence", + "W ell", + "Ġr ise", + "P S", + "Ġtamb ém", + "Ġë ĵ¤", + "i ance", + "y an", + "Ġaud io", + "ĠB et", + "ĠAmeric ans", + "ĠAs s", + "is chen", + "ìŀ ħ", + "Ġult imately", + "Ġpol ic", + "Ġmajor ity", + "éĢĻ åĢĭ", + "ĠFin ally", + "er ap", + "Ġgu ard", + "ĠMAT T", + "Ġbr own", + "м и", + "Ġch a", + "ĠHo ly", + "Ġnerv ous", + "ipp ing", + "ÄĻ d", + "ĠS a", + "ĵ ľë", + "¶ Ģ", + "l ie", + "çľ Ł", + "Ġn uc", + "ĠA pr", + "é Ľ", + "ĠKore a", + "eg o", + "ĠCan ada", + "Ġkön nen", + "Ġcomp ar", + "Ġg anz", + "ĠM ais", + "Ġthem e", + "Ġk i", + "Ġdraw ing", + "az on", + "ĠO ff", + "t t", + "ĠW ind", + "Ġtod os", + "Ġob vious", + "на Ñı", + "I M", + "ĠÐ ł", + "we ll", + "Ġbl ow", + "Ġho ok", + "Ġcir cle", + "Ġë³ ´", + "Ġarch itect", + "ĠK r", + "Ġc ó", + "Ġprotect ion", + "eg a", + "å ĩ", + "Ġwatch ed", + "Ġans wers", + "Ġdi et", + "iv o", + "Ġpow der", + "Ġyour s", + "Ġhigh est", + "çĤ º", + "F F", + "å º", + "Ġbo ys", + "ö yle", + "Ġl unch", + "è¬ Ŀ", + "ĠI I", + "Ġset s", + "Ġmo le", + "Û ģ", + "Ġwin ter", + "Ġluck y", + "Ġrespons ibility", + "Ġsign al", + "Ġwond ering", + "Ġa x", + "Ġcook ing", + "ов оÑĢ", + "le g", + "Ġп оÑĤ", + "Ġsurpr ise", + "Ġdem ocr", + "Ġlo op", + "Ġj ag", + "Ġcur ious", + "Ġmarket ing", + "Ð Ŀ", + "ar on", + "ĠApp le", + "Ġvirt ual", + "Ġ19 8", + "no on", + "ĠM et", + "оÑģ ÑĤо", + "об Ñĭ", + "it u", + "ĠA w", + "Ġbu ying", + "Ġrestaur ant", + "ĠB ud", + "Ġdou bt", + "Ġgr ant", + "Ġver d", + "Ġc ash", + "Ġfac ulty", + "Th at", + "ĠE in", + "å¤ ļ", + "Ġw ed", + "it ness", + "ĠM ag", + "n el", + "Ġn arr", + "Ġacc ident", + "Ġmed ium", + "em ents", + "Ġcr ow", + "n ight", + "ìĿ ¼", + "ä¹ Ł", + "Ġlibr ary", + "аÑİ ÑĤ", + "Ġtamb ién", + "Ġrefer ence", + "Ġfour th", + "h ouse", + "v ention", + "Ġfill ed", + "ĠC our", + "ib r", + "Ġn g", + "Ġdevelop ing", + "Ġprov ides", + "Ġpo ll", + "Ġtra ffic", + "arent ly", + "à® Ł", + "Ġform s", + "Ġcl ient", + "Ġg entle", + "Ġmus s", + "ĠCong ress", + "ĠInd ian", + "ce an", + "Ġp il", + "Ġc zy", + "st ood", + "ut y", + "Ġn ä", + "Ġsp ending", + "Ġconst ruction", + "ina udible", + "Ġë§ Ī", + "Īë¬ ´", + "Ġìĥ Ŀ", + "om a", + "os en", + "ag o", + "Ġlar gest", + "ãħĭ ãħĭ", + "Ġun iverse", + "b es", + "os a", + "Ġе го", + "Ġd ude", + "ĠM AR", + "Ġind eed", + "ε ι", + "Ġman aged", + "ĠSh ould", + "S o", + "Ġappl ied", + "Ġfair ly", + "ĠD en", + "Ġanal y", + "Ġconst antly", + "Ñģ п", + "H ow", + "ĠS ay", + "en cies", + "ĠP C", + "Ġegg s", + "à® °", + "Ġet h", + "ĠEnt ão", + "in ar", + "i ot", + "Ġc z", + "ĠEurope an", + "ãģ Ī", + "ĠA M", + "Ġc á", + "Ġrad io", + "§ Į", + "Ġh ide", + "ä» Ĭ", + "ĠSt art", + "Ġcl ub", + "ĠH ope", + "Ġeff orts", + "lus ion", + "Ġc ities", + "h one", + "Ġreach ed", + "Ġgu id", + "ro id", + "Ġhar m", + "Ġcut ting", + "Ġb ul", + "1 8", + "i est", + "ĠMe x", + "Ġ iron", + "çŁ ¥", + "Ġafter noon", + "Ġha ll", + "Ġpr zy", + "Ġg osh", + "Ġinflu ence", + "Ġв ид", + "Ġincre ased", + "ĠMin ister", + "Ġdis ci", + "ĠP eter", + "Ġver t", + "Ġmen u", + "Ġse lling", + "ur ally", + "Ġqu ote", + "Ġ ¡", + "Ġcontin ues", + "mp re", + "ĠÅŁ ey", + "it ution", + "Ġна Ñģ", + "c les", + "ĠGerm an", + "c zy", + "ĠÐ £", + "B e", + "Ġk itchen", + "ĠT ry", + "i pe", + "Ġic on", + "ar p", + "Ġprov iding", + "ĠTr ans", + "Ġtechn ique", + "Ġh är", + "Ġinf rast", + "Ġsus p", + "ü ck", + "ic ip", + "ĠÐ ķ", + "Ġc in", + "ìĸ ´ë", + "Ġpr z", + "Ġcompon ent", + "Ġby e", + "ĠB ible", + "iz er", + "C h", + "Ġsol utions", + "Ġaccom pl", + "Ġ201 6", + "I E", + "ĠT a", + "Ġass ume", + "Ġliqu id", + "Ġë¨ ¹", + "Ġquar ter", + "Ġfem ale", + "ĠTh ink", + "Ġstat us", + "it ute", + "Ġco ach", + "Ġre in", + "Ġcomb ination", + "è ·", + "ĠT er", + "Ġobject s", + "Ġdist rict", + "Ġmake up", + "Ġmur der", + "w as", + "f en", + "Ġbow l", + "Ġpub lished", + "Ġsp orts", + "ãģ ¡", + "Ġident ity", + "Ġseem ed", + "Ġact ing", + "л Ñİ", + "ri x", + "Ġup load", + "Ġh ast", + "Ġbo at", + "ĠM od", + "ri o", + "Ġ =", + "Ġcy cle", + "¯ ¸", + "Ġl oud", + "ust ed", + "com ing", + "Ġ201 7", + "Ġon t", + "Ġleg isl", + "Ġst ruct", + "ĠSomet hing", + "Ġconf lict", + "Ġu pper", + "Ġman ager", + "Ġm ort", + "Ġf ra", + "ĠÄ °", + "ĠM ike", + "ĠW ork", + "Ġn ó", + "ph ere", + "ĠìĤ ¬ë", + "ĠL and", + "Ġfil ter", + "Ġprom ot", + "æ °", + "æĻ Ĥ", + "ķ ¼", + "Ġrecord ing", + "× Ŀ", + "Ġassoci ated", + "Ġf uel", + "und er", + "Ġele ction", + "Ġemploy ees", + "ĠCom p", + "ÑĢÑĥ г", + "ĠW o", + "ro l", + "Ġsa ved", + "ĠH on", + "ĠV i", + "åĪ Ĩ", + "ac a", + "p ret", + "Ġw et", + "Ġst upid", + "Ġl ad", + "Ġf est", + "Ġw ake", + "Ġи н", + "Ġgreat est", + "ĠJ im", + "Ġserious ly", + "Ġì ¹", + "Ġfeel ings", + "Ġ3 00", + "i ation", + "Ġbeaut y", + "Ġìŀ ĺ", + "Ġs an", + "ĵ ł", + "Ġ- (", + "Ġcons cious", + "Ġд ел", + "b ye", + "ç Ļ", + "M an", + "Ġlet s", + "Ġsho es", + "y d", + "ä¹ Ī", + "Ġdisapp e", + "ĠCount y", + "ĠSc ott", + "Ġbut t", + "Ġaqu ÃŃ", + "Ġconf ig", + "resp ond", + "LAU GH", + "© ëĭĪëĭ¤", + "Ġdivid ed", + "Ġac qu", + "Ġz one", + "Ġk omm", + "a ção", + "ì§ ľ", + "c ut", + "Ġ2 3", + "Ġmaxim um", + "ro g", + "Ġrun s", + "Ġcompon ents", + "Ġarri ved", + "Ġconf ident", + "ÑĢ ов", + "Ġhe ight", + "Ġpro ced", + "E M", + "ĠÐŃ ÑĤо", + "ĠM en", + "Ġtalk s", + "Ġconf idence", + "ĠChr is", + "Ġlead s", + "Ġn ose", + "f all", + "b b", + "ĠNot hing", + "is er", + "Ġindepend ent", + "Ġmin or", + "Ġsy m", + "l en", + "ci ence", + "Ġf ashion", + "Ġsex ual", + "Ġb un", + "h ere", + "Ġso il", + "Ġdies e", + "Ġsh ap", + "Ġempt y", + "Ġjour nal", + "ag on", + "ĠThe ir", + "Ġweek end", + "ÃŃ t", + "Ġer ror", + "Ġn ar", + "à ¸", + "è ©", + "an cy", + "Ġìķ Ĭ", + "Ġfore st", + "Ġha cer", + "Ġmiss ed", + "ãģ ķ", + "åı¯ 以", + "Ġev il", + "Ġstor age", + "Ġsing ing", + "in ha", + "Ġkn ock", + "Ġimp ress", + "ĠоÑĩ енÑĮ", + "ĠGo ld", + "ĠS ur", + "ĠP ort", + "åİ »", + "ĠL ond", + "Ġfaz er", + "ot y", + "ot o", + "Ġan x", + "ĠWill iam", + "Ġexist ing", + "pl ace", + "ĠC D", + "Î ³", + "ĠColl ege", + "l or", + "ĠE ast", + "s en", + "f ach", + "o ft", + "Ġexperien ced", + "Ġlo ves", + "im m", + "Ġpo ly", + "Ġes se", + "ì ¤", + "ĠG rand", + "è §", + "ch er", + "Ġvict im", + "ĠG es", + "л ÑĮ", + "v ision", + "Ġt all", + "Ġl ens", + "Ġз на", + "ĠB oth", + "Ġì ²", + "Ġsust ain", + "Ġarg ument", + "Ġfact ors", + "Ġautom atically", + "Ġfr uit", + "Ġli ber", + "Ġa le", + "ĠP ress", + "ĠB a", + "ĠÐ ³Ð¾", + "Ġhundred s", + "th at", + "ĠR ich", + "Ġreci pe", + "ĠI T", + "è ĩ", + "Ạ¥", + "Ġdescri be", + "Ġdri ver", + "ĠO ct", + "ĠM at", + "д е", + "Ġme al", + "Ġlat est", + "Ġth erap", + "Ġcomp are", + "ĠAm azon", + "Ġì¢ Ģ", + "ĠRuss ia", + "Ġstr ing", + "Ġk a", + "ĠComm un", + "Ġd ia", + "I s", + "Ġmill ions", + "Ġcor por", + "Ġcor respond", + "Ġfix ed", + "ĠJo e", + "Ù İ", + "Ġview s", + "Ġr iver", + "Ġstud io", + "ig ger", + "Ġfl avor", + "Ġpres ence", + "Ġun its", + "Ġsa ving", + "av our", + "Ġp esso", + "or ith", + "Ġh ers", + "ĠN at", + "as ion", + "ĠFr ank", + "о ÑĪ", + "ÅĤ y", + "í Ħ", + "Ġein em", + "Ġfun ctions", + "um an", + "Ġn orth", + "Ġìł Ħ", + "Ġhor se", + "v id", + "Ġple asure", + "а ÑĪ", + "é es", + "ind a", + "Ġt ail", + "Ġexpl ore", + "S T", + "Ġcommer cial", + "ĠD uring", + "ar l", + "] :", + "f it", + "Ġr ates", + "æ ³", + "M USIC", + "Ġhous ing", + "Ġein er", + "Ġsitu ations", + "æ ĭ", + "Ġdec re", + "Ġappropri ate", + "ен но", + "% .", + "Ġb ac", + "Ġw at", + "ens ity", + "ä h", + "kn own", + "it z", + "Ġemot ional", + "erv ation", + "Ġbl ind", + "1 6", + "í ĥ", + "大 家", + "Ġjo ined", + "Ġloc ated", + "ĠÑģ м", + "ad as", + "ber g", + "Ġd ess", + "Ġde ar", + "ed en", + "c os", + "Ġad opt", + "1 00", + "ow e", + "ĠChe ck", + "ism o", + "Ġsim pl", + "Ġang ry", + "Ġмен Ñı", + "ĠC am", + "Ġp ad", + "Ġatt end", + "Ġsam ple", + "æĹ ¥", + "Ġì Ľ", + "ĠI N", + "ul ous", + "ĠS ar", + "ĠSh ow", + "Ġinfrast ructure", + "ĠAug ust", + "Ġless on", + "Ġn iet", + "æ İ", + "Ġfo i", + "Ġbro ke", + "t r", + "ç ķ", + "Ġ4 5", + "Ġg ew", + "Ñĥ п", + "at i", + "Ġmaint ain", + "Ġart ists", + "ing er", + "æĿ ¥", + "er ved", + "I A", + "Ġequ als", + "Ġoper ation", + "ill y", + "ĠëĤ ´", + "Ġcrow d", + "Ġintern al", + "Ġtest s", + "ĠR ock", + "ĠC ons", + "ĠëĦ Ī무", + "w ar", + "Ġs ou", + "Ġch art", + "ĠJ une", + "ĠApr il", + "g ent", + "Ġv ent", + "Ġqu and", + "ĠKore an", + "im o", + "ç ī", + "id ers", + "Ġmount ain", + "ÑģÑĤ ав", + "æľ Ī", + "ij k", + "Ġdiscover ed", + "ĠS und", + "ĠS il", + "Ġso lo", + " ´", + "Ġsch ol", + "ĠE ach", + "ç µ", + "Ġb are", + "Ġí Į", + "ĠvÃŃ de", + "Ġingred ients", + "ĠIt s", + "Ŀ¼ ê³ł", + "Ġì Ĭ", + "Ï į", + "ĠLe e", + "Ġsc ary", + "Ġprinci p", + "Ġspirit ual", + "ì ħ", + "ĠH old", + "æ²Ĵ æľī", + "Ġdef ine", + "ĠL es", + "ĠN or", + "ĠE nd", + "Ġbl og", + "ĠG reen", + "аеÑĤ ÑģÑı", + "p art", + "el es", + "äº ĭ", + "ĠUnd er", + "Ġpart e", + "Ġ3 5", + "Ġse ctor", + "ĠS ept", + "Ġaut h", + "à® ®", + "om in", + "Ġcl ients", + "Ġc i", + "ĠFr iday", + "er as", + "Ġtw e", + "ul ated", + "Ġcult ural", + "ĠÑģв о", + "Ġëį Ķ", + "Ġà º", + "Ġpar ce", + "à® ²", + "Ġtrad ition", + "Ġjud ge", + "ĠGen eral", + "Ġdeterm ine", + "ĠIs n", + "ĠP L", + "ne ath", + "Ġmatter s", + "íķ ´ì", + "! ]", + "а Ñħ", + "Ġpo ol", + "Ġvari able", + "Ġvacc ine", + "Ġcaus ed", + "Ġw est", + "ĠY ep", + "f ast", + "Ġph ilos", + "hor a", + "Ġcontinu ed", + "Ġunf ortunately", + "ãģ į", + "æ ķ", + "Ġfl ight", + "Ġw rap", + "Ġhu h", + "ĠAbs olutely", + "Ġp ink", + "Ġrem ains", + "Ġn é", + "Ġf le", + "ĠS ol", + "Ġlos ing", + "Ġalg orith", + "Ġrequ ires", + "Ġfound ation", + "ĠB ur", + "Ġprofess ion", + "ĠM id", + "Ġë ŃIJ", + "c an", + "ĠM il", + "Ġyoung er", + "Ġappe ars", + "ter m", + "íķĺ ê³ł", + "ac le", + "ĠLond on", + "Ġengine ering", + "ภ¢", + "Ġadv ent", + "ìĦ¸ ìļĶ", + "Ġê¸ °", + "ĠM aj", + "ÑĢ ем", + "ing u", + "ĠU K", + "u ro", + "s pe", + "Ġt ent", + "Ġreport ed", + "ĠA L", + "H ey", + "Ġë§ IJ", + "Ġd ent", + "ĠAustral ia", + "ĠJan uary", + "³ ´", + "ag ues", + "ars h", + "r ig", + "Ġtien e", + "ภ£", + "Î ®", + "Ġmach en", + "un te", + "Ñĥ Ñģ", + "Ġelect r", + "Ġtut orial", + "Ġpl aced", + "ĠìĿ´ ê±°", + "ĠCoun cil", + "í ĸĪ", + "°ë ¦¬", + "ah ren", + "Ġê·¸ë ŀĺ", + "Ġpro ve", + "f ol", + "Ġqu er", + "Ġche ap", + "ĠF ather", + "ĠP ower", + "ĵ ľ", + "Ġpur s", + "Ġes p", + "ĠB re", + "ê¸ °ë", + "om as", + "æĥ ³", + "ил ÑĮ", + "Ġge ht", + "os ter", + "ê³ ¼", + "Ġfil es", + "ĠÐ §", + "be ll", + "Ġwh om", + "Ġë ĺ", + "Ġex cellent", + "Ġdat ab", + "Ġg ö", + "Ġì§Ħ ì§ľ", + "Ġbelie f", + "j et", + "Ġj ack", + "Ġsw im", + "ri al", + "um in", + "a uc", + "Ġso ll", + "Ġess ential", + "íķĺ ëĬĶ", + "Ġev ol", + "cha ft", + "ain e", + "th let", + "Ġinc or", + "Ġreport s", + "Ġdefin ition", + "ke l", + "Ġcirc um", + "Ġprodu ced", + "Ġ× Ľ", + "ant ic", + "n et", + "Ġa ward", + "Ġd urch", + "Ġtrans p", + "Ġm ale", + "¦ ¬ë", + "Ġmo on", + "ĠGe orge", + "Ġfly ing", + "i ó", + "Ġs ources", + "Ġpl enty", + "ĠDem ocr", + "R O", + "Ġ 00", + "Ġsec ure", + "ĠB ir", + "ra in", + "Ġz ur", + "Ġeffic ient", + "Ġrepe at", + "Ġmethod s", + "Ġcal m", + "Ġdiscuss ed", + "ĠìŀĪ ëĬĶ", + "Ġser ver", + "an ie", + "ĠInst ead", + "Ġide al", + "Ġcon ven", + "Ġhop ing", + "ĠT or", + "Ġdep th", + "Ġhe aven", + "EN CE", + "Ġhab it", + "gr ad", + "Ġfl ag", + "Ġin e", + "Ġk h", + "ĠL I", + "Ġfac ing", + "ĠA U", + "ĠT im", + "Ġg em", + "ĠJ ul", + "Ġel a", + "iz za", + "Ġfe llow", + "Ġqu el", + "Ġsp oke", + "Ġcitiz ens", + "u ge", + "é ĥ½", + "Ġp ages", + "Ġf asc", + "Ġrelig ious", + "at en", + "Ġch apter", + "ĠV al", + "Ġcons ult", + "ĠM ill", + "g l", + "op er", + "Ġinf in", + "Ġmar riage", + "Ġmedic ine", + "Ġд в", + "Ġdog s", + "Ġinstr ument", + "ĠEx act", + "á n", + "Ġ20 21", + "Ġf er", + "Ġwe alth", + "Ġgr ade", + "Ñĭ Ñħ", + "Ġcr ime", + "Ġth read", + "Ġess a", + "Ġw ine", + "co hol", + "ph a", + "ภĩ", + "og ue", + "Ġins urance", + "arr ator", + "ĠSept ember", + "Ġv id", + "ĠSp irit", + "Ġg est", + "ĠRuss ian", + "Ġproper ties", + "Ġart icle", + "Ġunder neath", + "y er", + "Ġjo int", + "Ġrelative ly", + "Ġin ch", + "Ġdesp ite", + "ĠG ree", + "Ġclass ic", + "Ġsupport ing", + "Ġinst ruct", + "lus ive", + "Ġdi agn", + "æ Ĭ", + "Ġadminist ration", + "аб оÑĤ", + "ĠO pen", + "æīĢ 以", + "Ġп ок", + "Ġdoll ar", + "Ġconse qu", + "o ber", + "ĠGerm any", + "Ġter r", + "ĠQ U", + "ĠÐ ĵ", + "ç ¾", + "Ġstrong er", + "É Ļ", + "ĠÙ Ĭ", + "ĠiP hone", + "Ġfab ric", + "ü h", + "Ġen em", + "æ ¯", + "Ġsub t", + "E E", + "ond e", + "Ġcre w", + "Ġremo ved", + "Ġl ady", + "Ġpot entially", + "ĠÐĿ о", + "y al", + "Ġsym pt", + "Ġar my", + "Ġintrodu ced", + "t es", + "Ġaspect s", + "1 4", + "ĠL ou", + "Ġ )", + "Ġde ploy", + "p et", + "Ġh an", + "ĠW atch", + "Ġweap ons", + "Ġph en", + "Ġreg ister", + "Ġein fach", + "Ġsp ort", + "Ġbr idge", + "Ġin ner", + "Ġminim um", + "Ġw itness", + "Ġes o", + "Ġvill age", + "Ġown er", + "¦¬ ê³ł", + "Ġsc ream", + "il ed", + "Ġp itch", + "b ru", + "Ġadv ance", + "ä¸į æĺ¯", + "Ġsupp ose", + "ĠAt t", + "еÑĤ ÑģÑı", + "Ġdiffer ences", + "ak ed", + "Ġinter pret", + "à ¦", + "iend o", + "Ġabs ol", + "ĠбÑĥд еÑĤ", + "Ġë ²", + "Ġtri al", + "Ġthink s", + "ly ing", + "cept ion", + "ĠAfric an", + "Ġchem ical", + "Ġta pe", + "Ġconvers ations", + "Ġdistrib ution", + "t i", + "ĠA I", + "Ġfl ash", + "Ġunder stood", + "ĠGovern ment", + "å° ı", + "! ?", + "ĠS k", + "ê± °ë", + "ri er", + "T S", + "ĠAcc ording", + "Ñİ ÑĤ", + "Ġsp ons", + "ÑĤ обÑĭ", + "Ġval u", + "ere m", + "icht ig", + "Ġresist ance", + "ĠG al", + "ger y", + "Ġbeg ins", + "Ġadv anced", + "Ġrele vant", + "Ġpolit ics", + "ĠF am", + "Ġç ok", + "ĠN ever", + "ill ing", + "Ġfoot ball", + "и и", + "ĠI D", + "ĠAfric a", + "Ġfing ers", + "Ġб олÑĮ", + "Ġà ¡", + "Ġcl ip", + "ĠL at", + "ãĤ Ħ", + "Ġì§Ģ ê¸Ī", + "es se", + "Ġvo or", + "Ġas ide", + "æ ŀ", + "Ġto ward", + "Ġb at", + "Ġval id", + "ĠM ens", + "Ġcomplet ed", + "ı ÄŁ", + "Ġpod cast", + "ĠB on", + "Û Ĵ", + "ĠJ uly", + "il a", + "Ġpack age", + "Ġpull ed", + "ch ar", + "ĠM el", + "o is", + "Ġs outh", + "Ġë Ķ", + "Ġimport ance", + "Ġp ushing", + "Ġis ol", + "Ġstand s", + "c ill", + "ä ¼", + "Ġ ðŁ", + "or i", + "ê° ģ", + "Ġhom es", + "Ġconcern s", + "Ġb iz", + "å ½", + "b ie", + "Ġb is", + "Ġge ar", + "ĠM S", + "Ġh un", + "ĠM att", + "Ạ£", + "se y", + "ĠSec ret", + "Ġod d", + "ĠM ax", + "oll y", + "f ord", + "ĠS H", + "Ġrepl ace", + "Ġnav ig", + "Ġin i", + "и Ñı", + "Ġgi ant", + "Ġma nd", + "ĠH app", + "TI ON", + "g un", + "iam o", + "ìŀħ ëĭĪëĭ¤", + "Ġg ap", + "Ġê tre", + "Ġclass room", + "Ġhy p", + "ak i", + "è ®", + "is ters", + "ack s", + "ĠÑģ о", + "Ġb ug", + "Ġgra v", + "am in", + "Ġevery day", + "Ġì ¡°", + "Ġgard en", + "ce mber", + "Ġest o", + "åĹ İ", + "Ø ¬", + "Ł °", + "å ģ", + "Ġr om", + "Ġìłľ ê°Ģ", + "Ġfall ing", + "Ġfa ult", + "ell y", + "Ġch est", + "Ġл и", + "Ġpot ato", + "Ġbuild ings", + "Ġoper ating", + "Ġp are", + "w r", + "D on", + "ĠF our", + "Ġv ul", + "Ġl á", + "Ġfr ust", + "ĠD ann", + "ol es", + "ny a", + "Ġì ¶", + "ĠÑĢ аÑģ", + "× Ľ", + "Ġa ÃŃ", + "w ord", + "Ġweap on", + "Ġob t", + "ĠF all", + "ĠSte ve", + "Ġmix ed", + "Ġp ode", + "ĠA S", + "ĠL eg", + "Ġdes c", + "Ġspl it", + "Ġemer gency", + "ĠS ing", + "Ġprof it", + "Ġtyp ical", + "ĠDon c", + "Ġannoun ce", + "ĠTe x", + "Ġsac r", + "tern al", + "Ġcomm ittee", + "ig o", + "Ġdi am", + "ph as", + "Ġdef e", + "ĠProf ess", + "Ġdec l", + "Ñĥ ÑĢ", + "2 2", + "ol f", + "ĠM ond", + "u y", + "Ġa y", + "Ġl em", + "Ġlove ly", + "ĠC ould", + "Ġgu ar", + "H H", + "Ġcare fully", + "ĠL isten", + "Ġк ÑĢ", + "Ġyou th", + "ĠThere fore", + "Ġdream s", + "ĠJe ff", + "? ]", + "Ġë Ī", + "D A", + "Ġb odies", + "au x", + "Ġtechn iques", + "Ġmechan ism", + "× ĵ", + "Ġо ни", + "Ġdes ire", + "à ®", + "ĠV o", + "qu es", + "ĠÑĥ же", + "ĠWho a", + "ĠG ame", + "Ġh al", + "an ish", + "Ġpract ices", + "5 00", + "Ġsort s", + "up s", + "ate ful", + "Ġhers elf", + "Ġgu itar", + "Ġprop os", + "Ġsit es", + "Ġbe ach", + "Ġ× ¢", + "ç¬ ¬", + "н Ñĥ", + "Ġdr am", + "ĠNo ve", + "V E", + "r ant", + "Ġpl ot", + "ĠìŬ 기", + "ĠC a", + "Ġestab lished", + "Ġ201 5", + "Ġinsp ired", + "Ġannoun ced", + "ä¸ ª", + "ĠÑĤ ÑĢ", + "Ġ2 6", + "Ġv oy", + "Ġte ch", + "ìł ģ", + "Ġprocess es", + "ont o", + "ĠP an", + "Ġrap id", + "ist an", + "Ġ19 7", + "Ġrelig ion", + "Ġ2 8", + "Ġsm ile", + "Ġb ab", + "Ġ Ú©", + "ĠV ir", + "Ġsched ule", + "Ġexec ut", + "Ġpr on", + "Ñ į", + "ĠÐĿ Ñĥ", + "m usic", + "ìĽ IJ", + "Ġg an", + "ìĭ ł", + "Ġdef ault", + "Ġbe m", + "Ù ī", + "Ġfor ced", + "ĠOb viously", + "Ġst one", + "Ġt ie", + "Ġdrink ing", + "Ġser ved", + "C ause", + "Ġcon ference", + "ĠExact ly", + "ãĥ Ī", + "ł ľ", + "ìĻ Ģ", + "ĠR a", + "Ġf ake", + "Ġdif f", + "ãģ ©", + "Ġchalleng ing", + "Ġì¤ ij", + "Ï ĩ", + "ä»Ģ 麼", + "Ġintellig ence", + "re te", + "Ġstud ying", + "Ġapp oint", + "Ġt an", + "Ġи м", + "Ġcur ve", + "ĠTe am", + "ĠA z", + "Ġз д", + "ĠMus ic", + "f ield", + "ir ation", + "Ġfail ed", + "Ġno vel", + "Ġdifferent ly", + "Ġes cape", + "ĠY o", + "ĠOct ober", + "ı yor", + "Ġdescri bed", + "Ġcon vert", + "ac ement", + "Ġhot el", + "is ation", + "Ġsu is", + "ãģ ij", + "å ŃIJ", + "æĢ İ", + "Ġwalk ed", + "2 00", + "Ġneighbor hood", + "is p", + "ĠL os", + "Ġh idden", + "Ġ2 7", + "л е", + "Ġph r", + "ĠIs land", + "ĠSt reet", + "end a", + "hip s", + "os ure", + "Ġdefin ed", + "ภ§", + "Ġv ida", + "Ġlab el", + "ĠEvery body", + "Ġjo ke", + "ia o", + "ا ÙĨ", + "Ġa thlet", + "... \"", + "ĠF ire", + "D o", + "Ġdef ense", + "Ġent ertain", + "á t", + "Ġpolic ies", + "Ġal cohol", + "ĠEng ine", + "Ġg al", + "ĠJ ud", + "Ġvol unte", + "ick s", + "et a", + "ag t", + "Ġ× ķ", + "Ġm ö", + "1 3", + "Ġenc oun", + "Ġe h", + "Ġor ange", + "Ġabs or", + "Ġsp aces", + "ĠNove mber", + "êµ ¬", + "i at", + "Ġt am", + "ck now", + "Ġst orm", + "ĠDire ctor", + "Ġpre gn", + "ĠìĿ ¼", + "Ġо п", + "Ġres ource", + "Ġb ard", + "ne w", + "ĠDe cember", + "u its", + "Ġwe il", + "Ġconst ruct", + "s i", + "n ic", + "Ġfl our", + "Ġrest rict", + "ü t", + "Ġentire ly", + "Ġbreak ing", + "ent lich", + "Ġtw enty", + "Ġcaus es", + "Ġele v", + "ĠS pr", + "ĠIntern et", + "Ġk iss", + "Ġoper ations", + "s zy", + "Ġë Ĭ", + "Ġscient ists", + "Ġgr own", + "Ġown ers", + "out s", + "Ġcour ses", + "Ġus ual", + "Ġin n", + "Ġtrans m", + "ñ o", + "Ġnu est", + "к ов", + "Ġcateg ory", + "ĠL ife", + "ĠPl us", + "Ġat mos", + "wh ile", + "Ġrecord s", + "Ġde ÄŁ", + "ëĭ¤ ê³ł", + "ĠìĤ¬ë ŀ", + "Ġrequire ments", + "in n", + "Ġimm ig", + "Ġdeep er", + "ç ´", + "Ġapp s", + "Ġcolle agues", + "ż y", + "Ġoff ers", + "Ġt á", + "Ġcolum n", + "la ud", + "I R", + "ĠM s", + "Ġexch ange", + "l as", + "ĠL aw", + "ĠJ on", + "is se", + "ro gen", + "Ġmo i", + "× Ĺ", + "Ġs ending", + "Ġhe llo", + "е е", + "ÅĽ Äĩ", + "Ġsuc ceed", + "Ġsuff ering", + "Ġad vert", + "Ġì£ ¼", + "çŁ¥ éģĵ", + "Ġrec o", + "ın ı", + "Ġк ом", + "all ey", + "Ġfail ure", + "ie j", + "Ġëķ Į", + "Ġdrug s", + "Ġcu ando", + "Ġìĸ´ë ĸ", + "ĠAb out", + "Ġqu ando", + "9 0", + "ĠF ed", + "1 7", + "S h", + "in ho", + "ĠSund ay", + "ĠPh il", + "Ġacad emic", + "ĠIn c", + "Ġmaint en", + "åĩ º", + "Ġre ward", + "er d", + "Ġcomm itted", + "ìĬ ¤", + "г ÑĢ", + "Ġstand ards", + "Ġk al", + "Ġint ention", + "ĠZ h", + "Ġa cknow", + "ä ¿", + "Ġ== =", + "og y", + "å §", + "Ġfilm s", + "is k", + "Ġte eth", + "Ġstrugg le", + "r d", + "u en", + "Ġdis s", + "ĠD ar", + "am y", + "Ġenem ies", + "Ġve loc", + "ĠC all", + "um bs", + "иÑĤ елÑĮ", + "Ġo cean", + "é d", + "ìļ °", + "Ġtre m", + "ient o", + "еÑĪ ÑĮ", + "ffic ient", + "Ġbott le", + "Ġinstit ution", + "est y", + "ĠH an", + "h ab", + "ëĬ ĺ", + "Ġar rest", + "éĤ Ħ", + "Ġlet ters", + "oun ce", + "í Į", + "A n", + "Ġcreat es", + "Ġcl ock", + "Ġdeb t", + "Ġan cient", + "ific ations", + "g i", + "B ut", + "ĠT u", + "k l", + "Ġb order", + "Ġo ok", + "ĠB ay", + "est a", + "Ġë³ ´ì", + "Ġw ra", + "pre ne", + "Ġê² Į", + "ang le", + "Ġbelie ved", + "ien cy", + "ak a", + "Ġcrit ic", + "Ġb omb", + "Ġha m", + "ĠÐ Ľ", + "êµ Ń", + "ĠGu ys", + "ros oft", + "Ġcr im", + "et ch", + "AR R", + "Ġs ight", + "и на", + "Ġa in", + "á» ij", + "is che", + "Ġau x", + "Ġnum er", + "Ġsurv ive", + "A ll", + "B C", + "Ġs z", + "Ł ¬ë", + "Ġj am", + "ĠCour t", + "Ġall es", + "Ġtr igger", + "Ð ŀ", + "Ġform at", + "Ġdec ades", + "Ġc es", + "Ġsign s", + "Ġrob ot", + "ĠCh urch", + "Ġa z", + "Ġs oup", + "ĠTex as", + "ut en", + "ĠÑĩ ÑĤобÑĭ", + "Ġneigh b", + "ĸ ×Ķ", + "Ġcommunic ate", + "Å ¡", + "Ġel imin", + "Ġfrequ ency", + "her n", + "id os", + "Ġem phas", + "Ġmess ages", + "Ġg ender", + "ĠW enn", + "Ġв о", + "Ġpr ices", + "ol o", + "Ġп он", + "w ing", + "ĠF il", + "а ем", + "ĠC ur", + "Ġfal se", + "Ġfield s", + "Ġs é", + "2 4", + "Ġm ac", + "u ÅŁ", + "Ġlay ers", + "Ġadv oc", + "w an", + "Ġk ar", + "ĠÅ ŀ", + "Ġdec or", + "Ġwall s", + "o e", + "iss ions", + "Ġres ol", + "× ¢", + "ĠCar ol", + "ĠV ide", + "le ep", + "ĠY OU", + "Ġfl ip", + "Ġsur gery", + "Ġch op", + "U R", + ". ,", + "Ġag ency", + "Ġwant ing", + "Ġsol ar", + "Ġhor iz", + "ĠAd am", + "Ġstay ing", + "ol ic", + "Ġgr ateful", + "Ġrem ark", + "Ġtechn ologies", + "Ġprote in", + "å¿ ĥ", + "д ел", + "ĠM ont", + "Ġshould er", + "Ġz a", + "re y", + "ĠO oh", + "Ġst y", + "ic ar", + "оÑĤ ÑĢ", + "Ġrout e", + "ĠT urn", + "Ġb om", + "Ġdeb ate", + "Ġposs ibility", + "Ġíķ ´ì", + "ap a", + "Ġinv ent", + "ür lich", + "Ġprof ile", + "Ġsen ior", + "pp y", + "v as", + "Ġm undo", + "ate ver", + "Ġapp arently", + "en er", + "× IJ", + "ç Ń", + "Ġprec is", + "Ġal ign", + "Ġkn ife", + "ĠRo bert", + "å ĭ", + "Ġfo ol", + "Ġinv ite", + "us ing", + "Ġcircum st", + "Ġcapt ure", + "Ġd ough", + "ĠS and", + "Ġse u", + "ĠNew s", + "Ġb ite", + "Ġne ut", + "w ide", + "Ġlect ure", + "Ġëĺ IJ", + "Ġorigin ally", + "Ġcho ices", + "ĠG ar", + "Ġver se", + "Ġl it", + "Ġ19 6", + "íķ ł", + "Ġmeas ures", + "ç ões", + "w ater", + "ri ve", + "Ġz ijn", + "í ģ", + "ĠB us", + "Ġhe b", + "е Ñħ", + "ĠK ar", + "ĠN ão", + "Ġkill ing", + "à® ª", + "Ġmir ror", + "m od", + "Ġm ol", + "Ġcre ation", + "Ġest im", + "Ġatmos phere", + "Ġg am", + "Ġt ables", + "is i", + "ĠL ittle", + "Ġt as", + "ĠE le", + "é l", + "Ġscen es", + "Ġt one", + "Ġaffect ed", + "ĠAU DI", + "ĠBr own", + "I f", + "ĠÙ ĩ", + "ĠDan iel", + "羣 çļĦ", + "qu er", + "ch i", + "íķ ĺë", + "Ġmist akes", + "Ġs la", + "ãĤ ¤", + "Ġent r", + "Ġе Ñģли", + "Ġsh out", + "Ġport ion", + "Ñ Ĺ", + "Ġpre viously", + "á» Ļ", + "ĠпÑĢ ед", + "оÑģ ÑĮ", + "Ġhead s", + "ç İ", + "å Ń", + "åľ ĭ", + "Ġgr ass", + "ภ°", + "cri be", + "Ġqu é", + "ĠSp anish", + "Ġoffer ed", + "ĠбÑĭ ло", + "ĠCl oud", + "Ġve ctor", + "ĠH uh", + "Ġk ad", + "if ts", + "ĠÎ ½", + "Ġhung ry", + "Ð ¡", + "Ġpar all", + "AN D", + "ĠvÃŃde o", + "iz z", + "Ġocc up", + "Ġí Ķ", + "Ġsee k", + "h es", + "Ġdo ors", + "Ġhous es", + "Ġconsider ing", + "Ġgradu ate", + "Ġf ulf", + "è ¡Į", + "è £", + "Ġext reme", + "Ġflow ers", + "it ate", + "ĠP ri", + "Ġfundament al", + "Ñĩ аÑģ", + "è¯ ´", + "Ġtext ure", + "į ĺ", + "ĠAN D", + "à® ±", + "ĠT em", + "Ġn ada", + "ì§ Ħ", + "Ġcelebr ate", + "um s", + "Ġp ill", + "Ġи ли", + "go ing", + "Ġh ip", + "Ġsupport ed", + "Ġper man", + "Ġagre ement", + "Ġty m", + "Ġë ij", + "ĵ¤ ìĿ´", + "Ġpurch ase", + "í Ķ", + "ĠPl an", + "eg en", + "Ġrec over", + "P U", + "ĠMic rosoft", + "du c", + "Ġhol es", + "Ġdro pped", + "Ġp ig", + "Ġend ing", + "Ġattack s", + "be c", + "Ġre n", + "Ġr app", + "Ġìļ °ë¦¬", + "Ġter ror", + "Ġ× Ļ", + "Ġed it", + "Ġa o", + ". ", + "Ġhero es", + "ĠB oston", + "Ġdepend ent", + "Ġmotiv ation", + "fl ix", + "Ġse am", + "ки е", + "Ġdra in", + "od ed", + "Ġgu ilty", + "ĠJ enn", + "ing en", + "Ġgrant ed", + "ĠK elly", + "ĠS av", + "ĠUn cle", + "ĠHon estly", + "EL I", + "Ġnavig ate", + "Ġbless ed", + "c ore", + "Ġear ning", + "Ġsign als", + "Ġdis k", + "ial s", + "Ġag es", + "æ ħ", + "Ġpartic le", + "ĠÑĩ еÑĢ", + "Ġcan n", + "Ġt ier", + "Ġstat ements", + "ê³ł ìļĶ", + "ĠëķĮ문 ìĹIJ", + "ĠCh o", + "Ġpol ar", + "an ç", + "ĠK enn", + "ĠN i", + "ĠF ight", + "or gan", + "é ķ", + "ĠCh a", + "ĠS ÃŃ", + "ãĥ ª", + "Ġs lic", + "Ġcert ific", + "Ġtempl ate", + "ĠFed eral", + "Ġconsider ation", + "Ġexpl o", + "ĠM ain", + "ĠN E", + "Ġalong side", + "Ġd ressed", + "ĠP oint", + "Ġenviron ments", + "Ġpró xim", + "Ġda ar", + "Ġprom pt", + "Ġpurs ue", + "Ġentertain ment", + "Ġth roat", + "Ġproblem a", + "Ġm art", + "ì ¼", + "Ġprov ider", + "Ø Į", + "Ġ× Ĺ", + "int e", + "m aking", + "Ġstro ke", + "Ġtiss ue", + "U n", + "Ġpre cious", + "ĠAr ts", + "ink ing", + "ĠÐŀ н", + "Ġи Ñģ", + "n ah", + "ĠÐķ Ñģли", + "Ġcor ners", + "Ġtrick y", + "in ch", + "l ijk", + "Ġpress ing", + "le vel", + "AN G", + "Ġrad iation", + "ìĦ ł", + "Ġconf ront", + "Ġv et", + "Ġrepresent ative", + "Ġprop ag", + "Ġcra p", + "ĠDe c", + "Ġr amp", + "еп еÑĢÑĮ", + "u és", + "ess en", + "cri ption", + "Ġb ills", + "ĠMatth ew", + "Ġan ime", + "ấ t", + "Ġlow est", + "h as", + "sc reen", + "og rap", + "ал о", + "int on", + "ĠJ ah", + "èĢ ħ", + "it Ãł", + "Ġk ay", + "Ġrot ation", + "ĠW ere", + "abe i", + "Ġtri als", + "Ġle ver", + "ight y", + "Ġsp oon", + "Ġh unt", + "c ling", + "Ġdis m", + "ĠболÑĮ ÑĪ", + "Ġass ault", + "Ġíĺ ķ", + "Ġweek ly", + "Ġm ismo", + "Ġgen etic", + "ul pt", + "ĠStud ent", + "Ġreal istic", + "Ġauthent ic", + "æī ĵ", + "ast a", + "Ġarrest ed", + "Ġguid elines", + "Ġ×ľ× IJ", + "Ġд ав", + "ĠCom ing", + "f ür", + "Ġrequ ests", + "ĥ IJ", + "Ġanaly ze", + "Ġinter ess", + "Ġh alt", + "ĠO per", + "on om", + "Ġd uck", + "Ġwith d", + "s er", + "ĠÏ Į", + "ĠHist ory", + "Ġyout ube", + "ãĤ į", + "Ġsab er", + "w alk", + "f ont", + "Ġover view", + "3 9", + "ü y", + "ett i", + "Ġfro zen", + "Ġf lesh", + "ÄŁ i", + "ĠP M", + "ĠìĻ Ģ", + "é ¢", + "ÑĨи и", + "Ġê¸ °ë", + "íģ ¬", + "Ġpr ose", + "oo oo", + "r ates", + "W S", + "Ġautom atic", + "Ġcollect ing", + "Å ij", + "Ġneighb ors", + "» .", + "ĠEx pl", + "Ġcir cul", + "co ver", + "we g", + "Ġstick s", + "Ġe ller", + "Ġw ww", + "Ġd orm", + "ĠEx per", + "Ġstat istics", + "Ġemail s", + "Ġgra ve", + "im iz", + "H S", + "Ġu it", + ", '", + "Ġlas er", + "è ī", + "ĠÑĤ ем", + "Ñĭ ÑĪ", + "Ñī Ñij", + "Ġgen au", + "Ġtien en", + "Ġmed itation", + "ĠOr gan", + "Ġest imate", + "Ġë¬ ´ì", + "l ets", + "Ġn Ãły", + "Ġmind set", + "Ġres on", + "Ġm és", + "Ġnumer ous", + "Ġvie lleicht", + "ĠTh ird", + "u ous", + "ĠDe ad", + "ан д", + "H N", + "Ġrac ing", + "Ġag ents", + "ĠU t", + "Ġte ar", + "ĠH P", + "Ġchem istry", + "Ġsurv ival", + "æĸ °", + "Ġconvin ced", + "Ġ ;", + "Ġreg ulations", + "ĠE S", + "åĴ Į", + "3 00", + "Ġen se", + "Ġì µ", + "Ġd ict", + "G A", + "Ġah ÃŃ", + "åĭ ķ", + "Ġte j", + "Ġо ÑģÑĤ", + "ĠE lect", + "Ġintellect ual", + "Ġbi as", + "Ġbur den", + "çĤ ¹", + "Ġìĸ´ëĸ »", + "Ġche er", + "Ġso ph", + "Ġportfol io", + "ub a", + "Ġest os", + "T V", + "F or", + "Ġas h", + "Ġkom mer", + "Ġcollect ive", + "Ġw rest", + "ĠJ etzt", + "ĠW at", + "re ich", + "Ġprim er", + "act ive", + "Ġm ie", + "ick ed", + "Ġhun ting", + "Ġtest im", + "Ġcompass ion", + "ĠØ ±", + "Ġbr ut", + "Ġsal ad", + "об Ñīе", + "Ġsol ving", + "Ġflo ating", + "ç ·", + "Ġattract ive", + "ÙĪ ÙĦ", + "Ġper d", + "if fer", + "Ġsc ulpt", + "hh h", + "ĠWe ek", + "Ġent hus", + "Ġn ad", + "Ġmer ch", + "ĠíĻ ķ", + "Ġm ile", + "好 äºĨ", + "ĠÎ ¸", + "ĠëĤ ĺë", + "éĩ į", + "3 8", + "Ġch ains", + "ĠAl most", + "Ġtick ets", + "r in", + "ĠC C", + "Ġdistrib uted", + "abet es", + "Ġtemper atures", + "Ġg ained", + "Ġflex ibility", + "Ġscream ing", + "Ġab road", + "un o", + "Ġentreprene urs", + "ĠNet work", + "ĠCanad ian", + "Ġpre v", + "Ġs ö", + "ĠÑĤеб Ñı", + "ĠP oke", + "ĠP od", + "ĠTur key", + "çı¾ åľ¨", + "Ġabst ract", + "Ġsn ake", + "ĠAm y", + "ĠëĬIJëĤ Į", + "Ġbra ve", + "ĠìŀĪ ìĸ´ìļĶ", + "ĠK al", + "Ġ200 7", + "á rio", + "Ġmark ed", + "gin es", + "Ġall oc", + "ON G", + "Ġscient ist", + "Ġes ca", + "Ġrac ism", + "× ij×", + "ĠS ams", + "ĠP enn", + "Ġload s", + "Ġà® ¨", + "ü ber", + "M e", + "ix ò", + "Ġper ò", + "an ne", + "Ġexp ressed", + "м еÑĢ", + "Ġmo et", + "Ġret urning", + "n ia", + "Ġexp on", + "P ro", + "Ġlo yal", + "M L", + "Ġl amp", + "Ġsh y", + "Ġcomp osition", + "ĠL y", + "Ġmagn etic", + "Ġprem ier", + "Ġmeasure d", + "Ġsumm ary", + "Ġattack ed", + "Ġfin ishing", + "Ð Ĺ", + "ç ¥", + "Ġs its", + "Ġhyd rogen", + "Ġma i", + "ĠDeuts ch", + "as ı", + "Ġobt ain", + "v ie", + "Ġso it", + "Ġë° Ķ", + "Ġl ane", + "Ġconse gu", + "в о", + "Ġe ase", + "ak in", + "ĠF a", + "Ġunt uk", + "Ġbur st", + "Ġc um", + "al ım", + "ú blic", + "id i", + "ĠRoy al", + "ĠK on", + "Ġcommon ly", + "Ġremo ving", + "Ġj ur", + "il ib", + "Ġan ch", + "íĸ ī", + "Æ°á» £", + "ĠÐľ Ñĭ", + "ĠAn th", + "ĠS Ã¥", + "Ġinter rupt", + "Ġst ere", + "ĠO S", + "ony m", + "ter y", + "ĠMar ia", + "ê² ĥ", + "Ġexpl oring", + "Ġtransp arent", + "Ġf ate", + "ĠJ ung", + "Ġgr up", + "Ġdark er", + "ĠD oug", + "Ġman e", + "æĶ ¾", + "ạ i", + "d ri", + "lo ok", + "ĠDes ign", + "Ġtut aj", + "Ġhorizont al", + "re on", + "ort e", + "ĠCor rect", + "ĠSte ven", + "Ġv ine", + "0 2", + "i Äĩ", + "Ġsie mpre", + "ĠK ey", + "åĥ ı", + "ĠG ames", + "Ġna ar", + "Ġshock ed", + "el ve", + "ĠR ose", + "ìĭ ¬", + "Ġstop ping", + "oh l", + "ĠM ix", + "Ġsuff ered", + "Ġsig ma", + "Ġweak ness", + "ĠO w", + "ี à¹Ī", + "I F", + "Ġà® ħ", + "ad ed", + "ĠNet flix", + "an es", + "Ġrem ained", + "ir y", + "Ġr ip", + "ell t", + "Ġsil ent", + "Ġpro ven", + "Ġtox ic", + "Ġal umin", + "Ġmulti pl", + "al and", + "Ġ3 4", + "0 6", + "ĠB ru", + "Ġìłķ ë§IJ", + "J ust", + "b oy", + "Ġsho e", + "Ġcreat ure", + "Ġhead ed", + "ĠоÑĤ к", + "æ ±", + "Ġess ence", + "Ġremark able", + "Ġnú mer", + "Ġd rew", + "Ġpu zzle", + "ĠLibr ary", + "ĠF u", + "ash es", + "k k", + "ĠI st", + "¦ °", + "ĠB ry", + "Ġc eremony", + "Ġà® İ", + "Ġc ri", + "e qu", + "ãĤ ¢", + "Ġpri ze", + "Ġdim ensions", + "og ram", + "Ġle ather", + "Ġpop ulations", + "u um", + "Ġve gan", + "Ñı д", + "Ġcó mo", + "å Ħ", + "Ġstri p", + "å £", + "Ġvac ation", + "ħ ķ", + "Ġme als", + "ili pp", + "Ġ ents", + "ar am", + "ric ht", + "Ġgra in", + "ĠSp ain", + "Ġche ek", + "ĠA ff", + "I ON", + "ĠBr ing", + "Ġ3 8", + "iel en", + "ul u", + "ĠболÑĮ ÑĪе", + "Ġannounce ment", + "ĠÑĤ ÑĥÑĤ", + "ĠPro phet", + "ard o", + "3 7", + "Ġw oke", + "Ġtransl ation", + "ĠN OT", + "ĠC L", + "Ġd Ã¼ÅŁ", + "ÑĨ Ñĸ", + "ac er", + "ĠL oc", + "Ġper ception", + "N O", + "Ġdies en", + "L ook", + "he art", + "av ed", + "Ġbound ary", + "Ġfl ows", + "Ñij м", + "Ġarg uments", + "Ġelect ions", + "ı s", + "Ġhe ck", + "Ġsuit able", + "Ġf iber", + "ĠSt ra", + "x y", + "ĠH um", + "Ġmonth ly", + "u per", + "Ġgol f", + "Ġl ately", + "ĠG ard", + "ĠR en", + "ĠA st", + "ĠF ant", + "аÑģ Ñģ", + "Ġobs er", + "ë ¡ľ", + "Ġeas iest", + "į Ķë", + "Ġwebs ites", + "p ol", + "Ġco con", + "Ġà® ĩ", + "ĠV eg", + "Ġwalk s", + "Ġint ro", + "Ġdirect ed", + "ĠAn na", + "Ġëĵ¤ ìĸ´", + "ĠEaster n", + "ĠS aint", + "ĠB ow", + "Ġro ast", + "ĠU RL", + "Ġjed en", + "ur as", + "aj a", + "Ġse mi", + "Ġrapid ly", + "Ġtarget s", + "ĠCont rol", + "Ġb ah", + "Ġref lection", + "Ġcreat ivity", + "hold ers", + "Ġìĺ ¬ë", + "Ġamong st", + "Ġfeed ing", + "ÑįÑĤ омÑĥ", + "Ġвид е", + "Ġë§Įë ĵ¤", + "ĠSm art", + "Ġrel iable", + "Ġvez es", + "Ġ× ¨", + "ch uckles", + "az ione", + "ĠWilliam s", + "Ġa ç", + "Ġsle e", + "е Ñī", + "Ġtim eline", + "Ġthor ough", + "á» į", + "ĠO t", + "ạ n", + "Ġimag ination", + "Ġmechan ics", + "r ist", + "Ġclaim ed", + "ÏĦ η", + "ê te", + "ĠHur ry", + "ĠiP ad", + "Ġconst ru", + "ĠC la", + "ĠAl s", + "ä¼ ļ", + "ut z", + "Ġcult ures", + "Ġìĸ´ëĸ» ê²Į", + "Ġbelong s", + "Ġy er", + "ĠDoes n", + "Ġge omet", + "Ġb id", + "Ġfo am", + "Ġh ob", + "ĠBrit ain", + "Ġsubst ance", + "Ġann iversary", + "ĠëĦ Ī", + "Ġnot ed", + "Ġgovern or", + "Ġstock s", + "3 1", + "Ġdi ye", + "ìĬ ¤ë", + "Ġre b", + "z el", + "Ġmultip ly", + "Ġoper ator", + "Ħ¤ ìļĶ", + "Ġwat ers", + "Ġd är", + "Ġuns er", + "ĠEliz abeth", + "é« ĺ", + "Ġincreasing ly", + "ĠG ro", + "Ġen gines", + "ir s", + "Ø «", + "Ġtre asure", + "P C", + "in ction", + "ir i", + "Ġacc um", + "Ġvari ation", + "Ġp om", + "Ġtit les", + "ĠF est", + "ó s", + "Ġeld er", + "ny m", + "r un", + "Ñı в", + "Ġinnov ative", + "Ġnom bre", + "Ġco inc", + "Ġfr anch", + "Ġent onces", + "Ġnicht s", + "Ġexc lusive", + "ĠChe ers", + "ĠB i", + "u je", + "æŃ ¡", + "Ġp ok", + "ĠP rem", + "Ġrock et", + "ELI PE", + "Ġhosp itals", + "ri um", + "Ġjust e", + "Ġham mer", + "Ġquant um", + "Ġrespons es", + "ll y", + "end i", + "Ġact ively", + "Ġfr idge", + "i ate", + "l ong", + "Ġqu em", + "Ġdeath s", + "Ġsuper ior", + "ck en", + "ìĿ´ì ĹIJ", + "kt op", + "Ġgather ed", + "£ ¨", + "Ġd azu", + "Ġreci pes", + "Ġbu zz", + "c en", + "Ġany time", + "ons ense", + "Ġcirc les", + "Ġsol ved", + "Ġìĭ ł", + "Ġcoron avirus", + "ĠLu ke", + "Ġbu bb", + "Ġcont empor", + "r zy", + "ĠJ ane", + "Ġд ом", + "Ġscrew s", + "Ġhy brid", + "Ġcas ual", + "Ġsel bst", + "be ing", + "ĠÄ IJ", + "ĠCol umb", + "ĠÑħ оÑĩ", + "Ġbu cket", + "Ġevalu ate", + "Ġid ol", + "Ġrep utation", + "ĠìĨ Įë", + "ÙĪ ر", + "Ġhe cho", + "Ġpo em", + "Ġsubject s", + "pl ant", + "ĠBe h", + "ĠSpe aking", + "Ġbatter ies", + "Ġfollow ers", + "ö l", + "Ġg ently", + "Ġsi xt", + "Ġparam eter", + "Ġik ke", + "ĠT our", + "ĠD J", + "ot te", + "ĠJ ahren", + "Ġprepar ation", + "Ġд Ñĥм", + "Ġ8 00", + "c op", + "ik ing", + "Ġë¬ ¸", + "Ġн Ñĥ", + "Ġл еÑĤ", + "åIJ Į", + "ĠI de", + "Ġì¡° ê¸Ī", + "Ġla ughter", + "Ġmole cules", + "ĠR est", + "Ġobs erved", + "d zie", + "Ġadvert ising", + "ert o", + "Ġmo ins", + "ĠM IT", + "Ġexc it", + "Ġt um", + "Ġty l", + "Ġinvest ed", + "Ġph arm", + "Ġunex pected", + "Ġph i", + "oty pe", + "we ise", + "Ġge ç", + "jour d", + "Ġhors es", + "n Äħ", + "= \"", + "ĠS M", + "Ġf ib", + "Ġcl ips", + "çķ ¶", + "å¦Ĥ æŀľ", + "Ġreg ime", + "Ġrot ate", + "r ou", + "n ik", + "Ġarm or", + "ðŁ ĺ", + "еÑĢ а", + "åº ¦", + "ĠO ch", + "Ġr ichtig", + "üz el", + "ane ously", + "m ek", + "éĮ ¯", + "ĠX iao", + "Ġexist ed", + "w orth", + "ãģ£ ãģ¨", + "Ġna ught", + "Ġhe iÃŁt", + "ĠB al", + "Ġres id", + "iv ot", + "om atic", + "Ġh ired", + "Ġgrad ually", + "Ġon ions", + "Ġcomp at", + "Ġint im", + "Ġj ew", + "Ġcontrib ution", + "ĠI re", + "ac ji", + "Ġsl ice", + "Ġimm un", + "ĠR us", + "Ġgr ows", + "ĠSimilar ly", + "Ġhard est", + "Ġst ruck", + "Ġmeasure ment", + "... ]", + "th ey", + "Ġìł Ģë", + "Ġsne ak", + "Ġappl ies", + "Ġн ем", + "æ ĵ", + "×ij ר", + "ĠЧ ÑĤо", + "Ġout ro", + "Ġinnoc ent", + "Ġm og", + "ĠSams ung", + "Ġmer cy", + "Ġhand ling", + "Ġinter vention", + "id ays", + "g ot", + "Ġcur ric", + "Ġbound aries", + "Ġconf using", + "Ŀ¼ ëĬĶ", + "æ ĩ", + "Ġstitch es", + "ÃŃ vel", + "Ġtun nel", + "it ä", + "Ġg ost", + "im y", + "Ġcz as", + "Ġm é", + "Ġcat al", + "ĠSim on", + "ĠLI AM", + "m ic", + "ĠÐ ¤", + "Ġey el", + "is as", + "ĠC PU", + "ĠD ou", + "Ġnä ch", + "Ġinfin ity", + "Ġr if", + "ĠPe ace", + "ĠC u", + "Ġminim al", + "Ġlisten ed", + "Ġpo le", + "hal b", + "Ġload ed", + "Ġste ady", + "ĠBes ides", + "ê m", + "Ġl ap", + "Ġco op", + "Ġfriends hip", + "w orld", + "Ġge h", + "Ġtyl ko", + "ĠLa ura", + "Ġsurround ed", + "ĠE vent", + "Ġch ap", + "ĠW onder", + "bre ak", + "Ġdro ve", + "Ġbroad er", + "Ġch i", + "F i", + "Ġge hen", + "Ġwest ern", + "Ġintellig ent", + "Ġpers ist", + "Ġfound ed", + "ãģĵ ãģ¨", + "Ġhistor ic", + "Ġfr Ã¥", + "cks Ã¥", + "Ġhand y", + "Ġsy mp", + "Ġr ows", + "Ġnut ri", + "b ur", + "ĠLe on", + "Ġsist ema", + "Ġext ensive", + "ĠÑĥ в", + "í ı", + "Ġnight s", + "Ġcá c", + "Ġcount ing", + "ĠM ust", + "all ow", + "еÑģ Ñģ", + "M om", + "Ġнад о", + "Ġbar rel", + "ãĥ ŀ", + "AR D", + "Ġinstall ation", + "Ġin sect", + "Ġëħ ¸ë", + "uj Äħ", + "ĠÄij i", + "Ġpack ed", + "Ġf iction", + "N ow", + "ĠY ay", + "Ġper t", + "r ons", + "und e", + "ach es", + "Ġsty les", + "Ġapr ès", + "ok u", + "ĠV ice", + "ın ız", + "com m", + "Ġassign ed", + "Ġinteract ions", + "Ġac ab", + "F ELIPE", + "Ġresc ue", + "Ġindust ries", + "ĠAnd y", + "Ġpra ise", + "Ġfl ame", + "Ġsn ack", + "í Ĥ", + "ç ģ", + "Ġsw o", + "rend er", + "Ġbo ards", + "ĠÑĤ ом", + "en ne", + "Ġpast a", + "Ġdev il", + "ĠF el", + "Ġhat te", + "Ġcoll eg", + "e h", + "ì »", + "ãģĵ ãģ®", + "Ġproduct ive", + "for ward", + "и п", + "Ġsmart phone", + "Ġinv is", + "Ġb um", + "Ġwho a", + "ìŀ Ħ", + "Ġo cksÃ¥", + "ĠL ang", + "ĠSy ria", + "Ġses i", + "ί α", + "Ġappro val", + "4 8", + "Ġод ин", + "Ġë ĸ", + "ĠH arr", + "ĠAd minist", + "Ġ× ¤", + "ĠDe an", + "f i", + "Ġcitiz en", + "Ġsh ark", + "0 5", + "Ġbo il", + "Ġindic ate", + "å ¡", + "A re", + "Ġlay out", + "Ġref r", + "ĠPac ific", + "AA AA", + "ĠAustral ian", + "g ression", + "V oice", + "ал ÑģÑı", + "Ġshel ter", + "T o", + "au pt", + "Ġevalu ation", + "ap or", + "Ġcur rency", + "Ġм ного", + "ig os", + "ãģ °", + "Ġo ct", + "Ġro yal", + "è ³", + "as il", + "ĠChild ren", + "Ġr ien", + "Ġë ĵľë", + "Ġbar rier", + "Ġej emplo", + "Ġe k", + "N D", + "es p", + "ен а", + "Ġp ic", + "Ġkill er", + "Ġintegr ate", + "Ġfew er", + "Ġdis abilities", + "Ġ ....", + "Ġtri angle", + "Ġfe es", + "Ġwid ely", + "em i", + "Ġoverwhel ming", + "Ġz omb", + "Ġb ere", + "Ġho od", + "ĠA ye", + "ĠHar vard", + "e v", + "ĠÏĦ οÏħ", + "Ġcup s", + "ĠA uch", + "z ona", + "Ġ199 0", + "Ġwe iÃŁ", + "Ġcr unch", + "æ ¥", + "Ġз ав", + "Ġmeas uring", + "Ġst ations", + "ĠStep hen", + "Ġshort ly", + "Ġsig ning", + "Ġcom edy", + "om o", + "Ġsuggest ions", + "Ġsign ature", + "ĠпÑĢ ив", + "Ġdis order", + "as ka", + "Ġworld s", + "Ġprecis ely", + "n orm", + "ra v", + "ĠC ivil", + "In ter", + "ĠC ertain", + "Ġinj ured", + "Ġsuggest s", + "ĠGold en", + "Ġcy ber", + "ĠØ ´", + "Ġtempor ary", + "Ġco oper", + "Ġvot ed", + "Ġ ought", + "ấ y", + "x ual", + "Ġpan els", + "Ġ9 5", + "Ġhands ome", + "ĠпÑĢ ов", + "Ġper mit", + "Ġke in", + "Ġbad ly", + "Ġnot ifications", + "iz a", + "ĠNot ice", + "Ġinc lusive", + "Ġanswer ing", + "Ġí Ĺ", + "u ld", + "íħ Į", + "Ġnow adays", + "Ġ3 7", + "Ġb olt", + "Ġstat ic", + "ĠH op", + "Ġav ant", + "aj o", + "Ġ맼 ìŀĪ", + "Ġfif ty", + "ĠF inal", + "Ġsc ores", + "ĠT ap", + "Ġcy l", + "Ġconv ince", + "Ġany ways", + "od a", + "Ġìķ ¼", + "Ġser ves", + "ĠÑĤак ой", + "ĠZo om", + "Ġsaving s", + "ul o", + "Ġs outhern", + "view er", + "Ġho je", + "Ġse ja", + "Ġrepresent ing", + "Īë įĺ", + "l ik", + "ĠSome body", + "Ġbe ast", + "Ġstick ing", + "Ġins ist", + "Ġtal ented", + "Ġexplain ing", + "Ġatt orney", + "éĥ ¨", + "Ġst airs", + "ĠD og", + "í ĭ", + "Ġc ig", + "Ġshap ed", + "Ġs ons", + "Ïģ ι", + "ut t", + "Ġì Ķ", + "Ġpar ad", + "ìĿ¸ë į°", + "Ġh orn", + "ĠJ our", + "ann o", + "Ġworld wide", + "åĬ Ľ", + "Ġparticip ation", + "¦ Ħ", + "Ġm ów", + "Ġburn ed", + "Ġwrit ers", + "all ah", + "ĠF und", + "Ġcle ver", + "ĠLe ute", + "b in", + "Ġbe ating", + "f oot", + "ĠìĽ IJ", + "ĠStud io", + "Ġv ag", + "be y", + "r ze", + "Ġoppos ition", + "Ġж из", + "w ho", + "Ġê± ´", + "Ġtr ace", + "Ġд енÑĮ", + "Ġep id", + "Ġges ch", + "ĠN ar", + "ĠB E", + "Ñĥ й", + "ĠS ign", + "ed ly", + "Ġcl ay", + "Ġinst antly", + "Ġgather ing", + "ĠGal axy", + "Ġb ored", + "ĠBudd h", + "c é", + "Ġm am", + "Ġsl ope", + "Ġëĭ¤ ìĿĮ", + "Ġsch ön", + "Ġp ir", + "ge f", + "am er", + "Ġh ö", + "Ġcolle ague", + "Ġpres ents", + "ad ium", + "Ġà® µ", + "Ġfal ar", + "be ep", + "Ġdri ed", + "ism s", + "Ġro pe", + "Ġworks hop", + "Ġest ud", + "Ġb ands", + "Ġthem es", + "åħ ¬", + "ÙĬ ر", + "åIJ İ", + "Ġremind er", + "ÑĤ Ñĥ", + "ĠB h", + "Ġcocon ut", + "ĠÑģ ÑĤо", + "ĠCh annel", + "Ġimmig ration", + "ä s", + ".. ...", + "ä¸ »", + "çĻ ½", + "st op", + "Ġк аÑĢ", + "Ġco ins", + "ĠÑĩ аÑģ", + "Ġdest ruction", + "l ined", + "Ġbar riers", + "ant ine", + "Ġprint ed", + "Ġcongrat ulations", + "ĠHe art", + "Ġin qu", + "th a", + "Ġhard ly", + "ĠA ven", + "Ġt inha", + "ĠS ony", + "ĠN F", + "Ġgradu ates", + "Ġsque eze", + "ere my", + "ÏĦ ι", + "Ġep ic", + "ĠJ u", + "Ġol m", + "ĠLa ughter", + "Ġbelief s", + "ĠC ru", + "ĠTr ue", + "ĠS oul", + "owe en", + "Ġrom antic", + "Ġз в", + "Ġan os", + "ĠY up", + "éĺ ¿", + "d im", + "Ġin fer", + "Ġз ам", + "Ġso c", + "uk a", + "Ġprec ise", + "Ġdro pping", + "Ġcl ue", + "Ġer rors", + "char ge", + "ĠP u", + "omet er", + "Ġlamb da", + "ac ional", + "ĠD ong", + "Ġcham ber", + "Ġthank ful", + "ĠN u", + "ĠHaw ai", + "Ġinf o", + "Ġactiv ate", + "ĠQ ual", + "Ġqu ed", + "Ñĥ лÑĮ", + "Ġcl oth", + "åĸ ľ", + "Ġw ichtig", + "5 5", + "Ġot ra", + "ograp her", + "Ġcur ios", + "Ġ19 80", + "Ġemp res", + "d ess", + "e ur", + "Ġcl uster", + "ar ter", + "ob ile", + "ĠY an", + "ĠAd v", + "Ġdiscipl ine", + "Ġìłķ ëıĦ", + "ĠPl ace", + "ĠSe lect", + "T E", + "ĠбÑĭ ла", + "Ġwh is", + "Ġb ay", + "ĠD or", + "en cing", + "Ġrep et", + "Ġf icar", + "p ad", + "Ġf og", + "u yor", + "Ġsn ap", + "ib t", + "Ġso bie", + "Ġappoint ment", + "ĠR y", + "Ġce iling", + "our se", + "Ġwr ites", + "ĠAfghan istan", + "Ġm os", + "az e", + "Ġpen al", + "Ġcry stal", + "IC E", + "ê° IJ", + "é Ł", + "ĠTes la", + "Ġthe ories", + "Ġappe al", + "Ġnewsp aper", + "Ġcook ies", + "æ ©", + "ĠاÙĦ ÙĦ", + "Ġma j", + "ĠGet ting", + "k ommen", + "ĠHe aven", + "ell s", + "Ġdiv ine", + "Ä «", + "Ġa kt", + "Ġhop es", + "ĠCh en", + "we gen", + "** *", + "ĠFra ge", + "Ġн и", + "ภ¹", + "min ister", + "nes ota", + "wh ich", + "Ġexpl icit", + "Ġverd ad", + "Ġgradu ated", + "ĠPh ilipp", + "Q L", + "ĠM I", + "Ġdev ot", + "Ġc ure", + "Ġclos est", + "Ġà Ħ", + "Ġsex y", + "ãģ Ľ", + "ĠDe ath", + "ok o", + "ug u", + "ĠAn ne", + "itar ian", + "es a", + "ег од", + "ĠD ur", + "Ġ 000", + "ze it", + "Ġtour nament", + "Ġmel hor", + "ภª", + "Ġin du", + "Ġf law", + "Ġw ars", + "ĠM ind", + "ĠI ron", + "ÑĤ ак", + "ĠV R", + "Ġs iz", + "ĠS outhern", + "Ġê·¸ëŁ ¬ë", + "Ġaw ak", + "Ġìķ ŀ", + "Ġc ube", + "believ able", + "if all", + "d is", + "Ġabandon ed", + "m ind", + "Ġpar l", + "Ġclass ical", + "è ĭ", + "á»Ļ t", + "ĠAut o", + "ĠB or", + "ç ©", + "4 00", + "ĠSoci ety", + "Ġsubt le", + "Ġmiss ions", + "Ġremember ed", + "ĠE ither", + "Ġda für", + "OR D", + "Ġint ensity", + "ES IN", + "ĠC up", + "Ġrare ly", + "Ġto ys", + "ĠChar lie", + "á» Ł", + "Ġgla ube", + "Ġround s", + "T IN", + "Ġcap ability", + "Ġderiv ative", + "Ġrefer ring", + "Ġd Ã¥", + "ĠT ALI", + "Ġcott on", + "Ġcon fer", + "Ġcolum ns", + "Ġliber al", + "Ġnun ca", + "Ġμ ε", + "Ġind o", + "ib en", + "ĠBe ispiel", + "Ġê·¸ë łĩ", + "ĠÑĥ Ñĩ", + "Ġh oy", + "Ġfr y", + "ĠScott ish", + "è Ĭ", + "Ġc iv", + "Ġconserv ative", + "Ġair pl", + "Ġs ar", + "r us", + "Ġinvest ments", + "Ġinfin ite", + "Ġà® ķ", + "ĠTALI ESIN", + "ĠG ary", + "ue ll", + "Ġа к", + "ĠC ir", + "Ġrit ual", + "Ġ>> >", + "Ġtem pt", + "ĠTe ch", + "ĠPoke mon", + "Ġimprove ments", + "Ġsp are", + "Ġtransl ate", + "Ġson ra", + "ĠFil m", + "w ort", + "Ġм и", + "Ġperiod s", + "Ġje alous", + "ãģĦ ãģĦ", + "Ġt ir", + "M I", + "Ġconduct ed", + "ĠìķĪë ħķ", + "0 9", + "ĠPol it", + "ĠWhere as", + "Ġmoist ure", + "Ġs ins", + "Ġk ap", + "ĠÑį к", + "Ġben im", + "Ġelimin ate", + "Ġathlet es", + "ĠMan ager", + "Ġfeature d", + "ap ore", + "äº Ľ", + "Ġë° ľ", + "Ġper f", + "ĠTh us", + "Ġdeb ut", + "об ÑĢ", + "Ġse ñ", + "Ġmyster ious", + "w ords", + "Ķ ê°Ģ", + "Ġcheck s", + "Ġvolunte er", + "Ġwas hing", + "ĠMar vel", + "ĠA B", + "iss ors", + "! '", + "ĠF ull", + "ye on", + "Ġwe igh", + "ĠJO HN", + "Ġv os", + "Ġproced ures", + "Ġaddress ed", + "ĠBer lin", + "put er", + "ĠB an", + "Ġmedic ation", + "Ġdr one", + "ĠÑĥ б", + "ĠJe an", + "Ġcap s", + "Ġdisappoint ed", + "Ġw ore", + "Ġêµ Ń", + "Ġorgan ize", + "ĠHall oween", + "Ġfant asy", + "y ard", + "Ġnos otros", + "Ġjump ed", + "Ġphot ography", + "ĠN ame", + "re c", + "A B", + "Ġbless ing", + "ĠSh ut", + "Ġbit ter", + "p op", + "ãģĿ ãĤĮ", + "Ġde i", + "Ġfulf ill", + "çIJ Ĩ", + "Ġden gan", + "Ġbe lo", + "ĠMean while", + "Ġdep ois", + "Ġdi abetes", + "Ġbu nd", + "ĠZe aland", + "Ġdig est", + "Ġt ires", + "Ġdo d", + "ag ne", + "ế t", + "Ġpe el", + "Ġз аб", + "Ġn odes", + "Ġtrend s", + "ĠSw itch", + "ĠA ward", + "ĠOr ig", + "ĠH al", + "Ġest as", + "Ġ3 60", + "Ġsim ult", + "Ġcom ic", + "Ġm Ãł", + "Ġbal anced", + "ĠPrin cess", + "Ġkilomet ers", + "á» ©", + "Ġpart ir", + "ì¤ ij", + "so ft", + "ĠV iew", + "Ġbi ological", + "in st", + "4 4", + "Ġman era", + "Ġcompreh ensive", + "ĠS ab", + "Ġcr imes", + "y ers", + "ĠComp any", + "ĠPh ot", + "Ġpou co", + "i ac", + "Ġbe im", + "in ate", + "Ġsub sequ", + "ĠMay or", + "Ġcent uries", + "è res", + "ìŀĸ ìķĦìļĶ", + "Ġê·¸ëŁ ¼", + "ĠFra u", + "ĠO H", + "Ġëģ Ŀ", + "ĠN ah", + "ĠSer ies", + "Ġover night", + "íĴ Ī", + "ĠâĢ ¢", + "Ġtra ve", + "atter ed", + "Ġwar ri", + "ĠGru nd", + "ĠInd ones", + "Ġsc ra", + "ob y", + "ĠBro ok", + "Ġcur s", + "Ġë ¸", + "Ġexpl ains", + "ram atic", + "Ġparticip ating", + "Ġmin ut", + "Ġcontract s", + "Ġg egen", + "Ġdisappe ared", + "ĠS N", + "Ġrob ust", + "ap h", + "Ġsh rim", + "Ġdev ast", + "c ope", + "Ġme ets", + "Ġpeace ful", + "m ate", + "Ġwe ld", + "Ġ× ª", + "d on", + "Ñĥ ÑĤÑĮ", + "Ġregister ed", + "ĠN ik", + "j in", + "Ġc av", + "Ġe cht", + "io x", + "Ġflow ing", + "но ÑģÑĤи", + "Ġto e", + "Ġent ity", + "ов а", + "f its", + "ĠPat rick", + "ÑĤ ÑĢ", + "Ġle verage", + "Ġcor rel", + "i ah", + "Ġstr ings", + "ist inct", + "Ġg ue", + "arch y", + "Ġteng o", + "ım ız", + "Ġor bit", + "ä¸ º", + "Ġе ÑīÑij", + "ca ke", + "Ġ׾ ×Ķ", + "ĠMin nesota", + "Ġbra ke", + "ow ie", + "Ġcra w", + "ê¸°ë ¥¼", + "Ġprogram me", + "ĠÑģл ÑĥÑĩ", + "åı ª", + "ien ces", + "ĠO ui", + "ĠP ers", + "im iento", + "ĠIn vest", + "Ġsl ower", + "æĻĤ åĢĻ", + "ĠB eth", + "Ġnur se", + "ĠSpr ing", + "S p", + "Ġun employ", + "д и", + "Ġgen ius", + "ĠA aron", + "Ġê·¸ëŁ ¬", + "Ġe i", + "ãģĹ ãĤĩ", + "Ġtank s", + "Ġau jourd", + "Ġcomplex ity", + "ĠÑĢ еÑĪ", + "Ġold est", + "Ġlet z", + "åħ ¥", + "Ġphenomen on", + "pr int", + "ĠBund es", + "it at", + "ê» ĺ", + "Ġ4 2", + "ĠW i", + "Ġinc om", + "Ġg ek", + "Ġembr ace", + "Ġt ies", + "out e", + "Ġd ose", + "ĠF riends", + "Ñĭ ÑĤ", + "егод нÑı", + "Ġor g", + "Ħë ¡ľ", + "ó g", + "Ġex ceed", + "Ġgod s", + "Ġê±° ìĺĪìļĶ", + "Ġsoci et", + "ĠUn ivers", + "it ät", + "Ġword en", + "Ġsm oking", + "Ġint ens", + "ab ul", + "em ia", + "è ij", + "4 7", + "f ly", + "Ġ200 6", + "ĠSer iously", + "Ġprze z", + "æ ¼", + "c re", + "Ġn an", + "Ġmod es", + "ов аÑĤÑĮ", + "ĠH ang", + "em en", + "Ġbenefic ial", + "Ġvot ers", + "ĠBro ad", + "Ġb ent", + "W ow", + "Ġm ul", + "åĵ ¥", + "ĠU C", + "Ġdam aged", + "ĠUk raine", + "Ġw ipe", + "Ġst ones", + "Ġman agers", + "Ġr ab", + "ÑģÑĤÑĢ о", + "l at", + "Ġde ce", + "Ġgraph ic", + "Ġf oss", + "Ġdisag ree", + "ĠAm en", + "Ġsec rets", + "ho le", + "ink le", + "Ġfortun ate", + "Ġì ±", + "ìľ Ħ", + "èIJ ¬", + "Ġhab its", + "Ġbur ied", + "Ġh in", + "Ġvirt ually", + "ol as", + "ĠR P", + "ĠT ab", + "l ow", + "Ġsacr ific", + "Ġestim ated", + "ol n", + "Ù ĭ", + "c ur", + "ĠFe el", + "Ġcast le", + "Ġus eless", + "Ġdis g", + "ĠJac ob", + "Ġga an", + "Ġup side", + "Ġpare ce", + "ãĥ³ ãĥ", + "Ġsh ipping", + "ĠC R", + "Ġdis rupt", + "ac ter", + "UN D", + "f u", + "å® Į", + "ĠP ick", + "ĠChar l", + "ĠB ull", + "Ġenter prise", + "Ġpunish ment", + "ack ing", + "Ġfr action", + "Ġtab let", + "Ġch ord", + "Ġsimilar ly", + "åħ¶ 實", + "ĠTor onto", + "Ġcour ts", + "ÄŁ l", + "esz cze", + "Ġpron oun", + "ĠS ister", + "ĠM P", + "Ġgreat ly", + "ĠD ank", + "ic op", + "Ġgar bage", + "Ġresol ve", + "ĠS af", + "ĠG un", + "Ġcomp ound", + "Ġë° °", + "ĠMus ik", + "âĻ «", + "Ġcha os", + "ĠWhen ever", + "Ġe uros", + "Ġor chest", + "Ġrefr iger", + "al an", + "ภ·", + "ĠAm azing", + "Ġp ud", + "ag an", + "Ġj eszcze", + "is y", + "Ġaccur acy", + "ĠA ma", + "is ode", + "ë ĮĢ", + "Ġinterpret ation", + "ĠL iber", + "æ ·", + "c am", + "Ġevol ved", + "ĠK ay", + "ÑĨ Ñĭ", + "Ġcreat or", + "it as", + "Ġal arm", + "Ġcelebr ation", + "z ent", + "Ġfun cion", + "Ġo v", + "umb ling", + "Ġ %", + "ภĪ", + "Ġrestrict ions", + "Ġн ав", + "ĠK inder", + "Ġban ana", + "ÑĮ Ñı", + "Ġdiam eter", + "Ġnor thern", + "ur ers", + "ĠP as", + "æĪij çļĦ", + "Ġwork force", + "Ġj ung", + "Ġguar ante", + "Ġequ ilib", + "Ġsu ite", + "Ġeu ro", + "Ġdel iber", + "S te", + "Ġdownt own", + "Ġch in", + "Ġc odes", + "ed ia", + "Ġshe ep", + "res hold", + "wn ie", + "ó b", + "Ġunder lying", + "l ia", + "j er", + "ÏĢ ÏĮ", + "ç Ŀ", + "th rop", + "Ġz ap", + "Ġvac uum", + "ĠH ab", + "Ġwra pped", + "ì ¢", + "Ġinvent ory", + "м а", + "Ġco ord", + "Ġpl ates", + "Ġsy mm", + "T e", + "ĠwÅĤa ÅĽnie", + "Ġreach es", + "Ġlon ely", + "S cript", + "le e", + "ess er", + "Ġê± ¸", + "ĠGes ch", + "ĠMo ving", + "Ġré p", + "ĠV ill", + "åIJ Ī", + "ĠR achel", + "Ġtem os", + "ON E", + "Ġstra in", + "Ġang el", + "Ġf Ã¥", + "T r", + "Ġach o", + "Ġhighlight s", + "ĠW er", + "ĠCar l", + "Ġbl ur", + "Ġreg ards", + " ·", + "ил ÑģÑı", + "Ġrec re", + "ĠY ani", + "U CK", + "ł ¸", + "Ġelectr ons", + "ĠSp iel", + "Ġv ed", + "Ú ¾", + "Ġbe am", + "Ġid iot", + "ë ĵ¤", + "на Ñĩ", + "id d", + "Ġsk i", + "it ative", + "Ġhyp othes", + "ãģ§ãģĻ ãģŃ", + "ent er", + "ĠìķĦëĭĪ ë", + "Ġih re", + "Ġpre view", + "ang el", + "Ġdem on", + "Ġd us", + "Ġd ic", + "ĠK om", + "LE Y", + "... !", + "Ġsie ht", + "ĠSon ic", + "Ġten ho", + "an as", + "Ġdig it", + "ĠMa ar", + "Ġunder grad", + "oun cer", + "uff y", + "Ġconvers ion", + "Ġdis connect", + "Ġe cho", + "om er", + "Ġcurric ulum", + "Ġper ché", + "Ġw and", + ".. ?", + "Ġroll ed", + "Ġentreprene ur", + "Ġtheore t", + "ĠÑī о", + "Ġins ights", + "Ġzus ammen", + "o in", + "ret t", + "p rodu", + "Ġvisit ors", + "e ous", + "Ġgrand mother", + "Ġhum or", + "Ġн иÑħ", + "zen ia", + "ins on", + "Ġres et", + "Ġbase ball", + "Ġmatch ing", + "ëĭ¤ ê°Ģ", + "Ġpun to", + "ì ¡", + "Ġre de", + "Ġaddress ing", + "Ġfore cast", + "ĠB ol", + "Ġcol ored", + "Ġdocument ation", + "Ġexpect ation", + "ĠNor thern", + "Ġcre o", + "Ġà® ļ", + "f on", + "Ġuns ere", + "U M", + "Ġcop ies", + "Ġexpand ed", + "Ġveter ans", + "ĠAl m", + "Ġво обÑīе", + "Ġpsych ological", + "Ġnos so", + "Ġpay ments", + "im eters", + "Ġ-- >", + "ĠJenn ifer", + "Ġvolunte ers", + "os se", + "or ious", + "ĠбÑĭ ли", + "è Ĥ", + "ĠEs s", + "w s", + "ĠB C", + "ĠI C", + "W oman", + "Ġv ont", + "Ġeth nic", + "EN N", + "им о", + "Ġlo b", + "Ġou i", + "c s", + "Ġre he", + "Ġìł ģ", + "Ġch ick", + "ús ica", + "Ġk ont", + "ĠDist rict", + "Ġp ile", + "Ġа в", + "ей ÑģÑĤв", + "Ġ £", + "Ġiss ued", + "Ġком п", + "Ġpros per", + "Ġprof ound", + "ĠDe ar", + "Ġãģ ĵ", + "Ġfund ed", + "Ġb isa", + "ŀ ĺë", + "× Ł", + "ĠìĿ ĺ", + "Ġtw elve", + "ĠChamp ions", + "éĿŀ 常", + "Ñģ л", + "Ġ200 5", + "p m", + "Ġon de", + "Ġdiff é", + "ĠCh all", + "Ġdifficult ies", + "Ġgar age", + "Ġd á", + "ün k", + "Ġë¬ ¼", + "Ġtr an", + "Ġsubm itted", + "z w", + "ÙĪ ا", + "Ġar k", + "ĠìĦ ±", + "Ġgrocer y", + "он а", + "i ere", + "Ġa est", + "Ġexhib ition", + "Ġr és", + "Ġconsist ency", + "Ġcook ie", + "н ей", + "Ġrepl acement", + "æ² ¹", + "ĠS em", + "ĠìĤ¬ ìļ©", + "8 00", + "Ġgen es", + "Ġtrans action", + "ĠE L", + "Ġdur ante", + "ib les", + "ĠE at", + "t ail", + "iss ance", + "Ġto ss", + "Ġsurv ived", + "Ġoff ices", + "Ġsupport ive", + "Wh ere", + "Ġtout es", + "Ġë§ ī", + "Ġj okes", + "ier on", + "ap ers", + "Ġm ature", + "ĠM arsh", + "Ġs ido", + "k ind", + "Ġreal mente", + "ĠChe f", + "Ġquel que", + "Ġjud ges", + "e ft", + "ER S", + "Ġj et", + "Ġpers ons", + "è »", + "iz ations", + "ri k", + "Ġsh ops", + "ĠW y", + "Ġele g", + "qu è", + "qu oi", + "Ġjug a", + "Ġíķľë ²Ī", + "ĠQuest ion", + "ĠGlo bal", + "Ġìķ½ ê°Ħ", + "ĠSt ation", + "æİ ¥", + "ĠOh io", + "Ġstick y", + "Ġst ressed", + "Ġg ün", + "Ġí Ŀ", + "ÑģÑĤ Ñĥп", + "é ¡Į", + "ĠPh D", + "im mer", + "Ġment or", + "Ġinv ented", + "Ġre un", + "Ġine vit", + "Ġpol ÃŃt", + "Ġexec ute", + "ĠSt ory", + "Ġout standing", + "Ġgu er", + "ĠR ain", + "Ġch oses", + "ĠT it", + "ĠÑģ еÑĢ", + "ĠSing apore", + "ĠN one", + "Ġch ronic", + "°ë į°", + "Ġe go", + "æł ·", + "ES T", + "ãģĤ ãĤĬ", + "ĠW ang", + "ĠN AT", + "Ġa ug", + "Ġdes ktop", + "Ġetern al", + "ĠìĤ¬ ìĭ¤", + "ĠConst itution", + "ìĤ ¬ë", + "×Ļ× ľ", + "p res", + "ĠТ Ñĭ", + "Ġinter f", + "Ġlist s", + "Ġfight s", + "ft en", + "ĠI owa", + "Ġmotiv ated", + "ĠH osp", + "Ġelse where", + "Ġpath s", + "Ġinst ances", + "B l", + "r ange", + "á» ±", + "ĠS it", + "man a", + "Ġìĭľ ìŀij", + "Ġm ình", + "ans as", + "Ġs na", + "Ġphilos oph", + "Ġpas se", + "Æ°á» Ŀi", + "ak h", + "ent al", + "Ġih n", + "ru ctor", + "Ġв аÑĪ", + "Ġgener ous", + "Ġp ivot", + "п ол", + "Ġjam ais", + "Ġcom ent", + "ĠL ew", + "od zi", + "ĠX box", + "Ġв од", + "Ġcons ent", + "ī ìŀ¥", + "Ġdis par", + "l ass", + "ĠGovern or", + "Be ifall", + "Ġê° ľ", + "Ġbelo ved", + "׳ ×ķ", + "se ll", + "Ġhon ored", + "le h", + "Ġw äre", + "un ting", + "Ġfra ud", + "ĠR AM", + "ê± ¸", + "Ġkill s", + "Ġeconom ics", + "0 4", + "п еÑĢ", + "Ġco isas", + "Ġи гÑĢ", + "ÃŃ m", + "Ġmö chte", + "Ġìµ ľ", + "Ġstim ul", + "Ġfast est", + "l v", + "Ġg én", + "ĠS ounds", + "Ġ19 70", + "Ġhome work", + "spe aking", + "Ġencour aging", + "Ġqu ery", + "Ġre vers", + "pro fit", + "Ġd y", + "Ġìŀ ij", + "ëĬĶëį° ìļĶ", + "Ġso ap", + "ĠG all", + "ĠC N", + "ĠAn s", + "Ġf ic", + "ank s", + "Ġdess ert", + "ĠìłĢ íĿ¬", + "ĠM aking", + "Ġcome ç", + "ê³ Ħ", + "Ġassoci ation", + "D ad", + "he e", + "Ġh ogy", + "Ġap ro", + "Ġinvis ible", + "Americ an", + "í İ", + "Ġvi be", + "Ġem issions", + "Ġadvoc ate", + "Ġkick ed", + "Ġ vel", + "Ġsum mar", + "Ġfre aking", + "ch ron", + "Ġpin ch", + "Ġwszyst k", + "isc al", + "Ġpro ved", + "Ġmind ful", + "Ġt ä", + "Ġno ises", + "Ġisol ated", + "Ġcross ed", + "Ġê° ķ", + "Ġvo ilÃł", + "Ġch ore", + "ĠR A", + "C om", + "Ġrelax ed", + "at ro", + "Ġpre vention", + "Voice over", + "O D", + "ĠCo vid", + "Ġsepar ation", + "Ġ- [", + "иÑĩ его", + "çĻ ¼", + "ĠS D", + "ble ep", + "Ġindepend ence", + "Ġpart ial", + "Ġalgorith ms", + "ĠAny one", + "Ġassoci ate", + "h um", + "ic ular", + "Ġb ạn", + "Ġbatt les", + "G ood", + "App lause", + "Ġbast ante", + "Ġadv ant", + "ĠS weet", + "Ġref used", + "ãĤ ¸", + "ĠÑĤеб е", + "pl et", + "Ġencour aged", + "åĵ ¦", + "Ġmir acle", + "ĠB un", + "ĠV ar", + "rim ination", + "e lect", + "ĠM ult", + "Ġdeliver ing", + "e ing", + "Ġc m", + "ne hmen", + "ĠL ine", + "Ġë§ Į", + "en ced", + "ĠS ound", + "ĠCont in", + "ij d", + "UN G", + "k le", + "Ġth reshold", + "Ġcomp act", + "ad t", + "Ġto es", + "ĠP ur", + "own ed", + "ment ed", + "Ġdes igning", + "Ġvacc inated", + "Ġexha ust", + "Ġbas ics", + "Ġcons ists", + "ĠGu y", + "ac zy", + "Ġm ÃŃ", + "w on", + "å® ³", + "Ġ8 5", + "æ Ĥ", + "Ġm um", + "Ġign or", + "Ġprint ing", + "ac ular", + "p ow", + "Ġexpand ing", + "Ġg ir", + "ĠC ab", + "íĺ ¸", + "ÑĤÑĮ ÑģÑı", + "ĠìĹ¬ëŁ¬ë ¶Ħ", + "Ġang les", + "Ġterm inal", + "ĠW on", + "ĠInter esting", + "Ġcross ing", + "Ġbond s", + "Ġpu eden", + "Ġor b", + "lar ın", + "Ġcreep y", + "Ġnutr ition", + "Ġall ies", + "Ġwire less", + "Ġdes ired", + "Ġcomp ute", + "ĠAri zona", + "ĠBeaut iful", + "Ġprodu ces", + "Ġnuest ro", + "t ed", + "Ġel igible", + "ĠÑģ оз", + "ic ial", + "ĠH ero", + "Ġcons ume", + "Ġrob ots", + "Ġpurch ased", + "c ción", + "Ġ iz", + "ượ c", + "ίν αι", + "ĠØ£ ÙĨ", + "Ġshad ows", + "ĠMed ia", + "Ġprin cess", + "Ġk lar", + "Ġwood en", + "Ġus ar", + "Ġg üzel", + "Ġsl ot", + "r ade", + "Ġë Ĵ", + "Ġhar mon", + "Ġingred ient", + "ors hip", + "ek i", + "Ġgrand father", + "Ġexcit ement", + "Ġpolit icians", + ".. !", + "Ġout s", + "Ġsepar ately", + "ĠÑı к", + "ĠW elt", + "ĠP ow", + "j an", + "Ġorient ation", + "åı ĭ", + "L C", + "age m", + "ÛĮ Úº", + "åIJ Ĺ", + "Ġbran ches", + "ad en", + "rent e", + "ĠI hr", + "as m", + "Ġest ão", + "ĠN ic", + "Ġsla ve", + "Ġcomp ress", + "c rowd", + "Ġclim bing", + "ĠMan agement", + "ĠB ah", + "Ġpan ic", + "Ġk or", + "Ġcool ing", + "Ġb ind", + "Ġз ад", + "Ġr ack", + "Ġent it", + "Ġs ends", + "Ġyour selves", + "d es", + "ĠMuslim s", + "Ġí ļ", + "ism a", + "cy cle", + "un kt", + "ĠC ore", + "Ġinj uries", + "Ġident ical", + "ка Ñı", + "ĠDeutsch land", + "Ġе е", + "is an", + "Ġtr uc", + "let on", + "Ġback up", + "Ġult ra", + "Ġab und", + "ille urs", + "Ġby ÅĤo", + "åħ ĥ", + "ort ed", + "Ġearth qu", + "Ġк л", + "Ġobs ervation", + "Ġmainten ant", + "el en", + "Ġsett led", + "Ġp ela", + "ĠE conom", + "Ġ Õ", + "Ġste ering", + "ĠAL L", + "ĠC her", + "Ġpat ience", + "ĠS now", + "Ġb or", + "Ġworth y", + "Ġcá i", + "Ġ× §", + "Ġκ α", + "d og", + "ĠK aren", + "ill es", + "Î ²", + "Ġagric ulture", + "×ķ× Ł", + "ĠSe an", + "Ġsens ors", + "íķ ´ë", + "ag h", + "Ġpublic ly", + "Ġpe ux", + "ĠAlex ander", + "Ġprior it", + "Ġla zy", + "ard on", + "atter ing", + "Ġcost ume", + "س ت", + "è¿ ĺ", + "Ġun w", + "Ð Ľ", + "Ġthick ness", + "qu ito", + "g unt", + "ist as", + "ne ys", + "ĠëIJĺ ê²Į", + "ĠBr asil", + "Ġto ken", + "Ġaff ili", + "l on", + "Ġf Ã¥r", + "ĠBe ach", + "Ġw itch", + "ĠSe ven", + "Ġp ant", + "λ λ", + "Ġcapt ain", + "å Ŀ", + "Ġve ut", + "Ġpou voir", + "ac z", + "ĠBar b", + "Ġut ility", + "Ġcontempor ary", + "Ġobt ained", + "Ġpainting s", + "e ar", + "Ġpe an", + "ĠO g", + "Ġc ust", + "л ем", + "Ĥ ĺë", + "ĠIs so", + "Ġac onte", + "ĠTe le", + "ĠAss istant", + "à ī", + "íĸĪ ìĬµëĭĪëĭ¤", + "Ġcount s", + "Ġbu ck", + "ĠDe ep", + "Ġtack le", + "Ġh arsh", + "Ġdec ides", + "éĹ ľ", + ". âĢĭ", + "éĤ Ĭ", + "ĠAng el", + "Ġlay ing", + "Ġcal ories", + "Ġcontro lling", + "Ġadvant ages", + "ĠÑįÑĤ ой", + "Ġappro aching", + "Ġthreat s", + "ak an", + "em atic", + "m ann", + "ê³ µ", + "m umbles", + "ac ió", + "Ġmaint aining", + "Ġfound er", + "l ah", + "f ight", + "Ġadm itted", + "âĢ¦ .", + "ķ Į", + "ab ol", + "Ġus age", + "Ġn onsense", + "ĠPal est", + "Ġcont re", + "ĠDemocr atic", + "ĠE R", + "j ekt", + "Ġar bit", + "Ġг ол", + "ĠMich elle", + "ich er", + "es h", + "ĠP ho", + "к ом", + "4 9", + "ĠEner gy", + "ο Ïį", + "Ġc ents", + "Ġref ers", + "Ġg ospel", + "ĠSh a", + "ĠSh are", + "×Ļ× ł", + "Ġclin ic", + "ĠëĦ £", + "Ġequ ality", + "ug s", + "Ġsh ed", + "Ġplan es", + "Ġtout e", + "re ck", + "Ġstra nd", + "Ġbi ology", + "Ġle ague", + "ĠP ok", + "Ġnúmer o", + "ĠCo ast", + "Ġconsist ently", + "Ġnuc le", + "OO OO", + "Ġob jet", + "Ġch or", + "Ġg inger", + "Ġd abei", + "Ġcoop eration", + "à¯į .", + "nt en", + "ç ¤", + "l Ãł", + "ìĸ ij", + "r ado", + "Ġpass ive", + "Ġglo ves", + "Ġunder ground", + "Ġlog ical", + "Ġk et", + "Ġfunction ality", + "¸ë ¦¬", + "Ġport al", + "ell er", + "×Ļ× ¨", + "ĠT ed", + "ĠG re", + "IJ ľ", + "Ġperson nel", + "Ġemer ging", + "ĠF ür", + "Ġmeant ime", + "usal em", + "ĠC lear", + "Ġtra pped", + "Ġìļ °", + "Ġdis pl", + "Ġmet tre", + "Ġmun icip", + "Ġwithd raw", + "Ġsp at", + "un es", + "Ġaccess ibility", + "æĪij 们", + "Ġap are", + "Ġpros pect", + "Ġн аз", + "Ġcop per", + "ĠP RO", + "Ïħ ÏĦ", + "Ġattack ing", + "ĠV in", + "ĠSt one", + "Ġinvestig ate", + "st yle", + "ĠÎ »", + "ë ¡Ŀ", + "ë§ Ī", + "Ġins pect", + "Ġli ver", + "ал иÑģÑĮ", + "Ġser a", + "hal ten", + "em an", + "Ġmin istry", + "' '", + "Ġd ots", + "ãħĭãħĭ ãħĭãħĭ", + "Ñĥ ÑģÑĤ", + "ĠJ ak", + "AK E", + "Ġg aps", + "uck er", + "ĠинÑĤеÑĢ еÑģ", + "ĠEm ily", + "Ġinter val", + "Ġt ender", + "ĠTechn ology", + "g ame", + "Ġtri b", + "ÙĦ ا", + "ĠDevelop ment", + "Ùħ ا", + "Ġwr ist", + "Ġf ires", + "Ġtarget ed", + "ìł IJ", + "Ġso d", + "íļ Į", + "Ġoldu ÄŁ", + "Ġse asons", + "vent ions", + "Ġн его", + "Ġsomet ime", + "ли в", + "n é", + "Ġt ú", + "ĠDe us", + "Ġexec ution", + "á p", + "ĠCh ange", + "ĠInd eed", + "Ġreg ulation", + "ĠH ung", + "é is", + "Ġwish es", + "Ġj azz", + "Ġstruct ural", + "Ġblow ing", + "Ġby Äĩ", + "Ġtherm al", + "ph ant", + "ÑĢÑĥ з", + "ан ÑĤ", + "ĠP ull", + "Ġconf usion", + "нÑĭ ми", + "Ġscen arios", + "ìłģ ìľ¼ë¡ľ", + "Ġд еÑĤ", + "Ġtatto o", + "Ġaut re", + "Ġhe ating", + "Ġtreat ing", + "Ġпон им", + "Ġexc lus", + "ĠL OL", + "we ar", + "ag le", + "Ġzur ück", + "Ġr ational", + "s u", + "Ġdet er", + "ĠN ative", + "à®ķ ள", + "ach ed", + "Ġ ãĥ", + "ĠEnt onces", + "Ġhor a", + "ìĿ´ìĹIJ ìļĶ", + "Ġl ite", + "à «", + "Ġsix th", + "Ġбол ее", + "act or", + "Ġpsych ology", + "çĽ ¸", + "Ġdem ands", + "Ġpe er", + "Ġnew ly", + "ĠWW E", + "Don ald", + "ĠBo x", + "Ġp ine", + "Ġload ing", + "ĠN ico", + "Ġs ÅĤ", + "omm e", + "AR T", + "Ġrecru it", + "Ġbug s", + "arent s", + "ĠпÑĢ об", + "ĠIn side", + "ipp er", + "d ramatic", + "Ġplan ets", + "ord e", + "Ġy oga", + "ch ild", + "ĠMar ie", + "Ġãģ Ĥ", + "ĠB L", + "Ġfil med", + "Ġref resh", + "Ġtomato es", + "Ġf et", + "Qu é", + "Ġ !!", + "ĠëĤ ´ë", + "r ine", + "Ġinteract ive", + "s al", + "ann ah", + "pe z", + "ç¶ ĵ", + "Ġunderstand s", + "ĠTok yo", + "Ġlibr aries", + "Ġread er", + "ij IJ", + "o z", + "ĠEnd e", + "ĠF lo", + "Ġm ild", + "Ġpo etry", + "Ġж ив", + "æĦ Ľ", + "Ġbeh ave", + "Ġdo en", + "ĠSus an", + "p age", + "ra ham", + "Ġcommunic ations", + "Ġtun ing", + "Ġp ac", + "Ġanx ious", + "I O", + "M ark", + "Ġhi ç", + "book s", + "Ġp iss", + "Ġen abled", + "achel or", + "ĠF OR", + "Ġé c", + "ĠT R", + "il st", + "h at", + "ĠìĿ Į", + "Ġty ch", + "Ġj ar", + "Ġbuild s", + "ĠAr gent", + "Ġinter medi", + "Ġl ou", + "Ġa ra", + "Ġassign ment", + "Ġcabin et", + "Ġretire ment", + "ãģ »", + "Ġdis abled", + "ric a", + "Ġa wards", + "Ġbo ots", + "Ġacknow led", + "Ġth y", + "Ġêµ ¬", + "Ġsy nd", + "ни й", + "il ton", + "Ġprob l", + "ĠF al", + "Ġverd ade", + "Ġ7 00", + "ĠLe arning", + "oc us", + "Ġpal ace", + "N ot", + "t ain", + "c m", + "Ġmagn et", + "inc oln", + "Ġfig uring", + "ĠL yn", + "ĠB oss", + "ĠV O", + "Ġdiagn osis", + "Ġequ ipped", + "w atch", + "in os", + "ad ers", + "Ġsh elf", + "Ġorgan is", + "Ġn od", + "Ġk ız", + "pp ers", + "Ġrest ore", + "Ġart ic", + "ĠVo ice", + "ı yorum", + "ê² ©", + "Ġspread ing", + "Ġh ips", + "Ġw ard", + "ure au", + "Ġinter section", + "6 6", + "Ġ3 9", + "ç ³", + "Ġwait ed", + "ì ´", + "hh hh", + "Ġd ys", + "ĠE N", + "Ġb atch", + "Ġca f", + "Ġmark er", + "大家 好", + "or able", + "ó ria", + "Ġste pped", + "Ġcelebr ating", + "ан а", + "Ġwor n", + "ĠF ol", + "Ġpl a", + "Ġattempt s", + "Ġtwe et", + "Ġr ust", + "g ence", + "í Ĩµ", + "Ġre vel", + "Ġre cept", + "en ess", + "Ġ( (", + "ãĥ¼ ãĥ", + "! âĢĭ", + "ĠìĨ IJ", + "Ġinfluen ced", + "и ж", + "Ġкон еÑĩно", + "Ġcolleg es", + "ion i", + "Ġs ag", + "An n", + "ol ar", + "Ġexpress ions", + "Ġsu its", + "Ġowners hip", + "el and", + "pie ce", + "æĢİ ä¹Ī", + "Ġdesp ués", + "Ġt el", + "Ġins ult", + "Ġêµ īìŀ¥", + "ĠSm all", + "ĠF R", + "ok a", + "ber ries", + "ĠAnt on", + "ел Ñı", + "Ñı Ñģ", + "Ġval ve", + "act s", + "Ġwood s", + "à® £", + "Ġcult iv", + "Ġf á", + "ãģ¨ ãģĦãģĨ", + "Ġche ers", + "Ġassum ption", + "Ġfit ness", + "ÃŃ cul", + "Ġpod r", + "Ġwe it", + "ĠH ind", + "Ġd ign", + "Ġз н", + "Ġsqu ad", + "Ġdest ro", + "c ere", + "sh irt", + "imm t", + "eng ers", + "Ġs ä", + "k ÅĤad", + "Ġ ÈĻ", + "Ġocc as", + "Ġì¤ Ħ", + "Ġprocess or", + "ĠD M", + "ĠDad dy", + "Ġsoon er", + "Ġstraight forward", + "Ġdepart ments", + "ĠChr ome", + "Ġwork place", + "ĠPy thon", + "Ġm eng", + "ĠD AN", + "ĠI ce", + "ĠëĪ Ī", + "ĠG i", + "Ġh iring", + "Ġland ed", + "Ġdemocr atic", + "ied z", + "ãģĺ ãĤĥ", + "Ġse v", + "ic ia", + "Ġespe cial", + "ĠN ous", + "Ġh ät", + "Ġb ou", + "per t", + "ies z", + "åij Ģ", + "Ġv il", + "ÅĽ li", + "Ġî n", + "Ġloss es", + "éķ ·", + "Ġto ast", + "Ġreal m", + "ĠAust in", + "ĠIn formation", + "Ġres ume", + "Ġch ase", + "Ġsal ary", + "Ġë¶ Ħ", + "ли Ñĩ", + "ĠÑģл ед", + "ĠFur ther", + "Ġcar ing", + "Ġv ig", + "Ġval or", + "è¿Ļ 个", + "ĠÑĩ а", + "Ġanalyt ics", + "Ġglo be", + "ĠM AN", + "Ġn el", + "ìĿ´ì ķ¼", + "Ł ¼", + "Ġo y", + "íķĺ ìĦ¸ìļĶ", + "j en", + "Ġtrou bles", + "ah aha", + "Ġchurch es", + "u et", + "Ġmeasure ments", + "b il", + "ì ½", + "if ully", + "ин Ñĥ", + "ĠWil son", + "¦ ´", + "ĠíĮ Į", + "Ġì° ¨", + "Ġp úblic", + "ĠJer usalem", + "Ġn ails", + "Ġsp ine", + "Ġhe mos", + "Ġz n", + "qu is", + "ĠLe ben", + "Ġrefer ences", + "IT H", + "i per", + "ĠÑģеб Ñı", + "ì ģ", + "ĠW a", + "st ate", + "§ Ŀ", + "åħ ±", + "ĠGen er", + "Ġact ress", + "ĠEn joy", + "๠ĥ", + "Ġ× Ĵ", + "Ġinfect ed", + "Ġsh aking", + "Ġn ick", + "ภ¸", + "Ġf ot", + "Ġaccompl ished", + "u ke", + "Ġshe ets", + "Ġf ence", + "Ġnurs ing", + "Ġintrodu cing", + "Ġfe at", + "O ne", + "T O", + "Ġcl ubs", + "ĠBru ce", + "on ge", + "ch ange", + "ĠBat man", + "åı °", + "ĠOffic er", + "Ġhyd ro", + "Ġsupp lement", + "Ġc ela", + "Ġlong est", + "Ġcompet ing", + "Ġcon he", + "g iving", + "Ġbra ins", + "Ġlo ans", + "Ġw age", + "ĠCl inton", + "Ġs Äĥ", + "ane ous", + "Ġl ord", + "ÑĢÑĥ ж", + "Ġqu iz", + "Ġst iff", + "ĠL GB", + "s z", + "M E", + "m are", + "th ere", + "Ġn är", + "ĠM and", + "l ast", + "Ġd ag", + "Ġhalf way", + "ĠB and", + "Ġëĭ¤ ìĭľ", + "ĠA ren", + "Ġi le", + "P N", + "ent o", + "Ġalg um", + "Ġsoc cer", + "Ġblock ed", + "ĠJon athan", + "Ġse w", + "ĠTest ament", + "Ġv ale", + "Ġbehav i", + "å§ ĭ", + "Ġcon na", + "IC H", + "Ġaud iences", + "m l", + "amm ad", + "ĠìĤ ´ì", + "I GH", + "Ġr aces", + "em ed", + "Ġm á»Ļt", + "à ¯", + "Ġover s", + "Ġdecl ared", + "Ġs ana", + "ĠU na", + "ĠÑĢ е", + "uck s", + "Ġp airs", + "Ġan ge", + "N e", + "Ġup s", + "av y", + "ø r", + "ree k", + "Ġbehav iors", + "Ġreflect ed", + "Ġprior ities", + "Ġcon du", + "Ġret reat", + "Ġexp enses", + "Ġë´ IJ", + "Ġtri ple", + "Ġêµīìŀ¥ íŀĪ", + "ä lt", + "Ġind igenous", + "Ġmin ing", + "Ġaccept able", + "Ġru in", + "C A", + "u ine", + "Ġpip eline", + "ct ic", + "ê t", + "ĠвÑģ его", + "Ġb oun", + "ĠDig ital", + "ĠBo om", + "ÑĨ е", + "Ġл ÑĥÑĩ", + "Ġas c", + "ĮĢë ¡ľ", + "ĠGood bye", + "Ġrend er", + "ene z", + "ar re", + "ĠTH AT", + "b our", + "ic ión", + "ãĤ Ń", + "E very", + "Ġw ires", + "ĠPar liament", + "n ung", + "ate ur", + "ĠS ave", + "ĠPh ys", + "Ġam or", + "ĠE ve", + "Ġfr ight", + "Ġgam ma", + "Ġmic ros", + "m itt", + "ĠC ode", + "ĠBe y", + "pl ed", + "ĠиÑģп олÑĮз", + "ç Ĺ", + "ìĥ ī", + "å¥ ¹", + "Ġmon et", + "ĠJah re", + "Ġlux ury", + "Ġde af", + "Ġbet ray", + "Ġê² °", + "и ки", + "Ġdefe ated", + "Ġunder t", + "Ġwe g", + "Ġcool er", + "ãģķ ãĤĵ", + "iam i", + "éĤĦ æľī", + "ĠJess ica", + "ĠJ oy", + "Ġsoph istic", + "ени и", + "ðĿ ĺ", + "Ġch ili", + "ĠTy pe", + "Ġprote ins", + "Ġpresent ing", + "al ia", + "ìļ ¸", + "ĠMaj or", + "Ġmolec ule", + "um er", + "Ġcoll apse", + "ĠAny ways", + "ĠMount ain", + "ant ed", + "ãĢ IJ", + "Ġвиде о", + "æ° ´", + "A ud", + "Ġcon qu", + "Ġvo ll", + "Ġkn it", + "Ġmem br", + "ĠMark et", + "Ġd ari", + "Ġcalcul ated", + "г и", + "Ġshrim p", + "ĠM u", + "ĠпÑĢ оÑĤ", + "Ġìĺģ ìĥģ", + "Ġproduct ivity", + "Ġcogn itive", + "ĠHe b", + "ict ions", + "ê² ½", + "Ġcr é", + "f ör", + "Ġpray ing", + "ash i", + "ĠT ik", + "ó r", + "w en", + "ÑĮ Ñİ", + "ix o", + "Ġ( \"", + "ĠÑĤ ел", + "Ġìĸ´ëĸ ¤", + "ĠпеÑĢ ед", + "ĠD rive", + "ãĢ ij", + "ĠE qu", + "Ġequilib rium", + "Ġdescri bes", + "не е", + "4 2", + "ĠCur rent", + "y y", + "Ġabsor b", + "Ġsold ier", + "d ers", + "Ġtestim ony", + "Ġdec line", + "ľë ¡ľ", + "g age", + "Ġinsp ire", + "la pping", + "Ġspin ning", + "Ġsla very", + "Ġfac ial", + "Ġtrad itions", + "ári os", + "ĠHosp ital", + "Ġn est", + "ĠëĪ Ħ", + "Ġto i", + "Ġfe ars", + "ìħ ¨", + "ĠM uh", + "Ġgradu ation", + "Ġimpact ed", + "Ġa unt", + "ĠLet s", + "Ġalumin um", + "Ġdomin ant", + "ĠDav is", + "ĠNav y", + "Ġcom pt", + "op les", + "Ġest ava", + "è ¥", + "Ġsc al", + "Ġpres erve", + "ĠO pp", + "Ġpract ically", + "Ġmagn itude", + "Ġf itting", + "Ġcoordin ate", + "Ġfurn iture", + "ĠFam il", + "Ġexplos ion", + "Ġdocument ary", + "ĠS cript", + "Ġport ray", + "m at", + "Ġschedul ed", + "Ġdynam ics", + "ph y", + "ak y", + "ĠU I", + "C he", + "Ġcontinu ously", + "ĠPro v", + "å° ij", + "Ñĥ з", + "ra h", + "Ġger ne", + "pro of", + "Ġsecret ary", + "ĠPat reon", + "sc ream", + "ĠK ids", + "á»ĵ i", + "Ġk g", + "Ġuncertain ty", + "Ġк ажд", + "Ġmit ig", + "Ġread s", + "å· ²", + "ĠR u", + "Ġpri est", + "Ġн ед", + "Ġlimit ations", + "Ġflo at", + "6 00", + "ĠT oy", + "ĠJim my", + "Ġoff ensive", + "en i", + "ĠX i", + "Ġeye br", + "ĠTur k", + "Ġaccident ally", + "Ġoh ne", + "ĠS aud", + "9 5", + "ĠD utch", + "ан Ñģ", + "ĠSe attle", + "Ġëĵ ±", + "che ck", + "k ÄĻ", + "Ġcontrib utions", + "Ġbes ide", + "Ġqu indi", + "Ġfle w", + "æĹ ¶", + "Ø° ا", + "ĠL O", + "Ġwa ist", + "ĠE V", + "Ġhol idays", + "j on", + "Ġmis under", + "Ñı н", + "Ġb out", + "Ġd imin", + "Ạ½", + "ó l", + "ĠGr ace", + "Ġinput s", + "Ġden y", + "Ġform ing", + "ĠB ild", + "Ġad equ", + "Ġfol k", + "Ġreject ed", + "se mb", + "Ġfrust rated", + "op en", + "ĠBet ter", + "il on", + "Ġtow el", + "Ġdifferent ial", + "Ġsac red", + "Ġsa il", + "éĩ Į", + "ent imes", + "Ġgentle man", + "Ġicon ic", + "Ġcomp aring", + "Ġs agt", + "Ġtext s", + "Ġgrand ma", + "Ġroll s", + "Ġcont ents", + "ä¸į 好", + "оÑģ Ñģ", + "Ġsusp ension", + "ro it", + "¦ ¼", + "Ġasse z", + "Ġd ort", + "ĠM ath", + "ĠVict or", + "ĠJava Script", + "ä¸į å°į", + "Ġen han", + "Å Ļ", + "ĠB ush", + "Ġpromot ion", + "Ġk in", + "Ġmon sters", + "ĠColor ado", + "ĠÎ ²", + "íķ´ì ļĶ", + "æŃ £", + "iffer ent", + "Ġn aked", + "Ġpro d", + "et ics", + "ĠW oman", + "Ġtreat ments", + "Ġest oy", + "v é", + "Ġlif ting", + "Ġy apt", + "ĠRo ber", + "Ġì¹ ľ", + "Ġsubst itute", + "ak u", + "r idge", + "Ġê± °ë", + "Ġrespond ed", + "Ġb é", + "ĠEngine er", + "Ġtransfer red", + "ë ²", + "Ġha ber", + "o op", + "ĠW E", + "Ġv est", + "Ġfor ty", + "ĠD S", + "Ġ200 4", + "Ġco aching", + "n om", + "ĠB ab", + "Ġn ossa", + "ĠJ ake", + "Ġg y", + "Ġde leg", + "Ġìŀ ł", + "ĠкÑĢ аÑģ", + "Ġstand point", + "Ġdis ad", + "Ġart work", + "A d", + "ill o", + "ĠÄij ược", + "ĠPr om", + "ĠL ib", + "Ġcritic ism", + "Ġcontact s", + "ÑĢ ам", + "Ġachieve ment", + "ÐĶ а", + "Ġdiss ol", + "ĠVeg as", + "Ġstream s", + "ĠK ent", + "ĠعÙĦ Ùī", + "Ġrad ius", + "Ġsu cks", + "ĠA ch", + "Ġf i", + "ou st", + "ĠлÑİд и", + "Ġpal ette", + "ĠH az", + "ĠAnth ony", + "Ġtem a", + "ĠC os", + "Ġsa fer", + "α ÏĤ", + "Ġcont rad", + "Ġma ior", + "Ġinfl ation", + "ĠSil ver", + "Ġatt ending", + "íķľ íħĮ", + "art o", + "Ġapplaud ing", + "Ġcomput ing", + "ĠH at", + "æ »", + "k now", + "mak ers", + "Ġcon oc", + "Ġeduc ated", + "Ġmod ified", + "Ġinc lusion", + "ment al", + "ŀ IJ", + "is ia", + "ĠÏĢ οÏħ", + "Ġa un", + "ĠIre land", + "Ġk ö", + "Ġcompl iance", + "Ġinsp iring", + "иÑĤелÑĮ но", + "Ġdisp os", + "ì° ¨", + "Ġw ip", + "r ical", + "raw d", + "Ġt res", + "Ġmob il", + "olut ions", + "B O", + "Ġb ounce", + "Ġassum ed", + "ĠMed ical", + "Ġf iscal", + "Ġng Æ°á»Ŀi", + "ition ally", + "Ġst olen", + "ĠB M", + "Ġmechanism s", + "ε ί", + "Ġqual ified", + "Ġìŀ IJë", + "ught ers", + "ĠH IV", + "ĠL ots", + "Ġser vers", + "Ġcar r", + "ĠT ogether", + "Ġattract ed", + "Ġk r", + "æĪij æĺ¯", + "th ur", + "in in", + "ĠH alf", + "È Ľ", + "ĠP ap", + "Ġremind ed", + "AL L", + "Ġhel met", + "Ġbott les", + "Ġprofess ors", + "Ġse ine", + "ÅĤ Äħ", + "ãĥ ı", + "Ġê±° ìķ¼", + "Ġ×¢ ׾", + "f un", + "ĠB ird", + "Ġfight er", + "ĠëĶ °ë", + "ĠT ool", + "Ġt in", + "ino is", + "ë ¶Ħ", + "×Ļ× Ł", + "ĠC AR", + "åIJ į", + "irst y", + "Ġout door", + "ĠN S", + "ãħ İ", + "ff en", + "Ġl ud", + "H ello", + "Ġroll er", + "ie le", + "ĠPol and", + "Ġap a", + "ex p", + "Ġcertific ate", + "ĠT own", + "аÑİÑĤ ÑģÑı", + "ild e", + "Ġdeterm in", + "P R", + "Ġfree ze", + "Ġmain stream", + "Ġobject ives", + "b lo", + "Ġtak ie", + "åĵĪ åĵĪ", + "Ġë°Ķë ¡ľ", + "el et", + "ĠI V", + "ĠF ast", + "Ġd ere", + "em p", + "ĠD ra", + "ĠìŀĪ ìĹĪ", + "Ġdisc rimination", + "Ġε ίναι", + "ne cess", + "æ ®", + "ıģ ı", + "Ġpost ing", + "wi ÅĽcie", + "Ġl ub", + "Ġol ive", + "Ġr im", + "Ġmodel ing", + "Ġa ño", + "ĠPak istan", + "Ġover l", + "Ġinf lam", + "N E", + "ìĹIJ ê²Į", + "Ġatt ended", + "Ġdeal t", + "ĠAl t", + "ĠL incoln", + "Ġaw ake", + "Ġfil ters", + "ĠWith in", + "czy wiÅĽcie", + "Ġs û", + "ĠJohn ny", + "Ġintegr ity", + "Ġisol ation", + "ĠE asy", + "ĠпÑĢ ин", + "ĠAl ice", + "Ġsm iling", + "en ix", + ", ...", + "Î ¶", + "Ġbeg un", + "Ġjew el", + "Ġconvention al", + "Ġstat ist", + "Ġhand ed", + "Ġir re", + "Ġpro hib", + "Ġsatell ite", + "é¦ Ļ", + "ĠInd ust", + "Ġtra ged", + "Ġtra va", + "Ġih m", + "Ġcru el", + "ĠAg ora", + "ĠD oc", + "Ġz ones", + "Ġm all", + "Ġtr ay", + "×ķ× ł", + "Ġir rit", + "Ġk ans", + "ĠBe at", + "ud ge", + "ie lle", + "Ġtrust ed", + "Ġb ikes", + "ĠÑĥ п", + "ĠM ember", + "w ick", + "Ġcreat ors", + "Ġher itage", + "ind istinct", + "Ġres ur", + "enn en", + "C ome", + "Ġf iring", + "ĠBu eno", + "ĠТ о", + "ik an", + "ett es", + "Ġk es", + "Ġtri ps", + "Ġdivor ce", + "ĠK l", + "Ġcons ol", + "ke ep", + "기 ê°Ģ", + "ĠRep ort", + "Ġhost ing", + "Ġdiam ond", + "Ġcompl ic", + "Ġhel icop", + "Ġdep uis", + "d s", + "ĠCh an", + "Ñı л", + "Ġsc issors", + "il ation", + "Ġprop ortion", + "ER E", + "ĠÙĪ اÙĦ", + "int a", + "Ġmuch as", + "u ation", + "it is", + "æĬ Ĭ", + "Ñı Ñī", + "Ġni in", + "Ġemphas ize", + "uel a", + "Ġprodu cers", + "Ġr ze", + "änd er", + "ET H", + "æ º", + "Ġconst itu", + "åĽ ½", + "Ġperform ances", + "ist le", + "go v", + "ĠL iter", + "Ġincorpor ate", + "Ġeduc ate", + "ĠN in", + "ì ª½", + "Ùĩ Ùħ", + "el eration", + "×ķ× ij", + "Ġya ÅŁ", + "or ous", + "ĠC as", + "Ġgr ants", + "ëĬ ¥", + "am el", + "Ġê·¸ë łĩê²Į", + "ĠE ste", + "Ñħод иÑĤ", + "ĠпоÑģ ле", + "Ġg ent", + "Ġfocus es", + "al ities", + "ĠR h", + "ë ³´", + "æ° ij", + "ĠD ance", + "r r", + "Ġam er", + "Ġutil ize", + "Ġl ÃŃ", + "ĠAm ong", + "Ġpregn ancy", + "Ġlo ops", + "ал оÑģÑĮ", + "ĠM oh", + "Ġcatch ing", + "Ġglo b", + "Ġa jud", + "Ġ[ ?", + "ĠAn al", + "lo oking", + "Ġsurf aces", + "Ġprogress ive", + "Ġvir al", + "0 8", + "Î ¾", + "K A", + "Ġ ży", + "Ġpick s", + "ann on", + "Ġbul k", + "ĠR oss", + "Ġdescri bing", + "ĠG el", + "Ġloc ally", + "Ġend less", + "Ġmass age", + "Ġclean ed", + "Ġtravel ed", + "ен Ñĭ", + "Ġsent iment", + "ig ma", + "ĠN as", + "Ġchemical s", + "Ġright eous", + "ĠMag ic", + "Ġrel ates", + "Ġtruck s", + "Ġ19 60", + "åĪ ¥", + "Ġapp et", + "Ġsn acks", + "ĠSum mer", + "Ġy üz", + "Ġpr is", + "ĠMex ican", + "Ġtransp aren", + "Ġminor ity", + "Ġver te", + "Ġl assen", + "4 6", + "л ек", + "é p", + "ĠÑĦ илÑĮ", + "Ġi yi", + "Ġsp an", + "íķĺ ì§Ģ", + "Ġind icated", + "qu ar", + "Ġscholars hip", + "ĠLGB T", + "Ġhistor ically", + "ó ÅĤ", + "Ġmin ist", + "Ġpen et", + "ĠR ap", + "Ġcons ervation", + "çĽ ´", + "ĠH oney", + "ĠBe i", + "id el", + "Ġrespons ibilities", + "Ġmess y", + "ĠEx cept", + "OR E", + "Ġiniti atives", + "Ġjun ior", + "Ġdesign ers", + "Ġexpl oration", + "Ġspons or", + "Ġmob ility", + "Ġint eg", + "land o", + "Ġb ark", + "Ġindic ates", + "à ¶", + "Ġemploy er", + "å® ī", + "Ġcous in", + "Ġbo iling", + "Ġch rom", + "Ġç al", + "Ġper pet", + "Ġcont ained", + "Ġpark s", + "Ð «", + "ĠEngine ering", + "P lease", + "ĠStart ing", + "her o", + "Ġlaw yers", + "è¥ ¿", + "Ġz d", + "Ġfranch ise", + "ra ge", + "Ġint uit", + "ĠG L", + "re ach", + "ĠE lle", + "Ġnh Æ°", + "ĠN ord", + "Ġbe an", + "0 7", + "Ġple asant", + "å½ ĵ", + "v iron", + "Ġgrad ient", + "z us", + "ĠE M", + "Ġess ay", + "ìĹIJ ìļĶ", + "ế n", + "n u", + "á» «", + "ĠÃī s", + "Ġden omin", + "ĠGirl s", + "Ġperson nes", + "ĠاÙĦØ £", + "b ild", + "ĠSt at", + "Ġcompl iment", + "ĠK ate", + "Ġoptim al", + "Ġh id", + "د ÙĬ", + "Ġquick er", + "w all", + "E n", + "IN E", + "?? ?", + "ì² ´", + "ĠA ction", + "å Ł", + "Ġpenal ty", + "ĠK az", + "' ?", + "Ġc ried", + "Ġcan vas", + "ft e", + "Ġexc lud", + "¸ë ¡ľ", + "Ġemphas is", + "Ġen zy", + "ĠH ou", + "Ġoverse as", + "ÃŃ amos", + "å¸ «", + "ö glich", + "Ġhead phones", + "c n", + "ĠA ge", + "Ġa kan", + "Ġcharacter istic", + "íķĺë ©´", + "get s", + "Ġë¶ Ī", + "Ġr ival", + "Ġb orders", + "em ente", + "em ás", + "Ġy ol", + "Ġcom pe", + "end ers", + "ınd an", + "Ġmö glich", + "Ġbubb les", + "nat ural", + "Ġar med", + "Ġel abor", + "ĠìĿ´ë ²Ī", + "Ġwash ed", + "οÏħ με", + "è« ĭ", + "Ġfl avors", + "Ġexist e", + "Ġpre st", + "ĠThe ma", + "оп ÑĢоÑģ", + "er on", + "U E", + "er i", + "Ġconc er", + "Ġa ixò", + "åħ ©", + "Ġprotect ive", + "Ġзна Ñİ", + "ĠëĤ ł", + "ĠII I", + "Ġme er", + "ĠSh op", + "ll i", + "ĠOr der", + "ĠM Y", + "ĠG host", + "ãĤĤ ãģĨ", + "ad el", + "Ġst ole", + "Ġrele asing", + "ĠCom ment", + "Ġtra ins", + "ë ªħ", + "Ġw issen", + "ens ed", + "Ġdesc end", + "Ġf ier", + "Ġrad i", + "Ġpers u", + "ç ¢", + "Ġм н", + "ĠD est", + "Ġwor ries", + "it et", + "b as", + "Ġst ab", + "n ame", + "or ic", + "ĠCl ose", + "Ġalum ni", + "ĠS elf", + "ff e", + "it ating", + "ather ine", + "ĠRight s", + "Ġell os", + "Ġwar rant", + "Ġn erve", + "Ġveget able", + "ĠTe il", + "Ġê°Ļ ìĿ´", + "R Y", + "Ġsustain ability", + "Ġste ht", + "Ġbr id", + "ada ÅŁ", + "Ġt v", + "Ġdur ation", + "Ġpesso a", + "Ġmet rics", + "Ġad am", + "c as", + "аÑĢ и", + "Ġev ident", + "Ġdisplay ed", + "Ø§Ø ¦", + "Ġre ck", + "ĠBudd ha", + "Ġde le", + "ĠDie go", + "os ph", + "Ġb la", + "ĠM ik", + "ul ator", + "Ġ200 1", + "Ġpromot ing", + "y ch", + "ĠE X", + "Ġlast ly", + "Ġout line", + "Ġspir its", + "Ġve ux", + "Ġsubt ract", + "ĠÅŁ imdi", + "Ġp ins", + "Ġbur ger", + "Ġmol to", + "Ġhab ÃŃa", + "Ġë° ĺ", + "ig u", + "er st", + "Ġn en", + "Ġbac on", + "it ious", + "Ġcar ries", + "Ġprom ises", + "nd e", + "ĠLe ft", + "ĠL im", + "æ £", + "Ġ4 4", + "Ġcare ers", + "Ġì£ ¼ë", + "Ġspeed s", + "qu é", + "m ad", + "mark et", + "is me", + "Ġ200 3", + "Ġre cess", + "ĠJ UD", + "Ġrac ist", + "ĠSch l", + "Ġpar ler", + "Ġot ros", + "ish es", + "Ġconvert ed", + "aa aa", + "ани и", + "ĠAr k", + "ĠCh ance", + "Ġelement ary", + "ε ν", + "ink s", + "Inter viewer", + "Ġfre ely", + "al ah", + "Ġëĭ¤ë ¥¸", + "Ġrequest ed", + "Ġtor que", + "no ÅĽci", + "ou red", + "ĠSt aff", + "Ġst ain", + "ĠAl an", + "Ġv ere", + "ĠW inter", + "Ġdef ect", + "ied y", + "Ġbe ats", + "Ġh á", + "um n", + "o ons", + "it udes", + "Ġse it", + "o ly", + "Ġres erv", + "Ġext r", + "Ġphys ician", + "vis or", + "Ġhand ful", + "ĠN ations", + "Ġì¢ĭ ìĿĢ", + "uc cess", + "Ġup stairs", + "ĠSqu are", + "Ġhe in", + "ĠSe ason", + "ol is", + "Ġpr ince", + "Ġdef ensive", + "ç ½", + "Ġм еÑģÑĤ", + "Ñĸ й", + "Ġا ÙĨ", + "um ble", + "ê¹Į ìļĶ", + "Ġass ass", + "Ġcirc ular", + "Ġqual ities", + "Ġh mm", + "Ġbl own", + "ĠL iz", + "ĠK ur", + "ĠS A", + "Ġfind ings", + "Ġcol ours", + "Ġde lle", + "ĠI R", + "ĠA th", + "ĠD ub", + "ĠO x", + "ĠØ ®", + "Ġpo ckets", + "Ġgr ill", + "Ġswitch ing", + "Ġprefer red", + "ĠW ales", + "Ġex emplo", + "Ġchop ped", + "Ġvacc ination", + "Ġne uro", + "Ġspec ify", + "iv os", + "Ġser á", + "Ġz ie", + "Ġà® ®", + "Ġresult ing", + "ĠU gh", + "Ġmess ed", + "C D", + "Ġpa ar", + "Ġcom er", + "Ġcou ch", + "ĠFest ival", + "Ġ4 9", + "v ous", + "z ens", + "ç¨ ®", + "ĠKenn edy", + "ĠT s", + "Ġë³´ì Ĺ", + "Ġdemonst ration", + "Ġun to", + "Ġfrust rating", + "Ġlabor atory", + "Ġe gy", + "Ġbeaut ifully", + "Ġìŀ ¬ë", + "Ġal gu", + "Ġö yle", + "ä½ł çľĭ", + "ĠP H", + "Ġfort une", + "Ġclean er", + "ĠRob in", + "Ġsa us", + "ĠG eld", + "Ġk at", + "o bs", + "Ġol ur", + "Ġm att", + "Ġquest a", + "Ġsuggest ion", + "en cer", + "о ÑģÑĤ", + "Ġrad ar", + "Ġìŀ ¡", + "ish a", + "à® ¨", + "ãĤĵ ãģª", + "j es", + "Ġve el", + "ìĤ °", + "Ġauth ors", + "ãĢ İ", + "pl an", + "Ġcollabor ative", + "Ġinst inct", + "Ġfar ming", + "au ge", + "E du", + "Ġmembers hip", + "Ġsimult aneously", + "Ġb ake", + "Ġk ä", + "Ġlect ures", + "Ñĩ еÑģ", + "Ġprend re", + "Ġcoll aps", + "ĠS aya", + "ĠF ut", + "Ġy og", + "ĠR ather", + "ر ÙĬ", + "Ġcamp s", + "ол од", + "Ġsim ulation", + "ĠM ak", + "La ughs", + "Ġgre y", + "Ġsent ences", + "y en", + "ĠUn less", + "J e", + "ĠSat an", + "ĠÑĤак же", + "ĠN A", + "Ġbr on", + "Ġ? ]", + "Ġsoul s", + "Ġlight ning", + "Ġimag ined", + "Ġczy li", + "ps ilon", + "et ta", + "Ġbelie ving", + "Ġstrong est", + "ĠC ON", + "Ġquel ques", + "Ġimmig rants", + "Ġwall et", + "éĢĻ æĺ¯", + "ĠJer sey", + "Ġimplic ations", + "Ġfor b", + "ãĢ ı", + "Ġun believable", + "Ø§Ø ¡", + "Ġoper ational", + "ü s", + "ĠG M", + "Ġê·¸ëŁ °ëį°", + "Ġgrac ias", + "Ġent end", + "ĠReg ard", + "ro b", + "ĠÑĤ еÑħ", + "è ı", + "ĠRev olution", + "Ġwa ar", + "ĠB iz", + "th eless", + "Ġspons ored", + "qu ier", + "ĠìĿ ¼ë", + "Ġte k", + "ĠëIJ ł", + "ig keit", + "ĠL uck", + "ĠCertain ly", + "Ġto ll", + "Ġн иÑĩего", + "ĠM oney", + "ĠÑģ ÑĤоÑĢ", + "ĠDou ble", + "ĠW olf", + "Ġch unk", + "ά ν", + "it és", + "on ing", + "M ar", + "Ġgrand es", + "Ġcollect ions", + "ĠEurop a", + "Ġа ÑĢ", + "ĠâĢĭâĢĭ âĢĭ", + "Ġê·¸ëŁ¬ë ©´", + "Ġоб ÑĬ", + "Ġãģ ª", + "Ġìĭľ ê°Ħ", + "ĠC ustom", + "Ġì² ĺ", + "Ñĸ лÑĮ", + "Ġindivid ually", + "í Ĺ", + "Ġdo zen", + "Ġo we", + "ĠVict oria", + "åı¯ èĥ½", + "Ġbe et", + "ur b", + "Ġanal og", + "i ção", + "Ĥ ľ", + "so ever", + "Ġmod o", + "Ġsubscri bed", + "ìŀ ¬", + "Ġent ities", + "çī ĩ", + "Ġclos et", + "Ġrespond ing", + "Ġprin ter", + "ĠStep han", + "Ġby ÅĤ", + "ĠD om", + "ĠF ern", + "ĠP ier", + "ĠwiÄĻ c", + "Ġh ence", + "Ġmod ules", + "ãĥ ¬", + "ĠëĶ ±", + "ĠDann y", + "ĠÑģеб е", + "Ġv ad", + "ĠìĹ Ħ", + "Ġs ous", + "Ġsp here", + "B Y", + "ĠP ed", + "ign ed", + "Ġwhe at", + "Ġund ers", + "Ġevol ve", + "Ġdec lar", + "Ġlight ly", + "Ġident ifying", + "æĦı æĢĿ", + "Ġlegend ary", + "Ġgen uine", + "Ġgr ind", + "ĠU ne", + "ge ben", + "Ġb icy", + "Ġjump s", + "Ġprov ince", + "zi ÄĻ", + "Ġ×IJ× ł×Ļ", + "Ġh oc", + "Ġб л", + "ĠGr ad", + "Ġreven ge", + "ĠاÙĦ ت", + "o oh", + "æĭ ľ", + "аÑĨи и", + "å¹ ³", + "Ġelect ro", + "ĠëIJ IJ", + "ãģ§ ãģ¯", + "Ġf als", + "ri el", + "ok er", + "ĠEx cellent", + "ĠMor gan", + "Ġbr ick", + "Ġsubstant ial", + "Ġpoll ution", + "ĠT ür", + "ĠEv et", + "Ġl ung", + "ãģ ĸ", + "×Ļ× ©", + "omm es", + "Ġreal izing", + "Ġhum ble", + "ĠL ock", + "Ġb od", + "Ġìĸ ¸", + "Ġpe ers", + "uz z", + "Ġembed ded", + "Ġclar o", + "Ġag greg", + "Ġemploy ers", + "ĠR aj", + "Ġãģ ¨", + "ĠY i", + "Ġje u", + "at ers", + "Ġstri kes", + "n os", + "aut res", + "d r", + "op her", + "ĠApp arently", + "íĺ Ħ", + "Ġinf ant", + "ا ب", + "ÑĤ Ñĭ", + "í Ľ", + "Ú ¯", + "Ġred es", + "acaÄŁ ım", + "ĠDA VID", + "ĠCh icken", + "Ġperspect ives", + "Ġview er", + "Ġsh ar", + "ĠпÑĢо из", + "lig t", + "er os", + "it able", + "ил оÑģÑĮ", + "Ġdif ÃŃ", + "´ë į°", + "Ġret ired", + "Ġthat s", + "zen ie", + "be iten", + "Ġmy cket", + "ĠR ab", + "Ġinflam m", + "ì° ®", + "Ġd um", + "Ġdad dy", + "æľ Ł", + "Ġimm ers", + "Ġplay list", + "௠Ĩ", + "Ġtra um", + "Ġref use", + "st ep", + "à® ļ", + "c up", + "Ġpop s", + "r imin", + "ay ım", + "Ġa ld", + "Ġun necess", + "Ġd ah", + "ĠIr ish", + "Ġcomp r", + "la ÅŁ", + "T P", + "Ġtransl ated", + "S c", + "ce ÄŁim", + "´ IJ", + "Ġd rei", + "ĠлÑİд ей", + "Ġqu iero", + "Ġhe le", + "z lich", + "Ġapp les", + "Ġdistrict s", + "Ġcred its", + "Ġas p", + "Ġëĭ ¨", + "or al", + "å½ ±", + "Ġste pping", + "ĠV a", + "Ġg ains", + "6 5", + "Ġnuest ra", + "ed ay", + "ass ador", + "ĠL ind", + "Ġcrop s", + "ci endo", + "ig ue", + "Ġb ana", + "A m", + "Ġp ent", + "Ġadd iction", + "Ġpack aging", + "ä d", + "ª ¨", + "Ġper què", + "Ġcampaign s", + "Ġste ep", + "Ġne ue", + "Ġembarrass ed", + "Ġdist inction", + "it zer", + "åij Ĭ", + "Ġregist ration", + "Ġll am", + "ĠAlm ighty", + "li est", + "Ġu z", + "n ak", + "ç º", + "Ġter az", + "iam ente", + "Ġtrans actions", + "Ġc ôt", + "Ġswitch ed", + "Ġcom bo", + "Ġpray ers", + "Ġintern ship", + "Ġaddress es", + "Ġchar ity", + "ĠW OO", + "Ġb ait", + "è¿ ĩ", + "Ġ �", + "Ġf ica", + "ĠTy ler", + "ar u", + "Ġat oms", + "ĠLe vel", + "ĠпоÑĤ ом", + "Ġf ame", + "ul k", + "Ġteach es", + "Ġre build", + "ед ÑĮ", + "ĠIndones ia", + "ush i", + "ĠSh ort", + "Ġens uring", + "f s", + "e le", + "Ġmargin al", + "Ġconclud e", + "am t", + "Ġver ify", + "ĠMc Donald", + "Ġsk al", + "Ġrec onst", + "ĠM ann", + "Ġbas ement", + "Ġtransform ed", + "Ġoccasion ally", + "z one", + "ĠD ans", + "Ġкак ой", + "Ġdiagn osed", + "ĠÏĦ α", + "Ġcomm ands", + "Ġpresident ial", + "Ġab b", + "Ġbrack et", + "ĠL em", + "Ã¥ ng", + "Ġfavor ites", + "Ġrev ol", + "ĠíĬ ¹", + "Ġhar ass", + "é ħ", + "Ġcle ans", + "st änd", + "Ġknock ed", + "Ġpe oples", + "Ġmusic ians", + "Ġmut ual", + "ĠC old", + "8 8", + "ze j", + "at ie", + "ĠHon or", + "Ġobs essed", + "ĠM USIC", + "ĠBre ak", + "ú ng", + "Ġmod ify", + "Ġs öyle", + "Ġ×ŀ ×Ķ", + "ĠOn line", + "f o", + "ĠMill er", + "Ġlik ing", + "Ġin hab", + "Ġgrat itude", + "ĠJour nal", + "arn ess", + "J ohn", + "ĠG it", + "åī Ľ", + "Ġsin cere", + "ĠS ci", + "ĠE li", + "Ġsymbol s", + "Ġman ually", + "ε ÏĤ", + "Ġв Ñĸд", + "ĠF at", + "Ġlab els", + "Ġsophistic ated", + "ump s", + "Ġrele ases", + "Ġ4 7", + "ĠO M", + "ê°Ģ ë", + "ĠB ien", + "ĠRe f", + "è¨ ĺ", + "ĠSt a", + "ĠE gg", + "Ġindic ator", + "ps on", + "Ġnas ıl", + "R ight", + "Ġcon vey", + "Ġkn ot", + "Ġconnect s", + "ul as", + "Ġpre ced", + "Ġine quality", + "am iento", + "Ġrep ly", + "O Y", + "Ġdism iss", + "ĠëIJ ľ", + "çĦ ¡", + "ĠÑħоÑĢоÑĪ о", + "Ġm éd", + "Ġrandom ly", + "ĠO nt", + "u ard", + "Ġpull s", + "ĠÑĤ епеÑĢÑĮ", + "ĠNe ed", + "ĠSo ft", + "Ġstrength s", + "Ġgo ed", + "um en", + "æŃ »", + "Ġíİ ¸", + "Ġд об", + "Ġclar ity", + "ĠA i", + "Ġball oon", + "ĠP and", + "ĠìķĦ ëĭ", + "Ġsh iny", + "Ġsmall est", + "on ia", + "h ill", + "ot ing", + "Ġe ing", + "Ġmere ly", + "Ġse us", + "Ġн еп", + "Ġí Ĩµ", + "Ġgu ides", + "Ġspecial ist", + "Ġste ak", + "ãĤĪ ãģĨ", + "Ġmig ration", + "que le", + "Ġru ined", + "Ġpu pp", + "å¥ ³", + "Ġk end", + "ang an", + "Ġpal m", + "Ġunf air", + "Ġz m", + "ĠD V", + "ch ester", + "и Ñİ", + "Ġo oh", + "er g", + "AT H", + "° ©", + "åĵ ª", + "r ison", + "Ġinvol ving", + "Ġpart ly", + "anç ais", + "Ġv ow", + "Ġprom inent", + "Ġcry st", + "ib a", + "Ġdes erves", + "Ġover t", + "Ġsens it", + "ĠWh e", + "Ġtight en", + "Ġintim id", + "Ġal iment", + "w ill", + "Ġstrength en", + "ĠT an", + "åı Ī", + "ãģĹ ãģ¾ãģĻ", + "on i", + "ĠM un", + "Ġpro ph", + "Ġrehe ars", + "ĠK le", + "Ġve ces", + "Ġwonder ed", + "ok i", + "Ġsens es", + "´ì ĭ", + "Æ°á» Ľ", + "ĠÈĻ i", + "Ġmuch os", + "Ġwatch es", + "ortun ate", + "ĠJ uan", + "ìŀĸ ìķĦ", + "ÑĢ е", + "e i", + "ion en", + "Ġexperiment al", + "Ġda ughters", + "ภĽ", + "Ġment ally", + "bec ca", + "aw are", + "ìĦ Ŀ", + "Ġwhat soever", + "Ġen ables", + "ĠL ow", + "o id", + "ภĬ", + "ó d", + "Ø º", + "Ġconstruct ed", + "ĠLad ies", + "Ġaccus ed", + "Ġа н", + "D an", + "Ġsp awn", + "Ġcontain ers", + "Ġart istic", + "ı p", + "Ġdisc l", + "Ġaut res", + "in as", + "ĠN ation", + "Ġn ag", + "be an", + "w he", + "ľë ıĦ", + "ĠSe oul", + "Ġíı ¬", + "ĠN ich", + "Ġcomp lement", + "Ġinter ven", + "ĠMod el", + "ĠOr ange", + "nam on", + "Ġcalcul ation", + "se e", + "Ġusted es", + "Ġle b", + "Ġdo ct", + "Ñĸ н", + "Ġf oster", + "Ġel astic", + "ĠAh h", + "Ġa ce", + "ĠP ink", + "ĠJ eg", + "Ġde er", + "ãģĹ ãģĦ", + "s is", + "Ġjak o", + "ĠEm ma", + "ÑģÑĤв енно", + "Ġport rait", + "Ġmak er", + "Ġa ument", + "ÑĢ об", + "Ġairpl ane", + "Ġtransparen cy", + "Ġadjust ment", + "ĠCD C", + "ç on", + "Ġupload ed", + "Ġд ейÑģÑĤв", + "Ġго ÑĤов", + "Ġit er", + "Ġcur se", + "ô n", + "mer ce", + "ar an", + "Ġle ak", + "çµ IJ", + "Ġabs ence", + "Ñģ кий", + "Ġread ers", + "al er", + "Ġbene ath", + "ang o", + "h etic", + "Ġfin ns", + "Ġpo op", + "Ġdu plic", + "H i", + "ig s", + "olog ically", + "op p", + "Ġd izer", + "ĠAll en", + "Ġgl i", + "Ġacc eleration", + "Ġvit amin", + "ãĥ Ń", + "v ä", + "ĠAc cess", + "à® Ļ", + "r ás", + "Ġappreci ated", + "Ġn ah", + "Ġpos ter", + "Ġt ale", + "Ġhighlight ed", + "æĸ ĩ", + "ż eli", + "Ġblock chain", + "Ġmic row", + "Ġcin ema", + "ĠCh ang", + "ĠSe arch", + "ust ers", + "ĠZ ero", + "ĠDiv ision", + "ÑĢ аÑģ", + "Ġsca re", + "Ġj elly", + "ĠAdminist ration", + "S O", + "Ġl ined", + "Ġê° Ħ", + "Ġge ben", + "Ġso da", + "Ġwin ners", + "³ ¼", + "Ù Ĵ", + "ĠAm b", + "åķı é¡Į", + "å Ķ", + "Ġpe g", + "å· ±", + "4 3", + "Ġra us", + "Ġre wards", + "Ġinc lus", + "Ġhigh way", + "Ġha h", + "Ġmultipl ied", + "Ġs ẽ", + "Ġdisci ples", + "Ġn ing", + "Ġdress ing", + "Ġattrib utes", + "ĠM osc", + "ĠGree ce", + "Ġse k", + "ĠLe arn", + "Ġj us", + "rend re", + "Ġperson ne", + "pl ete", + "Ġpl acing", + "Ġl uego", + "ill ance", + "Ġоб Ñī", + "Ġprov ision", + "Ġl ion", + "t ra", + "bo ards", + "Ġbehavi our", + "he y", + "Ġsubscri ption", + "Ġprot agon", + "ãĥ £", + "Ġvar a", + "ĠÅŁ u", + "Ġha ha", + "Ġteas poon", + "æ Ł", + "av oir", + "Ġcrypt o", + "ĠÑģÑĤ аÑĢ", + "ĠSt ore", + "ab s", + "ĠStud ents", + "Ġla und", + "int o", + "Ġapproach ed", + "° ľ", + "ÑĥÑİ Ñī", + "ĠL abor", + "ot es", + "iat ric", + "Ġgro ÃŁ", + "ut ive", + "Ġи д", + "ĠG ib", + "Ġpl acement", + "ĠdifÃŃ cil", + "Ġf rog", + "ĠвÑģе Ñħ", + "ĠJ r", + "az ed", + "Ñĥ Ñī", + "Ġê ¼", + "fr ame", + "а еÑĪÑĮ", + "Ġlock down", + "åij ³", + "Ġmed i", + "Ġ×Ķ× ŀ×", + "ени й", + "em ale", + "ì¢ ħ", + "ater al", + "Ġdist ant", + "Ġbe ars", + "Ġjournal ist", + "è§ £", + "ĠMarsh all", + "ĠIh nen", + "uet ooth", + "b ag", + "ĠÄij ã", + "ĠHigh ness", + "Ġì° į", + "и ка", + "ĠW u", + "ĠFr an", + "Ġp eng", + "Ġf on", + "Ġhypothes is", + "ĠÑĢ Ñĥ", + "Ġl y", + "× ļ", + "ìĽ Ķ", + "ĠRad io", + "ภŀ", + "D av", + "Ġembarrass ing", + "ĠìŀĪ ìĸ´", + "Ġcast ing", + "Ġc age", + "ĠP sych", + "ĠìĿ¼ ëĭ¨", + "ĠÅ ¾", + "im b", + "Ġdirect ors", + "S H", + "ĠÏĦη ν", + "á»ģ u", + "Ġkon uÅŁ", + "Ġoption al", + "quar ters", + "ik er", + "ĠS ant", + "Ġvers es", + "ë ¶Ģ", + "Ġo lar", + "ĠÏ ĩ", + "ãĥ ķ", + "Ġγ ια", + "ĠI mm", + "Ġcontrovers ial", + "Ġer sten", + "Ġreci p", + "ĠChristian ity", + "Ġê´ ľ", + "ord on", + "×ķ× ©", + "Ġsl ash", + "ĠP f", + "Ñĥд ÑĮ", + "×ķ× Ŀ", + "ĠPer ry", + "Ġm amy", + "Ġbackground s", + "Ġà®İ ன", + "Ġpend ant", + "ĠColumb ia", + "Ġin verse", + "ĠÑĩеÑĢ ез", + "Ġs v", + "Ġdig ging", + "4 1", + "ch em", + "Ġnavig ation", + "ĠSh in", + "ĠFr ont", + "P D", + "Ġbe aring", + "ĠW asser", + "Ġw ax", + "ĠCH RIS", + "ch ing", + "Ġpress ed", + "E l", + "ĠD al", + "ons in", + "Ġb inding", + "Ñģк ой", + "po ons", + "Ġmo ck", + "are st", + "к ÑĢа", + "M M", + "Ġcor rupt", + "st orm", + "Ġref res", + "ĠCo ach", + "ll ä", + "ĠTH IS", + "Ġpar ag", + "Ġìĵ °", + "p ool", + "Ġbill ions", + "Ġê¹ Ģ", + "gr oup", + "Ġwel coming", + "cell ence", + "ĠDu ke", + "ê¸ ´", + "Ġprim era", + "ìł ¸", + "Ġp ond", + "Ġstat ue", + "Ġêµ ¬ë", + "Ġh atch", + "Ġinstrument al", + "Ġresident ial", + "ì» ¤", + "Ġaccept ing", + "osh i", + "d ate", + "ĠìĶ ¨", + "Ġplant ed", + "Ġj oking", + "Ġì Ħľ", + "Ġh ated", + "ĠÑĢаÑģ Ñģк", + "Ġsle pt", + "Ġpack ages", + "Ġisland s", + "es en", + "ÄŁ ı", + "Ġdi agon", + "ĠO sc", + "Ġmes h", + "Ġsc ales", + "ar ity", + "ĠDef ense", + "ãģ¡ ãĤĩ", + "ĠLew is", + "ĠÑģ егоднÑı", + "Ġfl ies", + "uin ely", + "ĠCons ider", + "Ġst ark", + "he w", + "ĠAs ÃŃ", + "³ ´ë", + "Ġprop ose", + "Ġíķĺë ©´", + "od o", + "ĠNorm ally", + "Ġhe eft", + "ĠHarr is", + "g ro", + "ĠBlo od", + "b ase", + "Ġi OS", + "Ġtouch es", + "Ġinsp ir", + "Ġ× ĵ", + "Ġb inary", + "Ġì¶ Ķ", + "Ġser ial", + "Ġ ion", + "Ġunemploy ment", + "Ġodd s", + "ĠF ab", + "ĠF BI", + "BR UN", + "Ġweight s", + "ν ο", + "at ile", + "Ġnurs es", + "Ġinvolve ment", + "ĠíĶ ¼", + "Ġgovern ance", + "Ġâ Ĥ¬", + "ÑĢÑĥ п", + "ier ra", + "íĺ ķ", + "ĠJ erry", + "Ġbe ard", + "Ġsal vation", + "ĠAl ong", + "g entle", + "ĠK i", + "b ol", + "ĠPl at", + "Ġhas ht", + "è¿ ij", + "Ġw are", + "Ġpart ie", + "y cz", + "Ġint r", + "F ih", + "n ent", + "Ġche at", + "il en", + "Ġë ¯", + "or ie", + "Ġfá cil", + "et ric", + "Ġaffect ing", + "unci ation", + "Ġaff airs", + "Ġbe e", + "Ġview ing", + "Ġor ang", + "ĠL an", + "ĠС ÑĤ", + "ä¸ ĸ", + "ĠM es", + "ĥ ģ", + "er ie", + "Ġes pa", + "Ġinter pre", + "Ġposs ess", + "Ġpure ly", + "rit o", + "f ound", + "as ma", + "ìłģ ìĿ¸", + "Ġexam ine", + "ĠÑĥ м", + "Ġbes ch", + "ĠTom orrow", + "ĠB lock", + "Ġvari ant", + "Ġprefer ence", + "Ġcoach es", + "Ġmedic ations", + "Ġíĺ Ħ", + "Ġemp ire", + "ë Ħ¤", + "ĠIll inois", + "Ġcris py", + "Ġth ì", + "Ġbe es", + "7 7", + "Ġgl ow", + "è º", + "ĠStud ies", + "åIJ Ħ", + "ĠChall enge", + "Ġunlike ly", + "Ð §", + "ıy orsun", + "DI E", + "Ġminim ize", + "iz ard", + "Ġú n", + "Ġencont rar", + "ĠK ill", + "å »", + "Ġvan illa", + "ĠGr ant", + "ĠG T", + "se a", + "Ġs ought", + "в од", + "Ġnä m", + "ĠA unt", + "OW N", + "Ġpump kin", + "st ellen", + "Ġr ag", + "ег да", + "Ġstory t", + "Ġfor um", + "æ© Ł", + "Ġestab a", + "uch e", + "Ġcon gress", + "ĠRe y", + "Ġdram atically", + "ĠSp ort", + "ĠYe llow", + "Ġê³Ħ ìĨį", + "Ġdisg usting", + "ĠRe cent", + "Ġacqu ired", + "Ġc ables", + "çĶ ļ", + "d in", + "Ġv isto", + "Ġcommunic ating", + "ÑģÑĤав лÑı", + "еÑģ ÑĤо", + "ãĥ»ãĥ» ãĥ»", + "Ġré g", + "Ġso cks", + "Ġpro ces", + "be cause", + "Ġut ter", + "Ġcoloc ar", + "Ġnew est", + "Ġgr amm", + "è¡ ¨", + "ä¸į çŁ¥éģĵ", + "Ġsh ifting", + "Ġcar rier", + "ĠÑģк оÑĢ", + "ĠSch w", + "Ġexec uted", + "Ġmaint ained", + "ĠÏ Ĩ", + "ĠM oses", + "Ġdis se", + "Ġhor r", + "ãĢ ľ", + "Ġr ally", + "Ġall em", + "ĠEvent ually", + "Ġdi yor", + "lv ania", + "Ġsch nell", + "Ġê³ ¼", + "Ġë§ ¤", + "Ġstrugg les", + "l ate", + "Ġclar ify", + "é ment", + "Ġmulti plic", + "иб о", + "Ġjour n", + "Ġfra gr", + "Ġsurprising ly", + "Ġdesper ate", + "5 2", + "Ġs ul", + "ĠRe ad", + "ĠF ried", + "Ġm ond", + "w oo", + "Ġorgan izing", + "ãģĹãĤĩ ãģĨ", + "ĠSo on", + "Ġв опÑĢоÑģ", + "ĠN ur", + "ĠÐĹ Ð´", + "Ġsp ider", + "е ÑģÑı", + "Ġtutorial s", + "Ġnutri ents", + "or er", + "Ġcoe fficient", + "Ġarrange ment", + "Ġpr icing", + "n an", + "y u", + "B L", + "Ġtri be", + "ĠHow ard", + "un ks", + "Ġnew er", + "Ġprov in", + "Ġpred iction", + "h os", + "Ġol sun", + "ĠAr ound", + "Ġv ier", + "ĠÑģÑĤоÑĢ он", + "Ġv alley", + "ĠE la", + "if i", + "Ġgal axy", + "Ġtran qu", + "Ġad vers", + "ĠTem ple", + "iff s", + "ig ence", + "èĩª å·±", + "Ġkön nte", + "ĠÄij ó", + "D id", + "Ġphotograph s", + "ĠA WS", + "ÑĨи Ñı", + "Ġgu ards", + "Ġappoint ed", + "ĠG il", + "Ġм ом", + "Ġc od", + "ĠUn like", + "Ġeven ly", + "isc onsin", + "Ġest ou", + "Ġm nie", + "ĠEx ec", + "ĠM V", + "ĠE ine", + "ä¿ ¡", + "ĠRog er", + "ĠF ac", + "ĠL ist", + "Ġf uer", + "аеÑĤ е", + "om ed", + "Ġattract ion", + "èī ²", + "Ġter rain", + "ĠD rop", + "Ġcorpor ations", + "Ġsci ences", + "Ġthr one", + "ãģĦ ãģŁ", + "Ġa j", + "ĠR ot", + "çī ¹", + "Ġsupp orters", + "ĠB ere", + "H ere", + "Ġdifer entes", + "Ġsignific ance", + "Ïĥ η", + "æĪij 覺å¾Ĺ", + "Ġcl amp", + "Ġë ĮĢë", + "Ġfab ulous", + "re z", + "æĮ ģ", + "Ġassum ptions", + "ut her", + "w id", + "p ot", + "è¿ İ", + "Ġy an", + "ul in", + "ÑĢ Ñĭв", + "ĠSl ow", + "ĠPenn sy", + "Ġíķ ´ìĦľ", + "Ġme io", + "Ġwealth y", + "ĠE ight", + "Ġpul se", + "Ġfr iction", + "id ity", + "ĠH oll", + "i yorum", + "Ġsound ed", + "ĠC arr", + "Ġfor k", + "â ĺ", + "ĠP A", + "Ġcons pir", + "Ġc oding", + "r t", + "ĠTy p", + "Ġìĸ ij", + "Ġп ог", + "Ġmis er", + "ĠÑģм оÑĤÑĢ", + "ĠSw eden", + "Ġolar ak", + "ĠZh ang", + "ĠCh i", + "ĠT itan", + "Ġscreen ing", + "ĠSp ider", + "ĠÅŀ imdi", + "Ġobst acles", + "lar a", + "Ġchalleng ed", + "p se", + "T ON", + "á» ¥", + "ĠP i", + "Ġlag i", + "ie urs", + "Ġhur ting", + "Ġneg lect", + "Ġgener ating", + "Ġyoung est", + "Ġaud it", + "ĠÑĢ ез", + "Ïģ ά", + "Ġdon ate", + "ĠPD F", + "Ġvis its", + "Ġcru ise", + "P P", + "as er", + "Ġw sp", + "back s", + "iv als", + "ãģĨ ãĤĵ", + "Ġde ve", + "Ġprop ort", + "Ġc ath", + "ĠE ffect", + "Ġwind s", + "ĠìĻ Ķ", + "Ġchart s", + "Ġs ama", + "Ġautom ation", + "Ġпок а", + "Ġol an", + "Ġbo ats", + "Ġca fe", + "Ġden ied", + "ĠM ama", + "Ġblock ing", + "ĠTh or", + "Ġphenomen al", + "Ġstake holders", + "Ġun os", + "Ñĥ еÑĤ", + "ĠAb raham", + "ãģ§ ãĤĤ", + "Ġdetect ion", + "Ġjur is", + "Ġpower ed", + "z ial", + "Ġwel fare", + "Ġup grad", + "Ġmoż na", + "ĠC ase", + "c ular", + "Ķ ìĿ´", + "ãĥ ģ", + "ĠGu ess", + "Ġcy cles", + "ä¾ ĭ", + "çµ ¦", + "ro ck", + "um i", + "Ġel ite", + "Ġqu è", + "åł ±", + "ÑĤ ом", + "Ġsh ore", + "gun ta", + "Ġk u", + "Ġfaith ful", + "ĠJ eremy", + "a id", + "à ·", + "ug al", + "å°į åķĬ", + "ĠV el", + "Ġvra i", + "st ell", + "¨ ¸", + "Ġk ol", + "è ½", + "Ġquant o", + "Ġз аÑĢ", + "Ġ200 2", + "es y", + "Ġres erve", + "Ġмом енÑĤ", + "Ġdeploy ed", + "Ġdefin ing", + "Ġsa u", + "Ġga at", + "\" )", + "Ġtrans mit", + "Ġpubl ishing", + "Ġrank ing", + "Ġoff ense", + "Ġ4 6", + "p in", + "ĠT aking", + "Ġentit led", + "Ġgen uinely", + "Ġvari ations", + "Ġfind e", + "Ġt au", + "Ġunf ortunate", + "ĠR ah", + "port s", + "Ġc Å", + "Ġmon key", + "Ġbr ac", + "we i", + "l ung", + "Ġart if", + "Ġsy rup", + "ĠÐĶ ав", + "Ġlift ed", + "Ġche z", + "ĠAd vent", + "ĠSt ock", + "Ġdo l", + "м ен", + "иÑĪ ÑĮ", + "Ġy n", + "g io", + "d et", + "Ġdes se", + "Ġg ri", + "ĠChair man", + "ç ħ", + "Ġcu enta", + "an im", + "Ġcra b", + "Ġesc al", + "Ġpremi ère", + "ĠGe f", + "Ġd ining", + "Ġsevent h", + "Ġch asing", + "ĠT ower", + "Ġbrut al", + "Ġfundament ally", + "ãģ¨ ãģĨ", + "л ениÑı", + "st age", + "Ġacqu is", + "Ġcyl inder", + "Ġcomm ander", + "m em", + "ĠU V", + "ha ppy", + "Ġe psilon", + "Ġinv itation", + "Ġfar mer", + "ch air", + "Ġdest iny", + "Ġso vere", + "ĠHeb rew", + "Ġserv ant", + "Ġbe w", + "Ġg ast", + "ut ies", + "Ġadministr ative", + "ĠComm and", + "é ta", + "Ġnit rogen", + "ê· ¼", + "Ġab i", + "Ġvill ain", + "Ġblank et", + "ĠS end", + "Ġbeat en", + "² Ħ", + "Ġvol unt", + "Ġschol ar", + "ĠEm peror", + "Ġ4 3", + "v able", + "ĠD us", + "ĠG U", + "Ġtarget ing", + "ww w", + "Ġamend ment", + "ìĨ Įë", + "Ġt ing", + "Ġn asty", + "Ġg auge", + "ĠÑĢ од", + "ĠH ans", + "Y our", + "α ν", + "Ġpro jet", + "ĠHawai i", + "Ġsusp icious", + "Ġsch w", + "Ġremo val", + "Ġint rig", + "ĠM U", + "Ġp onto", + "ठ¾", + "Ġоб ÑĢаз", + "Ġguess ing", + "p ace", + "Ġm others", + "Ġmill imeter", + "л ение", + "没 æľī", + "Ġavail ability", + "ic z", + "æŃ ¤", + "Ġfr act", + "Ġbas es", + "k m", + "ĠB TS", + "ĠF ield", + "Ġd zie", + "Ġseg undo", + "ĠëĤĺ ëĬĶ", + "Ġlegit imate", + "im as", + "Ġв н", + "Ġcor ruption", + "Ġsm ash", + "ĠVal ent", + "Ġalign ed", + "ĠPennsy lvania", + "Ġg ab", + "ĠE un", + "ent h", + "ĠMor ning", + "Ġcand le", + "Ġback pack", + "ĠIslam ic", + "a ções", + "Ġenc ry", + "Ġmushroom s", + "íĮ Į", + "d it", + "Ġtrans it", + "ĠW isconsin", + "Ġparticip ated", + "ĠIl s", + "Ġunf old", + "¶ Ģë", + "Ġprof its", + "Ġwar ming", + "ĠG ang", + "Ġnetwork ing", + "Ġme ga", + "Ġthorough ly", + "le ments", + "ĠH m", + "Ġdec iding", + "Ġemotion ally", + "Ġexha usted", + "ĠÐŁ оÑĤ", + "c ido", + "ĠHT ML", + "Ġcopy right", + "Ġmel ody", + "y im", + "Ġand ers", + "osh op", + "Ġë³ ¼", + "Ġathlet e", + "ĠG E", + "Ġfrequ ent", + "Ġdes ires", + "Ġneed ing", + "ĠY un", + "Ġrif le", + "Ġlo ver", + "' T", + "Ġd ense", + "Ġt ão", + "Ġnot ified", + "Ġid i", + "ìĹ Ń", + "í Ĩ", + "Ġinteract ing", + "Ġrapp ort", + "еÑĢ и", + "s ki", + "Ġb esser", + "Ġmanufact urer", + "ĠK yle", + "Ġaccount able", + "ĠS ak", + "ĠP il", + "ĠD omin", + "Ġpres um", + "ĠÐĴÑģ е", + "Ġvine gar", + "Ġguarante ed", + "çľĭ åĪ°", + "Ġhand led", + "éŁ ³", + "c at", + "Ġcivil ization", + "Ġaccom p", + "ĠV M", + "é mon", + "Ġde ze", + "Ġgrad es", + "Ġsoll te", + "Ġst aring", + "×IJ× ª", + "ar nt", + "Ġhoriz on", + "Ġtrav ail", + "h our", + "第 ä¸Ģ", + "ĠE D", + "ĠD ak", + "Ġn y", + "Ġcon ve", + "ĠCh am", + "Ġfir ms", + "ĠL iu", + "ĠÑģÑĤ ÑĢан", + "Ġli bert", + "Ġlens es", + "Ġint ake", + "ĠвÑĭ б", + "Ġmens en", + "h el", + "Ġpract ition", + "Ġ3 50", + "ãĤ ³", + "F O", + "Ġbed s", + "Ġancest ors", + "ĠìĹĦ ì²Ń", + "Ġdistur b", + "ĠLast ly", + "ĠSupp ort", + "ี à¹ī", + "ĠCor ona", + "Ġenthus i", + "Ġвоз м", + "ĠìĤ¬ëŀ Įë", + "Ġ5 2", + "b ird", + "Ġredu ces", + "ĠìŀĪ ìĿĦ", + "ĠG ene", + "êµ IJ", + "ÄĻ p", + "ĠÃľ ber", + "Ġconcer ning", + "us er", + "Ġconcent rate", + "ĠWH AT", + "ish op", + "onym ous", + "no ld", + "Ġsuggest ing", + "© °", + "ĠF ish", + ".... ....", + "Ġvess el", + "Ġtrabaj o", + "ãģ µ", + "ĠO cean", + "å§ IJ", + "y g", + "Ġtown s", + "d el", + "Ġterr ifying", + "Ġçal Ä±ÅŁ", + "Ġs ino", + "Ġe ats", + "Ġge z", + "Ġg eme", + "ĠìĻ Ħ", + "Ġcomp art", + "Ġimplement ing", + "ĠPot ter", + "ĠGerm ans", + "Ġg ÅĤ", + "Ġt ennis", + "Ġcar pet", + "au er", + "ĠSaud i", + "ye ong", + "Ġcur ry", + "ĠFore st", + "Ñĭ л", + "Ġfif teen", + "Ġbol ts", + "Ġ{ \\", + "¬ ´", + "Ġsett lement", + "Ġl ange", + "Ġb am", + "G et", + "íķ Ļ", + "Ġsw ap", + "ĠK han", + "Ġcomm ence", + "Ġquar antine", + "Ġsc ored", + "ç ĸ", + "Ġ19 50", + "Ġthick er", + "Ġsû r", + "åı £", + "ĠLar ry", + "Ġall ez", + "ìĭľ ëĬĶ", + "Ġg ü", + "Ġspect acular", + "/ /", + "b oth", + "Ġst ats", + "å¦ ³", + "ĠN ancy", + "Ġbun u", + "Ġcr ust", + "Ġactiv ated", + "Ġê·¸ë ŀ", + "out he", + "Ġport s", + "Ġne ural", + "Ġj aw", + "Ġobserv ations", + "Ġvo it", + "ab an", + "ả i", + "¦¬ë ¥¼", + "om es", + "௠ĭ", + "qu i", + "Ġkind ness", + "Ð ij", + "Ġ4 1", + "Ġmoder ate", + "Ġang els", + "ĠT amb", + "è t", + "Ġch lor", + "ĠBill y", + "ì² ĺë", + "ac on", + "Ġselect ing", + "ĠDel ta", + "Ġn ull", + "den ly", + "Ġci ud", + "Ġtend ency", + "Ġbreak down", + "Ġm int", + "ÑĦ оÑĢм", + "or ph", + "Ġda wn", + "s pr", + "ĠW ILL", + "äch lich", + "Ġpu ppy", + "7 00", + "Ġà® ¤", + "Ġfail s", + "ĠCon c", + "Ġrel atives", + "Ġinv iting", + "Ġaut onom", + "Ġcomp osed", + "Ġun ity", + "Ġdec is", + "Ġaccess ories", + "ĠC ass", + "Ġb ist", + "ĠT ip", + "ì§ ¸", + "Ġp unt", + "Ġr áp", + "éĢ ²", + "AN K", + "ãģ ļ", + "ex ist", + "Ġcompat ible", + "Ġn er", + "Ġе мÑĥ", + "Ġa plic", + "Ġb apt", + "Ġfail ing", + "ĠTam am", + "Ġos cill", + "Ġletz ten", + "Ġrepeated ly", + "Ġjung le", + "ĠP ush", + "h ai", + "ĠÎ ·", + "Ġdead ly", + "Ñı ж", + "wi Äħ", + "ĠComm on", + "ĠÎ ķ", + "Ġsk ate", + "T C", + "ĠMin i", + "Ġhob by", + "ầ n", + "Ġrout es", + "Ġam igos", + "Ġcon jun", + "Ġpartners hips", + "Ġno vo", + "Ġa ver", + "Ġpou vez", + "br idge", + "Ġpre oc", + "h im", + "Ġtur b", + "Ġso b", + "ĠSn ap", + "Ġì° ¸", + "min ute", + "Ġtra ject", + "uj ÄĻ", + "Ġe ager", + "Ġregul atory", + "Ġbank ing", + "b ling", + "ÑĪ ÑĮ", + "a ż", + "Ġbiz arre", + "it ated", + "d ire", + "Ġthreat ened", + "Ġsh ining", + "Ġn esse", + "Ġcor ps", + "ĠÑģ Ñĥ", + "Ġt eles", + "Ġtem p", + "t em", + "Ġк ан", + "Ġfe ver", + "N ew", + "Ġheav ier", + "ĠS ah", + "b ud", + "Ġout ros", + "Ġì° ¾", + "Ġëª ħ", + "arr ing", + "Ġê´ľ ì°®", + "ĠN ap", + "Ġse min", + "ĠTh an", + "if s", + "Ġdes en", + "ĠÑĤак ое", + "Ġlos es", + "ĠB alt", + "k on", + "Ġнап ÑĢ", + "Ġvo is", + "ĠMosc ow", + "Ġch airs", + "h is", + "Ġrefuge es", + "k g", + "Ġk ole", + "į ¨", + "аÑģ ибо", + "¦ ½", + "ĠUn iverse", + "ĠDire ct", + "Ġche ating", + "ĠC in", + "Ġpat ri", + "Ġadv ise", + "ĠN ether", + "Ġprime iro", + "Ġmention ing", + "n ut", + "5 6", + "ar ı", + "Ġpet ite", + "b led", + "Ġpens ar", + "ic io", + "IN D", + "Ġveter an", + "Ġlad der", + "Ġconsequ ence", + "ож ал", + "ĠB urn", + "Ġr ug", + "ĠM ade", + "Ġg it", + "\" ...", + "Ġcompet itors", + "Ġprz ed", + "Ġapp arent", + "ĠArgent ina", + "ĠWork ing", + "Ġcollabor ate", + "w oman", + "Ġret ain", + "Ġle urs", + "Ġdash board", + "×Ļ× ĵ", + "ĠEar ly", + "B M", + "Ġе Ñij", + "ол ог", + "Ġsatisf ying", + "Ġoft entimes", + "Ġma pping", + "ünk ü", + "ar th", + "f old", + "Ġlaunch ing", + "Ġa ura", + "Ġprec ision", + "work s", + "G od", + "Ġstra p", + "ĠIm per", + "Ġr ivers", + "Ġ |", + "Ġcu er", + "reg on", + "Ġarri val", + "ка Ñħ", + "ĠM iami", + "ан Ñĭ", + "Ġsurviv ors", + "ĠSen ior", + "Dav id", + "Ġest ado", + "Ġse ctors", + "Ġpop ping", + "Ġch im", + "ay ı", + "Ġkun nen", + "Ġgall ery", + "Ġsun light", + "ese hen", + "Ġye lling", + "ĠMe in", + "ĠPho enix", + "Ġman o", + "Ġhistor ia", + "Ġoccur ring", + "æ¬ ¸", + "ì ¸", + "ад и", + "å¾ ħ", + "Ġinstitution al", + "ĠT ut", + "ç ²", + "Ġsl aves", + "ãģ© ãģĨ", + "Ġforg iveness", + "Ġtw in", + "ĠHy un", + "н ÑĮ", + "ĠK omm", + "and ra", + "sh ot", + "ss ä", + "ĠÑĨ е", + "at ta", + "Ġexp ense", + "ĠG PU", + "ĠP ast", + "rib ly", + "ĠëŃIJ ìķ¼", + "Ġгод а", + "Ġresp ir", + "æĿ ±", + "ĠQue ens", + "h ops", + "Ġs érie", + "Ġpre f", + "Ġcom ed", + "Ġpl ut", + "ĠOver all", + "Ġãģ Ŀ", + "Ġc ush", + "Ġring ing", + "Ġincor rect", + "ĠÑģÑĤ ÑĢ", + "Ġgeomet ry", + "Ġadvert is", + "ĠÐ ¨", + "Ġreview ed", + "ãģĤ ãģĤ", + "Ġdo zens", + "Ġdeterm ination", + "ĠPh ill", + "Ġcontrib uted", + "ĠC it", + "Ġpass engers", + "Ġcôt é", + "Ġre ver", + "Ġtechn ological", + "Ġall en", + "Ġr aining", + "av i", + "Ġsal ty", + "Ġtyp ing", + "ĠÑĤ е", + "Ġt ilt", + "Ġì¹ ĺ", + "Ġо ÑĢ", + "ĠпÑĢ Ñıм", + "Ġr ou", + "Ġare na", + "ar at", + "åĪ «", + "HH HH", + "Ġmanufact urers", + "ĠEd ward", + "Ġt uck", + "Ġbl ows", + "ing o", + "ĠMar c", + "ìķĦ ìĦľ", + "M ich", + "ĠCle an", + "è ´", + "est o", + "ĠP ack", + "Ġsha ft", + "BRUN O", + "Ġa ven", + "u ur", + "Ñģк олÑĮко", + "ê´ Ģ", + "Ġautom ated", + "Ġvent ure", + "Ġsurve illance", + "ĠG row", + "ĠE mer", + "Ġд оÑĢ", + "Ġinvest or", + "ĠY ok", + "Ġl atter", + "ĠN I", + "Ġfunction ing", + "ĠHam ilton", + "Ġ5 1", + "Ġmurder ed", + "Ġanch or", + "Ġc uc", + "ĠSC P", + "ĠMad am", + "Ġconstra ints", + "Ġb arn", + "ank en", + "Ġë§İ ìĿĢ", + "ĠMot or", + "ĠDo ing", + "Ġam en", + "et ts", + "Ġinst ructor", + "eg t", + "ak o", + "Ġpost ure", + "iv ia", + "ĠPol ish", + "Ġдв а", + "Ġcolor ful", + "Ġel bow", + "Ġpar le", + "Ġpass er", + "Ġcond em", + "ort al", + "Ġfert il", + "ا د", + "ĠCol omb", + "Ġalign ment", + "Ġastron aut", + "ĠM ut", + "Ġsal mon", + "Ġstructure d", + "ŀ ר", + "Ġclick s", + "Ġm iej", + "æĶ ¿", + "ãģĦ ãĤĦ", + "ĠR ound", + "Ġrain bow", + "ĠV A", + "ãģĶ ãģĸ", + "ì§ Ī", + "ot z", + ", ", + "Ġch ords", + "ĠSand ers", + "Ġë¶ Ħë", + "B en", + "Ġdar über", + "ili ans", + "Ġorder ing", + "ĠMan h", + "Ġkil ogram", + "Ġkar ÅŁ", + "Ġgr asp", + "Ġghost s", + "al en", + "ĠJ edi", + "Ġб ли", + "Ġdownload ed", + "Ġconduct ing", + "ĠH ak", + "Ġresearch er", + "il an", + "go od", + "ĠH annah", + "ĠdÃ¼ÅŁ ün", + "ĠMess iah", + "u ity", + "ion a", + "Ġprob able", + "ĠY E", + "Ġindepend ently", + "Ġbuff er", + "b urn", + "our d", + "ĠMc K", + "Ġl ingu", + "uj emy", + "еÑĢ ÑĤ", + "Ġintuit ive", + "Ġcrack s", + "app ropri", + "nt y", + "Ġge en", + "Ġl end", + "Ġcert ification", + "ID S", + "un ter", + "pe es", + "Ġtr ump", + "Ġbank rupt", + "Ġfe as", + "è Ĺ", + "Ġdu ż", + "æ¸ ħ", + "Ġvirus es", + "Ġ5 8", + "g od", + "Ġж ел", + "Ġst alk", + "I nd", + "ach i", + "ĠC F", + "ĠC ond", + "Ġsan ct", + "Ġcont en", + "Ġfre ed", + "ĠR T", + "Ġment ors", + "ì¡ ±", + "Ġport able", + "ĠPaul o", + "r ane", + "HA HA", + "ĠS ection", + "ç Ĩ", + "hy un", + "ĠÎŃ Ïĩ", + "ĠP ub", + "ĠInd epend", + "Ġcomp ounds", + "ĠÑģ Ñĭ", + "Ġmess aging", + "Ġded ication", + "Ġnot icing", + "Ġdevot ed", + "ÑİÑĤ ÑģÑı", + "Ġsn akes", + "Ġbattle field", + "p ers", + "Ġdel a", + "9 2", + "Ġha i", + "ill ä", + "ér er", + "e very", + "Ġrespons ive", + "×Ļ ×ķ", + "op f", + "é ī", + "Ĭ ¸", + "Be cause", + "Ġtour ism", + "Ġê·¸ ê²Į", + "×ķ× ¦", + "Ġcan s", + "st üt", + "Ġdon ne", + "ĠD ios", + "ĠU ber", + "act ory", + "Ġorient ed", + "ĠH erm", + "Ġpat ron", + "ur f", + "be i", + "Ġprogram a", + "ĠOh h", + "gen er", + "Ġf ist", + "ĠW endy", + "Ġand a", + "Ġguess ed", + "Ġfre ak", + "ä¸Ń åľĭ", + "ĠK ings", + "ch ool", + "Ġoff line", + "ĠIndian a", + "ĠAll iance", + "Ġ5 3", + "Ġpartic ul", + "ĠF ocus", + "Ġinhab it", + "Ġê°ĻìĿĢ ëį°", + "ĠMc G", + "ows ki", + "ĠìĿ´ ê±´", + "Ġpa ÅĦst", + "он и", + "itt a", + "Ġconfirm ation", + "ĠBrook lyn", + "Ġnood le", + "f und", + "it ud", + "Ġgrand parents", + "Ġbar becue", + "ει ÏĤ", + "Ġ á", + "Ġball ot", + "ĠV eter", + "Ġpip es", + "ig ious", + "ĠG raph", + "est ed", + "Ġë¸ Įë", + "ĠK E", + "ãģ¡ãĤĩ ãģ£ãģ¨", + "Ġe ins", + "Ġhat red", + "ãģij ãģ©", + "Ġd ang", + "ee ee", + "Ġarch ae", + "ĠJes se", + "Ġdetect ed", + "Ġsen i", + "burg h", + "Ġdispl acement", + "Ġdo p", + "Ġcondition ing", + "Ġне ÑģколÑĮко", + "Ġdistur bing", + "P H", + "Ġthin ner", + "Ġwound ed", + "ĠCu ando", + "Ġcush ion", + "Ġwh ites", + "Ġprefer ences", + "Ġì¤Ģë ¹Ħ", + "Ġka ż", + "ĠG ate", + "ĠP ath", + "d les", + "à¸Ħ ร", + "im ore", + "Ġë³´ìĹ ¬", + "Ġdiscipl ines", + "á» ı", + "Ġmes ma", + "Ġìĥ Īë", + "Ġìĭ ¬", + "Ġg ing", + "Ġumbre lla", + "IGH T", + "Ġp ension", + "Ġcomb ining", + "S S", + "Ġrect angle", + "á»ĩ t", + "Ġpro xim", + "ĠC ow", + "¸ Į", + "Ġintention al", + "æķ Ļ", + "Ġdec id", + "ĠÑģк аж", + "ĠU ma", + "ias m", + "b uz", + "Ġdebr is", + "Ġc ass", + "ĠP rop", + "is ka", + "ë ł¥", + "ester ol", + "uss ian", + "ìĿ´ë ŀij", + "Ġun limited", + "Ġadm ire", + "Ġtight ly", + "Ġgen ome", + "ĠJun ior", + "ven ir", + "g us", + "Ġc Äĥ", + "ĠV lad", + "Ġí Ĥ", + "Ġrel ativ", + "in ci", + "Ġaun que", + "ĠBo ys", + "ÑĨи он", + "ĠSw iss", + "Ġphys icians", + "Ġíı ī", + "ĠP ET", + "Ġw ounds", + "ab out", + "Ãł i", + "on z", + "ur ities", + "ĠÑĥв ид", + "å· ¦", + "Ġment ality", + "Ġvari ance", + "Ġseg unda", + "Ġvol cano", + "al ie", + "ॠĩ", + "Ġt iles", + "ĠT erry", + "ĠاÙĦÙĦ Ùĩ", + "Ġcan on", + "Ġsc attered", + "pt on", + "Ġdefin itions", + "Ġal gebra", + "ot en", + "ab lo", + "ij uana", + "Ġwra pping", + "Ġses ame", + "ĠнаÑĩ ина", + "ĠAl f", + "ĠÐł оÑģÑģ", + "or no", + "Ġan kle", + "Ġspecial ty", + "Ġattempt ing", + "ili ation", + "Ġ19 20", + "Ġphen omena", + "ĠPro duct", + "ĠB uck", + "ĠA ww", + "se en", + "Ġvo id", + "ĠFrank lin", + "Ġadvoc acy", + "ĠS ep", + "Ġcool est", + "ĠÑģ ÑĢазÑĥ", + "ĠQu and", + "Ġ9 00", + "ĠTr ad", + "d ies", + "Ġhas h", + "æĪij å°±", + "ä¹Ł æĺ¯", + "Ġpot s", + "Ġsad ly", + "Ġvi able", + "ĠT iger", + "ĠON E", + "Ġneur ons", + "ow anie", + "Ä Ĺ", + "ĠSh ar", + "ĠLand es", + "Ġconfer ences", + "è© ²", + "Ġcred ential", + "Ġl ime", + "ine e", + "x it", + "p ay", + "Ġinc ons", + "Ġ>> :", + "èª į", + "Ġí ŀĺë", + "Ġless er", + "Ġsp ill", + "Ġprem ise", + "Ġ36 5", + "ĠH ost", + "Ġtom ar", + "×IJ× ľ", + "ë ²Ī", + "ĠWhat s", + "Ġlight weight", + "ĠM ap", + "f ia", + "ells chaft", + "Ġvend ors", + "uest o", + "ĠM ister", + "ĠÐŁ ÑĢи", + "åı ³", + "h ma", + "Ġintention ally", + "ĠT ang", + "éĹ ®", + "Ġident ification", + "Ġetc etera", + "ĠN ee", + "ĠÑĤ ÑĢи", + "ê· ¸", + "Ġcrypt ocur", + "Ġin hale", + "Ġadd ict", + "åIJĦ ä½į", + "Ġma u", + "ĠÑĤак аÑı", + "Ġë² Ħ", + "Ġcomp rar", + "ied zieÄĩ", + "ĠоÑĤ но", + "Ġbegin ner", + "Ġм Ñĥж", + "Ġobs c", + "Ġlim iting", + "asc ular", + "Ġins pection", + "ac i", + "Ġre jo", + "M us", + "Ġz aten", + "Ġsz cz", + "ĠMad rid", + "Ġvar ieties", + "Ġest Ãł", + "ĠSh akes", + "Ġk its", + "Ġad minister", + "Ġla va", + "Ġg Ã¥", + "è© ¦", + "ת ×Ļ", + "ĠWay ne", + "Ġinst agram", + "Ġr ated", + "p aper", + "Ġb ild", + "Ġpret ending", + "Ġobser ving", + "ĠÑģам ом", + "Ġtr or", + "Ġorgan isms", + "Ġfal ta", + "Ġh ometown", + "ç ±", + "Ġí ĭ", + "Ġche g", + "Ġì ¡", + "Ġcomm a", + "is é", + "Ġlike lihood", + "av ored", + "Ġgel di", + "ни ков", + "Ġmed io", + "Ġjak ie", + "ĠJ up", + "Ġgreen house", + "Ġsp it", + "ко е", + "Ġк аж", + "ĠG ram", + "ĠCon ference", + "Ġdef icit", + "s ın", + "in se", + "u ÄŁ", + "Ġr icht", + "Ġcoinc idence", + "åı į", + "Ġeu rop", + "Ġbutter fly", + "p read", + "Ġìĸ ¼", + "èĢ ¶", + "Ġwa vel", + "ĠIn fin", + "ĠPlan et", + "Ġself ie", + "ient ras", + "Ġar rog", + "os er", + "id al", + "ł×Š׳×ķ", + "üt ün", + "Ġfresh man", + "ĠMach ine", + "Ïĥ ÏĦ", + "ĠD ia", + "ìĿ´ ëĭ¤", + "ãģĵ ãģĨ", + "ne a", + "Ġlist ing", + "Ġconfig ure", + "ut or", + "U p", + "ts chaft", + "ri ère", + "Ġup wards", + "ĠÑħоÑĩ Ñĥ", + "Ġswe ep", + "B r", + "Ġexpress ing", + "Ġun happy", + "Ġmand atory", + "g ender", + "ĠA ÃŃ", + "Ġindic ators", + "Ġoil s", + "n ote", + "Ġseg ur", + "ож еÑĤ", + "yn asty", + "Ġdist ances", + "Ġmer ge", + "BER T", + "Ġsur render", + "Ġbu at", + "ĠA wards", + "Ġseñ or", + "od ox", + "Ġfl avour", + "Ġab dom", + "Ġconfig ur", + "8 6", + "ĠDI Y", + "Ġrig id", + "° ĺ", + "Ġcorpor ation", + "Ġg room", + "j aw", + "ĠNe ar", + "ил о", + "Ġoper a", + "ĠIn nov", + "и ÑĢа", + "ĵ ±", + "Ġspec ified", + "Ġcos m", + "ĠFre edom", + "Ġcl own", + "ĠN em", + "Ġв ол", + "Ñij н", + "Ġchar ger", + "à¹ģ ล", + "Ġinflu ential", + "äs ident", + "é ¤", + "ĠìĦ łë", + "Ġvol umes", + "æ IJ", + "Ġout ras", + "ĠTw itch", + "Ġfound ing", + "Ġa while", + "Ġco il", + "ê° Ļ", + "Ġc ả", + "ĠTh row", + "ĠH ence", + "omm t", + "ĠBen jamin", + "глÑı д", + "T ime", + "ob ic", + "Ġm our", + "Ġd read", + "ĠL Ãł", + "ĠCh ile", + "Ġpre val", + "Ġv ain", + "Ġart ık", + "Ġpres erved", + "ĠоÑĤ д", + "Ġware house", + "Ġbest e", + "ĠSever al", + "ĠS ituation", + "Ġcard board", + "T od", + "er na", + "Ġgar ant", + "Ġgest ure", + "Ġh en", + "Ġspe lling", + "ose xual", + "Ġan ne", + "Ġm ice", + "ĠMe ine", + "c ard", + "Ġre bell", + "Ġcert o", + "Ġìľ łë", + "Ġvers chied", + "ĠB os", + "Ġinv ention", + "Ġtr ze", + "Ġman ière", + "ĠCh ad", + "Ġsp re", + "Ġorganis ations", + "Ġpoor ly", + "Ġan terior", + "Ġst air", + "к ÑĢ", + "Ġatom ic", + "Ġsymp ath", + "Ġcontin ually", + "Ġkle ine", + "è te", + "и Ñī", + "ο ÏĤ", + "pe ut", + "Ġrep osit", + "Ġent ra", + "E m", + "Ġfinan cing", + "Ġмн ог", + "Ġthe sis", + "ĠCom puter", + "e au", + "ĠT ree", + "Ġbr ide", + "ons ieur", + "sh ire", + "w ic", + "D E", + "ĠìĪ ĺë", + "Ġac om", + "ĠP O", + "ers ch", + "Ġпом оÑī", + "ĠAr men", + "Ġì£ ½", + "Ġz or", + "Ġprint s", + "ĠD ass", + "æ¸ ¯", + "Ġdur able", + "ĠTrans port", + "ìŀIJ ê°Ģ", + "Ġл ег", + "Ġdé t", + "ô le", + "am ous", + "Y N", + "Ġcl iff", + "Ġgramm ar", + "ĠÐŁÐ¾ ÑįÑĤомÑĥ", + "ĠlÃł m", + "es ch", + "Ġmiser able", + "Ġvol ts", + "ĠC ad", + "uk an", + "ÑĤ ив", + "r ust", + "Ġìĺ¬ë Ŀ¼", + "Ġver k", + "Ġchick ens", + "ĠY oo", + "Ġout fits", + "c ode", + "Ġhier archy", + "net es", + "Ġcounter part", + "Ġt ôi", + "Ġt ed", + "ĠB art", + "Ġë Ŀ¼", + "ĠGen au", + "Ġinc oming", + "ĠA BC", + "ri que", + "ĠоÑĤ п", + "qu al", + "Ġincent ive", + "Ġih ren", + "׳ ×Ļ", + "lo e", + "Ġ19 30", + "Ġbar g", + "Ġd iction", + "Ġön ce", + "IN S", + "Ġre h", + "isia j", + "m outh", + "Ġsc oring", + "l ık", + "ĠìķĦ 주", + "OR IA", + "ĠEst ados", + "Ġcompan ion", + "Ġasse mble", + "Ġpun ished", + "Ġit al", + "Ġprev ents", + "ist es", + "ĠKent ucky", + "Ġloc ate", + "Ġfast ing", + "ãģ¨ æĢĿ", + "ĥ Ģ", + "ĠSe b", + "ĠCr own", + "op ia", + "Ġwh ip", + "us z", + "к ами", + "Ġdatab ases", + "åŃ Ĺ", + "Ġprose c", + "Ġ199 7", + "ĠìĤ´ì §Ŀ", + "ĠSol ar", + "ĠP ues", + "ĠZ en", + "oll o", + "ĠG uru", + "Ġsque ez", + "ĠÐĹ Ð°", + "ĠÄ į", + "cept ions", + "c ca", + "iz able", + "m and", + "Ġbreak through", + "Ġtables poon", + "ĠS EC", + "ik h", + "ĠS ão", + "Ġп ло", + "am en", + "Ġpr ac", + "Ġdar ling", + "Ġtall er", + "Ġrend ering", + "Ġìļ°ë¦¬ ê°Ģ", + "ĠÏĦη ÏĤ", + "Ġm ã", + "Ġes os", + "uer do", + "ĠÑģ ÑĩиÑĤ", + "all er", + "ìĹĪ ìĸ´ìļĶ", + "Ġmill ones", + "ler in", + "Ġpe gar", + "on ne", + "Ġenroll ment", + "Ġli egt", + "Ġbo a", + "w iÄĻ", + "bs p", + "Ġcy cling", + "ĠBern ie", + "Ġ198 9", + "Ġд алÑĮ", + "ĠDak ota", + "ĠÑģв Ñıз", + "ĠC P", + "Ġst are", + "íĤ ¤", + "Ġprosper ity", + "Ġarrange ments", + "Ġarri ving", + "m ä", + "Ġkay ak", + "ip t", + "Ġp ardon", + "Ġrel at", + "Ġver ste", + "ĠF ig", + "Ġfo il", + "ĠTalk ing", + "pe are", + "Ġno i", + "ĠпÑĢи ÑĪ", + "Ġhoc key", + "Ġad o", + "ĠO UT", + "6 7", + "Ġhorm ones", + "ĠAven ue", + "ĠSuper man", + "Ġpres cription", + "uber netes", + "C L", + "ot ive", + "N IS", + "ien en", + "Ġsad ness", + "ĠV it", + "T y", + "Ġstar ter", + "Ġbed e", + "Ġfound ations", + "Ġso re", + "åº Ĺ", + "Ñīе ÑģÑĤв", + "ìļ °ë", + "ĠÑĩ Ñĥв", + "l ink", + "Ġmane u", + "work ing", + "Ãł n", + "ĠAtt ack", + "ĠC art", + "ve is", + "ĠRes p", + "ens ing", + "Ġì¢ĭ ìķĦìļĶ", + "Ġesc uch", + "ĠR NA", + "Ĥ ´", + "Ġad op", + "Ġb ending", + "ع د", + "Ġman ages", + "us p", + "Ġt art", + "Ġrout er", + "B o", + "Ġestab lishing", + "Ġbal ancing", + "Ġathlet ic", + "ĠS lo", + "Ġf ills", + "Ġн аб", + "Ġд ал", + "Ġpos so", + "ĠV ielen", + "Ġcrit ics", + "Ġlaws uit", + "ĠIsa ac", + "ĠÑĦилÑĮ м", + "Ġtr as", + "Ġpra w", + "ĠCra zy", + "Ġne u", + "Ġk ull", + "Ġtum or", + "ĠAP P", + "g ate", + "ĠA RE", + "9 8", + "ĠSte am", + "Ġfuck ed", + "l age", + "ĠâĻ ¬", + "ĠM D", + "f y", + "Ġshell s", + "ĠSe ems", + "iz ers", + "Ġr anges", + "ĠAnton io", + "AT ION", + "ĠB aba", + "Ġìĥ ī", + "k un", + "Ġpray ed", + "ÑĢ Ñı", + "ĠпÑĢоÑĤ ив", + "Ġse as", + "b ury", + "Ġ×Ķ× ©", + "Ġtra it", + "ĠDep ending", + "Ġd re", + "Ġkön nt", + "ÑĨ Ñĥ", + "Ġlip stick", + "ee z", + "ĠпÑĢ имеÑĢ", + "Ġassign ments", + "B ob", + "Ġmet als", + "Ġspe cially", + "å°į ä¸įå°į", + "Ġìĺ Īë", + "ĠÅ ¡", + "Ġv ista", + "ĠÎ ¬", + "Ġtw ins", + "Ġnot able", + "ĠS au", + "Ġdé velop", + "Ġç ek", + "Ġpoly nom", + "av am", + "Ġtamb é", + "он ом", + "Ġpl asma", + "Ġe fect", + "Ġlä ng", + "Ġcas i", + "Ñģ а", + "ım ı", + "ãģĻ ãĤĭ", + "ĵ¤ ìĿĢ", + "Ġlab our", + "oss en", + "ĠP un", + "r if", + "Ġd oses", + "Ġoper ates", + "ил ли", + "Ġja ar", + "st aw", + "ĠìĤ¬ëŀ ij", + "Ġat m", + "Ġprotect s", + "Ġimp ed", + "H O", + "Ġc ima", + "Ġto ch", + "ab is", + "Ġsend o", + "la us", + "Ġcur l", + "ĠN um", + "Ġspons ors", + "Ġdé but", + "ĠAlex a", + "ĠB ür", + "ĠA mer", + "Ġc ope", + "Ġиз в", + "j al", + "Ġ199 5", + "ap at", + "res se", + "ĠPri ze", + "ĠCla ire", + "ĠBrand on", + "Ġwszyst ko", + "Ġval ued", + "à¸Ļ ะ", + "Ġse ct", + "Ġsecret ly", + "Ġdiam onds", + "ĠEv an", + "ĠRP G", + "ãģ« ãģª", + "Īë ıĦ", + "ĠUnivers al", + "Ġdoub ts", + "ĠP in", + "wiÄħ z", + "ļ ©", + "Ġal bo", + "Ġbra ucht", + "AU L", + "ĠM obile", + "gr ades", + "Ġsch em", + "wh y", + "ĠN icht", + "p i", + "g le", + "Ġchor us", + "Ġg ly", + "Ġrein force", + "Ġm uff", + "ĠSh en", + "ĠH ola", + "Ñĥ г", + "vid emment", + "v ial", + "ac ious", + "laim ed", + "ĠR ico", + "Ġve gg", + "Ġillust ration", + "ĠBut ter", + "ow ad", + "Ġeu x", + "Ġenf ants", + "ĠLe ader", + "ĠVill age", + "et ically", + "ÙĨ ÙĬ", + "Ġst ew", + "Ġsurpr ises", + "Ġc ue", + "ĠGrand ma", + "ĠC elsius", + "ĠR icht", + "en c", + "Ġpet ition", + "Ġher b", + "Ġw icked", + "Ġsch le", + "oc aly", + "Ġtrans f", + "Ġtok ens", + "ĠGr ay", + "ĠB BC", + "I K", + "Ġ15 00", + "z n", + "ĠNe v", + "Ġk oy", + "Ġz ar", + "Ġbull shit", + "ĠColomb ia", + "ul ative", + "Ġwides pread", + "y ect", + "k it", + "Ġempres a", + "Ġn our", + "Ġburn s", + "at in", + "a ired", + "Ġrevolution ary", + "Ġгод Ñĥ", + "ĠLog an", + "Ġ199 6", + "ĠGra ham", + "re b", + "ĠN HS", + "æľ Ľ", + "Ġcost umes", + "Ġnaw et", + "Ġlo vers", + "ĠLuc y", + "ĠInd igenous", + "íķĺ 기", + "Ġimmun ity", + "¥ ´ë", + "uit o", + "Ġexcess ive", + "Ġdon ations", + "Ġ×Ķ ר", + "Ġì² «", + "éī Ħ", + "Ġdry ing", + "mel on", + "Ġsurve ys", + "Ġ무ì Ĭ¨", + "é¢ ¨", + "aa a", + "Ġpro be", + "an cial", + "Ġlou der", + "Ġhot els", + "ü ÄŁ", + "ag ner", + "Ġorig ins", + "Ġë§Ī ì§Ģë§ī", + "Ġ* *", + "Ġstr angers", + "ĠHa us", + "com ed", + "Ġan throp", + "Ġus o", + "ĠìķĦ ì§ģ", + "ĠY uan", + "ĠíķĦ ìļĶ", + "pl er", + "ress ive", + "Ġsp raw", + "ĠSt ew", + "Ġ199 4", + "Ġeld ers", + "Ġme inen", + "Ġj unt", + "Ġac oust", + "ĠW ohn", + "Ġban anas", + "Ġproject ion", + "ĠSt ick", + "leg t", + "spe ed", + "ĠcÅ ©ng", + "ĠW ort", + "ĠBalt imore", + "ĠÑĨ ел", + "Ġdun no", + "å¼ ·", + "? ,", + "ãĥī ãĥ³", + "ĠLoc al", + "ost o", + "Ð Ń", + "од а", + "ĠPort uguese", + "Ġtheir s", + "Ġdé m", + "åı ¦", + "Ġdra uf", + "ĠBuddh ist", + "ert a", + "G e", + "Ġcar rot", + "ĠWonder ful", + "Ġso ak", + "Ġchair man", + "gg i", + "IC A", + "f ried", + "Ġfl ick", + "ĠThrough out", + "Ġìļ °ë", + "Ġc ough", + "Ġfl uffy", + "sch ool", + "Ġr ipped", + "---- ----", + "ĠZuk unft", + "Ġн еб", + "Ġst o", + "ĠB O", + "p ent", + "ĠLaw rence", + "Ïī ÏĤ", + "st icks", + "ĠE ins", + "ĠÑĢ Ñĭ", + "ĠStr ong", + "Ġcar amel", + "Ġsp ite", + "az ar", + "éĥ½ æĺ¯", + "Ġcrit ically", + "Ġob ra", + "ow itz", + "ĠZ one", + "ĠÑĢ ек", + "Ġsu g", + "ard ed", + "Ġg ì", + "ff entlich", + "an che", + "Ø Ł", + "ast ically", + "ìĿ ¼ë", + "л ав", + "Ġsimpl est", + "ĠF riend", + "Ġque llo", + "Ġamb ition", + "Ġabb iamo", + "åº ķ", + "ĠÑĦ оÑĢм", + "ĠEs sa", + "Ġeduc ators", + "Ġstatist ical", + "éĢĻ éĤĬ", + "Ġchang er", + "Ġat au", + "éta is", + "ĠShakes peare", + "ë IJĺ", + "Ġtr iggers", + "Ġreal iz", + "Ġcel ui", + "whe el", + "Ġloyal ty", + "Ġscream s", + "ke hr", + "ĠM ega", + "e ast", + "Ġtop s", + "ĠTot ally", + "ount ain", + "l ord", + "Ġviol ation", + "ĠG A", + "Ġnic er", + "ĠF resh", + "ĠMel issa", + "fun ction", + "Ġra pe", + "Ġexcept ions", + "Ġsil icon", + "Ġliber ty", + "Ġhousehold s", + "ãģį ãģ¾ãģĻ", + "ĠC A", + "ĠÐŀ б", + "Ġli b", + "ŀ Į", + "c ific", + "Ġtrop ical", + "Ġinvestig ating", + "H D", + "Ġad apter", + "ĠP itt", + "an cia", + "ĠShe ll", + "friend ly", + "Ġconclus ions", + "Ġtur tle", + "Ġdec omp", + "Ġanim ations", + "ĠÑģ ек", + "ins i", + "Ġret ention", + "k ie", + "Ġinject ion", + "ĠMad ison", + "ì° °", + "Ġv ient", + "Ġvar ied", + "Ġviol in", + "ĠB il", + "Ġluck ily", + "Ġh tt", + "l ä", + "Ġr anch", + "çľĭ çľĭ", + "Ġsó lo", + "ìķ ħ", + "ĠD erek", + "ĠScript ure", + "оÑĢ а", + "Ġclassroom s", + "av il", + "form ed", + "Ġbefore hand", + "ĠG em", + "pre ch", + "Ġl in", + "Ġgre ens", + "ÑĨ ев", + "ĠMer cedes", + "Ġdr ought", + "gas ps", + "Ġab ortion", + "Ġter ribly", + "Ġspos ób", + "Ġsec ured", + "Ġat rás", + "Ġwavel ength", + "Ġgra ins", + "ect ive", + "Ġspace craft", + "Ġtour s", + "Ġprof es", + "Ġsur geon", + "ĠP ie", + "Ġide ally", + "arn er", + "U P", + "op ard", + "s ce", + "Ġimm ense", + "ĠOr t", + "roll er", + "ĠD allas", + "ĠNich olas", + "Ġs ulf", + "ĠToy ota", + "Ġquant ities", + "ce ans", + "Ġcu i", + "an ça", + "ĠC AN", + "itzer land", + "åĦ ¿", + "Ġz ou", + "ĠCy ber", + "le gen", + "ĠIn it", + "ed u", + "Ġa pert", + "Ġad jac", + "ou v", + "èĢĮ ä¸Ķ", + "r s", + "Ġcab bage", + "Ġwheel chair", + "iny l", + "ĠD ynam", + "ĠìķĦëĭĪë Ŀ¼", + "Ġl ing", + "h l", + "Ġмог Ñĥ", + "Ġcris p", + "Ġm ij", + "Ġd ug", + "n in", + "Ġbl oss", + "Ġbelong ing", + "Ġloud ly", + "Ġminer als", + "Ġconclud ed", + "Ġsearch ed", + "9 6", + "ĠMe et", + "ĠS EO", + "ĠС к", + "ĠH ob", + "ot ta", + "Ġpropag anda", + "Ġcin namon", + "Ġhun ter", + "Ġgeme ins", + "Ġsculpt ure", + "uls ion", + "Ġv äl", + "Ġmagaz ines", + "Ġcontrovers y", + "ä¸Ģ 樣", + "Ġsequ ences", + "ãģĦ ãĤĭ", + "Ġíļ Į", + "Ġdel eted", + "ä½ ¿", + "IJë ıĦ", + "Ġvary ing", + "ãĥ Ĩ", + "Ġmount ing", + "Ġaff air", + "Ġpath ways", + "æ ¦", + "Ġdig o", + "äº ®", + "Ġд ок", + "A lex", + "Ġtob acco", + "ĠC V", + "Ġbother ed", + "Ġamb ient", + "ink y", + "ĠS L", + "Ġh ates", + "Ġje żeli", + "Ġcon greg", + "Ġel as", + "Ġde uts", + "ĠStud ios", + "ch ÄĻ", + "Ġdocument ed", + "ĠCru z", + "ĠL en", + "ĠDoug las", + "ĠPort ugal", + "ent i", + "Ġsp ouse", + "Ġanal ys", + "av ia", + "Ġed ited", + "Ġl ại", + "bu ilt", + "Ġv ille", + "ad ora", + "Ġbrac elet", + "Ġs ushi", + "Ġp m", + "Ġtra ils", + "Ġl ug", + "Ġö ver", + "Ġs orrow", + "Ġcol ony", + "ado x", + "Ġser ie", + "any ak", + "ĠØ ·", + "ĠG ulf", + "æĺ¯ ä¸įæĺ¯", + "ĠP V", + "ĠSam uel", + "ĠK it", + "ĠR al", + "ont in", + "ex pl", + "Ġent ries", + "Ġactiv ists", + "P s", + "Ġs ant", + "ĠÑĤо Ñĩ", + "ĠBr uno", + "ke ley", + "Ġtut to", + "é Ķ", + "Ġv intage", + "Ġterr ified", + "Ġпо Ñħ", + "us ive", + "ow ers", + "ай ÑĤ", + "ë ıĻ", + "Ġtwist ed", + "ĠTh ought", + "Ġt ah", + "Ġshr ink", + "Ġshe er", + "l it", + "Ġdal am", + "Ġd ib", + "Ġv ard", + "ow ane", + "Ġdo br", + "ĠR ena", + "ĠÑģво Ñİ", + "ĠpaÃŃs es", + "ĠE ra", + "ãģ® ãģ§", + "ĠB UT", + "s ighs", + "Ġê·¸ ê±°", + "Ġgro ÃŁen", + "Ġë¹ ¨ë¦¬", + "Ġn erves", + "Ġconst it", + "Ġpreoc up", + "ĠG ay", + "ĠX u", + "keep er", + "he ure", + ".. )", + "ĠCal m", + "ĠUn idos", + "ĠìĿ´ ê²ĥ", + "ĠAqu i", + "Ġìłľ ìĿ¼", + "d ır", + "ì¦ ĺ", + "y our", + "ĠÑįÑĤ им", + "20 20", + "Ġr und", + "ĠH O", + "ĠC atherine", + "iel i", + "Ġf usion", + "Ġide ology", + "Ġfor am", + "sh aped", + "ĠíĽ Ħë", + "Ġw t", + "Ġret r", + "Ġpr éc", + "Ġê° ij", + "Ġopen ly", + "v ity", + "구 ìļĶ", + "Ġobst acle", + "Ġbo o", + "Ġse iner", + "ic orn", + "Ġeigen lijk", + "Ġhead er", + "are mos", + "Ġso fter", + "ĠÐŁ од", + "Ġpre jud", + "Ġdefin es", + "ier te", + "Ġbl ending", + "Ġbelie vers", + "ĠWo chen", + "Ġник ак", + "ĠÐļ огда", + "ĠTyp ically", + "Ġíģ ¬", + "ç® ¡", + "ci os", + "Ġmiss iles", + "Ġsp onge", + "ĠK itchen", + "Ġt ren", + "ning en", + "Ġsc rap", + "Ġser ait", + "´ì ł", + "ç ¹", + "Ġë° ĺë", + "Ġrest ored", + "Ġprzy kÅĤad", + "ĠK ubernetes", + "Ġsa it", + "Ġu w", + "Ġen abling", + "Ġtra vers", + "amp s", + "åı Ĺ", + "ĠOM G", + "ens or", + "Ġz osta", + "Ġpronoun ced", + "A ng", + "norm al", + "Ġeconom ies", + "t in", + "ĠChamp ion", + "iz en", + "Ġar beiten", + "ĠG ospel", + "ĠZ u", + "ng a", + "Ġliter acy", + "ĠM ans", + "Ġcircul ation", + "Ġad ap", + "ĠTot al", + "Ġmere ka", + "Ġol acak", + "ÑģÑĤ аÑĤи", + "J ack", + "Ġm und", + "Ġth ief", + "b ies", + "Ġê² ģ", + "a que", + "ĠÚ© ÛĮ", + "ĠSc ar", + "å ²", + "Ġab ol", + "Ġdev ote", + "Ġ0 1", + "Ġs itten", + "ĠVis ual", + "we ek", + "s ome", + "ing t", + "Ġjournal ism", + "ĠH ir", + "ĠB achelor", + "in ery", + "Ãľ ND", + "ãĥ Ł", + "ç» Ļ", + "Ġcolor ing", + "ĠCr ist", + "Ġcelebr ities", + "ĠÑĩ иÑģ", + "ĠC rit", + "Ġdifferent iate", + "ĠÐľ не", + "el im", + "Ġse afood", + "Ġalgum as", + "otherap y", + "æĪ °", + "Ġgla ub", + "Ġarbitr ary", + "g ens", + "ĠбÑĥд ем", + "Ġt av", + "Ġcream y", + "ĠCount ry", + "a ñ", + "м еÑĤ", + "Ġh inter", + "Ġm ism", + "Ġillust rate", + "ÃľND NIS", + "Ġdecre asing", + "Ġwen iger", + "AK I", + "ix on", + "Ġн ей", + "Ġfat to", + "Ġn erd", + "ç ł", + "Ġb itte", + "P er", + "Ġt ane", + "Ġgö z", + "Ġfor te", + "ĠE y", + "Ġнав еÑĢ", + "è¢ «", + "ĠWord Press", + "ĠM is", + "Å ¯", + "z äh", + "Ġinté ress", + "osa urs", + "ĠFall s", + "Ġn essa", + "9 7", + "Ġmuseum s", + "Ġcorrespond s", + "Ġs ings", + "f our", + "Ġed er", + "ĠCommun ist", + "o a", + "ne k", + "ĠWH O", + "Ġcor po", + "Ġmess ing", + "ÏĦ αι", + "Ġbrush es", + "Ġb isc", + "ĠAr beits", + "ĠT ax", + "Ġse le", + "Ġflag s", + "ou pe", + "Ġanticip ated", + "ãĥ ij", + "ĠN ad", + "Ġpou red", + "Ġm l", + "Ġll ama", + "Ġvisual ize", + "Ġlisten ers", + "ÙĦ Ùĥ", + "al ten", + "Mich ael", + "Ġcos ì", + "Õ¡ Õ", + "op us", + "Ġíķ´ì £¼", + "Ġh ike", + "ĠAtt orney", + "ĠHill ary", + "ud ed", + "Ġíķĺ ì§Ģë§Į", + "Ġdo ve", + "Ġstorm s", + "ак Ñģ", + "Ġdoct rine", + "Ġhe x", + "ik s", + "no ÅĽÄĩ", + "Ġscript s", + "Ġδ εν", + "ĠÑįÑĤи Ñħ", + "ĠÐ Ĩ", + "ab er", + "ĠV as", + "Ġcent imeters", + "×ŀ ×Ķ", + "ни б", + "Ġrid ers", + "ĠT rib", + "åĮ ħ", + "Ġtak że", + "Ġn oun", + "Ġic ons", + "Ġsole ly", + "mind ed", + "Ġdisp on", + "ĠSw itzerland", + "Ġcl usters", + "Ġqu eda", + "ail ing", + "Ġman ga", + "Ġ6 8", + "Ħ Ī", + "Ġt et", + "g ins", + "ha us", + "ç© º", + "å· ¥", + "ĠO P", + "ot ed", + "Ġnouve au", + "AL LY", + "ÙĪ د", + "ò n", + "Ġmort ality", + "ĠGit Hub", + "d rop", + "Ġdis gu", + "Ġrec om", + "Ġloc als", + "Ġhome made", + "amb a", + "Ġpron unciation", + "Ġal phabet", + "ан ÑĮ", + "ow any", + "ir as", + "id ency", + "OM E", + "ĠÑĢаÑģ Ñģ", + "ar ak", + "v iamente", + "Ġnon profit", + "ĠYouT uber", + "Ġp arenth", + "ĠB oo", + "v at", + "ĠSt ir", + "Ġpre cip", + "Ġan ts", + "Ġall y", + "ĠMa ori", + "ĠëĮĢ íķľ", + "åı¯ æĺ¯", + "og ene", + "ĠLab our", + "aret te", + "Ġrecy cling", + "ens a", + "Ġpurs uit", + "Ġs ak", + "ĠÐĹд еÑģÑĮ", + "Ġtoler ance", + "Ġsa at", + "Ġclick ed", + "âĻ ¥", + "Ġface book", + "ĠInt o", + "Ġincent ives", + "기 ëĬĶ", + "ĠD ennis", + "ĠW ik", + "ges ch", + "à¹ĢภĽ", + "ĠÏĢ α", + "ĠWh oo", + "Ġround ed", + "Ġdo pe", + "Ġcapt uring", + "ĠWar ri", + "Ġcivil ian", + "Ġchar ming", + "Ġes as", + "Ġsust ained", + "Ġle aning", + "Ġabund ance", + "ÃŃ lia", + "алÑĮ нÑĭй", + "Ġph ải", + "ac ja", + "Ġê°Ļ ìķĦ", + "act iv", + "า ย", + "Ġ9 7", + "Ġм ой", + "c ro", + "ĠJack ie", + "itt ees", + "br acht", + "ul ent", + "Ġìł ľë", + "Ġplug in", + "v antage", + "part y", + "Ġsu as", + "Ġan te", + "Ñĥ л", + "ÐĿ ÐIJ", + "æĤ ¨", + "ĠÏĥ Ïħ", + "Ġmet h", + "Ġenthus iasm", + "ÑıÑĤ ÑģÑı", + "íĻ Ķë", + "Ġsynth etic", + "Ġseason ing", + "ĠL ost", + "on omy", + "ĠSp ark", + "Ġb ure", + "Ġass ured", + "Ġimag in", + "Ġcar ro", + "S ha", + "Äħ t", + "нÑĥ ÑĤÑĮ", + "át ica", + "T Y", + "Ġk ern", + "ĠBrazil ian", + "à °", + "Ġsusp ended", + "ĠCar ib", + "Ġbiz im", + "ĠOl iver", + "ãģ ¶", + "T om", + "Ġпл ан", + "Ġn ope", + "omet hing", + "Ġbe iden", + "ÑĨ ен", + "Ġflu ct", + "Ġμ οÏħ", + "Ġf athers", + "ĠBl ake", + "Ġup ward", + "ĠD ash", + "ĠL il", + "ĠìĪ ĺëıĦ", + "Ġrevel ation", + "Ġelev ated", + "ĠJi ang", + "LE D", + "ĠThom pson", + "Ġмог ÑĥÑĤ", + "ÑģÑĤ ÑĢÑĥ", + "if iers", + "Ġcome back", + "Ġbuy ers", + "ê² °", + "ĠS ales", + "иÑĩ е", + "c iones", + "Ġwh istle", + "Ġd ull", + "LE X", + "Ġíķĺ ê²łìĬµëĭĪëĭ¤", + "Ġcrimin als", + "Ġdes cent", + "ipp le", + "mas ı", + "Ġfool ish", + "ĠдÑĥм аÑİ", + "t ar", + "Ġman go", + "Ġchore ography", + "M att", + "Ġterr itor", + "Ġac aba", + "ĠEin stein", + "ĠI BM", + "ĠMet al", + "ĠCry stal", + "Ġr ah", + "Ġf oul", + "ĠIsland s", + "Ġint act", + "ĠR ail", + ". :", + "Ġac á", + "ĠпÑĢ оп", + "еÑĢ е", + "ĠWr ite", + "he he", + "ĠF O", + "ĠÏĥ ÏĦη", + "Ġdo in", + "h eld", + "Ġappropri ately", + "Ġdeliber ately", + "Ġarch ive", + "Ġgive away", + "ãģĵ ãģĵ", + "Ġfin ale", + "л аÑģ", + "ен о", + "Æ¡ n", + "æ£ Ĵ", + "og o", + "çī ©", + "ĠAud ience", + "ãħ ł", + "Ġsub ur", + "Ġhead ache", + "ан нÑı", + "ĠW itch", + "ĠSwed ish", + "ĠB I", + "Ġer ase", + "Ġk hi", + "Ġcomment ary", + "ĠS ultan", + "íĥ Ŀ", + "ĠLe ban", + "Ġë³´ì ĭ", + "ĠP am", + "pe kt", + "mon th", + "Ġground ed", + "ê ¾", + "ĠÅŁek ilde", + "2 50", + "ĠS CH", + "ios o", + "Ġin aug", + "he imer", + "Ġreflect ing", + "ĠR uth", + "ĠO il", + "Ġtrou ver", + "u ep", + ".. ]", + "Ġìŀ Īë", + "Ġol ha", + "Ġreason ably", + "Ġgl itch", + "U B", + "ĠGr an", + "Ġad alah", + "Ġl ent", + "ر ا", + "Ġtr action", + "Ġadjust ing", + "´ ¤", + "ниб ÑĥдÑĮ", + "Ġд оп", + "Ġstretch ed", + "Ġor t", + "Ġcos ine", + "vi ol", + "Ġì ħ", + "c ir", + "Ġbast ard", + "ä¸ ĩ", + "ĠÑħ од", + "Ġqu ier", + "Ġpress ures", + "ĠAn h", + "å¹ ¾", + "Ġell es", + "Ġд ÑĢÑĥз", + "ĠможеÑĤ е", + "Ġch á»", + "ĠM é", + "ö k", + "ầ u", + "ìł Ī", + "z in", + "Ġca ution", + "ib an", + "Ġjud ging", + "ÑĥÑİ ÑĤ", + "Ġb aj", + "ĠС ейÑĩаÑģ", + "ĠPo or", + "ĠNaz i", + "Ġup beat", + "y ang", + "Ġweek ends", + "ĠEss entially", + "Ġol uyor", + "Ġspat ial", + "ack er", + "Ġsell er", + "Ġ×IJ ×ķת", + "ij ׾", + "Ġv ivid", + "ĠB ond", + "ê ¶Į", + "is kt", + "ãĤ µ", + "Ġgo at", + "dri ver", + "Ġm ug", + "ict ional", + "Ġall t", + "ĠIn iti", + "ĠR and", + "Ġfinish es", + "Ġê° Ī", + "Ġvit am", + "Ġteen agers", + "ĠMor ris", + "ì¤ Ħ", + "ĠO ri", + "i ya", + "Ġmy ös", + "St ep", + "ĠK re", + "è¾ ¦", + "Ġdin osaur", + "Ġëª ĩ", + "aff e", + "ĠëIJ ©ëĭĪëĭ¤", + "Ġz eg", + "åĪ ĩ", + "ĠManh attan", + "Ġsu jet", + "ue lle", + "st off", + "Ġd ür", + "Ġsub mar", + "es es", + "Ġa quele", + "Ġn ou", + "ĠFa ith", + "t z", + "ĠÑĤ омÑĥ", + "ace ut", + "li ers", + "Ġband width", + "Æ°á» Ŀ", + "Ġrespect ive", + "ĠA ve", + "Ġspread she", + "ĠS ent", + "ic amente", + "Ġinf ra", + "Ġlearn ers", + "Ġà® ī", + "ai ah", + "ren al", + "Ġmust ard", + "Ġhab t", + "ç ĥ", + "ĠQu é", + "Ġanaly zing", + "æ¯ ı", + "Ġso lic", + "Ġ×Ķ ×ķ×IJ", + "Ġcaus a", + "Ġwel comed", + "ĠS uccess", + "Ġfac ile", + "ĠÐŁÐ¾ÑĤ омÑĥ", + "sche in", + "Ġf etch", + "Ġstr at", + "ĠÑģÑĤо иÑĤ", + "ìĹIJìĦľ ëĬĶ", + "ĠÑģп оÑģоб", + "m am", + "Ġser ÃŃa", + "nam ents", + "wr iter", + "Ġconsult ing", + "íĺ Ģ", + "ĠBer keley", + "e u", + "as ive", + "U U", + "ĠAnal yt", + "Ġsubm ission", + "Ġmagnific ent", + "en za", + "Ġe con", + "Ġprof iles", + "Ġinc ar", + "A b", + "ĠN un", + "Ġh ic", + "scream ing", + "Ġresil ient", + "åĪ ©", + "gr und", + "Ġconc ur", + "Ġbere its", + "L D", + "Ġnur t", + "ì ī", + "Ġfe ast", + "Ġenc uent", + "ĠMich el", + "Ġsup rem", + "\" ]", + "Ġfeed s", + "ĠKoll egen", + "iss er", + "ĠF eng", + "ĠW en", + "m un", + "Ġten ÃŃa", + "ĠW rest", + "Ġìĺ¤ëĬĺ ìĿĢ", + "Ġst ead", + "Ġrest oration", + "Ġdon ated", + "Ġdel s", + "Ġc ensus", + "Ġdesper ately", + "worth y", + "H E", + "ĠSp a", + "ĠBry an", + "Ġh j", + "ĠR aw", + "ìķĦ ë", + "ĠCam era", + "Ġz ien", + "Ġst yl", + "ĠT W", + "ĠChe ese", + "bor ne", + "Ġob l", + "ĠAl ready", + "Ġunst able", + "Ġfl ames", + "p ost", + "H a", + "rom agn", + "ĠìĹ Ħë§Ī", + "d est", + "Ġkole j", + "Ġtempor arily", + "Ġdeterm ining", + "ĠGl ass", + "ÑĢ он", + "ol an", + "Ġdom inated", + "åĮ ĸ", + "__ __", + "ĠÙĩ ذا", + "ĠD ana", + "Ġdin heiro", + "a qu", + "ë ¯¼", + "ĠÃł s", + "ĠJo ey", + "ĠGr iff", + "Ġatt ain", + "Ġtrans itions", + "ĠLiter ally", + "ен д", + "ĠHa ven", + "Ġgrab bing", + "Ġcryst als", + "ĠFour th", + "Ġcand les", + "ĠÑģлÑĥÑĩ а", + "ric o", + "Ġ5 000", + "et to", + "Ġund o", + "Ġk to", + "Ġdi vert", + "Ġch ir", + "Ġper sec", + "Ġh iking", + "Ġannounce ments", + "çĶ ±", + "з Ñĭ", + "Ġa uc", + "Ġsystem ic", + "ĠR M", + "Ïĥ α", + "ĠÐĶ ж", + "Ġy ar", + "ĠW ard", + "Ġpiss ed", + "Ġcar n", + "Ġautonom ous", + "ãħİ ãħİ", + "so ver", + "æ²Ĵ éĮ¯", + "å¾Ī 好", + "Ġref lex", + "Ġgard ens", + "Ġd ated", + "ì ±", + "ami ÄĻ", + "Ġcontinu ity", + "Ġcitizens hip", + "Ġsch wer", + "Ġz ak", + "t able", + "ĠÑģ Ñĩ", + "è§ ģ", + "ĠÏĥ ε", + "Ġgener ates", + "구ë Ĥĺ", + "ö h", + "ó m", + "al am", + "ĠJUD Y", + "ĠB ug", + "Ġãģ ¦", + "Ġdr ones", + "Ġá gua", + "ac aks", + "æ ļ", + "ĠÐļ он", + "× ĸ×Ķ", + "Ġstri ve", + "ĠAl tern", + "Ġne arest", + "Ġpro yect", + "ter a", + "ĠASH LEY", + "Ġwor m", + "Ġre play", + "Ġt ara", + "ĠInd ians", + "ãĤ °", + "ica id", + "ĠìĪ ľ", + "Ġappe aling", + "ĠW es", + "Ġment ions", + "Ġдел е", + "Ġk w", + "Ġfrag ile", + "is z", + "k ów", + "h ang", + "col or", + "Ġpresident e", + "8 7", + "е ÑĦ", + "çĪ ¸", + "Ġдоб ав", + "ĠN elson", + "á fic", + "ĠMIC HAEL", + "Ġmechan ic", + "Ġmet res", + "Ġo czywiÅĽcie", + "ĠC ind", + "Ġog sÃ¥", + "Ġlands ca", + "AC E", + "Ġhead lines", + "Ġcat alyst", + "ĠC atch", + "ink les", + "Ġp ills", + "ord o", + "Ġimmig rant", + "Ġexam ination", + "Ġacc idents", + "zÄħ d", + "Ġqui ere", + "Ġne lla", + "Ġ6 7", + "Ġpass a", + "Ġsuper fic", + "ist or", + "Ġno v", + "ëĭ µ", + "Ġmand ate", + "is ons", + "ĠVirt ual", + "Ġsel ber", + "Ġcounsel ing", + "ĠN BA", + "Ġse pt", + "Ġbelie ver", + "Ġmar vel", + "ĠInte gr", + "Ġм Ñĸ", + "Ġor ph", + "Ġback ward", + "ĠGen eration", + "ĠP ict", + "ĠÑĤо ÑĤ", + "Ġtap i", + "pro chen", + "Ġhall way", + "ht e", + "ĠÛģ ÛĴ", + "ĠZ um", + "èĢģ 師", + "ach ment", + "iqu er", + "fol g", + "ĠEd die", + "ĠK il", + "Ġwell ness", + "st ock", + "è¼ ĥ", + "Ġka ç", + "Ġterror ism", + "Ġpo inter", + "O f", + "her ic", + "ĠUlt imately", + "Ġmes es", + "ĠTr ade", + "Ġp int", + "Ġtu ition", + "Ġdisag re", + "Ġê²Į ìŀĦ", + "Ġmanus cript", + "Ġro omm", + "Ġoutput s", + "е ÑĨи", + "Ġr ies", + "Ġsal ud", + "otz dem", + "Ġmass es", + "Ġby ÅĤa", + "Ġclear ing", + "Ġdisc ourse", + "ats on", + "Ġfold ed", + "ĠJ ar", + "ÙĦ Ùī", + "9 00", + "ĠÑĥ Ñģп", + "Ġprophe cy", + "Ġinterf ere", + "иÑħ од", + "๠Į", + "Ġth ri", + "Ġ×ŀ× ©", + "Ġlaz ım", + "Ġ199 2", + "Ġfut uro", + "Ġlock ing", + "Ġembar go", + "ĠNe ither", + "iv amente", + "ĠmÃ¥ ste", + "Ġm ik", + "Ġcollect or", + "еко ÑĤоÑĢ", + "ĠG and", + "Ġsent ir", + "ĠM ight", + "å¡ Ķ", + "Ġgan zen", + "U C", + "Ġrel ating", + "S D", + "Ġmos quito", + "G R", + "Ġho llow", + "âĺ ħ", + "ĠWalk er", + "Ġaffili ate", + "Ġduplic ate", + "н ем", + "Ġgra pe", + "ĠOrgan ization", + "Ġsy nt", + "J oe", + "Ġg eg", + "Ġreve aling", + "ĠEth an", + "out er", + "Ġy ay", + "é« Ķ", + "л аÑĢ", + "Ġreported ly", + "Ġihr er", + "Ġrecogn ise", + "Ġbum per", + "ĠR andy", + "ĠVen us", + "t les", + "Ġappet ite", + "Ġgluc ose", + "Ġch odzi", + "ĠFurther more", + "t ir", + "Ġcont a", + "Ġint uition", + "Ġalt itude", + "Ġch unks", + "ĠJosh ua", + "ıģ ım", + "ry lic", + "le ans", + "ĠíĶ ¼ë", + "L L", + "Q ue", + "Ġg or", + "Ġзна ÑĩиÑĤ", + "Ġpo ems", + "Ġexc el", + "Ġexpl ored", + "Ġpop ul", + "Ġinclus o", + "st ä", + "ĠG avin", + "all ing", + "ĠÏĦο ν", + "é ©", + "ar beit", + "ĠG as", + "Ġgl orious", + "rie ben", + "Ġsp am", + "Ġindo or", + "Ġthr ust", + "ĠA ld", + "ĠPri or", + "Ġon board", + "ãģł ãģķãģĦ", + "o ca", + "AS H", + "£ ł", + "ĠChrist ine", + "Ġdra wer", + "Ġno on", + "Ġìŀ ĺë", + "Ġperman ently", + "æ· ±", + "ĠнапÑĢ имеÑĢ", + "Ġpodcast s", + "era peut", + "pr it", + "Ġstain less", + "ĠÚ© ÛĴ", + "Ġfamil ia", + "ĠÑĢаз ÑĢ", + "un to", + "ĠÑģÑĤ ол", + "Ġh ä", + "ĠH ai", + "ĠP B", + "iz on", + "Ġkon nte", + "Ġbüy ük", + "Ġutil izar", + "Ú Ĩ", + "Ġaqu esta", + "Ġmix er", + "ud ent", + "лек Ñģ", + "ÅĤ u", + "ĠÑģиÑģÑĤ ем", + "Ġн оÑĢм", + "Ġfat al", + "Ġconsider ations", + "Ġvalid ation", + "Ġo li", + "Ġk ardeÅŁ", + "ĠGL ORIA", + "Ġp all", + "еÑģÑĤ е", + "Ġrect ang", + "Ġmed ieval", + "allah i", + "ast i", + "ĠSy rian", + "Ġshe ar", + "Ġdeb ug", + "ĠM ai", + "Ġknock ing", + "ĠLe x", + "ard an", + "ro v", + "Ġmem orial", + "æ° £", + "ook y", + "Ġstuff ed", + "Ġpass é", + "Ġw ig", + "Ĥ ł", + "Ġpróxim a", + "Ġ199 1", + "Ġм еждÑĥ", + "Ġnuest ros", + "ĠBe ast", + "Ġsm o", + "atch ed", + "olog ia", + "Ġм од", + "Ġge e", + "Ġconcept ual", + "Ġà ´", + "Ġdecre ases", + "Ġquer ies", + "олÑĮ ÑĪ", + "ĠA part", + "Ġex empl", + "å± ±", + "Ġfl ed", + "ĠO FF", + "gg ak", + "Ġbe ad", + "h ir", + "l ies", + "ĠClear ly", + "ı lar", + "Ġch ess", + "Ġwhich ever", + "Ġ9 6", + "Ạ±", + "Ġrespect s", + "Ġм оÑĢ", + "Ġorgan ism", + "Ġgrand pa", + "ĠV ie", + "è·Ł ä½ł", + "Ġflo oding", + "Ġupgrad ed", + "Ñij ÑĢ", + "Ġcheek s", + "Ġcon quer", + "Ġstub born", + "Ġpuzz les", + "Ġau ction", + "Ġre lying", + "ĠPRO F", + "ĠEs per", + "ĠÐľ У", + "Ġhy pe", + "Ġposs ibil", + "Ġimp rison", + "ĠEr n", + "ìĹĪ ìĬµëĭĪëĭ¤", + "Ġenv ie", + "Ġresur rection", + "ä¸į è¡Į", + "Ġs per", + "ĠVenez uela", + "s om", + "Ġìŀł ê¹", + "Ġnouve lle", + "Ġclos es", + "Ġ19 40", + "Ġqu a", + "ĠJ ared", + "ĠP ir", + "Ġind e", + "Ġscr ub", + "uk u", + "Ġrequ iring", + "Ġв ами", + "Ġconsider able", + "åIJ Ľ", + "il ia", + "Ġin ne", + "Ġmein em", + "Ġhard ship", + "Ġtra ps", + "ro c", + "ĠìĦ ¤ë", + "Ġresearch ing", + "ĠMarg aret", + "Ġpen ny", + "Ġbı rak", + "Ñij л", + "Ġw ool", + "Ġr het", + "Ġflat ten", + "ç ĩ", + "à¹Ģภ£", + "Ġp ied", + "ĠCh ap", + "Ġunder m", + "Ġf ret", + "Ġcrash ed", + "ĠFra uen", + "Ø° Ùĩ", + "iv an", + "Ġliter ary", + "late go", + "Ġsp äter", + "Ġsimilar ities", + "â Ĩ", + "ĠCor on", + "ĠC reek", + "Ġboss es", + "Ġaccompan ied", + "Ġdeb ates", + "Ġassemb led", + "Ġà ģ", + "ĠV ai", + "Ġtr act", + "Ġsimple ment", + "ĠAr in", + "Ġvulner ability", + "Ġhorm one", + "I EL", + "OO K", + "Ġrel ay", + "ĠAnd rea", + "r il", + "Ġnecess ity", + "aceut ical", + "Ñİ Ñī", + "ous ing", + "nah men", + "Ġfoot print", + "m ap", + "ĠT ier", + "ann ya", + "int end", + "åĸ ®", + "å ¢", + "Ġdecor ate", + "Ġzomb ies", + "ĠHy d", + "ĠSu z", + "Ġcampus es", + "ĠE mb", + "Ġthr ottle", + "Ġad min", + "Ġop ortun", + "Ġmir rors", + "Ġident ities", + "ĠCl in", + "Ġë¹ Ħë", + "á¹ £", + "ĠO tt", + "Ġbl ues", + "Ġimpress ions", + "- ,", + "Ġv ague", + "a fe", + "Ġinfer ior", + "eral d", + "Ġmedic ines", + "Ġpre gunta", + "os ely", + "Ġt élé", + "ĠMon th", + "ĠLe aders", + "ĠEgypt ian", + "Ġr ation", + "k ers", + "he its", + "Ġre cht", + "P lay", + "Ġe g", + "Ġpoll s", + "ĠWOO DR", + "Ġsl ots", + "j am", + "B oth", + "ĠR at", + "ÑĢ аж", + "ĠBr ight", + "ä¸Ģ å®ļ", + "á»ij i", + "ur ious", + "Ġsing ers", + "Ġlo gin", + "Ġt êm", + "l ation", + "ĠM um", + "Æ°á»Ŀ ng", + "ĠEd itor", + "åIJ ij", + "Ġinnov ations", + "h ave", + "ĠS ek", + "Ġwe aker", + "ĠG ob", + "A fter", + "´ì §Ģ", + "Ġ문 ìłľ", + "ãĥ¼ ãĥ¼", + "Ġdisad vantage", + "ç¢ º", + "Ġg aze", + "ĠM ack", + "Ïģ ί", + "ĠK iss", + "ĠH olo", + "ĠBir th", + "iz i", + "b ab", + "ä¿ Ŀ", + "ìĭľ ê³ł", + "д еÑĢж", + "Ġsqu at", + "кÑĥ Ñģ", + "un i", + "ĠComm e", + "ĠWOODR UFF", + "ĠChampions hip", + "Ġwel che", + "ĠY outh", + "z em", + "Ġod pow", + "Ġpersist ent", + "r ut", + "ìĶ ©", + "íĸ ¥", + "la ir", + "ik u", + "Ġvend or", + "Ġch úng", + "Ġfinan ci", + "Ġover ly", + "â u", + "Ġgl uten", + "Ġ18 00", + "Ġdiv isions", + "Ġciud ad", + "Ġob ed", + "Ġwar um", + "Ġe her", + "Ġel im", + "ĠÐĴ о", + "Ġpeu vent", + "ĠW anna", + "Ġattend ance", + "Ġassess ments", + "ĠB og", + "Ġimag ery", + "Ġcollect ively", + "Ġinform al", + "ĠSch we", + "Ġde utlich", + "ĠCh el", + "ĠP E", + "ow ed", + "Ġb anner", + "Ġshel ves", + "ĠRet urn", + "æĭ ¿", + "LAUGH S", + "Ġcongrat ulate", + "ĠNor way", + "Ġd well", + "ĠCarib bean", + "Ġnorm s", + "ĠAn imal", + "ĠValent ine", + "Ġext ending", + "ĠV ou", + "or r", + "ĠCh eng", + " ¡", + "ĠдоÑĢ ог", + "Ġve g", + "Ġh Ã¥", + "ĠX in", + "Ġì¹ ´ë", + "em et", + "Ġhyp oth", + "Ġinteress ante", + "ric es", + "I Z", + "ĠUS D", + "Ġrun ner", + "ĠB ag", + "Ġê ½", + "Ġcomeç ar", + "Ġpig s", + "Ġweakness es", + "P h", + "ĠVi ol", + "ä¸į çĶ¨", + "Ġdra gging", + "ĠAqu ÃŃ", + "ĠCS S", + "Ġmill imeters", + "Ġest ás", + "Ġac ute", + "Ġde jar", + "i ÄŁ", + "ob ra", + "L ove", + "Ġsil k", + "** **", + "Ġjo ins", + "Ġpro l", + "Ġê°IJìĤ¬ íķ©ëĭĪëĭ¤", + "æĶ ¯", + "ØŃ Ø¯", + "agh etti", + "än ner", + "Ġstr ang", + "Ġdoub led", + "Ġdescri ptions", + "Ġst ellen", + "Ġpart i", + "ç« ĭ", + "² Ħë", + "Ġö ÄŁ", + "ig hing", + "Ġang ular", + "Ġnat uur", + "ĠSh el", + "Æ° Æ¡", + "Ġr ays", + "Ġse per", + "st art", + "v ised", + "Ġrush ed", + "Ġinternation ally", + "Ġnive l", + "Ġbox ing", + "fall en", + "á»ij c", + "Ġse inen", + "plic ity", + "Ġcarb oh", + "ĠTra vis", + "us o", + "ĠPh ase", + "Ġactiv ation", + "Ġop io", + "· ¨", + "Ġdecre ased", + "C ar", + "Ġbund le", + "Ġexp end", + "orm al", + "Ġadjac ent", + "Ġme e", + "ĠоÑĢ г", + "Ġtrans cript", + "ĠLang uage", + "G S", + "è§ ī", + "Ġse ul", + "Ãł nh", + "Ġn ya", + "ning s", + "Ġìĭ ľë", + "ĠëĶ°ë Ŀ¼", + "ĠA gr", + "ÃŃ d", + "çķ Ļ", + "Ġab y", + "ĠNe o", + "ıyor uz", + "ĠThink ing", + "a ime", + "Ġv ite", + "Ġtrav és", + "Ġ×ij× ¢", + "Ġм ед", + "O ur", + "ho ot", + "Ġl iner", + "ĠP izza", + "Ġhy g", + "fl ies", + "ĠContin ue", + "Ġdent al", + "ĠT ib", + "Ġreg ulate", + "lie ÃŁ", + "AL K", + "ĠTa e", + "ê¸ ¸", + "ĠBre xit", + "ĠG ut", + "Ġoccup ation", + "Ġz robi", + "â m", + "Ġwh isk", + "ä¸ĸ çķĮ", + "Ġkans ke", + "om on", + "ro be", + "Ġwar fare", + "Ġth á»ĥ", + "Ġjak i", + "Ġstro kes", + "Ġpe as", + "ĠDam it", + "H AN", + "Ġinter ference", + "Ġмин ÑĥÑĤ", + "N ER", + "out ing", + "Ġtext ures", + "Ł ī", + "ow i", + "Ġíķ Ļ", + "Ġd ens", + "Ġprotagon ist", + "än n", + "Ġgod dess", + "Ġwoll te", + "ij o", + "ĠWo che", + "ĠV PN", + "st ory", + "Ġkind erg", + "Ġfun nel", + "Ġdist ress", + "ноÑģÑĤÑĮ Ñİ", + "Ġno isy", + "ĠпÑĢод олж", + "Ġdar an", + "Ġenzy me", + "л ож", + "Ġm ute", + "Ġd war", + "Ġا س", + "Ġkom pl", + "Ġmer it", + "Ġf osse", + "ĠDr ink", + "Ġfor a", + "Ġw ohl", + "Ġbree ze", + "Ġsan it", + "Ġdr in", + "ĠìĿ´ê±° ëĬĶ", + "Ġ6 2", + "Ġì° ¨ë", + "aby tes", + "Ġde eds", + "ĠÐ ¹", + "i ème", + "igg ling", + "Ġ\" '", + "ĠÑĩа ÑģÑĤÑĮ", + "ĠAns wer", + "Ġev angel", + "Ġ10 80", + "ĠVis it", + "ic ient", + "Ġreli ability", + "Ñİ ÑģÑĮ", + "ĠEar lier", + "Ġf id", + "çŃī ä¸Ģä¸ĭ", + "Ġslee ves", + "iy orsun", + "Ġb ib", + "ĠAcc ount", + "Ñı ли", + "cipl inary", + "z as", + "Ġб еÑĢ", + "Ġneck lace", + "Ġbl ender", + "ĠPhill ips", + "et i", + "ĠJup iter", + "Ġprov oc", + "ĠYe ars", + "ent re", + "ac io", + "Ġk ü", + "Ġanten na", + "Ġnovel s", + "Ġf art", + "ĠS ugar", + "ĠJud y", + "Ġcollaps ed", + "ç °", + "rit is", + "Ġìĥģ íĻ©", + "ÐĹ Ð«", + "ĠVer f", + "rane an", + "ere um", + "ĠTar get", + "Ġ8 8", + "ĠÐĺ з", + "ide o", + "Ġreg ression", + "ì¶ ľ", + "Ġmów i", + "Ġstud ios", + "i ens", + "ip h", + "Ġfr ying", + "Ġfasc inated", + "ĠW ah", + "b ucks", + "m aya", + "ĠSat urn", + "ĠM ommy", + "Ġrating s", + "Ġaut umn", + "Æ°Æ¡ ng", + "Ġlos er", + "Ġcent ro", + "érie ur", + "ĠF old", + "Ġsuper visor", + "ĠNo bel", + "Ġunder est", + "ob ia", + "Ġв ÑģÑı", + "Ġver w", + "Ġfu els", + "Ġartif acts", + "Ġë¶ Ļ", + "ĠAut om", + "çļĦ æĺ¯", + "Û Ķ", + "×ķ× ¡", + "Ġih nen", + "Ġ5 9", + "ound ing", + "еÑĢ Ñĭ", + "in ars", + "ch ant", + "Ġadd icted", + "Ġexplos ive", + "Ġdisp ers", + "â ĸĪ", + "ax is", + "AR Y", + "Ġl um", + "ĠÑĥ Ñģл", + "ĠØ Į", + "Ġru pees", + "ĠPe arl", + "c amp", + "t v", + "oy a", + "Ġconclud es", + "Ġcoll ision", + "Ġbuy er", + "Ġplay ground", + "Ġspr ings", + "Ġfemin ine", + "ĠR as", + "Ġincar cer", + "íĹ ĺ", + "Ġdial ect", + "Ġclos ure", + "Ġchat ting", + "Ġb abe", + "Ġspot light", + "Ġnot ation", + "è· ¯", + "St ar", + "i ão", + "Ġt ête", + "Ġt ide", + "Ġjun to", + "Ġsen ator", + "Ð ¥", + "Ġexcus es", + "Ġbl ink", + "Ġadm ission", + "ĠL ily", + "Ñĭ ми", + "Ġam igo", + "Ġl ust", + "ëĭ ¬", + "Ġam ino", + "äºĭ æĥħ", + "Ġconsult ant", + "ĠElect ric", + "Ġëħ¸ë ŀĺ", + "uj ah", + "Ġshoot er", + "icht en", + "ĠUkrain ian", + "Ġaim s", + "ĠEnter tain", + "Ġmir acles", + "èŃ °", + "Ġze igen", + "Ġl am", + "Ġres s", + "ĠJ ill", + "yl an", + "Ġro ok", + "Ġh aya", + "Ġpass port", + "ad ata", + "Ġju icy", + "con f", + "л ей", + "ĠS z", + "Ġinter cept", + "ãģĤãĤĬãģĮãģ¨ãģĨ ãģĶãģĸ", + "ĠTe ams", + "Ġmak en", + "ir rel", + "ĠLI KE", + "áºŃ y", + "êµ °", + "Ġshort age", + "Ġparad igm", + "Ġpap el", + "Ġast ero", + "ãģ¾ ãģŁ", + "Ġsoll en", + "ĠMic key", + "ĠOr leans", + "Ġchol esterol", + "Ġgo ose", + "ÑĨи Ñİ", + "ãģĤ ãĤĭ", + "ĠF L", + "Ġгол ов", + "Ġtrib ute", + "ĠG am", + "Ġé videmment", + "Ñı Ñħ", + "å® ŀ", + "çĶ °", + "Ġin appropri", + "uh an", + "Ġorganiz ational", + "ail ed", + "Ġend ure", + "Ġ7 6", + "Ġshot gun", + "Ġliv re", + "Ġsu ited", + "Ġwarm th", + "ĠS IM", + "Ġenv ision", + "Ġde grad", + "î ne", + "La ughing", + "ĠWho ever", + "ĠBuddh ism", + "Ġspr inkle", + "ceÄŁ iz", + "Ġru ins", + "Ġst arch", + "ĠHer z", + "Ġinjust ice", + "Ġhum idity", + "ожал Ñĥй", + "ĠOb ject", + "ĠI gn", + "ĠEx am", + "ig ers", + "Ġth ou", + "ĠSo y", + "iv as", + "Ġpol es", + "m ath", + "Ġв ним", + "ING ING", + "ed ral", + "Ġexpl or", + "Ġroast ed", + "Ġcraw l", + "Ġco ff", + "Ġan om", + "Ġw ij", + "Ġimpro ves", + "Ġtreat y", + "Ġdiscover ing", + "Ġstat ute", + "Ġmerc ado", + "ĠÑģ ил", + "Ġint el", + "ĠChance llor", + "ĠMed icaid", + "ug i", + "Ġver bal", + "Ġd ön", + "Ġscript ure", + "Ġit eration", + "ek s", + "ĠOx ford", + "Ġw äh", + "ĠV ad", + "ĠA K", + "ĠìķĦ ìĿ´ë", + "Ġi ets", + "Ġneed les", + "Ùĥ Ùħ", + "Ġpas ado", + "Ġalbum s", + "Ġye a", + "et zen", + "Ħë ıĦ", + "Ġdeterm ines", + "Ġthe e", + "ĠPlay ing", + "är t", + "Ġ× ¦", + "c led", + "Ġdown ward", + "al one", + "Ġsol u", + "Ġpart ition", + "Ġw z", + "d d", + "Ġpesso al", + "å ª½", + "Ġfact ories", + "Ġble ibt", + "ม า", + "als a", + "ĠNF L", + "Ġfu era", + "Ġres erved", + "ĠE arn", + "Ġhel t", + "Ġshort cut", + "Ġconvin cing", + "sp ace", + "Ġen force", + "Ġc ores", + "Ġe fter", + "Ġrecess ion", + "x ico", + "Ġprop osition", + "ar ians", + "rop ol", + "Ġëª °ë", + "ĠÎ ľ", + "ĠìļĶ ì¦ĺ", + "Ġactiv ist", + "Ġconv iction", + "Ġz ab", + "Ġcancel ed", + "ÑĤо Ñĩно", + "ĠÎ ®", + "éĢĻ樣 åŃIJ", + "n ite", + "Ġfund ra", + "buz zer", + "ел о", + "ic ations", + "Ġz ona", + "Ġte ens", + "Ġmethod ology", + "Ġì¤ij ìļĶ", + "th an", + "ĠU l", + "ĠG rey", + "Ġh og", + "IN K", + "ĠS ung", + "ĠC laud", + "ĠCN N", + "Ġdel ivers", + "al in", + "ĠAd obe", + "ot he", + "ĠDes wegen", + "ภ³", + "Ġwer de", + "Ġgre ase", + "Ġup grades", + "ĠFin land", + "ac cept", + "Ġinter rog", + "be e", + "Ġãģ «", + "Ġpre de", + "ĠN ep", + "ĠCam bridge", + "Ġgraph s", + "Ġha unted", + "Ñģ ем", + "æ §", + "åħ ĭ", + "S ome", + "ĠM all", + "Ġrehears al", + "ĠUr ban", + "ĠL ag", + "Ġn im", + "ê° ķ", + "Ġposition ed", + "Ġavo ided", + "EM A", + "Ġlleg ar", + "Ġráp ido", + "Ġgou vern", + "Ġh ing", + "Ġdeal er", + "Ġreform s", + "Ġfat ty", + "к ол", + "ĠA ce", + "Ġne p", + "Ġì² Ń", + "Ġcomput ation", + "ĠSt ream", + "bour ne", + "t ur", + "P or", + "Ġsleep y", + "Ġbang et", + "ãģĤ ãģ®", + "Ġwe ighs", + "Ġble iben", + "ĠG ren", + "Ġun ions", + "Ġêµ IJ", + "Ġap render", + "uit ar", + "ĠJ est", + "um ing", + "ĠPlay er", + "ĠExt rem", + "Ġinteg er", + "аÑĩ е", + "Ġconcert s", + "×ķ× Ľ", + "Ġtro chÄĻ", + "ĠRe pe", + "éĩį è¦ģ", + "๠Ĥ", + "ż en", + "Ġsound ing", + "Ġan onymous", + "Ġex ca", + "ĠIran ian", + "Ġener getic", + "Ġw ives", + "ĠÑĨ веÑĤ", + "Ġa is", + "ãģĭ ãģª", + "Ġsud ah", + "Ġunder wear", + "Ġcrunch y", + "ĠP ain", + "Ġger çek", + "red ict", + "Ġm isma", + "Ñĸ ÑĤ", + "Ġsurv iving", + "ÎŃ ÏĤ", + "Ġparticip ant", + "ĠH essen", + "ári as", + "Ġsub way", + "ist ä", + "Ġcor al", + "Ġmar ijuana", + "ĠMem orial", + "ÑĪ ий", + "ri z", + "Ġsatell ites", + "Ġle ase", + "ĠCam eron", + "um ph", + "Ġclass mates", + "äh än", + "ÑģÑĤв е", + "Ġh ue", + "ĵ¤ ìĿĦ", + "Ġproport ional", + "Ġn oss", + "Ġl aps", + "r Ã¥", + "Ġbit coin", + "ÐĹЫ ÐļÐIJ", + "Ġì¶ ©", + "ĠÙĦ ÙĦ", + "ĠM ort", + "ĠEs p", + "arn os", + "ĠÑģказ ал", + "Ġä nd", + "åħ Ħ", + "×Ļ ×Ļ×Ŀ", + "ĠGe b", + "ge hen", + "I naudible", + "bor ough", + "ÑĦ ÑĦ", + "Ġfellow ship", + "ĠP aper", + "Ġcur ved", + "ĠGE OR", + "Ġcalcul ator", + "ĠCat al", + "ĠvÃł o", + "Ġby pass", + "л еÑĤ", + "à ³", + "tr ans", + "ren cies", + "ì ¡Į", + "ig ent", + "Ġtast ed", + "Ġo ceans", + "u ft", + "erv ice", + "ĠÐľÐ£ ÐĹЫÐļÐIJ", + "ĠClass ic", + "Ġrespect ively", + "~ )", + "î tre", + "ĠN ash", + "Ġz it", + "ĠìĽ ĥ", + "ĠëĨ Ĵ", + "qu ote", + "ĠUn s", + "Ġt ac", + "Ġpro ves", + "ĠPort land", + "b ly", + "Ġ ere", + "ì¶ Ķ", + "Ġépo ca", + "ĠÑĤÑĭ ÑģÑıÑĩ", + "7 6", + "Ġhad e", + "ĠF ro", + "ĠpolÃŃt ica", + "t ag", + "Ġíķ Ń", + "Ġsch ö", + "are tt", + "Ġprov isions", + "Ġmot ors", + "Ġimag ing", + "Ġdo k", + "ul ously", + "Ġme ille", + "çİ° åľ¨", + "ë IJ", + "ĠIS O", + "ĠST EM", + "ĠBow l", + "Ġto wers", + "ĠE e", + "ĠPerform ance", + "Ġlo in", + "cuss ion", + "Ġcoast al", + "ial e", + "com pass", + "Ġspell s", + "Ġdisappoint ing", + "Ġë²Ī 째", + "E ER", + "Ġvers atile", + "as ury", + "Ġen fin", + "Ġdown side", + "Ġgu iding", + "ĠاÙĦ ÙĤ", + "Ġnin ety", + "char ged", + "ĠF ans", + "Ġphilosoph ical", + "Ġg arn", + "ĠmÃ¥ nga", + "Ġwilling ness", + "Ġport ions", + "ab en", + "Ġ ï", + " ¿", + "ra ul", + "Ġspr int", + "if en", + "ıy la", + "Ġк Ñĥп", + "ãģı ãģłãģķãģĦ", + "Ġens uite", + "ĠCap itol", + "Ġ6 3", + "ĠговоÑĢ иÑĤ", + "Ġappoint ments", + "æī ¾", + "omi ast", + "Ġcare g", + "Ġpubl isher", + "Ġher aus", + "Ġε ί", + "ĠV S", + "ãģĿ ãģĹãģ¦", + "ä¸Ń åħ±", + "Ġsacrific es", + "th ird", + "Ġhuman itarian", + "ĠëĤ ´ì", + "im on", + "Ġine qu", + "Ġz ob", + "Ġcomfort ably", + "ĠD inge", + "Ġcancell ed", + "ĠPS AKI", + "ĠRob inson", + "Ġfin s", + ") ?", + "ĠHist or", + "ĠÑĩеловек а", + "Ġt bsp", + "te xt", + "k im", + "Ġupd ating", + "Ġgel d", + "f eld", + "ı ¼", + "Ġm ä", + "Ġcaf é", + "Ö Ģ", + "ĠS ri", + "ĠReg ion", + "ĠH ahaha", + "Ġfin ances", + "ĠاÙĦØ ´", + "Ġb unk", + "ru k", + "ha ft", + "Ġlater al", + "Ġext ensions", + "ĠìķĦ ìĿ´", + "Ġdefin ite", + "ĠZ hao", + "ĠLu is", + "st y", + "Ġcas os", + "ĠK lim", + "Ġ199 3", + "Ġreal ization", + "Ġhistor ian", + "Ġcrack ed", + "ëĤ ´", + "Ġsyst ème", + "ĠC IA", + "ĠÑĤ во", + "osp heric", + "Ġfle e", + "Ġr ất", + "ĠRegard less", + "Ġrel uct", + "Ġtim ely", + "ĠJul ian", + "G M", + "é Ĵ", + "ad ura", + "é£ Ł", + "Ġdress es", + "çģ £", + "ĠëĶ Ķ", + "Ġnom inated", + "Ġadvoc ates", + "ym ph", + "Ġrecord ings", + "Ġdev iation", + "Ġpriorit ize", + "Ġspir al", + "ĠYOU R", + "Ġtransp ose", + "amp oo", + "ĠìĽIJë ŀĺ", + "ĠV ision", + "Ġpol ite", + "Ġha mb", + "ĠPat ient", + "æ¯Ķ è¼ĥ", + "íģ ¬ë", + "Ġs ia", + "Ġê³ ³", + "Ġž e", + "è§ Ģ", + "Ġsuper market", + "ë ¹", + "ĠS ierra", + "Ġgr illed", + "ĠUp on", + "Ġabs ent", + "Ġme c", + "ĠAp ollo", + "Ġp unk", + "ĠPa ÅĦst", + "ĠÑģв ой", + "Ġê±° 기", + "G irl", + "Ġskin ny", + "ĠPrem ier", + "Ġterrit ories", + "Ġli ability", + "Ġj erk", + "r atic", + "Ġdan cers", + "ĠÑĥ ÑĢов", + "Ġê´ Ģë", + "on ly", + "ĠSt u", + "Ġske leton", + "ĠëŃ IJë", + "Ġзак он", + "ı kt", + "ĠMI KE", + "Ġl ö", + "m ie", + "Ġre iter", + "ãģĵãĤĮ ãģ¯", + "ĠKoll eg", + "ĠAd ams", + "lich er", + "Ġçoc uk", + "Ñı г", + "Ġbl ush", + "Ġsun shine", + "Ġe z", + "ĠDev il", + "Ġê¸ ¸", + "Ġãģ Ĭ", + "ad d", + "Ġlic ensed", + "Ġv inyl", + "ĠC zech", + "im ag", + "Ġcrack ing", + "Ġì º", + "Ġud ah", + "Ġs ommes", + "Ġìĸ¼ êµ", + "wa Äĩ", + "Ġf res", + "åij ½", + "ĠWal mart", + "ĠТ епеÑĢÑĮ", + "at isf", + "C I", + "l ang", + "Ġdiff usion", + "çĶ ·", + "Ġsom os", + "ĠM akes", + "æĪij æĥ³", + "ĠRick y", + "Ġmuch a", + "íķ ¨", + "Ġhorse power", + "as ia", + "Ġfib ers", + "Ġ erm", + "Ñģ кие", + "Ġjest e", + "Ġfire fight", + "Ġcu isine", + "Ġbesond ers", + "d ig", + "Ġì¢ ħ", + "ĠÑĥ ж", + "Ġtr acing", + "Ġcertain s", + "ĠApp ly", + "Ñĭв аÑĤÑĮ", + "ç Į", + "Ġbr u", + "ĠY ES", + "ĠB ai", + "ĠD it", + "ĠB is", + "Ġun le", + "ÑģÑĤа ÑĤоÑĩно", + "ĠAw ak", + ".. \"", + "Ġ12 5", + "Ġroot ed", + "Ġcaut ious", + "con st", + "Ġorchest ra", + "çľ ¼", + "Ġвн ÑĥÑĤ", + "Ġquel qu", + "ĠоÑĤ веÑĤ", + "ĠMet hod", + "ì¹ ľ", + "Ġμ αÏĤ", + "l ü", + "ĠìķĦ ê¹Į", + "Ġn aming", + "C har", + "ĠS icher", + "Ġprivile ged", + "ĠF ly", + "Ġãģ ĭ", + "áºŃ t", + "Ġadv ances", + "ĠZel da", + "Ġand ra", + "Ġgr inding", + "ĠEd ition", + "p f", + "Ġwarri ors", + "Ġh edge", + "Ġuns eren", + "ĠÑģÑİ Ð´Ð°", + "el iness", + "Ġpersonal ities", + "Ġf ö", + "' M", + "ĠÑĤо Ñĩно", + "Ġsh ipped", + "Ġmete or", + "Ġsurround ings", + "ĠF ill", + "u esta", + "ĠPerson al", + "ĠAll e", + "OR T", + "ä¹ ħ", + "ĠS che", + "V I", + "Ġcompar able", + "dam n", + "Ġd itch", + "Y AN", + "ism us", + "Ġpick up", + "Ġd ak", + "ĠE P", + "b est", + "ĠS ue", + "äll t", + "Ġpop corn", + "Ġfold ing", + "h ome", + "ив аеÑĤ", + "å·² ç¶ĵ", + "Ġan not", + "ch uck", + "Ġfier ce", + "Ġdam aging", + "Ġfl op", + "Ġpas ar", + "Ġre ef", + "ĠÑģво ей", + "Ġz oo", + "o vers", + "j ets", + "Ġpr ès", + "ĠSil icon", + "te ok", + "ĠS eth", + "at amente", + "Ġtransm itted", + "Ġrepl icate", + "Ġsl im", + "ĠC ream", + "æĦŁ ãģĺ", + "Ġside walk", + "ìĪ ĺë", + "Ġжиз нÑĮ", + "ĠMon ica", + "ä¾Ĩ äºĨ", + "Ġcop ied", + "ĠTer ra", + "ist ent", + "ç³ »", + "Ġо но", + "Ġwh ale", + "ĠW ITH", + "л ÑĥÑĪ", + "å½± çīĩ", + "ĠE en", + "ĠÑģво и", + "Ġord in", + "Ġpl ural", + "Ġsp okes", + "Ġdisp ute", + "Ġsens ible", + "Ġpre aching", + "Ġktó rzy", + "pt ed", + "av ier", + "Ġpist ol", + "ĠTap i", + "Ġ ÅĤ", + "ff ff", + "Ġac rylic", + "Ġignor ance", + "ĠZ iel", + "r ans", + "Ġweld ing", + "m id", + "æĪij ä¸į", + "Ġзан им", + "Ġlan es", + "Ġmin es", + "Ġmom s", + "×ķ× Ĺ", + "ĠCham ber", + "t ier", + "Ġmod est", + "ĠìĹ¬ê¸° ìĦľ", + "Ġun as", + "Ġw rench", + "hand ed", + "Ġsatur ated", + "ĠF ang", + "ĠCommission er", + "ठ°", + "Ġ× ĸ", + "ĠLouis iana", + "ĠM ask", + "Ġcub es", + "ìĶ ¨", + "Ġvidé os", + "ĠnÃ¥ gon", + "Ġr ider", + "Ġì¶ ľ", + "Ġs ón", + "ĠLat ino", + "b ank", + "íķ´ì £¼", + "ĠB rend", + "Ġsexual ity", + "... ,", + "Ġforget ting", + "Ġ ÛĮ", + "ĠAven gers", + "ĠBon jour", + "cess or", + "кÑĢа ÑĹ", + "c ence", + "Ġge ograph", + "cul o", + "о ÑģÑĤÑĮ", + "Ġswe ating", + "íĥ Ģ", + "Ġsymm etry", + "ts Ã¥", + "Ġj an", + "ĠFer r", + "é¦ ĸ", + "Ġamb assador", + "ziÄĻ k", + "Ġmus un", + "ĠÑĥ ÑĤ", + "ĠL G", + "iss ent", + "comm un", + "Ġcour s", + "Ġdevelop s", + "Ġbron ze", + "Ġsubst ances", + "dri ven", + "주 ìĦ¸ìļĶ", + "Ġa os", + "åĦ Ħ", + "ĠPROF ESS", + "h alf", + "Ġsort ed", + "ĠB omb", + "л аг", + "ĠMalays ia", + "ĠChrist ina", + "Ġteam mate", + "èģ ŀ", + "F T", + "Ġk ı", + "heart ed", + "+ +", + "ogen ic", + "Ġbell s", + "ĠOu ais", + "Ġspecial ists", + "б Ñĭ", + "dep th", + "lass es", + "g ies", + "ĠCo ffee", + "Ġmark ing", + "Ġfo ll", + "ul i", + "Ġad hesive", + "ĠB ot", + "ĠP unkt", + "e ye", + "ĠB ub", + "el ong", + "åĪ ¶", + "ĠпÑĢ ик", + "Ġdon or", + "8 4", + "Ġen for", + "Ġcatch es", + "Ġbr icks", + "Ġkn itting", + "ĠKnow ing", + "ok s", + "H Y", + "r ide", + "ĠFant asy", + "im an", + "Ġp se", + "Ġìĺ ¨", + "Ġв д", + "Ġrest ra", + "Ġevalu ated", + "ÑĢ ев", + "Ġfortun ately", + "Ġche gar", + "ر ب", + "Ġdom ains", + "ib i", + "ar ry", + "Ġshut ter", + "Ġfic ou", + "M ike", + "Ġinc lu", + "Ġdon ors", + "Ġa pl", + "ĠL ower", + "Ġimport ed", + "Ġacad emy", + "Ġfin als", + "Ġdisappe ars", + "ÙĬ ا", + "Ġadministr ator", + "j s", + "Ġcut ter", + "Ġr anging", + "ör per", + "Ġconstra int", + "ĠT able", + "ĠSh an", + "v ic", + "ĠF ix", + "ĠSw ift", + "oun ces", + "ĠWar um", + "Ġlett uce", + "app elle", + "Ġsh ave", + "Ġb ás", + "Ġ7 7", + "ĠO oo", + "a o", + "ĠMc M", + "ĠD rew", + "Ġl ump", + "Ġl ashes", + "schein lich", + "R ep", + "in is", + "ĠC ette", + "Ġcompos ite", + "emet ery", + "Ġsort e", + "ĠFin ancial", + "он е", + "ron es", + "ĠV oy", + "Ġt éc", + "ł ¹", + "ĠNin ja", + "ĠCor in", + "ен нÑı", + "ìĿ´ìĹ Ī", + "Ġn ich", + "Ġdetect ive", + "âĢ¦ \"", + "Ïĥ ε", + "Ŀ¼ë ıĦ", + "Ġë³ Ģ", + "Ġë¸ Ķë", + "Ġpro pe", + "ĠW right", + "Ġ×Ķ× ª", + "ĠSh i", + "Ġãģ Ł", + "Ġinvestig ations", + "éĤĦ æĺ¯", + "ĠPower Point", + "ĠCh u", + "Ġìĺ ¤í", + "ĠìĻĦ ìłĦ", + "ĠFra gen", + "un ning", + "Ġpour rait", + "Ġtext book", + "м Ñĭ", + "Ġf ahren", + "Ġ ÑĤоÑĢ", + "Ġl akes", + "ünd e", + "I nt", + "ĠMet ro", + "Ġmans ion", + "Ġа б", + "ĠZh ou", + "Ġcorrid or", + "Ġesc ol", + "Ġindic ating", + "ia ÅĤa", + "Ġm ommy", + "Ġarch ives", + "Ġfound ers", + "eng ine", + "ĠDie u", + "Ġsick ness", + "Ġë³´ ëĭĪê¹Į", + "Ġar b", + "Ġn ed", + "ĠCh op", + "Ġco vid", + "Ġsl am", + "Ġpublic ations", + "D C", + "Ġsp ends", + "æ ¾", + "Ġrefuge e", + "Ġd ile", + "Ġ×IJ× ĸ", + "ific ar", + "ĠS ach", + "G u", + "Ġre load", + "?? ??", + "Ġje ÅĽli", + "ĠÑģ оÑģÑĤо", + "Ġsim plicity", + "Ġbull ying", + "Ġм ол", + "Ġreal idad", + "Ġuncle ar", + "app a", + "le vant", + "ĠIS IS", + "ĠW atson", + "Ġde in", + "ĠMic ro", + "íķ ľë", + "ü g", + "Ġdev am", + "Ġtwe eted", + "å° İ", + "Ġunderstand able", + "at an", + "Ġvers a", + "Ġpre ca", + "Ġv á»ģ", + "ĠCop y", + "ĠOr acle", + "Ġmindful ness", + "Ġdisc ret", + "ern en", + "ĠP le", + "H ave", + "Ġisol ate", + "Ġde u", + "Ġsevent y", + "ĠH ills", + "Ġarc ade", + "ĠÑģп еÑĨи", + "Ġsigu iente", + "ĠB ÃľNDNIS", + "lig a", + "ĠвÑģÑĤÑĢ еÑĩ", + "ô m", + "Ġtwe ets", + "Ġsch auen", + "Ġcrit ique", + "ĠðŁİ µ", + "Ġst att", + "ĠÑģам ое", + "ân cia", + "Ġsuper natural", + "Ġplug ged", + "F l", + "yn ı", + "ĠTamb ién", + "Ġencourage ment", + "ĠSer ver", + "ëĤ ľ", + "up a", + "Ġast on", + "Ġhe ars", + "ÑĢа Ñħ", + "Ġsch e", + "Ġr ats", + "Ġrec uper", + "Ġun ten", + "ĠFight ing", + "Ġacadem ics", + "ç¤ º", + "ĠS ü", + "Ñģ киÑħ", + "Ġpa ired", + "Ģ ìĿĦ", + "Ġá rea", + "Ġsweet ness", + "åı Ĭ", + "Ġde fer", + "Ġmuit as", + "ĠAud io", + "Ġlock er", + "ÙĬ د", + "ĠÑģÑĤ ав", + "Ġbu ena", + "AN S", + "Ġdetect or", + "av o", + "be k", + "Ġα ν", + "íİ ¸", + "Ġdra gged", + "Ġдолж ен", + "à ĸ", + "ر Ø©", + "ìĿ´ì §Ģ", + "Ġcell e", + "ck ing", + "ĠاÙĦØ ¬", + "ĠCan vas", + "Ġespa ñ", + "Ġgl imp", + "Ġspread s", + "ong o", + "ĠM ason", + "ĠIn g", + "Ġê°Ģ ëĬ¥", + "ÏĦ ικ", + "Ġsec ular", + "Ġb ater", + "Ġinqu iry", + "Ġenerg ies", + "Ġmanufact ured", + "Ġveget arian", + "Ġpine apple", + "ÑıÑĤ а", + "Ġpractition ers", + "2 000", + "Ġíķ´ì ļĶ", + "ĠìĹ¬ëŁ¬ë ¶Ħëĵ¤", + "Ġë¶ Īë", + "ĠJeff erson", + "ĠJo an", + "Ġtr am", + "å® ¹", + "ch mal", + "ĠH ait", + "á¹ ĩ", + "Ġun real", + "Ġsymbol ic", + "Ġste alth", + "Ġspl ash", + "ĠEntertain ment", + "Ġmetall ic", + "?\" .", + "è¶ Ĭ", + "ar ound", + "Ġdesp air", + "ĠNev ada", + "ĠFin ance", + "Ġk rie", + "ĠL ux", + "ĠSm ash", + "ke eping", + "Ġз аг", + "Ġnarc iss", + "Ġdz isiaj", + "Ġtoler ate", + "o ard", + "Ġlink ing", + "ĠEconom ic", + "Ġì ¼", + "Ġmor ph", + "ĠN ak", + "ĠB aker", + "at on", + "r ings", + "ĠP eng", + "ĠAir port", + "ãģĭ ãģ£ãģŁ", + "íķĺ ëĭ¤", + "§ ģ", + "pr ints", + "Ġhad i", + "Ġemp ir", + "ĠL ives", + "ann ers", + "Ġн им", + "ĠPROFESS OR", + "Ġpositive ly", + "ant om", + "Ġbad ge", + "ke lt", + "Ġinter fer", + "Ġfulf illing", + "Ġvisual ization", + "éĹľ ä¿Ĥ", + "ĠPr ice", + "� �", + "Ġscen ery", + "Ġpr one", + "Ġw izard", + "Ġb anyak", + "ver b", + "s ky", + "Ġwish ed", + "Ġrail way", + "Ġü zer", + "Ġalgu ien", + "ĠA W", + "Ġкол иÑĩе", + "Ġreact ing", + "ĠB uch", + "ภ¶", + "Ġan th", + "Ġsi h", + "Ġh ust", + "ĠSc reen", + "il ant", + "ah o", + "Ġfragr ance", + "Ġelev ation", + "ĠMed iter", + "Ġë ¿", + "Ġé qu", + "Ġwra ps", + "Ġin ert", + "Ġrecre ate", + "л аÑĤ", + "Ġbo leh", + "Ġharass ment", + "unk y", + "Ġglimp se", + "reg ierung", + "Ġfut ur", + "Ġreposit ory", + "Ġeng ra", + "Ġtraff icking", + "ass is", + "ĠTre k", + "Ġë² Į", + "Ġë§ Īë", + "ĠK ab", + "ani u", + "g ive", + "Ġdin osaurs", + "Ġfe ather", + "Ġatt itudes", + "Ġpl um", + "ĠR S", + "ĠAn fang", + "ill ery", + "ĠìĬ ¤", + "M Y", + "Ġtrze ba", + "Ġsk ies", + "ĠA j", + "ur able", + "C U", + "ĠSh ane", + "Ġdepart ure", + "ĠT ON", + "iet en", + "r ats", + "æ° Ĺ", + "is u", + "Ġb ord", + "Ġinteresting ly", + "çĻ »", + "oug hing", + "Ġr ushing", + "Ġvol atility", + "Ġp yt", + "Ġform ats", + "Ġз аÑĤ", + "Ġê¼ Ń", + "Ġwhat not", + "Ġcomp ort", + "s w", + "ore an", + "ĠRel ax", + "Ġcl an", + "ĠA H", + "Ġpe w", + "Ġdiction ary", + "T ake", + "sh irts", + "ĠH ugh", + "ĠعÙĦ ÙĬ", + "ĠP ic", + "Ġenroll ed", + "Ġjed nak", + "Ġoffer ings", + "Ġcor az", + "L ife", + "Ġ !!!", + "Ġcl er", + "ĠVide os", + "ĠRod rig", + "ĠId ent", + "ĠP os", + "ĠSt age", + "ĠR ace", + "Ġen act", + "ãģĦ ãģ¾ãģĹãģŁ", + "ĠG y", + "ĠHis pan", + "Ġdef ence", + "ĠCamp bell", + "m atic", + "Ġrele v", + "Ġpe ach", + "Ħ¸ ìļĶ", + "Ġparad ise", + "Ġcere mon", + "Ġannoy ed", + "æĮ ĩ", + "la x", + "Ġexplo it", + "Ġcla use", + "ek er", + "ĠBlo om", + "n ant", + "ate urs", + "Ġhe ights", + "E ven", + "Ñģ он", + "Ġoutra ge", + "ĠVietnam ese", + "ãģ¯ ãģ¯", + "T R", + "Ġe er", + "Ġcann on", + "ĠCom b", + "IJë §Į", + "è» Ĭ", + "Ġê²ĥ ëıĦ", + "Ġaccomplish ments", + "ĠAnalyt ics", + "Ġshap ing", + "re iben", + "Ġb achelor", + "Ġfing ert", + "ack ed", + "Ġpyram id", + "ĠStew art", + "á st", + "Ġsurviv or", + "Ġdu ct", + "Ġdeal ers", + "æ´ »", + "ع Ùħ", + "ли н", + "Ġed e", + "×ķ× ¢", + "ĠÙĥ اÙĨ", + "ĠÏĦ ι", + "Ġcho oses", + "ĠO wn", + "го ÑĤов", + "h ire", + "алÑĮ нÑĭе", + "ĠÐĽ Ñİ", + "Ġо ÑģÑĤав", + "te ch", + "Ġdro it", + "Ġsubject ive", + "en es", + "Ġdiv is", + "ave z", + "Ġmaneu ver", + "à¹Ħ à¸Ķ", + "ade ce", + "ĠEn s", + "ac ial", + "ĠProt ection", + "ĸ ´", + "Ġform ally", + "Ġwy d", + "ingu ém", + "Ġz iem", + "Ġrecru iting", + "×Ļ× ļ", + "n em", + "Ġforb idden", + "ĠB apt", + "×IJ× ł×Ļ", + "Ġsubs et", + "ĠMag az", + "n ement", + "Ġaqu ela", + "rag on", + "Ġcomm ittees", + "Ġéta ient", + "ud i", + "ĠDa wn", + "Ġb ore", + "Ġcompos er", + "ĠwiÄĻ cej", + "ang a", + "Ġdis like", + "ĠD ays", + "åŁ º", + "Ġpar al", + "Ġm ientras", + "Ġheaven s", + "ãģ Ĵ", + "he id", + "Ġtrad ers", + "on ce", + "Ġmasc ara", + "ĠÏĢ Ïģο", + "Ġwhis per", + "ĠMus k", + "éĽ Ĩ", + "ĠFamil ie", + "All ah", + "ĠOl ivia", + "ĠPr os", + "Ġol ika", + "il im", + "Ġrép ond", + "ĠP eters", + "Ġ å¾Ī", + "Ġbit es", + "Ġv ic", + "ĠN Y", + "em ption", + "Ġ4 50", + "Ġvisual s", + "Ġlie u", + "ück en", + "ĠSte el", + "ĠG P", + "w ait", + "Ġnotice able", + "uch a", + "Ġreh abil", + "Ġreject ion", + "ĠÑģлед ÑĥÑİÑī", + "Ġsl ider", + "Ġregard ed", + "Ġgrav it", + "ĠRes erve", + "c ount", + "Ġbre eding", + "Ġlon ge", + "ale b", + "Ġkn ight", + "Ġв ой", + "Ġprés ent", + "Ĥĺ ìļĶ", + "ĠSpec ifically", + "Ġpos es", + "Ġve ure", + "ok ay", + "em as", + "Ġ ãģ§ãģĻ", + "Ġma jÄħ", + "Ġweb inars", + "Ġcann abis", + "Ġdam als", + "ĠNorth west", + "Ġp ada", + "Ġcrowd s", + "Ġfut ures", + "Ġä n", + "Ġciv ilians", + "ĠS achen", + "æ į", + "Ġtr aces", + "Ġ먹 ê³ł", + "Q U", + "é¡ĺ ãģĦ", + "ĠI F", + "an ın", + "ìĤ ´", + "Ġb iblical", + "ĠV ed", + "Ġst oring", + "ÑĢав лÑı", + "æĩī 該", + "Ġn ast", + "Ġd ö", + "ÑĢ оп", + "el ia", + "Ġside ways", + "ĠUnder stand", + "ĠQ ur", + "Ġper pend", + "ĠMill ionen", + "Ġwater melon", + "ĠDiv ine", + "ult ur", + "ab ord", + "Ġsuccess es", + "Ġhom bre", + "Ġcar p", + "Ġsus cept", + "ung kin", + "Ġk ij", + "ul us", + "Ø§Ø ¬", + "Ġnot ch", + "Ġpolynom ial", + "å¹ ²", + "å ©", + "Ġún ico", + "Ġteles cope", + "Ġpolit ique", + "k iem", + "ĠÎŃ Î½Î±", + "Ġaggreg ate", + "ĠGe off", + "Ġtr il", + "ĠG RA", + "Ġsubscri ber", + "im et", + "Ġдол лаÑĢ", + "op ing", + "Ġth erapeut", + "ĠCan cer", + "Ġpar ade", + "Ġir rig", + "âĻª âĻª", + "Ġclear er", + "Ġb og", + "ĠM aur", + "า à¸ĩ", + "ĠShang hai", + "acht e", + "ĠK ol", + "el ujah", + "Ġha v", + "ĠCr ime", + "se k", + "Ġë ¡ľ", + "ien na", + "ĠG or", + "è Ľ", + "ĠпоÑĤ ÑĢ", + "Ġкаж еÑĤÑģÑı", + "ĠL ift", + "ĠS ort", + "ĠP sal", + "Ġp ing", + "ĵ Ŀ", + "ph is", + "ĠF UCK", + "ĠS yn", + "Ġbam boo", + "¬ ìĺģ", + "c uts", + "Ġm mm", + "Ġfunktion iert", + "Ġ _", + "ÃŃ cio", + "St op", + "Ġimag inary", + "Ġnot amment", + "ĠIniti ative", + "ãĥ ¥", + "ĠK urt", + "Ġlo osen", + "Ġbus car", + "çģ «", + "Ġz elf", + "Ġpro ps", + "åĽ ī", + "Ġmoet en", + "Ġmill i", + "Ġhall s", + "ĠM atch", + "Ġbrack ets", + "ĠC ou", + "æ¦ Ĥ", + "ĠÐľ аÑĢ", + "IS A", + "Ġcig arette", + "Ġcompet itions", + "ĠM IN", + "Ġbeh ö", + "vo or", + "Ġ ust", + "ĠZ i", + "ĠO cc", + "ul ates", + "Ġball oons", + "Ġpr onto", + "ĠM iy", + "ĠF ile", + "Ġкл аÑģÑģ", + "нÑĥ л", + "Ġcere al", + "Ġincre ment", + "Ġref ined", + "åı¦ å¤ĸ", + "pr ising", + "ĠR F", + "Ġrespect ful", + "Ġlo ot", + "ask et", + "Ġdeix a", + "ing le", + "Ġfuncion a", + "ĠRe vel", + "Ġso ber", + "Ġperform s", + "ĠG entle", + "ãĤ ¨", + "Ġrecip ient", + "ĠHa use", + "Ġë ĥ", + "F rom", + "Ġmin isters", + "Ġpar adox", + "å°±æĺ¯ èªª", + "Ġtast ing", + "Ġ×Ķ× Ĺ", + "Ġre use", + "ĠL ane", + "ĠÑģов еÑĢÑĪ", + "Ġremem bers", + "Ġfemin ist", + "Ġcommit ments", + "Ġproject ed", + "Ġg az", + "iyor uz", + "Ġoblig ations", + "R o", + "z ar", + "Ġch w", + "ĠJ AM", + "ĠbÄĻd Äħ", + "asp berry", + "Ġм еÑģÑĤо", + "ë² ķ", + "Ġreg ulated", + "Ġw icht", + "ĠTre vor", + "Ġsecond ly", + "ĠIh re", + "els h", + "Ġrep orters", + "ÑĤоÑĢ а", + "oy o", + "G I", + "Ġinter connect", + "é IJĺ", + "OS H", + "æŃ ²", + "Ġbr ass", + "Ġign oring", + "ä»Ĭ æĹ¥", + "in fect", + "Ġpro jekt", + "ore t", + "ÏĦα ν", + "ĠÑĤ ип", + "Ġmut ta", + "Ġunbox ing", + "Ħ °", + "å¡ Ĭ", + "Ġadv ised", + "ĠDen ver", + "Ġsevere ly", + "ĠM hm", + "Ġfl ipped", + "Ġp ien", + "Ġkomm un", + "ĠF RE", + "Ġà®ĩ à®°", + "aint ed", + "Ġkn ives", + "Ġhab l", + "Ġgew orden", + "arett es", + "C S", + "Ġмал енÑĮ", + "Ġgal ax", + "Ġnin ete", + "ê±°ë Ĥĺ", + "Ġs is", + "Ġadvis ory", + "Ġdr illing", + "ĠWould n", + "ün f", + "gest ellt", + "ĠHel en", + "Ġ×ŀ× IJ", + "ap olis", + "Ġrze czy", + "Ġter ra", + "Ġhe p", + "Ġalg ún", + "ik k", + "Ġastron om", + "ĠStar bucks", + "k Äħ", + "Ġpat rol", + "Ġì½ Ķ", + "Ġg on", + "Ġ ãĢIJ", + "Ġson st", + "Ġencoun ters", + "Ġret rou", + "Ġshark s", + "Ġd or", + "ĠR ever", + "Ġev apor", + "Ġreserv oir", + "Ġalleg ed", + "ul er", + "Ġver m", + "Ġcommer ce", + "Ġf itted", + "ge m", + "Ġtact ical", + "Ġl ith", + "éīĦ å¡Ķ", + "h ad", + "è® Ĭ", + "Ġcarboh yd", + "Ġlength s", + "ι ο", + "Ġdem ographic", + "R ob", + "ĠS kin", + "cc oli", + "Ġsimpl ified", + "Ġread ily", + "ĠC um", + "ades h", + "ĠD Ã¥", + "us st", + "ig ne", + "et on", + "Ġmen or", + "q i", + "OO M", + "à¸Ń à¸Ļ", + "Ġpsych iat", + "Ġeight y", + "Ġм илли", + "ĠT ob", + "ed o", + "ç¶ ²", + "ĠÄij ến", + "Ġcirc uits", + "ĠLAU GH", + "ic ism", + "em or", + "Ġreg ener", + "eg ree", + "Ġbure auc", + "ĠAl ber", + "ä¹ĭ å¾Į", + "ĠW or", + "å¤ «", + "Ġres in", + "Ġby ÅĤy", + "ĠI G", + "à¯į ,", + "Ġ7 8", + "Ġwe eds", + "ĠMy th", + "9 3", + "æ ¿", + "ĠëĤĺ ìĻĶ", + "é v", + "á ½", + "ö ren", + "ç ar", + "ĠP AUL", + "Ġdisad vant", + "Ġposition ing", + "Ġcock tail", + "Ġagre es", + "n n", + "ĠS ally", + "M s", + "Ġinher ent", + "Ġmonet ary", + "Ġnat ur", + "ĠN h", + "ĠImp ort", + "Ġle ben", + "Ġw i", + "uss y", + "Ġob es", + "Ġwand ering", + "Ġìĭ łë", + "Äħ da", + "etch up", + "Ġdispos al", + "ĠJ A", + "ĠC er", + "z illa", + "Ġvir gin", + "ĠSl ide", + "and el", + "Ġrighteous ness", + "ĠÎ £", + "Ġide ia", + "ä½ł 好", + "иÑĢов аÑĤÑĮ", + "ר ×IJ", + "Com ment", + "Ġpre lim", + "ĠV ale", + "Ġì§Ģë Ĥľ", + "ĠV anc", + "OM AN", + "Ġп Ñĸд", + "Ġy um", + "st re", + "ce m", + "Ġpo cz", + "Ġfrag ment", + "ĠÑģлÑĥÑĩа е", + "Ġunder go", + "ĠH ank", + "ce ks", + "ĠF PS", + "Ġoc ur", + "Ġdeter ior", + "æ³ ¨", + "Ġempres as", + "Pa ul", + "Ġ) ))", + "ĠвÑĢем ени", + "Ġsc old", + "×Ļ× ¢", + "Ġsuspect ed", + "Ġaccess ing", + "Ġsubst it", + "Ġhistor ians", + "ä» »", + "Ġдел о", + "Ġsoci ed", + "r one", + "Ġre den", + "Ġext ends", + "epher d", + "Ġbal con", + "ä¸į èµ·", + "ĠSol o", + "Ġpolit ician", + "олÑĮ но", + "Ġirgend w", + "Ġtraum atic", + "Ġrapp er", + "ĠRO BERT", + "Re ally", + "æģ ¯", + "Ġline up", + "AS E", + "Ġcontract or", + "ĠCorpor ation", + "g or", + "ĠTod o", + "ÑģÑĤÑĢ ой", + "F BE", + "Ġnews letter", + "Ġko ÅĦ", + "alt ies", + "ĠпÑĢ иÑĩ", + "ĠHe avy", + "Ġsw ords", + "Ġmanip ulation", + "Ġfun k", + "Ġv Ã¥r", + "ĠTal iban", + "Ġë° ¥", + "Ġac ne", + "ür ü", + "Ġdes wegen", + "ĠD ust", + "Ġsil ic", + "Ġhook s", + "Ġbl ij", + "Ġpet its", + "Ġfil me", + "ĠBere ich", + "ĠSa id", + "Ġimp osed", + "Ġdi ary", + "Ġго ÑĢ", + "ĠG ates", + "Ġal ta", + "å¸ Į", + "Ġch cia", + "ple asant", + "Ġë° Ŀ", + "Ġmoż emy", + "ĠAust ria", + "Ġbro ker", + "Ġsuck ed", + "èĢ ĥ", + "Ġcomp artment", + "Ġcl one", + "Ġ×Ķ× ¢", + "ĠDan ke", + "Ġnoch mal", + "ез д", + "Ġad renal", + "Ġkle inen", + "ãģ¾ ãģĹãĤĩãģĨ", + "Ġsubsequ ently", + "Ġdecent ral", + "Ġgen etics", + "Ġê´ ij", + "Ġmon itors", + "ĠApp lic", + "ĠRep orter", + "w ert", + "Ġwie m", + "ĠMove ment", + "Ġinterview ing", + "Ġhair s", + "Ġpu ò", + "ĠChel sea", + "Ġco her", + "Ġc ot", + "Ġz as", + "Ġpatch es", + "Ġl ah", + "Ñĥн к", + "ĠRe agan", + "ĠMar co", + "c ity", + "Ġdef ender", + "Ġdecor ation", + "ij i", + "Ġl itter", + "Ð ¨", + "Ġj ego", + "RE W", + "ĠP ik", + "ĠHe e", + "ĠI v", + "Ġи де", + "ĠThe ater", + "ĠÑĩаÑģ ÑĤо", + "Ġswe ater", + "Ġhighlight ing", + "Ġa insi", + "Ġdipl omatic", + "ĠNever theless", + "å ³", + "AS ON", + "Ġpúblic o", + "Ġf erm", + "reat ed", + "c od", + "Ġë¬ ¼ë", + "Ġm ister", + "ĠVanc ouver", + "Ġrecogn izes", + "ec d", + "Ġcomplic ations", + "en cial", + "ãģĹ ãģı", + "Ġê°Ģ ì§Ģ", + "ĠUlt imate", + "Ġva ig", + "ĠM erry", + "×ķ× Ĵ", + "ĠMar cus", + "ç¸ ½", + "ow ego", + "Ġm ente", + "S m", + "Ġa ja", + "ĠTa o", + "Ġjud icial", + "Ġentrepreneurs hip", + "Ġнем ного", + "Ġp is", + "Ġer g", + "Ġch rist", + "ĠC urt", + "ĠÑĢаÑģ п", + "λ ε", + "ens ch", + "ÃŃ re", + "Ġfo cal", + "ĠDiam ond", + "av ÃŃa", + "Ġh anno", + "ĠSqu ad", + "Ġassoci ations", + "ĠCreat ive", + "Ġmess enger", + "Ġbe gging", + "Ġdec imal", + "Ġd Ä±ÅŁ", + "Ġmet adata", + "sel s", + "ĠÄ° ÅŁ", + "ữ a", + "Ġdiffic ile", + "d ı", + "Ġs laughter", + "ĠVer g", + "Ġ×Ĵ ×Ŀ", + "ç° ¡", + "æĮ ī", + "ĠTe a", + "ass es", + "O k", + "Ġsynth es", + "ot iation", + "Ġpain ter", + "Ġel bows", + "Ġarchitect ural", + "ĠÑĢ ад", + "Ġgl or", + "im age", + "amp a", + "cul iar", + "ł ¨", + "Ġte ve", + "ĠSt elle", + "ĠB am", + "Ġì´ Ī", + "as is", + "ip edia", + "ĠG I", + "ĠAct ive", + "çĦ¶ åIJİ", + "az i", + "ãĤĮ ãģ¦", + "ĠL ucky", + "íķ ©", + "ĠпÑĢ иÑħод", + "Ġrun way", + "Ġauthent ication", + "Ġpos ible", + "Ġsupp lements", + "Ġsurg ical", + "G en", + "Ġfeas ible", + "D O", + "Ġout look", + "Ġinter vals", + "Ġan ecd", + "Ãł ng", + "Ġstra ps", + "ĠSh u", + "ud d", + "iss enschaft", + "Ġport e", + "Ġcomm itting", + "Ġall ey", + "Ġco venant", + "ĠPed ro", + "less ness", + "ĠSol id", + "ĠM olly", + "Ġн екоÑĤоÑĢ", + "Ġcooper ate", + "åĮ Ĺ", + "oll en", + "Ġtun a", + "Ġkinderg arten", + "ĠS iz", + "Ġduż o", + "ĠM BA", + "ĠGEOR GE", + "ĠF isher", + "å¿ ĺ", + "ĠCa esar", + "ĠкÑĢаÑģ ив", + "ĠDel hi", + "zy m", + "Ġexpl icar", + "ê°Ģ ì§Ģ", + "un s", + "gr ow", + "ĠпÑĢ иÑģ", + "Ġ8 6", + "Ġst ating", + "Ġmass a", + "ch ter", + "Ġì»¬ë Ł¬", + "Ġdep uty", + "S M", + "n oc", + "Ġge ography", + "ĠEnter prise", + "ĠC ant", + "ö z", + "Ġun pack", + "ĠíĻ Ķë", + "Ġsearch es", + "Ġpres idency", + "Ġtri vial", + "Ġp ige", + "ou bt", + "ãĤ ļ", + "ì¼ ĢìĿ´", + "Ġbudget s", + "Ġu b", + "Ġp ne", + "ĠY ale", + "ĠÅŁ öyle", + "reg ular", + "Ġimper fect", + "AR A", + "Ġfam ÃŃlia", + "ur m", + "ĠAdvent ure", + "ãĥ Ĭ", + "c is", + "em ark", + "Ġne go", + "Ġinappropri ate", + "ĠпÑĢи з", + "ĠÑĢ ол", + "Ġdream ed", + "B ry", + "Ġshut tle", + "Ġpill ars", + "Ġb ik", + "in um", + "ĠÑĥ Ñģ", + "ĠNe br", + "Ġperpend icular", + "Ġbook ed", + "ber y", + "Ġv ikt", + "be ar", + "es us", + "Ġвозм ожно", + "¨ ¹", + "Ġpresum ably", + "ĠMem phis", + "Ġambul ance", + "×ķ× ŀר", + "Ġthumbna il", + "Ġmod ification", + "éĩ ı", + "Ġinterpret ed", + "Ġprom o", + "Ġκ ά", + "Ġε ÏĢ", + "Ġacoust ic", + "ĠD B", + "åĵ İ", + "Ġnon etheless", + "ou le", + "Ġpe qu", + "Ġkn ob", + "ãĤ £", + "ĠëıĮ ìķĦ", + "Ġpurch ases", + "ĠÃĩ ünkü", + "Ġdivid ing", + "per form", + "ract ion", + "health y", + "ĠTit le", + "Ġu k", + "Ġcer ca", + "Ġargu ably", + "Ġf ale", + "ë³ µ", + "Ġgam ers", + "Ġutil izing", + "Ġoff ended", + "Ġt ava", + "al ı", + "Ġmed ian", + "Ġinfect ious", + "ĠAn nie", + "Ġsmart phones", + "Ġpar ole", + "åĸ Ŀ", + "ĠEp ic", + "z za", + "Ġun ified", + "Ġê·¸ë ķĮ", + "Ġcur tain", + "ĠÄ ĥ", + "Ġsex ually", + "Ġuns erem", + "ĠCon vention", + "Ġalleg edly", + "Y a", + "ĠH oo", + "en ment", + "æĢ ª", + "íĽ Ħ", + "Ġgig antic", + "Ġnot ing", + "Ġre bo", + "ĠJ ama", + "ĠAl z", + "Ġborrow ed", + "ì¹ ¨", + "Ġper ipher", + "оÑĤ а", + "ĠG B", + "ĠGe ar", + "Ġeconom ically", + "Ġtele fon", + "Ġqu eremos", + "ĠдалÑĮ ÑĪе", + "Ġr as", + "ĠTe ach", + "ic ios", + "at os", + "Ġpl edge", + "b au", + "ĠHim self", + "L ink", + "Ġesper o", + "Ġchrom os", + "ĠP ER", + "Ġer le", + "Ġpod ium", + "ç os", + "Ġnie u", + "Ġf en", + "ĠGO D", + "ĠCh ocolate", + "wer k", + "Ġt ừ", + "Ġsupp ress", + "λ η", + "Ġ24 0", + "Ġsit ä", + "Ġhonest y", + "ĠB io", + "ĠB ard", + "ĠобÑī ем", + "Ġм Ñĥз", + "Ġmar ble", + "ĠÑĨ енÑĤ", + "Ġproc ure", + "Ġrot or", + "ber n", + "Ġtu h", + "Ġhead set", + "at em", + "Ġwarrant y", + "à® ´", + "Ġfil ing", + "ι ά", + "Ġcomp rendre", + "Ġimp ulse", + "Ġsal v", + "wr itten", + "Ġinstit ute", + "K im", + "ĠLGBT Q", + "fic iente", + "H is", + "ĠαÏħÏĦ ÏĮ", + "Ġteen age", + "or us", + "ĠÑĢаз б", + "S ee", + "ĠCons erv", + "á»ģ n", + "ful ness", + "Ġstraw berries", + "ĠAb u", + "и он", + "Ġo lla", + "NO ISE", + "ĠEm ploy", + "Ġwip ed", + "ur ger", + "Ġmod ifications", + "Ġíķĺ ì§Ģ", + "Ġfoot steps", + "Ġhon ors", + "Ġad ul", + "Ġfl ipping", + "ĠH U", + "Z Y", + "Ġintegr ating", + "ب ر", + "ull a", + "Ġnatuur lijk", + "ĠíĹ Ī", + "ĠEth ereum", + "ÙĬ ÙĦ", + "w ed", + "Ġpe aks", + "ĠK es", + "Ġblo om", + "Ġcr ashing", + "Ġ9 11", + "ĠоÑĤ лиÑĩ", + "Ġcontro llers", + "ĠD od", + "Ġвм еÑģÑĤе", + "Ġsort ir", + "å¥ ĩ", + "ĠStra ight", + "ĠGrac ias", + "Ġgro ove", + "Ġto gg", + "Ġìĭ¶ ìĿĢ", + "é ro", + "Ġout ward", + "ĠW A", + "ĠRock y", + "Ġsc am", + "Ġhay at", + "ig nty", + "â Ħ", + "pl ings", + "Ġantibiot ics", + "Ġ ä¸Ģ", + "Ġnever theless", + "j ang", + "com merce", + "Ġspo iler", + "Ġglo ve", + "Ġch atter", + "ĠB Y", + "~ ?", + "Ġíĺ ¸", + "Ġdem ol", + "we chsel", + "im ir", + "Ġra id", + "еÑĢ Ñħ", + "ìŀIJ 기", + "en f", + "Ġcomment ed", + "Ġoptim ized", + "Ġconv icted", + "Ġb ats", + "ĠS B", + "ĠA ur", + "ĠT ong", + "Ġimplic it", + "ĠJan et", + "Ġre ag", + "ãģ ²", + "ĠAdv anced", + "Ġimp ose", + "ש ×Ķ", + "Ġschem es", + "oug her", + "ab olic", + "Ġê±° ì£ł", + "Ġslow ing", + "Ġwt edy", + "Ġdest ructive", + "Ġоп ÑĢед", + "Ġland mark", + "Ġëı Ī", + "ĠWalk ing", + "Ạ¹", + "Ġt ijd", + "ĠK N", + "ĠQu ant", + "ìĺ ¤ë", + "Ġк ÑĢÑĥ", + "Ġper der", + "Ġno ve", + "änd e", + "Ġãģ Ĺ", + "b ia", + "Ġcust ody", + "Ġb iod", + "æĿ± 西", + "Ġdirect ing", + "... âĢĭ", + "Ġre loc", + "Ġdemand e", + "ãĤĵ ãģł", + "Ġo ÄŁlum", + "Ġод на", + "ĠMil k", + "åı ·", + "ĠK ra", + "ĠH onda", + "Ġp ue", + "Ġele kt", + "Ġbegin ners", + "Ġspe ar", + "ÃŃ nh", + "ĠLu ft", + "Ġn ig", + "ĠSchool s", + "Ġfor ums", + "ĠQ in", + "pp o", + "Ġz ag", + "ĠÐ ®", + "Ġtooth p", + "ĠSt yle", + "ì´ Ī", + "Ġpun ct", + "Ġrep s", + "ĠA ly", + "Ġamend ments", + "Ġö z", + "Ġdig its", + "ur ai", + "Ġcha otic", + "ĠMas ters", + "e on", + "ĠC ash", + "ĠC uz", + "Ġbede utet", + "Ġscan ning", + "Ġж д", + "н еÑĤ", + "Ġcertain ty", + "j ek", + "Ġdi jo", + "ĠCl imate", + "Ġr inse", + "Ġk rij", + "vel and", + "Ġsound track", + "ĠSa fe", + "ĠNo va", + "9 4", + "Ġa the", + "ĠVer b", + "ol er", + "ìĿ´ì £ł", + "Ġv in", + "Ġrespir atory", + "ĠStud y", + "ĠC AM", + "Ġav ocado", + "ĠZ hen", + "Ġlat ency", + "Ġfe athers", + "Ġcont ar", + "Ġв еÑī", + "Ġf ark", + "Ġbl ended", + "Ġexpl oded", + "ĠX X", + "ĠBen im", + "Ġalgu ém", + "isto ire", + "Ġconfident ial", + "Ġm ast", + "Ġì ¿", + "ge h", + "Ġdis respect", + "ĠSystem s", + "Æ° a", + "E d", + "Ġw ys", + "Ġex otic", + "Ġgl owing", + "ù ng", + "oun ge", + "è Ħ", + "ани з", + "Ġpal av", + "ĠSw ord", + "Ġg im", + "ĠC row", + "Ġpot ent", + "b ish", + "Ġab used", + "ĠJ ed", + "Ġg ambling", + "ĠS pect", + "Ġinvestig ators", + "æĻ ļ", + "Ġr att", + "Ġdo b", + "ĠD ES", + "h og", + "ĠоÑĤк ÑĢÑĭ", + "íĮ ħ", + "ĠденÑĮ ги", + "Ġíĺ ¹", + "Ġë¨ ¸ë¦¬", + "Ġsat uration", + "Ġinher ited", + "ĠInnov ation", + "ìĹ Īëįĺ", + "Ġtang ible", + "Ġdep ri", + "h ed", + "Ġпом ог", + "Ġslic ed", + "ॠį", + "Ġth ế", + "Å ¥", + "6 8", + "Ġcor ona", + "Ġgift ed", + "Ġso ir", + "Ġhum ility", + "ĠìĿ´ 걸", + "Ġflaw s", + "ĠпÑĢ акÑĤи", + "Ġk ald", + "wa ż", + "y w", + "ãĤĵ ãģ§ãģĻ", + "ir teen", + "Ġcroch ets", + "¦¬ ê°Ģ", + "ĠìłĦ ìĹIJ", + "Ġdes e", + "æ¥ Ń", + "Ġм аг", + "Ġdz iaÅĤ", + "Ġl ég", + "ch anging", + "Ġlle v", + "ÅĦ sk", + "çĶ »", + "Ġ198 4", + "orn s", + "ĠW elsh", + "Ġpharm aceutical", + "Ġpump ing", + "ĠSh aw", + "p unk", + "Ġva ult", + "Ġkin etic", + "Ġhur ricane", + "ĠInc luding", + "ứ c", + "ĠGrand pa", + "ans hip", + "é¦Ļ 港", + "ĠвÑĭ Ñħод", + "н ож", + "ľ ł", + "ut ta", + "Ġê²ģ ëĭĪëĭ¤", + "Ġb az", + "Ġпо ÑĪ", + "Ġpe culiar", + "zy Äĩ", + "ĠEll ie", + "Ġlearn s", + "ĠKr ishna", + "Ġconse cut", + "Ġemp ath", + "ĠD in", + "Ġtrad ed", + "ĠBor is", + "ugg age", + "oll a", + "Ġназ в", + "Ġetern ity", + "Ġв п", + "è mes", + "Ġgra pp", + "b é", + "ĠпÑĢед ÑģÑĤав", + "ĠF C", + "į ëĭĪëĭ¤", + "e ven", + "ĠNebr aska", + "ortun e", + "Ġk arena", + "ĠAg ent", + "Ġst ing", + "ĠP I", + "Ġmunicip al", + "power ed", + "Ġconse gue", + "ĠMan chester", + "Ġrain y", + "Ġbl i", + "Ġk ost", + "Ġhal ten", + "ĠAh hh", + "ins ula", + "er ting", + "ĠاÙĦ Ùģ", + "Ġrel acion", + "Ġk omen", + "Ġd ome", + "Ġpri ests", + "ĠInt rodu", + "rop he", + "sh ore", + "vel t", + "clip se", + "ĠÑĢ ÑĥÑģ", + "×Ļ× ¡", + "Ġsab emos", + "ĠHoll and", + "og i", + "ank i", + "ĠM ats", + "Ġsm oked", + "ull ie", + "Ġeuro pe", + "ĠдейÑģÑĤв иÑĤелÑĮно", + "Ġbard ziej", + "Ġtransform ing", + "ĠE z", + "op ath", + "Ġìĸ¸ ëĭĪ", + "ĠÑģÑĤ ан", + "ằ ng", + "ั à¹ī", + "ĠO uch", + "Ġclear ance", + "ust ain", + "Ġsolid arity", + "Ġpro ving", + "ĠÐĺ н", + "ĠÑģ ÑĬ", + "Ġpro long", + "ад но", + "Ġs os", + "ĠDe al", + "Ġ17 0", + "m ons", + "Ġз ем", + "Ġlo gged", + "Ġlif elong", + "Ġsens ory", + "Ġbe hold", + "ĠF AR", + "èt ement", + "ĠFed eration", + "Ġdod ge", + "ĠSh ir", + "Ġdrag ons", + "ĠAr ctic", + "Äħ ż", + "Å į", + " º", + "Ġden ke", + "Ġpodr ÃŃa", + "co le", + "ÑĥлÑĮÑĤ аÑĤ", + "Ġsystem atic", + "ам а", + "ch os", + "Ġclin ics", + "ĠB S", + "Ġtal es", + "us ions", + "Ġí Ī¬", + "Ġpres ervation", + "Ġl ore", + "ĠProt est", + "á» Ľ", + "å¸ Ĥ", + "Ġacknowled ged", + "ĠIs aiah", + "ĠëķĮ ëĬĶ", + "Ġ× ĺ", + "Ġcompet itor", + "Ġadv ancing", + "z ip", + "Ġtent h", + "ĠLa ure", + "Ġh ints", + "Ġexerc ising", + "ŀ ľë", + "ĠIntell igence", + "u ated", + "OU T", + "op ed", + "Ġaut onomy", + "Ġbrand ing", + "ĠMediter ranean", + "Ñĸ к", + "Ġscrew driver", + "Ġsu pre", + "Ġst ap", + "Ġjurisd iction", + "ĠSetting s", + "Ġfore front", + "ĠF emale", + "com fort", + "Ġmultiplic ation", + "ĠMur ray", + "Ġbo b", + "ĠT as", + "Ġt ahu", + "Ġon un", + "et ter", + "Ġproph ets", + "l ag", + "Ġreven ues", + "Ġpr á", + "Ġupload ing", + "Ġmach inery", + "asc al", + "ĠEst á", + "ĠG oth", + "ĠB ald", + "ĠS aw", + "Ġstri pes", + "ìł ij", + "Ġpow in", + "æĹ¥ æľ¬", + "Ġhost ile", + "Ġdar um", + "Ġprevent ed", + "ожалÑĥй ÑģÑĤа", + "Ġalgun as", + "Ġhop eless", + "Ġz naj", + "Ġread ings", + "Ġcra ving", + "t at", + "ĠP ig", + "Ġli ar", + "çĪ ±", + "Ġmulti player", + "Ġd ale", + "ĠCour se", + "íģ ¼", + "ĠK ita", + "Ġcustom s", + "Ġrespond s", + "end ra", + "è¦ ĸ", + "Ġmet ro", + "Ñģ ол", + "Ġmitig ate", + "Ġopp ression", + "Ġ æĪijåĢij", + "qu inho", + "Ġam mo", + "Ġen fer", + "Ġp ony", + "Ġ ounces", + "° Ķ", + "ĠìĪĺ ê°Ģ", + "Ġdich o", + "ĠDe b", + "Ġwond ers", + "ĠRo ose", + "Ġpri zes", + "ĠA LEX", + "Ġthank fully", + "Ġtiss ues", + "ĠÑĢав но", + "ĠL una", + "intell igible", + "ĠìĻ ¸", + "ê° ij", + "ĠHe at", + "ĠÑģ ид", + "ĠQu i", + "Ġ ions", + "Ġaccommod ation", + "ä¾ ¿", + "ĠK art", + "ien st", + "Ġt arde", + "Ġso aked", + "ĠCase y", + "Ġì´ Ŀ", + "ĠÑĢ Ñĥб", + "Ġdifferent i", + "Ġleft over", + "Ġexch anges", + "sec ond", + "Ġfirst ly", + "Ġbuild er", + "ri en", + "Ġd w", + "Ġboun cing", + "? <", + "olog ÃŃa", + "we alth", + "Ġmed itate", + "ĵ¤ ìĿĺ", + "ĠC raft", + "è§ī å¾Ĺ", + "æĻ ®", + "ri v", + "ĠAgain st", + "Ġcer amic", + "esp ère", + "Ġcompet ent", + "ĠHop kins", + "Ġkil os", + "Ġgra vel", + "Ġpist on", + "Ġfriends hips", + "Ġesc re", + "Ġvo z", + "ĠGes ellschaft", + "Ġunter stüt", + "Ġmu j", + "Ġwarning s", + "p os", + "ĠProfess ional", + "w szy", + "od le", + "b ands", + "Ġteam work", + "stell ung", + "Ġd x", + "åį Ĭ", + "Ġatt orneys", + "Ġweit ere", + "ãħĭãħĭ ãħĭ", + "ĠOrig inal", + "×Ļ× Ĺ", + "Ġbroadcast ing", + "ĠпеÑĢв Ñĭй", + "uch i", + "Ġhe ure", + "Ġgra bs", + "ĠW OR", + "ĠPla id", + "M in", + "Ġp az", + "ĠP uis", + "um u", + "it ates", + "Ġco ats", + "Ġbu en", + "Ġhe ir", + "Ġpne um", + "ש ר", + "ens er", + "ĠJUD GE", + "Ġbl onde", + "á¹ Ľ", + "Ġg ak", + "Ġs ık", + "Ġquot ed", + "Ġequip o", + "Ġw ishing", + "ÃŃ cia", + "Ġver bs", + "çµ Ħ", + "ĠCanad ians", + "Ġgover ning", + "ĠEv ans", + "E uro", + "Ġgen res", + "Ġunters chied", + "ĠBeck y", + "³¼ ê²ĮìļĶ", + "Ġe inge", + "ĠRa ise", + "ol and", + "ĠStr ateg", + "Ġer es", + "ĠVeter ans", + "Ġbreak out", + "Ġsant é", + "Ġad el", + "Ġinvestig ated", + "Ġpe ur", + "Ġag ile", + "Ġrail road", + "ans ka", + "Ġе й", + "Ġexp os", + "ator ies", + "ĠCont ent", + "Ġtruth s", + "ĠTra il", + "Ġgu a", + "Ġp ores", + "Ġwrit ings", + "ĠU hr", + "ĠThat s", + "Ġic ing", + "O C", + "ĠProdu ction", + "Ġcar ne", + "IS S", + "Ġn inguém", + "n on", + "Ġv icious", + "×ķ× Ķ", + "Ġrecon nect", + "Ġcent res", + "ĠK em", + "Ġcre ase", + "ĠìĿ´ë ¯¸", + "айÑĤ еÑģÑĮ", + "Ġб оÑĢ", + "ĠHay ır", + "ĠÑģ Ñĥд", + "Ġún ica", + "owa ÅĤ", + "Ġad her", + "h ua", + "Z Z", + "Ġprecis o", + "Ġcurrent s", + "Ġseason ed", + "ĠIo T", + "ĠB ishop", + "è¨ Ī", + "st ed", + "ĠBern ard", + "ì¤ ĺ", + "æ² »", + "ĠGl enn", + "Ġktóry m", + "ื à¹Ī", + "Ġast rolog", + "ĠK ot", + "å¤ ľ", + "Ġparf ois", + "Ġfor wards", + "ĠW iÄĻ", + "ĠÎ ĺ", + "Ġn ano", + "è» į", + "s ub", + "ĠBr ill", + "Ġgr it", + "Ġc ited", + "g ado", + "Ġmel ts", + "Ġfor cé", + "âĸĪ âĸĪ", + "Ġb ajo", + "Ġdiscret ion", + "° °", + "at ivity", + "Ġsitu ated", + "ãĥ« ãĤ¯", + "Ñīе е", + "åľ° æĸ¹", + "ĠпÑĢин ÑĨип", + "am az", + "Ġaqu arium", + "Ġdissol ve", + "ĠGod s", + "S uper", + "Ġam id", + "z k", + "Ġ ãģĦ", + "éł IJ", + "amp f", + "Ġhel a", + "' !", + "Ġdevelopment al", + "ĠD ise", + "ĠÑĢабоÑĤ аеÑĤ", + "Ġsnaps hot", + "好 好", + "Õ ¸", + "ĠY ue", + "ĠH ulk", + "ĠDo om", + "ĠFel ix", + "Ġré f", + "M ale", + "ç· Ĭ", + "ph ants", + "EN S", + "ĠMe chan", + "ĠG olf", + "åĨį è¦ĭ", + "Ġgener osity", + "ät ze", + "Ġunlock ed", + "Ġ ãĤĴ", + "íĥ ģ", + "ocaly pse", + "Al right", + "Ġê° ľë", + "Ġ×IJ× ij׾", + "ĠKeep ing", + "Ġcollabor ating", + "ch ief", + "ĠFern ando", + "Ġchef s", + "ĠíĶ¼ë ¶Ģ", + "Ġsk ipped", + "Ġperson n", + "Ġax e", + "che z", + "Ġextract ion", + "ĠA V", + "ĠGib bs", + "Ġí ľ", + "Ġs ı", + "I AM", + "V iew", + "ĠGR ANT", + "Ġëª ¸", + "Ġver ification", + "Ġdep icted", + "ĠMo z", + "ou x", + "Ġt ul", + "Ġsc anner", + "Ġcomed ian", + "ĠVol ks", + "ĠJE FF", + "è¨Ĥ éĸ±", + "§ Ħ", + "Ġdistract ion", + "r á", + "ĠIN TER", + "Ġsin cer", + "Ġ×ŀ× ª", + "Ġש ׳", + "Ġconstruct ive", + "ar f", + "ĠëĪ Ħë", + "Ġe co", + "r amos", + "Ġrenew ed", + "in ement", + "ĠU b", + "ĠPe pper", + "ì§Ģ ê°Ģ", + "ĠDar win", + "Ġmerch and", + "Ġv árias", + "è ce", + "N G", + "ĠìľĦ íķ´ìĦľ", + "Ġак ÑĤив", + "ĠUn ters", + "ع ÙĦ", + "Ġint ric", + "omm a", + "ie ving", + "ĠCarol ine", + "åĵ ģ", + "ĠPR ES", + "Ġperform er", + "Ġaut our", + "ãģ¾ãģĽ ãĤĵ", + "Ġutter ly", + "Ġsynth esis", + "Ġles bian", + "Ġretrie ve", + "Ġmane ira", + "Ġimp air", + "Ġment oring", + "ĠSoul s", + "ĠGo Pro", + "ÑĢ аÑĤÑĮ", + "Ġc ose", + "ĠSS D", + "I RE", + "Ġup front", + "ĠA un", + "Ġgam er", + "Ġl itt", + "Ġag gression", + "ĠLike wise", + "ĠBet ty", + "ĠD art", + "ĠD LC", + "ish ment", + "ìŀ¥ ìĿĦ", + "Ġ 对", + "ç» ı", + "c ream", + "ĠBaby lon", + "Ġn ug", + "br ar", + "Ġa ynı", + "am ily", + "b ike", + "ahah aha", + "lo yd", + "Ġmir a", + "Ġper me", + "ĠG aming", + "Ġfirm ware", + "M a", + "Ġassist ed", + "at ics", + "Ġìķŀ ìľ¼ë¡ľ", + "ĠM ental", + "niej s", + "ĠI z", + "ow Äħ", + "Ġt ougher", + "Ġde ed", + "èĭ ¦", + "Ġsty lish", + "ĠTool s", + "ĠH amp", + "Ġsun screen", + "Ġartic ulate", + "i ye", + "и ÑĦ", + "ĠSp read", + "ĠHA VE", + "Ġsw irl", + "Ġspons oring", + "ä» ĭ", + "iov ascular", + "mes i", + "Ġrelax ation", + "ĠÑģво иÑħ", + "Ġmar gins", + "Ġsa ÄŁ", + "ĠPr ide", + "ĠÏĦοÏħ ÏĤ", + "и ÑĨи", + "en ci", + "Do es", + "Ġcor pse", + "Ġend urance", + "Ġí ŀĺ", + "ì¹ ´", + "Ġhair cut", + "Ġinterrupt ed", + "Ġwind y", + "ĠC aleb", + "Ïģ Ïĩ", + "ĠPour quoi", + "Ġhol istic", + "uc lear", + "ĠWho le", + "å£ «", + "A ct", + "Ġgall on", + "c ade", + "ĠReg ional", + "ro ads", + "ĠSch ne", + "á ng", + "Ġиз мен", + "ãĤĪ ãģŃ", + "Ġmen us", + "Ġspl itting", + "Ġpr iced", + "ĠÎ ĵ", + "Ġus ername", + "ĠÐŀ Ñĩ", + "Ġcomp ressed", + "y in", + "Ġguard ian", + "Ġgo of", + "Ġcheck list", + "Ġinter change", + "Ġexped ition", + "Ġex tern", + "Ġinfra red", + "eng o", + "Ġden ying", + "Ġpack ets", + "on ent", + "B B", + "ĠInc re", + "Ġsin i", + "ÃŁ er", + "è g", + "ma al", + "gen eration", + "Ġminor ities", + "Ġlle var", + "Ġnom ination", + "Ġcons id", + "Ġ×ľ× ¢", + "m uÅŁ", + "ĠEs c", + "Ġnumer ator", + "Ġka ik", + "Ġktóry ch", + "ies en", + "Ġv ê", + "ĠUS S", + "ĠPri vate", + "Ġод но", + "Ġal ém", + "ÃŃt ulo", + "Ġlim b", + "Ġforg iven", + "Ġdiscl osure", + "ÏĦ ί", + "Ġning ún", + "Ġtherapeut ic", + "Ġnegoti ating", + "ĠN ike", + "ense ful", + "Ġin cap", + "Ġflag ship", + "t own", + "â Ī", + "ĠÏĢ ολ", + "Ġwol ves", + "Ġviol ations", + "ĠAr nold", + "Ġinterven e", + "Ġhe ater", + "Ġrecurs os", + "Ġma id", + "ê² ¼", + "Ġдав айÑĤе", + "ĠCe lebr", + "Ġca pe", + "ĠSt y", + "ain en", + "s ite", + "b ij", + "Ġп олÑĮз", + "Ġfr amed", + "Ġpublish ers", + "ĠÑĩ ÑĥÑĤÑĮ", + "Ġtempt ation", + "Ġcert eza", + "Ġex empt", + "ìĬ ¹", + "se lling", + "ĠT ask", + "ho on", + "ĠC oc", + "ĠPark s", + "Ġrepet ition", + "ĠÑĤ Ñĥда", + "Ġens l", + "ĠdeÄŁ iÅŁ", + "ĠOr lando", + "ĠMain ten", + "æŃ ¢", + "oc ument", + "ĠH C", + "Ġscoot er", + "Ġнап иÑģ", + "Ġtight er", + "Ġte ase", + "Ġremo ves", + "Ġkij ken", + "ĠÑģÑĥ ÑīеÑģÑĤв", + "Ġth é", + "ĠвÑĭ глÑıд", + "Ġrel ieve", + "Ġmit ä", + "Ġstation ary", + "ö ff", + "p able", + "Ġar ter", + "Ġdé f", + "r ative", + "Ġcon ect", + "Ġsad dle", + "ĠD iane", + "Ġcomm emor", + "fend im", + "S ÃŃ", + "Ġíģ ´ë", + "Ġman ge", + "at te", + "Ġarrog ant", + "Ġrobot ic", + "Ġgi Ãł", + "æĺ¯ çļĦ", + "Ġneighbour hood", + "iss on", + "Ġдв иж", + "ĠR I", + "ĠNorm an", + "b rand", + "am ation", + "Ġraz or", + "Ġmur ders", + "ĠÑĤ Ñĥ", + "Ġwszystk im", + "Ġut ilities", + "Ġmicros cop", + "ê ¿", + "Ġda qui", + "oll ar", + "ĠÐĶав айÑĤе", + "Ġann ée", + "Ġkilomet res", + "Ġhom osexual", + "Ġarchitect s", + "ãģ¡ ãģ¯", + "Ġni ye", + "L ER", + "Ġmicro phones", + "ĠSt unden", + "Ġconsecut ive", + "iend a", + "v änd", + "D ER", + "Ġlif ts", + "ĠMe at", + "Ġsave z", + "íĸ Īëįĺ", + "M en", + "Ġdism ant", + "ê±°ë ¥¼", + "Ġins ulation", + "Ġsc all", + "Ġsp ooky", + "Ġpar c", + "Ġball et", + "ĠWhats App", + "Ġfr anc", + "Ġdeliber ate", + "Ġíħ Į", + "Ġm ars", + "ĠZ ur", + "P r", + "dis ciplinary", + "Ġobs ession", + "м е", + "Ġmarch ing", + "ĠEmer gency", + "ig uous", + "Ġs zy", + "ĠL ands", + "Ġboard ing", + "ĠпоÑĩ ÑĤи", + "Ġenv y", + "Ġcompassion ate", + "Ġmer ci", + "Ġdes irable", + "d ale", + "Ġcan ım", + "ĠAnt ar", + "tem ps", + "Ġconfig ured", + "ĠComp ared", + "ne h", + "ic ating", + "Ġnic kel", + "ÙĪ ÙĤ", + "Ùĥ ÙĪÙĨ", + "op es", + "Ġform ulas", + "ĠÐķ ÑģÑĤÑĮ", + "Ġpo bl", + "ĠP J", + "ĠL ud", + "ä»Ĭ åĽŀ", + "ĠBr id", + "ĠH og", + "ĠBr is", + "J en", + "Ġshad ing", + "ĠY as", + "Ġdistur bed", + "Ġrecomm ending", + "Ġc é", + "ĠH OW", + "ìĹĪ ìĸ´", + "Ġrevers ed", + "ĠInteresting ly", + "iox id", + "åħ Ń", + "Ġìĺ¤ ì¼ĢìĿ´", + "ế u", + "x x", + "Ġou ais", + "ĠYouT ubers", + "ĠR osa", + "ĠH aupt", + "j adi", + "Ġvlog s", + "Ġcult ura", + "ĠLeaders hip", + "ĠH ep", + "Ġill um", + "´ë ıĻ", + "Ġcustom ized", + "Ġmar ca", + "Ġqu atro", + "Ġн аг", + "ĠSpace X", + "ĠE igen", + "ast ing", + "ĠolduÄŁ u", + "Ġfor ts", + "ãģ ī", + "r iment", + "ien cia", + "Ġten ir", + "ro ffen", + "Ġ197 9", + "Ġc ie", + "ĠëIJĺ ê³ł", + "Ġes cri", + "ÏĮ ÏĤ", + "íı ¬", + "uz zy", + "C ong", + "ìĿ¸ ìĿ´", + "G reat", + "s il", + "é ch", + "ãģ¨ ãģĭ", + "Ġmult ic", + "ĠDis k", + "² ķ", + "Ġfaz la", + "Ġle vant", + "Ġab ajo", + "ur ry", + "st ru", + "Ġ먹 ëĬĶ", + "Ġaccess ory", + "Ġдв иг", + "ĠR id", + "20 19", + "Ġdown stream", + "æķ ¸", + "Ġk az", + "ut an", + "Ġchar coal", + "Ġa fect", + "w u", + "Ġcontext s", + "Ġfe ared", + "ĠìĦ ¤", + "Ġhist ories", + "Ġf as", + "ens ible", + "Ġcoco a", + "ill ar", + "ge ons", + "Ġspiritual ity", + "ĠP ew", + "Ġpharm acy", + "Ġpass ions", + "Ġb os", + "Ġall á", + "Ġthri ving", + "ĠRe act", + "Ġoccup y", + "Ġwithdraw al", + "Ġallow ance", + "ĠFra ktion", + "Ġbud dies", + "Ġid le", + "Ġdissol ved", + "Ġpreval ent", + "Ġmil itar", + "Ġsens ing", + "Ġpo jaw", + "Ġanc ora", + "Ġabund ant", + "Ġha irst", + "ãģĤ ãĤĮ", + "Ġtw ee", + "Ġnäch ste", + "ĠMöglich keit", + "Ġho o", + "uff icient", + "Ġfant ast", + "Ġed ible", + "Ġëĸ¨ ìĸ´ì", + "ìĽ ĥ", + "Ġve in", + "uc ci", + "Ġdevot ion", + "Ġconce aler", + "in come", + "Ġrecy cled", + "ĠìĬ¤í ĥĢ", + "Ġpont os", + "Ġdess us", + "Ġvé rit", + "Ġreflect ions", + "ĠA A", + "Ġtake away", + "b are", + "ĠCont act", + "e il", + "ĠHe ar", + "Ġmir ac", + "ĠGer ilim", + "ĠÑģам Ñĭй", + "Ġv ivo", + "Ġkilogram s", + "ĠCr im", + "û t", + "7 8", + "Ġsincere ly", + "ra z", + "Ġë³ µ", + "Ġarri v", + "Ġconcept ion", + "ĠPers ian", + "Ġsj äl", + "Ġst arring", + "ĠìķĦë ¬´", + "ĠFore ver", + "е ÑģÑĤÑĮ", + "Ġve il", + "Ġsubt it", + "od ka", + "ĠоÑĤно ÑĪ", + "Ġcook s", + "ен Ñı", + "K ay", + "Ġni ños", + "ĠPh one", + "Ġstitch ing", + "Ġfinger print", + "é¢ ĺ", + "λ ά", + "Ġded icate", + "ĠL ob", + "Ġblack s", + "ĠB le", + "b out", + "ĠÄij ang", + "Ġe ks", + "Ġsqu ash", + "ĠK ü", + "od i", + "Ġn Æ°á»Ľc", + "Ġvoy age", + "Ġplay ful", + "ĠØ¥ ÙĦÙī", + "an ic", + "Ġcondem n", + "ĠB öyle", + "ĠPol ize", + "ãĤ¿ ãĥ¼", + "Ġay uda", + "Ġp am", + "à¹Ħ à¸Ľ", + "ĠK athy", + "ед ин", + "нов а", + "Ġbr ig", + "eg er", + "Ġe agle", + "Ġvis ions", + "ĠíķŃ ìĥģ", + "Ġsh itty", + "Ġh ott", + "ĠBr itt", + "ut ors", + "ENT E", + "æĽ ²", + "Ġph on", + "ĠB ing", + "Ġпод деÑĢж", + "spr ing", + "æĸ ¯", + "et ten", + "Ġpil gr", + "Ġed iyor", + "енÑĤ Ñĭ", + "ag gio", + "Ġj ul", + "Ġcomp rend", + "te il", + "ĠØ ²", + "Ġperform ers", + "Ġinf amous", + "ĠM K", + "ç ª", + "æ³ ģ", + "ot le", + "e ff", + "ĠH ash", + "Ġcow ard", + "ĠB RA", + "ĠD D", + "Ġcom ida", + "Ġpl ata", + "Ġfl ap", + "ĠMe hr", + "rib ution", + "ĠY emen", + "Ġmyster ies", + "ĠÄ° yi", + "Ġst ell", + "Ġeyel iner", + "Ġdel es", + "Ġnail ed", + "Ġillness es", + "Ġst acks", + "Ġtrabaj ar", + "fl ower", + "ci u", + "Ġcr ude", + "Ġsubstant ially", + "Ġhome m", + "Ġnep hew", + "Ġstamp s", + "Ġcar bs", + "ÑĮ ÑĤе", + "mo oth", + "Ġtun nels", + "ac ie", + "æ³ ¢", + "ĠSe ñ", + "ĠH era", + "ĠìķĦëĭĪ ìĹIJìļĶ", + "ĠWy oming", + "ĠHD MI", + "ĠL is", + "u ción", + "Ġste er", + "о Ñİ", + "иÑĤ а", + "N T", + "Ġìĸ¼êµ ´", + "Ġpal ms", + "Ġne on", + "ов аниÑı", + "Ġfilter ing", + "Ġjou er", + "ĠH ö", + "Ġне Ñģ", + "ê²ł ìĸ´ìļĶ", + "Ġ8 1", + "Ġstory line", + "Ġprz ep", + "Ġthank ing", + "ĠBo eing", + "Ġsoft ly", + "j em", + "алÑĮ нÑĭÑħ", + "Ġflash light", + "Ġп Ñĥ", + "ĠW OMAN", + "ắ c", + "ÃŃ ch", + "Ġlux urious", + "Ġw ün", + "Ġimpact ful", + "Ġcons on", + "re u", + "ir ring", + "if ter", + "Ġconstitu ents", + "èIJ ½", + "Ġ9 4", + "ĠT ou", + "g om", + "ĠìĥĿê°ģ ìĿĦ", + "Ġstere otypes", + "Ġmoż li", + "åĪĨ 享", + "Ĥ ¨", + "Ġpencil s", + "ĠÑģл ож", + "Ġih rem", + "ĠBes ch", + "ĠK oh", + "ĠEnt scheid", + "Ġle k", + "Ġför s", + "Ġtotal mente", + "Ġlive ly", + "Ġent ropy", + "Ġdisc ern", + "ĠÐĹ Ð½Ð°", + "Ġdo v", + "Ġmyth ology", + "è¨ĺ å¾Ĺ", + "apan ese", + "Ġapprox imate", + "аÑĤ ив", + "if iable", + "ĠSe o", + "åĢ Ĵ", + "´ìĭ¬ íŀĪ", + "Ġìĺ ·", + "Ġtempor al", + "Ġi T", + "Ġest at", + "к им", + "Ġspr ink", + "Ġgr und", + "Ġinfant ry", + "Ġsch affen", + "ç´ Ħ", + "Ġan k", + "ri ages", + "ĠYe on", + "ĠMor oc", + "Ġinv asive", + "ģ Ķ", + "Ġparent ing", + "ĠR is", + "ib ile", + "Ġmod s", + "å½ ¢", + "ĠпÑĢов еÑĢ", + "ĠTh ing", + "ĠWhere ver", + "Ġacknowled ging", + "Ġpa wn", + "um mer", + "or b", + "6 9", + "Ġretr ouve", + "Ġrel ies", + "ĠHigh way", + "Ġa we", + "ãģ§ãģĻ ãģĭ", + "ita ire", + "Ġapplic ant", + "Ġais le", + "w orm", + "Ġpay load", + "Ġcar re", + "ĠB ach", + "æł ¼", + "Ġì¹ľ 구ë", + "ни е", + "Ġit ÃŃs", + "onna ise", + "s ol", + "èı ¯", + "alg ia", + "Ġrock ing", + "Ġbest en", + "rit es", + "^ ^", + "ин ой", + "Ġba ixo", + "Ġ기 ìĸµ", + "оÑĤ ÑĢи", + "s im", + "Ġinc arn", + "ëĭ¤ ìĿĮ", + "Ġl ick", + "s ided", + "Ġ7 1", + "f order", + "Ġreson ance", + "Ġte gen", + "Ġmet aph", + "ows er", + "Ġ×IJ× ł×Ĺ׳×ķ", + "? ãĢį", + "Ġsp ielen", + "Ġvoll ey", + "ĶìĿ´íģ¬ ìĹħ", + "lo oked", + "Ġsent enced", + "Ġmultip lying", + "Ġide als", + "Ġwahr scheinlich", + "Ġdepos its", + "bil ir", + "Ġeff et", + "ill on", + "Īë §Į", + "Ġtestim on", + "Ġz awsze", + "ĠпÑĢоÑĨ еÑģÑģ", + "ĠL av", + "ä¸į éĮ¯", + "Ġtrava iller", + "Ġla isse", + "ĠMount ains", + "ĠÑĢ об", + "Ġexam ined", + "it us", + "W as", + "л Ñĭ", + "Ġattrib uted", + "ĠìĬ ¹", + "ĠBar on", + "Ġg ep", + "Ġatt ent", + "ĠColl ection", + "Ġthe at", + "ĠC ai", + "Ġwell s", + "Ġhuman o", + "çĹ ħ", + "ĠH ast", + "ĠÑħоÑĤ Ñı", + "cz as", + "Ġperm its", + "Ġle gg", + "Ġe po", + "ĠF en", + "Ġth i", + "ĠF oi", + "Ġé lect", + "Ġ8 3", + "Ġover th", + "Ġ è¬Ŀè¬Ŀ", + "Ġten ant", + "è² ·", + "N ext", + "Ġpra ised", + "sec urity", + "ĠImp act", + "为 ä»Ģä¹Ī", + "Ġv ouch", + "Ġneg ó", + "Ġun ve", + "Ġcritic ize", + "ĠKen ya", + "Ġtact ic", + "Ġlo gr", + "Ġpo is", + "Ġpap a", + "spe aks", + "ðŁ ij", + "isp ers", + "Ġsur plus", + "Ġcold er", + "åį Ĺ", + "åIJ ¬", + "pl ets", + "ĠV ienna", + "ĠLe ad", + "Ġaer ial", + "ĠT ah", + "енÑĤ ов", + "ĠGree ks", + "C am", + "Ġmá xim", + "Ġk uin", + "ch io", + "Ġdemonst rates", + "an os", + "ĠC ert", + "ĠÑį н", + "Ġblog s", + "ĠìĦľ ìļ¸", + "Ġbe ams", + "ик ов", + "Ġprompt ed", + "Ġfright ening", + "ĠPors che", + "ãģĪ ãģ¦", + "lar ını", + "Ġch illing", + "is phere", + "Ġfl ashing", + "ĠK ard", + "b read", + "Ġex h", + "Ġty cker", + "Ġec ological", + "ĠMa e", + "Ġ×ŀ×IJ ×ķ×ĵ", + "ĠëĤ ĺëıĦ", + "л он", + "ys s", + "Ġper gunt", + "Ġpri x", + "izz ard", + "Ġcan cers", + "Ġ9 1", + "s usp", + "ĠIt em", + "ÅŁ a", + "Ġp est", + "Ġtak Äħ", + "Ġl ymph", + "ĠPat ri", + "f ill", + "Ġrec onna", + "Ġoptim ism", + "Ġmim ic", + "Ġì² ľ", + "ĠMad ame", + "oc y", + "l ining", + "åijĬ 訴", + "erm e", + "Ġfold ers", + "Ġcz ÅĤ", + "uch ar", + "Ġcur so", + "Ġbre ach", + "ни ÑĤÑĮ", + "Ġp amiÄĻ", + "Ġel ig", + "Ġaut op", + "F low", + "Ġprogram med", + "ĠPro cess", + "Ġfig ur", + "ĠS F", + "ĠE les", + "Ġprogram mes", + "Ġdiz zy", + "ìĭľ ê°Ħ", + "Ġли бо", + "Ġsn iff", + "ĠSeb astian", + "ĠH ye", + "Ġ4 000", + "Ġperm ite", + "æ¢ Ŀ", + "Ġза Ñī", + "Ġgu it", + "ĠD ais", + "Ġaccord ance", + "Ġmod ular", + "ogene ous", + "æĭ į", + "Ġpou quinho", + "Ġart illery", + "Ġlub ric", + "Ġvol can", + "ĠN H", + "ðŁ ¤", + "Ġde an", + "R h", + "Ġminist re", + "åĿ IJ", + "ĠIn v", + "ĠBul gar", + "ĠD aten", + "è İ", + "I m", + "Ġorigin ated", + "ĠN ixon", + "inte gr", + "Ġlack s", + "ĠN acht", + "ìĸ´ë Ĥĺ", + "cam era", + "Ġrad ish", + "ki ye", + "Ġang es", + "Ġpré f", + "j uk", + "ĠBe e", + "ĠB U", + "ĠвоÑģ п", + "ĠB T", + "ê mes", + "ĠSt ück", + "ĠIn k", + "æĪĸ èĢħ", + "ĠSerge ant", + "ĠMult ip", + "Ġhiç bir", + "ĠС ам", + "ĠD é", + "ol ph", + "ìĸ ¸", + "Ġimp at", + "ĠìķĬ ê³ł", + "ĠÑĤак ого", + "ĠнавеÑĢ ное", + "Ġunpredict able", + "Ġm end", + "ĠìĹĨ ìĸ´ìļĶ", + "Ġjakie ÅĽ", + "Ġann i", + "Ġdon né", + "ĠK irsty", + "Ġrectang ular", + "Ġempez ar", + "ĠEx change", + "ê° Ķ", + "Ġé conom", + "ãģĵ ãĤĵ", + "el in", + "re ibt", + "Ġ×Ķ× ¤", + "Ġc emetery", + "Ġespañ ol", + "ol in", + "лÑİ Ð´", + "Ġgr âce", + "all en", + "ĠPh ilos", + "ĠEr st", + "Ġìĥ Ī", + "ĠV id", + "G ive", + "O H", + "μ ο", + "ĠP are", + "Ġmetabol ism", + "Ġma ple", + "Ġax le", + "ĠD y", + "Ġkomm e", + "Ïİ Î½", + "Ġgreat ness", + "Ġver ified", + "Ġsp é", + "ĠFahren heit", + "ĠB ren", + "ĠConf eder", + "Ġhist oire", + "Ġelimin ating", + "ĠAd ding", + "ĠAb i", + "æĿ İ", + "Ġhospital ity", + "t im", + "Ġbon ito", + "Ġpart es", + "ĠдÑĢÑĥг иÑħ", + "ĠSh ay", + "ĠS ed", + "Ġreg rets", + "Ñı ми", + "Ġten ants", + "éĢ Ł", + "ĠP TS", + "Ġdev i", + "ĠL ate", + "ue z", + "Ġsö yl", + "ãĤ »", + "Ġìŀ¬ë °Į", + "Ġtogg le", + "Ġmas king", + "алÑĮ ного", + "Ġpers ön", + "Ġamer ican", + "f ik", + "ĠR GB", + "ens on", + "ĠK A", + "ww ww", + "ĠÑĢ ег", + "met ics", + "Ġeduc ator", + "ãĤ· ãĥ«ãĤ¯", + "p ark", + "елÑĮ зÑı", + "ar us", + "ÑĢ еÑĤ", + "Ġfe ito", + "Ġcho ir", + "Ġlar go", + "Ġe ens", + "Ġwat ts", + "ĠSing le", + "Ġsuscept ible", + "ic er", + "Ġв клÑİÑĩ", + "Ġp us", + "íĻ ĺ", + "E ng", + "Ġfant as", + "Ġspecific ation", + "Ġconfront ed", + "ĠColumb us", + "ив еÑĤ", + "ar ım", + "Ġcaffe ine", + "mun ition", + "Ġmig rants", + "l ide", + "it ations", + "ĠG eme", + "Ạ«", + "Ġpl anner", + "Ġstim ulate", + "Ġapro xim", + "ce u", + "ĠN om", + "Ġv og", + "ĠÑĢ аÑģÑĤ", + "Ġense ñ", + "Ġsell ers", + "Ġgut en", + "z d", + "C al", + "Ġdescri pt", + "Ġrecon ciliation", + "z inho", + "á¹ĩ a", + "ãģĺãĤĥ ãģĤ", + "acy j", + "ĠCO L", + "s aw", + "ĠíĻķ ìĿ¸", + "Ġvar it", + "Ġpartner ing", + "Ġdet ention", + "Ġbomb ing", + "c lapping", + "ien cies", + "ond u", + "AM E", + "Ġê°Ļ ìĬµëĭĪëĭ¤", + "c ÃŃa", + "ĠпоÑģ ÑĤо", + "ĠAS MR", + "Ġhome page", + "Ġsi è", + "an tha", + "ĠP oll", + "Ġ igen", + "cy ch", + "Ġê°ij ìŀIJ기", + "Ġconsider ably", + "ä»ĸ çļĦ", + "ĠAr ist", + "Ġwith stand", + "Ġqual itative", + "ĠK raft", + "ĠÑį лекÑĤ", + "ĠBe ad", + "екÑĤ ив", + "Ġcr ushing", + "ì³ IJ", + "Ġnav y", + "ÙĪ Úº", + "s ho", + "Ġo ak", + "ipp ers", + "Ġso ils", + "Ġpig ment", + "Ġev itar", + "ãĥ ĩ", + "Ġf use", + "ĠD ale", + ": \"", + "Ġcompl ètement", + "Ġke l", + "๠Ĩ", + "Ġqu atre", + "ĠU M", + "Ġë§ IJë", + "æł ¹", + "ÃŃ r", + "Ġle isure", + "ĠH ousing", + "Ġfold s", + "est ion", + "AR S", + "Ġm ash", + "urp ose", + "Ġaccum ulated", + "ĠSt uff", + "èª ŀ", + "Ġtap es", + "ĠÑģ илÑĮно", + "ĠLO VE", + "Ġ198 2", + "Ġsc ars", + "Ġcapital ist", + "ĠN ed", + "Ġsoft en", + "Ġnot ably", + "Ġforcé ment", + "ĠRa um", + "Ġнеоб Ñħод", + "Ġtrad emark", + "Ġfert ig", + "Ġ? !", + "æĹ ł", + "Ġreinfor ced", + "Ġre charge", + "ĠPut ting", + "Ġvill ains", + "Ġhand ic", + "Ġadvertis ement", + "ت ÙĬ", + "ĠÑģ Ñĥм", + "ĠR iley", + "×ķ× ij×", + "äº ¬", + "O s", + "Ø§Ø ²", + "B oy", + "Ġsqu ish", + "ock et", + "Ġtest ify", + "æ¼ Ķ", + "Ġ×ľ× ŀ×", + "Ġм аÑģÑģ", + "man uel", + "ĠArk ansas", + "if fe", + "Ġanalyst s", + "ĠDe af", + "Ġj ó", + "Ġgrocer ies", + "ĠWhe el", + "ĠÑĢ иÑģ", + "Ġc òn", + "ĠC ob", + "Ġpris ons", + "è ve", + "ĠCab inet", + "Ġpos ed", + "Ġguer re", + "ĠL loyd", + "Ġcl erk", + "Ġcr ises", + "ĠSh o", + "ĠO re", + "ĠFoot ball", + "ĠAd vis", + "ĠZh eng", + "è į", + "ĠAM Y", + "Ġun for", + "Ġmon aster", + "Ġcomp ile", + "Ġimm ortal", + "at able", + "Ġpar ano", + "Ġt iver", + "ĠStep h", + "ĠFu ÃŁ", + "Ġdisc ontin", + "Ġr ipe", + "Ġhack ing", + "Ġs iendo", + "Ġsegu ro", + "alt res", + "Ġand eres", + "Ġë ¦¬ë", + "Ġexp orts", + "æŃ ¥", + "Ġtab ii", + "Ġ기 ëĭ¤ë", + "Ġbother ing", + "Ġpick le", + "ĠBRI AN", + "Ġalt ar", + "ĠпÑĢи б", + "Ġtransfer ring", + "ĠV ors", + "ĠÙĩ ÙĪ", + "ĠZ a", + "ĠFr ances", + "Ġbrow se", + "em it", + "Ġche wing", + "ĠFred dy", + "Ġedit ors", + "ä lle", + "Ġí ĮĢ", + "ĠS que", + "ĠC ultural", + "aw k", + "ĠS ache", + "ĠCar bon", + "ắ t", + "F L", + "ĠN GO", + "pe ÅĤ", + "ĠS ou", + "Ġh vor", + "un intelligible", + "Ġë² ķ", + "Ġ °", + "i in", + "Ġ×¢ ×Ŀ", + "Ġder rière", + "Ġczy m", + "ĠAp ost", + "Ġregard er", + "Ġag rade", + "ĠC andy", + "Ġma re", + "Ġintrodu ces", + "bird s", + "Ġuniqu ely", + "Ġm uk", + "Ġcook er", + "Ġcrew s", + "Ġje ito", + "ER T", + "¶ Ħë", + "n isse", + "Ġe f", + "Ġcart e", + "ĠY ak", + "ĠP AT", + "и но", + "bok ki", + "Ġm ates", + "Ġdist int", + "Ġì½Ķë¡ľ ëĤĺ", + "Ġy ıl", + "Ġκ άν", + "Ġconfigur ations", + "eng a", + "re cht", + "H appy", + "ãĤĦ ãģ£ãģ¦", + "in vest", + "Ġreconst ruct", + "ĠÑįÑĤ омÑĥ", + "Ġmos que", + "ra um", + "Ġvoy ez", + "ĠN BC", + "ĠìŀIJ ìĭł", + "Ġstur dy", + "Ġк ап", + "Ġans ch", + "al id", + "Ġmas ih", + "ĠR EP", + "Ġì½ Ķë", + "Ġded uct", + "Ġsal ir", + "w urf", + "il ot", + "ĠM utter", + "old s", + "ĠF EMA", + "ĠB ib", + "Ġneighb oring", + "Ġbl iss", + "Ġíĺ ¼", + "ли ÑģÑĮ", + "ĠÑĤÑĢ еб", + "Ġ å°±æĺ¯", + "Ġgren ade", + "Ġe gal", + "Ġfin ely", + "Ġpet als", + "Ġke er", + "Ġch yba", + "Ġsk ipping", + "Ġth irteen", + "Ġgrav y", + "ĠS AT", + "6 1", + "Ġн ог", + "Ġmin s", + "IT E", + "Ġso zial", + "íķĺë ©´ìĦľ", + "rukt ur", + "Ġвозм ож", + "Ġоп ÑıÑĤÑĮ", + "Ġar th", + "ĠCub an", + "Ġtre asures", + "Ġfertil izer", + "Ġawak ening", + "Ġë°± ìĭł", + "Ġr all", + "Ġdep ict", + "ĠP ablo", + "Ġninete en", + "Ġw att", + "Ġentire ty", + "K S", + "ĠWood s", + "S ch", + "ĠÚ© ÙĪ", + "ĠD ry", + "ãģ ŀ", + "u ve", + "Ġreconst ruction", + "Ġanat omy", + "Īë ¥¼", + "Ġb aba", + "Ġlisten er", + "Ġshar pen", + "ĠPer u", + "ĠвÑĭ з", + "Ġrecre ation", + "Ġiniti ate", + "Ġcal or", + "ĠN aj", + "ge e", + "ĠFe els", + "ĠSnap chat", + "ĠT et", + "ĠN est", + "ĠD af", + "ĠFin ish", + "ĠÑĤак им", + "ú c", + "iz ens", + "Ġsp ins", + "Ġemb ry", + "Ġpass ages", + "Ġc ient", + "Ġjust ification", + "ä»ĸ 說", + "Ġolm az", + "Ġflood ed", + "Ġemo ji", + "Ġembr acing", + "Ġdisc ard", + "ĠBas ic", + "ag og", + "ĠìľĦ íķ´", + "Ġas ylum", + "er in", + "Ġf im", + "Ġnin ja", + "Ġautom ate", + "Ġaller gic", + "ÿÿ ÿÿ", + "am am", + "Ġм аÑĢ", + "ĠO i", + "ä us", + "Ġin duct", + "ĠB EN", + "Ġz ÅĤ", + "Ġkaż dy", + "ĠAM P", + "n ÄĽ", + "S ure", + "Ġqu il", + "Ġespe c", + "ro k", + "BS CRI", + "Ġlie be", + "p us", + "ach sen", + "Ġcr icket", + "ëĬ IJ", + "ĠFr ame", + "ekk ür", + "ar b", + "Ġp ÅĻ", + "иÑģ Ñģ", + "Ġzeg gen", + "Ġdou bles", + "ĠD re", + "t est", + "ins p", + "bo ys", + "Ġm ão", + "ĠVer se", + "Ġmus cular", + "ĠMA LE", + "Ġd ulu", + "Ġoccas ional", + "L o", + "conom ic", + "Ġv ak", + "Ġrem edy", + "å¤ ł", + "ĠâĻªâĻª âĻª", + "ve m", + "Ġön em", + "ĠkarÅŁ ı", + "ĠSh arp", + "h ur", + "Ġë°© ë²ķ", + "Ġgrand son", + "Ġakt iv", + "ĠTh rones", + "ĠìķĪ ìĹIJ", + "Ġto ts", + "Ġsub d", + "ĠPa ula", + "Ġgra ves", + "ĠB rent", + "Ġник ÑĤо", + "Ġsö z", + "Ġcre c", + "ĠVlad imir", + "çĸ «", + "Ġп ой", + "Ġ\" -", + "Ġp sy", + "at ri", + "id an", + "Ġa ún", + "Ġstandard ized", + "ì¹ ĺë", + "Ġк ÑĢов", + "ĠZh u", + "s omething", + "Ġ7 50", + "Ġmuj eres", + "Ġa it", + "éĹ ´", + "ag u", + "Ġcorrect ed", + "ik ka", + "el ed", + "ĠCare er", + "ow ym", + "Ġroomm ate", + "Ġdescend ants", + "ĠNapole on", + "ĠÐĶ о", + "íĸĪ ìĸ´ìļĶ", + "Ġbun un", + "ĠMich a", + "ç· ļ", + "Ġdesc ob", + "P I", + "Ġpalab ra", + "Ġtrack ed", + "Ġdepend ence", + "ĠBar ack", + "åģ ĩ", + "Ġfert ility", + "ĠSouth west", + "Ġincom plete", + "Ġcomun ic", + "Ġcomp ris", + "ĠRest aur", + "Ġac ron", + "κ α", + "Ġapprent ices", + "Ġmus st", + "ĠA br", + "Ġpent ru", + "ĠCons ort", + "ĠAve c", + "Ġdum plings", + "L R", + "Ġwszystk ie", + "Ġsw amp", + "н ев", + "ugg le", + "Ġwater color", + "Ġprot on", + "ĠEspa ña", + "ock ing", + "ов ал", + "Ġtak im", + "V ery", + "Ġdement ia", + "ĠÅŁey i", + "J ac", + "ĠMac Book", + "ĠL iv", + "ffic ients", + "ĠH unt", + "Ġover lay", + "æĦŁ 覺", + "ĠSky pe", + "p unkt", + "Ġconf ined", + "ĠAd rian", + "ر Ùĥ", + "ĠJe ep", + "Ġenqu anto", + "Ġan est", + "оÑĤ веÑĤ", + "Ġм енÑĮ", + "Ġirrig ation", + "á»ij n", + "Ġeight een", + "ĠP on", + "Ġresc ued", + "Ġ198 3", + "r ü", + "ja e", + "ĠJe ong", + "Ġamazing ly", + "ĠF DP", + "Ġback stage", + "c ue", + "ĠÏĥÏĦη ν", + "ĠاÙĦØ µ", + "Ġlivest ock", + "ĠW arner", + "Ġmaj ors", + "ãĥģ ãĥ£", + "Ġcooper ative", + "ĠBr ady", + "ra ined", + "rie b", + "Ġ×ij× ŀ×", + "Ġдов олÑĮно", + "ĠF E", + "Ġle aked", + "ĠMerc ury", + "Ġpersu ade", + "Ġtransform er", + "ĠNor weg", + "ĠìĹ¬ë Ł¬", + "Ġzrobi Äĩ", + "Ġcard iovascular", + "ĠCr ash", + "Ġg ossip", + "а ÑģÑĤÑĮ", + "Ġì ª½", + "Ġsw ept", + "ĠH orn", + "ĠAt é", + "Ġbu kan", + "ĠK aw", + "K Y", + "ĠSt ories", + "G ary", + "Ġgard ening", + "ĠQuick ly", + "ĠFal con", + "Ġov at", + "c ı", + "ĠCom plet", + "ĠD ate", + "ĠпÑĢ им", + "Ġlä uft", + "ĠAud rey", + "ĠW ent", + "Ġpel ÃŃcul", + "Ġcar riage", + "Ġun acceptable", + "ny mi", + "ĠÑģл ÑĭÑĪ", + "Ġter re", + "uell ement", + "EE EE", + "Ġpharm ac", + "h ões", + "Ġz ich", + "Ġmig rate", + "ĠF ry", + "ñ ana", + "ĠM uito", + "EO VER", + "Ġfort ress", + "ĠCom pan", + "ĠJ SON", + "ord nung", + "Ġw arto", + "Ġun gef", + "ìħĶ ìĦľ", + "ĠÑĢ ок", + "Ġpad dle", + "J ared", + "Ġsubm itting", + "Ġl atch", + "Ġf ug", + "Ġк оÑģ", + "ĠE f", + "Ġlaunch es", + "Ġf t", + "ote chn", + "Ġtrave lled", + "ا Ùģ", + "éģ ķ", + "Ġpro ch", + "Ġded im", + "8 3", + "Ġreb ound", + "ĠL U", + "p ath", + "ĠÑģп ÑĢав", + "Ġö l", + "ĠíĤ ¤", + "Ġpriv at", + "Ġtr actor", + "ĠAtt ention", + "S er", + "Ġcos es", + "á ria", + "p al", + "ĠìĿ Ģ", + "Ġsuccess or", + "Ġconnect ors", + "ĠÑĥÑģÑĤ анов", + "Ġgen ocide", + "Ġsufficient ly", + "ĠA ixò", + "Ġstabil ize", + "Ġcon gest", + "Ġcar ving", + "Ġz ost", + "ĠбÑĭ ÑģÑĤÑĢо", + "Ġshort est", + "Ġli vel", + "Ġ8 9", + "éģ Ĭ", + "Ġer k", + "Ġport raits", + "ॠĢ", + "è ĺ", + "bo at", + "ll ah", + "AN C", + "Ġempir ical", + "ĠE cho", + "ĠNeder land", + "è¿Ļ ä¹Ī", + "N et", + "Ġcuid ado", + "ĠR oma", + "Ġc alf", + "Ġgi ants", + "ĠExpl orer", + "ĠColl ect", + "al ition", + "ĠDest iny", + "Ġaus ge", + "ĠE du", + "ĠC lo", + "Ġear rings", + "ĠTr ack", + "ĠR OS", + "ĠBe lle", + "çĻ ¾", + "Ġpu eda", + "Ġday time", + "Ġsupp lier", + "ĠS V", + "ĠEx hale", + "Ġgal era", + "c ourse", + "Ġcent imeter", + "ĠB ast", + "m ud", + "Ġsang at", + "ĠPhys ical", + "Ġpriv ately", + "Ġtr ata", + "lyn n", + "ill i", + "Ġë© ĶìĿ´íģ¬ìĹħ", + "Ġcryst all", + "Ġpod s", + "ả n", + "in ator", + "ĠRec ords", + "å® ĺ", + "ÄŁim iz", + "isse ment", + "h are", + "h adow", + "ĠD K", + "ĠìķĮ ê³ł", + "Ġw yn", + "Ġrequest ing", + "ĠD onna", + "ĠìĹ ´ìĭ¬íŀĪ", + "ine a", + "Ġex ert", + "ĠDun can", + "Ġв еÑĩ", + "ĠH ah", + "ठĤ", + "ĠL if", + "ĠF inding", + "ĠNo v", + "Ġзн ак", + "Ġо ÑĦ", + "ĠQu è", + "Ġquarter back", + "ĠÑĦ ак", + "Ġbipart isan", + "ÄŁ in", + "Ġné cess", + "Ġrefer endum", + "Ġcomp iler", + "Ġprob abil", + "ед и", + "Ġtrad er", + "æĺ ĵ", + "ĠR um", + "ge me", + "Ġd io", + "ĠbÄĻdzie my", + "ĠÏĢ ά", + "ê¾ ¸", + "×ķ× ĺ", + "Ġठķ", + "Ġбл аг", + "Ġscal p", + "ĠPa use", + "Ġcapt ion", + "Ġend anger", + "Ġen lar", + "Ġrot ten", + "ãĥĥ ãĥĪ", + "Ġw ah", + "èĤ ī", + "Ġd zi", + "ĠInst all", + "A y", + "Ġcre ar", + "енÑĤ а", + "Ġwe ighing", + "Ġbutter flies", + "ĠG ast", + "äº ķ", + "h orn", + "war z", + "IC EOVER", + "Ġнай ÑĤи", + "Ġcoe fficients", + "ç°¡ åĸ®", + "ĠSp encer", + "ĠH igher", + "Ġcow ork", + "å¨ ĺ", + "ĠкоÑĤоÑĢ ое", + "Ġmon it", + "Ġdys function", + "ĠÑģÑĤ анов", + "Ġtour naments", + "Ġoy ster", + "B N", + "Ġtr ud", + "sl ow", + "ĠPen ny", + "ĠOd ys", + "æ r", + "Ġf ou", + "Ġenjoy ment", + "аÑĤ Ñĭ", + "Ġwygl Äħda", + "алÑĮ наÑı", + "ĠProt ect", + "Ġmo y", + "Ġcl aw", + "Ġsusp icion", + "Ġsacrific ed", + "Ġgost o", + "B ig", + "Ġaggress ively", + "Ġvor ne", + "ãĥ ł", + "Ġbl amed", + "ĠSe hr", + "פ ר", + "c ito", + "Ġse als", + "Ġmu jer", + "ĠWe ird", + "Ġfore ns", + "Ġcontrib utes", + "est ra", + "Ġp og", + "L OL", + "Ġhacer lo", + "о ÑĤÑĮ", + "f iction", + "7 9", + "λ ο", + "大 æ¦Ĥ", + "å£ °", + "ĠÑĤ об", + "ĠG S", + "ĠCl ara", + "ite z", + "Ġadvoc ating", + "ĠíĶ Ħë", + "s ung", + "Ġvert ices", + "Ġnavig ating", + "Ġeurop é", + "çļ Ĩ", + "Ġslow ed", + "Ġfore ground", + "ĠIndust rial", + "Ġad ore", + "ìĭ Ń", + "Ġcré er", + "æŀ Ĺ", + "chn itt", + "Ġun aware", + "Ġcur ly", + "ent ar", + "Ġl er", + "Ġprohib ited", + "ĠHero es", + "ĠRe ed", + "u ca", + "Ġsm ok", + "Ġkun na", + "zeit ig", + "im men", + "ĠL un", + "Ġаб ÑģолÑİÑĤ", + "Ġdeg li", + "Ġvill agers", + "Ġpres et", + "z ept", + "ud s", + "Ġem it", + "ä½ł è¦ģ", + "Ġë ī", + "ëĬĶ ì§Ģ", + "нак о", + "Ġos ób", + "Ġ196 9", + "ĠÐIJ ÑĢ", + "Ġman chmal", + "ĠBro ck", + "Ġmant ra", + "ĠW IL", + "b ach", + "in ä", + "el as", + "kel n", + "Ġdisci ple", + "Ġqual c", + "Ġde hyd", + "ìĿ´ë Ŀ¼ëĬĶ", + "A f", + "ìĦ± ìĿ´", + "R yan", + "Ġpupp et", + "ĠдÑĢÑĥг ие", + "Ġr ud", + "Ġp ending", + "P lus", + "ĠìķĬ ìĿĦ", + "Ġb á»ĭ", + "ĠSe ga", + "ç e", + "Ġprogram mer", + "b li", + "Ġun l", + "Ġensl aved", + "Ġsoci été", + "Äģ h", + "Ġinherit ance", + "ĠBang l", + "erm aid", + "Ġpractition er", + "ĠSt alin", + "ĠUs er", + "ci ble", + "Ġcard iac", + "ĠKore ans", + "Ġdump ed", + "Ġ×Ķ ×Ļ×Ķ", + "á is", + "Ġhydraul ic", + "oubt edly", + "ĠP it", + "Ġpic nic", + "Ġbehö ver", + "ĠÑģм ог", + "Ġbra king", + "é» ij", + "ut ar", + "ĠìĦ ¸ë", + "ub l", + "Ġü z", + "Ġmaj esty", + "Ġb ers", + "ut able", + "Ġhot ter", + "çħ §", + "ÛĮ ÙĨ", + "Ġbi ases", + "Ġsubject ed", + "Ġnaught y", + "Ġcir cus", + "ãģĹ ãģĭ", + "ĠIm medi", + "ĠSte fan", + "ĠTri ple", + "en k", + "Ġw it", + "Ġrecy cle", + "em ie", + "d ated", + "Ġun load", + "Ġpop ula", + "ch in", + "Ġyield s", + "Ġeng lish", + "ĠBon nie", + "Ġsp iders", + "à ģ", + "Ġer osion", + "éĥ¨ åĪĨ", + "ĠN ICK", + "иÑı Ñħ", + "Ġimp art", + "Ġк ни", + "Ġres olutions", + "Ġlith ium", + "Ġconver gence", + "ĠT ara", + "Ġдв е", + "th s", + "ĠCind y", + "æĪij è¦ģ", + "å¹ «", + "ĠD IE", + "Ġass urance", + "Ġоп иÑģ", + "Ġbu ckets", + "Ġc ues", + "ĠQu iet", + "Ġsimilar ity", + "Ġfound ational", + "ĠMin ist", + "æ» ¿", + "Ġp ian", + "Ġcent r", + "Ġnum b", + "Ġmon ks", + "uj ourd", + "en zie", + "Ġskate board", + "Ġd latego", + "ĠÑģ оÑĤ", + "ĠA E", + "Ġmaster piece", + "ĠSol omon", + "ĠRed dit", + "Ġr iot", + "ab l", + "ĠJ azz", + "Ġelectromagn etic", + "Ġinsec ure", + "ĠComp et", + "ger ies", + "об од", + "ł ×ķ", + "ðŁ Ĵ", + "Ġsen ators", + "ĠBris bane", + "ĠAl b", + "utter ing", + "ĠAll ow", + "z ero", + "Ġp ai", + "ĠÐIJ лекÑģ", + "ĠDis play", + "ĠBl ade", + "ĠApp s", + "Ġp ä", + "Ġд еÑģÑı", + "Ġque lla", + "ĠGa o", + "ен нÑĭÑħ", + "Ġspoil ers", + "Ġgall ons", + "ĠÙĦ ÙĬ", + "ĠZ ion", + "æľī ä¸Ģ", + "on ie", + "rag t", + "ĠCh and", + "Ġë³ ij", + "Ġbl unt", + "Ġus u", + "ĠK ad", + "ra kt", + "Ġcin ematic", + "Ġam munition", + "re ne", + "Ġfour teen", + "ĠC arn", + "c rit", + "Ġten ure", + "v u", + "Ġprincipal mente", + "Ġalle en", + "éĢĻ ä¸Ģ", + "Ġkompl ett", + "Ġdü ny", + "J ames", + "Ġrecept or", + "Ġones elf", + "g uru", + "Ġmerch ant", + "l iness", + "Ġover looked", + "Ġharmon ic", + "éķ ¿", + "ies o", + "×ķ× ŀ", + "col m", + "ĠпÑĢо екÑĤ", + "ĠAd a", + "ا س", + "T im", + "Ġrecur ring", + "Ġproceed s", + "ĠPart icularly", + "ĠDown load", + "et rical", + "Ġmat rices", + "Ġproyect o", + "anc ies", + "ĠUh m", + "Ġc aves", + "Ġìĸ´ë ł¤", + "ĠLe af", + "Ġоб ÑĭÑĩ", + "ĠìĿ´ì ľł", + "Euro pe", + "Ġt Äħ", + "Ġpul s", + "Ġtak iego", + "ÐĿ е", + "G U", + "Ġfor s", + "Ïģ γ", + "Ġfot os", + "Ġ) )", + "Ġë© ¤ë", + "Ġaqu ilo", + "ĠK urd", + "ï¸ ı", + "pt ic", + "ĠD ort", + "Ġmis ery", + "aus o", + "åĬ Ł", + "chuck ling", + "ĠR idge", + "ĠíĸĪ ìĬµëĭĪëĭ¤", + "Ġ* **", + "å® ¢", + "ĠHmm m", + "Ġge ographic", + "Ġany s", + "Ġtal vez", + "Ġske let", + "Ġsign atures", + "Ġlit ers", + "IJë ©´", + "ĠÑģво его", + "Ġski ing", + "ĠÐľ оÑģ", + "Ġadop ting", + "Ġha ft", + "Ġsymm etric", + "ĠL iqu", + "Ġthy roid", + "Ġmis in", + "lud e", + "Ġh ull", + "ĠX D", + "ĠG ust", + "ze ich", + "Ġvibr ations", + "Ġes emp", + "ĠвÑģ Ñİ", + "ĠQu em", + "Ġü brig", + "ĠS ke", + "ĠLyn ch", + "room s", + "art et", + "f est", + "Ġfr üher", + "Ġl ure", + "ä¸į好 æĦıæĢĿ", + "ĠìķĮ ìķĦ", + "ĠW IN", + "ĠR YAN", + "ĠкоÑĤоÑĢ ÑĥÑİ", + "ĠK ash", + "Ġ×Ķ× ŀ", + "Ġsaf eg", + "ĠHall elujah", + "Ġдв ÑĥÑħ", + "Ġstap le", + "Ġsed iment", + "ĠAct s", + "Ġbl aming", + "Ġmain land", + "Ġsport ing", + "Ġdecor ations", + "Ġexecut ing", + "Ġpar an", + "ĠDoll ar", + "Ġproject ions", + "Ġcommission ed", + "Ġb our", + "ö m", + "Ġste amed", + "ĠëŃ ĺ", + "Ġpet rol", + "Ġcel ular", + "å¸ ¶", + "ĠHung ary", + "Ġrent ed", + "Ġв аÑĢи", + "bb ie", + "Ġsé cur", + "ü ll", + "Ġsw ings", + "bet ween", + "Ġи ÑĤ", + "est ro", + "Ġnie mand", + "ĠìĤ ¼", + "ĠP ardon", + "ess es", + "ĠM ID", + "Ġcentral ized", + "ĠAl ien", + "cul os", + "Ġcr ise", + "裡 éĿ¢", + "Ġcl asse", + "beit et", + "i ÄŁi", + "Ġwh ales", + "Ġper imeter", + "Ġty ing", + "Ġstr ony", + "Ġlike wise", + "ĠP unch", + "D a", + "ĠBapt ist", + "Ġsort ing", + "Ġ iv", + "Ġíķ ©", + "Ġre hab", + "Ġet a", + "ri ver", + "Ġsa i", + "ãģĦãģŁ ãģł", + "od us", + "ãģĬé¡ĺãģĦ ãģĹãģ¾ãģĻ", + "Ġess ayer", + "Ġtur tles", + "ĠHaz rat", + "Ġfab rics", + "Ġcav ity", + "Ġpon ieważ", + "Ġschle cht", + "Ġs alsa", + "ÅŁ ekkür", + "Ġse ating", + "Ġeconom ists", + "Ġman g", + "Ġsegu inte", + "Ġr ang", + "Ġrat ios", + "Ġconst ell", + "Ġlong temps", + "u ating", + "Ġspo iled", + "Ġrecip ients", + "Ġsn iper", + "ä¹ĭ åīį", + "ìĬµ ëĭĪê¹Į", + "Ġw p", + "ĠLIN KE", + "Ġfl are", + "ĠAd ri", + "ñ as", + "Ġback l", + "mä ÃŁ", + "ĠB end", + "Ġworkload s", + "ĠÑģ Ñĥп", + "Ġ197 5", + "им ÑģÑı", + "ан е", + "Ġм он", + "Ġaspir ations", + "ĠA er", + "ĠговоÑĢ иÑĤÑĮ", + "ĠQ ian", + "å¦ Ī", + "Ġcomprom ised", + "Ġyol k", + "ла ÑģÑĤ", + "Ġhe men", + "ro ve", + "d ens", + "Ġком менÑĤ", + "Ġ- --", + "Ġflu ores", + "но Ñģ", + "ĠLiver pool", + "ĠÑģоб ой", + "ĠZ we", + "Ġl umin", + "ĠO G", + "á ¸", + "hol m", + "pro fits", + "S N", + "Ġproport ions", + "Ġm ica", + "ĠB oh", + "ĠAt las", + "Ġuns ure", + "Ġtour ing", + "Ġn ied", + "Ġt ÄĻ", + "Ġimper ative", + "Ġdem ek", + "ĠSher iff", + "r ance", + "Ġhom eland", + "ĠH ail", + "ĠG anz", + "y mm", + "M on", + "åĨ ·", + "v ida", + "Ġdesar roll", + "æĬ Ģ", + "Ġintrig uing", + "ĠH ugo", + "Ġ ãĤĤ", + "é ¬", + "а ÑĨ", + "ĠWiÄĻ c", + "att ed", + "ĠìķĦëĭĪ ê³ł", + "ĠV ari", + "á d", + "Ġsur real", + "Ġdispar ities", + "Ġm ó", + "ull en", + "ĠìŀĪ ëĭ¤ê³ł", + "Ġп ожалÑĥйÑģÑĤа", + "Ġma ins", + "Ġe ject", + "Ġmeth ane", + "Ġmarginal ized", + "Ġchill i", + "r ès", + "Ġy em", + "ä½ł æĺ¯", + "ĠCh un", + "Ġdeb ts", + "Ġdownload ing", + "ĠAth ens", + "is ierung", + "ry n", + "Ġte kn", + "ĠQu indi", + "éľ Ģ", + "Ġtara f", + "Ġh é", + "Ġconscious ly", + "Ġfix es", + "uck le", + "may ın", + "Ġfre i", + "Ġsp a", + "Ġì§Ħ íĸī", + "ĠاÙĦØ °", + "ĠÑĥ к", + "let t", + "Ġolm uÅŁ", + "Ġche esy", + "า à¸ģ", + "na ire", + "Ġw iden", + "Ġli en", + "Ġesca ping", + "igg s", + "ĠBl ick", + "c Äħ", + "ĠìĦ ľë", + "Ġ×Ķ× ¡", + "Ġв пеÑĢ", + "oph one", + "ie ll", + "ĠSU BSCRI", + "Ġl ions", + "Ġê·¸ ê²ĥ", + "Ġinsp ires", + "Ġguarante es", + "Ġcome ça", + "ĠGrow ing", + "Ġneg lig", + "ĠFrank f", + "Ġge geben", + "ĠÄij ầu", + "Ġend lich", + "Ġì į¨", + "ĠT T", + "ĠL ith", + "ÏĢ α", + "aster n", + "ĠA zer", + "Ġlun ar", + "h ic", + "Ġна ÑĢод", + "Ġnen hum", + "è· ij", + "ĠSalv ador", + "ĠPro gress", + "Ġprivile ges", + "ĠëıĻ ìķĪ", + "Ġant agon", + "ĠImp f", + "Ġdesc ub", + "ĠLe i", + "ĠìĥĪë ¡ľ", + "Ñĩ е", + "Ġdó lares", + "ĠMeg han", + "ĠW ire", + "to o", + "ay ing", + "us c", + "Ġt ud", + "Ġappe als", + "ed uc", + "Ġp ane", + "Ġj i", + "Ġde cks", + "ĠAl ter", + "Ġ å°±", + "ìĦ ¤", + "åĪĨ éIJĺ", + "Ġproduct ions", + "ĠWILL IAM", + "Ġimpl ied", + "Ġfulfill ment", + "ĠA ah", + "Ġsa ja", + "x us", + "ĠÎļ αι", + "Ãł s", + "uc ch", + "ок о", + "ĠDisc ord", + "ĠS Y", + "j sk", + "ĠWall ace", + "un ction", + "Dan iel", + "Ġk öt", + "ij ah", + "Ġmarch e", + "Ġdis gr", + "Ġm ungkin", + "Ġal ma", + "³ µ", + "Ġextensive ly", + "ĠFl oren", + "ĠAll ison", + "ãĤ ±", + "ÙĬ Ùħ", + "Ġju ven", + "ĠRena issance", + "Ġfundra ising", + "ĠCha os", + "Ġpar aly", + "Ġnarr ator", + "Ġecosystem s", + "A sh", + "Ġmitig ation", + "ĠA ujourd", + "ĠIde e", + "! ,", + "Ġ ½", + "Ġland lord", + "Ġdefect s", + "Ġac re", + "uls ive", + "Ġalg ae", + "pe k", + "Ġem ba", + "ĠR oc", + "éĽ ¢", + "ks om", + "ä che", + "Ġle uk", + "Ġlever aging", + "Ġê·¸ëłĩ ì§Ģ", + "ĠPal m", + "Ġä ven", + "Ġl is", + "ĠIn sp", + "ĠR ita", + "ĠAb b", + "ith m", + "Ġsuper vision", + "Ġrevis it", + "Ġpi ÄĻ", + "Ġeu h", + "Ġf ades", + "Ġmot to", + "åį ¡", + "ез ж", + "ĠSh im", + "Ġrelev ance", + "Ġo o", + "Ġo stat", + "n ica", + "Ġcho ix", + "ĠFac ulty", + "Ġì¤ij ìĹIJ", + "ĠAb ove", + "Ġнеб олÑĮÑĪ", + "Ġsequ encing", + "Ġnutri ent", + "Ġconqu ered", + "Ġdigest ive", + "Ġback drop", + "ĠL ori", + "ail able", + "G ame", + "Ġneglect ed", + "om orph", + "ill ah", + "Ġkn e", + "Ġsi itä", + "Ġworks pace", + "ĠVen ice", + "ĠK ne", + "Ñī о", + "ħ Ģ", + "ĠH ass", + "Ġv ita", + "Ŀ¼ë ©´", + "Ġlay s", + "ên cias", + "é rica", + "ĠL l", + "æ± Ĥ", + "ĠCo ca", + "ĠWH Y", + "èĪ ŀ", + "Ġrout ing", + "Ġperm issions", + "Ġd ings", + "pre nd", + "pro gram", + "Ġcro cod", + "br al", + "AAAA AAAA", + "ag it", + "ĠN ä", + "Ġgek ommen", + "at ten", + "Ġrefer enced", + "Ġpair ing", + "ĠPart ner", + "ĠCoron avirus", + "Ñĸ Ñģ", + "è½ ī", + "Ġ×Ķ× ĵ", + "Ġespec ÃŃfic", + "ars i", + "qu elle", + "Ġspont aneous", + "çĨ ±", + "Ġê²ĥ ìĿĦ", + "ĠÐŁÐ¾Ñģ ле", + "ĠاÙĦ د", + "ĠSh out", + "Ġн ал", + "Ġdisgu ise", + "ĠJ ord", + "Ġwe e", + "Ġmiej sc", + "Ġser um", + "Ġplais ir", + "Ġcred ible", + "Ġb Ã¥", + "ĠA J", + "ma res", + "Ġrod s", + "Ġer an", + "ãģ¾ ãģĤ", + "Ġp ää", + "ĠU A", + "ĠUn known", + "ĠÙĦ Ùħ", + "ĠRab bi", + "Ġla at", + "Ġhairst yle", + "ĠØ º", + "éģ ĭ", + "Ġc ach", + "ĠWr iting", + "оÑĩ ки", + "ab ad", + "Ġstraight en", + "-- \"", + "w ife", + "Ġhott est", + "Ġpun ya", + "ĠF ashion", + "gr iff", + "ĠQ R", + "ot ch", + "ĠÐľ ожеÑĤ", + "Cl oud", + "ĠStri ke", + "ĠHe in", + "Ġ 羣çļĦ", + "Ġle i", + "ĠFl ow", + "weg s", + "Ġha br", + "åīĽ åīĽ", + "nah me", + "Ì ģ", + "Ġple asing", + "op ping", + "Ġ구ë ıħ", + "Ġdr an", + "Ġbang s", + "Ġ7 9", + "Ġsk et", + "Ġcav al", + "ĠMac ron", + "Ġweight ed", + "Ġm uted", + "Ġnuest ras", + "EE P", + "Ġmath ematic", + "ĠM RI", + "ag us", + "Ġtherap ies", + "θ ε", + "Ġun pl", + "Ġcomm encer", + "f ull", + "Ġtow els", + "Ġpr ue", + "Ġlic enses", + "׼ ×ķ׾", + "ĠÐŁ оÑĩемÑĥ", + "Ġpoint less", + "B ye", + "Ġelig ibility", + "Ġscra pe", + "Ġab usive", + "ĠM ant", + "Ġje unes", + "t al", + "ĠPrin cip", + "ĠOrth odox", + "Ġmel od", + "ĠмаÑĤ еÑĢи", + "Ġprosecut or", + "Ġopio id", + "ĠÑĥ веÑĢ", + "ĠBe en", + "Ġìłij ì¢ħ", + "Ġd ynasty", + "Ġajud a", + "Ġent reg", + "Ġweigh ed", + "Ġe ure", + "ĠB em", + "Ġab normal", + "8 2", + "ĠJ R", + "ĠA kt", + "ĠB ri", + "ú t", + "Ġst agn", + "! *", + "Ġwe gen", + "Ġle aking", + "ĠW ords", + "ĠM au", + "Ġv ue", + "ĠL iam", + "ани ем", + "Ġclin icians", + "ĠP ump", + "Ġför st", + "? ...", + "Ġautom otive", + "ĠOw en", + "zus agen", + "ĠH undred", + "Ġdecentral ized", + "Ġbul bs", + "Ġ×ľ× Ľ", + "Ġprovin ces", + "ĠMil an", + "8 1", + "k as", + "Ġëĵ £", + "Ġfor ça", + "Ġright ly", + "å³ ¶", + "r Äħ", + "Ġven ues", + "Ġw ai", + "Ġpred icting", + "ĠWi Fi", + "Ġê¶ģ ê¸Ī", + "ر ÙĪ", + "Ġ×Ķ× ĸ", + "cent ury", + "Ġgrad ual", + "ĠProblem e", + "ĠìĹ ħ", + "Ġcop ing", + "ĠBr us", + "Ġpean uts", + "irts chaft", + "Ġз ал", + "ĠT roy", + "Ġsper m", + "ĠM itar", + "ĠTür kiye", + "g rand", + "¦ Ń", + "Ġ×ŀ× ¡", + "Ġp ans", + "ĠKnow ledge", + "ber ly", + "ĠÐķ го", + "Ġdan ced", + "ĠFr ost", + "ĠB urg", + "Ġbit ing", + "ìłķ ìĿĦ", + "me al", + "Ġhero ic", + "Ġmother board", + "ĠL icht", + "ãģ£ ãģ", + "ll an", + "ай н", + "ĠÑĢ Ñıд", + "Ġ à¹Ģà¸", + "on en", + "ir ie", + "Ar t", + "r ang", + "ν η", + "Ġnew born", + "Ġam is", + "Ġا ÙĪر", + "Ġsoph om", + "ĠCare ful", + "Ġprospect s", + "ens en", + "Ġthr ill", + "ĠVi á»ĩt", + "A dam", + "r ition", + "ent ric", + "ud en", + "Ġcertific ates", + "Ġas hes", + "èª ¿", + "play ing", + "Ġs adece", + "Ġo st", + "Ġairpl anes", + "ÑĢ ок", + "on er", + "Ġmagnes ium", + "Ġgod damn", + "Ġ197 2", + "ĠSch ule", + "Ġtem at", + "Ġpart out", + "௠Ĥ", + "Ġin ve", + "ĠScient ists", + "ĠHud son", + "win ning", + "ceks in", + "Ġcongress ional", + "or u", + "Ġro pes", + "в ед", + "Ġmad re", + "Ġf erry", + "ĠCoh en", + "ĠP red", + "Ġvag y", + "Ġб еÑģп", + "Ġmult im", + "Ġdrain age", + "Ġsim ulator", + "g iggles", + "ĠSt adium", + "об Ñī", + "Ġnot ices", + "Ġcraw ling", + "Ġgr oupe", + "åı ¸", + "Ġkto ÅĽ", + "ĠY oga", + "Ġmed ida", + "ĠÑħ ваÑĤ", + "ĠL ite", + "Ġr av", + "or ama", + "Ġdisc ord", + "ĠDI RE", + "Ġte h", + "ĠN urs", + "ç² ī", + "Ġpitch ed", + "Ġbark ing", + "ĠC oke", + "wi ad", + "Ġpop ulated", + "éĻ ¤", + "pe lled", + "Ġб ог", + "Ġpe wno", + "ĠC ube", + "Ġrecru ited", + "éĢĻ 種", + "ĠC ara", + "ıģ ını", + "im ated", + "ĠÑĪ кол", + "ic ional", + "ĠпÑĢо ÑĦ", + "Ġcontam ination", + "Ġúlt imos", + "Ġfear ful", + "Ġele phants", + "us i", + "ĠiT unes", + "ĠSw ami", + "ê ¼", + "ĠìĦ¤ë ªħ", + "ĠRich ards", + "Ġmagn ets", + "ĠRicht ung", + "ĠLeg ion", + "èı ľ", + "Ġk itty", + "Ġkiss ed", + "Ġwater ing", + "Ġcon o", + "ĠPalest ine", + "id ir", + "Ġma ze", + "Ġflu ids", + "ĠProdu cer", + "ĠKr sna", + "好 åķ¦", + "la f", + "Ġ×IJ ×ķ", + "Ġm iesz", + "ĠX ing", + "oint ed", + "se in", + "ĠF uk", + "ĠDep ression", + "ĠD uty", + "ĠPan ther", + "Ġsu nd", + "Ġref ere", + "Ġexc lusion", + "Ġnav al", + "ĠWin ston", + "Ġsl ogan", + "Ġhypoth etical", + "Ġelev ate", + "ë ł¹", + "Ġcabe ça", + "ĠGes und", + "m eter", + "ĠìķĦëĭĪë ©´", + "Ġcloud y", + "âĢ¦ ?", + "ĠSch ritt", + "ĠJ S", + "ì į", + "ĠSpr ings", + "ĠB atter", + "· °", + "Ġtail or", + "ĠPTS D", + "ĠG ent", + "Ġba ÄŁ", + "Ġspat ula", + "Ġcr ay", + "ĠLeg isl", + "Ġs ú", + "Ġle ve", + "า ม", + "Ġer ad", + "Ġdon g", + "Ġd erm", + "ĠBank s", + "ich o", + "åħĪ çĶŁ", + "ĠFr anz", + "ra vel", + "éģ Ķ", + "ол о", + "Ġfl ute", + "ĠE k", + "Ġjoy ful", + "Ġch ased", + "ĠLar ge", + "O ver", + "Ġentrepreneur ial", + "Ġcons iders", + "Ñĥ ем", + "op a", + "Ġdorm ir", + "ĠElement ary", + "Ġprzy pad", + "ÑĥÑģ ка", + "ĠоÑĩ еÑĢ", + "ug ene", + "Ġten ido", + "Ġlug ares", + "ë ¥", + "ĠÑĩ аÑģÑĤ", + "Ġsa o", + "Ġbra id", + "ĠV ere", + "ĠRe ich", + "ĠP oss", + "Ġin an", + "w and", + "re f", + "Ġmont rer", + "Ġ198 1", + "çķ ª", + "as ında", + "Ġch rome", + "ĠTr inity", + "Ġexplo itation", + "ĠS ense", + "ĠC MS", + "ĠNo ble", + "ĠìĦł íĥĿ", + "Ġswe lling", + "elect ronic", + "] ?", + "Ġbr ushing", + "Ġliquid ity", + "ĠH ook", + "ĠCon nor", + "ĠAl um", + "Ġgu cken", + "su ite", + "Ġwie le", + "Ġbarrel s", + "ĠReg el", + "ĠM ent", + "ĠT rip", + "ĠBr ush", + "ĠE rik", + "ur ate", + "ÉĻ r", + "ĠC yr", + "ou ble", + "ĠBe cca", + "Ġpass words", + "Å ±", + "bor g", + "Ġv endo", + "ĠCla us", + "ĠF az", + "ind est", + "Ġdece ased", + "Ġcompar isons", + "ĠL CD", + "ĠP ork", + "Ġevent ual", + "Ġpat reon", + "Ġin ability", + "Ġext inction", + "Ġì¢ĭìķĦ íķĺëĬĶ", + "ĠÑģ оÑģ", + "aj u", + "Ġ×ij× IJ×", + "Ġso fort", + "Ġdest ined", + "ĠR in", + "Ġmouth s", + "ĠNat ürlich", + "Ġpres erving", + "Ġlim p", + "é» ¨", + "oc used", + "ин г", + "Ġexp osing", + "ĠÎ ¾", + "ë į", + "la ugh", + "Ġhis s", + "ãģł ãģĭãĤī", + "Ġind ie", + "Ġdet al", + "ÑĢав ÑģÑĤв", + "Ġtr ên", + "æķ °", + "Ġog ni", + "Ġsimple mente", + "Ġ197 8", + "Ġgo o", + "Ġ196 7", + "Ġgen ug", + "h ö", + "Ġhist ó", + "å® Ł", + "Ġlob ster", + "c endo", + "Ġte il", + "Ġalle vi", + "00 00", + "OL D", + "Ġpes os", + "Ġbon uses", + "Ġam i", + "Ġrev ival", + "ĠHor se", + "Ġs ack", + "T alk", + "Ġmul her", + "ĠпоÑģÑĤо Ñıн", + "ĠH ood", + "H uh", + "Ġë¶ ģ", + "Ġhy ung", + "ĠMe eting", + "Ġimport a", + "Ġì°¾ ìķĦ", + "ĠV ern", + "Ġstri pped", + "Ġref uses", + "Ġqual ifications", + "op l", + "Ģë ıĦ", + "ix ÃŃ", + "Ġdi ab", + "it ime", + "fl ows", + "Ġin ac", + "ĠG ong", + "Ġmeaning less", + "Ġcourage ous", + "Ġmicro bi", + "az y", + "h ist", + "Ġvolunte ering", + "V IE", + "Ġviol ated", + "Ġsymp athy", + "ĠEd it", + "好 åĥı", + "elect ric", + "produ ct", + "Ġpand emia", + "Ġgeomet ric", + "ĠCon vers", + "g re", + "Ġgl ut", + "ist ed", + "ĠاÙĦ Ùĥ", + "ĠCh ain", + "ĠPres ent", + "ĠY in", + "ĠÑģ ог", + "ĠV log", + "Ġìĸ´ë ¨¸", + "Ġdon n", + "Ġh itch", + "uck ing", + "ãģĬ ãģĦ", + "w ald", + "ris k", + "Ġhar i", + "ĠK ens", + "ĠId ol", + "Ġвним ание", + "Ġtod d", + "Ġsm ashed", + "Ġinv ari", + "Ġкон ÑĤÑĢ", + "Ġaut istic", + "ìŀ¥ ëĭĺ", + "R es", + "д Ñĭ", + "ch au", + "Ġsel v", + "Ġhät ten", + "ठ¿", + "Ġexpect s", + "Ïģ η", + "Ġaç ık", + "ĠHT TP", + "le ÅŁ", + "Ġswe eping", + "ĠBet a", + "Ġcounterpart s", + "ab ile", + "ĠSim s", + "C s", + "Ġrep ar", + "s qu", + "Ġprovin cial", + "Ġshare holders", + "Ġrun ter", + "Ġged acht", + "ĠTe en", + "Ġgrand s", + "çĶ ¢", + "ag les", + "Ġrock y", + "ven s", + "Ġr ivals", + "un al", + "Ġreact s", + "ë ©", + "Ġmerc ury", + "ĠLu igi", + "Ġо г", + "ĠJ UST", + "Ġl od", + "Ġcort ex", + "w ig", + "Ġl akh", + "ì¤ij ìĹIJ", + "ĠV ic", + "ĠM und", + "Ġma pped", + "ĠD ell", + "ĠD ruck", + "Ġlif es", + "алÑĮ ное", + "ivid ual", + "ad ım", + "Ġat rav", + "ĠFl ug", + "ĠKle in", + "ê±° ìķ¼", + "ห à¸Ļ", + "Ġapp li", + "ா ?", + "ü yorum", + "ĠинÑĤеÑĢеÑģ но", + "Ġdis infect", + "> -", + "Ġchamp agne", + "Ġk la", + "op ers", + "Tr ans", + "ĠDes ert", + "Ġcultiv ate", + "ĠFuck ing", + "idel ity", + "ĠÑĤ ан", + "Ġinc ub", + "Ġtem u", + "Ġlearn er", + "found er", + "ĠSy l", + "ãĤ Ģ", + "Ġf ato", + "z ier", + "ĠìĹĨ ìĿ´", + "ĠìĪ ¨", + "Ġpsych o", + "ĠÑĤел еÑĦ", + "Ġregard e", + "Ġrepresent ations", + "Ġlit igation", + "Ġsp ann", + "ult s", + "b ior", + "è¦ĭ ãģ¦", + "ä¸į å¤ļ", + "ĠSur vey", + "ĠLED s", + "Ġtr ä", + "Ġl ên", + "Ġant ioxid", + "еÑĢ ом", + "Ġindu ction", + "Ġfool ed", + "ät zlich", + "ĠговоÑĢ ÑıÑĤ", + "ĠF act", + "umb ai", + "Ġw iggle", + "NO UN", + "Ġdévelop p", + "ĠCl aro", + "Ġì ¸", + "ë ¬", + "ãģªãĤĵ ãģł", + "Ġaccum ulate", + "Ġmaint ains", + "ë Ħ", + "ĠFight er", + "íĨ ł", + "Ġmat in", + "Ġcoup on", + "Ġst unt", + "Ġdeb uted", + "å¾ħ ãģ£ãģ¦", + "Ġpra g", + "ив аем", + "7 3", + "Ġexp res", + "Ġìĺ¤ë ¹ł", + "ĠпеÑĢ Ñģон", + "Ġcalcul us", + "Ġab rupt", + "ĠInspect or", + "our t", + "æĸ Ļ", + "ź niej", + "int ense", + "B a", + "Ġl ounge", + "Ġast hma", + "ĠHi ç", + "ª »", + "Ġeditor ial", + "Ġse ize", + "Ġk ır", + "Ġm ouve", + "Ġtier ra", + "Ġtestoster one", + "Ġr h", + "ĠKing ston", + "EL LE", + "ĠRepresent ative", + "Ġ197 4", + "Ġi ba", + "T s", + "Ġsort a", + "Ġ( ?)", + "Ġت ÙĪ", + "ĠëĤ´ë ł¤", + "Ġbek ommt", + "Ġspirit ually", + "Ġdist orted", + "M ad", + "Ġre im", + "á nh", + "ĠOtt oman", + "ĠRel ig", + "ĠEl s", + "Ġret ained", + "ĠLa ughs", + "æĢ »", + "ĠS AS", + "ĠколиÑĩе ÑģÑĤво", + "×ķת ר", + "Ġinnov ate", + "Ġk ork", + "ĠÑĢаÑģÑģк азÑĭв", + "ond ere", + "iv i", + "ay e", + "ount y", + "ĠполÑĥÑĩ аеÑĤÑģÑı", + "Ġbun s", + "åħ «", + "Ġyüz den", + "Ġsur geries", + "Ø£ ÙĨ", + "Ġbankrupt cy", + "w elt", + "Ġsi amo", + "Ġdark est", + "ĠH ann", + "gg a", + "Ġform as", + "ĠD j", + "n amed", + "Ġshield s", + "ue ller", + "ĠF ew", + "Ġl ace", + "Ġfur ious", + "ĠY U", + "Ġsociet al", + "Ġjudge ment", + "ĠD os", + "Ġj ab", + "law s", + "Ġrein vent", + "ĠK atherine", + "ĠCh oi", + "ad ows", + "Ġr ans", + "od en", + "ĠMid west", + "n ın", + "Ġdep ort", + "ĠD ip", + "ç´ ħ", + "Ġaten ción", + "ĠCourt ney", + "ivid ad", + "ĠÚ© Ûģ", + "Ġeffic acy", + "ĠBrook s", + "Ġrefer ral", + "Ġкон ÑĨ", + "Ġmal icious", + "Ġk ir", + "ĠGod dess", + "Ġfun ky", + "Ġinter im", + "ĠK örper", + "Ġìĸ¼ë §", + "k ur", + "Ġк ли", + "Ġtruc s", + "ges etz", + "Ġz ug", + "ĠGl ück", + "ĠMin ute", + "Ġprest igious", + "Ġnie z", + "Ġconcent rations", + "ла ÑģÑĤи", + "ĠS is", + "ĠVit amin", + "ko v", + "ĠP BS", + "Ġне е", + "Ġretail ers", + "Ġcon ventions", + "ĠSam antha", + "Ġproud ly", + "J ordan", + "ĠJ ASON", + "at k", + "Ġtr iste", + "Ġst är", + "Ġreiter ate", + "Ġpos terior", + "Ġ197 3", + "ĠP ine", + "ĠJul iet", + "Ġped ir", + "k il", + "Ġover lapping", + "Ġexclud e", + "Ġecon óm", + "Ġaccept s", + "ĠS ter", + "æ± º", + "Ġìļ ´ëıĻ", + "est ab", + "Ġt ug", + "ar g", + "Ġliv ro", + "Ø§Ø µ", + "Ġse ams", + "Ġbur aya", + "Ġe llo", + "ĠT M", + "ĠP aw", + "ĠInd ex", + "Ex c", + "Ġinspir ational", + "Ġd unk", + "è° ģ", + "ak ter", + "Ġcondition er", + "ĠSal ut", + "ÅĤ ec", + "Ġìī ½", + "ĠÑĥз на", + "ĠRome o", + "f ruit", + "ĠY O", + "Ġchá» ī", + "б Ñĥ", + "b ons", + "Ġreprodu ctive", + "Ġor ada", + "Ġíļ ¨", + "Ġtent ar", + "Ġma ñana", + "ãĤ ¬", + "Ġsol vent", + "Jess ica", + "ĠLeg al", + "Ġtu a", + "Ġs ic", + "ĠE Q", + "au kee", + "ìĭľ ëĭ¤", + "ĠÅŀ u", + "Ġad here", + "ĠT ul", + "Ġà® Ĩ", + "Ġtext books", + "ĠFif th", + "Ġexper i", + "Ġch ic", + "Ġhe ap", + "in ely", + "at ra", + "T wo", + "Ġhele maal", + "Ġf ren", + "æİ ¨", + "Ġbis her", + "Ø§Ø ´", + "ĠìĦł ìĥĿ", + "ĠT ages", + "Ġs á»±", + "Ġbull ied", + "Ø ¤", + "Ġbenef ited", + "ĠPre viously", + "ĠÑį ÑĦÑĦ", + "Ù į", + "Ġsen ate", + "ĠM orm", + "ij ke", + "ĠF lu", + "Ġincorpor ating", + "j ack", + "Ġп иÑĤ", + "Ġimp ly", + "Ġha cks", + "ĠR ICH", + "Ġк ваÑĢ", + "ĠпÑĢек ÑĢаÑģ", + "Ġdepend ency", + "Ġìļ ©", + "Ġì± ħ", + "Ġwäh rend", + "Ġsu lla", + "ĠPitts burgh", + "Ġesemp io", + "¼ë ¡ľ", + "pr ot", + "ĠR osen", + "ĠIndepend ence", + "Ġpars ley", + "ie gen", + "Ġha w", + "Ġaqu ell", + "ĠC AP", + "ĠÑĢабоÑĤ аÑĤÑĮ", + "ĠCl iff", + "ion ar", + "Ġsec uring", + "æĪijåĢij çļĦ", + "ν ε", + "Ġutil is", + "Ġcou le", + "ĠP ing", + "Ġtre k", + "Ġf ak", + "Ġenorm e", + "Ġìĭ «", + "è® ©", + "Ġdoub ling", + "ĠнÑĢав иÑĤÑģÑı", + "Ġh ed", + "ho ven", + "ĠStand ing", + "Ġm ÃŃn", + "ĠJ imin", + "Ġmon arch", + "Ġco ke", + "Ġm r", + "Ġcl ic", + "à į", + "Ġimpe achment", + "Ġdur ability", + "Ġvar ios", + "Ġcommercial s", + "Ġgreet ings", + "ĠR i", + "ĠApp reci", + "ìŀĪ ëĬĶ", + "Ġrés ult", + "ér t", + "Ġsal ute", + "Ġpoder ia", + "Ġsun rise", + "ve ck", + "Ġreluct ant", + "Ġcommission er", + "å¿ µ", + "â te", + "ĠKen ny", + "ĠSir i", + "ãĥĥ ãĥĹ", + "ĠëĬ ĺ", + "ĠE E", + "Ġun ch", + "к он", + "ĠاÙĦØ ¥", + "Ġbel ts", + "Ġhas s", + "Ġмо Ñı", + "Ġdispl aced", + "Ġab ra", + "ÎŃ Î»", + "Ġscratch es", + "Ġcom et", + "Ġauthor ization", + "ĠL LC", + "Ġprodu k", + "Ġrehabil itation", + "å ŀ", + "Ñĸ Ñĩ", + "ud ing", + "ol it", + "Ġ10 5", + "Ġexp ands", + "Ġalt ri", + "ĠKom ment", + "Ġan f", + "P l", + "ĠM ana", + "f ed", + "Ġb ri", + "Ġor a", + "G s", + "ĠG ur", + "uck land", + "Ġjun ction", + "Ġiron ic", + "ĠFe ed", + "Ġpra kt", + "ĠHam mer", + "Įë ıĦ", + "ĠTr acy", + "çµ ±", + "ĠAs ide", + "н его", + "ĠиÑģполÑĮз оваÑĤÑĮ", + "Ġz aj", + "Ġequ itable", + "Ġcur b", + "Ġãģĵ ãĤĮ", + "Ġderiv atives", + "Ġpupp ies", + "ĠKenn eth", + "ĠCom pl", + "ig ram", + "ĠGar cia", + ") \"", + "ĠHar bor", + "est ial", + "Ġ ä¾Ĩ", + "Ġ ers", + "æ ¹", + "Ġunw anted", + "Ġbel ang", + "аР³Ð¾", + "em b", + "d os", + "ĠìĻ ľë", + "ĠBud get", + "Ġbatt ling", + "ØŃ Øª", + "k ok", + "наÑĩ ала", + "Ġpl ag", + "Ġcant idad", + "Ġgrup os", + "Ġplug ins", + "ler ini", + "Ġиме еÑĤ", + "Ġso zusagen", + "ol ics", + "Ġpue blo", + "Ġrem inis", + "r än", + "ĠMor rison", + "Ġl inha", + "Ġbreath s", + "ĠT aste", + "Ġenf rent", + "ĠDo cker", + "Ġд ен", + "Ġethnic ity", + "Ġw ob", + "Ġsuff ers", + "Ġtransition ing", + "ĠR ange", + "ÄĻd zy", + "Ġк аÑĤ", + "Ġsy ner", + "Ġdon ut", + "Ġprob abilities", + "ĠO mar", + "Wh ich", + "u ish", + "is in", + "Ġdem os", + "ĠìłĢ 기", + "Ġëĺij ê°Ļ", + "Ġед ин", + "Ġc erve", + "Ġj oka", + "I AN", + "Ġkilomet er", + "Ġhorizont ally", + "ĠBh ag", + "Ġ- >", + "ĠMon itor", + "Ġknowledge able", + "Ġf av", + "Ġpin ned", + "Ġe Bay", + "ick er", + "Ġìŀłê¹ IJë§Į", + "ĠXia omi", + "Ġcap it", + "Ġn p", + "Ġ196 5", + "ho e", + "Ġn ok", + "ĠS age", + "Ġн елÑĮзÑı", + "ĠT ow", + "g am", + "Ġdic en", + "ĠSUBSCRI BE", + "Ġrebo ot", + "Ġp aj", + "Ġë³´ìĹ ¬ë", + "Ġth icken", + "ĠRe ality", + "id än", + "N a", + "Ġê²ĥ ìĿĢ", + "!! )", + "Ġrout ines", + "Ġод ного", + "Ġex ting", + "Ġì¦ Ŀ", + "Ġsulf ur", + "Ġcar ve", + "Ġastero id", + "ĠWarri or", + "Ġphotograph ers", + "Ġpe ll", + "Ġcros sover", + "æĪij çŁ¥éģĵ", + "Ġhace mos", + "ĠNe j", + "Ġsett ling", + "Ġir m", + "ĠBook s", + "ient ôt", + "Ġesp acio", + "ĠSchol ars", + "Ġdo omed", + "ĠIR S", + "w ohl", + "Ġseg ue", + "ĠëĪĦ ê°Ģ", + "Ġpr atic", + "B T", + "ĠConsider ing", + "ĠBuff alo", + "Ġtrain ings", + "Ġge bru", + "ĠG leich", + "Ġpir ates", + "Ġen velop", + "Ġre open", + "im at", + "Ġte e", + "Ġsu ed", + "fe h", + "Ġ×Ķ× §", + "Ġdi ets", + "Ġjunt os", + "ast o", + "Ġmisunder stood", + "Ġru im", + "Ġclass ify", + "ĠпÑĢод Ñĥк", + "Ġin se", + "Ġillust rated", + "Ġcorros ion", + "Ġacc red", + "ĠAunt ie", + "ĠпÑĢив еÑĤ", + "ĠLI VE", + "Ġre k", + "Ġrece ipt", + "åĪ° åºķ", + "ĠBar bie", + "ĠSn ake", + "t urn", + "Je ff", + "ãģĬ ãģĬ", + "ķ Ħ", + "VO ICEOVER", + "co ll", + "Ġrun ners", + "ìł ľë", + "os os", + "mo on", + "Ġkey note", + "ĠInst it", + "S PEAK", + "Ġplug s", + "Ġcur v", + "ĠY uri", + "ĠTh eres", + "ĠP s", + "Ġμ ÏĢο", + "Ġconver ter", + "Ġref ine", + "Ġbad ass", + "Ġο ι", + "Ġreg en", + "az zi", + "ÙĬ Ùģ", + "Ġse ized", + "Ġiç er", + "ile e", + "Ġup stream", + "Ġbud s", + "Ġp im", + "Ġíķĺë £¨", + "Ġall uded", + "Ġthem ed", + "Ġconsist ing", + "Ġb ons", + "un uz", + "ĠпÑĢов од", + "ĠLove ly", + "ॠĭ", + "Ġpar ach", + "ĠSta ats", + "éļ Ĭ", + "Ġselect ive", + "Ġf ase", + "ĠGeor get", + "Ġcoc aine", + "Ġreprodu ction", + "ĠL ara", + "ĠL D", + "Ġg h", + "J on", + "Ġl Ã¥", + "Ġëij IJë", + "Ġtyp ed", + "ĠB ana", + "ë ĵľë", + "Ġsav ory", + "ĠZ omb", + "stand en", + "Ġpedest rian", + "Ġdifférent s", + "Ġìĭ ¸", + "èī ¯", + "Ġcompl ained", + "ç¦ ı", + "ĠÐļ ÑĤо", + "Ġ×ľ× ¤", + "ali ÅĽmy", + "Ġmort ar", + "Ġverd ict", + "Ġsu ficiente", + "ĠMill ion", + "mitt el", + "in als", + "ĠاÙĦØ ®", + "аÑİ ÑģÑĮ", + "Ġmi ÄĻdzy", + "ĠO le", + "Ġin vert", + "czy Äĩ", + "озм ожно", + "star ter", + "Ġaud itor", + "ĠSc out", + "ch ien", + "ĠSver ige", + "uff led", + "Ġze hn", + "ĠA uckland", + "Ġarg ent", + "Ġ197 6", + "ĠHo e", + "Ġboth ers", + "Ġsocial ist", + "Ġpl iers", + "Ġemer gen", + "ĠX P", + "еÑĢ ов", + "M ore", + "ĠLe vi", + "ĠAnd ers", + "ibil idad", + "ĠP arents", + "Ġindu ced", + "ìĸ´ì ¤", + "Ġbal ances", + "ĠвÑĭ ÑĪ", + "Ġsubmar ine", + "St art", + "Ġdri es", + "Ġvol ver", + "Ġtick ing", + "c ott", + "Ġf aj", + "pr és", + "ĠS abb", + "Ġза Ñĩ", + "Ġпок Ñĥп", + "Ġbapt ized", + "ĠBrill iant", + "ĠÐij ог", + "Ġm ots", + "b its", + "Ġlatt ice", + "æĪij è·Łä½ł", + "Ġcor iander", + "Ġresid ency", + "yn c", + "Ġpier wszy", + "ĠKn ock", + "ĠZ ap", + "ĠÐķ в", + "ê² ¬", + "å°ı å¿ĥ", + "Ġune ven", + "ĠJ as", + "od or", + "ç¿ Ĵ", + "7 4", + "ĠS ite", + "Ġacontece u", + "ym pt", + "Ġtril ogy", + "Ġlan tern", + "ĠZ ucker", + "v ari", + "we lling", + "ĠPot ato", + "gom ery", + "Ġreact ed", + "ĠChr on", + "Ġj ede", + "be eld", + "Ġtw ent", + "Ġl act", + "æ¨ Ĥ", + "Ġré se", + "Ġrel ent", + "Ġfurn ace", + "Ġwid get", + "Ġearthqu akes", + "ĠAd just", + "il it", + "ĠØ£ ÙĪ", + "Ġhear ings", + "Ġdefend ant", + "irs iniz", + "Ġbas k", + "c ja", + "ľ ¨", + "Ġrif les", + "Ġinst al", + "ĠFor give", + "p ical", + "ĠÐŀÑĩ енÑĮ", + "Ġpet ites", + "Ġh p", + "Ġren owned", + "ĠIn n", + "Ġ주 ìĦ¸ìļĶ", + "Ġemphas ized", + "éĹ® é¢ĺ", + "ĠìŀĪ ì£ł", + "Ġê²ĥ ìľ¼ë¡ľ", + "ãĤ Ĩ", + "Å ĵ", + "g ili", + "D ave", + "Ġexha usting", + "ÅĤ ug", + "Ġsch ema", + "μ ά", + "cy cl", + "Ġaut ant", + "Ġpar cel", + "Ġmater ia", + "ĠB erry", + "ĠÑģ ами", + "Ġextract ed", + "ĠSay ing", + "ism atic", + "Ġпоп ÑĢоб", + "Ġneur on", + "g raph", + "ľë ©´", + "Ġencl osure", + "ĠJoh ann", + "Ġafter math", + "ÑĤ об", + "Ġu ży", + "Ġs amp", + "3 60", + "ĠMe i", + "Ġt aco", + "Ġrecept ors", + "Ġpunch es", + "ĠHo je", + "ĠÙĩ ÙĨا", + "=\" #", + "ĠAng ular", + "Ġmus ique", + "Ġro l", + "Ġà ±", + "ster reich", + "Ġcl am", + "ĠTre asury", + "chem ical", + "Ġap ar", + "Ġapp end", + "Ġforb id", + "ĠHamb urg", + "ак ов", + "Ġê¸ Ī", + "ild a", + "Ġprepar ations", + "Ġmog Äħ", + "Ġcam ino", + "E ric", + "ĠBl ind", + "èĪ ĩ", + "å¹´ çļĦ", + "ĠDis covery", + "ì¸ ł", + "çĪ ¶", + "Ġinterpre ter", + "Ġb red", + "ĠPsal m", + "Ġdef ended", + "ìī ¬", + "ĠEr fahr", + "ĠPe ach", + "Ġmo ons", + "ĠO st", + "Ġspé cial", + "Ġarri ver", + "ĠW is", + "u ci", + "Ġrobot ics", + "I VE", + "Ġsie ge", + "ar la", + "Ġsepar ates", + "ĠT C", + "íı °", + "quis ite", + "Ġparenth eses", + "ик е", + "ç« Ļ", + "Ġtr ous", + "å» º", + "ĠÑģ илÑĮ", + "Ġbe ers", + "Ġпл аÑĤ", + "ãģĻãģĶ ãģĦ", + "Ġso la", + "Ġd ès", + "ming ham", + "ik te", + "Ġo ops", + "Ġtw itch", + "å° ĩ", + "Ï Ī", + "ĠShould n", + "uv re", + "Ġle er", + "cript ions", + "Ġeyes hadow", + "ĠGu o", + "ĠPow ell", + "Ġsup uesto", + "Ġan a", + "r als", + "ĠMont real", + "Ġsurf ing", + "ĠÐŁÐµÑĢ в", + "×ŀ ×ķ", + "Ġmillise conds", + "Ġsubur bs", + "Ġplanet a", + "ÑĥÑĪ ка", + "hr lich", + "ĠH Y", + "Ġس ÛĴ", + "ĠM M", + "ĠE ff", + "åı¯ æĦĽ", + "ĠH S", + "ans on", + "Ġì§ģ ìłij", + "Ġsu o", + "Ġdeploy ing", + "Ġk unt", + "ter ing", + "Ġere ct", + "ìŀ¥ ìĿ´", + "ĠìĿĮ ìĭĿ", + "Ġspec imen", + "! ...", + "æĪij 說", + "Ġlig ne", + "Ġk onst", + "ade qu", + "Ġìĥģ íĥľ", + "Ġaccess ed", + "ĠP ole", + "k ill", + "Ġë² Ħë", + "Ġauthentic ity", + "Ġapp elle", + "ull e", + "Ġrev ision", + "Ġgo ats", + "г ли", + "Ġp au", + "ĠR anger", + "ĠIm ag", + "aut hor", + "Ġe ve", + "ĠMess enger", + "Ġn ay", + "Ġwh oles", + "ät te", + "Ġon wards", + "ĠDep ois", + "Ġíijľ íĺĦ", + "ĠSAR S", + "Ġwszystk ich", + "Ġdest ru", + "umb ing", + "Ġcompat ibility", + "Ġmis information", + "od ore", + "ĠF avor", + "ek o", + "ı Į", + "w aukee", + "ĠTe aching", + "ĠK O", + "Ġbet ting", + "Ġquest s", + "Ġviv re", + "ĠмÑĥз Ñĭ", + "Ġs aga", + "Ġswe ll", + "Ġge he", + "æĢİ麼 樣", + "ĠоÑĢг аниз", + "Ġg ide", + "ĠG ross", + "Ġdale j", + "Ġcl aws", + "á»Ļ c", + "Ġprejud ice", + "Ġins ign", + "i hood", + "Ġpl ed", + "Ġdó nde", + "ĠPolit ical", + "Ġprem ises", + "und ert", + "ع ت", + "on nen", + "Ġespa ço", + "Ġf é", + "ĠHarr ison", + "ĠC ensus", + "Ġcard io", + "Ġdi y", + "Ġmil ieu", + "Ġjourn ée", + "ĠRe lease", + "N IE", + "ĠM uk", + "id ée", + "á»į i", + "Ġiç inde", + "ŀ Ļ", + "Ġreson ate", + "Ġm oles", + "ĠF lying", + "ĠGl oria", + "ĠPast or", + "ĠAre na", + "好 ä¸į好", + "N ON", + "ол ов", + "Ġall ÃŃ", + "om at", + "ìĸ´ë ıĦ", + "Ġcaracter ÃŃst", + "Ġdecl ining", + "Ñĸ Ñı", + "an co", + "ĠIn form", + "Ġbarg ain", + "Ġbus hes", + "ĠNat urally", + "Ġre chts", + "ĠT ensor", + "ĠPat ricia", + "Ġprincip io", + "ĠM umbai", + "Ġwom b", + "Ġnost ra", + "Ġdile mma", + "Ġirgendw ann", + "Ġ196 4", + "Ġenerg ÃŃa", + "Ġна ÑĢ", + "Ġseg regation", + "ĠA thlet", + "Ġ» ,", + "Ġy eni", + "ĠSe it", + "Ġven om", + "Ġdak ika", + "Ġëı Įë", + "ĠÃī l", + "Ġf us", + "ĠM og", + "¦½ ëĭĪëĭ¤", + "Ġrem ar", + "ĠTed dy", + "Ġbreast s", + "ic ans", + "æĶ¶ çľĭ", + "k ap", + "Ġh Æ¡n", + "ĠJ P", + "ãĥ³ ãĤ¿", + "Ġresur rect", + "ĠìĿ ¸ë", + "her ical", + "Ġfot ograf", + "ĠJos é", + "Ġlivel ihood", + "Ġbib li", + "ter i", + "Ġvor stellen", + "ĠA AA", + "Ġassess ing", + "Y A", + "Ġspl end", + "Ġexca v", + "Ġbapt ism", + "y ll", + "w ow", + "M ac", + "Ġpl astics", + "teok bokki", + "Ġintéress ant", + "Ġcommand ed", + "Ġfamous ly", + "ĠÐĺ ли", + "ĠMan uel", + "Ġsouth west", + "Ġde formation", + "ÃŃcul o", + "ĠнаÑħод иÑĤÑģÑı", + "ĠP atter", + "d egree", + "ĠczÄĻ sto", + "\" -", + "Ġìħ ĭ", + "Ġman ger", + "ĠTrust ee", + "Ģë ¦¬", + "Ġpunt os", + "iv able", + "Ġvol atile", + "ĠëĬ IJ", + "Ġinst ability", + "Ġc iel", + "ci Äħ", + "Ġpur ity", + "но ÑģÑĤ", + "S il", + "ed ar", + "åĻ ¨", + "NOUN CER", + "Ġspe lled", + "G ER", + "Ġsanct uary", + "Ġacceler ating", + "Ġsc out", + "ĠпÑĢ ев", + "f ahren", + "ãģĵ ãģ¡ãĤī", + "ĠëĤĺìĺ ¨", + "Ġpocz Äħt", + "ĠMe u", + "ka ar", + "³´ ê³ł", + "ak ra", + "D own", + "ĠÃĦ r", + "ĠEl ite", + "Ġall ons", + "Ġmay onnaise", + "ĠS ustain", + "prising ly", + "Ġsuper vis", + "Ġê·¸ëłĩ ì£ł", + "Ġunemploy ed", + "Ġfresh ly", + "Ġ×ŀ× ¢", + "ĠD h", + "Ġtack ling", + "Ġo gr", + "Ġì´ Īë", + "ãĤĪ ãĤį", + "Ġlo ft", + "ar ah", + "ĠA irl", + "ĠD ir", + "ĠÐľ ожно", + "Ġbook ing", + "ĠC RA", + "Ġhtt ps", + "Ġcho ke", + "Ġg own", + "Ġno ite", + "Ġz ac", + "ist ol", + "Ġsec re", + "Ġresemb les", + "Ġcu ad", + "ìĤ¬ ê°Ģ", + "sh ow", + "Ġbl anc", + "Ġag u", + "ĠPr int", + "ast ed", + "ĠWe ather", + "i pl", + "Ġobsc ure", + "Ġcont e", + "ough s", + ") ;", + "ĠD ame", + "ä¸Ģ 缴", + "Ġclar ification", + "Ġintim acy", + "Ġup hold", + "ĠMir ror", + "Ġw agon", + "x ide", + "Ġcl og", + "app er", + "ĠImmedi ately", + "ú de", + "Ġtouch down", + "Ġro oft", + "аÑĪ а", + "Ġç ıkt", + "Ġla isser", + "ĠUn real", + "ens itive", + "Ġ12 3", + "Ġpl aster", + "Ġduck s", + "Ġet me", + "Ġb ishop", + "bre vi", + "Ġb ic", + "ä¸ĭ åİ»", + "Ġrun time", + "Ġamb itions", + "м аÑĤ", + "ĠWe in", + "ĠMar i", + "ĠíĬ ¸ë", + "Ġresol ver", + "Ġng Ãły", + "ĠR ise", + "ãĤĪãģĨ ãģ«", + "ĠCr us", + "Ġmerchand ise", + "Ġel i", + "Ġstate wide", + "Ġow l", + "éģ ł", + "æĶ ¹", + "Ġtwist ing", + "Ġcontam inated", + "ĠCom merce", + "hy thm", + "Ġà Ī", + "Ġìĭ ¤ë", + "Ġmus ste", + "u ir", + "Ġsum s", + "ĠSome where", + "ãĥ İ", + "Ġk ami", + "Ġa ired", + "ĠAND REW", + "Ġê º", + "Ġv iendo", + "Ġantib ody", + "Ġabsol ument", + "Ġprotest ers", + "ĠQué bec", + "st adt", + "Sha un", + "Ġcham bers", + "ĠWe ar", + "ĠEffect s", + "Ġhaz ards", + "Ġne i", + "Ġcoraz ón", + "Ġá ¼", + "ĠS G", + "Ķ ©", + "ĠìĹŃ ìĭľ", + "Ġcom fy", + "ĠC ody", + "Ġpens ando", + "Ġg anska", + "ĠAc ross", + "öll ig", + "aby te", + "Ġwed ge", + "Ġkal ian", + "Ġsig ue", + "end es", + "ĠGro ÃŁ", + "Ġutil iser", + "Ġfl own", + "ани Ñİ", + "Ġle var", + "rest rial", + "Ġillust rations", + "Ġas lında", + "BLE EP", + "Ġдо ÑģÑĤ", + "Ġtur ret", + "Ġsuit case", + "ziÄĻ ki", + "Ġsket ches", + "Ġac red", + "ĠRe i", + "Ġt sun", + "ĠS ag", + "Ġthird s", + "ĠKIR BY", + "ra i", + "Ġhuman os", + "Ġrecomm ends", + "Ġextraordin arily", + "Ġcommence ment", + "K N", + "ope z", + "Ġ×ij× ©", + "Ġlet hal", + "ĠEst amos", + "Ġinspect or", + "ĠSe ok", + "e un", + "Ġoff shore", + "Ġget tin", + "ye ars", + "ĠSil ence", + "ĠNat ur", + "up un", + "Ġtr zy", + "Ġno get", + "Ġhamb urger", + "ĠPra ise", + "é nd", + "Ġ197 1", + "yl ie", + "k rit", + "ĠìĥĿê°ģ ìĿ´", + "çļ ®", + "Ġmoment os", + "Ġest é", + "Ġdisse min", + "Ġgig s", + "Ġdes af", + "Ġav is", + "ĠZ oo", + "ĠìķĬ ìĿĢ", + "h äng", + "åı ¥", + "h ake", + "ĠB ism", + "Ġre think", + "ĠMal colm", + "Ġident ifies", + "l ower", + "ix el", + "Ġtv Ã¥", + "k ed", + "ier z", + "Ġö ffentlich", + "Ġproc laim", + "so on", + "l ol", + "Ġlo i", + "Ġb itten", + "ro llo", + "Ġser mon", + "Ġes qu", + "Ġjack ets", + "Ġgr áfic", + "Ġпок азÑĭв", + "Ġcabe za", + "ch odzi", + "Ġpel vis", + "Ġnost algia", + "Ġbre w", + "Ġshort cuts", + "ĠAd emás", + "Ġsuperfic ial", + "åħ© åĢĭ", + "Ġbo ca", + "ĠæĪij æĺ¯", + "iment os", + "åĽł 为", + "Ġspr outs", + "é£ Ľ", + "ĠJon as", + "ĠFloren ce", + "st atic", + "da ughter", + "* )", + "ÅĤ by", + "f ashion", + "ĠG inger", + "Ġë§ ¤ë", + "Ġhust le", + "ut os", + "ĠÑĤ Ñıж", + "ĠL ös", + "ש ×Ļ×Ŀ", + "any ch", + "tu ber", + "Ġtid y", + "Ġfront al", + "Ġwhis key", + "Ġhum id", + "ĠÎ Ł", + "Ġr idge", + "Ġmar in", + "Ġb ientôt", + "ĠCarr ie", + "ch w", + "Ġtah un", + "ĠEr geb", + "F R", + "Ġìłķ ë¶Ģ", + "ĠSold ier", + "Ġenlight enment", + "Ġexam ining", + "ĠNot re", + "Ġer am", + "ĠSun ny", + "Ġlay ered", + "ĠD azu", + "r ades", + "好 åIJĥ", + "ĠнаÑĪ ей", + "Ġtim ber", + "Ġman ners", + "ĠBir mingham", + "Ġmini ature", + "omet ers", + "Ġfill er", + "ĠR ip", + "ĠK omb", + "own er", + "ì ¿", + "id ian", + "Ġdem ás", + "ĠÙĪ ت", + "Ġpreca utions", + "Ġgovern o", + "z elf", + "ĠCom plete", + "å¸ ĥ", + "ĠPh antom", + "ãģ¾ ãģļ", + "Ġн ез", + "ĠкаÑĢ ÑĤ", + "ĠAnt wort", + "ĠPf izer", + "ĠFran co", + "Ġw ÅĤ", + "Ġfr ig", + "es per", + "Ġk ale", + "Ġfilm maker", + "Ġk urt", + "Ġinv alid", + "å± Ģ", + "are lla", + "Äĥ ng", + "ram ento", + "Ġnutr itional", + "Ġdict ators", + "Ġaf in", + "Ġf uzzy", + "ĠG ina", + "ó t", + "ĠExtrem adura", + "Ġdemonst rations", + "ĠMont gomery", + "íķ´ì Ħ¤", + "ĠGand hi", + "ãĥ Ŀ", + "ç½ ®", + "Ġreun ion", + "Ġjaki ÅĽ", + "ĠZ ug", + "OU GH", + "l ifting", + "Ġ à²", + "á¹Ľ á¹£", + "e b", + "ĠW OW", + "ĠSh iva", + "omet ry", + "Ġwild ly", + "Ġt ended", + "Ġmeg ap", + "ì² ĺ", + "Ġna use", + "Ġg erek", + "ãĥ ĭ", + "ĠMar cel", + "Ġn este", + "Ø® ر", + "Ġfe h", + "åĨ ħ", + "susp enseful", + "ĠWrest le", + "ĠPalestin ians", + "ĠG ORD", + "iy et", + "ĠÑĢ ади", + "Ġvers uchen", + "Ġtrans istor", + "ĠÐŁÑĢ оÑģÑĤо", + "Ġпон ÑĢав", + "Ġrhy me", + "ĠVerm ont", + "pl atz", + "è® °", + "ĠÄ°ÅŁ te", + "ĠH ag", + "ĠÐĺ м", + "ĠÑĢаÑģÑģк аз", + "Ġmet ros", + "ĠInfin ity", + "w olf", + "ib al", + "ft ig", + "Ġ ÚĨ", + "Ġíĺ¹ ìĭľ", + "Ġo ggi", + "Ġdisp osit", + "ĠпÑĢ ил", + "ĠвÑĭ пол", + "Ġth ôi", + "ĠK ENN", + "Ġhand ing", + "act us", + "Ġtac os", + "Ġformer ly", + "ĠCorinth ians", + "ãģ« ãģ¯", + "ÑĨÑĸ ÑĹ", + "Ġpad re", + "Ġcongreg ation", + "æ ij", + "fer t", + "Ġsub ir", + "ais er", + "qu a", + "ara oh", + "ĠCur ry", + "ĠìķĬ ëĬĶ", + "ел Ñİ", + "Ġf uss", + "Ġbo oty", + "Ġl ows", + "Ġh ommes", + "ĠM H", + "ĠDisney land", + "w ent", + "Ġresid ue", + "Ġbe eping", + "è¼ ķ", + "ät ta", + "Ġm ould", + "ĠPro jekt", + "st alk", + "Ġartif act", + "ĠAnt rag", + "ĠAM D", + "ĠCry pt", + "Ġë© Ķ", + "ĠFel ipe", + "ĠCO B", + "el u", + "Ġself ies", + "ĠS anti", + "ch utz", + "ĠУ кÑĢаÑĹ", + "ges amt", + "Ġflo ck", + "j az", + "pl ain", + "Ġwr inkles", + "Ġre ais", + "Ġpal jon", + "Ġempower ment", + "Ġattend ees", + "pp a", + "Ġn eden", + "он Ñĭ", + "Ġtime frame", + "ĠCher ry", + "Ġid ée", + "Ġg ag", + "Ġdon key", + "Ġô ng", + "ĠH are", + "éļ Ľ", + "ĠK ara", + "Ġacom pan", + "pl aces", + "im ientos", + "ĠH amm", + "б и", + "ub en", + "ili yor", + "Ġth irst", + "Ġk ry", + "ĠGeorget own", + "׳ ×Ķ", + "Ġor ch", + "Ġheart beat", + "Ġtransform ations", + "est ones", + "ĠK H", + "Ġcart oons", + "Ġan ci", + "Ġworth less", + "Ġtail ored", + "p u", + "Americ ans", + "Ġp iles", + "ĠMon key", + "Ġbas in", + "ĠTem per", + "ĠP aint", + "Ġpunch ing", + "Ġba ik", + "ĠOak land", + "v re", + "ÅŁ allah", + "yd d", + "Ġcas ually", + "od u", + "Ġc oded", + "ĠNorweg ian", + "ĠV ince", + "Ġprem ature", + "ĠProm ise", + "ек ÑģÑĤ", + "Ġdevast ated", + "ĠPrem ium", + "ĠPar am", + "ĠÃĸ yle", + "um uz", + "P O", + "r ators", + "Ġlamp s", + "Ġterritor ial", + "Ġback bone", + "list ed", + "D Y", + "ĠاÙĦ ر", + "Ġpurs ued", + "ĠComm ons", + "Ġê³ ¡", + "lo cks", + "ed or", + "Ġconce ived", + "g ere", + "Ġdisappe aring", + "ĠS ull", + "ĠìĹ °ë", + "Ġho ffe", + "Ġdet ox", + "íĶ Į", + "Ġret ir", + "ĠëģĿ ëĤ", + "Ġper gunta", + "ĠB OY", + "ç² ¾", + "Ġp enn", + "æĿ¥ äºĨ", + "h és", + "h on", + "Ġcatastroph ic", + "Ġa ust", + "Ġtor so", + "Ġìĸ´ ëĬIJ", + "ĠìĤ¬ëŀĮë ĵ¤ìĿ´", + "Ġmarvel ous", + "ĠHar ley", + "ach ine", + "Ġti ế", + "itt o", + "ĠI ÃŃm", + "yl on", + "Ġshut down", + ".' '", + "Ġap ologies", + "ĠCommun ication", + "ĠговоÑĢ Ñİ", + "ãģĤ ãĥ¼", + "âĦ ¢", + "ÃŃ veis", + "ac un", + "Ġret aining", + "Ġcontrad iction", + "ĠAD AM", + "C OM", + "Bry an", + "ĠM onsieur", + "Ġadap ting", + "Ш ÐIJ", + "ĠSc r", + "änd ert", + "Ġpl aus", + "ä»Ĭ天 çļĦ", + "Ġon set", + "Ġassist ants", + "Ġval ves", + "Ġsc atter", + "ĠR ust", + "aw ia", + "Ġread iness", + "Ġp ais", + "Ġb ible", + "Ġamb iente", + "Ġа меÑĢик", + "Ġunc ond", + "Ġk alk", + "åĬ ¨", + "Ġmo c", + "un n", + "Ġact u", + "Ġhum ming", + "iss imo", + "ĠPat rol", + "g ow", + "ãĥ ¤", + "ĠTHE Y", + "ĠBod en", + "ĠB ie", + "Ġre el", + "ĠÑĥÑģл ов", + "Ġende avor", + "ĠPer iod", + "ustom ed", + "m als", + "al on", + "B ox", + "ĠÏĥ αÏĤ", + "Ġom dat", + "Ġal tre", + "ĠHe h", + "k ad", + "Ġprotect or", + "Ġdomin ance", + "odynam ic", + "Ġcommunic ated", + "k ö", + "Ġprede cessor", + "ĠL uk", + "ĠFl ower", + "Ġãģ ©", + "po que", + "ÑĤи ÑĢов", + "Ġret rospect", + "Ġdecis ive", + "Ġexem pel", + "{ \\", + "ĠR ück", + "r ite", + "ĠZe us", + "Ġcal orie", + "Ġattract ions", + "ĠH inter", + "Ġuh m", + "ĠíĮ IJ", + "Ġrul ers", + "Ġdiscour aged", + "Ġaconte cer", + "Ġacc ents", + "ĠOpt im", + "ĠAl g", + "k ids", + "20 21", + "ĠLind say", + "Ġfilm makers", + "pr owad", + "Ġter ug", + "ëĭ ´", + "ĠSom mer", + "20 18", + "Ġborrow ing", + "ĠTrans fer", + "н оп", + "ari as", + "Ġhead phone", + "ì¼ ľ", + "Ġtransl ating", + "Ġauf ge", + "ப à®Ł", + "we is", + "av ant", + "pa id", + "b aby", + "Ġtough est", + "Ġrepe ats", + "ĠTer esa", + "L ord", + "Ġacab ar", + "ĠR ide", + "d ir", + "Ġl eng", + "Ġd wa", + "Ġhead aches", + "Ġn ữa", + "ĠнаÑģ ÑĤоÑıÑī", + "Ġbo ils", + "Ġlong ing", + "ri as", + "ó rio", + "ĠParad ise", + "ĠSeñ or", + "erd em", + "Ġrein st", + "Ġsal aries", + "Ġinsec urity", + "ÅĤo ÅĽci", + "ĠабÑģолÑİÑĤ но", + "ink en", + "ĠEd dy", + "ud os", + "Ġd ummy", + "Ðļ ак", + "s ix", + "Ġin box", + "Ạ©", + "Pe ople", + "á»ĵ ng", + "Ġorganiz ers", + "f ind", + "Ġü l", + "ĠCO M", + "ż a", + "we ile", + "Comment ary", + "íĬ¸ë ¥¼", + "ĠMitt el", + "k us", + "èĽ ĭ", + "ठ¨", + "ir al", + "Ġgar ment", + "ικ ά", + "Ġst ool", + "pay ers", + "Ġsh immer", + "ĠO llie", + "ĠJe żeli", + "è¿ĺ æľī", + "Ġ197 7", + "Ġje ux", + "Ġext inct", + "ĠTransport ation", + "ĠM aker", + "Ġj ohn", + "Ġrich est", + "Ġtraum at", + "Ġli egen", + "´ë ¥¼", + "è¿Ļ éĩĮ", + "Ġun rest", + "ĠSt raw", + "æĭľ æĭľ", + "Ġcom a", + "ĠKr isten", + "ĠÐļон еÑĩно", + "ĠBry ce", + "ĠÑıк Ñĸ", + "Ġpearl s", + "Ġпоним аÑİ", + "Ġadd itions", + "Ġas ympt", + "ĠменÑĮ ÑĪе", + "Ġsc ans", + "Ch ild", + "ĠH ide", + "к ÑĥÑİ", + "et as", + "Ġd ank", + "Ġple as", + "Ġess ays", + "Ġj ets", + "åħ Ĵ", + "Ġв ед", + "Ġposit ives", + "ho f", + "- )", + "zz o", + "Ġstar ters", + "Ġsm iled", + "Ġ194 4", + "qu iera", + "Ġro k", + "Ġpu esto", + "N ico", + "Ġsim ulations", + "Ġ à¶", + "Ġintrig ued", + "ĠOver watch", + "åĸ Ĥ", + "s igh", + "b ai", + "Ġë§IJ ê³ł", + "id é", + "Ġcra bs", + "áºŃ p", + "ĠIraq i", + "ìĿ´ë ¥¼", + "ÑĤ Ñı", + "ĠSoph ia", + "ĠDN S", + "Ġönem li", + "ĠLu o", + "Ŀ ¤", + "ĠCoun sel", + "l igen", + "анÑĮ ÑĪе", + "Ġtrump et", + "Ġd apat", + "ĠJ M", + "ĠEVER Y", + "Ġå°į ä¸įå°į", + "å¤ ¢", + "ĠL ayer", + "Ġc ô", + "н ал", + "ĠJ oo", + "ĠH ack", + "Ġs unt", + "ĠLeon ard", + "ĠFire base", + "äng er", + "Ġexpl oding", + "v oy", + "Ġì¦ IJ", + "ĠÑģ еÑĢÑĮ", + "Ġsever ity", + "Ġbest imm", + "çµIJ æŀľ", + "Ġt iring", + "Ġprocure ment", + "Ġdiplom acy", + "Ġdecor ative", + "ĠÙĬ ا", + "Ġpenet ration", + "Õ «", + "Ġout right", + "EN E", + "ĠUn i", + "od les", + "Ġz eros", + "Ġdelight ful", + "j m", + "Ġdo po", + "没 äºĭ", + "Ġposit ivity", + "ĠVIS TA", + "ĠRes ource", + "íĥ Ģë", + "ÑĪ ие", + "C arl", + "Ġpip ing", + "Ġchop ping", + "ĠGan ze", + "ü ss", + "ĠA o", + "Ġsh attered", + "ĠDet ective", + "Ġund oubtedly", + "Ġhall uc", + "Ġen ch", + "Ñĭ Ñĩно", + "ÑĥлÑı ÑĢ", + "is esti", + "Ġped als", + "Ġdur um", + "¤í Ķ", + "la imer", + "Ġprop re", + "C u", + "Ġtransl ator", + "Ġca ÅĤ", + "Ġê·¸ 걸", + "Ġca ÅĤy", + "U A", + "Ġrev ised", + "Ġпод об", + "ĠArt icle", + "ĠHait i", + "Ġà ĵ", + "ĠC trl", + "Ġroz m", + "la it", + "Ġletz te", + "is pering", + "dis play", + "Ġalumin ium", + "Ġpalab ras", + "Ġconoc er", + "Ġz itten", + "Ġdir ig", + "åıª æľī", + "Ġbrain storm", + "Ġw ifi", + "ĠPart icip", + "Ġview point", + "ĠQu an", + "Ġhier arch", + "W elcome", + "å¯ ¾", + "Ġoff en", + "ĠRe covery", + "gan o", + "W ould", + "Ġrep ro", + "Ġper ceptions", + "Ġdem asi", + "ĠBangl adesh", + "ĠIncred ible", + "Ġlet zt", + "Ġbehav ing", + "Ġaston ishing", + "Ġâ Ĩ", + "ĠëĤ¨ ìŀIJ", + "èµ° äºĨ", + "ãĥ Ķ", + "ĠGORD ON", + "C AR", + "? !\"", + "ĠP rest", + "Ġë§ŀ ìķĦìļĶ", + "Ġt and", + "Ġl ash", + "ç Ĭ", + "ific ant", + "Ġint oler", + "Ġг еÑĢо", + "Ġte u", + "as o", + "ĠÑģов еÑĤ", + "Ġtravel ers", + "ĠSy nd", + "ĠвеÑĢ Ñģ", + "F onda", + "ad ı", + "Ġtrans cription", + "Ġtit anium", + "Ġtw ists", + "Ġgear box", + "ens ation", + "f at", + "C oll", + "ĠCommon wealth", + "z on", + "ĠPolize i", + "ĠAPP LAUSE", + "f ry", + "ĠJud a", + "este em", + "Ġso ck", + "ĠJug end", + "Ġк ÑģÑĤаÑĤи", + "ĠD ro", + "Ġproch aine", + "ãĥ¼ ãĥ«", + "Ġli ksom", + "ĠEner gie", + "ĠMar ina", + "Ġ2 30", + "Ġê°Ģ ìĦľ", + "ump ing", + "Ġl one", + "ç´ ļ", + "Ġfont s", + "Ġbusiness man", + "Ġp ly", + "Ġdo e", + "gr id", + "ĠMil waukee", + "ĠE den", + "! \".", + "ĠÛĮ Ûģ", + "og ens", + "Ġteas er", + "Ġqui én", + "Ġincent iv", + "go vern", + "Ġchild care", + "Ġsneak ers", + "Ġimprison ed", + " ®", + "иÑĤ еÑģÑĮ", + "an bul", + "Ġreg ain", + "Ġtranqu il", + "Red ner", + "éĽ ¨", + "IF A", + "Ġide ological", + "Ġmayor ÃŃa", + "Ġb ureau", + "et erm", + "ĠD ID", + "ìĬ ·", + "Ġw aving", + "Ġbe b", + "Ġá r", + "Ġк в", + "Ġenv oy", + "an ut", + "ик Ñĥ", + "ĠEnviron ment", + "ĠAss ass", + "ãĤĵ ãģ§", + "ĠB read", + "ĠТ ÑĥÑĤ", + "Ġstair case", + "ĠDise ase", + "Ġauc un", + "Ġëĭ Ī", + "Ġconfront ation", + "Ġ194 1", + "Ġiron y", + "Ġwor sh", + "ãĤĮ ãĤĭ", + "Ġf ick", + "ĠNa omi", + "Ġback side", + "ie ux", + "K ap", + "Ġved ere", + "Ġlength y", + "Ġbreak er", + "ĠRoll e", + "Ġpred ator", + "Ġnoss os", + "Ġadvert ise", + "è³ ĩ", + "ÑĢод е", + "Redner wechsel", + "re ten", + "Ġcollect ors", + "ıģ ımız", + "Ġtr ig", + "Ġax es", + "in ters", + "Ġpen alties", + "ĠOs man", + "ĠJen na", + "Ġfl akes", + "Ġtrain ers", + "Ġstun ned", + "ĠSc roll", + "ĠP ip", + "Ġна ÑģÑĤ", + "Ġnh Ãł", + "ĠSm ack", + "ẫ n", + "rat os", + "ĠÑĢабоÑĤ Ñĭ", + "Ġu cz", + "ĠLem on", + "ĠS ind", + "Ġpsych ic", + "ĠAb g", + "Ġmamm als", + "Ġimmers ive", + "Ġb ots", + "Ġverschied ene", + "Ġg eral", + "Ġfoll ower", + "Ġ ä»ĸ", + "Ġsegur idad", + "Ġimmers ed", + "fe ito", + "c ross", + "Ġö ld", + "íĥ Ħ", + "Ġãģĵ ãģ®", + "Ġ×Ķ ×Ļ×IJ", + "ĠJ ian", + "Ġbili yor", + "are a", + "Ġk af", + "Ġgod t", + "缸 ä¿¡", + "Ġë°© ìĨ¡", + "Ġdet riment", + "æ¥ ļ", + "Ñĸ л", + "ĠÄij âu", + "Ġchlor ide", + "ø re", + "le i", + "Ġmont e", + "Ġdifférent es", + "à¯ģ .", + "Ġcareg ivers", + "Ġin adequ", + "Ġfare well", + "ĠÑĤип а", + "ont ec", + "ĠE ph", + "HH H", + "ĠTod os", + "ĠС ШÐIJ", + "Ġtro v", + "Ġl ige", + "Ġc ông", + "ĠC iv", + "Ġcap az", + "ĠV allahi", + "Ġquest e", + "Ġrepl ica", + "س ب", + "z na", + "ĠÑģл Ñĥж", + "ĠP T", + "w ave", + "ien i", + "Ġrel ied", + "de velop", + "Ġdem e", + "ĠA man", + "Ġ[ ...]", + "Ġcompl iments", + "u ais", + "ĠíĮ ¨", + "Ġsmell ing", + "Ġdad urch", + "ÙĪ ت", + "Ġor anges", + "Ġл ай", + "Ġstabil ization", + "åĢ į", + "ãĤĮ ãģŁ", + "æ¥ ½", + "Ġappl iances", + "Ġh m", + "ĥ IJë©´", + "odynam ics", + "Ġc iÄĻ", + "ĠC ott", + "M ON", + "ĠM ang", + "æĶ¯ æĮģ", + "Ġall erdings", + "ικ ή", + "sh ots", + "Ġt s", + "ĠG ör", + "ĠCH AR", + "Ġ: (", + "Ġwr ath", + "Ġf ique", + "Ġfüh ren", + "Ġtest ament", + "Ġ^ ^", + "á¹Ľá¹£ á¹ĩa", + "AL D", + "Ġtext o", + "ĠDog s", + "Ġs ib", + "Ġpath etic", + "ock s", + "Ġrad ically", + "ĠM ORE", + "ĠJAM ES", + "Ġing l", + "ĠTechn ical", + "Ġpor ch", + "ĠU T", + "ĠобÑıз аÑĤелÑĮно", + "Ġrenew al", + "Ġaesthet ics", + "ik um", + "Ġbe verage", + "der n", + "Ġpredict ive", + "Ġch uy", + "ĠRegard ing", + "ĠFor ward", + "ĠÙĪ ÙĦ", + "Ġcontext ual", + "Ġdwar f", + "Ġpre he", + "Ġgovern ed", + "ħ Ħ", + "Ġtrabal har", + "Ġnegó cio", + "ĠболÑĮÑĪ ой", + "еÑĩ аÑĤ", + "Ġд ÑĥÑħ", + "Ġflood s", + "Ġbow ling", + "ĠO B", + "ĠH är", + "Ġgrad ing", + "주 ëĬĶ", + "Ġg ars", + "d ling", + "Ġr ak", + "ë Ī", + "c reat", + "ĠÑī е", + "Ġneighb ours", + "f ood", + "Qu ery", + "Ġhero in", + "ice ps", + "ĠK inda", + "N ET", + "Ġmar i", + "Ġim itate", + "Ġach ter", + "Ġsettle ments", + "ra re", + "cc iones", + "Ġë ĵľ", + "Ġf ik", + "it ung", + "Ġм акÑģим", + "Ġel f", + "Ġd alla", + "ĠPol sce", + "ĠP ul", + "Ч ÑĤо", + "ĠMor gen", + "ØŃ Ùħ", + "Ġsuprem acy", + "Ġk ys", + "ĠHur ricane", + "ĠG TA", + "ĠFe h", + "Ġfinal mente", + "m und", + "ĠK rie", + "é poque", + "ĠT ucker", + "IT T", + "Ġl ur", + "Ġdi pping", + "ä v", + "Ġeer ste", + "ĠFl int", + "bild ung", + "ู à¹ī", + "Ġto im", + "Ġpr acy", + "Ġtransform s", + "Ġspeed ing", + "Ġpresent er", + "Ġfellow s", + "f illed", + "ie za", + "Ġadv ising", + "ĠInter view", + "и гÑĢ", + "we hr", + "ĠD ante", + "pt ure", + "Īë¬ ¸", + "¯ ¸ë", + "IJ IJ", + "ĠCoun ter", + "Ġcr ist", + "Ġì§ ľ", + "Ġje une", + "ĠÑģÑĤ ÑĢаÑĪ", + "Ġmie Äĩ", + "Ġtut or", + "Ġmas ala", + "Ġpowder ed", + "Ġn au", + "ĠFreder ick", + "Ġbill ing", + "ĠE isen", + "Ġд обÑĢ", + "Ġm est", + "æ ½", + "Ġsn ipp", + "Ġmon o", + "ĠA lo", + "ĠMer cy", + "éri ence", + "Ġcasual ties", + "ĠAN NOUNCER", + "ä» İ", + "Ġto car", + "Ġbacter ial", + "H o", + "Ġstre ak", + "ĠJ ENN", + "Ġpl ast", + "Ñģ лед", + "Ġre app", + "Ġpay check", + "Ġmin ers", + "hab t", + "ĠJ ap", + "н ÑĥÑĤ", + "Ġred emption", + "Ġqu ir", + "hn lich", + "Ġaccum ulation", + "Ġsh ove", + "Ġadrenal ine", + "M ake", + "ĠH ern", + "oss ing", + "ĠV il", + "ub by", + "her tz", + "bre aks", + "Ġsp ur", + "ĠD aha", + "US TIN", + "Ġcontinu er", + "ĠSa ul", + "ãģ® ãģ¯", + "Ġíı Ń", + "ĠëIJĺë ©´", + "Ġë§IJìĶ Ģ", + "Ġо ж", + "Ġsuspect s", + "Ġla quelle", + "ĠMuch as", + "Ġv öllig", + "ul en", + "Ġimp res", + "Ġlo bb", + "ene e", + "Ġн аж", + "T a", + "Ġréal ité", + "ĠRe x", + "Ġharvest ing", + "Ġest r", + "æ ¶", + "osp ace", + "OS S", + "Ġdisturb ance", + "ass ic", + "ĠIs ab", + "Ġdéc ouv", + "ĠHamp shire", + "Ġor nament", + "Ġlu ôn", + "ĠU W", + "Ġj Äħ", + "éĤ£ ä¹Ī", + "Ġrespect o", + "Ġcomun idad", + "Ġcom igo", + "ag na", + "Ġintrins ic", + "ĠAlum ni", + "Ġses leri", + "Ġestim ation", + "âĢĶ âĢĶ", + "Ġprodu it", + "ãĢĤ ãĢį", + "Ġв ÑĢ", + "Ġwh irl", + "Ġac ces", + "ç u", + "Ġvari ability", + "Ġv odka", + "its u", + "Ġinternship s", + "Ġalloc ate", + "R R", + "íĽ Ī", + "Ġinstruction al", + "t ant", + "Ġà®ħ த", + "Ġinv ites", + "Ġha k", + "Ġsca res", + "Ġe clipse", + "п ов", + "к олÑĮ", + "ativ as", + "Ġstab bed", + "ĠD OM", + "ä¸į åĪ°", + "ro ots", + "ĠPict ure", + "íĺ ¼", + "ĠC HA", + "ie c", + "ı ı", + "han ol", + "Ġmisunder stand", + "R ay", + "Ġroad map", + "ocument ed", + "iz ione", + "ĠOl ive", + "r ift", + "Ġ×Ķ× ł", + "æ¯ į", + "l est", + "; ;", + "ĠE A", + "éľĢ è¦ģ", + "од Ñĥ", + "Ġhob bies", + "Ġbur ial", + "ãģ« ãģ¡ãģ¯", + "Ð ¤", + "le ge", + "ĠH J", + "Ġobject ion", + "Ġãģ Ń", + "ct ory", + "Ġincre mental", + "Ġgym n", + "Ġepid emi", + "Ñģ Ñĭл", + "à ij", + "Ġadvance ment", + "Ġpar ch", + "New s", + "Ġa yr", + "л ам", + "Ġ×ľ× ©", + "Ġdipl oma", + "ãģ¡ãĤĥ ãĤĵ", + "Ġrob bed", + "On ly", + "Ġinc ur", + "Ġch anting", + "Ġíķ´ë ıĦ", + "Ġrich es", + "ĠCar men", + "Ġnost ro", + "λ ÎŃ", + "ĠPow der", + "à¹Ģภ«", + "ĠìŀĪ ìľ¼ë©´", + "Ġgerçek ten", + "ĠPik achu", + "ем он", + "OL L", + "Ġplanet ary", + "Ġsl ows", + "Ġclock wise", + "al ion", + "Ġì Į", + "Ġver n", + "Ġh omme", + "Ġend point", + "Ġinnoc ence", + "Ġelement os", + "Ġsophom ore", + "Ġnot ions", + "ĠCould n", + "p ur", + "Ġz at", + "Ġobs ess", + "Ġmotiv o", + "ĠK ub", + "ĠDr ug", + "A nt", + "ĠPlay ers", + "ĠHum ans", + "Ġme lee", + "ĠWild life", + "ĠV P", + "Ġvolcan ic", + "Ġcom in", + "ĠGu ang", + "ĠÏĦι ÏĤ", + "ĠоÑģоб енно", + "ĠS ize", + "L isten", + "ĠA aa", + "app ro", + "Ġbar bar", + "ĠPark inson", + "нÑı ÑĤÑĮ", + "å į°", + "Ġunderest imate", + "Ġsubst itution", + "Ġcosm etic", + "ä¸ĭ 次", + "Ġwill en", + "Ġbe ide", + "ann i", + "Ġcondition ed", + "ĠDe bbie", + "Ġis to", + "ĠEd wards", + "ìĽĮ ìļĶ", + "ĠÑĤ ов", + "Ġab brevi", + "ĠM ün", + "ĠPr inc", + "ĠLi ang", + "Ġst ink", + "Ġradio active", + "ãģĨ ãĤı", + "Ġac ontec", + "Ġun con", + "ĠTur bo", + "ãģ IJ", + "Ġkiss es", + "æĺ¯ ä»Ģ麼", + "еÑĤ ÑĢов", + "Ġfront ier", + "ĠSp y", + "ĠBel arus", + "ĠC BS", + "á» Ĺ", + "am oto", + "íķľë į°", + "ĠÑģÑĤ ÑĢо", + "ĠEn fin", + "Ġbread th", + "éĺ ²", + "ĠCa fe", + "ĠDaf ür", + "ĠB our", + "ar as", + "Ġbl ueprint", + "an ı", + "Ġconst ants", + "Ġattack er", + "ĠForm ula", + "za Äĩ", + "Ġs owie", + "Ġeyebr ow", + "ob ook", + "Ġset zen", + "第 ä¸ī", + "ons ider", + "aw ning", + "Ġsöyle ye", + "Ġinv aded", + "Ġpronoun s", + "Ġdob ry", + "S i", + "ĠÐ¥ оÑĤ", + "Ġvolley ball", + "Ġl ament", + "is ches", + "ar me", + "ap i", + "ĠW iki", + "ли ÑĪ", + "Ġkas ih", + "Ġp ess", + "ĠÑĦ оÑĤ", + "ĠS ul", + "å¾ ·", + "Ġpse udo", + "Ġmem o", + "ĠìĹ° ìĬµ", + "ĠдоллаÑĢ ов", + "ĠпеÑĢ ем", + "ĠRe ach", + "mir al", + "alt ed", + "Ġstat ut", + "read ing", + "Ġsöy led", + "ĠLind sey", + "ĠAh mad", + "ë ¶Ģë", + "ĠС егоднÑı", + "Ġprzy got", + "Ġhy ster", + "U RE", + "ĠNe igh", + "Rep orter", + "ĠB unu", + "ĠTreat y", + "ĠR ank", + "ĠF ame", + "in ished", + "Ġge ared", + "Ġcomp ose", + "od ia", + "ĠL on", + "Ġjeste ÅĽmy", + "ĠDIRE CTOR", + "Ġel kaar", + "ĠV iel", + "×IJ× ©", + "ynth ia", + "ä¸ ¦", + "Ġm ère", + "ĠTom ato", + "Ġex atamente", + "ni ÄĻ", + "ĠFre i", + "ĠD if", + "Ġopen ings", + "Ġgraph ical", + "ĠÑĥд об", + "ĠвÑģ п", + "ĠWeek ly", + "ев а", + "Ġhang s", + "Ġuns afe", + "Ġem blem", + "ĠKolleg innen", + "al ay", + "Ġk si", + "Ġh ides", + "Ġol may", + "Ġent ste", + "Ġarth ritis", + "ÃŁ erdem", + "Ġbin nen", + "Ġlist ens", + "ĠH ess", + "åĨį ä¾Ĩ", + "ĠLou ise", + "ld en", + "ен Ñģ", + "ĠVers ion", + "ĠAgric ulture", + "ìĬ¤ë ¥¼", + "м ан", + "ë Ħ¤ìļĶ", + "Ġw ines", + "ĠIN F", + "r ul", + "ĠJ K", + "ıyor lar", + "sh ield", + "reat h", + "Ġter us", + "ĠL um", + "Ġanticip ation", + "Ġacc ustomed", + "ĠM ina", + "Ġw ield", + "io è", + "mer a", + "Ġcount down", + "Ġcl ing", + "Ġcomm end", + "Ġfakt iskt", + "Ġdef enses", + "Ġcock pit", + "Ġком анд", + "Ġdish was", + "ĠThan os", + "Ġkid neys", + "Ġse he", + "Ġmicro bes", + "Ġc uff", + "ĠвÑĭÑģ ок", + "ĠSp icy", + "çŃī çŃī", + "வ à®°", + "cul us", + "or c", + "ç¾ ħ", + "ix es", + "ĠC redit", + "Ġr aj", + "Ġbring t", + "ĠN iss", + "Ġgr im", + "ĠS OL", + "Ġten im", + "ĠSud an", + "ĠSp art", + "Ġpromot es", + "ĠN ossa", + "ĠÑģоÑģÑĤо Ñıни", + "Ġì° ©", + "Ġunc ont", + "ĠLiber al", + "ĠТ олÑĮко", + "ĠV iele", + "Ġktóre j", + "Ġ* ***", + "M ax", + "ĠЧ ÑĤобÑĭ", + "3 50", + "Ġíĺ¼ ìŀIJ", + "Ġë¶Ħë ĵ¤ìĿ´", + "Ġwar p", + "Ġteng a", + "Ġsympath etic", + "Ġbiz i", + "ĠZ ack", + "ied o", + "Ġëī ´ì", + "p iel", + "ĠÑĤ ол", + "Ġsc aled", + "ĠPET ER", + "ĠCO MM", + "ĠC ame", + "Ġcatast rophe", + "Ġsweat y", + "ig ration", + "Ġstuff ing", + "ĠÏĢολ Ïį", + "ĠDri ver", + "zy st", + "T ech", + "Ġassess ed", + "ĠSur face", + "ır ım", + "s ur", + "ler weile", + "Ġд ог", + "Ġshut ting", + "Ġfr actions", + "ĠÑģ ол", + "every one", + "Ġer n", + "ĠÐĿ ов", + "Ġdefend ers", + "Ġvers ucht", + "ãĥ³ãĥ Ģ", + "Ġpol ity", + "ĠÐŁ он", + "ver ständ", + "Ġbrows ers", + "Ġtransform ative", + "Ġdict ate", + "ĠLE GO", + "Ġning una", + "ê´ ij", + "Ġp izz", + "ĠHar old", + "ĠL opez", + "Ú¾ ÛĮ", + "an ız", + "atch et", + "ÙĬ ت", + "Ġl ernen", + "Ġê·Ģ ìŬ", + "Ġhous ed", + "Ġclean se", + "ĠW AT", + "lar ation", + "Ġby tes", + "Ġtuck ed", + "Ġfault s", + "д о", + "F X", + "Ġìĸ¼ë§ ĪëĤĺ", + "Ġde form", + "Ġcontract ing", + "ĠTIM E", + "ir se", + "Ġne ben", + "Ġc erc", + "ĠArm strong", + "Ġtest er", + "Ġparf ait", + "Ġjealous y", + "Ġtox ins", + "Ġdis bel", + "ÑĥÑĢ Ñĭ", + "imp ression", + "Ġprost ate", + "Ġfire wall", + "Ġclass ics", + "еÑĩ ÑĮ", + "Ġsocial ism", + "Ġgrac ious", + "ĠÑģ нова", + "Ġд нÑı", + "Ġburn er", + "ĠMin or", + "Ġìļ°ë ¦¬ë", + "Ġjed es", + "Ġcontinu um", + "Ġh ots", + "Ġoccur rence", + "Ġadminister ed", + "Ġзам еÑĤ", + "Ġhes itation", + "Ġdr ills", + "er ca", + "ĠвÑĤоÑĢ ой", + "Ġstead ily", + "Ġinsan lar", + "Ġi han", + "í ij", + "Ġhel per", + "ĠSen in", + "åģ ľ", + "ов ание", + "ĠER IC", + "b la", + "ĠAcad emic", + "Ġhuman ities", + "bl ack", + "ump y", + "ort ex", + "Ġìł Īë", + "ĠØ¥ ÙĨ", + "Ġdiscl ose", + "ĠEl ijah", + "Ġλ ÎŃ", + "ĠQu er", + "ب ÙĦ", + "ãĤ ¡", + "T ell", + "ar le", + "Ñĸ ÑĢ", + "Ġaug mented", + "Ġë¹Ħ ìĬ·", + "Ġand roid", + "ठ¤", + "ar ma", + "Ġs zer", + "ge ord", + "Ġge ek", + "Ġye ux", + "Ġp ong", + "ĠãģĿ ãģĨ", + "Ġtort ured", + "ĠB ath", + "z ig", + "ason able", + "Ġn ets", + "Ġbar u", + "ĠFl at", + "ĠV ater", + "ĠTer ror", + "ĠA vo", + "Ġceremon ies", + "ro e", + "Ùģ س", + "O ps", + "Ġhy vin", + "Ġap resent", + "ol or", + "ĠигÑĢ Ñĭ", + "ort on", + "Ġê·¸ëŀ ¬", + "Ġlook in", + "ĠT Y", + "ĠM int", + "Ad d", + "Ġm ite", + "ĠSm oke", + "Ġnot a", + "Ġm oss", + "ĠAb end", + "Ġì» ¨", + "Ġexagger ated", + "f ires", + "Ġred ist", + "ff iti", + "Ġopen ness", + "ê°IJ ìĿ´", + "ende u", + "ен ной", + "W atch", + "Ġav atar", + "ĠP ey", + "ur un", + "Ġsen za", + "Ġì§Ģ ìĹŃ", + "ĠNat omiast", + "Ġemer gence", + "ray s", + "Ġcraft ed", + "g ary", + "ãģł ãģij", + "ü ng", + "- \"", + "Ġhack ed", + "Ġstr ay", + "en cie", + "em o", + "Ġcom en", + "ĠK ız", + "ĠJ asmine", + "ĠH indi", + "man as", + "Ġinfin itely", + "em on", + "ìĿ¸ëį° ìļĶ", + "j ak", + "Ġro aring", + "éri que", + "s weise", + "ĠRo lex", + "åł± å°İ", + "ĠStu art", + "bn b", + "Ġdiagn ose", + "Ġcoher ent", + "ĠM J", + "æºĸ åĤĻ", + "Ġp ike", + "l av", + "Ġorchest ral", + "а ÑģÑĤи", + "Ġterm inar", + "Ġgather ings", + "Ġcompl iant", + "Ġupgrad ing", + "Ġregul ator", + "Ġlan ç", + "éĢ £", + "Ġmerch ants", + "ta wa", + "Ġmonit ored", + "Ġrend re", + "ä¸ ¤", + "Ġunter wegs", + "ang uard", + "g ard", + "ĠBel ow", + "du ino", + "ĠЦ е", + "Ġimped ance", + "ìľ ¡", + "ä» ½", + "Ġakt uell", + "ĠV atic", + "åŃ ©", + "Ġste wards", + "Ġbright est", + "Ġk enn", + "Ġk au", + "ĠMat rix", + "ĠB ark", + "ĠðŁ ij", + "Ġt aper", + "Ġcas ino", + "ר ×Ķ", + "ys ical", + "Ġbuild ers", + "ĠczÅĤ owie", + "ĠNep al", + "Ġ! \"", + "Ġterm e", + "Ġin nych", + "Ġmath s", + "Ġdraft ed", + "ĠB alk", + "Ġhesit ant", + "Ġvolt ar", + "Ġrev ive", + "ĠÑĦилÑĮ ма", + "Ġassass in", + "ĠS olutions", + "Ġdu el", + "Ġbear ings", + "à¸Ħ ะ", + "Ġrook ie", + "ik at", + "Ġbisc uits", + "Ġc ords", + "Ñĥв аÑĤи", + "AR IN", + "Ġprogress ing", + "ĠG ir", + "Ġpenet rate", + "ĠSt orage", + "e ight", + "ĠÑĤ ÑĢÑĥ", + "Ġdon ÃŃt", + "Ġsiz in", + "Ġout dated", + "ĠнаÑĪ и", + "Ġaff ir", + "Ġspo ons", + "Ġon i", + "Ġfl ank", + "ĠG ol", + "h ã", + "Ġp éri", + "Ġhonor able", + "ĠBreat he", + "sc enes", + "Ġob viamente", + "ик Ñģ", + "Ġש ×ŀ×", + "Ġsmooth ie", + "ŀ Īë", + "Ġd ime", + "ĠíĸĪ ìĸ´ìļĶ", + "Ġapp el", + "ĠCath olics", + "Ġsing les", + "Ġlat en", + "Ġç ünkü", + "ĠV ader", + "æı Ľ", + "Ġvard ı", + "ĠIst anbul", + "gr é", + "ĠEl sa", + "ë l", + "Ġinve ce", + "Ġcr ane", + "Ġo be", + "ĠSh ark", + "Ġsm ack", + "Ġrest oring", + ". \\", + "Ġë¹ łë", + "Ġf aded", + "um bers", + "S inging", + "Ġdep ressing", + "th est", + "ĠW ahr", + "Ġmult itude", + "ÑĢавÑģÑĤв ÑĥйÑĤе", + "rij k", + "ek a", + "Ġcomplet es", + "ĠWell s", + "Ġro y", + "ĠPr ay", + "ĠKal au", + "iz in", + "iaÅĤ em", + "Ġlo com", + "ĠNash ville", + "ĠPent agon", + "ë ¯¸", + "ĠNE W", + "Äħ Äĩ", + "ÃŃ ss", + "Ġmarry ing", + "Ġfe ud", + "íĻ ķ", + "æĢ ¥", + ") !", + "ĠOper ations", + "Ñĥ ÑĶ", + "Ġmo je", + "Ġinstruct ed", + "ĠëĪĦ 구", + "Ġ×Ķ× Ĵ", + "ĠпомоÑī ÑĮÑİ", + "Ġsab ia", + "ìķĺ ìĸ´ìļĶ", + "pl ane", + "p ri", + "Ġпол ноÑģÑĤÑĮÑİ", + "ĠK itty", + "Ġpróp rio", + "ed ere", + "Ġinteres ante", + "Ġд е", + "Ġcond ensed", + "Ġav ent", + "T OR", + "Ġgre asy", + "AR K", + "ort a", + "A J", + "Ġdis reg", + "Ġcorrect ions", + "Ġst ero", + "Ġinfluen za", + "Ġdess es", + "Ġball ots", + "Ġme get", + "Ġma fia", + "Ġb öl", + "n ost", + "ĠÑģÑĤ аÑĤÑĮ", + "Ġrespond er", + "Ġhint en", + "g rav", + "à¸Ń ะ", + "yn chron", + "Ġvi ens", + "Ġsam o", + "Ġd t", + "pan nt", + "ĠÅĽwi at", + "Ġзап иÑģ", + "Ġmer ged", + "Ġke p", + "Ġmis leading", + "Ġdig amos", + "Ġam mon", + "è¾ Ľ", + "ch et", + "Ġê°Ģ ìł¸", + "Ġun i", + "ĠëIJĺ ëĬĶëį°", + "Ġнап ÑĢав", + "ĠкоÑĤоÑĢ ого", + "Ġanim ate", + "×ķ× IJ×", + "еÑĢ в", + "Ġmin ced", + "Ġka um", + "ãģĤ ãģģ", + "ÏĢ ε", + "л ег", + "exist ing", + "Ġplata form", + "ĠK RIS", + "ìĽ ł", + "ĠFamil ien", + "ĠLib ya", + "Ġbiod iversity", + "Ġidi ots", + "ird i", + "Ġszy b", + "ĠRoll ing", + "ü cht", + "ĠÑĥд ив", + "Ñģ Ñĥд", + "Ġreal izar", + "Ġcan ned", + "ĠÑĢ ан", + "Ġmet abolic", + "ĠBe ef", + "Ġkil ka", + "лÑİ Ñģ", + "Ġreg istry", + "моÑĤÑĢ иÑĤе", + "Ġviel ä", + "Ġod c", + "Ġcondem ned", + "æ© ĭ", + "f al", + "ĠD il", + "wo ÅĽci", + "A w", + "Ġstatist ically", + "Ġso gen", + "ĠB ETH", + "Ġsh aving", + "å¹ ¸", + "oc al", + "ĠFun ny", + "Ġpeace fully", + "Ġaddict ive", + "ĠIns ert", + "la uf", + "Ġexperien cia", + "é¦ĸ åħĪ", + "иÑĤ елÑı", + "ÃŃ gen", + "ág ina", + "Ġabdom en", + "íķľ ëĭ¤", + "ic us", + "im ana", + "ì į¨", + "arch ing", + "Ġkonk ret", + "ìķ ĺë", + "ек а", + "ou fl", + "ive l", + "Ġn ude", + "èt res", + "Ġm onsieur", + "Ġcl ash", + "Ġtherap ists", + "Ġcub ed", + "Ġretrou ver", + "Ġwave form", + "Ġpot em", + "ĠForm er", + "is ión", + "åº ľ", + "Ġ×IJ× Ŀ", + "und os", + "ĠMein ung", + "ص ÙĦ", + "ĠJ ude", + "Ġn Ã¥r", + "ĠLeon ardo", + "ĠCr isto", + "ĠG OT", + "ÑģÑĤÑĢÑĥ к", + "L AN", + "Ġg Ã¥ng", + "Ġdé b", + "ĠFrankf urt", + "Ġcra ppy", + "Ġli l", + "ann ée", + "ĠмеÑģÑĤ е", + "RE T", + "ĠN er", + "ĠCO STA", + "Ġjed em", + "Ġcurt ains", + "Ġiter ations", + "Ġun av", + "Ġpla que", + "or um", + "ĠÎ ¶", + "Ġnúmer os", + "Ġdes ap", + "² ½", + "Ġcomp iled", + "Ġref le", + "Ġrank ings", + "Ġrep aired", + "ĠÐĿап ÑĢ", + "Ġdownload s", + "Ġarm our", + "Ġ×Ļ ×ķתר", + "Ġlonge vity", + "ĠTON ER", + "ĠкомменÑĤ аÑĢ", + "Ġcz ego", + "Ġnot ify", + "Ġairport s", + "Ġend uring", + "let te", + "Ġapp arat", + "Ġhab il", + "á»ĩ c", + "n ad", + "IC O", + "ĠBra h", + "Ġseg ún", + "Ġgovern ors", + "k aha", + "ĠSchl uss", + "Ġodpow ied", + "ir ting", + "Ġrem pl", + "ĠAb original", + "ident ally", + "Ġenhan cing", + "lic ting", + "ĠHawai ian", + "Ġstri ving", + "ĠN iet", + "Ġzn aczy", + "Ġobed ience", + "ĠnÃ¥ got", + "Ġexp ired", + "Ġ19 18", + "pres ented", + "Ġpr owad", + "ĠTer r", + "ĠPrinc eton", + "Ġmor gen", + "Ġattract ing", + "ĠS igma", + "ign er", + "ĠRe chts", + "ĠP eki", + "Ġmet hy", + "Ġha mm", + "Ġdire ito", + "Ġdeleg ation", + "ив аÑİÑĤ", + "Ġg in", + "You ng", + "Ġdepend encies", + "ĠBrad ley", + "bud s", + "Ġf is", + "Ġpyt anie", + "Ġinterconnect ed", + "Ġemba ixo", + "ĠS as", + "Ġr uh", + "ĠS icht", + "S ur", + "Ġsuper b", + "ĠSabb ath", + "ĠD anger", + "k ol", + "Ġh ou", + "s upp", + "ĠN acional", + "Ġsuccess ion", + "Ġv á", + "ĠMaÃŁ nahmen", + "ĠJess ie", + "ĠId aho", + "fore st", + "ħ ĺ", + "Ġ×ŀ× ĵ", + "ĠØ£ ÙĬ", + "Ġsweet heart", + "Ġneat ly", + "ĠEv angel", + "ê³ ¡", + "ĠSu ite", + "úblic a", + "ĠÑĥ ли", + "ĠAnn ouncer", + "l igh", + "Ġsens ations", + "Ġshel ters", + "Ġh art", + "Ġsqueez ing", + "ĠR ivers", + "ĠCook ing", + "ì± ħ", + "person al", + "Ġman os", + "ÑijÑĤ ÑģÑı", + "w ij", + "Ġgo gg", + "ĠMill i", + "ĠF P", + "ün st", + "ĠL S", + "Ġspray ing", + "Ġf aux", + "Ġaut ograph", + "olog ic", + "Ġtor ment", + "Ġencry pted", + "á» ħ", + "Ġest re", + "ç¹ ¼", + "à ±", + "Ġst umbled", + "Ġa ider", + "Ġsab en", + "x ter", + "ĠC ities", + "ĠTür k", + "ëĭ ¥", + "ch ine", + "Ġto pping", + "Ġpoison ed", + "ĠRoman ia", + "×ĵ ×Ļ", + "Ģë ¡ľ", + "ĠпоÑĢ Ñıд", + "Ġchir ping", + "ĠìĻ Ħë", + "×ij× ¢", + "Ġcu anto", + "Ġdon ating", + "ĠReg ent", + "ĠBer uf", + "Ġdistract ing", + "Ġstam ina", + "ĠDar ren", + "Ġì¶ ķ", + "l ists", + "d al", + "ch uss", + "Ġeconom ist", + "ãģĪ ãĥ¼", + "org t", + "Ġist iyorum", + "è¿ Ľ", + "ĠSur prise", + "ĠHa o", + "Ġìµľ ê³ł", + "ĠG W", + "ĠIn ner", + "Ġqu ieren", + "Ġmind ed", + "Ġsupercom puter", + "Ġdiagram s", + "íĬ ľë", + "ê²ł ìĸ´", + "ĠобÑĬ ÑıÑģ", + "Ġestab an", + "Ġdestro ys", + "ĠBre aking", + "Ġkar Ä±ÅŁ", + "Ġrebuild ing", + "ľë ĮĢ", + "ли во", + "ĠSau ce", + "ĠF usion", + "×ķ× ŀ×", + "ĠQu inn", + "Ġga uche", + "ĠÙĪ Ø£", + "Ġ È", + "ç ĵľ", + "Ġtechn o", + "Ġdisp atch", + "ĠaÅŁ k", + "Ġein zel", + "ĠG mail", + "ç ŀ", + "Ġê°ľ ìĿ¸", + "ĠÑģем ÑĮ", + "Ġjour neys", + "Ġi ht", + "Ġfib re", + "Ġdram as", + "ouch ed", + "Ġren ame", + "Ġоп еÑĢ", + "Ġpo o", + "ĠD ru", + "ĠиÑĤ ог", + "Ġz ast", + "Ġco z", + "Ġz ucch", + "Ġobt aining", + "Ġcomm ute", + "Ġsub mer", + "ĠV ish", + "ĠR abb", + "og g", + "Ġh ut", + "íĸĪ ìĸ´", + "æ¯Ķ å¦Ĥ", + "ere mi", + "Ġμ α", + "Ġdisk ut", + "Ġб Ñĥк", + "Ġimp aired", + "d epend", + "ĠÙĪ ا", + "ĠÑĢ Ñĥк", + "Ġб аÑĢ", + "Ġoxid ation", + "Ġsitu ação", + "ÉĻ n", + "u ção", + "Ġsag te", + "ĠS ER", + "ĠC ake", + "Ġtur meric", + "ĠK ak", + "b ung", + "ĠK á¹Ľá¹£á¹ĩa", + "Ġpoison ing", + "Ġsl ipping", + "ĠS ays", + "å°± åı¯ä»¥", + "ò ng", + "çŁ ³", + " «", + "ĠClaud ia", + "ĠChar acter", + "ни ÑĨ", + "co at", + "Ġprogress ed", + "ĠFer gus", + "Ġìĺ¤ ëĬ", + "Ġo at", + "ord able", + "ĠLe y", + "ĠHera us", + "Ġresult ados", + "ĠKay la", + "Ġr iff", + "Ġcheg ou", + "Ġx i", + "Ġsp acious", + "Ġrecogn ised", + "Ġe ch", + "ĠT ie", + "Ġlaunch er", + "J im", + "Ġsupp ression", + "ĠImp ossible", + "Ġguit ars", + "ĠFour ier", + "иÑĩеÑģ кий", + "ĠTh erap", + "ĠK af", + "cent ered", + "ĠÑģо оÑĤвеÑĤ", + "Ġk lim", + "Ġcarbohyd rates", + "ign ant", + "ĠAst ron", + "Ġem ple", + "Ġdr astic", + "ĠмиÑĢ е", + "в ин", + "u w", + "Ġpret tier", + "Ġdon uts", + "ĠAth ena", + "Ġdiss ert", + "Ġpl ante", + "Ġur anium", + "ìĿ Įë", + "ar é", + "Ġrze cz", + "Ġdisplay ing", + "æĪ ²", + "Ġsar c", + "r ão", + "Ġtamp oco", + "Ġphilosoph ers", + "ĠRe cht", + "æĵ ļ", + "Ġcoment arios", + "y se", + "Ġìľ ¤", + "Ġm ise", + "ĠG in", + "Ġн ом", + "ĠFR OM", + "l iner", + "at if", + "Ġspo ÅĤec", + "x a", + "ĠÑĤ ÑĢÑĥд", + "Ġw ag", + "기 ìĹIJ", + "ĠM G", + "Ġoff spring", + "ĠUnder standing", + "åıª æĺ¯", + "OR A", + "Ġwh irring", + "Ġsur rend", + "Ġpok er", + "Ġmon uments", + "ĠâĻ ©", + "Ġorgan ised", + "ĠSo zial", + "ĠF actory", + "Ñħ а", + "Ġrese mble", + "з д", + "Ġexplos ions", + "Ġpay roll", + "Ġom n", + "ĠJ orge", + "ι Ïĥ", + "Ġfract ure", + "Ġpersec ution", + "Ġdem ais", + "E CH", + ", )", + "Ġcri ar", + "ĠJ OSH", + "Ġdem ographics", + "Ġ16 00", + "Ġcur rencies", + "ĠT ips", + "Ġ éĢĻåĢĭ", + "ĠRe fer", + "ĠDan cing", + "Ġincons istent", + "Ġde h", + "Ġimm ens", + "Ġme ist", + "Ġimpat ient", + "Ġbehav es", + "æĿ ¾", + "ĠëĤ´ì ļ©", + "Ġback story", + "Ġagree ing", + "ĠÅ ģ", + "ih in", + "Ġtemper atura", + "ĠBack ground", + "Ġnut zen", + "Ġëħ ¹", + "ĠM änner", + "Ġcollabor ations", + "ĠK os", + "éģİ åİ»", + "Ġnight mares", + "ë ĵ±", + "ĠQueens land", + "Ġassoci ates", + "ĠK ok", + "Ġfact orial", + "ĠHy ung", + "Ġê·¸ ëĭ¤ìĿĮ", + "Ġfil ho", + "Ġel ét", + "Ġíĸī ë³µ", + "° ±", + "Ġgef unden", + "Ġsemic ondu", + "Ġcounsel ors", + "ĠU pper", + "ĠA ub", + "ick ers", + "V er", + "Ġnorth west", + "ĠMainten ant", + "ĠL akes", + "аÑı в", + "int é", + "ì° ½", + "Ġг аз", + "Ġgi orn", + "Ġdigit ally", + "ĠCirc uit", + "ì¼ Ģ", + "ãĤĬ ãģ¾ãģĹãģŁ", + "Ġcheer ful", + "ĠPet erson", + "ĠDan ish", + "ativ os", + "Ġli ken", + "Ġhar bor", + "али ÑģÑĤ", + "x e", + "Ġcur ls", + "ĠR hod", + "E nd", + "ĠE T", + "Ġacqu aint", + "ĠKel vin", + "Ġtr if", + "ĠA way", + "ìŀIJ ëĬĶ", + "v s", + "Ġp ágina", + "Ġin let", + "ĠSant os", + "Ġìļ° ìĻĢ", + "Ġyap ıyorsun", + "th eme", + "Ġsou ff", + "Ġinject ed", + "Ġpó źniej", + "iver so", + "amp ed", + "Ġda her", + "Ġd agger", + "ĠлÑİб им", + "Ġt ummy", + "Ġenlight ened", + "c ents", + "ĠD ah", + "Ġcu est", + "ä¾Ĩ 說", + "IL Y", + "Ġ×ij ר", + "Ġbang ing", + "ĠEm il", + "ĠC ler", + "ĠB order", + "иж Ñĥ", + "Ġpresent ers", + "ĠST UD", + "co ins", + "ĠíĻ į", + "Ġper ks", + "Ġpar ap", + "Ġcertain es", + "ĠL ore", + "ö st", + "ĠMAR TIN", + "Ġb ios", + "Ġwhere by", + "ver ts", + "ĠMir anda", + "Ġst ip", + "æ¾ ¤", + "and ez", + "׼ ׾", + "uj in", + "Ġê ¾", + "Ġaller gies", + "pl ate", + "Ġyap ıl", + "Ġundert ake", + "ĠëĤĺ ê°Ģ", + "P art", + "Ġkız ım", + "h guru", + "ãģĤ ãģ¨", + "ĠJohn s", + "Ġeyel ashes", + "Ġdra ined", + "Ġst Ã¥r", + "ãģĤãĤĬ ãģ¾ãģĻ", + "ĠJ ade", + "Ġcal end", + "fil m", + "Ġmes a", + "Ġlud zie", + "Ġattract s", + "Ġju ices", + "Ġк ил", + "Ġnieu we", + "Ġmen cion", + "Ġign ition", + "Ġbl adder", + "anda ag", + "ĠExt ension", + "íĤ ¨", + "fe ed", + "ĠÙĪ Ùĩ", + "Ġsp un", + "Ġt ät", + "оÑĢ оÑĤ", + "ty ard", + "ron ics", + "ĠH uge", + "Ñĥж д", + "st ring", + "Ġun just", + "Ġpra wn", + "Ġfrost ing", + "Ġdisappear ance", + "ios a", + "Ġcard i", + "ĠPri est", + "Ġcient ÃŃfic", + "åĵª 裡", + "ĠÐĴ аÑģ", + "Ġë¶Ģ íĥģ", + "Ġth ieves", + "Ġphys ique", + "ĠE ugene", + "Ġбли з", + "Ġmon opoly", + "Ġbi ography", + "Ġho ÅŁ", + "Ġt ö", + "m ac", + "Ġshock s", + "ìĦ ¸ë", + "h it", + "Ġsn ug", + "Ġinc l", + "Ġded ic", + "Ġult ras", + "Ġизв еÑģÑĤ", + "Ġutil ization", + "ĠÑģовеÑĢÑĪ енно", + "Ġserv i", + "st ag", + "1 80", + "Ġse wer", + "ĠCh oice", + "Ġdis charged", + "ĠJ D", + "ол еÑĤ", + "ĠкваÑĢ ÑĤи", + "Ġteles cop", + "ĠJe ÅĽli", + "ĠN ana", + "c ale", + "ĠÑĤ он", + "mm m", + "äºĨ åIJ§", + "Ġge habt", + "ëĤ ł", + "æĬ ķ", + "à¸Ļ à¸Ļ", + "Ġet her", + "Ġz en", + "Ġresearch ed", + "ĠCzy li", + "å®Į åħ¨", + "work ers", + "Ġê²½ ì°°", + "Ġsher iff", + "all o", + "Ġtip os", + "Ġprosec ution", + "Ġfrog s", + "Ġf alt", + "j d", + "ĠíĮ Ķ", + "Ġfilter ed", + "ĠO ft", + "Ġì į", + "Ġdis fr", + "ĠMust ang", + "Ġwo ah", + "ĠRE ALLY", + "Ġмог ли", + "Ġentr ada", + "Ġиг ÑĢа", + "Ġmix es", + "ĠавÑĤом об", + "Ð Ļ", + "Ġsh in", + "Ġparan ormal", + "Ġsome place", + "Ġdish on", + "eta an", + "Ġfu erte", + "Ù ¹", + "Ġdo om", + "ìĪ ľ", + "Ġexist ential", + "Ġbu ld", + "ĠSD K", + "ĠпÑĢав да", + "Ġturn over", + "ĠìĹ¬ê¸° ìĹIJ", + "Ġठ¹", + "Ġmodel ed", + "Ġbug ün", + "Ġexperiment ation", + "Ġmorning s", + "Ġmed o", + "Ste vie", + "Ġplay able", + "Ġairl ines", + "g ments", + "Ġê¸°ë ¶Ħ", + "ĠT omb", + "ĠMV P", + "AUDI ENCE", + "Ġcheck out", + "Ġpas st", + "Ġbe ispiel", + "ĠLink s", + "he avy", + "Ġquestion able", + "Ġìĵ °ë", + "Ġs ill", + "Ġmanip ulated", + "ĠL oren", + "Ġìľ ¼", + "Ġver ge", + "á k", + "I ES", + "Ġsab ot", + "ĠCustom er", + "ale ży", + "Ġnom inee", + "ĠG ad", + "Ġnouve lles", + "ĠS PE", + "ist ling", + "Ġo val", + "обÑĢ аж", + "if ty", + "éĩ İ", + "Ġbez el", + "y et", + "Ġfre ight", + "ĠHan ım", + "r ÃŃa", + "Ġz oning", + "Ġind em", + "ĠB ü", + "Ġfemin ism", + "Ġvo ix", + "Ġof icial", + "Ġdi yorum", + "» IJ", + "Ġar ose", + "Ġpar ar", + "ìĿ¸ ì§Ģ", + "ĠMart ine", + "ĠL ect", + "Ġrest er", + "Ġdrown ing", + "u ya", + "c ida", + "ĠAri el", + "Ġ0 2", + "Ġ×Ķ ×Ķ", + "ç´ ł", + "ĠW ert", + "Т Ñĭ", + "Ġwid ow", + "Ġparch ment", + "Ġcott age", + "ĠX L", + "ĠSl ack", + "ĠN ES", + "Ġro be", + "Ġg imm", + "Ġcam inho", + "ĠHar per", + "Ġcit rus", + "Ġfirefight ers", + "Ġdop amine", + "el ets", + "Ġdemocr at", + "ìł ľë¡ľ", + "Ġplay back", + "o j", + "ĠпÑĢ ок", + "ĠSull ivan", + "se mble", + "ĠW orth", + "ĠMust afa", + "า ร", + "Ġmet s", + "éĸ Ģ", + "л оÑģÑĮ", + "Ġinert ia", + "Ġuniform s", + "è¶ ³", + "é rio", + "×ķר ×Ķ", + "é nt", + "Ġà® Ĵ", + "ĠÑģам ÑĭÑħ", + "Ġvou lais", + "ĠZ immer", + "ê² łë", + "Ġн оÑģ", + "en cias", + "Ġrel ación", + "Ġê± ¸ë", + "Ġfact ion", + "Ġg osp", + "пол ож", + "n ap", + "h ak", + "Ġproceed ings", + "ĠìĨ Ķ", + "ìķĦ ëĭĪ", + "ĠìŀIJ 기", + "Ġwer d", + "Ġso f", + "Ġsch lim", + "Ġfl avored", + "Ġquad ratic", + "ĠBo ot", + "Ġpublic ity", + "ĠCar o", + "Ġ ?\"", + "ни ÑĨа", + "man ia", + "ĠS UR", + "ĠB UR", + "l ance", + "ét ica", + "Ġzob aczy", + "Ġtri o", + "s ama", + "Ġta ÅŁ", + "Ġas ymm", + "ress er", + "Ġت ع", + "Ġп еÑģ", + "Ġbeginning s", + "lad ım", + "ĠбÑĭ ÑģÑĤÑĢ", + "Ġmo o", + "ĠGene va", + "Ġ åľ¨", + "er us", + "bor ah", + "Ġref using", + "b ull", + "ĠWait ing", + "ĠInd ividual", + "Ġan onym", + "im ens", + "Ġmed idas", + "Ġfragr ant", + "Ġdirect ement", + "ĠìķĦ ë§Ī", + "ur ia", + "Ġsp herical", + "Ġab ge", + "ĠVictor ian", + "Ġspect acle", + "ĠRodrig uez", + "Ġoc up", + "ĠN är", + "mark s", + "ng ulo", + "ĠLu ci", + "Ġshout ed", + "Ġregul ators", + "ÄŁ ini", + "Ġdis ent", + "ĠÑĢÑĭ н", + "ëĤ ¨", + "ĠìĤ ´ë", + "Ġprobl èmes", + "ĠF inger", + "asse mble", + "Ġpe ar", + "Ġdro ite", + "ĠEvery where", + "t am", + "оÑĤ ив", + "в ой", + "ordin ate", + "ĠL ak", + "Ġm Ỽi", + "ĠTele vision", + "Ġexpon entially", + "av as", + "Ġble v", + "ĠM T", + "ä¿ º", + "Con nell", + "ĠêµŃ 민", + "ĠÑģво им", + "Ġach a", + "ĠD ynasty", + "J in", + "Ġto re", + "Ġfl or", + "Ġмног ие", + "æ²Ĵ äºĭ", + "ow an", + "b ah", + "Ġì£ Ħ", + "ĠC ela", + "Ġìµľ ê·¼", + "Ġpermett re", + "Ġab ras", + "Ġverste hen", + "Ġesc ort", + "ĠThe m", + "är ke", + "por ter", + "Ġkah kaha", + "Ġhe ct", + "Ġda u", + "w ah", + "ol ve", + "ĠAg es", + "s chaft", + "ĠSt ell", + "ne lle", + "ĠEn suite", + "ĠÐĴÑģ ем", + "Ġcr éd", + "ĠP P", + "l ords", + "gr unting", + "Ġcontract ion", + "G ot", + "Ġacqu iring", + "Ġso pr", + "Ġpoison ous", + "R NA", + "Ġan ar", + "ĠH of", + "' )", + "Ġremark ably", + "Ġintern acional", + "ü cke", + "in qu", + "Ġdu y", + "Ġbeast s", + "ĠL AN", + "Ġpreced ent", + "ĠRP M", + "åij ¨", + "Ġsel on", + "Ġmort e", + "Ġcomeç ou", + "Ñı ла", + "Ġinterpre ting", + "ĠBur ke", + "ÑĤ ÑĢа", + "ĠìĿ´ë Ł¬", + "Ġpess im", + "ĠN ok", + "íĮ Ŀ", + "F emale", + "Ġìĭ ¤í", + "Ļ Ģ", + "Ġstim ulation", + "Ġsl ick", + "Ġê°Ģ ëĬĶ", + "Ġк аз", + "ĠH BO", + "Ġpap ier", + "Ġkön nten", + "Ñĥб ли", + "ĠConst ant", + "SPEAK ING", + "Ġktó rÄħ", + "Ġcos metics", + "ĠT rend", + "Ġrob bery", + "Ġt itt", + "Ġgj ort", + "Ġdiet ary", + "ł Į", + "ĠKir by", + "ĠпÑĢимеÑĢ но", + "Ġqual ification", + "Ġìķ ī", + "Ġcabin ets", + "Ġhtt p", + "ĠEric a", + "ç¾ ©", + "Ġdisadvant ages", + "Ġch attering", + "y z", + "fe it", + "Ġgu ild", + "ĠE TF", + "ĠDrag ons", + "ĠH ERE", + "vent h", + "ÙĦ اÙħ", + "Ġmarch é", + "D am", + "Ġphot on", + "Ġest able", + "M ag", + "Ġol har", + "Ġcou pling", + "ĠHil fe", + "ĠW izard", + "Ġм ало", + "hel p", + "ĠlÃŃ nea", + "Ġì «", + "Ġstand alone", + "Ġmor ale", + "Ġzwe ite", + "ãĤĪãĤį ãģĹãģı", + "ähr t", + "Ġd otted", + "Ġdri pping", + "ĠFl ag", + "éĿ Ĵ", + "ro cket", + "rate gy", + "ir im", + "Ġíķĺë ©´ìĦľ", + "Ġsogen an", + "ĠUn o", + "ĠSch utz", + "Ġest ilo", + "ĠS ubs", + "ĠDais y", + "ÐĿ еÑĤ", + "' ...", + "Ġplat inum", + "Ġb irl", + "ĠSo vi", + "Ġviol ate", + "Ñĥ еÑĤÑģÑı", + "r ill", + "Ġtra z", + "Ġsn ip", + "Ġcum pl", + "à¸Ń à¸ģ", + "Ġc uk", + "éħ Ĵ", + "ĠParl ament", + "Ġhyper t", + "Ġpul p", + "Ġtong ues", + "at to", + "Ġbus ca", + "ih n", + "ER O", + "ĠÙĬ ع", + "Ġvari as", + "ĠMar ian", + "Ġbound ed", + "Ġpitch ing", + "Ġdefic iency", + "ĠBless ed", + "ĠEx erc", + "uch s", + "ĠnhÆ° ng", + "æľ¬ å½ĵ", + "Ġrap ed", + "h ales", + "Ġmal a", + "p ic", + "Ġ40 1", + "ÅĽ niej", + "ar ina", + "ëĵ¤ ìĿĦ", + "ott i", + "Ġдол го", + "Ġtrack er", + "ĠShel by", + "Ġvan ished", + "Ġbak ery", + "Kap ı", + "J esus", + "ĠK R", + "J O", + "ħ ¸", + "Ġdisc s", + "ìĦ ¯", + "ì§Ģ ë", + "×Ļ× ¦", + "em ary", + "K endra", + "Ġy ük", + "ück t", + "Ġv az", + "Ġk up", + "akt u", + "ĠÑģп аÑģибо", + "Ġa ik", + "Ġnurs ery", + "Ġendanger ed", + "êm ement", + "emat ics", + "Ġrespond ers", + "ĠRepresent atives", + "Ġsculpt ures", + "ig keiten", + "Ġde pl", + "Ġinterpret ations", + "Ġdead lines", + "Ġ194 2", + "à Ĺ", + "Ġsug ars", + "em u", + "l ively", + "Ġrecre ational", + "Ġdist ort", + "Ġunders core", + "Ġun quote", + "Ġsaf est", + "Ġsw ollen", + "Ġanalys es", + "Ġcommen cé", + "å¦ ¹", + "and in", + "ĠÐ¥ оÑĢоÑĪо", + "Ġdi arr", + "ãģ¾ ãģģ", + "zi est", + "Ġtooth brush", + "éł» éģĵ", + "u ations", + "Ġc ade", + "Ġbackl ash", + "h ind", + "Ġris que", + "z ess", + "ĠìĿ´ìķ¼ 기", + "Ġesper ar", + "Ġtransl ations", + "ion ed", + "gro ans", + "Ġп ÑĥÑĤ", + "Ġgen etically", + "éĢ ł", + "Ġhapp iest", + "Ġwer k", + "ato on", + "Ġmus i", + "Ġfun ção", + "Ġìŀħ ëĭĪëĭ¤", + "ĠÑĢ ай", + "Ġbe vor", + "BL ANK", + "Ġrepent ance", + "P ut", + "Ġpotrze b", + "Ġsal a", + "Ġcamp a", + "W ER", + "Ġdec ÃŃa", + "Ġsécur ité", + "ĠAppreci ate", + "Ñĩ и", + "ĠR andom", + "ë³ Ħ", + "k ah", + "Ġmö j", + "Ġsä ger", + "Ġ×Ļ ׼×ķ׾", + "Ġ19 0", + "xt ures", + "E u", + "Ġg ä", + "Ġ×ij× ª", + "ĠC roat", + "ap o", + "P LE", + "Ġpersist ence", + "åĬ ©", + "Ġbl ends", + "Ġtre ffen", + "ĠSanti ago", + "yd ia", + "al do", + "ĠTensor Flow", + "ĠD ual", + "ãĥ ľ", + "Ġch iff", + "ìĹ ´", + "Ġcontract ed", + "Ġseg reg", + "ĠFair y", + "Ġwis ely", + "Ġvulner abilities", + "Ġhand held", + "Ġgad gets", + "Ġbo ÅŁ", + "ĠPop ular", + "Ġcurv ature", + "ë ¬¸", + "ĠMAR Y", + "ìĿ´ì Ĭ", + "Ġform ulation", + "Ġcel ery", + "Ġblur ry", + "ĠT S", + "ale z", + "Ġw s", + "Ġprogram m", + "ĠSt ack", + "ĠJ IM", + "ов али", + "ı ll", + "Ġp ère", + "ĠKan ye", + "ĠDel aware", + "Ġãģ ł", + "Ġda unting", + "Ġб еÑģ", + "ĠSt upid", + "b ig", + "ffic ial", + "Ġprecip itation", + "Ġpl ung", + "ụ c", + "bur se", + "Ġdar le", + "Ġcri pp", + "Ġpione er", + "Ġdis put", + "Ġse an", + "ãģĵ ãĤĵãģª", + "Ġresist or", + "Ġalle in", + "ipp les", + "are l", + "Ġend ors", + "z ust", + "ĠÑĢеб ÑıÑĤа", + "ed ed", + "Ġì¹´ë ©Ķë", + "Ġlle va", + "Ġken nt", + "Ġб ал", + "ĠDoc ument", + "ĠKn ights", + "Ġbuck le", + "Ġìī ¬", + "Ġal k", + "ĠEvery day", + "atter s", + "Ġtoil ets", + "Ġj ugar", + "ĠìŀĪ ì§Ģ", + "Ġgen auso", + "ĠLandes regierung", + "ãģ£ãģ ±", + "ij e", + "Ġtrail ers", + "ĠT igers", + "Ġg itti", + "Ġforg iving", + "Ġconcur rent", + "ĠV u", + "ĠíĬ¹ íŀĪ", + "ĠBR OWN", + "ound ed", + "\" ;", + "Ġtre mb", + "Ġt iet", + "ĠÑĢеж им", + "Ġnuts hell", + "ел иÑĩ", + "Ġlos ers", + "ric ting", + "Ġrede em", + "def ined", + "N ice", + "Ġbroad band", + "K O", + "Ġte asing", + "Ġpart isan", + "ı ma", + "Ġìŀ¬ë ¯¸", + "ĠJour ney", + "Ġslop es", + "un ing", + "gr unts", + "Ġt äll", + "Ġuncover ed", + "Ġmy ÅĽlÄĻ", + "ĠEst her", + "äº İ", + "ĠHealth y", + "Ġë° ij", + "r ée", + "Ġpolar ization", + "Ġfl av", + "Ġcambi ar", + "Ġy r", + "ĠR anch", + "Ġspl its", + "Ġtrou vé", + "åľĭ 家", + "Ġrecord er", + "Ġdé part", + "ÙĪ ب", + "ĠK ry", + "Ġinteress ant", + "Ġeder im", + "ÅĽ wiad", + "il ateral", + "w right", + "Ġpour ra", + "ê ter", + "Ġcam el", + "á ŀ", + "Ġrapid ement", + "Ġme j", + "Ġstiff ness", + "AD AS", + "Ġdiff ers", + "Ġal ot", + "ĠS ig", + "ÑıÑĤ елÑĮ", + "Ġabstract ion", + "åľ ĺ", + "Ġke iner", + "gr upp", + "ĠSher lock", + "íĺ Ķ", + "Ġc ite", + "Ġover flow", + "Ġt ại", + "ú car", + "b ula", + "Ġconjun to", + "ĠC I", + "Ġmoder ator", + "Ġindirect ly", + "Ġalle ine", + "â Ĥ", + "ÑĪ иб", + "Ġб аб", + "Ġdan ach", + "Ġ19 39", + "Ġpr omet", + "Ġdest inations", + "ĠIll ust", + "ικ ÏĮ", + "Ġsab es", + "Ġhe h", + "ĠGesetz ent", + "ĠM iz", + "ен ко", + "ĠM ys", + "Ð ¬", + "ĠJuda ism", + "Ġmust ache", + "Ġst immt", + "ĠG aza", + "Ġvol te", + "Ġnu o", + "Ġm ón", + "ĠCom put", + "ู à¹Ī", + "ĠR adi", + "Ġexception ally", + "Ġassum es", + "éĸĭ å¿ĥ", + "ãģĪ ãģ°", + "in form", + "Ġshr ine", + "æĵ Ĭ", + "Ġimplic ation", + "ĠF itz", + "æ²Ĵ éĹľä¿Ĥ", + "! .", + "Ġl t", + "Ġall oy", + "Ġeth ic", + "Ġmonaster y", + "ìĭľ ì£ł", + "ica ção", + "Ġcoordin ating", + "ĠM oto", + "Ġover look", + "Ġcho is", + "Ġantibiot ic", + "ĠMin ne", + "ĠB J", + "ĠA pa", + "or ian", + "Ġsp illed", + "J am", + "Ġhus bands", + "Ġcre ations", + "Ġa ñ", + "üs sel", + "ĠìĿ´ì ļ©", + "Ġanaly se", + "r ose", + "Ġpunch ed", + "Ġpres que", + "Ġastron omy", + "Ġschwier ig", + "ĠEb ola", + "Ġc is", + "Ġac et", + "ĠF X", + "end re", + "ĠìĿĮ ìķħ", + "Ġweb page", + "Ġfre aked", + "Ġlat te", + "Ġì¿ ł", + "Ġë¨ ¸ë", + "N ever", + "G ra", + "íĻĶë ¥¼", + "ey ed", + "Ġë°ľë Ŀ¼", + "Ġesper a", + "Ġapare ce", + "ra ção", + "Ġdisrupt ive", + "ĠJo int", + "ur ous", + "re as", + "Ġquer ÃŃa", + "Ġdistrib utions", + "Ġexpon ent", + "ì¹ ĺ를", + "Ġd l", + "z hou", + "ĠHe aring", + "å·® ä¸įå¤ļ", + "ĠC raw", + "Ġflo ats", + "oun ced", + "L ab", + "W orld", + "Ġbur dens", + "Ġauthor itarian", + "ĠB olt", + "Ġод нÑĥ", + "Ġpige on", + "Ġdistract ions", + "ĠHeraus forder", + "Ġz est", + "es c", + "Ġsh akes", + "at as", + "ĠÙħ Ø´", + "hol es", + "Ġthink ers", + "al ta", + "Ġar che", + "ĠS uk", + "an ha", + "Ġtempt ing", + "Ġyou tuber", + "Ġv ì", + "Ġdz iaÅĤa", + "ĠVatic an", + "P ark", + "Ġsup ers", + "ĠNik ki", + "ëĬ IJë", + "or ang", + "ram ient", + "é ¬¼", + "Ġê°ĸ ê³ł", + "Ġdessert s", + "Ġav ere", + "ĠGreg ory", + "Ġëĵ¤ìĸ´ì ĺ", + "Ġcost ing", + "ĠClin ic", + "Ġreb els", + "ĠM ob", + "Ġbun lar", + "ĠYour s", + "ert ime", + "Ġret ali", + "m ara", + "at us", + "all es", + "Ġд ÑĢ", + "Ġд иÑģ", + "Ġdiscount s", + "ĠGU Y", + "Ġкак ое", + "ĠExper iment", + "re ment", + "ĠXi ang", + "Ġb ate", + "W E", + "Ġspecial ize", + "Ġde ity", + "ĠL oki", + "m ag", + "ĠN it", + "W est", + "Ġmater nal", + "Ġqu is", + "åŁº æľ¬", + "bro ken", + "Ġlas ers", + "Ġha kk", + "ĠAng els", + "Ġmaster y", + "ant is", + "T iffany", + "ee e", + "ç ij", + "ore m", + "Ġin acc", + "Ġjurisd ictions", + "ĠKard ash", + "æľ º", + "I l", + "ĠS inn", + "åĭķ çĶ»", + "Ġathlet ics", + "c ÄĻ", + "Ġlo osely", + "Ġdiet a", + "A g", + "Ġ? ?", + "ĠëĮĢ íijľ", + "Ġsuper v", + "Ġnut rit", + "Ġdr ifting", + "ĠìĦłìĥĿ ëĭĺ", + "Ġпон Ñıл", + "ĠVict ory", + "ÙĦ Ø©", + "×ķ׳ ×Ķ", + "Ġп иÑĪ", + "Ġsh aved", + "Ġmes ure", + "ond en", + "Ùĥ ر", + "Ġex ile", + "ĠDes de", + "ĠP interest", + "Ġattach ments", + "Ġh ombres", + "Ġfin es", + "ĠìĦ¸ ìĥģ", + "Ġsleep s", + "ĠT aco", + "ĠI RA", + "ri os", + "Ġo ll", + "et es", + "Ġun ut", + "fashion ed", + "Ġtre ball", + "ĠNear ly", + "ĠÑĢе алÑĮно", + "Ġch il", + "éĢ ±", + "ÄŁ a", + "ĠM EL", + "ros cop", + "ĠC G", + "Ġv enge", + "Ġdishwas her", + "al gic", + "Ġmod ifier", + "Ġemb assy", + "t imer", + "em ics", + "Ġintric ate", + "Ġev et", + "ĠëĮĢë °ķ", + "Ġis ot", + "Ġна ÑĥÑĩ", + "ĠQu iz", + "res o", + "δ Ïİ", + "Ġye lled", + "Ġfed er", + "ELL ER", + "Ġexceed ed", + "on as", + "ic ano", + "Ġжив оÑĤ", + "ĠMa o", + "ĠKaz uto", + "Ġ ãħĭãħĭãħĭãħĭ", + "Ġfront line", + "ĠHung arian", + "Ġüber all", + "aw at", + "Ġgri ps", + "i ções", + "arn ya", + "ĠÍ ¡", + "Ġse id", + "Ġan ak", + "Ġacab ou", + "íķ ij", + "Ġnot orious", + "ĠGod zilla", + "Ġover coming", + "ĠP end", + "Ġol abilir", + "ül me", + "Ġer halten", + "ãĤī ãģĦ", + "ê· ¹", + "ĠM eter", + "Ġsta an", + "O l", + "Ġch ats", + "ĠBu enos", + "ÃŃ ve", + "alu able", + "Ġstrateg ically", + "Ġcompr ised", + "ĠпеÑĢÑģон аж", + "Ġw ann", + "ĠC en", + "н иÑĤе", + "Ł ģ", + "ĠÑĤоб ой", + "i ad", + "ĠkardeÅŁ im", + "ĠCongress man", + "ream ing", + "h omme", + "Ġcommun aut", + "Ġalcohol ic", + "Ġpick led", + "Ġac ord", + "p osition", + "eg ól", + "Ġtrou bling", + "ĠMarch eg", + "Ġzum indest", + "Ġseam lessly", + "Ġol un", + "ĠTV s", + "ĠпÑĢакÑĤи ÑĩеÑģки", + "Ġback end", + "ãģĵãĤĵ ãģ«ãģ¡ãģ¯", + "id able", + "Ġgad get", + "Ġfa ço", + "ĠMarcheg iani", + "Ġë° ¤", + "Ġaccident al", + "ĠL P", + "Ġeld est", + "ĠAd miral", + "Ġn Äĥm", + "le ver", + "Ġpast el", + "Ġfond o", + "Con nie", + "Ġter cer", + "Ġp act", + "ĠMont e", + "Ġme ats", + "ĠS MS", + "ĠAustral ians", + "ç ¼", + "Rh ett", + "Ġexact ement", + "Ġë¹ ¼", + "ĠM OD", + "ç ¡", + "ĠR apt", + "ĠNo ch", + "Ġab ort", + "ĠNav al", + "ĠFu ji", + "IN TER", + "Ġнов Ñĭй", + "Ġmiej sce", + "ĠIC U", + "ĠGrad uate", + "ĠGl en", + "ard i", + "ĠÈ ĺ", + "Ġsold er", + "Ġprofess ions", + "Ġorth og", + "om n", + "int rodu", + "ĠDen ise", + "ìŀIJë ¥¼", + "Ġcorrespond ence", + "AM A", + "Ġinf lict", + "Ġf and", + "ĠG ü", + "ĠÑĩ еÑĤ", + "Ġtr aced", + "Ġpat ents", + "Ġamb ush", + "Ġlot ta", + "ff er", + "ĠW agner", + "Ġimp erson", + "Ġextr êmement", + "ÙĤ ت", + "cond uct", + "A tt", + "ĠM ueller", + "ĠAl icia", + "Ġcy c", + "Ġha cker", + "Ġt ys", + "Ġha il", + "Ġз аÑıв", + "Ġpas so", + "Ġì¶ Ķê°Ģ", + "ĠÎ Ī", + "Ġpack aged", + "ĠC ynthia", + "he et", + "ä¸Ń åĽ½", + "ĠNiss an", + "ĠQuest o", + "é ¨", + "d id", + "Ġμ ια", + "ĠEll is", + "ĠAnal ysis", + "ce mos", + "Ġas eg", + "ĠMy ster", + "ĠCa o", + "Ġtu v", + "ĠIndust ry", + "주 ê³ł", + "ot al", + "Ġpeque ño", + "br as", + "Ġcompreh end", + "ĠSim pson", + "ÑģÑĤв ие", + "ocr acy", + "иÑĩеÑģ ки", + "ĠM ush", + "ĠLaur ie", + "Ġtriang ular", + "ĠPres ents", + "ĠK unden", + "ç´ ¹", + "æŃ ¦", + "ĠIs s", + "ĠDe ck", + "á»ĥ n", + "ĠDark ness", + "Ġinflamm atory", + "eremi ah", + "Ġwar med", + "vey ard", + "ĠMem ory", + "et ty", + "Ġtax payers", + "ภĵ", + "Ø ¡", + "Ġpract ise", + "ëĭ ¬ë", + "Ġdr illed", + "m Ã¼ÅŁ", + "log o", + "ĠF ach", + "¤ë ¡ľ", + "Ġübrig ens", + "Ġkon nten", + "Ġnormal mente", + "Ġarg ues", + "iling ual", + "°ë ¥¼", + "eg al", + "Ġtrava ill", + "ov y", + "а ÑĤо", + "Ġr uth", + "ĠL ights", + "Ġconsist ed", + "×ijר ×Ļ×Ŀ", + "Ġstere otype", + "Ġpay er", + "ĠRe e", + "ĠAir bnb", + "Ġdr owned", + "ĠZ oe", + "Ġcan opy", + "Ġbar r", + "Ġн оÑĩ", + "Ġpag an", + "Ġj ars", + "Ġr ê", + "er ver", + "æĪ ¿", + "ie ben", + "Ġes pect", + "ĠF i", + "Ġunw illing", + "Ġtechn ician", + "ặ t", + "m ember", + "ĠCan al", + "س Ùħ", + "Ġlie ber", + "Ġin ference", + "Ġhon oring", + "åij µ", + "ĠCamp aign", + "Ġline age", + "ĠSt ress", + "Ġvict ories", + "Ġde ja", + "× £", + "ê tes", + "bl ick", + "Ġмен ее", + "oth s", + "ĠCou ple", + "J ason", + "ĠNic olas", + "ек Ñģ", + "l ib", + "Ġher ramient", + "Ġ×IJ ×ķ×ŀר", + "Ġвид им", + "mill imeter", + "Ġsil houette", + "Ġdrive way", + "Ġcher ish", + "ãħł ãħł", + "Ġrans om", + "Ġinter disciplinary", + "ĠPort al", + "Ġtra g", + "th ood", + "Ġted ious", + "Ġgloss y", + "Ġpré par", + "ĠC ay", + "ĠT ook", + "ĠBott om", + "Ġz ig", + "å «", + "åį ±", + "re presented", + "à¹Ģล ย", + "Ġdesar rollo", + "ìĦ ľë", + "Ġvis cos", + "Ġmill igram", + "ĠG und", + "Ġfer ment", + "d rum", + "Ġdraw ers", + "La ugh", + "Ġpel os", + "Ġpave ment", + "Ġmem oir", + "av ait", + "Ġ20 50", + "¤ë ¥¼", + "Ġraz ón", + "Ġflour ish", + "Ġst ern", + "ä¸ Ī", + "ĠCh ung", + "Ġser pent", + "ĠGentle men", + "羣çļĦ å¾Ī", + "k ook", + "Ġl ut", + "import e", + "p arent", + "Ġw sz", + "Ġsc ree", + "ĠMitar beiter", + "å· ´", + "m ut", + "Ġìĸĺ 기를", + "Ġsem ble", + "ĠO W", + "Ġinvestig ator", + "ĠCher yl", + "ĠG erald", + "Ġpr ere", + "Ġcomp ares", + "ny t", + "Ġdiferen ça", + "? -", + "Ġqu á", + "ר ×Ļ", + "S en", + "Ġhe ps", + "Ġgrat uit", + "Ġcons ort", + "ĠST OP", + "ĠProtest ant", + "Ġelectro de", + "â Ĺ", + "Ġsecure ly", + "иÑĩеÑģ кой", + "Ġt ää", + "Ġreg isters", + "ĠHeaven ly", + "og ly", + "iss ä", + "ĠPhys ics", + "ĠMer kel", + "Ġré v", + "éĻ ¢", + "Ġer ased", + "ĠSac ramento", + "Ġcoff in", + "Ġex acer", + "Ġl anz", + "Ġpo ets", + "ul if", + "Ġì¹ ĺë", + "ĠN erd", + "ĠN CT", + "ĠH our", + "neh mer", + "ŀ ĺëıĦ", + "ĠPrin ci", + "S w", + "m ies", + "ar med", + "ĠBeat les", + "Ġpropag ation", + "Ġexch anged", + "Ġcum ulative", + "Ġì§ij ìĹIJ", + "Ġdefe ating", + "æĬ ±", + "b els", + "Ġw es", + "ĠOdys sey", + "ä½ł æĥ³", + "av ior", + "ĠìľĦ ìĹIJ", + "Ġbr it", + "Ġhij o", + "D AY", + "ĠاÙĦت ÙĬ", + "ĠС еÑĢг", + "Ñĥ ка", + "eds iÄĻ", + "Ġimp os", + "Ġell as", + "Ġfire arms", + "ĠN R", + "Ġ×ij× IJ", + "ĠÐŁ ока", + "aw i", + "ĠìĦ± ê³µ", + "Ġpup ils", + "ĠT ack", + "Ġfr ase", + "ĠSh ip", + "Ġst ad", + "ä¸ ľ", + "ĠGreat er", + "un un", + "imm ung", + "gr own", + "ĠN XT", + "ĠAmeric as", + "f ox", + "Ġmant en", + "éłIJ åĤĻ", + "ĠÑģ ок", + "Ġr ikt", + "lect ric", + "de ep", + "Ġзна еÑĪÑĮ", + "Ġben ut", + "ĠInf rast", + "ĠEm ir", + "ĠоÑĤп ÑĢав", + "ĠKim chi", + "ĠFinn ish", + "´ìł ģ", + "ina ire", + "Ġo ike", + "æ¸ħ æ¥ļ", + "Ġhost age", + "ĠBut ton", + "ÙĤ ÙĬ", + "ek ing", + "ĠKaz akh", + "Ġcomfort ing", + "Ġso g", + "Ġgreet ed", + "g uitar", + "p ayer", + "Ġrel ational", + "Ġconstru ir", + "çī¹ åĪ¥", + "op ian", + "ĠVol ume", + "iet h", + "ÑģÑĤв ом", + "ur rection", + "li ÅĽmy", + "Ġhem isphere", + "ĠBe an", + "IG N", + "Ġköt ü", + "ĠFall out", + "Ġbr ace", + "ç¹¼ çºĮ", + "ÏĢ ά", + "ĠH AS", + "Ġg é", + "Ġcharacter ize", + "ặ c", + "ĠMil ky", + "Ġtum ors", + "Ġn uit", + "ĠG az", + "ĠìŀĪ ëĭ¤ëĬĶ", + "Ġг аÑĢ", + "ess ment", + "ĠA be", + "Ġë½ ij", + "ĠEins atz", + "J IN", + "j ä", + "C ry", + "ĠProm ised", + "ĠÑģеÑĢ д", + "ok us", + "Ġscal able", + "ĠпоÑģмоÑĤÑĢ еÑĤÑĮ", + "ück lich", + "Ġreal ism", + "Ġmay o", + "Ġjuven ile", + "Ġhead lights", + "Ġgör Ã¼ÅŁ", + "ĠRe form", + "Ġhal ves", + "cz ne", + "Ġbreak up", + "że j", + "Ġr ätt", + "D ay", + "ĠìĿ¼ë ³¸", + "Ġmu erte", + "Ġtun es", + "ĠSm ile", + "rec ord", + "Ġrecher che", + "atisf ied", + "Ġpo zi", + "Ġcelebr ations", + "ise xual", + "ĠRO B", + "third s", + "ĠF ortune", + "ĠÑĤ ой", + "Ġbrand ed", + "lo o", + "Ġd ud", + "Ġrandom ized", + "Ġcomb in", + "ä¸Ģ äºĽ", + "ier an", + "c zenia", + "į ãĥ«", + "Ġcur ator", + "Ġar tery", + "ĠÑĥ ÑĪ", + "ĠÑĩ иÑĤ", + "Ġsubsid ies", + "Ġbloss om", + "ĠTw ilight", + "Ġhy vä", + "ĠPom pe", + "ĠC isco", + "ĠÐŁÑĢ о", + "Ġbir i", + "Ġg ern", + "Ġre built", + "Ġw cze", + "Ġbenefic i", + "Ġdrum mer", + "Ġsol ids", + "Ġdi yorsun", + "ãģĤãĤĬãģĮãģ¨ãģĨãģĶãģĸ ãģĦãģ¾ãģĹãģŁ", + "l ated", + "Ġmud dy", + "Ġh olog", + "Ġcl aps", + "ĠR ings", + "ĠO key", + "ĠBra ve", + "Ġvalu ation", + "Ġmig rant", + "Ġinter mitt", + "Ġeig ene", + "ili ary", + "ãĥ¼ ãĥĪ", + "mark t", + "k r", + "ĠR ib", + "á»Ļ i", + "Ġaccus ations", + "Ġa rab", + "w ash", + "ĠBard zo", + "Ġu gh", + "est ers", + "oph ren", + "Ġaliment os", + "ĠU z", + "Ö Ĥ", + "Ġ6 50", + "ĠпÑĢи еÑħ", + "F I", + "Ġsamp ai", + "Ġparl é", + "hes ion", + "Ġs ır", + "Ġapparat us", + "Ġcor related", + "ĠPrincip al", + "Ġcor r", + "ĠOffic ial", + "иÑĩеÑģ кие", + "Ġtermin als", + "Sh ould", + "Ġvac un", + "Ġst ellt", + "Ġmo oi", + "etz ung", + "Ġк ÑĢа", + "Ġda i", + "Ġп ож", + "Te am", + "ĠP PE", + "ĠÐŀ Ñģ", + "ĠLe ah", + "ĠI vy", + "y st", + "Ġuh hh", + "Ġnight time", + "Ġtrend y", + "Ġsec urities", + "Ġcontin ents", + "Ġfirst hand", + "ĠVer on", + "ĠëĤ ®", + "Ġbrows ing", + "ĠC ada", + "t ro", + "Ġtr amp", + "re ib", + "Ġerst mal", + "irl er", + "Ġps ic", + "Ġget ir", + "ĠN P", + "Ġdzie ci", + "об ÑĢаз", + "Ġmagic ian", + "Ġscrut iny", + "Ġsl ab", + "ĠO T", + "ist y", + "ir ies", + "ore st", + "Ġtask ed", + "Ġmor ally", + "ìķ¼ ì§Ģ", + "ust ered", + "Ġfool s", + "Ġir respons", + "Ġein f", + "Ġvi á»ĩc", + "Ġsc or", + "Ġpill ows", + "ĠG egen", + "Ġtut te", + "Ġquarter ly", + "Ġdid nt", + "ĠG ym", + "ĠE ther", + "ĠØ «", + "лиÑĪ ком", + "Ġsign aling", + "ĠN ode", + "ĠDonc s", + "Ġy ah", + "ĠKan al", + "Ġf ading", + "et in", + "Ġinfluen cers", + "Ġmed als", + "Ġengine ered", + "Ġfer mented", + "ê²ł ì§Ģë§Į", + "ĠBeet hoven", + "×ŀ× ©", + "inent al", + "ĠìķĮë ł¤", + "üt fen", + "al nya", + "Ġo vere", + "Ġden kt", + "ак ÑĤеÑĢ", + "Ġâ ĺ", + "Ġneces it", + "Ġgener ators", + "gr ass", + "Ġпод Ñĥм", + "lie ÃŁen", + "B ar", + "ľë ıĻ", + "ĠдеÑĤ ей", + "Ġsuck ing", + "Ġsten cil", + "Ġprim o", + "ĠBreat h", + "st rom", + "Ġimmens ely", + "Ġapp reh", + "ìłķ ìĿ´", + "P op", + "Ġj ong", + "ĠGi ul", + "ĠAD HD", + "Ġhö ren", + "Ġe lo", + "iv ent", + "Ġr us", + "Ġoutrage ous", + "Ġmaster ed", + "Ġì» ¤", + "ÙĪ Ùģ", + "ip es", + "ĠRud y", + "Jac ob", + "Ġbull ish", + "Ġt apped", + "Ġfa ud", + "iz ophren", + "ĠÑģо Ñħ", + "ĠDar ling", + "Ġ196 3", + "ĠPre vention", + "² Ķ", + "Ġabdom inal", + "st ones", + "Ġav aient", + "á»ķ i", + "m ake", + "Ġs are", + "ĠInst ant", + "к ам", + "Ġkeep er", + "Ġblank ets", + "ãģ§ ãģĹãĤĩãģĨ", + "Ġswe ats", + "ĠMinne apolis", + "åħ¨ éĥ¨", + "Ġgen ommen", + "Ġfast en", + "ĠBrus sels", + "åij ¼", + "Ġcaf eter", + "Ġabsor bing", + "Ġha go", + "ĠEl mo", + "Ġgust o", + "ĠY ap", + "M úsica", + "Ġt ert", + "Ġband a", + "Ġm ily", + "Ġthere after", + "ĠStock holm", + "ĠC arson", + "Ġcalib ration", + "ava ÅŁ", + "ans a", + "ik ke", + "Ġfore see", + "Ġqual che", + "Ġdest e", + "æ ¤", + "ün üz", + "Ġfor ge", + "D is", + "est en", + "Ġδ ια", + "Ġenca ps", + "ĠGes pr", + "Ġcher cher", + "ick ets", + "ÑĤоÑĢ Ñĭ", + "C r", + "ĠТак же", + "Ġrabb its", + "ĠD ot", + "he iten", + "Ġcaus al", + "ĠF oster", + "ajÄħ c", + "Ġbere it", + "Ġayud ar", + "é« Ļ", + "ãģ ³", + "s ong", + "com b", + "Ġfr inge", + "Ġcyber security", + "Ġëľ ¨", + "Ġk ier", + "Ġbesch äft", + "Ġкон ÑĨе", + "Ġfacil it", + "ĠNam en", + "Ġbil ateral", + "t x", + "ĠW issenschaft", + "Ġnu ances", + "Ġr ipping", + "Ġf y", + "ĠSicher heit", + "ĠGh ana", + "ol on", + "Ġto pped", + "ĠMoroc co", + "Ġrad ial", + "ĠL EE", + "ĠAndre as", + "ed d", + "ĠìĹ ´ë", + "ĠAirl ines", + "ãģĵ ãĤį", + "Ġval ores", + "ê· ľ", + "H y", + "Ġзад аÑĩ", + "ĠKend all", + "ĠÑħ аÑĢ", + "ĠV amp", + "Ġpy thon", + "Ġmanage able", + "ĠG ente", + "o ise", + "ici ary", + "Ġimp oss", + "ĠBun ny", + "iest a", + "And rew", + "Ġser t", + "ĠC ec", + "zz arella", + "Ġautom obile", + "ĠT iere", + "all ows", + "åĨ Ĩ", + "Ġë° Ģ", + "ĠSc orp", + "ĠJ elly", + "ag ara", + "ĠSt retch", + "Ġrede f", + "Ġexacer b", + "ĠS HA", + "é f", + "ors a", + "Ġflaw ed", + "ĠNo el", + "?! ?", + "Ġpro cent", + "Ġmen stru", + "ĠпÑĢо Ñĩ", + "Ġinf ants", + "ðŁİ µ", + "pa use", + "ĠR acing", + "Ġ194 8", + "Ġsuper intendent", + "id ores", + "id y", + "bra him", + "Ġunl ucky", + "Ġper k", + "an ci", + "Ġë§Įë Ĥĺ", + "ĠÐľÐ¾Ñģ кв", + "Ġfin ans", + "Ġdiferen cia", + "łĪ ìĿ´", + "éħ į", + "OR Y", + "ĠT ac", + "ÛĮ ا", + "Ġdes em", + "Ġваж но", + "ĠJ U", + "ĠìŀĪ ìŀĸìķĦìļĶ", + "ĠÎ Ŀ", + "Ġinform ations", + "ĠH EL", + "h st", + "Ġпог овоÑĢ", + "Ġvo iture", + "Ġre us", + "änd ig", + "ĠпоÑħ ож", + "j ing", + "Ġd ru", + "alt ra", + "Ġprodu its", + "Ġk ite", + "Ġeye ball", + "ĠB elt", + "ĠRestaur ant", + "Ġg amb", + "Ġpor ridge", + "it ters", + "Ġconver ts", + "Ġyard ım", + "Ġmáxim o", + "w irtschaft", + "Ġíķĺë Ĥĺë", + "Ġì¤ Ģ", + "Ġice berg", + "Ġvor bei", + "Ġ25 6", + "ocr atic", + "Ġreck less", + "on ner", + "Ġm ús", + "Ġlog ically", + "ĠPr ison", + "ĠNet z", + "Ġvac ant", + "Ġn immt", + "ĠH ARR", + "Ġз ов", + "ĠDe e", + "ring e", + "ni est", + "ĠR ules", + "ìĬ¤ë Ł½", + "cuss ions", + "Ġfl oral", + "Ġconstra ined", + "Ġdifferent iation", + "ĠQue bec", + "ĠÛģ ÛĮÚº", + "Ġpúblic a", + "it el", + "Ġaccommod ations", + "ĠGr ü", + "í ľ", + "Ġpick les", + "иÑĩеÑģ киÑħ", + "Ġcomm issions", + "ĠBa ek", + "Ġçoc uÄŁ", + "ĠMed ium", + "Ġperiod ically", + "Ġwonder fully", + "Ġstaff ing", + "ìĽ IJë", + "ri re", + "f le", + "ĠMc L", + "ĠÑĤ еп", + "ĠпеÑĢ ек", + "н олог", + "Ġíģ¬ ê²Į", + "çĻ¼ çı¾", + "Ġprosper ous", + "ĠSpirit ual", + "ĠCh ick", + "DI A", + "ĠÐŁÑĢ ивеÑĤ", + "Ġper ÃŃ", + "ÑĮ ÑİÑĤ", + "Ġconsult ants", + "ĠEar l", + "ä»Ĭ å¹´", + "Ġru ining", + "оÑĢ е", + "Ġpens er", + "Ġtak iej", + "Ġstrength ened", + "ĠLiqu id", + "он еÑĨ", + "ав аÑĤÑĮ", + "Ġcam er", + "Ġdisagre ement", + "Ġbat hing", + "ĠY osh", + "a al", + "pre chen", + "RIS ADAS", + "Ġsuper star", + "æģ Ń", + "лÑı ÑĤÑĮ", + "Ġn ib", + "ĠTh erm", + "ĠDAN IEL", + "Ġp aw", + "Ġliqu ids", + "Ġcapac it", + "ark en", + "Ġvag ina", + "Ġm ashed", + "Ġemer ges", + "ys cy", + "Ġun related", + "ĠGu ild", + "Ġin verted", + "it ives", + "T ra", + "Ġbe gr", + "Ġal te", + "ì§ ķ", + "ãĤģ ãģ¦", + "ĠÑĢазÑĢ абоÑĤ", + "f inder", + "Ġдал ее", + "Ġблаг одаÑĢ", + "walk er", + "Ġcr ater", + "ass adors", + "ren ces", + "ins ki", + "ĠK IM", + "ĠEll iot", + "20 17", + "ĠS r", + "ink a", + "ano v", + "Ġìŀĺë ª»", + "Ġpropriet ary", + "display style", + "ĠÑģ им", + "Ġиз б", + "ĠPan el", + "Ġinstinct s", + "ĠCommun ications", + "éº »", + "mid t", + "Ġë§Įëĵ¤ ìĸ´", + "ĠÑģл ова", + "ĠGil bert", + "缮 åīį", + "Т ак", + "voor beeld", + "е ÑİÑģÑĮ", + "ary n", + "que z", + "Ġd art", + "Ñĸ ÑĪ", + "ĠH ut", + "S al", + "Ġs outheast", + "Ġpestic ides", + "Ġhelicop ters", + "Ġend ured", + "i ada", + "Ġbre wing", + "ìĹ ¬ë", + "ĠÑģв обод", + "ĠS aints", + "ĠFr ançais", + "ĠEconom ics", + "Ġdis loc", + "oph obia", + "C amer", + "Ġnegoti ated", + "ĠÑģÑĤ али", + "ìĬ¤í ģ", + "og ie", + "Ġtsun ami", + "Ġpeel ed", + "Ġmotiv ations", + "è¨ Ń", + "ost at", + "fl an", + "ĠD AC", + "Ġk av", + "' RE", + "ĠPe arson", + "b be", + "c zenie", + "Ġaten ção", + "íĨµ ëł¹", + "ãģ£ ãģ¡", + "ĠÑĥд аÑĢ", + "Ġintrodu ctory", + "ĠI ci", + "ë ĮĢë", + "ak at", + "Ġt rench", + "Ġproceed ed", + "ĠCo in", + "Ġdere cho", + "ĠRed e", + "æ¯ Ľ", + "ан нÑĭй", + "Ġincarcer ated", + "ĠRich mond", + "R ock", + "ĠP av", + "ĠKar ma", + "ug es", + "Ġconte ú", + "ë ¹Ħ", + "Ġê·¸ë §Į", + "ĠG one", + "Ġwsp óÅĤ", + "ĠRah men", + "un ken", + "Ġì¤ijìļĶ íķľ", + "Ġi b", + "Ġatt aching", + "H ay", + "Ġsu ka", + "ìį ¹", + "Ġpivot al", + "ĠRes pect", + "ÃŃ da", + "I B", + "ĠVer antwort", + "w iet", + "Ġforens ic", + "ÑĢи ÑģÑĤ", + "ĠпÑĢинÑĨип е", + "Ġmark ings", + "Ġk ettle", + "ĠOper a", + "ĠDo ctors", + "Ġshred ded", + "Ġrec uer", + "Ġvig il", + "ĠF ail", + "Ġentre v", + "Ġд ÑĥÑĪ", + "Ġout breaks", + "èµ° åIJ§", + "ĠÏĢ ο", + "Ġro gue", + "ang led", + "Ġyear ly", + "ĠCre ed", + "Ġw am", + "Ġlot us", + "ê³ ¼ë", + "ãĢģ ãĢģ", + "ĠSp it", + "ĠIt u", + "Ġstra ins", + "Ġstamp ed", + "Ġpl aint", + "Ġpot ion", + "Ġconsolid ation", + "è© ķ", + "оÑĩ кÑĥ", + "Ġvlog ging", + "Ġsl ate", + "ĠAu ft", + "ĠInc or", + "ừ ng", + "§ IJ", + "en h", + "Ġhe iÃŁ", + "Ġdom est", + "ĠSt rom", + "åį ³", + "ak is", + "Ġfra gen", + "Ġfin er", + "ĠS ug", + "Ġup hill", + "Ġé én", + "âĢ¦ )", + "ĠÑģ оп", + "ĠCore y", + "Ġsie bie", + "Ġm use", + "Ġclo ves", + "Ġp ous", + "ĠFin anz", + "ĠR oute", + "am at", + "Ġmut ually", + "ĠвнÑĥÑĤ ÑĢи", + "ĠSel ena", + "ë Ķ", + "ĠGa ussian", + "ë ¶ĢíĦ°", + "Ġ×ij× Ľ", + "Ġej erc", + "å¾ ®", + "ke a", + "ĠG erry", + "ĠS ic", + "大 çļĦ", + "Ġ196 6", + "ies e", + "Ġfoss ils", + "Ġest ad", + "ĠK ane", + "ci Äĩ", + "Ġìľł íĬľë", + "Ġп ам", + "ĠCru ise", + "int érieur", + "Ġbe kannt", + "ĠP ode", + "Ġdem ander", + "R em", + "Ġinv ade", + "Ġdecor ating", + "rop ic", + "Ġcow boy", + "ĠPh oto", + "opol it", + "Ġì»¬ë Ł¬ë", + "Ġre ap", + "Ġhand writing", + "à¹Ħ ร", + "Ġë ļ", + "Ġب عد", + "ĠM t", + "Ù Ģ", + "Ġspaces hip", + "Ġnational ism", + "Ġcouncil s", + "ĠGriff in", + "ĠAh med", + "Ġcl ich", + "ĠO L", + "w l", + "ĠPil ot", + "å® ®", + "Ġacron ym", + "Ġg els", + "Ġelectro ly", + "è ĵ", + "Ġм ной", + "Ġepis od", + "ĠDies es", + "ĠAT P", + "Ġed iyorum", + "Ġexpress es", + "Ġexhib its", + "C omm", + "Ġк ÑĢÑĥп", + "Ġmat ar", + "Ġ20 25", + "ĠArt em", + "vas ive", + "r Ãł", + "Ġbe ÅŁ", + "é» ĥ", + "Ġliz ard", + "Ġfill e", + "Ġì§ Ī문", + "Ġмо Ñī", + "Ġt ür", + "Ġcul prit", + "Ġwo ven", + "ĠAN Y", + "n im", + "Ġt ay", + "Ġprom in", + "Ġacom pa", + "Ġid é", + "Ġbo iler", + "ĠThe men", + "Ġaven ue", + "ĠM ud", + "Ġнов Ñĭе", + "Ġwitness ing", + "Ġl ance", + "ĠCH AN", + "ĠBe ver", + "ت Ùħ", + "Ġchem otherapy", + "K ing", + "ĠbÄĻd ÄĻ", + "Ġat ual", + "Ġt ive", + "Ġtalk in", + "Ġqued ar", + "ie ÃŁ", + "ed el", + "Ġìĸ´ì łľ", + "Ġjog ar", + "Ġö r", + "Ġundert aking", + "ĠStre ngth", + "Ġmil hões", + "ĠW ine", + "ĠM olt", + "è® ²", + "ãģij ãĤĮ", + "Ġunderm ine", + "ĠArch ives", + "v ana", + "mer cial", + "M C", + "Ġcast e", + "п ÑĢ", + "Ġlegisl ators", + "ul ators", + "ên io", + "Ġëį °ë", + "ĠÑħоÑĤ иÑĤе", + "Ġн ек", + "Ġs urn", + "Ġcons ci", + "ĠP OW", + "Ġcul inary", + "ĠK AT", + "ĠFol ks", + "Ñĭв аем", + "Ġв ок", + "ãģij ãĤĭ", + "s ervice", + "pt s", + "Ġпоб ед", + "æĺ¯ åķĬ", + "Ġt ents", + "Ġn ord", + "ST E", + "Ġrepublic an", + "Ġwy k", + "Ġmin ions", + "èĻ ķ", + "Ġmem ang", + "j est", + "Ġcompar ative", + "Ġty le", + "car bon", + "bed ingt", + "ks en", + "Ġneg ativity", + "Ġsjäl v", + "Ġd ú", + "æīĢ æľī", + "Ġrec alled", + "c ra", + "ĠT ada", + "ĠÑĢÑĥ ки", + "ĠопÑĢед ел", + "Ġproc rast", + "Ġjog os", + "ĠO o", + "ĠHe arts", + "Ġé ch", + "Ġksi Äħż", + "Ġco arse", + "ĠT ube", + "ĠG reens", + "Ġé n", + "Ġdumb bell", + "ĠÑĤ и", + "Ġquer er", + "ا ØŃ", + "Ïĥ ει", + "ĠпÑĢав илÑĮно", + "Ġп ап", + "Ġcomp ra", + "Ġt ér", + "ĠAnt es", + "Ġoptim um", + "Ġbisc uit", + "κ ι", + "acz ego", + "Ġìĭľê°Ħ ìĿ´", + "ĠMar ines", + "ver o", + "Ġvacc inations", + "Ġpet ty", + "rit ers", + "Ġа л", + "count ry", + "Ġcoun ters", + "Ġattend ant", + "ĠH ui", + "ãģ¨ãģĦãģĨãģĵãģ¨ ãģ§", + "ck a", + "ÑģÑĤвен нÑĭй", + "gu y", + "Ġtrick ed", + "ĠR ED", + "Ġthr illing", + "ÏĢο ι", + "Ġpig gy", + "Ġan unci", + "OR TER", + "ĠVal ue", + "Ġr ond", + "ĠA DA", + "Ġpos er", + "h ores", + "ĠR oland", + "ĵ ¯", + "Ġno ir", + "Ġש ×IJ×", + "ë° ľ", + "iem and", + "ĠпоÑĤ еÑĢ", + "ê³ ³", + "Ġê± ±", + "Ġformat ting", + "ĠL ed", + "è§Ģ çľ¾", + "Ġkill ers", + "ĠÄij ấy", + "Ġha ar", + "ag ain", + "! > [", + "min ster", + "Ġв ли", + "Ġident ifier", + "ĠLamb da", + "Ġtr os", + "Ġflaw less", + "Ġdetriment al", + "Ġbun ları", + "W ar", + "Ġreg ião", + "羣çļĦ æĺ¯", + "ĠB ike", + "cess ors", + "Ġc ùng", + "ĠR N", + "Ġê½ ĥ", + "Ġküç ük", + "ĠBegin ning", + "íĺ ¸ë", + "Ġge we", + "Ġden ote", + "ĠAlber to", + "Ġprob iot", + "Ġo de", + "Ġmol ar", + "Ġburst ing", + "ass umed", + "Ġfoot prints", + "ved a", + "Ġstero ids", + "Ġfl aming", + "ĠE ller", + "Ġerk ennen", + "ät zen", + "Ġlife cycle", + "ĠD OU", + "ĠK arena", + "ĠGuer ra", + "è¿ĺ æĺ¯", + "Ġsin ister", + "Ġpod éis", + "Ġpar ab", + "Ġok o", + "Ġmat éri", + "Ġcar ic", + "son aro", + "Ġpratic amente", + "ÑĥÑģ а", + "Ġcomun que", + "Ġvig ilant", + "Ġreg imes", + "ĠShoot ing", + "Ġra ids", + "ĠN ora", + "ĠW ieder", + "m ens", + "ĠÑģ од", + "Ġê²½ìļ° ìĹIJëĬĶ", + "Ġв Ñħод", + "Ġaut obi", + "ĠS chn", + "ĠRob bie", + "ĠF itness", + "Ġкон ÑĦ", + "Ġpeng uin", + "моÑĤÑĢ Ñı", + "Ġми ним", + "play s", + "Ġdeleg ates", + "M er", + "Ġsist em", + "ĠMicha els", + "m ale", + "ا ع", + "Ġcá ch", + "ĠH ä", + "Ġ×Ļ ×ķ×ĵ×¢", + "Ġsuper power", + "Ġstr on", + "Ġro ver", + "Ġdé pend", + "éĻ ³", + "Ġret iring", + "Ġvamp ires", + "Ġmer de", + "ĠCh anging", + "Ġt ame", + "Ġspokes person", + "Ġc ay", + "Ġfl irting", + "ĠGr ö", + "Ġw är", + "Ġwy b", + "Ġcoe ur", + "ạ nh", + "ĠìĻĢ ìĦľ", + "Ġconna is", + "ĠHundred s", + "ĠBe a", + "Ġα ÏĢ", + "pr uch", + "Ġsocied ade", + "ĠWh ilst", + "ĠK ait", + "esp ace", + "Ġch ia", + "ĠEr m", + "Ġë°Ķ ê¿", + "Ġf ences", + "ĠM ortal", + "ê² ģ", + "Ġг ÑĢаÑĦ", + "ĠHom eland", + "ĠJ UN", + "is st", + "Ġpar lar", + "Ġsport y", + "é o", + "Ġdeep en", + "ĠBeh avior", + "éĢ ı", + "åĵĪåĵĪ åĵĪ", + "Ġer rand", + "Ġrot ary", + "ĠWell ington", + "W ind", + "Ġmes ela", + "ả ng", + "iend e", + "Ġex cell", + "ĠGen ius", + "ĠEdu ardo", + "æľī 人", + "ĠÅŁ unu", + "ĠÄ° stanbul", + "Ġprod uto", + "Ġ ãħİãħİ", + "O FF", + "Ġwoll t", + "çĪ Ĩ", + "Ġëī´ì Ĭ¤", + "Ġl ass", + "Ġher tz", + "Ġar omatic", + "Ġзв он", + "Ġaut oc", + "ĠL ust", + "Ġ11 2", + "ĠÎ Ĺ", + "Ġreview ers", + "Ġrecept ive", + "å°į äºĨ", + "â nd", + "og lo", + "ĠìķĦëĭ Ļ", + "Ġn go", + "Ñĸ ÑĤи", + "Ã¥ t", + "con o", + "Ġtek rar", + "Ġ주 ê³ł", + "Ġgel miÅŁ", + "Ġbed time", + "ĠAr gh", + "AD A", + "ĠгоÑĢод а", + "ĠÄ ĩ", + "Ġall iances", + "g iggling", + "Ġyer de", + "Ġsp ies", + "Ġg utes", + "ç i", + "Ġallt id", + "ĠL ah", + "ŀ IJë", + "Ġdo kÅĤad", + "ÙĪ ÙĬ", + "Ġtoxic ity", + "Ġcancell ation", + "Ġ195 8", + "d ro", + "Ġìŀij ìĿĢ", + "ĠMotor ola", + "Ġmult in", + "Ġenthusi asts", + "ĠM ighty", + "ĠCoc onut", + ": ãĢĮ", + "ĠPict ures", + "Ġsang re", + "Ġbl inking", + "ol esome", + "ĠìĬ¤íĥĢ ìĿ¼", + "F P", + "Ġboom ing", + "ĠдеÑģÑı ÑĤ", + "Ġr atchet", + "Ġtim elines", + "len ess", + "Ġc ages", + "ĠGood night", + "omet imes", + "Ġc unning", + "ĠR isk", + "ul ed", + "d ade", + "Ġpr ata", + "Ġgust arÃŃa", + "am us", + "ĠJin ping", + "Ġest rut", + "Ġdescob rir", + "ĠM Äģ", + "ĠAll an", + "Ġ åĪĨ", + "Ġ×ľ× §", + "Ġpres erv", + "ĠStraw berry", + "Ä ı", + "L u", + "Ġk ro", + "ĠRep orts", + "ìħĶ ìķ¼", + "Ġval t", + "Ġpouv ait", + "Ġapp ar", + "ĠB one", + "Ġprefer ably", + "ĠRep ública", + "å°± åĪ°", + "Ġher zlich", + "Ġchim ney", + "Ġç ev", + "Ġvis as", + "Ġver r", + "Ġcultiv ation", + "ĠArmen ia", + "Ġвд ÑĢÑĥг", + "Ġcock ro", + "retch ed", + "art z", + "ĠлÑİд Ñıм", + "ĠpolÃŃt icas", + "ĠP anz", + "ĠA KA", + "ĠëĪ Į룬", + "Ġer ro", + "Ġcam per", + "Ġ10 2", + "ठ¸", + "d one", + "Ġho ard", + "ĠÐŁÐ¾ÑĤ ом", + "je ong", + "Ġdest a", + "p ak", + "Ġin im", + "Ġgrow ers", + "ĠMess age", + "Ġele ctor", + "eng age", + "ĠFor bes", + "ĠCincinn ati", + "Ġdiffé rence", + "d f", + "Ġsp ar", + "Ġawait s", + "ĠUSS R", + "ĠR ising", + "ĠHo ÅŁ", + "Ġfoot ing", + "Ġcond iciones", + "ÑĤоÑĢ ов", + "Ġclin ician", + "ĠDisk uss", + "å£ ĵ", + "ר ×Ĵ", + "× ¥", + "ite it", + "g ren", + "Ġchar isma", + "Ġle uke", + "Ġirrit ating", + "Ġcir ca", + "ĠRhod es", + "Ġp ior", + "Ġhandic ap", + "roy able", + "Ġv ull", + "O G", + "Ġin ÃŃcio", + "ier i", + "Ġspl ashing", + "Ġdem ise", + "Ġassist ir", + "Ñĩ ÑĤо", + "Ġcover t", + "ĠG ud", + "ภī", + "kl är", + "ĠìŀIJ 꾸", + "Ġver ändert", + "ĠR EM", + "ĠCon ven", + "at ge", + "Ġpierws ze", + "Ġcler gy", + "ling ton", + "l iv", + "V PN", + "ĠÑģ ожал", + "ĠH ate", + "ãģ¨ ãģĵãĤį", + "ÏĨ ο", + "ĠResp ons", + "оз д", + "Ġet mek", + "Ġchem in", + "Ùħ Ø©", + "Ġê°Ģ 족", + "T re", + "Ġum as", + "ĠBur ton", + "Ġpatri arch", + "ĠSmithson ian", + "¥ ĺ", + "M oon", + "A ir", + "Ġmed ios", + "Ġer aser", + "Ġwoll ten", + "Ġpare il", + "ĠBill ie", + "æĬ ½", + "еÑĢÑĤ в", + "Ġparl ament", + "Ġag ony", + "ĠQU E", + "sequ ently", + "An other", + "ĠWh ew", + "ĠAnn ual", + "Ġse ben", + "ìĥģ ìĿĦ", + "val ues", + "ŀľë §Į", + "Ġsin on", + "ere al", + "ĠEn light", + "ĠChem istry", + "ĠCatal unya", + "Ġdoct r", + "ant on", + "Ġst uk", + "ĠPl ate", + "ĠKardash ian", + "Ġfil os", + "ĠW et", + "Ġпоп ÑĭÑĤ", + "Ġunknown s", + "ĠSch on", + "ĠBald win", + "Ġtelescop es", + "ĠG ucci", + "ox ide", + "ĠConserv ative", + "ìĦ± ìĿĦ", + "Ġhina us", + "P ower", + "Ġê±´ ê°ķ", + "Ġprev ail", + "orm an", + "m achine", + "Ġ194 6", + "Ġun bel", + "Ġsch aut", + "Ġp iel", + "e enth", + "Ġobject ively", + "Ġch akra", + "aud io", + "Ġch icos", + "ĠV ault", + "å° Ī", + "Ġmedic inal", + "ĠT ail", + "Wh ile", + "Ġas phalt", + "Ġfro ze", + "ĠE K", + "unch ing", + "n osis", + "20 15", + "ĠG ri", + "Ġodd ly", + "ĠM är", + "ĠA eg", + "c olo", + "P ar", + "Ġëĵ¤ ìĸ´ë", + "Ġv inden", + "ĠO VER", + "Ġ iced", + "Ġsc orp", + "Ġha c", + "qual ified", + "ĠÑĥвид еÑĤÑĮ", + "erm o", + "H EN", + "Ġso i", + "Ġmulti ples", + "Ġlay outs", + "Ġblind ness", + "ĠB owser", + "Ġпод ÑĤ", + "Ġà İ", + "vention al", + "Ġm ata", + "mad ı", + "Ġge ez", + "Ġcad ence", + "Ġważ ne", + "ĠChrist ie", + "ven ge", + "C all", + "Ġturn around", + "Ġblo b", + "ĠЯ к", + "ĠVoice over", + "Ġper il", + "ĠJa ime", + "ĠH OY", + "l ane", + "Ġse bel", + "ĠDu o", + "ĠHistor ical", + "Ġd ni", + "Ġg ema", + "y k", + "Ġsab em", + "ắ ng", + "Ġv ars", + "ĠRon nie", + "ĠRon aldo", + "ĠPer què", + "ns inn", + "h air", + "Ġrelent less", + "Ġl yn", + "Ġtravel er", + "æĢİ麼 äºĨ", + "n ine", + "Ġant im", + "Ġì¼ Ģ", + "Ġsnow ball", + "ĠÑħаÑĢ акÑĤеÑĢ", + "Ġintern s", + "Ġconstitu ency", + "ĠÐĿ ам", + "׾ ׾", + "V EL", + "Ġvikt igt", + "Ġap oyo", + "ÙĦ ب", + "Ġj ard", + "Ġheight ened", + "ÑĢо ÑģÑĤ", + "ĠSM ITH", + "Ġдел а", + "Ġrepair ing", + "Ġr igt", + "ĠShe ikh", + "ĠBrit ney", + "Ġevery time", + "Ġadvent urous", + "oc key", + "er nt", + "Ġat aque", + "ĠAltern atively", + "e ffect", + "Ġpalav ras", + "ĠElli ott", + "Ġréuss i", + "Ġhypert ension", + "ĠMan ual", + "Ġproph etic", + "Ġhand c", + "ÑĮ е", + "Ġref rain", + "ĠSqu id", + "ìŀ ¡", + "Ġком ан", + "äll en", + "Ġlleg ó", + "Ġbas h", + "ion y", + "ĠÑģк лад", + "Ġк аб", + "Ġcare less", + "ĠP ool", + "Ġtr ás", + "Ġfil s", + "ĠSch r", + "Ġsp rawd", + "ĠMon aten", + "Ġunfor gettable", + "ĠCott on", + "Ġinconven ient", + "ĠR X", + "or is", + "Ġhum bled", + "ת ×Ĺ", + "ĠØ¢ Ù¾", + "Ġincre ÃŃ", + "ĠKomment are", + "èĪ Ĵ", + "r ación", + "Ġv antage", + "ĠSe al", + "ĠìĿ´ 거를", + "Ġjou e", + "ãģĿãģĨ ãģ§ãģĻãģŃ", + "Ġìĺ¤ë ŀĺ", + "ĠиÑģп ÑĭÑĤ", + "ob en", + "Ġgr ate", + "Ġcontro le", + "ĠPer cy", + "ÅĤ ada", + "Ġsimult aneous", + "Ġprot oty", + "ĠgroÃŁ er", + "Ġbew usst", + "iniz i", + "Ġpass ieren", + "ĠHapp iness", + "åī ĩ", + "sh i", + "ge ht", + "Ġstation ed", + "ĠErgeb nis", + "Ġdirect amente", + "Ġsurv ives", + "Ġperson es", + "BER G", + "Ġvom iting", + "Ġconhe cer", + "Ġad jour", + "ĠCiv ic", + "pe i", + "bur st", + "Ġëĭ¤ ëĭĪ", + "é ı", + "Ġsl ed", + "Ġplataform a", + "ĠS ect", + "ĠDe fin", + "çĻ» éĮ²", + "én om", + "chn et", + "Ġprofit ability", + "Ġerre icht", + "á»ı i", + "c ation", + "Ġì§Ģ ê¸", + "Ġperd re", + "Ġfel ony", + "Ġ195 7", + "æĪij å¾Ī", + "Ġunsuccess ful", + "Ġnag yon", + "Ġelastic ity", + "Ġfac ade", + "Ġearth ly", + "ĠамеÑĢик ан", + "Ġcon n", + "c la", + "D u", + "Ġpolit iques", + "Ġhal o", + "iant es", + "Ġмо ей", + "ãĥ³ ãĥī", + "ton es", + "el ier", + "è® ļ", + "ht aking", + "Ġwicht ige", + "Ġan no", + "ĠL ok", + "ill ions", + "Ġv iver", + "Ġsol chen", + "Ġsu f", + "ĠSal z", + "ĠN vidia", + "z uge", + "ĠSp ike", + "V ideo", + "Ġtw or", + "ĠA la", + "èij ī", + "Ġh anya", + "ĠAd m", + "ìĿ µ", + "ĠPatient en", + "ĠOn ion", + "ĠKo be", + "ĠSc ene", + "ĠR ash", + "æ¨ Ļ", + "ÑĢа ÑģÑĤ", + "ist ani", + "Gen eral", + "le ye", + "imb ap", + "Ġconce aled", + "ĠFr idays", + "ĠW ool", + "Ġнов ÑĭÑħ", + "Ø´ ر", + "Ġê²° ê³¼", + "Ġjed och", + "´ìĭ ľ", + "ĵ¤ ëıĦ", + "Ġìŀ¥ ëĤľ", + "uk t", + "L ou", + "Ġ먹 ìĸ´", + "ĠEx pect", + "Ġдом ой", + "Ġirrespons ible", + "Ġac erca", + "ĠZ ust", + "ר ×ĺ", + "U I", + "Ġyout ubers", + "ĠPos itive", + "Ġsoci oe", + "Ġsn atch", + "èĥ Į", + "Ġrefresh ed", + "Ġnom inations", + "ĠP att", + "Ġobsol ete", + "Ġdem iÅŁ", + "åı ¤", + "orm uÅŁ", + "ĠìĨĶì§ģ íŀĪ", + "Ġf la", + "Ġcra ziest", + "ĠZ ie", + "ĠT ú", + "z ep", + "ic em", + "Ġë©ĭ ìŀĪ", + "Ġcyn ical", + "ãģĿ ãĤĵãģª", + "Ġt resp", + "Ġcra z", + "Õ¥ Õ", + "Ġne lle", + "Ġm ph", + "ĠN ered", + "ĠK ob", + "ĠE ck", + "¨¸ ëĭĪ", + "J an", + "ĠТ огда", + "Ġde ci", + "ĠV og", + "Ġbubb ling", + "éĢ Ģ", + "ú a", + "Ġproduct os", + "iber al", + "Ġrepl icated", + "ĠImp rove", + "ill ary", + "C ha", + "Ġré du", + "ĥIJ íķĺë©´", + "Ġcon not", + "ĠK rit", + "ĠдÑĥÑħ ов", + "Ġtread mill", + "ĠP W", + "Ġзов ÑĥÑĤ", + "Ġcl ams", + "Ġdra fting", + "Ġ195 6", + "un ta", + "Ġexpend itures", + "ĠHoo ver", + "W OO", + "ÑĪе е", + "Ġded uction", + "mon ary", + "Ġreci b", + "Ġpo vo", + "Ġëį Ķë", + "ĠP AL", + "ĠBl ow", + "Ġwy p", + "Ġdest ac", + "de al", + "Gra eme", + "Ġnécess aire", + "Ġdamn ed", + "Ġ19 38", + "Ġìĭ¤ ìłľë¡ľ", + "Ġtro op", + "Ġinsight ful", + "ĠT J", + "ĠоÑģ в", + "Ġf idelity", + "ĠSk ip", + "ĠMay o", + "ë§ Ŀ", + "app e", + "Ġbl as", + "ĠW Y", + "ĠG N", + "ct ar", + "S u", + "Ġcu ent", + "he ws", + "Ġcorps es", + "A bs", + "Ġwaste water", + "Ġc iek", + "ĠOn u", + "Ġexplos ives", + "Ġar ma", + "ĠSTEP HAN", + "polit ik", + "ĠOs aka", + "ta ÅĤ", + "Ġyap ıyor", + "Ġiz quier", + "Ġbele za", + "ĠWy att", + "åIJ ¸", + "Ġsu k", + "Ġspec jal", + "Ġdan ke", + "wh istle", + "ĠfÃŃs ica", + "ĠHar riet", + "ĠìķĦ íĮĮ", + "Ġwill kommen", + "ip ing", + "ĠÑģмоÑĤÑĢ иÑĤе", + "Ġмож еÑĪÑĮ", + "Ġinacc urate", + "Ġarrog ance", + "ĠRem o", + "γ ά", + "ass ed", + "Ġdeliver ies", + "Ġst inky", + "ĠпеÑĢ еж", + "j ay", + "Ġtrans itional", + "Ġr ere", + "ĠNGO s", + "ĠAT M", + "Ø® ت", + "i ology", + "Ġв лад", + "Ġsch me", + "ĠSh ine", + "ìķ ¡", + "p ants", + "Ġser ge", + "Ġsen hor", + "Ġab duct", + "ĠBry ant", + "V ES", + "Ġawak ened", + "ĠL az", + "rop olis", + "ĠLa o", + "è¾Ľ èĭ¦", + "Ġvill a", + "Ġsumm ers", + "Ġent hal", + "Ġ194 9", + "V ia", + "Ġìĸ´ì ¨", + "Ġtend on", + "Ġviol et", + "Ġintellect ually", + "Ġboun ced", + "ara us", + "Ġ19 19", + "Ġvra ag", + "Ġsp el", + "ĠSch war", + "Sc ott", + "ĠInd o", + "Ġë§ Ŀ", + "Ġcanon ical", + "ĠI KE", + "Ġthat ÃŃs", + "Ġme llan", + "æ¯ Ĵ", + "ig mat", + "C ould", + "... ?)", + "Ġfo arte", + "ĠKum ar", + "rend o", + "Ġél é", + "à ´", + "val uation", + "c ases", + "Ġintuit ively", + "h ong", + "ett ed", + "Ġsou ven", + "Ġmor b", + "Ġc ors", + "ĠN V", + "ĠHas an", + "æĥħ åĨµ", + "ie ved", + "Ġì§Ģê¸Ī ìĿĢ", + "Ġdum pling", + "Ġcontr ôle", + "Ġambigu ity", + "æ©Ł æľĥ", + "Ġco g", + "ĠScript ures", + "Ġc ai", + "Ġbe ver", + "大家 éĥ½", + "Ġhu is", + "Ġa ime", + "Ġerkl ären", + "ĠL M", + "ĠF ey", + "éļ ¾", + "à®± த", + "Ġsuper vised", + "Ġje we", + "s pl", + "ĠÑĨенÑĤ ÑĢ", + "Ġcoll isions", + "ÙĦ Ùģ", + "ĠHog warts", + "ĠDur ham", + "×ķ× £", + "Ġphosph ate", + "Ġoverse e", + "Ġinspect ions", + "Ġbr inc", + "ĠZ ak", + "Ġpay off", + "Ġch aud", + "ĠHung er", + "ã os", + "v ir", + "Ġf iance", + "Ġb oug", + "l ived", + "c ry", + "åĽŀ ä¾Ĩ", + "Ġjoint ly", + "Ġgirl friends", + "ĠNe xus", + "¦¬ ê²łìĬµëĭĪëĭ¤", + "ĠK wang", + "åĵĪ åĽī", + "å§ ij", + "ÅĤ ÄĻ", + "ĠN eden", + "ie ce", + "Ġins erting", + "æŁ ĵ", + "ĠM ummy", + "ĠGlo be", + "Ġle e", + "Ġg erman", + "Ġcre ams", + "ach o", + "Ġch Æ°a", + "ĠGal ile", + "Ġfür s", + "Ġest iver", + "c idos", + "Christ ian", + "Ġlors qu", + "Ġcut est", + "v ale", + "ĠкÑĢ еп", + "Ġw ary", + "Ġslic ing", + "Ġesper ando", + "ĠV ander", + "ĠDe ixa", + "Ġ195 4", + "Ġmów iÄħ", + "Ñĸ ÑĶ", + "Ġtool ing", + "Ġrest or", + "Ġpos ición", + "Ġintent ar", + "ĠAp ache", + "OU L", + "ĠÙĪ ب", + "Ġmat ière", + "ãĥ¼ ãĤĵ", + "Ġl inen", + "Ġestrat ég", + "ĠMut ta", + "é¡ ¯", + "è¡Į äºĨ", + "Ġpart ing", + "Ġminim izing", + "Ġapp rendre", + "æľ Ŀ", + "Ġан глий", + "ĠDo o", + "ĠFire fox", + "c ómo", + "Ġge opolit", + "Ġmak an", + "Ġmog elijk", + "ĠÏĢε Ïģι", + "Ġcá» ©", + "Ġinstall er", + "Ġdib uj", + "ĠHe ath", + "lo op", + "ĠBro ken", + "HY UN", + "sh elf", + "Ġf izer", + "Ġenh ances", + "ä¾ĭ ãģĪãģ°", + "Ġдо ÑģÑĤи", + "ĠP UB", + "ĠKolleg in", + "Ġatt ained", + "Ä ¾", + "Ġmist ress", + "ĠOft entimes", + "×ŀ ×Ļ×Ŀ", + "Ġbe we", + "ĠS ora", + "ra uen", + "ba um", + "Ġroll ers", + "Ġm ering", + "ĠP AC", + "Ġн Ñĸ", + "ĠRép ublique", + "ĠÑĤ ÑĢав", + "ĠV anguard", + "uc iones", + "Ġ무ë ĮĢ", + "Ġg our", + "¯ ¤", + "ĠÏ ī", + "Ġsa una", + "Ġpe ine", + "ĠVal erie", + "ĠS ikh", + "fend imiz", + "ber o", + "ĠÑĩ и", + "Ġdo ÅĽwiad", + "ĠE uros", + "Ġcomment aires", + "Ġtwe aks", + "ĠF aster", + "ĠÑĢаÑģ к", + "Ġprogress ively", + "ĠE uch", + "bor o", + "ĠIng red", + "C ap", + "Ġun check", + "Ġìĺ¤ë ¥¸", + "Ġw re", + "ĠF T", + "ör ung", + "Ġmemor ized", + "ĠD inner", + "ĠP hew", + "ou bl", + "Ġput a", + "Ġadm its", + "ез де", + "op od", + "Ġpand a", + "Ġhing es", + "ci pe", + "Ġtrans act", + "Ġpod ia", + "Ġp ics", + "Ġcriter ion", + "ĠOrchest ra", + "ĠBl og", + "Ġsolem n", + "ĠPix ar", + "Th ree", + "Ġв низ", + "ĠVol unte", + "ĠSav age", + "ĠPV C", + "ĠC af", + "Ġwy kon", + "Ġgrad ers", + "Ġcr ouch", + "Ġcl iche", + "Ġsoy beans", + "ĠM UR", + "ĠGonz alez", + "ĠM imi", + "ĠBol sonaro", + "Ġdi aphrag", + "Ġbil ang", + "ëIJĺ ëĬĶ", + "éĤ£ æĪijåĢij", + "Ġregul ating", + "M c", + "J udge", + "Ġн ож", + "Ġjak Äħ", + "ites se", + "ĠW ij", + "Ġl ata", + "gro aning", + "POS ING", + "Ġ×IJ×ķת ×ķ", + "Ġha ga", + "Ġground ing", + "Ġviol ently", + "Ġt ills", + "Ġeng ag", + "ĠHo llow", + "Ġпоп ÑĥлÑıÑĢ", + "Ġw prowad", + "Ġrepl aces", + "Ġfluores cent", + "urg ical", + "igg ly", + "ĠTrad itional", + "t te", + "ĠÙĦ Ùĩ", + "Ġphosph orus", + "Ġapr on", + "ĠWat ers", + "ĠK ultur", + "ав ай", + "Ġol ives", + "Ġ×Ķ×IJ× ľ", + "Ġteil weise", + "Ġsen cill", + "Ġprend s", + "Ġnarr ower", + "Ġj ätte", + "ĠInformation en", + "ìĥģ ìĿ´", + "Ġstar ve", + "Ġfr ick", + "ĠBe weg", + "ठ²", + "Ġdolph in", + "ĠLAUGH TER", + "ĠINTER VIE", + "åĶ ī", + "Ġyan lÄ±ÅŁ", + "Ġtor pedo", + "Ġshort ages", + "ìĿ´ë ĵľ", + "ıld ı", + "Ġp aws", + "Ġo zone", + "Ġcultiv ated", + "ĠF ot", + "Ġnot or", + "н оз", + "Ġко ÑĪ", + "Ġtouch screen", + "ĠAll y", + "æľĢ è¿ij", + "Ġ맼ìŀĪ ìĸ´ìļĶ", + "ĠС еÑĢ", + "Ġв полне", + "Ġpap rika", + "ĠDust in", + "Ġefect o", + "Ġop ini", + "Ġmu ut", + "Ġhá»į c", + "Ġinter ject", + "ÄĻ t", + "Ġbut ts", + "ure z", + "ĠP ike", + "ĠH ok", + "ĠGu inea", + "ĠCath edral", + "Ġ14 00", + "C ra", + "+ ,", + "ë§ Ľ", + "³´ë ıĦë¡Ŀ", + "aby rin", + "Ġvide og", + "Ġо ÑĢÑĥж", + "Ġu ž", + "Ġbus cando", + "ĠAss istance", + "éĻ ½", + "Ġmel hores", + "ì¡ ´", + "Ġëģ ¼", + "ĠR J", + "Ġت Ùħ", + "Ġo min", + "Ġmotor cycles", + "ĠS app", + "Ġsupply ing", + "ĠAl gun", + "Ġaer ospace", + "×¢ ׾", + "oc cup", + "le ist", + "Ġê±° ëĬĶ", + "Ġcomplet a", + "b res", + "! (", + "ĠÐŁÑĢ ед", + "Ġdisadvant aged", + "ĠAtt end", + "ĠJud ah", + "á»ĭ ch", + "yl ene", + "act ly", + "Ġset ups", + "Ġammon ia", + "ĠSchwe iz", + "ĠSh ame", + "Ġband e", + "ĠF uel", + "Ġtroubles ome", + "Ġnum ero", + "ĠM OM", + "ĠпÑĢед лаг", + "ment ioned", + "ĠболÑĮÑĪ ое", + "ĠVikt or", + "ĠSty les", + "Ġcruc ified", + "ructure d", + "en viron", + "Ġmor als", + "Ġmed itating", + "Ġax ial", + "is ance", + "ĠAb st", + "G reen", + "Ġê± ´ì", + "Ġquad rant", + "Ġper gi", + "Ġcamer aman", + "ĠSe qu", + "Ġpa used", + "ĠLa ughing", + "ê· Ģ", + "? ..", + "ĠÅ» e", + "Ġpermit ir", + "Ġdetect ors", + "ĠH UD", + "av al", + "ĠìĹ¬ê¸° ê¹Įì§Ģ", + "Ġh ubs", + "Ġbest immt", + "ĠбÑĥдеÑĤ е", + "INTER POSING", + "Ġten gan", + "Ġcra ve", + "ĠBundes regierung", + "ĠBlo ody", + "Ġus ability", + "ĠE as", + "ĠÄijá»Ļ ng", + "Ġ195 5", + "Ġkrie gen", + "Ġhabit ual", + "Ġessential s", + "rim inal", + "Ġroomm ates", + "éĤ£ å°±", + "ĠпеÑĢе Ñħод", + "Ġng hi", + "Ġmen ing", + "ĠSym phony", + "ĠH ug", + "ag gi", + "Ġw ied", + "Ġmit ad", + "ãģ£ãģ¦ ãģĦãģĨ", + "te enth", + "ida Äĩ", + "S ave", + "Ġrob iÄĩ", + "Ġboun ces", + "° ĸìĹIJ", + "st ars", + "Ġprag matic", + "Ġcogn ition", + "Ġwra pper", + "Ġw arten", + "ad h", + "Ġpens a", + "ĠHert z", + "Ġn ÄĽ", + "ĠRe id", + "ĠPC s", + "ĠMo le", + "Ġ.. ...", + "Ġpre cio", + "ĠChampions hips", + "ê°Ģë Ŀ½", + "Ġv ér", + "Ġcorrid ors", + "ĠElect ronic", + "S l", + "Ġа ле", + "Ġoverth row", + "Ġk abul", + "ĠR ES", + "ĠCyber punk", + "ог од", + "ĠÐĿ ав", + "Ġw an", + "Ġmanifest ations", + "Ġcual es", + "ĠW ise", + "ĠLös ung", + "Ġex fol", + "Ġearn s", + "ÑĥÑģÑĤ иÑĤÑĮ", + "Ġsa pp", + "ĠBra un", + "ĠBRAND ON", + "ì¹ Ļ", + "Ġs ano", + "ĠF EL", + "Ñĭв айÑĤеÑģÑĮ", + "ожд ениÑı", + "Ġse wn", + "F un", + "Ġrecipro cal", + "Ġexpans ive", + "ĠTra ffic", + "Ġktóre go", + "ĠÙĪ س", + "æĺ ¥", + "Ġë¹ ¨", + "pro ve", + "ig are", + "Ġlo h", + "Ø§Ø ¶", + "H ope", + "Ġdevote es", + "ĠG om", + "Ġste als", + "ĠU ms", + "ĠTw ice", + "ãĤ ²", + "iy im", + "Ġrhythm ic", + "ĠV orte", + "Ġpref ix", + "om ination", + "Ġdat o", + "Ġcust ard", + "ĠVO ICE", + "å· ŀ", + "Ġmen y", + "ist ors", + "Ġíĺ ij", + "ĠìĤ´ì ķĦ", + "Ġíĥ Ħ", + "Ġk ort", + "Ġab a", + "ĠV era", + "ep y", + "Ġì¹´ë©Ķë Ŀ¼", + "Ġsubmer ged", + "ĠC lock", + "Ġthumbna ils", + "Ġbo ast", + "ĠF are", + "!! ]", + "ĠÅĽ m", + "Ġkaik ki", + "ĠTechn ologies", + "ìĻ ¸", + "ãĥ Ĵ", + "иÑĤ ай", + "å°ı æĻĤ", + "Ġа ÑĤ", + "Ġkn obs", + "Ġre icht", + "ượ ng", + "gl io", + "Ġ맼 ìĿ´", + "ê°IJ ìĿĦ", + "Ġjot ka", + "ĠHand y", + "ĠHab en", + "n ous", + "Ġin land", + "Ġam azon", + "ho oting", + "S L", + "Ġle isten", + "~ \"", + "Ġprov oke", + "ĠTw ist", + "Ġ×ij× Ĺ", + "Ġdepart ed", + "ê° ľë¥¼", + "Ġk onse", + "ĠCar wyn", + "íķĺ ìĭł", + "ident al", + "ES CO", + "Ġt teokbokki", + "Ġdiz endo", + "ç· ´", + "ınd aki", + "imas u", + "af ar", + "Ġland fill", + "Ġcorrect ing", + "Ġcle ars", + "ĠNum mer", + "H AM", + "Ġcart ridges", + "ĠDies el", + "p aced", + "Ġobl iv", + "Ġmoy ens", + "ĠSin ne", + "ĠPre is", + "il iz", + "ĠÑģм ож", + "Ġbroad en", + "ä»ĸ æĺ¯", + "x es", + "Ġcarbohyd rate", + "íĺ ¹", + "se ok", + "Ġecho es", + "Ġc ess", + "ë° Ķ", + "Ġб изнеÑģ", + "Ġllam ado", + "Ġess ent", + "ĠìĿ¼ë °ĺ", + "ĠA ires", + "ph en", + "Ġze bra", + "Ġsymbol ism", + "On ce", + "Ġr acks", + "ĠKaf ka", + "ĠÑģеÑĢÑĮ ез", + "Ġsin n", + "p icious", + "ka a", + "Ġmotherf ucker", + "Ġapprentices hip", + "Ġr pm", + "Ġtax ation", + "Ġfur ry", + "ĠSac red", + "ĠÑĢаз м", + "por a", + "eng es", + "ĠíĹ Īë", + "ĠÑģ ин", + "Ġsanit izer", + "Ġcr inge", + "ĠS ca", + "оÑĩ но", + "Ġof ere", + "Ġmel odies", + "ĠVel vet", + "ĠIhr er", + "ĠHy brid", + "ĠG iov", + "Ġirgend was", + "Ġdep ende", + "ĠUs ers", + "Ġh ump", + "dri ving", + "Ġs f", + "Ġruth less", + "à¹ĢภĦ", + "Ġlem ons", + "Ġfö ret", + "ĠO j", + "Ġм ама", + "Ġinter personal", + "Ġge v", + "Ġab norm", + "иÑģ л", + "Ġин д", + "Ġkont roll", + "Ġreg res", + "Ġled ge", + "Ġerzäh lt", + "ĠT act", + "Ġarri vé", + "Ġsubstant ive", + "Ġspoon ful", + "zw ischen", + "oooo o", + "Ġconten ido", + "Ġbes l", + "á»ĥ m", + "k ten", + "Jam ie", + "Ġsand y", + "ä¸į åIJĮ", + "â ĭ", + "Ġp ase", + "Ġdet te", + "ĠBelg ian", + "ê° ľë", + "ula res", + "r ud", + "ig or", + "ĠíĮ ¬ë", + "Ġremed ies", + "Ġblast ing", + "ĠS ich", + "Ġож ид", + "Ġmon str", + "Ġmanif old", + "Ġglaub en", + "ĠE ST", + "Ġstream line", + "Ġlobb ying", + "ĠGoth ic", + "to ire", + ".. '", + "Ġdém ocr", + "Ġнаб лÑİд", + "Ġwsp ól", + "ĠczÄĻ ÅĽÄĩ", + "ä¸ĭ éĿ¢", + "is és", + "g angen", + "Ġbez pie", + "rem lin", + "ê° Ŀ", + "St ill", + "Ġres ides", + "Ġgele cek", + "Ġtélé phone", + "Ġpe wn", + "Ġle opard", + "Ġcompliment ary", + "Ġc rib", + "ĠAnim als", + "Ġge il", + "ess el", + "Ġgard er", + "Ġcatch y", + "æ¨ ¹", + "ĠE ts", + "ĠCom mercial", + "ĠD ENNIS", + "ĠCoordin ator", + "ĠAb igail", + "ffff ff", + "ấ p", + "Ġpeque ña", + "Ġinject ions", + "ce kt", + "Ġphilanthrop y", + "Ġp uck", + "Ġcelebr ates", + "ĠD unk", + "ĠD latego", + "ãģ¾ ãģł", + "δ ή", + "grad uate", + "ĠM obil", + "t ill", + "ac am", + "Ġyol ks", + "Ġtang led", + "Ġman iac", + "Ġoblig ed", + "ĠLa ink", + "Ġver der", + "ĠDam on", + "Ġmut ant", + "Ġhop ping", + "Ġre ins", + "Ġinver ter", + "Ġcont empt", + "׳ ס", + "le arning", + "M iss", + "ĠÐĵ оÑģ", + "ĠMe yer", + "ê»ĺ ìĦľ", + "é£ İ", + "×ķ׳ ×Ļ×Ŀ", + "ask ing", + "Ġtrim ming", + "Ġtre asury", + "Ġs ente", + "A ust", + "ĠUnterstüt zung", + "ĠCom edy", + "ĠAn akin", + "é ¹", + "ÑĢÑĥ ÑĤ", + "ĠH ari", + "ograph ers", + "Ġoat meal", + "ĠB ots", + "ä¸į äºĨ", + "Ġп алÑĮ", + "Ġacknowledge ment", + "x ic", + "Ġê´Ģ ìĭ¬", + "gas ping", + "Ġãģ ķ", + "Ġterr ace", + "Ġor naments", + "ĠM ER", + "comm ittee", + "ĠìĹĨ ìĬµëĭĪëĭ¤", + "Ġr ij", + "é ³", + "צ ×Ŀ", + "le me", + "Ġlibert ies", + "Ġfell as", + "ĠCop per", + "ben ch", + "ĠIde a", + "á»į n", + "ÑĪ а", + "Ġvers ión", + "ÏĦο Ïį", + "ĠÐľ и", + "ĠпÑĢил ож", + "Ġbox er", + "ĠT anner", + "ĠM oy", + "ì¹ĺ ëĬĶ", + "T hr", + "Ġtin ham", + "Ġpol ishing", + "Ġconsequ ently", + "Ġamen ities", + "ĠK I", + "ĠGRE EN", + "ĠFrank ie", + "н иÑĤ", + "itt el", + "Ñģ кое", + "urs ed", + "Ġup bringing", + "Ġth ứ", + "ĠìĭĿ ìľ¼ë¡ľ", + "Ġwh im", + "Ġchin ese", + "conf idence", + "ĠJ eder", + "ãģª ãģ®ãģ§", + "aj cie", + "ĠT ous", + "ĠPow ers", + "ừ a", + "other mal", + "ĠвÑĭ ÑĪе", + "r ale", + "Ø§Ø ®", + "Ġì§Ģ ìĽIJ", + "Ġép isode", + "Ġsul ph", + "Ġenc ara", + "k raft", + "alar ı", + "ĠCom es", + "Ġdiv ul", + "ĠRud olph", + "ĠM use", + "Ġut ens", + "ĠìŀIJ 주", + "Ġp ana", + "ĠVeget a", + "ĠPH P", + "ĠN SA", + "ent in", + "ĠCarne gie", + "ا ÙĬ", + "iÄĻ cy", + "H arry", + "Ġf ır", + "С п", + "Ġglad ly", + "Ġaver aging", + "íķĺ ê²łìĬµëĭĪëĭ¤", + "лÑı ÑİÑĤÑģÑı", + "ĠÐľ енÑı", + "Ġquot ation", + "ri res", + "itch ens", + "ay ed", + "Ġun att", + "ĠP erez", + "ĠоÑĤ меÑĤ", + "Ġtact ile", + "ĠEu h", + "is ini", + "b uh", + "Ġhat ır", + "ĠìŀĪ ìľ¼", + "Ġpolicy makers", + "³´ì Ħ¸ìļĶ", + "ac ı", + "Ġκ ι", + "Ġregister ing", + "re to", + "ĠSpr inkle", + "ĠGram my", + "ax ter", + "Ġб и", + "Ġsit ter", + "Ġpred ic", + "Ġthin ly", + "Ġstr um", + "Ġag grav", + "Ġa ha", + "ر ج", + "m ellow", + "Ġconst ante", + "ĠL aut", + "ist on", + "Ġtransition ed", + "ĠCamb odia", + "ãģĦ ãģįãģ¾ãģĻ", + "è·Ł 大家", + "art ed", + "Ġmis f", + "ĠPunk te", + "Įë ĵł", + "Ġtremb ling", + "Ġges pannt", + "ĠعÙĦÙĬ Ùĩ", + "Ġникак иÑħ", + "Ġë¶Ģë ĵľë", + "ĠÑĢазв иÑĤ", + "Ġit chy", + "Ġc iento", + "Ġpl ains", + "Ġk ittens", + "Ġback log", + "ĠPres iding", + "pt a", + "Ġha voc", + "ĠDarr in", + "ĠÐĽÑİ Ð±", + "Ġsegreg ated", + "Ġg hetto", + "Ġerle bt", + "Ġdrug iej", + "ĠSi xt", + "åı ĥ", + "ร ะ", + "uen cia", + "Ġíķĺ 기", + "ĠëĨ į", + "Ġrob i", + "Ġpione ers", + "Ġmilli ards", + "ĠWitch er", + "Ġ무ìĹ ĩ", + "or ro", + "m ass", + "Ġdiver gence", + "ĠRiver a", + "ĠNo odles", + "Ġend roit", + "ĠK osten", + "ĠдÑĢÑĥг а", + "ĠmÃŃn imo", + "ĠKazakh stan", + "ت Ùĩ", + "Ġвоз дÑĥ", + "Ġgesch rieben", + "ĠN il", + "Ñģ ки", + "ĠFr üh", + "Ġbever ages", + "æº IJ", + "ĠG on", + "æĺ ¨", + "Ar in", + "ĠInt ro", + "ocaly ptic", + "Ġexhaust ion", + "ĠStat us", + "ĠBatter y", + "és z", + "£ ¼ë", + "air y", + "Ġë³´ìŬë ĵľë", + "Ġdispar ity", + "Ù Į", + "ĠTuc son", + "Ġbright ly", + "pro blem", + "Ġbiom ass", + "éĻ į", + "§ ī", + "Ġhur dle", + "Ġwavelength s", + "Ġ< <", + "Ġteam ed", + "FF FF", + "ĠS lim", + "om ial", + "Ġunve iled", + "ĠVere in", + "ÙĤ Ø·", + "est ry", + "Ġcl ás", + "Ġch eddar", + "Ġaccus ing", + "ĠScient ific", + "ĠбÑĥд е", + "ĠCyr us", + "ε ÏĦε", + "Ĩĵ ê³ł", + "Ġë³ Ħ", + "Ġcur d", + "Ġrefer rals", + "sh ift", + "åį ķ", + "nik ów", + "Ġm ier", + "Ġconf ronting", + "ê²ĥ ëıĦ", + "aw l", + "Ġtry in", + "Ġê·¸ëŀĺ ìļĶ", + "Ġch iar", + "Ġìĺ¤ëĬ ĺëıĦ", + "æĶ¿ æ²»", + "es que", + "Ġmism os", + "ĠSh ak", + "Ġsoci aux", + "Ġpi ÅŁ", + "ĠkiÅŁ i", + "Ġcy an", + "h ay", + "be w", + "b od", + "ĠÎ ¹", + "ĠMain ly", + "Ñİ ÑĤÑĮ", + "hab itude", + "ĠÑģп окой", + "è·Ł æĪij", + "Ġpre con", + "ĠM andy", + "ðŁ¤ £", + "ill os", + "Ġgr upp", + "Ġcr umble", + "Ġconstru ctor", + "erv ices", + "Ġlight house", + "ĠCon cept", + "ан ÑĤи", + "alt ro", + "h ope", + "ĠAll eg", + "ìĸ´ë ¥¼", + "pie ces", + "oun ter", + "Ġíķĺ ëĭĪê¹Į", + "ĠìĿ¸ íĦ°ë", + "Ġvérit able", + "Ġthread ed", + "bl ind", + "Ĥĺë Ŀ¼", + "Ġtr ays", + "ĠEd ison", + "ĠÃĸ z", + "ĠSte vie", + "Ġl ender", + "Ġbrig ade", + "Ġdeuts che", + "m uffled", + "b art", + "Ġinsan ity", + "Ġsav vy", + "Ġsens ational", + "Ġdere chos", + "ĠM X", + "ĠпÑĢ еп", + "Ġthreat ens", + "Ġrealt Ãł", + "Ġindic ative", + "Ġch ops", + "Ġbenef iting", + "ĠVern on", + "ĠSt rand", + "n un", + "qu ently", + "10 1", + "Ġe el", + "ìĪ Ļ", + "r ints", + "ĠÙħ س", + "Ġب د", + "Ġпо ÑģÑĤÑĢо", + "Ġyap mÄ±ÅŁ", + "Ġol ması", + "Ġi edereen", + "ol é", + "ke f", + "Ġë°ľ ìĥĿ", + "Ġr ained", + "Ġalm ighty", + "ĠвÑĭ д", + "ĠC PR", + "F re", + "Ġinhab ited", + "Ġarb ets", + "Ġa kin", + "а ÑģÑĤв", + "v ania", + "Ġhäuf ig", + "ĠMat te", + "s orry", + "Jen ny", + "ĠгÑĢ ад", + "Ġwh it", + "Ġbro kers", + "å¯ Ł", + "Ġh ine", + "ast en", + "Ġг ÑĢÑĥ", + "M B", + "ĠP RI", + "S ab", + "Ġwrest ler", + "Ġfacil itating", + "Ġeh kä", + "ĠC red", + "Ġ12 7", + "Ġnot hin", + "Ġmand ated", + "å¯ Į", + "ÑĥÑĤ ÑģÑĤв", + "F rank", + "Ġwor s", + "Ġdzie ÅĦ", + "ĠUnder ground", + "Ġznaj du", + "ĠB ä", + "ĠPrin zip", + "аÑĤ елей", + "Ġveter inar", + "Ġsplend id", + "Ġroz p", + "Ġpsych opath", + "ig on", + "Ġh ops", + "Ġc ần", + "ĠX ian", + "Ġtro isième", + "Ġproduct o", + "ĠdeÄŁ er", + "ĠContin uing", + "ив ал", + "c ık", + "Ġmoistur izer", + "Wh ite", + "Ġsi is", + "ĠEver est", + "ien ced", + "Ġcả m", + "ĠJ apon", + "´ìł Ħ", + "Ġten ÃŃan", + "Ġenc anta", + "M m", + "Ġdrop down", + "ĠI ya", + "³´ë ©´", + "Ġword ing", + "ĠSque eze", + "ĠMap le", + "Ġclar ified", + "ĠMun icip", + "ĠRou ge", + "ĠNick i", + "ĠGo o", + "v olt", + "t ek", + "fect ure", + "f red", + "ar rive", + "ãĥ¼ ãģĦ", + "te z", + "E p", + "Ġob ras", + "ĠV ID", + "ĠR iv", + "ĠMod i", + "i be", + "Ġacontec endo", + "Ġim itation", + "Ġcamoufl age", + "Ġspan ning", + "ĠSEC RET", + "ĠOre o", + "ìĨĮë ¦¬", + "Ġh unch", + "Ġca ÅĤe", + "Ġspont aneously", + "ĠPer d", + "Ġet ap", + "ĠHo le", + "ĠDis ability", + "Ġafter life", + "æģ ©", + "Ġtest ified", + "Ġpres up", + "Ġpet roleum", + "Ġcontr ario", + "ĠAss essment", + "ÄŁ lu", + "Ġp ests", + "Ġdil ig", + "ĠвÑģÑĤÑĢ еÑĤ", + "Ġcons équ", + "Ġcann ons", + "Ġcan oe", + "ĠM ile", + "Ġcit oy", + "Ġbe gged", + "ĠMin nie", + "ÅĤy ch", + "Ġprinci pe", + "ÏĢÏĮ ν", + "m niej", + "Ġw ert", + "Ġëĭ¤ë ĵ¤", + "an se", + "Ġunc les", + "Ġprovoc ative", + "Ġinter sections", + "Ġdemocr ats", + "ĠJul ius", + "ин ки", + "yg usal", + "Ġ׾ ×ķ", + "Ġgj orde", + "Ġg asket", + "ĠB ock", + "ĠÄ° n", + "b reat", + "ĠEqu ity", + "ard ı", + "Ġкан але", + "Ġд ней", + "Ġt Ỽi", + "Ġfi xture", + "Ġab uses", + "Ġv aya", + "Ġou vert", + "Ġmultic ultural", + "Ġcontext o", + "ĠSes ame", + "Ġdé pl", + "Ġcons omm", + "ĠPart e", + "Ġp em", + "ĠCon an", + "Ġб ÑĸлÑĮ", + "Ġpersu aded", + "Ġdra ins", + "M oo", + "F ORE", + "Ġб аÑĤ", + "Ġf od", + "ĠProduct s", + "ì§Ħ ì§ľ", + "Ġ\" [", + "ĠW ick", + "ĠNar uto", + "н али", + "ry w", + "Ġl odge", + "Ġin h", + "Ġvont ade", + "Ġdi j", + "ĠJes ús", + "Look ing", + "Ġfore arm", + "ĠIntegr ation", + "ĠHARR IS", + "Ġtool bar", + "le ader", + "Ġsel dom", + "Ġб ÑĢоÑģ", + "ĠK ook", + "он д", + "Ġmon opol", + "Ġmill et", + "Ġl ira", + "ĠAs ians", + "Ġ18 90", + "ci ÄŁim", + "Ġed en", + "ĠIKE A", + "ĠNeigh bor", + "ĠKazu ya", + "ü d", + "Ġpsych edel", + "Ġenvision ed", + "åĿ Ĺ", + "Ġï· »", + "Ġw under", + "ĠBulgar ia", + "B rid", + "Ġmar row", + "Ġdep iction", + "ĠT in", + "ĠPhar ise", + "Ġeinz ige", + "Ġblind ly", + "ãģĽ ãģ¦", + "Ġdef ens", + "D ire", + "Ġvibr ating", + "Ġtroll s", + "Ġdisrespect ful", + "Ġw od", + "Ġstimul i", + "Ġcreep ing", + "Ġcla irement", + "Ġsc ariest", + "Ġdécouv rir", + "Ġ10 4", + "ĠвеÑĢ Ñħ", + "ĠÅĤ at", + "Ġróż ne", + "Ġbar ley", + "ĠRe pl", + "ĠT we", + "k ke", + "ĠãģĿ ãĤĮ", + "ĠRed mi", + "ĠMet roid", + "Ġή ÏĦαν", + "Che ck", + "ĠS EN", + "Ġ ido", + "ÑĤоÑĢ ии", + "ó p", + "UN KNOWN", + "Ġänd ern", + "ĠJu ice", + "ĠGes icht", + "å°± æľĥ", + "ĠнаÑģÑĤ олÑĮко", + "íĥ ķ", + " Ń", + "ex hales", + "Ġì´ ī", + "Ġj sem", + "ÏĢ ÏīÏĤ", + "Ġit t", + "ëªħ ìĿ´", + "Ġrem ix", + "Ġbloss oms", + "ĠR enee", + "is ations", + "ìĬ¤í Ħ°", + "Ġë³´ ìĿ´ëĬĶ", + "uest as", + "op edia", + "ĠA im", + "ìĿ´ì¦ Ī", + "sc ene", + "Ġleak age", + "uck t", + "S ad", + "A sk", + "Ġsusp ense", + "Ġimp ost", + "ĠStrateg ic", + "ĠIt ÃŃs", + "âĢ Į", + "Ġkey boards", + "Ġam using", + "og r", + "id erman", + "ŀ ĸ", + "Ġв ижÑĥ", + "Ġd ips", + "Ġapolog ized", + "ĠST AR", + "Ġesc uela", + "ĠC hing", + "н ениÑı", + "Ġë¶Ģë¶Ħ ìĿ´", + "ĠFle et", + "Ġs amb", + "Ġentsprech end", + "Ġelectrod es", + "ĠFrei heit", + "æĪij ä¸įçŁ¥éģĵ", + "ĠSh rim", + "iÃŁ e", + "Ġselect ions", + "Ġfor di", + "Ġd oss", + "Ñı Ñĩ", + "Ġdiscrimin ate", + "ĠAu ÃŁerdem", + "Ġdesenvol v", + "ĠIntern al", + "ĠBened ict", + "å¯ Ĩ", + "ĠSh iv", + "M issy", + "Ġоб наÑĢÑĥж", + "Ġна ÑģÑĤÑĢо", + "Ġcontrol ar", + "ĠL ia", + "Ġopio ids", + "ant u", + "Ġcup board", + "æģ IJ", + "г е", + "acht s", + "Ġcur ated", + "Ġx em", + "Ġwe ary", + "Ġbre thren", + "Ġbudget ing", + "Ġpour tant", + "éļ »", + "ais ia", + "ĠоÑĤв еÑĩ", + "ĠG IS", + "μ αι", + "Ġש×Ķ ×ķ×IJ", + "Ġsa ud", + "Ġl Ỽ", + "Ðķ Т", + "ub ine", + "ĠнÑĥж ен", + "Ġkidna pping", + "Ġbr at", + "ĠTer re", + "ĠMon et", + "Ġë§Ī ìĬ¤íģ", + "Ġflash y", + "ĠIS BN", + "Ġfreel ance", + "i age", + "Ġjun ge", + "ì¶ ©", + "cer al", + "ĠÑĤоÑĩ ки", + "Ġform ulate", + "ĠF ER", + "ĠDart mouth", + "ìľ¼ë ©´ìĦľ", + "å¢ ĥ", + "ow iÄħ", + "ĠëĶĶ ìŀIJ", + "Ġreg iment", + "Ġmetabol ismo", + "ĠP arr", + "Ġ충 ë¶Ħ", + "Ġsan ity", + "ĠL al", + "ĠG ö", + "ĠG la", + "Ġprot o", + "Ġmicroscop ic", + "Ġk ang", + "ĠSc alia", + "Ġp ug", + "ĠSc ore", + "ĠSav annah", + "Ġgard e", + "ĠN OR", + "å°į åIJ§", + "Ġsche int", + "Ġp óÅĤ", + "Ġcor ri", + "Ġbr ute", + "Ġ ÅĤad", + "ä»ĸ 们", + "Ġsucceed ing", + "Ġbicy cles", + "N on", + "Ġseek ers", + "Ġuncond itional", + "Ġrhy mes", + "ĠGar age", + "Ġinv oice", + "Ġcan vi", + "ne ck", + "Ġcustom izable", + "irit ual", + "Que en", + "íķĺ ìĭľëĬĶ", + "Ġpower less", + "Ġcs ak", + "ä¸į ä¼ļ", + "is oft", + "Ġìłķ íĻķ", + "Ġnh ân", + "ĠM AND", + "ĠH af", + "Ġrevol ves", + "ä¹Ł åı¯ä»¥", + "ov an", + "ar oo", + "ĠGr ind", + "éĽ ª", + "Ġindispens able", + "Ġconsult ed", + "ĠClin ical", + "A cc", + "Ġol hos", + "Ġmon ter", + "ĠH ana", + "et ah", + "Ġva an", + "Ġt igers", + "Ġcau cus", + "ðŁĺ Ĥ", + "³´ì ŀIJ", + "pow ers", + "ium s", + "ĠíĨ łë", + "Ġtrad icional", + "Ġreson ated", + "Ġìĭł 기", + "th em", + "Ro bert", + "Ġelement o", + "Ġant id", + "Ġоб Ñģ", + "Ġnat ives", + "Ġlo ca", + "ow ment", + "ĠT ight", + "Ġ æĢĿ", + "Ġmel an", + "ĠN ue", + "am is", + "Ġsor gen", + "as ına", + "H ome", + "ĠPUB G", + "Ġaw fully", + "ĠSh ore", + "ĠPer ché", + "ĠL au", + "ĠCind erella", + "ĠCh est", + "Ġsem antic", + "Ġdesert ed", + "ĠMom o", + "ĠHern andez", + "gen es", + "ĠAd ult", + "иÑĩеÑģ кого", + "osh ima", + "ĠcaracterÃŃst icas", + "ĠK L", + "´ìŀ ¥", + "oc ar", + "Ġfeh lt", + "Ġd ruk", + "ĠPop py", + "EN GLISH", + "ĠVerg leich", + "B rien", + "Ġrec omp", + "ĠÑģ д", + "Ġmer ger", + "Ġmarket ers", + "Ġhoney moon", + "Ġpen so", + "Ġbell i", + "еÑĤ Ñĥ", + "Ġbank er", + "Cam era", + "ĠSt all", + "ĠSt amp", + "ĠB ite", + "еж де", + "Ġs ür", + "Ġgü ç", + "ĠPas sover", + "ĠBug ün", + "ĠÑģожал ениÑİ", + "Ġн из", + "Ġman ure", + "Ġglac ier", + "è« ĩ", + "RA Y", + "ter ror", + "Ġsal ads", + "Ġhur ricanes", + "ĠDesign er", + "ator io", + "Ġfact ual", + "ĠTam my", + "Ġзв ÑĥÑĩ", + "Ġintrodu ctions", + "Ġhouse keeping", + "Ġh anger", + "ëĭ ĺë", + "ak te", + "ĠCol a", + "' ]", + "ĠG ender", + "оÑĢ он", + "ip se", + "ic ias", + "Ġsuccess ive", + "Ġpolit ic", + "Ġhö her", + "ĠQ iao", + "ĠG imme", + "Ġл ож", + "Ġse b", + "ĠWe iter", + "ĠSak ura", + "ĠB oulder", + "ĠAm érica", + "peÅĤ nie", + "Ġtecn ologÃŃa", + "ish ops", + "f ur", + "Ġmoon light", + "Ġdispers ed", + "Ġre z", + "ен ное", + "алÑĮ нÑĥÑİ", + "ĠTw elve", + "ĠH OR", + "ìĭ¤í ŀĪ", + "il age", + "Ġshad ed", + "Ġres umes", + "ĠPe anut", + "ĠM ILL", + "ap ons", + "ĠU FC", + "ĠSo le", + "Ġjoy stick", + "ĠOliv ier", + "war ming", + "Ġsyll abus", + "Ġоб Ñīе", + "Ġhi á»ĩn", + "Ġfest a", + "Ġcr adle", + "ĠZ ac", + "Ġremem brance", + "Ġê°Ļ ìķĦìĦľ", + "ĠpiÄĻ k", + "Ġco exist", + "ĠV II", + "Ġá reas", + "Ġu waż", + "Ġobser vers", + "Ġmännisk or", + "co on", + "ĠD AM", + "Ġnas zym", + "Ġall igator", + "ĠFree ze", + "ĠEst ate", + "ĠÑĤÑĢ ади", + "Ġunder cover", + "Ġn ies", + "ĠFeh ler", + "pl in", + "ĠK abul", + "il ate", + "Ġê³ł ìĸij", + "Ġm op", + "ìĦ ¼", + "Ġand erer", + "ĠK ELL", + "ок и", + "Ġж еÑģÑĤ", + "Ġgra zing", + "Ġda ÃŃ", + "Ġcapital ize", + "Ġa pex", + "Ġnurt uring", + "Ġcort ar", + "Ġcontr ac", + "ımız ı", + "Ġtand em", + "éĥ½ æľī", + "ge ment", + "ĠÑģиÑģÑĤем а", + "Ġman que", + "ia jÄħ", + "W OR", + "Ġا ب", + "Ġcart s", + "AN O", + "Ġë°Ľ ê³ł", + "ĠC ena", + "ĠBi ology", + "id ar", + "Ġa ż", + "er ne", + "an u", + "Ġthank ed", + "Ġsubmar ines", + "Ġman ic", + "Ġм оз", + "ä¼ Ĭ", + "inst ant", + "ess ential", + "Ġsam urai", + "Ġpast i", + "Ġal an", + "Ġbro ch", + "Ġb aker", + "ĠGu ill", + "¨ ¼", + "Ġwithd rawn", + "ëĭ Ŀ", + "Per fect", + "qu ency", + "Ġstream lined", + "Ġ13 00", + "´ë ıĦ", + "Ġëĸ łë", + "Ġãģ¯ ãģĦ", + "Ġh vad", + "ä¸Ģå®ļ è¦ģ", + "Ġverb ally", + "ĠK ons", + "Ġì¡° ìĭ¬", + "Ġdie z", + "æİ° æİ°", + "Ġchuck ling", + "ĠM ih", + "Ġrall ies", + "Ġman ter", + "Ġearn est", + "s uper", + "Ġge ce", + "ĠR end", + "ĠGer ade", + "jen igen", + "ĠV all", + "Ġìŀ ĪëĤĺ", + "ĠÑģказ ала", + "Ġtrabal h", + "ĠнаÑĪ ем", + "Ġм еÑħ", + "ik it", + "Ġnoun s", + "Ġneurolog ical", + "Ġmotiv ational", + "ĠMcM ahon", + "ĠFin ished", + "Ġë³´ ìĿ´", + "ĠField s", + "Ġadoles cents", + "ĠT isch", + "ĠNe ben", + "ĠFl owers", + "ĠEner g", + "Ġdire t", + "ĠTh i", + "ĠP icas", + "æĥ ľ", + "æĢİä¹Ī æł·", + "Ġav ete", + "ĠF ors", + "ĠChap el", + "N ão", + "E t", + "ĠÑģод еÑĢж", + "ren o", + "Ġs ven", + "Ġdost ÄĻp", + "ne e", + "ĠSnap dragon", + "ĠID s", + "ìķĺ ëĬĶëį°", + "ר ×ļ", + "Ġsun flower", + "Ġperpet ual", + "ç³ ĸ", + "Ġkn ights", + "Ġg ird", + "ĠTo ld", + "Ġvolcano es", + "Ġadvers ary", + "ĠEconom y", + "Ġextra pol", + "Ġbl uetooth", + "Ġzoom ing", + "Ġsk ys", + "Ġgen ial", + "ÃŃcul os", + "amb re", + "Ġм еÑĢ", + "Ġteen y", + "Ġstress ing", + "ìķ Į", + "ON Y", + "Ġtransluc ent", + "Ġround ing", + "Ġgr ues", + "×Ļ׳ ×Ķ", + "ap rès", + "Ġprue ba", + "Ġpoly gon", + "Ġblue berry", + "ĠProgram m", + "Ġtren ches", + "Ġse bagai", + "Ġpal ate", + "Ġla ude", + "Ġbehav ed", + "Ġlongitud inal", + "ĠMod ule", + "Ġadm ir", + "λ ι", + "G reg", + "Ġwy st", + "Ġpropag ate", + "Ġmold s", + "ĠT ub", + "ĠL oud", + "ust o", + "Ġun stoppable", + "Ġreinfor cing", + "éĿŀ常 çļĦ", + "ĠпÑĢоблем а", + "Ġpot encial", + "Ġhe mp", + "ìŀ Ķ", + "ठ¯", + "Ġopt ic", + "Ġerfolg reich", + "Ñģ Ñĭ", + "олÑĮ ÑĪе", + "ur st", + "ĠPo is", + "Ġrespond ents", + "Ġneh me", + "ĠEx ternal", + "ol ate", + "H yun", + "Ġquart z", + "Ġmathematic ian", + "Ġbás icamente", + "Ġa il", + "ìł ľë¥¼", + "att utto", + "Ġno oit", + "Ġaff lict", + "ĠOl ga", + "èŃ ·", + "Ġна ÑĤ", + "Ġd ites", + "Ġreal idade", + "Ġk än", + "Ġuniqu eness", + "Ġpad res", + "Ġsubs idi", + "Ġpige ons", + "β α", + "st ad", + "Ġder en", + "ĠС лед", + "d oo", + "ĠопиÑģ ании", + "Ġam ber", + "Ġgoose bumps", + "ĠfrÃ¥ gor", + "ĠV ital", + "ĠIsrael ites", + "w asser", + "Is n", + "Ġcomm its", + "ĠSTE VEN", + "ĠBev ölker", + "uit ive", + "Ġleg en", + "Ġbr uk", + "иÑĢов ан", + "yn en", + "hel m", + "Ġgener ational", + "ĠL ändern", + "οι ÏĢÏĮν", + "uz u", + "Ġcall er", + "он ÑĮ", + "üm ü", + "Ġbes ar", + "Ġpl ats", + "Ġmig rated", + "Ġj ap", + "ĠW AR", + "Ġdis sect", + "ĠZus ch", + "ĠZe iten", + "ĠL ions", + "ĠD F", + "â Ķ", + "ки в", + "Ġpedest rians", + "ĠMar ilyn", + "d ock", + "Ġy ht", + "Ġre incarn", + "ĠSon o", + "ĠGrow th", + "ÑĥÑģ ов", + "Ġdun geons", + "Ġbag us", + "k ich", + "ĠÑĥ кÑĢаÑĹ", + "éĨ «", + "ĠK eller", + "chem istry", + "J apanese", + "Ġwill st", + "Ġdecomp osition", + "ĠÑģÑĤ ен", + "Ġrev ived", + "íķĻ êµIJ", + "ĠÅ ĵ", + "ä½ IJ", + "ìĭ ¸", + "ipp y", + "Ġhour ly", + "j än", + "ĠWork shop", + "Ŀ¼ ìĦľ", + "Ġcu arto", + "Ġpat rim", + "ĠB urch", + "ĠìŀĪ 기", + "Ġhe pat", + "Ġh Ãłng", + "ĠëĮĢ íķ´", + "ĠваÑĪ и", + "Ġre work", + "Ġpar se", + "Ġçıkt ı", + "ĠS ax", + "ĠMong o", + "ĠAa ah", + "ram ble", + "D J", + "Ġstabil ized", + "ĠSpe ech", + "Book s", + "Ġhur dles", + "ĠW O", + "ĠLamb org", + "Ġ19 33", + "Ġvor bere", + "Ġclin ically", + "Ġbreat htaking", + "ĠGate way", + "пеÑĢв ÑĭÑħ", + "ut ers", + "Ġë¹ µ", + "Ġyet er", + "Ġpull ey", + "Ġmuff in", + "ĠPre fer", + "ĠP ence", + "Ġinform ação", + "ìĬ¤í Ĭ¸ë", + "ãĤ¸ ãĥ£", + "ĠTur tle", + "ĠReg ina", + "ĠLo ad", + "do es", + "pan ze", + "¸ Ķ", + "Ġmin a", + "ĠLatin os", + "amm ers", + "ĠT ort", + "ĠBey once", + "имо ÑģÑĤи", + "ĠвопÑĢоÑģ Ñĭ", + "Ġbul un", + "èĢĮ å·²", + "ine k", + "bere ich", + "Ġpast ure", + "ĠO A", + "ĠM elt", + "ĠEt t", + "ĠD Y", + "Ġob wohl", + "Ġle agues", + "ÑĤ еÑģÑĮ", + "Ġк ÑĥÑģ", + "Ġv ors", + "Ġto pp", + "ograph ical", + "as st", + "Ġl indo", + "Ġë°Ŀ íĺĶ", + "Ġré fl", + "Ġclim bs", + "Ġv arsa", + "Ġmethy l", + "ĠKar ere", + "Æ°á» Ł", + "R ad", + "Ġprepared ness", + "он Ñĩ", + "ĠO D", + "ĠC GI", + "Ġठ®", + "Ġspeech less", + "Ġlas ci", + "Ġbol ag", + "ĠÑħоÑĩ еÑĤÑģÑı", + "Ġgr ieving", + "ĠJohann es", + "ĠCar roll", + "ad aki", + "Ī ¬ë", + "ĠsÅĤ u", + "Ġinner halb", + "Ġgymn astics", + "п ÑĢи", + "if iques", + "Ġkar ate", + "Ġdom u", + "ãģĿãĤĮ ãģ§", + "OTH ER", + "Ġdemand é", + "Ġbook let", + "ĠKy oto", + "Ġw oh", + "ĠMar ÃŃa", + "viol ent", + "J E", + "Ġl óg", + "Ġbrut ally", + "c ot", + "ĠÙħ ÛĮ", + "ĠWars z", + "å® Ī", + "w ol", + "Ġmik ä", + "ĠPron ounce", + "ĠBrend an", + "Ġr oup", + "Ġital iano", + "å¦Ĥ æѤ", + "Ġкомп ÑĮÑİÑĤ", + "Ġur ging", + "ed es", + "Ġcarbon o", + "ĠRichards on", + "ĠÐĿ аÑĩ", + "ĠTra iner", + "ĠCrime a", + "Ġdi apers", + "Ġco vet", + "ĠMah ar", + "ĠH utch", + "ĠAus w", + "ber ty", + "Ġind ifferent", + "кÑĢ еÑĤ", + "uld ade", + "Ġhar ms", + "¢ ÙĨ", + "les ia", + "Ġg io", + "ĠMist ress", + "ĠK nox", + "ĠFRE E", + "Ġë £¨ë", + "ĠнаÑĪ а", + "Ġinvinci ble", + "Ġma iden", + "ĠJ eez", + "Ġbre ve", + "po le", + "Ġcritic isms", + "ĠRus ia", + "ठ®", + "ph in", + "ĠComp are", + "ĠB ON", + "Ġsne aking", + "ĠR ails", + "ĠG eral", + "Ġ195 3", + "H ola", + "Ġоп ÑĭÑĤ", + "Ġrain forest", + "Ġbel um", + "ĠOb i", + "ĠIS S", + "ãĤĮ ãģªãģĦ", + "ĠС в", + "Ġbl ond", + "Ġwz gl", + "Ġpowiedz iaÅĤ", + "Ġch oking", + "ĠSong s", + "ĠBir az", + "Ġyell s", + "Ġstyl ist", + "ÏĮ ÏĦε", + "Ġsch reiben", + "ĠJ aw", + "ĠEle ven", + "ĠR if", + "/ .", + "Ġìĺ¤ë ŀľë§Į", + "Ġtreat ies", + "uff ed", + "ĠâĪ Ĵ", + "Ġroof s", + "à¹Ģภª", + "Ġë »", + "Ġspark le", + "ĠK iev", + "ĠAr gu", + "ere cht", + "ĠÐĿад о", + "ĠF IL", + "Ġmol ta", + "ĠDe vi", + "Ġcam pe", + "Ġbene vol", + "ĠT ough", + "Ġmo im", + "Ġevac uate", + "Ġer rado", + "å© Ĩ", + "ÑĢÑĥ го", + "Ġíİ ĺ", + "ĠÎĵ ια", + "Ġweak en", + "Ġillum inated", + "Ġsig lo", + "ĠV acc", + "и ей", + "al is", + "ĠÑĥ ÑģÑĤÑĢой", + "Ġdon a", + "ÅĤ os", + "ü man", + "Ġprodu cción", + "Ġcl ot", + "ĠM ango", + "Ġune asy", + "Ġsh uts", + "ĠExam ples", + "ve ll", + "e be", + "Ġprompt ly", + "ĠT eles", + "ĠпÑĢоÑĪ л", + "Ġpu erta", + "Ġüber zeug", + "Ġco ch", + "so cial", + "ĠB enson", + "ĠM eth", + "ĠEx ped", + "Ġsupplement al", + "Ġconce ive", + "Ġ×ĺ ×ķ×ij", + "Ġcapt ivity", + "ıĻ ìķĪ", + "ĠÑħ Ñĥд", + "form ing", + "Ġupload s", + "Ġturbul ence", + "j oint", + "Ġsatisf actory", + "ĠAn ime", + "Ġwash es", + "Ġliber als", + "ĠSun shine", + "ĠRE AL", + "ub lik", + "b inary", + "T ony", + "Ġpolar ized", + "Ġenrich ed", + "t aking", + "ĠëģĿ ëĤĺ", + "Ġple asures", + "Ġex termin", + "in ese", + "at l", + "v är", + "аÑĢ Ñĭ", + "Ġmy ÅĽ", + "n arrator", + "Ġод ном", + "Ġnaj wiÄĻ", + "Ġmobil ize", + "Ġmill or", + "Ġat a", + "æ· ·", + "ĠpolÃŃt ico", + "Ġple ad", + "Ġpain ters", + "ĠS ow", + "о ÑĦ", + "ĠìĺĽ ëĤł", + "ĠÑĩ ÑĤоб", + "Ġs abor", + "ĠUnd ert", + "ĠJER RY", + "Å¡ ÃŃ", + "Ġë° ĸìĹIJ", + "Ġpréc éd", + "Ġannot ation", + "ĠI naudible", + "Ġtext ured", + "Ġfisher man", + "v ordan", + "icher ung", + "Ġìłģ ìĿ´", + "Ġge zeigt", + "Ġmand ates", + "Ġbe ak", + "ĠTW O", + "ĠAk bar", + "il ian", + "Ġtiế p", + "Ġsuperior ity", + "ink u", + "Ġl ys", + "ĠF CC", + "ĠC PA", + "ust ering", + "nic os", + "an ja", + "Ġch ills", + "ĠC age", + "Ġse aling", + "Ġsa ç", + "Ġded ans", + "ĠAl ger", + "Ġspe zie", + "Ġcol oss", + "ıy ı", + "clock wise", + "Ġexact amente", + "Ġ iemand", + "am ı", + "Ġmand ar", + "ra j", + "f aced", + "ag ua", + "Ġê¹ Ķë", + "Ġins besondere", + "Ġdri zzle", + "Ġdimin ish", + "ĠY oda", + "A I", + "Ġbil miyorum", + "ĠM MA", + "ateg ory", + "ĠпеÑĢ еп", + "Ġparticip ar", + "Ġnormal ized", + "Ġcomplex ities", + "æ´ ²", + "æİ §", + "аÑĢ ов", + "m ist", + "ich a", + "Gr oup", + "Ġresil iency", + "Ġnog le", + "ĠCN C", + "pr ü", + "Ġphysic ists", + "н ок", + "L I", + "Ġstuff s", + "Ġsist emas", + "Ġinterfer ing", + "ĠMar vin", + "ér cito", + "ĠìĹĨ ê³ł", + "Ġson ic", + "Ġequ iv", + "Ġab ord", + "ĠRam en", + "Ġ0 9", + "med im", + "at iques", + "Ġдел аÑİÑĤ", + "Ġunanim ously", + "Ġsk irts", + "ĠíĬ¹ ë³Ħ", + "ĠP rix", + "k ami", + "Ġfr uition", + "Ġbirthday s", + "ик ом", + "Ġinaug ural", + "Ġcorrel ate", + "ĠT ory", + "ĠëĤĺ ìģ", + "Ġde w", + "ĠPre cis", + "ih i", + "Ġë¬¸ìłľ ê°Ģ", + "Ġc iting", + "ĠL ana", + "ĠK ag", + "Ġplay through", + "ĠProt ocol", + "fr ist", + "hov ah", + "Ġmerc iful", + "Ġb ilingual", + "ĠG uitar", + "r h", + "Ġglam orous", + "ĠVik ings", + "ĠOoo oh", + "íķĺ ëĬĶëį°", + "ĠUg anda", + "Ġcollaps es", + "ent ry", + "Ġantioxid ants", + "ëĤ ĺë", + "ÑĪ аÑı", + "Ġtri via", + "Ġgä ller", + "Ġfun gi", + "Ġmil ks", + "Ġd icht", + "μ η", + "po ke", + "ĠвÑĭп ÑĥÑģк", + "Ġfeed er", + "ĠAl cohol", + "h ower", + "Ġdes erving", + "ĠRe bel", + "ios is", + "Ġ10 3", + "Ġhand out", + "Ġen m", + "Ġland lords", + "Ġge ology", + "r ils", + "Ġco bra", + "ĠV old", + "ĠP anch", + "ĠGRE G", + "Ġpr oss", + "Ġbrac elets", + "ĠV ega", + "Ġroz um", + "æ¬ ¾", + "аз д", + "ĠLy nd", + "ĠHon ors", + "Ġsurrend ered", + "Ġlibr arians", + "12 5", + "ĠÑģ иг", + "Ġuniform ly", + "ĠE agles", + "ìķ Ļ", + "иÑĤ ан", + "and id", + "ĠìłĪë ĮĢ", + "ĠØ ¶", + "Ġarrest s", + "ĠCS V", + "ĠAzerbai jan", + "ort ic", + "ĠD X", + "ĠAdvent ures", + "Ġab us", + "ĠF au", + "Ġschlim m", + "Ġratt ling", + "Ġconsum es", + "ĠTol kien", + "Ġresurrect ed", + "ĠX Y", + "íĬ¸ ê°Ģ", + "ĠвÑĭ ÑģÑĤÑĥп", + "ĠAng ie", + "żen ia", + "M ic", + "ĠShe ila", + "acht et", + "Ġover st", + "Ġl â", + "Ġine ffective", + "æĿ ¡", + "æĢİä¹Ī äºĨ", + "å¿ Ļ", + "Ġwicht iger", + "Ġv ino", + "Ġp um", + "Ġang led", + "ĠP ione", + "ĠM ỹ", + "ãģĿãĤĮ ãģ¯", + "wo ÅĽÄĩ", + "d raw", + "ั à¹Ī", + "mark ets", + "Ġcaf es", + "ĠC em", + "â Ŀ¤", + "ĠS uit", + "M K", + "Ġemphas izes", + "Ġtort illa", + "Ġmejor ar", + "ĠSur viv", + "cast ing", + "Ġeduc ación", + "ĠG um", + "u ely", + "ĠìĹ¬ê¸° ëĬĶ", + "Ġstretch y", + "en ça", + "Ġwith hold", + "Ġex iting", + "Ġenthal py", + "ĠTrans it", + "ıl mÄ±ÅŁ", + "al ies", + "Ġsal var", + "Ġlean ed", + "ĠgroÃŁ es", + "Ġf itt", + "ак и", + "S arah", + "Ġhost el", + "Ġfinger na", + "Ġnadzie jÄĻ", + "w ives", + "R ec", + "Ġsp ool", + "аÑĤ ов", + "ĠEn emy", + "Ġf ury", + "Ġdet ta", + "ĠF ay", + "éļ ¨", + "Ñı ÑİÑĤ", + "Ġaproxim adamente", + "Ġsil os", + "Ġmag ist", + "Ġc ree", + "ĠKr ank", + "ĠD OWN", + "Ġstart led", + "Ġre born", + "ĠUm welt", + "ĠSuz anne", + "ни ÑĨÑĭ", + "out ez", + "ĠJ AC", + "y ards", + "rad as", + "ra u", + "ip ts", + "h ail", + "Ġparagraph s", + "Ġme glio", + "Ġisol ating", + "Ġace ite", + "ĠH arsh", + "Ġcy st", + "ĠBlock chain", + "ĠÑħоÑĢоÑĪ ий", + "Ġvirt uous", + "Ġinvestig ación", + "Ġdev oir", + "Ġmast urb", + "ĠS ale", + "ÙĬر Ø©", + "ĠÎ §", + "ĠStra ÃŁen", + "Ġdi kk", + "Ġa fore", + "ĠJung kook", + "Ġcho ciaż", + "ĠDebat te", + "Ġweird ly", + "Ġvia je", + "reg ist", + "H elp", + "Ġkind eren", + "Ġform ulated", + "Ġenf im", + "ĠTow ards", + "ко ÑĹ", + "iver ing", + "ĠдеÑĤ и", + "char ger", + "Ġpur l", + "Ġacadem ically", + "ĠNur se", + "Ġdel eting", + "ay o", + "Ġref usal", + "Ġdepict s", + "ĠDr acula", + "Ġtoast ed", + "ĠZomb ie", + "ĠSuper ior", + "ĠB old", + "Ġquizz es", + "Ġg le", + "4 50", + "Ġcome ço", + "yn n", + "Ġver st", + "ĠO laf", + "Ġpom oc", + "ĠS ask", + "ë ĺ", + "ĠT CP", + "ĠProper ty", + "íķĺ ì£ł", + "à¸ľ ม", + "bo om", + "ar os", + "ĠÑĢоÑģÑģ ий", + "ĠбÑĭв аеÑĤ", + "åĩº åİ»", + "ĠìĿ´ìķ¼ 기를", + "Ġcomb ien", + "v acc", + "Ġeben falls", + "par a", + "Ġз м", + "Ġdesper ation", + "ord re", + "Ġש׾ ×Ļ", + "Ġgener ously", + "ĠÐŀ к", + "Ġorb iting", + "> ", + "lstrip": false, + "normalized": 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